PA4 Report - Geriatric

Initial Analysis

1 Define scoring 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)
  )
}
##

2 Quick ggwithinstats function

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)

2.1 Behavioral scales

folder <- "C:/Users/Mihai/Desktop/R Notebooks/notebooks/PA4-geronto"
file_beh_pre <- "Set teste zi 1 pre grup vârstnici.xlsx"
file_beh_post <- "Set teste zi 5 post grup vârstnici.xlsx"


### Pre
behav_pre <- rio::import(file.path(folder, file_beh_pre))
behav_pre <- behav_pre[-1,]
names(behav_pre)[6] <- "id"
behav_pre[behav_pre$id == "CU16PA1",]$id <- "CN16PA1"     # typo, should be "CN_16" instead of "CU-16" -- in vas its "CN_16"
behav_pre <- behav_pre %>% 
  janitor::clean_names() %>%
  dplyr::mutate(id = stringr::str_remove(id, "PA1"),
                id = gsub('^(.{2})(.*)$', '\\1_\\2', id)) %>%        # insert "_" afeter code
  tidyr::separate(id, into = c("forma", "ID_no"), remove = FALSE, sep = "_") %>%
  dplyr::mutate(forma = gsub('^(.{1})(.*)$', '\\1_\\2', id)) %>%
  tidyr::separate(forma, into = c("Cond", "Cog_imp"), remove = FALSE, sep = "_") %>%
  dplyr::mutate(Cond = dplyr::case_when(Cond == "C" ~ "CTRL",
                                        Cond == "E" ~ "TR",
                                        TRUE ~ NA_character_)) %>%
  dplyr::mutate(id = stringr::str_replace(id, "O", "0"),
                id = stringr::str_replace(id, "o", "0")) %>%
  dplyr::mutate(ID_no = stringr::str_replace(ID_no, "O", "0"),
                ID_no = stringr::str_replace(ID_no, "o", "0"),
                ID_no = as.numeric(ID_no))

## Post
behav_post <- rio::import(file.path(folder, file_beh_post))
behav_post <- behav_post[-1,]
behav_post <- behav_post[,-c(6:9)]
names(behav_post)[6] <- "id"
behav_post <- behav_post %>% 
  janitor::clean_names() %>%
  dplyr::mutate(id = stringr::str_remove(id, "PA1"),
                id = gsub('^(.{2})(.*)$', '\\1_\\2', id)) %>%        # insert "_" afeter code
  tidyr::separate(id, into = c("forma", "ID_no"), remove = FALSE, sep = "_") %>%
  dplyr::mutate(forma = gsub('^(.{1})(.*)$', '\\1_\\2', id)) %>%
  tidyr::separate(forma, into = c("Cond", "Cog_imp"), remove = FALSE, sep = "_") %>%
  dplyr::mutate(Cond = dplyr::case_when(Cond == "C" ~ "CTRL",
                                        Cond == "E" ~ "TR",
                                        TRUE ~ NA_character_)) %>%
  dplyr::mutate(id = stringr::str_replace(id, "O", "0"),
                id = stringr::str_replace(id, "o", "0")) %>%
  dplyr::mutate(ID_no = stringr::str_replace(ID_no, "O", "0"),
                ID_no = stringr::str_replace(ID_no, "o", "0"),
                ID_no = as.numeric(ID_no))



# checks
bla <- data.frame(Pre = names(behav_pre), Post = names(behav_post))

behav_pre %>%
  dplyr::count(id) %>%  # 40 x 1
  dplyr::arrange(id) %>%
  dplyr::pull(id) -> behav_pre_ids
 
  
behav_post %>%
  dplyr::count(id) %>%  # 40 x 1
  dplyr::arrange(id) %>%
  dplyr::pull(id) -> behav_post_ids

identical(behav_pre_ids, behav_post_ids)    # one has CU_16, the other CN_16

[1] TRUE

## 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 <- 11:30
colnames(behav_pre)[index_item_panas] <- sprintf("PANAS_%d", 1:20)
colnames(behav_post)[index_item_panas] <- sprintf("PANAS_%d", 1:20)

behav_pre[, index_item_panas] <- data.frame(lapply(behav_pre[, index_item_panas], 
                                      function(x) {gsub(".*în foarte mică măsură.*", "1", x)}), stringsAsFactors = FALSE)
behav_pre[, index_item_panas] <- data.frame(lapply(behav_pre[, index_item_panas], 
                                      function(x) {gsub(".*în mică măsură.*", "2", x)}), stringsAsFactors = FALSE)
behav_pre[, index_item_panas] <- data.frame(lapply(behav_pre[, index_item_panas], 
                                      function(x) {gsub(".*într-o oarecare măsură.*", "3", x)}), stringsAsFactors = FALSE)
behav_pre[, index_item_panas] <- data.frame(lapply(behav_pre[, index_item_panas], 
                                      function(x) {gsub(".*în mare măsură.*", "4", x)}), stringsAsFactors = FALSE)
behav_pre[, index_item_panas] <- data.frame(lapply(behav_pre[, index_item_panas], 
                                      function(x) {gsub(".*în foarte mare măsură.*", "5", x)}), stringsAsFactors = FALSE)

behav_post[, index_item_panas] <- data.frame(lapply(behav_post[, index_item_panas], 
                                                      function(x) {gsub(".*în foarte mică măsură.*", "1", x)}), stringsAsFactors = FALSE)
behav_post[, index_item_panas] <- data.frame(lapply(behav_post[, index_item_panas], 
                                                      function(x) {gsub(".*în mică măsură.*", "2", x)}), stringsAsFactors = FALSE)
behav_post[, index_item_panas] <- data.frame(lapply(behav_post[, index_item_panas], 
                                                      function(x) {gsub(".*într-o oarecare măsură.*", "3", x)}), stringsAsFactors = FALSE)
behav_post[, index_item_panas] <- data.frame(lapply(behav_post[, index_item_panas], 
                                                      function(x) {gsub(".*în mare măsură.*", "4", x)}), stringsAsFactors = FALSE)
behav_post[, index_item_panas] <- data.frame(lapply(behav_post[, index_item_panas], 
                                                      function(x) {gsub(".*în foarte mare măsură.*", "5", x)}), stringsAsFactors = FALSE)

# Scoring
behav_pre$PA_Total <- ScoreLikert(behav_pre[, index_item_panas][c(1, 3, 5, 9, 10, 12, 14, 16, 17, 19)], 
                                    tonumeric = TRUE, napercent = .11)                              # not more than 1 NAs for 10 items
behav_pre$NA_Total <- ScoreLikert(behav_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

behav_post$PA_Total <- ScoreLikert(behav_post[, index_item_panas][c(1, 3, 5, 9, 10, 12, 14, 16, 17, 19)], 
                                     tonumeric = TRUE, napercent = .11)                              # not more than 1 NAs for 10 items
behav_post$NA_Total <- ScoreLikert(behav_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 <- 31:44
index_item_revPSS <- c(4, 5, 6, 7, 9, 10, 13)
colnames(behav_pre)[index_item_pss] <- sprintf("PSS_%d", 1:14)
colnames(behav_post)[index_item_pss] <- sprintf("PSS_%d", 1:14)

behav_pre[, index_item_pss] <- data.frame(lapply(behav_pre[, index_item_pss], 
                                      function(x) {gsub(".*niciodată.*", "0", x)}), stringsAsFactors = FALSE)
behav_pre[, index_item_pss] <- data.frame(lapply(behav_pre[, index_item_pss], 
                                      function(x) {gsub(".*aproape niciodată.*", "1", x)}), stringsAsFactors = FALSE)
behav_pre[, index_item_pss] <- data.frame(lapply(behav_pre[, index_item_pss], 
                                      function(x) {gsub(".*uneori.*", "2", x)}), stringsAsFactors = FALSE)
behav_pre[, index_item_pss] <- data.frame(lapply(behav_pre[, index_item_pss], 
                                      function(x) {gsub(".*destul de des.*", "3", x)}), stringsAsFactors = FALSE)
behav_pre[, index_item_pss] <- data.frame(lapply(behav_pre[, index_item_pss], 
                                      function(x) {gsub(".*foarte des.*", "4", x)}), stringsAsFactors = FALSE)

behav_post[, index_item_pss] <- data.frame(lapply(behav_post[, index_item_pss], 
                                                    function(x) {gsub(".*niciodată.*", "0", x)}), stringsAsFactors = FALSE)
behav_post[, index_item_pss] <- data.frame(lapply(behav_post[, index_item_pss], 
                                                    function(x) {gsub(".*aproape niciodată.*", "1", x)}), stringsAsFactors = FALSE)
behav_post[, index_item_pss] <- data.frame(lapply(behav_post[, index_item_pss], 
                                                    function(x) {gsub(".*uneori.*", "2", x)}), stringsAsFactors = FALSE)
behav_post[, index_item_pss] <- data.frame(lapply(behav_post[, index_item_pss], 
                                                    function(x) {gsub(".*destul de des.*", "3", x)}), stringsAsFactors = FALSE)
behav_post[, index_item_pss] <- data.frame(lapply(behav_post[, index_item_pss], 
                                                    function(x) {gsub(".*foarte des.*", "4", x)}), stringsAsFactors = FALSE)

# Score
behav_pre[, index_item_pss] <- colstonumeric(behav_pre[, index_item_pss])
behav_post[, index_item_pss] <- colstonumeric(behav_post[, index_item_pss])

behav_pre[, index_item_pss][index_item_revPSS] <- ReverseCode(behav_pre[, index_item_pss][index_item_revPSS], tonumeric = FALSE, min = 0, max = 4)
behav_post[, index_item_pss][index_item_revPSS] <- ReverseCode(behav_post[, index_item_pss][index_item_revPSS], tonumeric = FALSE, min = 0, max = 4)

behav_pre$PSS_Total <- ScoreLikert(behav_pre[, index_item_pss], napercent = .11)
behav_post$PSS_Total <- ScoreLikert(behav_post[, index_item_pss], napercent = .11)



## PS int
index_item_PSint <- 45:48    # 45:55
colnames(behav_pre)[index_item_PSint] <- sprintf("PSint_%d", 1:4)
colnames(behav_post)[index_item_PSint] <- sprintf("PSint_%d", 1:4)


behav_pre[, sprintf("PSint_%d", 1:4)] <-
  behav_pre[, sprintf("PSint_%d", 1:4)] %>%
    dplyr::mutate_all(readr::parse_number)


behav_post[, sprintf("PSint_%d", 1:4)] <-
  behav_post[, sprintf("PSint_%d", 1:4)] %>%
    dplyr::mutate_all(readr::parse_number)


# Score
behav_pre$PSint_Total <- ScoreLikert(behav_pre[, index_item_PSint], tonumeric = TRUE, napercent = .33)
behav_post$PSint_Total <- ScoreLikert(behav_post[, index_item_PSint], tonumeric = TRUE, napercent = .33)



## PS mot
index_item_PSmot <- 49:52      
colnames(behav_pre)[index_item_PSmot] <- sprintf("PSmot_%d", 1:4)
colnames(behav_post)[index_item_PSmot] <- sprintf("PSmot_%d", 1:4)


behav_pre[, sprintf("PSmot_%d", 1:4)] <-
  behav_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_))


behav_post[, sprintf("PSmot_%d", 1:4)] <-
  behav_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_))


# Score
behav_pre$PSmot_Total <- ScoreLikert(behav_pre[, index_item_PSmot], tonumeric = TRUE, napercent = .33)
behav_post$PSmot_Total <- ScoreLikert(behav_post[, index_item_PSmot], tonumeric = TRUE, napercent = .33)



## PS iden
index_item_PSiden <- 53:55     
colnames(behav_pre)[index_item_PSiden] <- sprintf("PSiden_%d", 1:3)
colnames(behav_post)[index_item_PSiden] <- sprintf("PSiden_%d", 1:3)


behav_pre[, sprintf("PSiden_%d", 1:3)] <-
  behav_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_))


behav_post[, sprintf("PSiden_%d", 1:3)] <-
  behav_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_))


# Score
behav_pre$PSiden_Total <- ScoreLikert(behav_pre[, index_item_PSiden], tonumeric = TRUE, napercent = .33)
behav_post$PSiden_Total <- ScoreLikert(behav_post[, index_item_PSiden], tonumeric = TRUE, napercent = .33)


## IOS
colnames(behav_pre)[56] <- "IOS"
colnames(behav_post)[56] <- "IOS"

behav_pre$IOS <- as.numeric(behav_pre$IOS)
behav_post$IOS <- as.numeric(behav_post$IOS) 



## Add some columns
behav_pre <- behav_pre %>%
  dplyr::mutate(PrePost = rep("Pre", nrow(.)))

behav_post <- behav_post %>%
  dplyr::mutate(PrePost = rep("Post", nrow(.)))

# Long format
behav_long <- dplyr::bind_rows(behav_pre, behav_post) %>%
  dplyr::mutate(PrePost = factor(PrePost, levels = c("Pre", "Post")))

# PSS
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Stress",
                   title = "CTRL")

Registered S3 methods overwritten by 'parameters':
  method                           from      
  as.double.parameters_kurtosis    datawizard
  as.double.parameters_skewness    datawizard
  as.double.parameters_smoothness  datawizard
  as.numeric.parameters_kurtosis   datawizard
  as.numeric.parameters_skewness   datawizard
  as.numeric.parameters_smoothness datawizard
  print.parameters_distribution    datawizard
  print.parameters_kurtosis        datawizard
  print.parameters_skewness        datawizard
  summary.parameters_kurtosis      datawizard
  summary.parameters_skewness      datawizard
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Stress",
                   title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# NA
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Negative Affect",
                   title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Negative Affect",
                   title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# PA
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Positive Affect",
                   title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Positive Affect",
                   title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# Psint
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Prosocial intention",
                   title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Prosocial intention",
                   title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# Psmot
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Prosocial motivation",
                   title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Prosocial motivation",
                   title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# IOS
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Closeness",
                   title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Closeness",
                   title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

2.2 VAS Stress

folder <- "C:/Users/Mihai/Desktop/R Notebooks/notebooks/PA4-geronto"
file_pre <- "date VAS STRES PRE varstnici - modif.xls"
file_post <- "date VAS STRES POST varstnici.xls"

stres_pre <- rio::import(file.path(folder, file_pre))

stres_pre <- stres_pre[-1, c(3:5, 10:11)]

stres_pre <- stres_pre[-37,]  # Sofi notebook: on 31-07 id 19 wanted 0 but put 25 ... he has two recodings with 25 and 0 ... delete the one with 25
#stres_pre <- stres_pre[-122,]   # no data
names(stres_pre)[4:5] <- c("id", "vas_stres")
stres_pre[stres_pre$id == "EN08PA1",]$id <- "EU08PA1"    # typo "EN_08", should be "EU_08"
stres_pre <- stres_pre %>% 
  janitor::clean_names() %>%
  dplyr::mutate(id = stringr::str_remove(id, "PA1"),
                id = gsub('^(.{2})(.*)$', '\\1_\\2', id)) %>%        # insert "_" after code
  tidyr::separate(id, into = c("forma", "ID_no"), remove = FALSE, sep = "_") %>%
  dplyr::mutate(forma = gsub('^(.{1})(.*)$', '\\1_\\2', id)) %>%
  tidyr::separate(forma, into = c("Cond", "Cog_imp"), remove = FALSE, sep = "_") %>%
  dplyr::mutate(Cond = dplyr::case_when(Cond == "C" ~ "CTRL",
                                        Cond == "E" ~ "TR",
                                        TRUE ~ NA_character_)) %>%
  dplyr::mutate(vas_stres = as.numeric(vas_stres)) %>%
  dplyr::mutate(id = stringr::str_replace(id, "O", "0"),
                id = stringr::str_replace(id, "o", "0")) %>%
  dplyr::mutate(ID_no = stringr::str_replace(ID_no, "O", "0"),
                ID_no = stringr::str_replace(ID_no, "o", "0"),
                ID_no = as.numeric(ID_no))


stres_post <- rio::import(file.path(folder, file_post))

stres_post <- stres_post[-1, c(3:5, 10:11)]
names(stres_post)[4:5] <- c("id", "vas_stres")
stres_post[stres_post$id == "CN12PA1",]$id <- "CU12PA1"    # typo "CN_12", should be "CU_12"
stres_post <- stres_post %>% 
  janitor::clean_names() %>%
  dplyr::mutate(id = stringr::str_remove(id, "PA1"),
                id = gsub('^(.{2})(.*)$', '\\1_\\2', id)) %>%        # insert "_" after code
  tidyr::separate(id, into = c("forma", "ID_no"), remove = FALSE, sep = "_") %>%
  dplyr::mutate(forma = gsub('^(.{1})(.*)$', '\\1_\\2', id)) %>%
  tidyr::separate(forma, into = c("Cond", "Cog_imp"), remove = FALSE, sep = "_") %>%
  dplyr::mutate(Cond = dplyr::case_when(Cond == "C" ~ "CTRL",
                                        Cond == "E" ~ "TR",
                                        TRUE ~ NA_character_)) %>%
  dplyr::mutate(vas_stres = as.numeric(vas_stres)) %>%
  dplyr::mutate(id = stringr::str_replace(id, "O", "0"),
                id = stringr::str_replace(id, "o", "0")) %>%
  dplyr::mutate(ID_no = stringr::str_replace(ID_no, "O", "0"),
                ID_no = stringr::str_replace(ID_no, "o", "0"),
                ID_no = as.numeric(ID_no))

  

# retrasi
retrasi_post <- c(1, 6, 7)

# check
stres_pre %>%
  dplyr::count(id) %>%
  dplyr::pull(id) -> stres_pre_ids
setdiff(stres_pre_ids, behav_pre_ids)   
stres_post %>%
  dplyr::count(id) %>%
  dplyr::pull(id) -> stres_post_ids
setdiff(stres_post_ids, behav_pre_ids)  
setdiff(stres_pre_ids, stres_post_ids)
# Add variable Time
# its: pre-post x4 + pre-post-post => pre = 1,3,5,7,9  post = 2,4,6,8,10,11
# in pre EN_19 have 6 instead of 5 data -- fixed now; EN_04 fixed in excel file

stres_pre_time <- 
  stres_pre %>%
  dplyr::mutate(row_numb = dplyr::row_number()) %>%
  dplyr::arrange(desc(row_numb)) %>%                     # order of rows needs to be inverted to be in chronological order
  dplyr::select(-row_numb) %>%
  dplyr::group_by(id) %>%
  dplyr::mutate(Time = dplyr::row_number()) %>%
  dplyr::mutate(Time = dplyr::case_when(Cond == "CTRL" & Time == 1 ~ 9,
                                        Cond == "TR" & Time == 1 ~ 1,
                                        Cond == "TR" & Time == 2 ~ 3,
                                        Cond == "TR" & Time == 3 ~ 5,
                                        Cond == "TR" & Time == 4 ~ 7,
                                        Cond == "TR" & Time == 5 ~ 9,
                                        TRUE ~ NA_real_)) %>%
  dplyr::ungroup()                    

stres_post_time <- 
  stres_post %>%
  tidyr::drop_na(vas_stres) %>%                      # there is one NA that ruins the flow
  dplyr::mutate(row_numb = dplyr::row_number()) %>%
  dplyr::arrange(desc(row_numb)) %>%                     # order of rows needs to be inverted to be in chronological order
  dplyr::select(-row_numb) %>%
  dplyr::group_by(id) %>%
  dplyr::mutate(Time = dplyr::row_number()) %>%
  dplyr::mutate(Time = dplyr::case_when(Cond == "CTRL" & Time == 1 ~ 10,
                                        Cond == "CTRL" & Time == 2 ~ 11,
                                        Cond == "TR" & Time == 1 ~ 2,
                                        Cond == "TR" & Time == 2 ~ 4,
                                        Cond == "TR" & Time == 3 ~ 6,
                                        Cond == "TR" & Time == 4 ~ 8,
                                        Cond == "TR" & Time == 5 ~ 10,
                                        Cond == "TR" & Time == 6 ~ 11,
                                        TRUE ~ NA_real_)) %>%
  dplyr::ungroup()


# check
stres_pre_time %>%
  dplyr::group_by(id) %>%
  filter(Time == max(Time)) %>% 
  print(n = Inf)               # all are 1 and 9 -- good

stres_post_time %>%
  dplyr::group_by(id) %>%
  filter(Time == max(Time)) %>% 
  print(n = Inf)              # all are 11 -- good because last post is same in TR and CTRL

# Long data
stres_time_long <- dplyr::bind_rows(stres_pre_time, stres_post_time) %>%
  dplyr::mutate(Time = factor(Time, levels = c(1:11)))

2.2.1 CTRL

stres_time_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  ggstatsplot::ggwithinstats(x = Time, y = vas_stres, outlier.label = id, 
                type = "np")

2.2.2 TR

stres_time_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Time %in% c(9, 10, 11)) %>%
  ggstatsplot::ggwithinstats(x = Time, y = vas_stres, outlier.label = id, 
                type = "np")

stres_time_long %>%
  dplyr::filter(Cond == "TR") %>%
  ggstatsplot::ggwithinstats(x = Time, y = vas_stres, outlier.label = id, 
                type = "np")

Warning: Number of labels is greater than default palette color count.
 Try using another color `palette` (and/or `package`).

stres_time_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(!Time %in% c(9, 10, 11)) %>%
  ggstatsplot::ggwithinstats(x = Time, y = vas_stres, outlier.label = id, 
                type = "np")

2.3 Only for Normal Cognition

# deficienta cognitiva (N-normal, U-usor deficit cognitiv)

# PSS
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  dplyr::filter(Cog_imp == "N") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Stress",
                   title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Cog_imp == "N") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Stress",
                   title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# NA
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  dplyr::filter(Cog_imp == "N") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Negative Affect",
                   title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Cog_imp == "N") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Negative Affect",
                   title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# PA
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  dplyr::filter(Cog_imp == "N") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Positive Affect",
                   title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Cog_imp == "N") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Positive Affect",
                   title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# Psint
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  dplyr::filter(Cog_imp == "N") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Prosocial intention",
                   title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Cog_imp == "N") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Prosocial intention",
                   title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# Psmot
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  dplyr::filter(Cog_imp == "N") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Prosocial motivation",
                   title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Cog_imp == "N") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Prosocial motivation",
                   title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# IOS
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  dplyr::filter(Cog_imp == "N") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Closeness",
                   title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Cog_imp == "N") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = ID_no, type = "np", 
                   xlab = "", ylab = "Closeness",
                   title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

2.4 Only for Light Cognitive Deficit

# deficienta cognitiva (N-normal, U-usor deficit cognitiv)

# PSS
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  dplyr::filter(Cog_imp == "U") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = ID_no, type = "np", 
                    xlab = "", ylab = "Stress",
                    title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Cog_imp == "U") %>%
  my_ggwithinstats2(x = PrePost, y = PSS_Total, outlier.label = ID_no, type = "np", 
                    xlab = "", ylab = "Stress",
                    title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# NA
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  dplyr::filter(Cog_imp == "U") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = ID_no, type = "np", 
                    xlab = "", ylab = "Negative Affect",
                    title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Cog_imp == "U") %>%
  my_ggwithinstats2(x = PrePost, y = NA_Total, outlier.label = ID_no, type = "np", 
                    xlab = "", ylab = "Negative Affect",
                    title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# PA
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  dplyr::filter(Cog_imp == "U") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = ID_no, type = "np", 
                    xlab = "", ylab = "Positive Affect",
                    title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Cog_imp == "U") %>%
  my_ggwithinstats2(x = PrePost, y = PA_Total, outlier.label = ID_no, type = "np", 
                    xlab = "", ylab = "Positive Affect",
                    title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# Psint
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  dplyr::filter(Cog_imp == "U") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = ID_no, type = "np", 
                    xlab = "", ylab = "Prosocial intention",
                    title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Cog_imp == "U") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = ID_no, type = "np", 
                    xlab = "", ylab = "Prosocial intention",
                    title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# Psmot
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  dplyr::filter(Cog_imp == "U") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = ID_no, type = "np", 
                    xlab = "", ylab = "Prosocial motivation",
                    title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Cog_imp == "U") %>%
  my_ggwithinstats2(x = PrePost, y = PSint_Total, outlier.label = ID_no, type = "np", 
                    xlab = "", ylab = "Prosocial motivation",
                    title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

# IOS
behav_long %>%
  dplyr::filter(Cond == "CTRL") %>%
  dplyr::filter(Cog_imp == "U") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = ID_no, type = "np", 
                    xlab = "", ylab = "Closeness",
                    title = "CTRL")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

behav_long %>%
  dplyr::filter(Cond == "TR") %>%
  dplyr::filter(Cog_imp == "U") %>%
  my_ggwithinstats2(x = PrePost, y = IOS, outlier.label = ID_no, type = "np", 
                    xlab = "", ylab = "Closeness",
                    title = "TR")

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

3 Test VAS Stress differences in response to Bucharest Stress Task

3.1 see Brunner et al. (2002)

# experiment has the F1-LD-F1 designstructure with treatment being the whole-plot factor. (p. 13 art R Journal)

stres_f1ldf1 <- 
  stres_time_long %>%
    dplyr::filter(Time %in% c(9, 10, 11)) %>%
    dplyr::mutate(Time = factor(Time, levels = c(9, 10, 11))) 

nparLD::f1.ld.f1(y = stres_f1ldf1$vas_stres, time = stres_f1ldf1$Time, group = stres_f1ldf1$Cond, subject = stres_f1ldf1$id)

Attaching package: ‘MASS’

The following object is masked from ‘package:rstatix’:

    select

The following object is masked from ‘package:dplyr’:

    select

 Total number of observations:  120 
 Total number of subjects:   40 
 Total number of missing observations:  0 

 Class level information 
 ----------------------- 
 Levels of Time (sub-plot factor time) :  3 
 Levels of Group (whole-plot factor group) :  2 

 Abbreviations 
 ----------------------- 
 RankMeans = Rank means
 Nobs = Number of observations
 RTE = Relative treatment effect
 case2x2 = tests for 2-by-2 design
 Wald.test = Wald-type test statistic
 ANOVA.test = ANOVA-type test statistic with Box approximation
 ANOVA.test.mod.Box = modified ANOVA-type test statistic with Box approximation
 Wald.test.time = Wald-type test statistic for simple time effect
 ANOVA.test.time = ANOVA-type test statistic for simple time effect
 N = Standard Normal Distribution N(0,1)
 T = Student's T distribution with respective degrees of freedom
 pattern.time (time effects) = Test against patterned alternatives in time using normal distribution ( no pattern specified ) 
 pair.comparison = Tests for pairwise comparisions (without specifying a pattern) 
 pattern.pair.comparison = Test for pairwise comparisons with patterned alternatives in time ( no pattern specified ) 
 pattern.group (group effects) = Test against patterned alternatives in group ( no pattern specified ) 
 covariance = Covariance matrix 
 Note: The description output above will disappear by setting description=FALSE in the input. See the help file for details. 

 F1 LD F1 Model 
 ----------------------- 
 Check that the order of the time and group levels are correct.
 Time level:   9 10 11 
 Group level:   CTRL TR 
 If the order is not correct, specify the correct order in time.order or group.order.

$RTE

$case2x2
NULL

$Wald.test
             Statistic df          p-value
Group       0.11896719  1 0.73015736878659
Time       34.63043649  2 0.00000003020621
Group:Time  0.00187216  2 0.99906435792943

$ANOVA.test
              Statistic      df         p-value
Group       0.118967187 1.00000 0.7301573687866
Time       14.613795919 1.86256 0.0000009784007
Group:Time  0.001182357 1.86256 0.9981935278646

$ANOVA.test.mod.Box
      Statistic df1      df2   p-value
Group 0.1189672   1 37.99886 0.7320593

$Wald.test.time

$ANOVA.test.time

$pattern.time
NULL

$pair.comparison

$pattern.pair.comparison
NULL

$covariance
NULL

$model.name
[1] "F1 LD F1 Model"

4 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] MASS_7.3-54                rio_0.5.27                 scales_1.1.1               ggpubr_0.4.0               summarytools_1.0.0        
 [6] rstatix_0.7.0              broom_0.7.9                PerformanceAnalytics_2.0.4 xts_0.12.1                 zoo_1.8-9                 
[11] psych_2.1.6                plyr_1.8.6                 forcats_0.5.1              stringr_1.4.0              dplyr_1.0.7               
[16] purrr_0.3.4                readr_2.0.1                tidyr_1.1.3                tibble_3.1.4               ggplot2_3.3.5             
[21] 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           splines_4.1.0             gmp_0.6-2                
  [6] kSamples_1.2-9            ipmisc_6.0.2              TH.data_1.0-10            pryr_0.1.5                digest_0.6.28            
 [11] SuppDists_1.1-9.5         htmltools_0.5.2           magick_2.7.3              fansi_0.5.0               magrittr_2.0.1           
 [16] checkmate_2.0.0           memoise_2.0.0             paletteer_1.4.0           tzdb_0.1.2                openxlsx_4.2.4           
 [21] modelr_0.1.8              matrixStats_0.60.1        sandwich_3.0-1            colorspace_2.0-2          rvest_1.0.1              
 [26] ggrepel_0.9.1             haven_2.4.3               xfun_0.25                 prismatic_1.0.0           tcltk_4.1.0              
 [31] crayon_1.4.1              jsonlite_1.7.2            zeallot_0.1.0             survival_3.2-13           glue_1.4.2               
 [36] gtable_0.3.0              emmeans_1.6.3             MatrixModels_0.5-0        statsExpressions_1.1.0    car_3.0-11               
 [41] Rmpfr_0.8-4               abind_1.4-5               rapportools_1.0           mvtnorm_1.1-2             DBI_1.1.1                
 [46] PMCMRplus_1.9.0           Rcpp_1.0.7                performance_0.7.3         xtable_1.8-4              tmvnsim_1.0-2            
 [51] foreign_0.8-81            datawizard_0.2.0.1        httr_1.4.2                ellipsis_0.3.2            farver_2.1.0             
 [56] pkgconfig_2.0.3           reshape_0.8.8             multcompView_0.1-8        dbplyr_2.1.1              utf8_1.2.2               
 [61] janitor_2.1.0             labeling_0.4.2            effectsize_0.4.5          tidyselect_1.1.1          rlang_0.4.11             
 [66] munsell_0.5.0             cellranger_1.1.0          tools_4.1.0               cachem_1.0.6              ggprism_1.0.3            
 [71] cli_3.0.1                 generics_0.1.0            fastmap_1.1.0             BWStest_0.2.2             rematch2_2.1.2           
 [76] knitr_1.33                fs_1.5.0                  zip_2.2.0                 pander_0.6.4              WRS2_1.1-3               
 [81] pbapply_1.4-3             nlme_3.1-152              xml2_1.3.2                correlation_0.7.0         compiler_4.1.0           
 [86] rstudioapi_0.13           curl_4.3.2                ggsignif_0.6.2            reprex_2.0.1              stringi_1.7.4            
 [91] parameters_0.14.0         lattice_0.20-44           Matrix_1.3-4              vctrs_0.3.8               pillar_1.6.3             
 [96] lifecycle_1.0.1           mc2d_0.1-21               BiocManager_1.30.16       estimability_1.3          data.table_1.14.0        
[101] insight_0.14.4            patchwork_1.1.1           R6_2.5.1                  BayesFactor_0.9.12-4.2    codetools_0.2-18         
[106] boot_1.3-28               gtools_3.9.2              assertthat_0.2.1          nparLD_2.1                withr_2.4.2              
[111] mnormt_2.0.2              multcomp_1.4-17           bayestestR_0.11.0         parallel_4.1.0            hms_1.1.0                
[116] quadprog_1.5-8            grid_4.1.0                coda_0.19-4               snakecase_0.11.0          carData_3.0-4            
[121] lubridate_1.7.10          base64enc_0.1-3           ggstatsplot_0.8.0        

 

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A work by Claudiu Papasteri

claudiu.papasteri@gmail.com