PA4 - HRV

Report

1 Define functions

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 ID data
folder <- "C:/Users/Mihai/Desktop/R Notebooks/notebooks/PA4-full-report"
file <- "Scale complete triate Sofi pa4.xlsx"
setwd(folder)

id_df <- xlsx::read.xlsx2(file.path(folder, file),
                          startRow = 1, header = FALSE, sheetName = "incadrari")

id_df <- id_df[, 1:8]
colnames(id_df) <- c("Grup", "Cond", "id", "email", "gen", "dir1", "dir2", "dir3")



id_df <- 
  id_df %>%
  dplyr::na_if("") %>%
  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)) %>% 
  dplyr::mutate(email = tolower(email),
                email = stringr::str_remove_all(email, "[[:blank:]]"))

id_df <- 
  id_df %>%
  dplyr::mutate(across(starts_with("dir"),  as.numeric)) %>%
  dplyr::filter(id != "9RMN")


# check if info in dir colums is correct
check_ordered <- sort(c(id_df$dir1, id_df$dir2, id_df$dir3))  # 8.9,10 from 1GSR/9RMN
check_ordered <- unique(check_ordered)

hrv_all <- c(5:228, 230:257)    # 229 was skipped

all.equal(check_ordered, hrv_all)  # GOOD
# Read HRV data
root_folder <- "E:/CINETIC diverse/PA4 HRV"
temp_folder <- "temp_PPG"
dir1_path <- file.path(root_folder, "dir1", temp_folder)
# dir2_path <- file.path(root_folder, "dir2", temp_folder)
dir3_path <- file.path(root_folder, "dir3", temp_folder)

dir1_file <- paste0("IBI_", "dir1", ".csv")
# dir2_file <- paste0("IBI_", "dir2", ".csv")
dir3_file <- paste0("IBI_", "dir3", ".csv")


dir1_df <- read.csv(file.path(dir1_path, dir1_file))
names(dir1_df) <- c("rec", "base", "b_first5", "b_last5")
dir1_df <- 
  dir1_df %>%
  mutate_all(~ifelse(is.nan(.), NA, .)) %>%
  mutate(rec = stringr::str_remove(rec, "BUN_M2_Session"),
         rec = stringr::str_remove(rec, "_idCD5C_Calibrated_PC.csv"),
         rec = as.numeric(rec))


dir3_df <- read.csv(file.path(dir3_path, dir3_file))
names(dir3_df) <- c("rec", "whole", "first5", "last5")
dir3_df <- 
  dir3_df %>%
  mutate_all(~ifelse(is.nan(.), NA, .)) %>%
  mutate(rec = stringr::str_remove(rec, "BUN_M2_Session"),
         rec = stringr::str_remove(rec, "_idCD5C_Calibrated_PC.csv"),
         rec = as.numeric(rec))


# Make merged dataframe
dir_merge <- dplyr::left_join(id_df, dir1_df, by = c("dir1" = "rec"))
dir_merge <- dplyr::left_join(dir_merge, dir3_df, by = c("dir3" = "rec"))

dir_merge <-
  dir_merge %>%
  tidyr::drop_na(base, first5, last5)

dir_merge_long <-
  dir_merge %>%
  dplyr::select(-c(b_first5, b_last5, whole)) %>%
  tidyr::pivot_longer(cols = c(base, first5, last5), names_to = "Time", values_to = "HRV" )

3 Whole sample

dir_merge_long %>%
  dplyr::filter(Cond == "CONTROL") %>%
  my_ggwithinstats2(x = Time, y = HRV, outlier.label = id, 
                    xlab = "", ylab = "HRV",
                    title = "CONTROL")

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

dir_merge_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL") %>%
  my_ggwithinstats2(x = Time, y = HRV, outlier.label = id, 
                    xlab = "", ylab = "HRV",
                    title = "EXPERIMENTAL")

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

4 By group

4.1 Normal Pop

dir_merge_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = Time, y = HRV, outlier.label = id, 
                    xlab = "", ylab = "HRV",
                    title = "CONTROL")

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

dir_merge_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "pop gen") %>%
  my_ggwithinstats2(x = Time, y = HRV, outlier.label = id, 
                    xlab = "", ylab = "HRV",
                    title = "EXPERIMENTAL")

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

4.2 PTSD

• only 1 CONTROL with PTSD had HRV data

# dir_merge_long %>%
#   dplyr::filter(Cond == "CONTROL", Grup == "PTSD") %>%
#   my_ggwithinstats2(x = Time, y = HRV, outlier.label = id, 
#                     xlab = "", ylab = "HRV",
#                     title = "CONTROL")

dir_merge_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "PTSD") %>%
  my_ggwithinstats2(x = Time, y = HRV, outlier.label = id, type = "np",
                    xlab = "", ylab = "HRV",
                    title = "EXPERIMENTAL")

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

4.3 Burnout

dir_merge_long %>%
  dplyr::filter(Cond == "CONTROL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = Time, y = HRV, outlier.label = id, 
                    xlab = "", ylab = "HRV",
                    title = "CONTROL")

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

dir_merge_long %>%
  dplyr::filter(Cond == "EXPERIMENTAL", Grup == "Burnout") %>%
  my_ggwithinstats2(x = Time, y = HRV, outlier.label = id, type = "np",
                    xlab = "", ylab = "HRV",
                    title = "EXPERIMENTAL")

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

5 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] ggstatsplot_0.8.0          rio_0.5.29                 scales_1.1.1               ggpubr_0.4.0               rstatix_0.7.0             
 [6] broom_0.7.11               PerformanceAnalytics_2.0.4 xts_0.12.1                 zoo_1.8-9                  psych_2.1.6               
[11] forcats_0.5.1              stringr_1.4.0              dplyr_1.0.7                purrr_0.3.4                readr_2.0.1               
[16] tidyr_1.1.3                tibble_3.1.4               ggplot2_3.3.5              tidyverse_1.3.1            papaja_0.1.0.9997         
[21] 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         lmerTest_3.1-3            fansi_0.5.0               magrittr_2.0.1            memoise_2.0.0            
 [16] xlsx_0.6.5                paletteer_1.4.0           tzdb_0.1.2                openxlsx_4.2.4            modelr_0.1.8             
 [21] sandwich_3.0-1            colorspace_2.0-2          rvest_1.0.1               ggrepel_0.9.1             haven_2.4.3              
 [26] xfun_0.25                 crayon_1.4.1              jsonlite_1.7.2            lme4_1.1-27.1             zeallot_0.1.0            
 [31] survival_3.2-13           glue_1.4.2                gtable_0.3.0              emmeans_1.6.3             MatrixModels_0.5-0       
 [36] statsExpressions_1.1.0    car_3.0-11                Rmpfr_0.8-4               abind_1.4-5               mvtnorm_1.1-2            
 [41] DBI_1.1.1                 PMCMRplus_1.9.0           Rcpp_1.0.7                xtable_1.8-4              performance_0.7.3        
 [46] tmvnsim_1.0-2             foreign_0.8-81            datawizard_0.2.0.1        httr_1.4.2                ellipsis_0.3.2           
 [51] farver_2.1.0              pkgconfig_2.0.3           reshape_0.8.8             rJava_1.0-4               multcompView_0.1-8       
 [56] dbplyr_2.1.1              utf8_1.2.2                janitor_2.1.0             labeling_0.4.2            reshape2_1.4.4           
 [61] tidyselect_1.1.1          rlang_0.4.11              effectsize_0.4.5          munsell_0.5.0             cellranger_1.1.0         
 [66] tools_4.1.0               cachem_1.0.6              ggprism_1.0.3             cli_3.0.1                 generics_0.1.0           
 [71] fastmap_1.1.0             BWStest_0.2.2             rematch2_2.1.2            knitr_1.33                fs_1.5.0                 
 [76] zip_2.2.0                 WRS2_1.1-3                pbapply_1.4-3             nlme_3.1-152              xml2_1.3.2               
 [81] correlation_0.7.0         compiler_4.1.0            rstudioapi_0.13           curl_4.3.2                ggsignif_0.6.2           
 [86] reprex_2.0.1              afex_1.0-1                stringi_1.7.4             parameters_0.14.0         lattice_0.20-44          
 [91] Matrix_1.3-4              nloptr_1.2.2.2            vctrs_0.3.8               pillar_1.6.3              lifecycle_1.0.1          
 [96] mc2d_0.1-21               estimability_1.3          data.table_1.14.0         insight_0.14.4            patchwork_1.1.1          
[101] R6_2.5.1                  BayesFactor_0.9.12-4.2    codetools_0.2-18          boot_1.3-28               MASS_7.3-54              
[106] gtools_3.9.2              assertthat_0.2.1          xlsxjars_0.6.1            withr_2.4.2               mnormt_2.0.2             
[111] multcomp_1.4-17           bayestestR_0.11.0         parallel_4.1.0            hms_1.1.0                 quadprog_1.5-8           
[116] grid_4.1.0                minqa_1.2.4               coda_0.19-4               snakecase_0.11.0          carData_3.0-4            
[121] numDeriv_2016.8-1.1       lubridate_1.7.10         

 

---

A work by Claudiu Papasteri

claudiu.papasteri@gmail.com