PA4
Report
Claudiu Papasteri
16 12 2021
1 Define functions
## Define function that recodes to numeric, but watches out to coercion to not introduce NAs
colstonumeric <- function(df){
tryCatch({
df_num <- as.data.frame(
lapply(df,
function(x) { as.numeric(as.character(x))}))
},warning = function(stop_on_warning) {
message("Stoped the execution of numeric conversion: ", conditionMessage(stop_on_warning))
})
}
##
## Define function that reverse codes items
ReverseCode <- function(df, tonumeric = FALSE, min = NULL, max = NULL) {
if(tonumeric) df <- colstonumeric(df)
df <- (max + min) - df
}
##
## Define function that scores only rows with less than 10% NAs (returns NA if all or above threshold percentage of rows are NA); can reverse code if vector of column indexes and min, max are provided.
ScoreLikert <- function(df, napercent = .1, tonumeric = FALSE, reversecols = NULL, min = NULL, max = NULL) {
reverse_list <- list(reversecols = reversecols, min = min, max = max)
reverse_check <- !sapply(reverse_list, is.null)
# Recode to numeric, but watch out to coercion to not introduce NAs
colstonumeric <- function(df){
tryCatch({
df_num <- as.data.frame(
lapply(df,
function(x) { as.numeric(as.character(x))}))
},warning = function(stop_on_warning) {
message("Stoped the execution of numeric conversion: ", conditionMessage(stop_on_warning))
})
}
if(tonumeric) df <- colstonumeric(df)
if(all(reverse_check)){
df[ ,reversecols] <- (max + min) - df[ ,reversecols]
}else if(any(reverse_check)){
stop("Insuficient info for reversing. Please provide: ", paste(names(reverse_list)[!reverse_check], collapse = ", "))
}
ifelse(rowSums(is.na(df)) > ncol(df) * napercent,
NA,
rowSums(df, na.rm = TRUE) * NA ^ (rowSums(!is.na(df)) == 0)
)
}
##my_ggwithinstats <- function(data, title, x, y, outlier.label, xlab, ylab) {
x <- rlang::enquo(x)
y <- rlang::enquo(y)
outlier.label <- rlang::enquo(outlier.label)
data %>%
ggstatsplot::ggwithinstats(
x = !!x,
y = !!y,
title = title,
xlab = xlab,
ylab = ylab,
outlier.tagging = TRUE, # whether outliers need to be tagged
outlier.label = !!outlier.label, # variable to be used for tagging outliers
outlier.coef = 2,
pairwise.comparisons = TRUE,
pairwise.display = "significant",
results.subtitle = TRUE,
type = "parametric",
bf.message = FALSE,
p.adjust.method = "none",
point.path = TRUE,
ggtheme = ggprism::theme_prism(),
# package = "RColorBrewer", # "ggsci",
# palette = "Dark", # "default_jco",
violin.args = list(width = 0.9, alpha = 0.2, size = 1, color = "black"),
centrality.point.args = list(size = 5, color = "darkred"),
centrality.label.args = list(size = 3, nudge_x = 0.2, segment.linetype = 5, fill = "#FFF8E7"),
ggplot.component = list(
theme(
plot.title = element_text(hjust = 0, size = 16),
plot.subtitle = element_text(hjust = 0, size = 12),
plot.caption = element_text(hjust = 0, size = 12),
text = element_text(size = 14)
))
) + scale_colour_grey(start = 0.2, end = 0.2) # hacky way to change point color
}
# For publication
my_ggwithinstats2 <- function(data, title, x, y, outlier.label, xlab, ylab,
outlier.tagging = FALSE, results.subtitle = TRUE,
centrality.label.args = TRUE, point.path = TRUE,
type = "parametric",
...) { # ... for limits and breaks
x <- rlang::enquo(x)
y <- rlang::enquo(y)
outlier.label <- rlang::enquo(outlier.label)
if(centrality.label.args){
centrality.label.args <- list(size = 3, nudge_x = 0.2, segment.linetype = 5, fill = "#FFF8E7")
}else{
centrality.label.args <- list(size = 0, nudge_x = 10, segment.linetype = 0, alpha = 0) # very hacky way of not showing label
}
data %>%
ggstatsplot::ggwithinstats(
x = !!x,
y = !!y,
title = title,
xlab = xlab,
ylab = ylab,
outlier.tagging = outlier.tagging, # whether outlines need to be tagged
outlier.label = !!outlier.label, # variable to be used for tagging outliers
outlier.coef = 2,
pairwise.comparisons = TRUE,
pairwise.display = "all",
results.subtitle = results.subtitle,
type = type,
bf.message = FALSE,
p.adjust.method = "none",
point.path = point.path,
ggtheme = ggprism::theme_prism(),
# package = "RColorBrewer", # "ggsci",
# palette = "Dark", # "default_jco",
violin.args = list(width = 0.9, alpha = 0.2, size = 1, color = "black"),
centrality.plotting = TRUE,
centrality.type = "parameteric",
centrality.point.args = list(size = 5, color = "darkred"),
centrality.label.args = centrality.label.args,
ggplot.component = list(
theme(
plot.title = element_text(hjust = 0, size = 16),
plot.subtitle = element_text(hjust = 0, size = 12),
plot.caption = element_text(hjust = 0, size = 12),
text = element_text(size = 14)
))
) + scale_colour_grey(start = 0.2, end = 0.2) + # hacky way to change point color
scale_y_continuous(...)
}
# Fast ggsave - saves plot with filename of R plot object
fast_ggsave <- function(plot, device = "png", path = NULL,
units = "in", dpi = 300, width = 5, height = 5, ...){
plot_name <- deparse(substitute(plot))
ggplot2::ggsave(filename = paste0(plot_name, ".", device), plot = plot,
device = device, path = path,
units = units, dpi = dpi,
width = width, height = height,
...
)
} # use: fast_ggsave(jrad_ox_p, path = savefolder)2 Read, Clean, Recode
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Read, Clean, Recode, Unite
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Read files
folder <- "C:/Users/Mihai/Desktop/R Notebooks/notebooks/PA4-full-report"
file <- "Scale complete triate Sofi pa4.xlsx"
setwd(folder)
## ID df
id_df <- rio::import(file.path(folder, file),
skip = 0, colNames = FALSE, which = "incadrari") id_df <- id_df[, 1:4]
colnames(id_df) <- c("Grup", "Cond", "id", "email")
id_df <-
id_df %>%
janitor::remove_empty("rows") %>%
dplyr::mutate(id = stringr::str_remove(id, "^0+"), # remove leading zeros
id = stringr::str_remove_all(id, "[[:blank:]]"), # remove any white space
id = toupper(id)) %>%
dplyr::mutate(Cond = stringr::str_replace(Cond, "12CONTROL", "CONTROL"), # fix typo
Grup = stringr::str_replace(Grup, "burnout", "Burnout"),
Grup = stringr::str_replace(Grup, "pop generala", "pop gen"),
Grup = stringr::str_replace(Grup, "old", "pop gen")) %>%
dplyr::mutate(Grup = dplyr::if_else(is.na(Grup), "pop gen", Grup))
id_df <-
id_df %>%
tidyr::separate(id,
into = c("id_num", "Exp_type"),
sep = "(?<=[0-9])(?=[A-Za-z])", # ?<= is "look behind"
remove = FALSE
) %>%
dplyr::select(-id_num) %>%
dplyr::mutate(Exp_type = dplyr::if_else(Exp_type %in% c("A", "B", "C", "D", "E", "R", "X"), "online", Exp_type))
## Scale
scale_df_pre <- rio::import(file.path(folder, file),
skip = 0, which = "Scale zi1")
scale_df_post <- rio::import(file.path(folder, file),
skip = 0, which = "Scale zi5")
dif_col <- which(colnames(scale_df_pre) == setdiff(colnames(scale_df_pre), colnames(scale_df_post)))
scale_df_pre <- scale_df_pre[, - dif_col]
scale_df_pre <- scale_df_pre[, - c(1, 3:7)]
scale_df_post <- scale_df_post[, - c(1, 3:7)]
identical(colnames(scale_df_pre), colnames(scale_df_post))
scale_df_pre <- scale_df_pre[-1, ]
colnames(scale_df_pre)[1] <- "Date"
colnames(scale_df_pre)[2] <- "id"
scale_df_pre <-
scale_df_pre %>%
janitor::remove_empty("rows") %>% # remove empty rows if any
dplyr::mutate(id = stringr::str_remove(id, "^0+"), # remove leading zeros
id = stringr::str_remove_all(id, "[[:blank:]]"), # remove any white space
id = toupper(id)) %>%
dplyr::mutate_if(is.character, ~dplyr::na_if(., "Not Answered")) %>%
dplyr::mutate(Date = lubridate::as_datetime(Date)) %>%
dplyr::arrange(Date)
scale_df_post <- scale_df_post[-1, ]
colnames(scale_df_post)[1] <- "Date"
colnames(scale_df_post)[2] <- "id"
scale_df_post <-
scale_df_post %>%
janitor::remove_empty("rows") %>% # remove empty rows if any
dplyr::mutate(id = stringr::str_remove(id, "^0+"), # remove leading zeros
id = stringr::str_remove_all(id, "[[:blank:]]"), # remove any white space
id = toupper(id)) %>%
dplyr::mutate_if(is.character, ~dplyr::na_if(., "Not Answered")) %>%
dplyr::mutate(Date = lubridate::as_datetime(Date)) %>%
dplyr::arrange(Date)
setdiff(id_df$id, scale_df_pre$id)
# Merge with id
scale_df_pre <- dplyr::left_join(scale_df_pre, id_df, by = "id")
scale_df_pre$PrePost <- rep("Pre", nrow(scale_df_pre))
unique(scale_df_pre$Cond)
unique(scale_df_pre$Grup)
table(scale_df_pre$Cond, scale_df_pre$Grup)
scale_df_pre[, c("id", "Cond", "Grup")]
scale_df_post <- dplyr::left_join(scale_df_post, id_df, by = "id")
scale_df_post$PrePost <- rep("Post", nrow(scale_df_post))
unique(scale_df_post$Cond)
unique(scale_df_post$Grup)
scale_df_post[, c("id", "Cond", "Grup")]NAgender_match <-
Data[, c("Sex", "ID")] %>%
mutate_if(is.character, ~na_if(., "Not Answered"))
fem_ids <-
gender_match %>%
dplyr::filter(Sex == "Feminin") %>%
dplyr::pull(ID)
masc_ids <-
gender_match %>%
dplyr::filter(Sex == "Masculin") %>%
dplyr::pull(ID)–>
3 Outcome Measures
–>
## PANAS: Positive Affect Score = sum items 1, 3, 5, 9, 10, 12, 14, 16, 17, 19. Negative Affect Score = sum items 2, 4, 6, 7, 8, 11, 13, 15, 18, 20.
index_item_panas <- 3:22
colnames(scale_df_pre)[index_item_panas] <- sprintf("PANAS_%d", 1:20)
colnames(scale_df_post)[index_item_panas] <- sprintf("PANAS_%d", 1:20)
scale_df_pre[, index_item_panas] <- data.frame(lapply(scale_df_pre[, index_item_panas],
function(x) {gsub(".*în foarte mică măsură.*", "1", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_panas] <- data.frame(lapply(scale_df_pre[, index_item_panas],
function(x) {gsub(".*în mică măsură.*", "2", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_panas] <- data.frame(lapply(scale_df_pre[, index_item_panas],
function(x) {gsub(".*într-o oarecare măsură.*", "3", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_panas] <- data.frame(lapply(scale_df_pre[, index_item_panas],
function(x) {gsub(".*în mare măsură.*", "4", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_panas] <- data.frame(lapply(scale_df_pre[, index_item_panas],
function(x) {gsub(".*în foarte mare măsură.*", "5", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_panas] <- data.frame(lapply(scale_df_post[, index_item_panas],
function(x) {gsub(".*în foarte mică măsură.*", "1", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_panas] <- data.frame(lapply(scale_df_post[, index_item_panas],
function(x) {gsub(".*în mică măsură.*", "2", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_panas] <- data.frame(lapply(scale_df_post[, index_item_panas],
function(x) {gsub(".*într-o oarecare măsură.*", "3", x)}), stringsAsFactors = FALSE)scale_df_post[, index_item_panas] <- data.frame(lapply(scale_df_post[, index_item_panas],
function(x) {gsub(".*în mare măsură.*", "4", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_panas] <- data.frame(lapply(scale_df_post[, index_item_panas],
function(x) {gsub(".*în foarte mare măsură.*", "5", x)}), stringsAsFactors = FALSE)
# Scoring
scale_df_pre$PA_Total <- ScoreLikert(scale_df_pre[, index_item_panas][c(1, 3, 5, 9, 10, 12, 14, 16, 17, 19)],
tonumeric = TRUE, napercent = .11) # not more than 1 NAs for 10 items
scale_df_pre$NA_Total <- ScoreLikert(scale_df_pre[, index_item_panas][c(2, 4, 6, 7, 8, 11, 13, 15, 18, 20)],
tonumeric = TRUE, napercent = .11) # not more than 1 NAs for 10 items
scale_df_post$PA_Total <- ScoreLikert(scale_df_post[, index_item_panas][c(1, 3, 5, 9, 10, 12, 14, 16, 17, 19)],
tonumeric = TRUE, napercent = .11) # not more than 1 NAs for 10 items
scale_df_post$NA_Total <- ScoreLikert(scale_df_post[, index_item_panas][c(2, 4, 6, 7, 8, 11, 13, 15, 18, 20)],
tonumeric = TRUE, napercent = .11) # not more than 1 NAs for 10 items
## PSS-SF 14 (likert 0-4). Items 4, 5, 6, 7, 9, 10, and 13 are scored in reverse direction.
index_item_pss <- 23:36
index_item_revPSS <- c(4, 5, 6, 7, 9, 10, 13)
colnames(scale_df_pre)[index_item_pss] <- sprintf("PSS_%d", 1:14)
colnames(scale_df_post)[index_item_pss] <- sprintf("PSS_%d", 1:14)
scale_df_pre[, index_item_pss] <- data.frame(lapply(scale_df_pre[, index_item_pss],
function(x) {gsub(".*niciodată.*", "0", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_pss] <- data.frame(lapply(scale_df_pre[, index_item_pss],
function(x) {gsub(".*aproape niciodată.*", "1", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_pss] <- data.frame(lapply(scale_df_pre[, index_item_pss],
function(x) {gsub(".*uneori.*", "2", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_pss] <- data.frame(lapply(scale_df_pre[, index_item_pss],
function(x) {gsub(".*destul de des.*", "3", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_pss] <- data.frame(lapply(scale_df_pre[, index_item_pss],
function(x) {gsub(".*foarte des.*", "4", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_pss] <- data.frame(lapply(scale_df_post[, index_item_pss],
function(x) {gsub(".*niciodată.*", "0", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_pss] <- data.frame(lapply(scale_df_post[, index_item_pss],
function(x) {gsub(".*aproape niciodată.*", "1", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_pss] <- data.frame(lapply(scale_df_post[, index_item_pss],
function(x) {gsub(".*uneori.*", "2", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_pss] <- data.frame(lapply(scale_df_post[, index_item_pss],
function(x) {gsub(".*destul de des.*", "3", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_pss] <- data.frame(lapply(scale_df_post[, index_item_pss],
function(x) {gsub(".*foarte des.*", "4", x)}), stringsAsFactors = FALSE)
# Score
scale_df_pre[, index_item_pss] <- colstonumeric(scale_df_pre[, index_item_pss])
scale_df_post[, index_item_pss] <- colstonumeric(scale_df_post[, index_item_pss])
scale_df_pre[, index_item_pss][index_item_revPSS] <- ReverseCode(scale_df_pre[, index_item_pss][index_item_revPSS], tonumeric = FALSE, min = 0, max = 4)
scale_df_post[, index_item_pss][index_item_revPSS] <- ReverseCode(scale_df_post[, index_item_pss][index_item_revPSS], tonumeric = FALSE, min = 0, max = 4)
scale_df_pre$PSS_Total <- ScoreLikert(scale_df_pre[, index_item_pss], napercent = .11)
scale_df_post$PSS_Total <- ScoreLikert(scale_df_post[, index_item_pss], napercent = .11)
# PS int (Pro social scales - 37:47)
index_item_PSint <- 37:40
colnames(scale_df_pre)[index_item_PSint] <- sprintf("PSint_%d", 1:4)
colnames(scale_df_post)[index_item_PSint] <- sprintf("PSint_%d", 1:4)
scale_df_pre[, sprintf("PSint_%d", 1:4)] <-
scale_df_pre[, sprintf("PSint_%d", 1:4)] %>%
dplyr::mutate_all(readr::parse_number)
scale_df_post[, sprintf("PSint_%d", 1:4)] <-
scale_df_post[, sprintf("PSint_%d", 1:4)] %>%
dplyr::mutate_all(readr::parse_number)
scale_df_pre$PSint_Total <- ScoreLikert(scale_df_pre[, index_item_PSint], tonumeric = TRUE, napercent = .33)
scale_df_post$PSint_Total <- ScoreLikert(scale_df_post[, index_item_PSint], tonumeric = TRUE, napercent = .33)
# PS mot
index_item_PSmot <- 41:44
colnames(scale_df_pre)[index_item_PSmot] <- sprintf("PSmot_%d", 1:4)
colnames(scale_df_post)[index_item_PSmot] <- sprintf("PSmot_%d", 1:4)
scale_df_pre[, sprintf("PSmot_%d", 1:4)] <-
scale_df_pre[, sprintf("PSmot_%d", 1:4)] %>%
dplyr::mutate_all(~case_when(stringr::str_detect(., "dezacord puternic") ~ 1,
stringr::str_detect(., "dezacord") ~ 2,
stringr::str_detect(., "neutru") ~ 3,
stringr::str_detect(., "acord") ~ 4,
stringr::str_detect(., "acord puternic") ~ 5,
TRUE ~ NA_real_))
scale_df_post[, sprintf("PSmot_%d", 1:4)] <-
scale_df_post[, sprintf("PSmot_%d", 1:4)] %>%
dplyr::mutate_all(~case_when(stringr::str_detect(., "dezacord puternic") ~ 1,
stringr::str_detect(., "dezacord") ~ 2,
stringr::str_detect(., "neutru") ~ 3,
stringr::str_detect(., "acord") ~ 4,
stringr::str_detect(., "acord puternic") ~ 5,
TRUE ~ NA_real_))
scale_df_pre$PSmot_Total <- ScoreLikert(scale_df_pre[, index_item_PSmot], tonumeric = TRUE, napercent = .33)
scale_df_post$PSmot_Total <- ScoreLikert(scale_df_post[, index_item_PSmot], tonumeric = TRUE, napercent = .33)
# PS iden
index_item_PSiden <- 45:47
colnames(scale_df_pre)[index_item_PSiden] <- sprintf("PSiden_%d", 1:3)
colnames(scale_df_post)[index_item_PSiden] <- sprintf("PSiden_%d", 1:3)
scale_df_pre[, sprintf("PSiden_%d", 1:3)] <-
scale_df_pre[, sprintf("PSiden_%d", 1:3)] %>%
dplyr::mutate_all(~case_when(stringr::str_detect(., "dezacord puternic") ~ 1,
stringr::str_detect(., "dezacord") ~ 2,
stringr::str_detect(., "neutru") ~ 3,
stringr::str_detect(., "acord") ~ 4,
stringr::str_detect(., "acord puternic") ~ 5,
TRUE ~ NA_real_))
scale_df_post[, sprintf("PSiden_%d", 1:3)] <-
scale_df_post[, sprintf("PSiden_%d", 1:3)] %>%
dplyr::mutate_all(~case_when(stringr::str_detect(., "dezacord puternic") ~ 1,
stringr::str_detect(., "dezacord") ~ 2,
stringr::str_detect(., "neutru") ~ 3,
stringr::str_detect(., "acord") ~ 4,
stringr::str_detect(., "acord puternic") ~ 5,
TRUE ~ NA_real_))
scale_df_pre$PSiden_Total <- ScoreLikert(scale_df_pre[, index_item_PSiden], tonumeric = TRUE, napercent = .33)
scale_df_post$PSiden_Total <- ScoreLikert(scale_df_post[, index_item_PSiden], tonumeric = TRUE, napercent = .33)
# IOS
colnames(scale_df_pre)[48] <- "IOS"
colnames(scale_df_post)[48] <- "IOS"
scale_df_pre$IOS <- as.numeric(scale_df_pre$IOS)
scale_df_post$IOS <- as.numeric(scale_df_post$IOS)
scale_df_pre %>%
dplyr::count(id) %>%
dplyr::filter(n > 1) # correct unique: 7RMN 01-27, 43GSR 04-23
scale_df_pre <-
scale_df_pre %>%
dplyr::filter(!(id == "7RMN" & Date == lubridate::as_datetime("2020-07-17 14:12:37"))) %>%
dplyr::filter(!(id == "43GSR" & Date == lubridate::as_datetime("2021-04-12 21:10:00")))
scale_df_post %>%
dplyr::count(id) %>%
dplyr::filter(n > 1) # correct unique: 43GSR 05-03, 30GSR 05-20, 41GSR 04-28
scale_df_post <-
scale_df_post %>%
dplyr::filter(!(id == "43GSR" & Date == lubridate::as_datetime("2021-04-27 15:37:57"))) %>%
dplyr::filter(!(id == "30GSR" & Date == lubridate::as_datetime("2021-04-25 13:06:02"))) %>%
dplyr::filter(!(id == "41GSR" & Date == lubridate::as_datetime("2021-04-25 09:15:30")))
# Unite scale df - Long Format
scale_united_long <- rbind(scale_df_pre, scale_df_post)
scale_united_long <-
scale_united_long %>%
dplyr::group_by(id) %>%
dplyr::filter(n() > 1) %>%
dplyr::ungroup() %>%
dplyr::mutate(PrePost = factor(PrePost, levels = c("Pre", "Post")))4 Whole samples
4.0.1 PSS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
4.0.2 PANAS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
4.0.3 Prosocial
scale_united_long %>%
dplyr::filter(Cond == "CONTROL") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
4.0.4 IOS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
5 By group
5.1 Normal Pop
5.1.1 PSS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
5.1.2 PANAS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
5.1.3 Prosocial
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
5.1.4 IOS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
5.2 PTSD
5.2.1 PSS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
5.2.2 PANAS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
5.2.3 Prosocial
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
5.2.4 IOS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
5.3 Burnout
5.3.1 PSS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
5.3.2 PANAS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
5.3.3 Prosocial
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
5.3.4 IOS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
6 Only online
6.1 Normal Pop
6.1.1 PSS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
6.1.2 PANAS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
6.1.3 Prosocial
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
6.1.4 IOS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
6.2 PTSD
6.2.1 PSS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
6.2.2 PANAS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
6.2.3 Prosocial
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
6.2.4 IOS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
6.3 Burnout
6.3.1 PSS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
6.3.2 PANAS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
6.3.3 Prosocial
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
6.3.4 IOS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
7 Only GSR
7.1 Normal Pop
7.1.1 PSS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
7.1.2 PANAS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
7.1.3 Prosocial
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
7.1.4 IOS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
7.2 PTSD
7.2.1 PSS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
7.2.2 PANAS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
7.2.3 Prosocial
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
7.2.4 IOS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
7.3 Burnout
7.3.1 PSS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
7.3.2 PANAS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
7.3.3 Prosocial
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
7.3.4 IOS
scale_united_long %>%
dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "CONTROL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
8 Only RMN & OXT
8.1 Normal Pop (has only Normal Pop and EXPERIMENTAL)
8.1.1 PSS
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
8.1.2 PANAS
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
8.1.3 Prosocial
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
8.1.4 IOS
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
9 GSR, RMN & OXT
9.1 Normal Pop (has only Normal Pop and EXPERIMENTAL)
9.1.1 PSS
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id,
xlab = "", ylab = "PSS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
9.1.2 PANAS
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id,
xlab = "", ylab = "NA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id,
xlab = "", ylab = "PA",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
9.1.3 Prosocial
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id,
xlab = "", ylab = "PSint",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id,
xlab = "", ylab = "PSmot",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id,
xlab = "", ylab = "PSiden",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
9.1.4 IOS
scale_united_long %>%
dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id,
xlab = "", ylab = "IOS",
title = "EXPERIMENTAL")Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
10 Session Info
R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 8.1 x64 (build 9600)
Matrix products: default
locale:
[1] LC_COLLATE=Romanian_Romania.1250 LC_CTYPE=Romanian_Romania.1250 LC_MONETARY=Romanian_Romania.1250 LC_NUMERIC=C
[5] LC_TIME=Romanian_Romania.1250
system code page: 1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] rio_0.5.27 scales_1.1.1 ggpubr_0.4.0 rstatix_0.7.0 broom_0.7.9
[6] PerformanceAnalytics_2.0.4 xts_0.12.1 zoo_1.8-9 psych_2.1.6 forcats_0.5.1
[11] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.0.1 tidyr_1.1.3
[16] tibble_3.1.4 ggplot2_3.3.5 tidyverse_1.3.1 papaja_0.1.0.9997 pacman_0.5.1
loaded via a namespace (and not attached):
[1] readxl_1.3.1 pairwiseComparisons_3.1.6 backports_1.2.1 plyr_1.8.6 splines_4.1.0
[6] gmp_0.6-2 kSamples_1.2-9 ipmisc_6.0.2 TH.data_1.0-10 digest_0.6.28
[11] SuppDists_1.1-9.5 fansi_0.5.0 magrittr_2.0.1 memoise_2.0.0 paletteer_1.4.0
[16] tzdb_0.1.2 openxlsx_4.2.4 modelr_0.1.8 sandwich_3.0-1 colorspace_2.0-2
[21] rvest_1.0.1 ggrepel_0.9.1 haven_2.4.3 xfun_0.25 crayon_1.4.1
[26] jsonlite_1.7.2 zeallot_0.1.0 survival_3.2-13 glue_1.4.2 gtable_0.3.0
[31] emmeans_1.6.3 MatrixModels_0.5-0 statsExpressions_1.1.0 car_3.0-11 Rmpfr_0.8-4
[36] abind_1.4-5 mvtnorm_1.1-2 DBI_1.1.1 PMCMRplus_1.9.0 Rcpp_1.0.7
[41] xtable_1.8-4 performance_0.7.3 tmvnsim_1.0-2 foreign_0.8-81 datawizard_0.2.0.1
[46] httr_1.4.2 ellipsis_0.3.2 farver_2.1.0 pkgconfig_2.0.3 reshape_0.8.8
[51] multcompView_0.1-8 dbplyr_2.1.1 utf8_1.2.2 janitor_2.1.0 labeling_0.4.2
[56] tidyselect_1.1.1 rlang_0.4.11 effectsize_0.4.5 munsell_0.5.0 cellranger_1.1.0
[61] tools_4.1.0 cachem_1.0.6 cli_3.0.1 ggprism_1.0.3 generics_0.1.0
[66] fastmap_1.1.0 BWStest_0.2.2 rematch2_2.1.2 knitr_1.33 fs_1.5.0
[71] zip_2.2.0 WRS2_1.1-3 pbapply_1.4-3 nlme_3.1-152 xml2_1.3.2
[76] correlation_0.7.0 compiler_4.1.0 rstudioapi_0.13 curl_4.3.2 ggsignif_0.6.2
[81] reprex_2.0.1 stringi_1.7.4 parameters_0.14.0 lattice_0.20-44 Matrix_1.3-4
[86] vctrs_0.3.8 pillar_1.6.3 lifecycle_1.0.1 mc2d_0.1-21 estimability_1.3
[91] data.table_1.14.0 insight_0.14.4 patchwork_1.1.1 R6_2.5.1 BayesFactor_0.9.12-4.2
[96] codetools_0.2-18 boot_1.3-28 MASS_7.3-54 gtools_3.9.2 assertthat_0.2.1
[101] withr_2.4.2 mnormt_2.0.2 multcomp_1.4-17 bayestestR_0.11.0 parallel_4.1.0
[106] hms_1.1.0 quadprog_1.5-8 grid_4.1.0 coda_0.19-4 snakecase_0.11.0
[111] carData_3.0-4 lubridate_1.7.10 ggstatsplot_0.8.0 A work by Claudiu Papasteri
Â
---
title: "<br> PA4" 
subtitle: "Report"
author: "<br> Claudiu Papasteri"
date: "`r format(Sys.time(), '%d %m %Y')`"
output: 
    html_notebook:
            code_folding: hide
            toc: true
            toc_depth: 2
            number_sections: true
            theme: spacelab
            highlight: tango
            font-family: Arial
            fig_width: 10
            fig_height: 9
    # pdf_document: 
            # toc: true
            #  toc_depth: 2
            #  number_sections: true
            # fontsize: 11pt
            # geometry: margin=1in
            # fig_width: 7
            # fig_height: 6
            # fig_caption: true
    # github_document: 
            # toc: true
            # toc_depth: 2
            # html_preview: false
            # fig_width: 5
            # fig_height: 5
            # dev: jpeg
---


<!-- Setup -->


```{r setup, include=FALSE}
# kintr options
knitr::opts_chunk$set(
  comment = "#",
  collapse = TRUE,
  error = TRUE,
  echo = TRUE, warning = FALSE, message = FALSE, cache = TRUE       # echo = False for github_document, but will be folded in html_notebook
)

# General R options and info
set.seed(111)               # in case we use randomized procedures       
options(scipen = 999)       # positive values bias towards fixed and negative towards scientific notation

# Load packages
if (!require("pacman")) install.packages("pacman")
packages <- c(
  "papaja",
  "tidyverse",       
  "psych", "PerformanceAnalytics",          
  "broom", "rstatix",
  "ggpubr", "scales",        
  "rio"
  # , ...
)
if (!require("pacman")) install.packages("pacman")
pacman::p_load(char = packages)

# Themes for ggplot2 ploting (here used APA style)
theme_set(theme_apa())
```


<!-- Report -->

# Define functions

```{r def_func}
## Define function that recodes to numeric, but watches out to coercion to not introduce NAs
colstonumeric <- function(df){
  tryCatch({
    df_num <- as.data.frame(
      lapply(df,
             function(x) { as.numeric(as.character(x))})) 
  },warning = function(stop_on_warning) {
    message("Stoped the execution of numeric conversion: ", conditionMessage(stop_on_warning))
  }) 
}
##
## Define function that reverse codes items
ReverseCode <- function(df, tonumeric = FALSE, min = NULL, max = NULL) {
  if(tonumeric) df <- colstonumeric(df)
  df <- (max + min) - df
}
##
## Define function that scores only rows with less than 10% NAs (returns NA if all or above threshold percentage of rows are NA); can reverse code if vector of column indexes and min, max are provided.
ScoreLikert <- function(df, napercent = .1, tonumeric = FALSE, reversecols = NULL, min = NULL, max = NULL) {
  reverse_list <- list(reversecols = reversecols, min = min, max = max)
  reverse_check <- !sapply(reverse_list, is.null)
  
  # Recode to numeric, but watch out to coercion to not introduce NAs
  colstonumeric <- function(df){
    tryCatch({
      df_num <- as.data.frame(
        lapply(df,
               function(x) { as.numeric(as.character(x))})) 
    },warning = function(stop_on_warning) {
      message("Stoped the execution of numeric conversion: ", conditionMessage(stop_on_warning))
    }) 
  }
  
  if(tonumeric) df <- colstonumeric(df)
  
  if(all(reverse_check)){
    df[ ,reversecols] <- (max + min) - df[ ,reversecols]
  }else if(any(reverse_check)){
    stop("Insuficient info for reversing. Please provide: ", paste(names(reverse_list)[!reverse_check], collapse = ", "))
  }
  
  ifelse(rowSums(is.na(df)) > ncol(df) * napercent,
         NA,
         rowSums(df, na.rm = TRUE) * NA ^ (rowSums(!is.na(df)) == 0)
  )
}
##
```


```{r}
my_ggwithinstats <- function(data, title, x, y, outlier.label, xlab, ylab) {
  x <- rlang::enquo(x)
  y <- rlang::enquo(y)
  outlier.label <- rlang::enquo(outlier.label)
  
  data %>%
    ggstatsplot::ggwithinstats(
      x = !!x,
      y = !!y,
      title = title,
      xlab = xlab,
      ylab = ylab,
      outlier.tagging = TRUE,            # whether outliers need to be tagged
      outlier.label = !!outlier.label,   # variable to be used for tagging outliers
      outlier.coef = 2,
      pairwise.comparisons = TRUE,
      pairwise.display = "significant",
      results.subtitle = TRUE,
      type = "parametric",
      bf.message = FALSE, 
      p.adjust.method = "none",
      point.path = TRUE,
      ggtheme = ggprism::theme_prism(),
      # package = "RColorBrewer",  # "ggsci",
      # palette = "Dark",         # "default_jco",
      violin.args = list(width = 0.9, alpha = 0.2, size = 1, color = "black"),
      centrality.point.args = list(size = 5, color = "darkred"),
      centrality.label.args = list(size = 3, nudge_x = 0.2, segment.linetype = 5, fill = "#FFF8E7"), 
      ggplot.component = list(
        theme(
          plot.title = element_text(hjust = 0, size = 16),
          plot.subtitle = element_text(hjust = 0, size = 12), 
          plot.caption = element_text(hjust = 0, size = 12), 
          text = element_text(size = 14)
      ))
    ) + scale_colour_grey(start = 0.2, end = 0.2)   # hacky way to change point color
}

# For publication
my_ggwithinstats2 <- function(data, title, x, y, outlier.label, xlab, ylab, 
                              outlier.tagging = FALSE, results.subtitle = TRUE, 
                              centrality.label.args = TRUE, point.path = TRUE,
                              type = "parametric", 
                              ...) {  # ... for limits and breaks
  x <- rlang::enquo(x)
  y <- rlang::enquo(y)
  outlier.label <- rlang::enquo(outlier.label)
  
  if(centrality.label.args){
    centrality.label.args <- list(size = 3, nudge_x = 0.2, segment.linetype = 5, fill = "#FFF8E7")
  }else{
    centrality.label.args <- list(size = 0, nudge_x = 10, segment.linetype = 0, alpha = 0) # very hacky way of not showing label
  }
  
  data %>%
    ggstatsplot::ggwithinstats(
      x = !!x,
      y = !!y,
      title = title,
      xlab = xlab,
      ylab = ylab,
      outlier.tagging = outlier.tagging,                    # whether outlines need to be tagged
      outlier.label = !!outlier.label,                      # variable to be used for tagging outliers
      outlier.coef = 2,
      pairwise.comparisons = TRUE,
      pairwise.display = "all",
      results.subtitle = results.subtitle,
      type = type,
      bf.message = FALSE, 
      p.adjust.method = "none",
      point.path = point.path,
      ggtheme = ggprism::theme_prism(),
      # package = "RColorBrewer",  # "ggsci",
      # palette = "Dark",         # "default_jco",
      violin.args = list(width = 0.9, alpha = 0.2, size = 1, color = "black"),
      centrality.plotting = TRUE,
      centrality.type = "parameteric",
      centrality.point.args = list(size = 5, color = "darkred"),
      centrality.label.args = centrality.label.args,
      ggplot.component = list(
        theme(
          plot.title = element_text(hjust = 0, size = 16),
          plot.subtitle = element_text(hjust = 0, size = 12), 
          plot.caption = element_text(hjust = 0, size = 12), 
          text = element_text(size = 14)
      ))
    ) + scale_colour_grey(start = 0.2, end = 0.2) +  # hacky way to change point color
    scale_y_continuous(...)
}

# Fast ggsave - saves plot with filename of R plot object
fast_ggsave <- function(plot, device = "png", path = NULL,
                        units = "in", dpi = 300, width = 5, height = 5, ...){ 
  plot_name <- deparse(substitute(plot))
  ggplot2::ggsave(filename = paste0(plot_name, ".", device), plot = plot,
                  device = device, path = path,
                  units = units, dpi = dpi,
                  width = width, height = height,
                  ...
  )
  
} # use: fast_ggsave(jrad_ox_p, path = savefolder)
```




# Read, Clean, Recode

```{r red_clean_recode_merge, results='hide', message=FALSE}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Read, Clean, Recode, Unite
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Read files
folder <- "C:/Users/Mihai/Desktop/R Notebooks/notebooks/PA4-full-report"
file <- "Scale complete triate Sofi pa4.xlsx"

setwd(folder)

## ID df
id_df <- rio::import(file.path(folder, file),
                     skip = 0, colNames = FALSE, which = "incadrari")   

id_df <- id_df[, 1:4]
colnames(id_df) <- c("Grup", "Cond", "id", "email")

id_df <- 
  id_df %>%
  janitor::remove_empty("rows") %>% 
  dplyr::mutate(id = stringr::str_remove(id, "^0+"),              # remove leading zeros
                id = stringr::str_remove_all(id, "[[:blank:]]"),  # remove any white space
                id =  toupper(id)) %>%
  dplyr::mutate(Cond = stringr::str_replace(Cond, "12CONTROL", "CONTROL"),   # fix typo
                Grup = stringr::str_replace(Grup, "burnout", "Burnout"),
                Grup = stringr::str_replace(Grup, "pop generala", "pop gen"),
                Grup = stringr::str_replace(Grup, "old", "pop gen")) %>%
  dplyr::mutate(Grup = dplyr::if_else(is.na(Grup), "pop gen", Grup))

id_df <- 
  id_df %>%
  tidyr::separate(id, 
           into = c("id_num", "Exp_type"), 
           sep = "(?<=[0-9])(?=[A-Za-z])",     # ?<= is "look behind"
           remove = FALSE
           ) %>%
  dplyr::select(-id_num) %>%
  dplyr::mutate(Exp_type = dplyr::if_else(Exp_type %in% c("A", "B", "C", "D", "E", "R", "X"), "online", Exp_type))

## Scale 
scale_df_pre <- rio::import(file.path(folder, file),
                            skip = 0, which = "Scale zi1") 
scale_df_post <- rio::import(file.path(folder, file),
                             skip = 0, which = "Scale zi5") 

dif_col <- which(colnames(scale_df_pre) == setdiff(colnames(scale_df_pre), colnames(scale_df_post)))
scale_df_pre <- scale_df_pre[, - dif_col]
scale_df_pre <- scale_df_pre[, - c(1, 3:7)]
scale_df_post <- scale_df_post[, - c(1, 3:7)]
identical(colnames(scale_df_pre), colnames(scale_df_post))

scale_df_pre <- scale_df_pre[-1, ]
colnames(scale_df_pre)[1] <- "Date"
colnames(scale_df_pre)[2] <- "id"
scale_df_pre <- 
  scale_df_pre %>%
  janitor::remove_empty("rows") %>%                            # remove empty rows if any
  dplyr::mutate(id = stringr::str_remove(id, "^0+"),              # remove leading zeros
                id = stringr::str_remove_all(id, "[[:blank:]]"),  # remove any white space
                id =  toupper(id))  %>%
  dplyr::mutate_if(is.character, ~dplyr::na_if(., "Not Answered")) %>%
  dplyr::mutate(Date = lubridate::as_datetime(Date)) %>%
  dplyr::arrange(Date)


scale_df_post <- scale_df_post[-1, ]
colnames(scale_df_post)[1] <- "Date"
colnames(scale_df_post)[2] <- "id"
scale_df_post <- 
  scale_df_post %>%
  janitor::remove_empty("rows") %>%                            # remove empty rows if any
  dplyr::mutate(id = stringr::str_remove(id, "^0+"),              # remove leading zeros
                id = stringr::str_remove_all(id, "[[:blank:]]"),  # remove any white space
                id =  toupper(id))  %>%
  dplyr::mutate_if(is.character, ~dplyr::na_if(., "Not Answered")) %>%
  dplyr::mutate(Date = lubridate::as_datetime(Date)) %>%
  dplyr::arrange(Date)


setdiff(id_df$id, scale_df_pre$id)

# Merge with id
scale_df_pre <- dplyr::left_join(scale_df_pre, id_df, by = "id")
scale_df_pre$PrePost <- rep("Pre", nrow(scale_df_pre)) 
unique(scale_df_pre$Cond)
unique(scale_df_pre$Grup)
table(scale_df_pre$Cond, scale_df_pre$Grup)
scale_df_pre[, c("id", "Cond", "Grup")]


scale_df_post <- dplyr::left_join(scale_df_post, id_df, by = "id")
scale_df_post$PrePost <- rep("Post", nrow(scale_df_post))
unique(scale_df_post$Cond)
unique(scale_df_post$Grup)
scale_df_post[, c("id", "Cond", "Grup")]

```



<!-- No gender data here
## Gender matching to IDs
```{r gender_match}
gender_match <- 
  Data[, c("Sex", "ID")] %>%
  mutate_if(is.character, ~na_if(., "Not Answered"))

fem_ids <- 
  gender_match %>%
  dplyr::filter(Sex == "Feminin") %>%
  dplyr::pull(ID)

masc_ids <- 
  gender_match %>%
  dplyr::filter(Sex == "Masculin") %>%
  dplyr::pull(ID)
```
-->


# Outcome Measures

<!-- No Dictator in data...
# Dictator Game

```{r dg_scoring, results='hide'}
# DG df
dg_df_pre <- rio::import(file.path(folder, file),
                                   skip = 0, which = "DictatorGame pre")
dg_df_pre <- 
  dg_df_pre %>%
  janitor::remove_empty("rows") 
  
colnames(dg_df_pre)[4] <- "ID"
dg_df_pre <- 
  dg_df_pre %>%
  dplyr::mutate(ID = stringr::str_replace_all(ID, fixed(" "), "")) %>%    # remove white spaces
  dplyr::mutate(ID = toupper(ID))                                         # to upper
all(dg_df_pre$ID == toupper(dg_df_pre$ID)) 

dg_df_post <- rio::import(file.path(folder, file),
                                   skip = 0, which = "DictatorGame post")
dg_df_post <- 
  dg_df_post %>%
  janitor::remove_empty("rows")

colnames(dg_df_post)[4] <- "ID"
dg_df_post <- 
  dg_df_post %>%
  dplyr::mutate(ID = stringr::str_replace_all(ID, fixed(" "), "")) %>%    # remove white spaces
  dplyr::mutate(ID = toupper(ID))                                         # to upper
all(dg_df_post$ID == toupper(dg_df_post$ID)) 

colnames(dg_df_pre)[5:8] <- sprintf("DG_%d", 1:4)
colnames(dg_df_pre)[2] <- "Response_Status"
colnames(dg_df_post)[5:8] <- sprintf("DG_%d", 1:4)
colnames(dg_df_post)[3] <- "Response_Status"

dg_df_pre <-
  dg_df_pre %>%
  dplyr::mutate(`Date Modified` = lubridate::ymd_hms(format(`Date Modified`, "%Y-%m-%d %H:%M:%S", tz = "UTC"))) %>%
  mutate_if(is.character, ~na_if(., "Not Answered")) %>%
  dplyr::filter(Response_Status == "completed") %>%
  dplyr::filter(!stringr::str_detect(ID, "PA4OXT")) %>%
  dplyr::filter(!stringr::str_detect(ID, "PA4RMN"))

dg_df_post <-
  dg_df_post %>%
  dplyr::mutate(`Date Modified` = lubridate::ymd_hms(format(`Date Modified`, "%Y-%m-%d %H:%M:%S", tz = "UTC"))) %>%
  mutate_if(is.character, ~dplyr::na_if(., "Not Answered")) %>%
  dplyr::filter(Response_Status == "completed") %>%
  dplyr::filter(!stringr::str_detect(ID, "PA4OXT")) %>%
  dplyr::filter(!stringr::str_detect(ID, "PA4RMN"))

dg_df_pre <-
  dg_df_pre %>%
  dplyr::mutate_at(vars(starts_with("DG_")), ~stringr::str_extract(., "[0-9]+")) %>%   # extracts first number (all games start with Player A, so always first number)
  dplyr::mutate_at(vars(starts_with("DG_")), as.numeric) %>%
  dplyr::mutate(Time = rep("Pre", nrow(.))) %>%
  dplyr::mutate(Cond = ifelse(stringr::str_detect(ID, "X"), "CTRL", "TR")) %>%
  select(`Date Modified`, ID, starts_with("DG_"), Time, Cond)

dg_df_post <-
  dg_df_post %>%
  dplyr::mutate_at(vars(starts_with("DG_")), ~stringr::str_extract(., "[0-9]+")) %>%   # extracts first number (all games start with Player A, so always first number)
  dplyr::mutate_at(vars(starts_with("DG_")), as.numeric) %>%
  dplyr::mutate(Time = rep("Post", nrow(.))) %>%
  dplyr::mutate(Cond = ifelse(stringr::str_detect(ID, "X"), "CTRL", "TR")) %>%
  select(`Date Modified`, ID, starts_with("DG_"), Time, Cond)

# Transform DG 0-900 egoism to 0-9 altruism
dg_trans_func <- function(x){trans <- 9 - x / 100}

dg_df_pre <-
  dg_df_pre %>%
  dplyr::mutate_at(vars(starts_with("DG_")), dg_trans_func) 

dg_df_post <-
  dg_df_post %>%
  dplyr::mutate_at(vars(starts_with("DG_")), dg_trans_func) 

# Unite DG data - Long Format
dg_united_long <- rbind(dg_df_pre, dg_df_post)
which(table(dg_united_long$ID)> 2 )     # IDs "3B" & "31X" have more than 2 trials
dg_united_long[dg_united_long$ID == "3B",]  # two Pres -- keep the first
dg_united_long <- dg_united_long[-55, ]
dg_united_long[dg_united_long$ID == "31X",] # two Pres -- keep the first
dg_united_long <- dg_united_long[-73, ]
dg_united_long[dg_united_long$ID == "5E",] # two Pres -- keep the first
dg_united_long <- dg_united_long[-106, ]

dg_united_long$DG_Total <- rowSums(dg_united_long[, sprintf("DG_%d", 1:4)], na.rm = TRUE) 

# Unite DG data - Wide Format
dg_united_wide <-
  dg_united_long %>%
  tidyr::pivot_wider(id_cols = c(ID, Cond), names_from = Time, values_from = c("Date Modified", sprintf("DG_%d", 1:4), "DG_Total"))
# dg_united_wide2 <- dplyr::left_join(dg_united_wide, ids_groups_df, by = "ID")     # no need to merge, already have ids for groups stored

# Merge gender
dg_united_wide <- dplyr::left_join(dg_united_wide, gender_match, by = "ID")
```

-->


```{r scale_scoring, results='hide'}
## PANAS: Positive Affect Score = sum items 1, 3, 5, 9, 10, 12, 14, 16, 17, 19. Negative Affect Score = sum items 2, 4, 6, 7, 8, 11, 13, 15, 18, 20.  
index_item_panas <- 3:22
colnames(scale_df_pre)[index_item_panas] <- sprintf("PANAS_%d", 1:20)
colnames(scale_df_post)[index_item_panas] <- sprintf("PANAS_%d", 1:20)

scale_df_pre[, index_item_panas] <- data.frame(lapply(scale_df_pre[, index_item_panas], 
                                      function(x) {gsub(".*în foarte mică măsură.*", "1", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_panas] <- data.frame(lapply(scale_df_pre[, index_item_panas], 
                                      function(x) {gsub(".*în mică măsură.*", "2", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_panas] <- data.frame(lapply(scale_df_pre[, index_item_panas], 
                                      function(x) {gsub(".*într-o oarecare măsură.*", "3", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_panas] <- data.frame(lapply(scale_df_pre[, index_item_panas], 
                                      function(x) {gsub(".*în mare măsură.*", "4", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_panas] <- data.frame(lapply(scale_df_pre[, index_item_panas], 
                                      function(x) {gsub(".*în foarte mare măsură.*", "5", x)}), stringsAsFactors = FALSE)

scale_df_post[, index_item_panas] <- data.frame(lapply(scale_df_post[, index_item_panas], 
                                                      function(x) {gsub(".*în foarte mică măsură.*", "1", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_panas] <- data.frame(lapply(scale_df_post[, index_item_panas], 
                                                      function(x) {gsub(".*în mică măsură.*", "2", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_panas] <- data.frame(lapply(scale_df_post[, index_item_panas], 
                                                      function(x) {gsub(".*într-o oarecare măsură.*", "3", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_panas] <- data.frame(lapply(scale_df_post[, index_item_panas], 
                                                      function(x) {gsub(".*în mare măsură.*", "4", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_panas] <- data.frame(lapply(scale_df_post[, index_item_panas], 
                                                      function(x) {gsub(".*în foarte mare măsură.*", "5", x)}), stringsAsFactors = FALSE)
# Scoring
scale_df_pre$PA_Total <- ScoreLikert(scale_df_pre[, index_item_panas][c(1, 3, 5, 9, 10, 12, 14, 16, 17, 19)], 
                                    tonumeric = TRUE, napercent = .11)                              # not more than 1 NAs for 10 items
scale_df_pre$NA_Total <- ScoreLikert(scale_df_pre[, index_item_panas][c(2, 4, 6, 7, 8, 11, 13, 15, 18, 20)],
                                    tonumeric = TRUE, napercent = .11)                              # not more than 1 NAs for 10 items

scale_df_post$PA_Total <- ScoreLikert(scale_df_post[, index_item_panas][c(1, 3, 5, 9, 10, 12, 14, 16, 17, 19)], 
                                     tonumeric = TRUE, napercent = .11)                              # not more than 1 NAs for 10 items
scale_df_post$NA_Total <- ScoreLikert(scale_df_post[, index_item_panas][c(2, 4, 6, 7, 8, 11, 13, 15, 18, 20)],
                                     tonumeric = TRUE, napercent = .11)                              # not more than 1 NAs for 10 items


## PSS-SF 14 (likert 0-4). Items 4, 5, 6, 7, 9, 10, and 13 are scored in reverse direction.
index_item_pss <- 23:36
index_item_revPSS <- c(4, 5, 6, 7, 9, 10, 13)
colnames(scale_df_pre)[index_item_pss] <- sprintf("PSS_%d", 1:14)
colnames(scale_df_post)[index_item_pss] <- sprintf("PSS_%d", 1:14)

scale_df_pre[, index_item_pss] <- data.frame(lapply(scale_df_pre[, index_item_pss], 
                                      function(x) {gsub(".*niciodată.*", "0", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_pss] <- data.frame(lapply(scale_df_pre[, index_item_pss], 
                                      function(x) {gsub(".*aproape niciodată.*", "1", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_pss] <- data.frame(lapply(scale_df_pre[, index_item_pss], 
                                      function(x) {gsub(".*uneori.*", "2", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_pss] <- data.frame(lapply(scale_df_pre[, index_item_pss], 
                                      function(x) {gsub(".*destul de des.*", "3", x)}), stringsAsFactors = FALSE)
scale_df_pre[, index_item_pss] <- data.frame(lapply(scale_df_pre[, index_item_pss], 
                                      function(x) {gsub(".*foarte des.*", "4", x)}), stringsAsFactors = FALSE)

scale_df_post[, index_item_pss] <- data.frame(lapply(scale_df_post[, index_item_pss], 
                                                    function(x) {gsub(".*niciodată.*", "0", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_pss] <- data.frame(lapply(scale_df_post[, index_item_pss], 
                                                    function(x) {gsub(".*aproape niciodată.*", "1", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_pss] <- data.frame(lapply(scale_df_post[, index_item_pss], 
                                                    function(x) {gsub(".*uneori.*", "2", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_pss] <- data.frame(lapply(scale_df_post[, index_item_pss], 
                                                    function(x) {gsub(".*destul de des.*", "3", x)}), stringsAsFactors = FALSE)
scale_df_post[, index_item_pss] <- data.frame(lapply(scale_df_post[, index_item_pss], 
                                                    function(x) {gsub(".*foarte des.*", "4", x)}), stringsAsFactors = FALSE)

# Score
scale_df_pre[, index_item_pss] <- colstonumeric(scale_df_pre[, index_item_pss])
scale_df_post[, index_item_pss] <- colstonumeric(scale_df_post[, index_item_pss])

scale_df_pre[, index_item_pss][index_item_revPSS] <- ReverseCode(scale_df_pre[, index_item_pss][index_item_revPSS], tonumeric = FALSE, min = 0, max = 4)
scale_df_post[, index_item_pss][index_item_revPSS] <- ReverseCode(scale_df_post[, index_item_pss][index_item_revPSS], tonumeric = FALSE, min = 0, max = 4)

scale_df_pre$PSS_Total <- ScoreLikert(scale_df_pre[, index_item_pss], napercent = .11)
scale_df_post$PSS_Total <- ScoreLikert(scale_df_post[, index_item_pss], napercent = .11)



# PS int  (Pro social scales - 37:47)
index_item_PSint <- 37:40
colnames(scale_df_pre)[index_item_PSint] <- sprintf("PSint_%d", 1:4)
colnames(scale_df_post)[index_item_PSint] <- sprintf("PSint_%d", 1:4)


scale_df_pre[, sprintf("PSint_%d", 1:4)] <-
  scale_df_pre[, sprintf("PSint_%d", 1:4)] %>%
  dplyr::mutate_all(readr::parse_number)


scale_df_post[, sprintf("PSint_%d", 1:4)] <-
  scale_df_post[, sprintf("PSint_%d", 1:4)] %>%
  dplyr::mutate_all(readr::parse_number)

scale_df_pre$PSint_Total <- ScoreLikert(scale_df_pre[, index_item_PSint], tonumeric = TRUE, napercent = .33)
scale_df_post$PSint_Total <- ScoreLikert(scale_df_post[, index_item_PSint], tonumeric = TRUE, napercent = .33)



# PS mot
index_item_PSmot <- 41:44   
colnames(scale_df_pre)[index_item_PSmot] <- sprintf("PSmot_%d", 1:4)
colnames(scale_df_post)[index_item_PSmot] <- sprintf("PSmot_%d", 1:4)

scale_df_pre[, sprintf("PSmot_%d", 1:4)] <-
  scale_df_pre[, sprintf("PSmot_%d", 1:4)] %>%
  dplyr::mutate_all(~case_when(stringr::str_detect(., "dezacord puternic") ~ 1,
                               stringr::str_detect(., "dezacord") ~ 2,
                               stringr::str_detect(., "neutru") ~ 3,
                               stringr::str_detect(., "acord") ~ 4,
                               stringr::str_detect(., "acord puternic") ~ 5,
                               TRUE ~ NA_real_))

scale_df_post[, sprintf("PSmot_%d", 1:4)] <-
  scale_df_post[, sprintf("PSmot_%d", 1:4)] %>%
  dplyr::mutate_all(~case_when(stringr::str_detect(., "dezacord puternic") ~ 1,
                               stringr::str_detect(., "dezacord") ~ 2,
                               stringr::str_detect(., "neutru") ~ 3,
                               stringr::str_detect(., "acord") ~ 4,
                               stringr::str_detect(., "acord puternic") ~ 5,
                               TRUE ~ NA_real_))

scale_df_pre$PSmot_Total <- ScoreLikert(scale_df_pre[, index_item_PSmot], tonumeric = TRUE, napercent = .33)
scale_df_post$PSmot_Total <- ScoreLikert(scale_df_post[, index_item_PSmot], tonumeric = TRUE, napercent = .33)



# PS iden
index_item_PSiden <- 45:47
colnames(scale_df_pre)[index_item_PSiden] <- sprintf("PSiden_%d", 1:3)
colnames(scale_df_post)[index_item_PSiden] <- sprintf("PSiden_%d", 1:3)

scale_df_pre[, sprintf("PSiden_%d", 1:3)] <-
  scale_df_pre[, sprintf("PSiden_%d", 1:3)] %>%
  dplyr::mutate_all(~case_when(stringr::str_detect(., "dezacord puternic") ~ 1,
                               stringr::str_detect(., "dezacord") ~ 2,
                               stringr::str_detect(., "neutru") ~ 3,
                               stringr::str_detect(., "acord") ~ 4,
                               stringr::str_detect(., "acord puternic") ~ 5,
                               TRUE ~ NA_real_))

scale_df_post[, sprintf("PSiden_%d", 1:3)] <-
  scale_df_post[, sprintf("PSiden_%d", 1:3)] %>%
  dplyr::mutate_all(~case_when(stringr::str_detect(., "dezacord puternic") ~ 1,
                               stringr::str_detect(., "dezacord") ~ 2,
                               stringr::str_detect(., "neutru") ~ 3,
                               stringr::str_detect(., "acord") ~ 4,
                               stringr::str_detect(., "acord puternic") ~ 5,
                               TRUE ~ NA_real_))

scale_df_pre$PSiden_Total <- ScoreLikert(scale_df_pre[, index_item_PSiden], tonumeric = TRUE, napercent = .33)
scale_df_post$PSiden_Total <- ScoreLikert(scale_df_post[, index_item_PSiden], tonumeric = TRUE, napercent = .33)


# IOS
colnames(scale_df_pre)[48] <- "IOS"
colnames(scale_df_post)[48] <- "IOS"

scale_df_pre$IOS <- as.numeric(scale_df_pre$IOS)
scale_df_post$IOS <- as.numeric(scale_df_post$IOS) 


scale_df_pre %>%
  dplyr::count(id) %>%
  dplyr::filter(n > 1)   # correct unique: 7RMN 01-27, 43GSR 04-23

scale_df_pre <- 
  scale_df_pre %>%
  dplyr::filter(!(id == "7RMN" & Date == lubridate::as_datetime("2020-07-17 14:12:37"))) %>%
  dplyr::filter(!(id == "43GSR" & Date == lubridate::as_datetime("2021-04-12 21:10:00")))
  

scale_df_post %>%
  dplyr::count(id) %>%
  dplyr::filter(n > 1)   # correct unique: 43GSR 05-03, 30GSR 05-20, 41GSR 04-28

scale_df_post <- 
  scale_df_post %>%
  dplyr::filter(!(id == "43GSR" & Date == lubridate::as_datetime("2021-04-27 15:37:57"))) %>%
  dplyr::filter(!(id == "30GSR" & Date == lubridate::as_datetime("2021-04-25 13:06:02"))) %>%
  dplyr::filter(!(id == "41GSR" & Date == lubridate::as_datetime("2021-04-25 09:15:30")))


# Unite scale df - Long Format
scale_united_long <- rbind(scale_df_pre, scale_df_post)

scale_united_long <- 
  scale_united_long %>%
  dplyr::group_by(id) %>%
  dplyr::filter(n() > 1) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(PrePost = factor(PrePost, levels = c("Pre", "Post")))
```

# Whole samples
### PSS

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                   xlab = "", ylab = "PSS",
                   title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                   xlab = "", ylab = "PSS",
                   title = "EXPERIMENTAL")
```

### PANAS

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                   xlab = "", ylab = "NA",
                   title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                   xlab = "", ylab = "NA",
                   title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                   xlab = "", ylab = "PA",
                   title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                   xlab = "", ylab = "PA",
                   title = "EXPERIMENTAL")
```

### Prosocial

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                   xlab = "", ylab = "PSint",
                   title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                   xlab = "", ylab = "PSint",
                   title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                   xlab = "", ylab = "PSmot",
                   title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                   xlab = "", ylab = "PSmot",
                   title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                   xlab = "", ylab = "PSiden",
                   title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                   xlab = "", ylab = "PSiden",
                   title = "EXPERIMENTAL")
```

### IOS

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                   xlab = "", ylab = "IOS",
                   title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                   xlab = "", ylab = "IOS",
                   title = "EXPERIMENTAL")
```


# By group

## Normal Pop

### PSS

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "EXPERIMENTAL")
```

### PANAS

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "EXPERIMENTAL")
```

### Prosocial

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "EXPERIMENTAL")
```

### IOS

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "EXPERIMENTAL")
```


## PTSD

### PSS

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "EXPERIMENTAL")
```

### PANAS

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "EXPERIMENTAL")
```

### Prosocial

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "EXPERIMENTAL")
```

### IOS

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "EXPERIMENTAL")
```


## Burnout

### PSS

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "EXPERIMENTAL")
```

### PANAS

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "EXPERIMENTAL")
```

### Prosocial

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "EXPERIMENTAL")
```

### IOS

```{r, message=FALSE, fig.height=6, fig.width=8}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "EXPERIMENTAL")
```



# Only online

## Normal Pop

### PSS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "EXPERIMENTAL")
```

### PANAS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "EXPERIMENTAL")
```

### Prosocial

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "EXPERIMENTAL")
```

### IOS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "EXPERIMENTAL")
```


## PTSD

### PSS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "EXPERIMENTAL")
```

### PANAS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "EXPERIMENTAL")
```

### Prosocial

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "EXPERIMENTAL")
```

### IOS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "EXPERIMENTAL")
```


## Burnout

### PSS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "EXPERIMENTAL")
```

### PANAS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "EXPERIMENTAL")
```

### Prosocial

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "EXPERIMENTAL")
```

### IOS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "online") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "EXPERIMENTAL")
```



# Only GSR

## Normal Pop

### PSS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "EXPERIMENTAL")
```

### PANAS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "EXPERIMENTAL")
```

### Prosocial

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "EXPERIMENTAL")
```

### IOS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "EXPERIMENTAL")
```


## PTSD

### PSS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "EXPERIMENTAL")
```

### PANAS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "EXPERIMENTAL")
```

### Prosocial

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "EXPERIMENTAL")
```

### IOS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "EXPERIMENTAL")
```


## Burnout

### PSS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "EXPERIMENTAL")
```

### PANAS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "EXPERIMENTAL")
```

### Prosocial

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "EXPERIMENTAL")
```

### IOS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "CONTROL")

scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout", Exp_type == "GSR") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "EXPERIMENTAL")
```



# Only RMN & OXT

## Normal Pop (has only Normal Pop and EXPERIMENTAL)

### PSS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "EXPERIMENTAL")
```

### PANAS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "EXPERIMENTAL")
```

### Prosocial

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "EXPERIMENTAL")
```

### IOS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "EXPERIMENTAL")
```



# GSR, RMN & OXT

## Normal Pop (has only Normal Pop and EXPERIMENTAL)

### PSS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = id, 
                    xlab = "", ylab = "PSS",
                    title = "EXPERIMENTAL")
```

### PANAS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = id, 
                    xlab = "", ylab = "NA",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = id, 
                    xlab = "", ylab = "PA",
                    title = "EXPERIMENTAL")
```

### Prosocial

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = id, 
                    xlab = "", ylab = "PSint",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = PSmot_Total, outlier.label = id, 
                    xlab = "", ylab = "PSmot",
                    title = "EXPERIMENTAL")
```

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = PSiden_Total, outlier.label = id, 
                    xlab = "", ylab = "PSiden",
                    title = "EXPERIMENTAL")
```

### IOS

```{r, message=FALSE, fig.height=5, fig.width=7}
scale_united_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen", Exp_type %in% c("GSR", "RMN", "OXT")) %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = id, 
                    xlab = "", ylab = "IOS",
                    title = "EXPERIMENTAL")
```






<!-- Session Info and License -->

<br>

# Session Info
```{r session_info, echo = FALSE, results = 'markup'}
sessionInfo()    
```

<!-- Footer -->
&nbsp;
<hr />
<p style="text-align: center;">A work by <a href="https://github.com/ClaudiuPapasteri/">Claudiu Papasteri</a></p>
<p style="text-align: center;"><span style="color: #808080;"><em>claudiu.papasteri@gmail.com</em></span></p>
&nbsp;
