Code
library(grid)
library(tidyverse)
library(ggcorrplot)
library(ggpubr)
library(gt)
library(readxl)
library(devtools)
library(knitr)
library(corrr)
write_bib(file = "packages.bib")1 R packages
2 Adjusting function from ggcorrplot package
Code
mycorrr <-
function (corr, method = c("square", "circle"), type = c("full",
"lower", "upper"), ggtheme = ggplot2::theme_minimal, title = "",
show.legend = TRUE, legend.title = "Corr", show.diag = NULL,
colors = c("blue", "white", "red"), outline.color = "gray",
hc.order = FALSE, hc.method = "complete", lab = FALSE, lab_col = "black",
lab_size = 4, p.mat = NULL, sig.level = 0.05, insig = c("pch",
"blank"), pch = 4, pch.col = "black", pch.cex = 5, tl.cex = 12,
tl.col = "black", tl.srt = 45, digits = 2, as.is = FALSE)
{
type <- match.arg(type)
method <- match.arg(method)
insig <- match.arg(insig)
if (is.null(show.diag)) {
if (type == "full") {
show.diag <- TRUE
}
else {
show.diag <- FALSE
}
}
if (inherits(corr, "cor_mat")) {
cor.mat <- corr
corr <- .tibble_to_matrix(cor.mat)
p.mat <- .tibble_to_matrix(attr(cor.mat, "pvalue"))
}
if (!is.matrix(corr) & !is.data.frame(corr)) {
stop("Need a matrix or data frame!")
}
corr <- as.matrix(corr)
corr <- base::round(x = corr, digits = digits)
if (hc.order) {
ord <- .hc_cormat_order(corr, hc.method = hc.method)
corr <- corr[ord, ord]
if (!is.null(p.mat)) {
p.mat <- p.mat[ord, ord]
p.mat <- base::round(x = p.mat, digits = digits)
}
}
if (!show.diag) {
corr <- .remove_diag(corr)
p.mat <- .remove_diag(p.mat)
}
if (type == "lower") {
corr <- .get_lower_tri(corr, show.diag)
p.mat <- .get_lower_tri(p.mat, show.diag)
}
else if (type == "upper") {
corr <- .get_upper_tri(corr, show.diag)
p.mat <- .get_upper_tri(p.mat, show.diag)
}
corr <- reshape2::melt(corr, na.rm = TRUE, as.is = as.is)
colnames(corr) <- c("Var1", "Var2", "value")
corr$pvalue <- rep(NA, nrow(corr))
corr$signif <- rep(NA, nrow(corr))
if (!is.null(p.mat)) {
p.mat <- reshape2::melt(p.mat, na.rm = TRUE)
corr$coef <- corr$value
corr$pvalue <- p.mat$value
corr$signif <- as.numeric(p.mat$value <= sig.level)
p.mat <- subset(p.mat, p.mat$value > sig.level)
if (insig == "blank") {
corr$value <- corr$value * corr$signif
}
}
corr$abs_corr <- abs(corr$value) * 10
p <- ggplot2::ggplot(data = corr, mapping = ggplot2::aes_string(x = "Var1",
y = "Var2", fill = "value"))
if (method == "square") {
p <- p + ggplot2::geom_tile(color = outline.color)
}
else if (method == "circle") {
p <- p + ggplot2::geom_point(color = outline.color, shape = 21,
ggplot2::aes_string(size = "abs_corr")) + ggplot2::scale_size(range = c(4,
10)) + ggplot2::guides(size = "none")
}
p <- p + ggplot2::scale_fill_gradient2(low = colors[1], high = colors[3],
mid = colors[2], midpoint = 0, limit = c(-1, 1), space = "Lab",
name = legend.title)
if (class(ggtheme)[[1]] == "function") {
p <- p + ggtheme()
}
else if (class(ggtheme)[[1]] == "theme") {
p <- p + ggtheme
}
p <- p + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = tl.srt,
vjust = 1, size = tl.cex, hjust = 1), axis.text.y = ggplot2::element_text(size = tl.cex)) +
ggplot2::coord_fixed()
label <- round(x = corr[, "value"], digits = digits) %>%
str_replace_all("\\b0(\\.\\d+)", "\\1") %>%
str_replace_all("(\\.\\d)\\b", "\\10")
if (!is.null(p.mat) & insig == "blank") {
ns <- corr$pvalue > sig.level
if (sum(ns) > 0)
label[ns] <- " "
}
if (lab) {
p <- p + ggplot2::geom_text(mapping = ggplot2::aes_string(x = "Var1",
y = "Var2"), label = label, color = lab_col, size = lab_size)
}
if (!is.null(p.mat) & insig == "pch") {
p <- p + ggplot2::geom_point(data = p.mat, mapping = ggplot2::aes_string(x = "Var1",
y = "Var2"), shape = pch, size = pch.cex, color = pch.col)
}
if (title != "") {
p <- p + ggplot2::ggtitle(title)
}
if (!show.legend) {
p <- p + ggplot2::theme(legend.position = "none")
}
p <- p + .no_panel()
p
}
.no_panel <- function() {
ggplot2::theme(
axis.title.x = ggplot2::element_blank(),
axis.title.y = ggplot2::element_blank()
)
}3 Data import and manipulation
Code
DATA <- read_xlsx("SURVEY_DATA.xlsx")
DATA <-
DATA %>%
select(-where( ~ all(is.na(.)))) %>%
filter(`teilnahme[teilnahme]` == "Y")
DATA <-
DATA[9:81] %>%
mutate(Nitem_CHECK = rowSums(!is.na(.))) %>%
filter(Nitem_CHECK > 0) %>%
filter(AB7 == 0) %>% # exclude special education schools
mutate(AB12 = (AB12 - 6) %>% abs()) %>%
rename_with(~ stringr::str_replace_all(., "\\[", "_") %>%
stringr::str_replace_all(., "\\]", "")) %>%
mutate(across(
c("AB6_1", "AB6_2", "AB6_3", "AB7", "AB8_1",
"AB8_3", "AB8_4", "AB8_5", "AB8_6", "AB8_7",
"AB8_8", "AB8_9", "AB9_1", "AB9_2", "AB9_3",
"AB9_4", "AB9_5"),
~ ifelse(. == "Y", 1, 0))) %>%
mutate(across(
c("AB6_1", "AB6_2", "AB6_3", "AB7", "AB8_1",
"AB8_3", "AB8_4", "AB8_5", "AB8_6", "AB8_7",
"AB8_8", "AB8_9", "AB9_1", "AB9_2", "AB9_3",
"AB9_4", "AB9_5"),
~ ifelse(is.na(.), 0, .)))Code
item_ID <-
c(
"item_1", "item_2", "item_3", "item_4", "item_5", "item_6", "item_7", "item_8",
"item_9", "item_10", "item_11", "item_12", "item_13", "item_14", "item_15",
"item_16", "item_17", "item_18", "item_19", "item_20", "item_21", "item_22",
"item_23", "item_24", "item_25", "item_26", "item_27", "item_28", "item_29",
"item_30"
)Code
DAT <-
DATA[
c(paste0("SE",
c(1:8,14,9,15,12,13,10:11,16:20)),
paste0("VA",
c(1:5,10,6:9)))] %>%
rename_with(~ item_ID)
DAT_COR <- cbind(DAT, DATA[31:73])Code
DAT_COR <-
DAT_COR %>%
rowwise() %>%
mutate(
`NAI-ST` = mean(c(item_1, item_2, abs(item_3 - 5), abs(item_4 - 5)), na.rm = TRUE),
`IRP-ST` = mean(c(item_5, item_6), na.rm = TRUE),
`NAI-PA` = mean(c(item_16, abs(item_17 - 5), abs(item_18 - 5)), na.rm = TRUE),
`IRP-PA` = mean(c(item_19, item_20), na.rm = TRUE),
CSFAS = mean(c(item_21, item_22, item_23, item_24), na.rm = TRUE),
CCMESS = mean(c(item_25, item_27, item_28, item_29, item_30), na.rm = TRUE),
WA1 = (WA1 == 2) %>% as.numeric(), # "Notarzt nicht immer notwendig",
WA2 = (WA2 == 2) %>% as.numeric(), # "keinen Gegenstand in den Mund",
WA3 = (WA3 == 2) %>% as.numeric(), # "Mund nicht mit physischer Kraft öffnen",
WA4 = (WA4 == 2) %>% as.numeric(), # "nicht festhalten",
#WA5 = (WA5 == 2) %>% as.numeric(), # "nicht wachrütteln",
WA6 = (WA6 == 2) %>% as.numeric(), # "nicht mit Wasser bespritzen",
WA7 = (WA7 == 2) %>% as.numeric(), # "Notfallmedikament geben",
#WA8 = (WA8 == 2) %>% as.numeric(), # "keine Form von geistiger Beeinträchtigung",
#WA9 = (WA9 == 3) %>% as.numeric(), # "Hochbegabung",
#WA10 = (WA10 == 3) %>% as.numeric(), # "Lernschwächen",
#WA11 = (WA11 == 3) %>% as.numeric(), # "Aggression",
#WA12 = (WA12 == 3) %>% as.numeric(), # "Sozialekompetenzen",
WA13 = (WA13 == 2) %>% as.numeric(), # "Sport kein Auslöser",
WA14 = (WA14 == 2) %>% as.numeric(), # "Ballsport kein Verletzungsrisiko"
) %>%
mutate(SED_INC = case_when(AB10 == 0 & AB11 == 0 ~ 0,
AB10 == 1 & AB11 == 0 ~ 1,
AB10 == 0 & AB11 == 1 ~ 1,
AB10 == 1 & is.na(AB11) ~ 1,
is.na(AB10) & AB11 == 1 ~ 1,
AB10 == 1 & AB11 == 1 ~ 1,
is.na(AB10) & is.na(AB11) ~ NA,
AB10 == 0 & is.na(AB11) ~ NA,
is.na(AB10) & AB11 == 0 ~ NA)) %>%
select(c("WA1", "WA2", "WA3", "WA4", "WA6", "WA7",
"WA13", "WA14",
"AB0", "AB1", "AB2", "AB3", "AB4", "AB5", "AB6_1",
"AB9_1", "AB9_2", "AB9_3",
"AB9_4", "SED_INC", "AB12", "AB13", "AB14",
"AB15", "CCMESS", "CSFAS", "IRP-PA", "NAI-PA", "IRP-ST", "NAI-ST"))4 Correlation matrix (1)
Code
COR <-
DAT_COR %>%
correlate() %>%
focus(!c(CCMESS, CSFAS, `IRP-PA`, `NAI-PA`, `IRP-ST`, `NAI-ST`)) %>%
as.data.frame()
rownames(COR) <- c("CCMESS", "CSFAS", "IRP-PA", "NAI-PA", "IRP-ST", "NAI-ST")
colnames(COR) <-
c("term",
"seizure: emergency medical treatment not always necessary",
"during seizure: do not put anything in the mouth",
"during seizure: mouth should not be opened with physical force",
"during seizure: student should not be held down",
"seizure can not be stopped by splashing with cool water",
"teachers are allowed to administer emergency medication on their own",
"physical activity and exertion are not considered to be major triggers for seizures",
"ball sports: risk of injury is not significantly higher (compared to non-sport activities)",
"prior knowledge about epilepsy (self-rating)",
"currently teaching students with epilepsy",
"previously taught students with epilepsy",
"witnessed a student having an epileptic seizure",
"witnessed a person having an epileptic seizure",
"have or have had regular contact with people with epilepsy",
"elementary education teacher",
"school subject: German",
"school subject: math",
"school subject: natural science",
"school subject: physical education",
"teacher training in special and/or inclusive education",
"attitude towards inclusive education",
"work experience as a teacher in years",
"age in years",
"sex: female")
FIG <-
COR %>%
rev() %>%
arrange(-row_number()) %>%
mutate(term = NULL) %>%
mycorrr(lab = T, outline.color = "white") +
theme(legend.title = element_blank()) +
theme(legend.position = "top")
annotate_figure(FIG,
bottom = text_grob(
"NAI-ST: Negative affect and insecurity (school trips)
NAI-PA: Negative affect and insecurity (physical activities)
IRP-ST: Intention to restrict participation (school trips)
IRP-PA: Intention to restrict participation (physical activities)
CSFAS: Confidence in seizure first aid skills
CCMESS: Confidence in classroom management and emotional support skills",
size = 10, x = 0.41, hjust = 0),
right = textGrob(label =
c("knowledge:\nseizure first aid & physical activity",
"|\n|",
"prior\nknowledge & experience",
"|\n|",
"professional background &\nsociodemographic characteristics"),
x = c(-1.2, -1.2, -1.2, -1.2, -1.2), # Adjust these values as needed
y = c(0.8, 0.676, 0.588, 0.498, 0.35), # Adjust these values as needed
rot = 270,
gp = gpar(fontsize = 10),
just = "center"))
5 Correlation matrix (2)
Code
COR <-
DAT_COR %>%
select(!c(CCMESS, CSFAS, `IRP-PA`, `NAI-PA`, `IRP-ST`, `NAI-ST`)) %>%
correlate() %>%
focus(c("AB0", "AB1", "AB2", "AB3", "AB4", "AB5", "AB6_1",
"AB9_1", "AB9_2", "AB9_3", "AB9_4", "SED_INC", "AB12",
"AB13", "AB14", "AB15")) %>%
as.data.frame()
rownames(COR) <- c("seizure: emergency medical treatment not always necessary",
"during seizure: do not put anything in the mouth",
"during seizure: mouth should not be opened with physical force",
"during seizure: student should not be held down",
"seizure can not be stopped by splashing with cool water",
"teachers are allowed to administer emergency medication on their own",
"physical activity and exertion are not considered to be major triggers for seizures",
"ball sports: risk of injury is not significantly higher (compared to non-sport activities)")
colnames(COR) <-
c("term",
"prior knowledge about epilepsy (self-rating)",
"currently teaching students with epilepsy",
"previously taught students with epilepsy",
"witnessed a student having an epileptic seizure",
"witnessed a person having an epileptic seizure",
"have or have had regular contact with people with epilepsy",
"elementary education teacher",
"school subject: German",
"school subject: math",
"school subject: natural science",
"school subject: physical education",
"teacher training in special and/or inclusive education",
"attitude towards inclusive education",
"work experience as a teacher in years",
"age in years",
"sex: female")
FIG <-
COR %>%
rev() %>%
#arrange(-row_number()) %>%
mutate(term = NULL) %>%
mycorrr(lab = T, outline.color = "white") +
theme(legend.title = element_blank()) +
theme(legend.position = "top")
annotate_figure(FIG,
right = textGrob(label =
c("prior\nknowledge & experience",
"|\n|",
"professional background &\nsociodemographic characteristics"),
x = c(-2.9, -2.9, -2.9), # Adjust these values as needed
y = c(0.833, 0.747, 0.605), #0.498, 0.35), # Adjust these values as needed
rot = 270,
gp = gpar(fontsize = 10),
just = "center"))
6 Correlation matrix (3)
Code
COR <-
DAT_COR %>%
select(!c(CCMESS, CSFAS, `IRP-PA`, `NAI-PA`, `IRP-ST`, `NAI-ST`) &
c("AB0", "AB1", "AB2", "AB3", "AB4", "AB5", "AB6_1",
"AB9_1", "AB9_2", "AB9_3", "AB9_4", "SED_INC", "AB12",
"AB13", "AB14", "AB15")) %>%
correlate() %>%
#focus(c("AB0", "AB1", "AB2", "AB3", "AB4", "AB5", "AB6_1",
# "AB9_1", "AB9_2", "AB9_3", "AB9_4", "SED_INC", "AB12",
# "AB13", "AB14", "AB15")) %>%
as.data.frame()
rownames(COR) <-
c("prior knowledge about epilepsy (self-rating)",
"currently teaching students with epilepsy",
"previously taught students with epilepsy",
"witnessed a student having an epileptic seizure",
"witnessed a person having an epileptic seizure",
"have or have had regular contact with people with epilepsy",
"elementary education teacher",
"school subject: German",
"school subject: math",
"school subject: natural science",
"school subject: physical education",
"teacher training in special and/or inclusive education",
"attitude towards inclusive education",
"work experience as a teacher in years",
"age in years",
"sex: female")
colnames(COR) <-
c("term",
"prior knowledge about epilepsy (self-rating)",
"currently teaching students with epilepsy",
"previously taught students with epilepsy",
"witnessed a student having an epileptic seizure",
"witnessed a person having an epileptic seizure",
"have or have had regular contact with people with epilepsy",
"elementary education teacher",
"school subject: German",
"school subject: math",
"school subject: natural science",
"school subject: physical education",
"teacher training in special and/or inclusive education",
"attitude towards inclusive education",
"work experience as a teacher in years",
"age in years",
"sex: female")
COR %>%
rev() %>%
#arrange(-row_number()) %>%
mutate(term = NULL) %>%
mycorrr(lab = T, outline.color = "white") +
theme(legend.title = element_blank()) +
theme(legend.position = "top")
7 Elementary education vs. secondary education: contact with individuals with epilepsy
Code
table(DATA$AB6_1, DATA$AB1,
dnn = c("elementary",
"currently teaching students with epilepsy")) %>%
prop.table(1) %>% round(2) currently teaching students with epilepsy
elementary 0 1
0 0.90 0.10
1 0.92 0.08Code
table(DATA$AB6_1, DATA$AB2,
dnn = c("elementary",
"previously taught students with epilepsy")) %>%
prop.table(1) %>% round(2) previously taught students with epilepsy
elementary 0 1
0 0.63 0.37
1 0.66 0.34Code
table(DATA$AB6_1, DATA$AB3,
dnn = c("elementary",
"witnessed a student having an epileptic seizure")) %>%
prop.table(1) %>% round(2) witnessed a student having an epileptic seizure
elementary 0 1
0 0.83 0.17
1 0.83 0.17Code
table(DATA$AB6_1, DATA$AB4,
dnn = c("elementary",
"witnessed a person having an epileptic seizure")) %>%
prop.table(1) %>% round(2) witnessed a person having an epileptic seizure
elementary 0 1
0 0.53 0.47
1 0.58 0.42Code
table(DATA$AB6_1, DATA$AB5,
dnn = c("elementary",
"have or have had regular contact with people with epilepsy")) %>%
prop.table(1) %>% round(2) have or have had regular contact with people with epilepsy
elementary 0 1
0 0.80 0.20
1 0.75 0.258 R session info
Code
session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.2 (2024-10-31 ucrt)
## os Windows 11 x64 (build 22631)
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate German_Germany.utf8
## ctype German_Germany.utf8
## tz Europe/Berlin
## date 2024-11-21
## pandoc 3.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-8 2024-09-12 [1] CRAN (R 4.4.1)
## backports 1.5.0 2024-05-23 [1] CRAN (R 4.4.0)
## broom 1.0.7 2024-09-26 [1] CRAN (R 4.4.2)
## cachem 1.1.0 2024-05-16 [1] CRAN (R 4.4.2)
## car 3.1-3 2024-09-27 [1] CRAN (R 4.4.2)
## carData 3.0-5 2022-01-06 [1] CRAN (R 4.4.2)
## cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.4.2)
## cli 3.6.3 2024-06-21 [1] CRAN (R 4.4.2)
## colorspace 2.1-1 2024-07-26 [1] CRAN (R 4.4.2)
## corrr * 0.4.4 2022-08-16 [1] CRAN (R 4.4.2)
## cowplot 1.1.3 2024-01-22 [1] CRAN (R 4.4.2)
## devtools * 2.4.5 2022-10-11 [1] CRAN (R 4.4.2)
## digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.2)
## dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.4.2)
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.4.2)
## evaluate 1.0.1 2024-10-10 [1] CRAN (R 4.4.2)
## fansi 1.0.6 2023-12-08 [1] CRAN (R 4.4.2)
## farver 2.1.2 2024-05-13 [1] CRAN (R 4.4.2)
## fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.2)
## forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.4.2)
## Formula 1.2-5 2023-02-24 [1] CRAN (R 4.4.0)
## fs 1.6.5 2024-10-30 [1] CRAN (R 4.4.2)
## generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.2)
## ggcorrplot * 0.1.4.1 2023-09-05 [1] CRAN (R 4.4.2)
## ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.4.2)
## ggpubr * 0.6.0 2023-02-10 [1] CRAN (R 4.4.2)
## ggsignif 0.6.4 2022-10-13 [1] CRAN (R 4.4.2)
## glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.2)
## gridExtra 2.3 2017-09-09 [1] CRAN (R 4.4.2)
## gt * 0.11.1 2024-10-04 [1] CRAN (R 4.4.2)
## gtable 0.3.6 2024-10-25 [1] CRAN (R 4.4.2)
## hms 1.1.3 2023-03-21 [1] CRAN (R 4.4.2)
## htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.2)
## htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.2)
## httpuv 1.6.15 2024-03-26 [1] CRAN (R 4.4.2)
## jsonlite 1.8.9 2024-09-20 [1] CRAN (R 4.4.2)
## knitr * 1.49 2024-11-08 [1] CRAN (R 4.4.2)
## labeling 0.4.3 2023-08-29 [1] CRAN (R 4.4.0)
## later 1.3.2 2023-12-06 [1] CRAN (R 4.4.2)
## lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.2)
## lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.4.2)
## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.2)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.4.2)
## mime 0.12 2021-09-28 [1] CRAN (R 4.4.0)
## miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.4.2)
## munsell 0.5.1 2024-04-01 [1] CRAN (R 4.4.2)
## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.4.2)
## pkgbuild 1.4.5 2024-10-28 [1] CRAN (R 4.4.2)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.2)
## pkgload 1.4.0 2024-06-28 [1] CRAN (R 4.4.2)
## plyr 1.8.9 2023-10-02 [1] CRAN (R 4.4.2)
## profvis 0.4.0 2024-09-20 [1] CRAN (R 4.4.2)
## promises 1.3.0 2024-04-05 [1] CRAN (R 4.4.2)
## purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.4.2)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.2)
## Rcpp 1.0.13-1 2024-11-02 [1] CRAN (R 4.4.2)
## readr * 2.1.5 2024-01-10 [1] CRAN (R 4.4.2)
## readxl * 1.4.3 2023-07-06 [1] CRAN (R 4.4.2)
## remotes 2.5.0 2024-03-17 [1] CRAN (R 4.4.2)
## reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.4.2)
## rlang 1.1.4 2024-06-04 [1] CRAN (R 4.4.2)
## rmarkdown 2.29 2024-11-04 [1] CRAN (R 4.4.2)
## rstatix 0.7.2 2023-02-01 [1] CRAN (R 4.4.2)
## rstudioapi 0.17.1 2024-10-22 [1] CRAN (R 4.4.2)
## scales 1.3.0 2023-11-28 [1] CRAN (R 4.4.2)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.2)
## shiny 1.9.1 2024-08-01 [1] CRAN (R 4.4.2)
## stringi 1.8.4 2024-05-06 [1] CRAN (R 4.4.0)
## stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.4.2)
## tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.4.2)
## tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.4.2)
## tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.2)
## tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.4.2)
## timechange 0.3.0 2024-01-18 [1] CRAN (R 4.4.2)
## tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.4.2)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.4.2)
## usethis * 3.0.0 2024-07-29 [1] CRAN (R 4.4.1)
## utf8 1.2.4 2023-10-22 [1] CRAN (R 4.4.2)
## vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.2)
## withr 3.0.2 2024-10-28 [1] CRAN (R 4.4.2)
## xfun 0.49 2024-10-31 [1] CRAN (R 4.4.2)
## xml2 1.3.6 2023-12-04 [1] CRAN (R 4.4.2)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.4.2)
## yaml 2.3.10 2024-07-26 [1] CRAN (R 4.4.1)
##
## [1] C:/Users/Pawel Kulawiak/AppData/Local/Programs/R/R-4.4.2/library
##
## ──────────────────────────────────────────────────────────────────────────────9 References
Iannone, Richard, Joe Cheng, Barret Schloerke, Ellis Hughes, Alexandra Lauer, JooYoung Seo, Ken Brevoort, and Olivier Roy. 2024. Gt: Easily Create Presentation-Ready Display Tables. https://gt.rstudio.com.
Kassambara, Alboukadel. 2023a. Ggcorrplot: Visualization of a Correlation Matrix Using Ggplot2. http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2.
———. 2023b. Ggpubr: Ggplot2 Based Publication Ready Plots. https://rpkgs.datanovia.com/ggpubr/.
Kuhn, Max, Simon Jackson, and Jorge Cimentada. 2022. Corrr: Correlations in r. https://github.com/tidymodels/corrr.
R Core Team. 2024. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wickham, Hadley. 2023. Tidyverse: Easily Install and Load the Tidyverse. https://tidyverse.tidyverse.org.
Wickham, Hadley, and Jennifer Bryan. 2023. Readxl: Read Excel Files. https://readxl.tidyverse.org.
Wickham, Hadley, Jim Hester, Winston Chang, and Jennifer Bryan. 2022. Devtools: Tools to Make Developing r Packages Easier. https://devtools.r-lib.org/.
Xie, Yihui. 2024. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://yihui.org/knitr/.