R Notebook

## Set-Up
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(sparkline)
TODAY<-Sys.Date()
## ADD SPARKLINE FUNCTION (Only needed once)
formattableWithSL <- function(TableDF,rawDF,key,SLfield){
  ## Add sparkline field to TableDF
  TableDF$sparkline <- TableDF[[key]]
  ## Now basically call formattable, passing in the vars
  formattable(TableDF,list(
    "sparkline"=function(z){
      sapply(z,function(zz){knitr::knit(text = sprintf(
              '`r sparkline(c(%s))`',
              paste0(
                rawDF[which(rawDF[,key] == zz),SLfield],
                collapse=","
              )),
            quiet = TRUE
          )})}  ))
}

eBACH_QC_Database1 <- readxl::read_excel( "/Volumes/eBACH/MRI/QC_Output/eBACH_QC_Database.xlsx", 
    sheet = "PRE", col_types = c("numeric", 
        "text", "text", "date", "text", "text", 
        "text", "text", "text", "text", "text", 
        "text", "numeric", "text", "text", 
        "text", "text", "text", "text", "text", 
        "numeric", "text", "numeric", "numeric", 
        "numeric", "text", "text", "text", 
        "text", "numeric", "text", "text", 
        "text", "text", "text", "text", "text"))
eBACH_QC_Database1<-eBACH_QC_Database1[complete.cases(eBACH_QC_Database1$Subject),]
eBACH_QC_Database1$WeeklyReport<-eBACH_QC_Database1$`Weekly report?`
eBACH_QC_Database1$Date<-eBACH_QC_Database1$`Date of MRI Scan`
eBACH_QC_Database1$Session<-"MR1"
eBACH_QC_Database1$ScanNotes<-eBACH_QC_Database1$`Overall Scan Notes`
eBACH_QC_Database1$QC03<-eBACH_QC_Database1$Notes...20
eBACH_QC_Database1$QC04<-eBACH_QC_Database1$Notes...27
eBACH_QC_Database1<-eBACH_QC_Database1%>% select(Subject,Session,Date, WeeklyReport,ScanNotes,QC03, QC04)
eBACH_QC_Database1<-eBACH_QC_Database1[complete.cases(eBACH_QC_Database1$Subject),]
eBACH_QC_Database1<-as.tibble(eBACH_QC_Database1)
#eBACH_QC_Database1.tb<-kable(eBACH_QC_Database1) %>%
# kable_styling(bootstrap_options = c("responsive", "bordered"),full_width = T,font_size = 10)%>%
#  row_spec(0, align='c')%>%
#  column_spec(1, bold = T) %>%
#    column_spec(2,  width = "1cm",border_right = T) %>%
#    column_spec(3, bold = F,width = "3cm" ) %>%
#      column_spec(4, bold = T, width = "1cm", border_left = T) %>%
#    column_spec(5, width = "10cm", border_right = F) %>%
#      column_spec(6, width = "10cm") 
# kableExtra::scroll_box(eBACH_QC_Database1.tb, height = "300pt", width = "700pt" )

-
/
                                                                                                                   

eBACH_QC_Database3 <- readxl::read_excel("/Volumes/eBACH/MRI/QC_Output/eBACH_QC_Database.xlsx", 
    sheet = "POST")
eBACH_QC_Database3<-eBACH_QC_Database3[complete.cases(eBACH_QC_Database3$Subject),]
eBACH_QC_Database3$WeeklyReport<-eBACH_QC_Database3$`Weekly report?`
eBACH_QC_Database3$Session<-"MR3"
eBACH_QC_Database3$Date<-eBACH_QC_Database3$`Date of MRI Scan`
eBACH_QC_Database3$ScanNotes<-eBACH_QC_Database3$`Overall Scan Notes`
eBACH_QC_Database3$QC03<-eBACH_QC_Database3$Notes...27
eBACH_QC_Database3$QC04<-eBACH_QC_Database3$Notes...38
eBACH_QC_Database3<-eBACH_QC_Database3%>% select(Subject,WeeklyReport,Date,Session, ScanNotes,QC03, QC04)
eBACH_QC_Database3<-eBACH_QC_Database3[complete.cases(eBACH_QC_Database3$Subject),]

tmp<-rbind(eBACH_QC_Database1, eBACH_QC_Database2)
tmp2<-rbind(tmp, Exclude1)
eBACH.Admin<-rbind(tmp2, eBACH_QC_Database3)

MRIQC Admin Database

Baseline: 30

Withdrew After Baseline MRI: 4

--- Randomized: E1058, E1067

--- Dropped before randomized: E1027, E1031

Mid-Point: 7

Post-Intervention: 3

Import Master Spreadsheets for Report...

Check: MASTER Spreadsheets

September 10 2020

library(formattable)
# Average by Site
################
# Make Summary table w/ SPARK_LINE
eBACH<-arrange(eBACH, "Date")
eBACH<-eBACH[complete.cases(eBACH$cnr),]
res <- eBACH %>% 
  dplyr::group_by(Session) %>% 
  dplyr::summarise(N=n(),
            'CNR'=round(mean(cnr),4),
            'sd'=round(sd(cnr),4),
            'cnr'=as.character(htmltools::as.tags(sparkline(c(cnr)))),
            'FWHM'=round(mean(fwhm_avg),4),
            'sd '=round(mean(fwhm_avg),4),
            'fwhm'=as.character(htmltools::as.tags(sparkline(c(fwhm_avg)))),
            SNR.mean=round(mean(snr_total),4),
            'sd'=round(sd(snr_total),4),
            "snr"=as.character(htmltools::as.tags(sparkline(c(snr_total)))),
            
            SNRd.mean=round(mean(snrd_total),4),
            'sd'=round(sd(snrd_total),4),
            snrd=as.character(htmltools::as.tags(sparkline(c(snrd_total))))) %>%
  formattable() %>%
  formattable::as.htmlwidget()
res$dependencies <- c(res$dependencies,htmlwidgets:::widget_dependencies("sparkline", "sparkline"))
res

eBACH<-arrange(eBACH, "Date")
eBACH$months<-format(as.Date(eBACH$Date), "%b/%y")
# select part of the dataset and use it for plotting
eBACH$months<-factor(eBACH$months, levels=c("Jun/19", "Jul/19"  ,"Sep/19" , "Oct/19" ,"Nov/19", "Dec/19" ,"Jan/20" ,"Feb/20",  "Aug/20"))
TMP2<-eBACH %>% select(SubID, Session, months, cnr, fwhm_avg,snr_total,snrd_total)
TMP2<-reshape2::melt(TMP2, id.vars=c("SubID", "Session", "months"))
TMP2$Session<-as.factor(TMP2$Session)
ggstatsplot::grouped_ggbetweenstats(
    data = TMP2,
    x=months,
    y = value,
    grouping.var = variable,
 plot.type = "violin",
    type = "np",
    conf.level = 0.99,
    xlab = "report",
    ylab = "value",
   ggtheme = ggplot2::theme_gray(), # a different theme
    package = "yarrr", # package from which color palette is to be taken
    palette = "info2", # choosing a different color palette
    outlier.tagging = TRUE,
    outlier.label.args = list(color = "red"), # outlier point label color
    ggstatsplot.layer = FALSE,
    outlier.label = "SubID",
 pairwise.comparisons = TRUE, # display results from pairwise comparisons
    pairwise.display = "significant", # display only significant pairwise comparisons
    # arguments relevant for ggstatsplot::combine_plots
    title.text = "Annual Strutural IQM Report",
 title.size = 12 )

snr_mean.compar<-compare_means(snr_total ~ months, eBACH)
datatable(snr_mean.compar, rownames = FALSE,
          options = list(pageLength = 5)) 

MASTER Weekly Report == 0

eBACH$date<-format(as.Date(eBACH$Date),"%b/%d/%y")
NEW.table<-eBACH %>% filter(WeeklyReport=="0") %>% group_by(Session) %>% select(Subject, Session, date, ScanNotes, QC03,QC04)
DT::datatable(NEW.table, rownames = FALSE)

Structural Report

eBACH$cnrZ<-as.numeric(scale(eBACH$cnr, scale = T))
eBACH$fwhmZ<-as.numeric(scale(eBACH$fwhm_avg, scale = T))
eBACH$snrZ<-as.numeric(scale(eBACH$snr_total, scale = T))
eBACH$snrdZ<-as.numeric(scale(eBACH$snrd_total, scale = T))
eBACH$Report<-if_else(eBACH$WeeklyReport=="0", "New", "Current")
eBACH$Report<-as.factor(eBACH$Report)
eBACH<-arrange(eBACH, "Date")
res <- eBACH %>% 
  dplyr::group_by(Report) %>% 
  dplyr::summarise(N=n(),
      'CNR'=round(mean(cnr),3),
      ' sd'=round(sd(cnr),3),
      ' z'= as.character(htmltools::as.tags(sparkline(c(cnrZ), type = "bar"))), 
       'FWHM'=round(mean(fwhm_avg),3),
       'sd '=round(mean(fwhm_avg),3),
       'Z'= as.character(htmltools::as.tags(sparkline(c(fwhmZ), type = "line"))), 
       "SNR"=round(mean(snr_total),3),
       '.sd '=round(sd(snr_total),3),
        'z '= as.character(htmltools::as.tags(sparkline(c(snrZ), type = "box"))),
        'SNRd'=round(mean(snrd_total),3),
        'sd.'=round(sd(snrd_total),3),
        'Z '= as.character(htmltools::as.tags(sparkline(c(snrdZ), type = "box")))) %>%
  formattable( align =c("l","l","r","l","l","r","l","l","r","l","l","r","l","l") ) %>%
  formattable::as.htmlwidget()
res$dependencies <- c(res$dependencies,htmlwidgets:::widget_dependencies("sparkline", "sparkline"))
res

TMP<-eBACH %>% select(SubID, Session, Date, Report, cnr, fwhm_avg,snr_total,snrd_total)
TMP<-reshape2::melt(TMP, id.vars=c("SubID", "Session", "Date", "Report"))
TMP$Session<-as.factor(TMP$Session)
TMP$Report<-as.factor(TMP$Report)
# select part of the dataset and use it for plotting
ggstatsplot::grouped_ggbetweenstats(
    data = TMP,
    x=Report,
    y = value,
    grouping.var = variable,
 plot.type = "violin",
    type = "np",
    conf.level = 0.99,
    xlab = "report",
    ylab = "value",
   ggtheme = ggplot2::theme_gray(), # a different theme
  package = "yarrr", # package from which color palette is to be taken
  palette = "info2", # choosing a different color palette
    outlier.tagging = TRUE,
    outlier.label.args = list(color = "red"), # outlier point label color
    title.size = 12,
    ggstatsplot.layer = FALSE,
    outlier.label = "SubID",
    # arguments relevant for ggstatsplot::combine_plots
    title.text = "Weekly MPRAGE Report",
 sub.size = 3,
 greedy=TRUE,
 pairwise.comparisons = TRUE, # display results from pairwise comparisons
    pairwise.display = "significant", # display only significant pairwise comparisons
 stats.label.args = list(size = 2, direction = "y"))

SNR_Dens.plot<-ggdensity(eBACH, x = "snr_total",
   add = "mean", rug = TRUE, na.rm=TRUE,
   color = "Session", fill = "Session")

g <- ggplot(eBACH, aes(months, snr_total))
gbox_SNR_month<-g + geom_boxplot(aes(fill=factor(Session))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="MPRAGE SNR Grouped by Month & Session",
       x="Month",
       y="MPRAGE SNR")


gridExtra::grid.arrange(SNR_Dens.plot, gbox_SNR_month, ncol=2)

# Violin plots with box plots inside
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change fill color by groups: dose
# add boxplot with white fill colorRest.DF
my_comparisons.df<-compare_means(snr_total ~ Session , eBACH)
my_comparisons <- list( c("MR1", "MR2"),
                        c("MR1", "MR3"),
                        c("MR2", "MR3")
                       )
plot.anova1<-ggviolin(eBACH, 
                          x = "Session", y = "snr_total", 
                          fill = "Session",  na.rm=TRUE,
          add = "boxplot", add.params = list(fill = "white"))+
          stat_compare_means(comparisons = my_comparisons, label = "p.signif") + # Add significance levels
          stat_compare_means(label.y = 2)  +
  ggtitle("SNR by Session")+
  theme(legend.position='right')
# Violin plots with box plots inside
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change fill color by groups: dose
# add boxplot with white fill colorRest.DF
my_comparisons.df<-compare_means(cnr ~ Session , eBACH)
my_comparisons <- list( c("MR1", "MR2"),
                        c("MR1", "MR3"),
                        c("MR2", "MR3")
                       )
plot.anova2<-ggviolin(eBACH, 
                          x = "Session", y = "cnr", 
                          fill = "Session",  na.rm=TRUE,
          add = "boxplot", add.params = list(fill = "white"))+
          stat_compare_means(comparisons = my_comparisons, label = "p.signif") + # Add significance levels
          stat_compare_means(label.y = 2)  +
  ggtitle("CNR by Session")+
  theme(legend.position='right')
gridExtra::grid.arrange(plot.anova1, plot.anova2, ncol=2)

MPRAGE_snr.compar<-compare_means(snr_total~Session, eBACH)
MPRAGE_snr.compar<-as.data.frame(MPRAGE_snr.compar)
DT::datatable(MPRAGE_snr.compar, rownames = FALSE)

Functional Report

Emotional Reappraisal Task

BOLD.emoreap.IQMs.df<-eBACH_BOLD
BOLD.emoreap.IQMs.df<-BOLD.emoreap.IQMs.df[BOLD.emoreap.IQMs.df$Task=="emoreap",]
BOLD.emoreap.IQMs.df<-BOLD.emoreap.IQMs.df[complete.cases(BOLD.emoreap.IQMs.df$dvars_nstd),]
BOLD.emoreap.IQMs.df$dvars_nstd<-digits(BOLD.emoreap.IQMs.df$dvars_nstd, digits = 4)
BOLD.emoreap.IQMs.df$fd_mean<-digits(BOLD.emoreap.IQMs.df$fd_mean, digits = 4)
BOLD.emoreap.IQMs.df$fwhm_avg<-digits(BOLD.emoreap.IQMs.df$fwhm_avg, digits = 4)
BOLD.emoreap.IQMs.df$snr<-digits(BOLD.emoreap.IQMs.df$snr, digits = 4)
BOLD.emoreap.IQMs.df$tsnr<-digits(BOLD.emoreap.IQMs.df$tsnr, digits = 4)
BOLD.emoreap.IQMs.df$gcor<-digits(BOLD.emoreap.IQMs.df$gcor, digits = 4)
BOLD.emoreap.IQMs.df<-BOLD.emoreap.IQMs.df[complete.cases(BOLD.emoreap.IQMs.df$Subject),]
BOLD.emoreap.IQMs.df<-as.data.frame(BOLD.emoreap.IQMs.df, rownames=FALSE)
BOLD.emoreap.IQMs.df<-arrange(BOLD.emoreap.IQMs.df,"Date" )
BOLD.emoreap.IQMs.df$Scan<-paste(BOLD.emoreap.IQMs.df$SubID,BOLD.emoreap.IQMs.df$Session, sep = "_" )
TMP3<-BOLD.emoreap.IQMs.df %>% select(SubID, Session,Date, Report,dvars_nstd ,fd_mean, snr,tsnr)
TMP3<-reshape2::melt(TMP3, id.vars=c("SubID","Session" ,"Date", "Report"))
# select part of the dataset and use it for plotting
ggstatsplot::grouped_ggbetweenstats(
    data = TMP3,
    x=Report,
    y = value,
    grouping.var = variable,
 plot.type = "violin",
    type = "np",
    conf.level = 0.99,
    xlab = "report",
    ylab = "value",
   ggtheme = ggplot2::theme_gray(), # a different theme
  package = "yarrr", # package from which color palette is to be taken
  palette = "info2", # choosing a different color palette
    outlier.tagging = TRUE,
    outlier.label.args = list(color = "red"), # outlier point label color
    title.size = 12,
    ggstatsplot.layer = FALSE,
    outlier.label = "SubID",
    # arguments relevant for ggstatsplot::combine_plots
    title.text = "Weekly EmoReap. IQMs.df IQM Report",
 sub.size = 3,
 greedy=TRUE,
 pairwise.comparisons = TRUE, # display results from pairwise comparisons
    pairwise.display = "significant", # display only significant pairwise comparisons
 stats.label.args = list(size = 2, direction = "y"))

BOLD.emoreap.IQMs.df<-eBACH_BOLD
BOLD.emoreap.IQMs.df<-BOLD.emoreap.IQMs.df[BOLD.emoreap.IQMs.df$Task=="emoreap",]
BOLD.emoreap.IQMs.df<-BOLD.emoreap.IQMs.df[complete.cases(BOLD.emoreap.IQMs.df$dvars_nstd),]
BOLD.emoreap.IQMs.df$dvars_nstd<-digits(BOLD.emoreap.IQMs.df$dvars_nstd, digits = 4)
BOLD.emoreap.IQMs.df$fd_mean<-digits(BOLD.emoreap.IQMs.df$fd_mean, digits = 4)
BOLD.emoreap.IQMs.df$fwhm_avg<-digits(BOLD.emoreap.IQMs.df$fwhm_avg, digits = 4)
BOLD.emoreap.IQMs.df$snr<-digits(BOLD.emoreap.IQMs.df$snr, digits = 4)
BOLD.emoreap.IQMs.df$tsnr<-digits(BOLD.emoreap.IQMs.df$tsnr, digits = 4)
BOLD.emoreap.IQMs.df$gcor<-digits(BOLD.emoreap.IQMs.df$gcor, digits = 4)
BOLD.emoreap.IQMs.df<-BOLD.emoreap.IQMs.df[complete.cases(BOLD.emoreap.IQMs.df$Subject),]
BOLD.emoreap.IQMs.df<-as.data.frame(BOLD.emoreap.IQMs.df, rownames=FALSE)
BOLD.emoreap.IQMs.df<-arrange(BOLD.emoreap.IQMs.df,"Date" )
# Violin plots with box plots inside
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change fill color by groups: dose
# add boxplot with white fill colorRest.DF
my_comparisons.df<-compare_means(snr ~ Session , BOLD.emoreap.IQMs.df)
my_comparisons <- list( c("MR1", "MR2"),
                        c("MR1", "MR3"),
                        c("MR2", "MR3")
                       )
plot.anova1<-ggviolin(BOLD.emoreap.IQMs.df, 
                          x = "Session", y = "snr", 
                          fill = "Session",  na.rm=TRUE,
          add = "boxplot", add.params = list(fill = "white"))+
          stat_compare_means(comparisons = my_comparisons, label = "p.signif") + # Add significance levels
          stat_compare_means(label.y = 2)  +
  ggtitle("SNR by Session")+
  theme(legend.position='right')
# Violin plots with box plots inside
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change fill color by groups: dose
# add boxplot with white fill colorRest.DF
my_comparisons.df<-compare_means(fd_mean ~ Session , BOLD.emoreap.IQMs.df)
my_comparisons <- list( c("MR1", "MR2"),
                        c("MR1", "MR3"),
                        c("MR2", "MR3")
                       )
plot.anova2<-ggviolin(BOLD.emoreap.IQMs.df, 
                          x = "Session", y = "fd_mean", 
                          fill = "Session",  na.rm=TRUE,
          add = "boxplot", add.params = list(fill = "white"))+
          stat_compare_means(comparisons = my_comparisons, label = "p.signif") + # Add significance levels
          stat_compare_means(label.y = 2)  +
  ggtitle("fd_mean by Session")+
  theme(legend.position='right')
plot.anova3<-ggviolin(BOLD.emoreap.IQMs.df, 
                          x = "Session", y = "tsnr", 
                          fill = "Session",  na.rm=TRUE,
          add = "boxplot", add.params = list(fill = "white"))+
          stat_compare_means(comparisons = my_comparisons, label = "p.signif") + # Add significance levels
          stat_compare_means(label.y = 2)  +
  ggtitle("tsnr by Session")+
  theme(legend.position='right')
gridExtra::grid.arrange(plot.anova1, plot.anova2,plot.anova3, ncol=3)

Resting State

BOLD.rest.IQMs.df<-eBACH_BOLD
BOLD.rest.IQMs.df<-BOLD.rest.IQMs.df[BOLD.rest.IQMs.df$Task=="rest",]
BOLD.rest.IQMs.df<-arrange(BOLD.rest.IQMs.df,"Date" )
BOLD.rest.IQMs.df<-BOLD.rest.IQMs.df[complete.cases(BOLD.rest.IQMs.df$dvars_nstd),]
BOLD.rest.IQMs.df$dvars_nstd<-digits(BOLD.rest.IQMs.df$dvars_nstd, digits = 4)
BOLD.rest.IQMs.df$fd_mean<-digits(BOLD.rest.IQMs.df$fd_mean, digits = 4)
BOLD.rest.IQMs.df$fwhm_avg<-digits(BOLD.rest.IQMs.df$fwhm_avg, digits = 4)
BOLD.rest.IQMs.df$snr<-digits(BOLD.rest.IQMs.df$snr, digits = 4)
BOLD.rest.IQMs.df$tsnr<-digits(BOLD.rest.IQMs.df$tsnr, digits = 4)
BOLD.rest.IQMs.df$gcor<-digits(BOLD.rest.IQMs.df$gcor, digits = 4)
BOLD.rest.IQMs.df<-as.data.frame(BOLD.rest.IQMs.df, rownames=FALSE)
TMP3<-BOLD.rest.IQMs.df %>% select(SubID, Session,Date, Report,dvars_nstd ,fd_mean, snr,tsnr)
TMP3<-reshape2::melt(TMP3, id.vars=c("SubID","Session" ,"Date", "Report"))
# select part of the dataset and use it for plotting
ggstatsplot::grouped_ggbetweenstats(
    data = TMP3,
    x=Report,
    y = value,
    grouping.var = variable,
 plot.type = "violin",
    type = "np",
    conf.level = 0.99,
    xlab = "report",
    ylab = "value",
   ggtheme = ggplot2::theme_gray(), # a different theme
  package = "yarrr", # package from which color palette is to be taken
  palette = "info2", # choosing a different color palette
    outlier.tagging = TRUE,
    outlier.label.args = list(color = "red"), # outlier point label color
    title.size = 12,
    ggstatsplot.layer = FALSE,
    outlier.label = "SubID",
    # arguments relevant for ggstatsplot::combine_plots
    title.text = "Weekly Resting State IQMs.df IQM Report",
 sub.size = 3,
 greedy=TRUE,
 pairwise.comparisons = TRUE, # display results from pairwise comparisons
    pairwise.display = "significant", # display only significant pairwise comparisons
 stats.label.args = list(size = 2, direction = "y"))

BOLD.rest.IQMs.df<-eBACH_BOLD
BOLD.rest.IQMs.df<-BOLD.rest.IQMs.df[BOLD.rest.IQMs.df$Task=="rest",]
BOLD.rest.IQMs.df<-arrange(BOLD.rest.IQMs.df,"Date" )
BOLD.rest.IQMs.df<-BOLD.rest.IQMs.df[complete.cases(BOLD.rest.IQMs.df$dvars_nstd),]
BOLD.rest.IQMs.df$dvars_nstd<-digits(BOLD.rest.IQMs.df$dvars_nstd, digits = 4)
BOLD.rest.IQMs.df$fd_mean<-digits(BOLD.rest.IQMs.df$fd_mean, digits = 4)
BOLD.rest.IQMs.df$fwhm_avg<-digits(BOLD.rest.IQMs.df$fwhm_avg, digits = 4)
BOLD.rest.IQMs.df$snr<-digits(BOLD.rest.IQMs.df$snr, digits = 4)
BOLD.rest.IQMs.df$tsnr<-digits(BOLD.rest.IQMs.df$tsnr, digits = 4)
BOLD.rest.IQMs.df$gcor<-digits(BOLD.rest.IQMs.df$gcor, digits = 4)
BOLD.rest.IQMs.df<-as.data.frame(BOLD.rest.IQMs.df, rownames=FALSE)
# Violin plots with box plots inside
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change fill color by groups: dose
# add boxplot with white fill colorRest.DF
my_comparisons.df<-compare_means(snr ~ Session , BOLD.rest.IQMs.df)
my_comparisons <- list( c("MR1", "MR2"),
                        c("MR1", "MR3"),
                        c("MR2", "MR3")
                       )
plot.anova1<-ggviolin(BOLD.rest.IQMs.df, 
                          x = "Session", y = "snr", 
                          fill = "Session",  na.rm=TRUE,
          add = "boxplot", add.params = list(fill = "white"))+
          stat_compare_means(comparisons = my_comparisons, label = "p.signif") + # Add significance levels
          stat_compare_means(label.y = 2)  +
  ggtitle("SNR by Session")+
  theme(legend.position='right')
# Violin plots with box plots inside
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change fill color by groups: dose
# add boxplot with white fill colorRest.DF
my_comparisons.df<-compare_means(fd_mean ~ Session , BOLD.rest.IQMs.df)
my_comparisons <- list( c("MR1", "MR2"),
                        c("MR1", "MR3"),
                        c("MR2", "MR3")
                       )
plot.anova2<-ggviolin(BOLD.rest.IQMs.df, 
                          x = "Session", y = "fd_mean", 
                          fill = "Session",  na.rm=TRUE,
          add = "boxplot", add.params = list(fill = "white"))+
          stat_compare_means(comparisons = my_comparisons, label = "p.signif") + # Add significance levels
          stat_compare_means(label.y = 2)  +
  ggtitle("fd_mean by Session")+
  theme(legend.position='right')
plot.anova3<-ggviolin(BOLD.rest.IQMs.df, 
                          x = "Session", y = "tsnr", 
                          fill = "Session",  na.rm=TRUE,
          add = "boxplot", add.params = list(fill = "white"))+
          stat_compare_means(comparisons = my_comparisons, label = "p.signif") + # Add significance levels
          stat_compare_means(label.y = 2)  +
  ggtitle("tsnr by Session")+
  theme(legend.position='right')
gridExtra::grid.arrange(plot.anova1, plot.anova2,plot.anova3, ncol=3)

MSIT Stress Task

BOLD.msit.IQMs.df<-eBACH_BOLD
BOLD.msit.IQMs.df<-BOLD.msit.IQMs.df[BOLD.msit.IQMs.df$Task=="msit",]
BOLD.msit.IQMs.df<-BOLD.emoreap.IQMs.df[complete.cases(BOLD.msit.IQMs.df$dvars_nstd),]
BOLD.msit.IQMs.df$dvars_nstd<-digits(BOLD.msit.IQMs.df$dvars_nstd, digits = 4)
BOLD.msit.IQMs.df$fd_mean<-digits(BOLD.msit.IQMs.df$fd_mean, digits = 4)
BOLD.msit.IQMs.df$fwhm_avg<-digits(BOLD.msit.IQMs.df$fwhm_avg, digits = 4)
BOLD.msit.IQMs.df$snr<-digits(BOLD.msit.IQMs.df$snr, digits = 4)
BOLD.msit.IQMs.df$tsnr<-digits(BOLD.msit.IQMs.df$tsnr, digits = 4)
BOLD.msit.IQMs.df$gcor<-digits(BOLD.msit.IQMs.df$gcor, digits = 4)
BOLD.msit.IQMs.df$SubID<-as.character(BOLD.msit.IQMs.df$SubID)
BOLD.msit.IQMs.df<-as.data.frame(BOLD.msit.IQMs.df, rownames=FALSE)
BOLD.msit.IQMs.df<-BOLD.msit.IQMs.df[complete.cases(BOLD.msit.IQMs.df$SubID),]
BOLD.msit.IQMs.df<-arrange(BOLD.msit.IQMs.df,"Date" )
TMP3<-BOLD.msit.IQMs.df %>% select(SubID, Session,Date, Report,dvars_nstd ,fd_mean, snr,tsnr)
TMP3<-reshape2::melt(TMP3, id.vars=c("SubID","Session" ,"Date", "Report"))
# select part of the dataset and use it for plotting
ggstatsplot::grouped_ggbetweenstats(
    data = TMP3,
    x=Report,
    y = value,
    grouping.var = variable,
 plot.type = "violin",
    type = "np",
    conf.level = 0.99,
    xlab = "report",
    ylab = "value",
   ggtheme = ggplot2::theme_gray(), # a different theme
  package = "yarrr", # package from which color palette is to be taken
  palette = "info2", # choosing a different color palette
    outlier.tagging = TRUE,
    outlier.label.args = list(color = "red"), # outlier point label color
    title.size = 12,
    ggstatsplot.layer = FALSE,
    outlier.label = "SubID",
    # arguments relevant for ggstatsplot::combine_plots
    title.text = "Weekly Resting State IQMs.df IQM Report",
 sub.size = 3,
 greedy=TRUE,
 pairwise.comparisons = TRUE, # display results from pairwise comparisons
    pairwise.display = "significant", # display only significant pairwise comparisons
 stats.label.args = list(size = 2, direction = "y"))

Stroop Stress Task

BOLD.stroop.IQMs.df<-eBACH_BOLD
BOLD.stroop.IQMs.df<-BOLD.stroop.IQMs.df[BOLD.stroop.IQMs.df$Task=="stroop",]
BOLD.stroop.IQMs.df<-BOLD.stroop.IQMs.df[complete.cases(BOLD.stroop.IQMs.df$SubID),]
BOLD.stroop.IQMs.df<-BOLD.emoreap.IQMs.df[complete.cases(BOLD.stroop.IQMs.df$dvars_nstd),]
BOLD.stroop.IQMs.df$dvars_nstd<-digits(BOLD.stroop.IQMs.df$dvars_nstd, digits = 4)
BOLD.stroop.IQMs.df$fd_mean<-digits(BOLD.stroop.IQMs.df$fd_mean, digits = 4)
BOLD.stroop.IQMs.df$fwhm_avg<-digits(BOLD.stroop.IQMs.df$fwhm_avg, digits = 4)
BOLD.stroop.IQMs.df$snr<-digits(BOLD.stroop.IQMs.df$snr, digits = 4)
BOLD.stroop.IQMs.df$tsnr<-digits(BOLD.stroop.IQMs.df$tsnr, digits = 4)
BOLD.stroop.IQMs.df$gcor<-digits(BOLD.stroop.IQMs.df$gcor, digits = 4)
BOLD.stroop.IQMs.df<-as.data.frame(BOLD.stroop.IQMs.df, rownames=FALSE)
BOLD.stroop.IQMs.df<-arrange(BOLD.stroop.IQMs.df,"Date" )
TMP3<-BOLD.stroop.IQMs.df %>% select(SubID, Session,Date, Report,dvars_nstd ,fd_mean, snr,tsnr)
TMP3<-reshape2::melt(TMP3, id.vars=c("SubID","Session" ,"Date", "Report"))
# select part of the dataset and use it for plotting
ggstatsplot::grouped_ggbetweenstats(
    data = TMP3,
    x=Report,
    y = value,
    grouping.var = variable,
 plot.type = "violin",
    type = "np",
    conf.level = 0.99,
    xlab = "report",
    ylab = "value",
   ggtheme = ggplot2::theme_gray(), # a different theme
  package = "yarrr", # package from which color palette is to be taken
  palette = "info2", # choosing a different color palette
    outlier.tagging = TRUE,
    outlier.label.args = list(color = "red"), # outlier point label color
    title.size = 12,
    ggstatsplot.layer = FALSE,
    outlier.label = "SubID",
    # arguments relevant for ggstatsplot::combine_plots
    title.text = "Weekly Resting State IQMs.df IQM Report",
 sub.size = 3,
 greedy=TRUE,
 pairwise.comparisons = TRUE, # display results from pairwise comparisons
    pairwise.display = "significant", # display only significant pairwise comparisons
 stats.label.args = list(size = 2, direction = "y"))