Swisstransplant cookbook for R

Statistical report

Author

Simon Schwab

Published

March 26, 2025

Abstract

This document aims to demonstrate statistical reporting in our in-house style.

Introduction

This cookbook introduces the Swisstransplant R package swt to make high-quality reports and publication-ready graphics in our in-house style—much more.

The source code for swt is maintained on GitHub and can be accessed here. Additionally, a package manual is available for reference.

Code

# TODO: add devel/tidy_rmsfit stuff to rcookbook

library(ggplot2)
library(gridExtra)
library(reshape2)
library(swt)
library(readxl)

Quarto report

The swt package contains the Swisstransplant statistical report as a Quarto project template. Quarto documents enable dynamic reporting and unite written text, statistical programming code, results, tables, and figures into a reproducible document. All the steps for obtaining the results from the data are precisely specified. This enables transparency, reproducibility, and trust in statistical analyses.

A new project in RStudio can be created using the Swisstransplant project template: Click New Project… then choose New Directory, and select the Swisstransplant Project at the bottom of the list.

Writing a report

The Swisstransplant Quarto template is written in markdown. It is possible to add callout blocks to highlight important information.

::: {.callout-note appearance="simple"}
Note that there are five types of callouts, including:
`note`, `warning`, `important`, `tip`, and `caution`.
:::

Note that there are five types of callouts: note, warning, important, tip, and caution.

Something important.

Data analysis workflow

The new project will suggest a structure for an analysis pipeline. However, every project is different; therefore, only a rough structure is suggested.

flowchart TB
O(Objectives) --> I(Data import)
I --> P(Data processing)
P --> Q(Quality control)
Q --> D(Descriptive statistics)
D --> A(Primary analysis)
A --> S(Secondary analyses)
S --> C(Computing information)
O --- OC[Study objectives<br>Primary outcome<br>Secondary outcomes<br>Methods]
I --- IC[Custom R functions<br>Load libraries<br>Two-pass reading when necessary<br>Data class conversion<br>Data cleaning<br>Reshape<br>Merge<br>Aggregate]
P --- PC[Data overview<br>Definition of outcomes<br>Consent status<br>Inclusion, exclusion and subsets<br>Missing data and imputations<br>Transform<br>Matching<br>Create analysis data set]
Q --- QC[Data checks<br>Assert logical expressions<br>Variable distributions]
D --- DC[Sample size<br>Table 1<br>Consort Diagram]
A --- AC[Results<br>Tables<br>Figures]
S --- SC[Results<br>Tables<br>Figures]
C --- CC[Runtime<br>Environment<br>Package names and versions]

Corporate design

Colors

The swt_colors() function creates an object for user-friendly access to the Swisstransplant color scheme:

col = swt::swt_colors()
col$blue.dark

The following colors are available:

Code

col = swt_colors()
par(mfrow=c(1,1), mai=c(0.5,0.1,0.2,0.1))
barplot(rep(1,12), axes=FALSE, col=c(col$blue.dark,
                                     col$blue.alt,
                                     col$turkis.tpx,
                                     col$yellow.donation, 
                                     col$strongred.akzent,
                                     col$pink.heart,
                                     col$red.liver,
                                     col$darkyellow.kidney,
                                     col$green.pancreas,
                                     col$lightblue.lungs,
                                     col$beige.intestine,
                                     col$purple.alt
                                     
),
names.arg = c("blue\ndark", "blue\nalt", "turkis\ntpx", "yellow\ndonat", 
              "strongr\nakzent", "pink\nheart", "red\nliver", "darkylw\nkidney", 
              "green\npancr", "lightb\nlungs", "beige\nintest", "purple\nalt"),
cex.names = 0.8

)

Color palettes

The swt_color object also includes single hue palettes with three additional color strengths: 75%, 50%, and 25%.

Code

par(mfrow=c(12,1), mai=c(0.1,0.1,0.2,0.1))

barplot(rep(1,4), axes=FALSE, col=c(col$pal.blue.swt[1],
                                    col$pal.blue.swt[2],
                                    col$pal.blue.swt[3],
                                    col$pal.blue.swt[4])
)

barplot(rep(1,4), axes=FALSE, col=c(col$pal.blue.alt[1],
                                    col$pal.blue.alt[2],
                                    col$pal.blue.alt[3],
                                    col$pal.blue.alt[4])
)

barplot(rep(1,4), axes=FALSE, col=c(col$pal.turkis.tpx[1],
                                    col$pal.turkis.tpx[2],
                                    col$pal.turkis.tpx[3],
                                    col$pal.turkis.tpx[4])
)

barplot(rep(1,4), axes=FALSE, col=c(col$pal.yellow.donation[1],
                                    col$pal.yellow.donation[2],
                                    col$pal.yellow.donation[3],
                                    col$pal.yellow.donation[4])
)

barplot(rep(1,4), axes=FALSE, col=c(col$pal.strongred.akzent[1],
                                    col$pal.strongred.akzent[2],
                                    col$pal.strongred.akzent[3],
                                    col$pal.strongred.akzent[4])
)

barplot(rep(1,4), axes=FALSE, col=c(col$pal.pink.heart[1],
                                    col$pal.pink.heart[2],
                                    col$pal.pink.heart[3],
                                    col$pal.pink.heart[4])
)

barplot(rep(1,4), axes=FALSE, col=c(col$pal.red.liver[1],
                                    col$pal.red.liver[2],
                                    col$pal.red.liver[3],
                                    col$pal.red.liver[4])
)

barplot(rep(1,4), axes=FALSE, col=c(col$pal.darkyellow.kidney[1],
                                    col$pal.darkyellow.kidney[2],
                                    col$pal.darkyellow.kidney[3],
                                    col$pal.darkyellow.kidney[4])
)

barplot(rep(1,4), axes=FALSE, col=c(col$pal.green.pancreas[1],
                                    col$pal.green.pancreas[2],
                                    col$pal.green.pancreas[3],
                                    col$pal.green.pancreas[4])
)

barplot(rep(1,4), axes=FALSE, col=c(col$pal.lightblue.lungs[1],
                                    col$pal.lightblue.lungs[2],
                                    col$pal.lightblue.lungs[3],
                                    col$pal.lightblue.lungs[4])
)

barplot(rep(1,4), axes=FALSE, col=c(col$pal.beige.intestine[1],
                                    col$pal.beige.intestine[2],
                                    col$pal.beige.intestine[3],
                                    col$pal.beige.intestine[4])
)

barplot(rep(1,4), axes=FALSE, col=c(col$pal.purple.alt[1],
                                    col$pal.purple.alt[2],
                                    col$pal.purple.alt[3],
                                    col$pal.purple.alt[4]),
        names.arg = c("100%", "75%", "50%", "25%"), 
        cex.names = 0.8
)

Plots with `ggplot2`

Below are a few examples of creating various types of data plots in the SWT color scheme. The function swt_style() adds the correct styling to the plot.

Scientific plots

Code

set.seed(1980)
n = 100
var1 = c(rnorm(n/2, mean=0), rnorm(n/2, mean=3) )
d = data.frame(var1 = var1,
               var2 = var1 + rnorm(n, sd = 0.4),
               group = as.factor(rep(c("abc", "mno" ), each=n/2))
)

p1 = ggplot(d, aes(x=group, y=var1, group=group, col=group)) + 
  geom_boxplot(fill=col$grey.bg) + 
  geom_point(size=2, shape=1, position = position_jitter(height = 0, width = 0.25),
             col=c(rep(col$pal.blue.swt[1], n/2),
                   rep(col$pal.red.liver[1], n/2))) +
  labs(title = "Title", tag = "A") +
  scale_color_manual(values = c(col$blue.swt,
                                col$red.liver)) +
  swt_style(legend_position = "none", grey_theme = TRUE, font_size = 14, title_size = 16) + 
  theme(plot.tag = element_text(size = 16, face = "bold"))

p2 = ggplot(d, aes(x=group, y=var1, group=group, col=group)) + 
  geom_boxplot() + 
  geom_point(size=2, position = position_jitter(height = 0, width = 0.25),
             col=c(rep(col$pal.turkis.tpx[3], n/2), 
                   rep(col$pal.darkyellow.kidney[3], n/2))) +
  scale_color_manual(values = c(col$turkis.tpx,
                                col$darkyellow.kidney)) +
  swt_style(legend_position = "none")

p3 = ggplot(d, aes(x=var2, fill=group)) + 
  geom_histogram(position = "identity", bins=20, alpha=0.5) +
  scale_fill_manual(values = c(col$lightblue.lungs,
                               col$beige.intestine)) +
  swt_style(grey_theme = FALSE)

p4 = ggplot(d, aes(x=var2, y=var1, group=group, col=group)) + 
  geom_point(size=2, alpha=0.5) +
  scale_color_manual(values = c(col$blue.swt,
                                col$yellow.donation)) +
  labs(title = "Title", tag = "D") +
  swt_style(legend_position = "bottom", grey_theme = TRUE)

grid.arrange(p1, p2, p3, p4, nrow = 2, ncol = 2)

Line plot

Code

# Data taken from Annual Report 2020 (p. 32)
table3.2 = t(array(c(
  13.3,17.2,18.6,18.4,17.0,
  11.5,12.6,14.9,11.7,11.2,
  1.8,4.6,3.8,6.7,5.8), dim = c(5,3)))

colnames(table3.2) = 2016:2020
rownames(table3.2) = c("Total", "DBD", "DCD")

data3.2 = melt(table3.2, varnames = c("Gruppe", "Jahr"), value.name = "Anzahl")

Code

number_height = -0.8

ggplot(data3.2, aes(x=Jahr, y=Anzahl, col=Gruppe, group=Gruppe)) +
  
  # plot line with numbers
  geom_line(data = data3.2, linewidth=1) +
  geom_text(data = data3.2, aes(label=Anzahl), vjust=number_height,
            col="black", size=4) +
  
  # some adjustments (colors, axes, etc)
  scale_color_manual(values=c(col$strongred.akzent,
                              col$yellow.donation,
                              col$blue.swt)) +
  scale_y_continuous(breaks = seq(0,22,4), limits = c(0,22)) +
  
  ylab("pmp") +
  labs(title="Title",
       subtitle = "Subtitle"
  ) +
  swt_style(grey_theme = TRUE) +
  theme(legend.position = "top")

Barplot with lines

Code

# Data taken from Annual Report 2020 (p. 31)
table3.1 = t(array(c(
  96,106,126,100,96,
  15,39,32,57,50), dim = c(5,2)))

table3.1.totals = array(colSums(table3.1), dim = c(1,5))
colnames(table3.1) = 2016:2020
colnames(table3.1.totals) = 2016:2020
rownames(table3.1) = c("DBD", "DCD")
rownames(table3.1.totals) = c("Total")

data3.1 = melt(table3.1, varnames = c("Gruppe", "Jahr"), value.name = "Anzahl")
data3.1.totals = melt(table3.1.totals, varnames = c("Gruppe", "Jahr"),
                      value.name = "Anzahl")

Code

number_height = -0.8
bar_with = 0.5

ggplot(data3.1, aes(x=Jahr, y=Anzahl, fill=Gruppe, group=Gruppe)) +
  
  # plot bars with numbers
  geom_bar(data = data3.1, stat="identity", position="dodge", width=bar_with) +
  geom_text(data = data3.1, aes(label=Anzahl), vjust=number_height,
            position = position_dodge(width=bar_with)) +
  
  # plot line with numbers
  geom_line(data = data3.1.totals, col = col$strongred.akzent, linewidth=1) +
  geom_text(data = data3.1.totals, aes(label=Anzahl), vjust=number_height,
            position = position_dodge(width=bar_with), col=col$strongred.akzent) +
  
  # some adjustments (colors, axes, etc)
  scale_fill_manual(values=c(col$yellow.donation, 
                             col$blue.swt,
                             col$strongred.akzent)) +
  scale_y_continuous(breaks = seq(0,180,20), limits = c(0,180)) +
  ylab("Personen") +
  swt_style(grey_theme = TRUE)

Tiny little helpers

Several helper functions are implemented to support statistical computing.

Formatting

mean_sd for the mean and standard deviation
median_irq for the median and interquartile range
freq_perc for the frequency count and percent
miss_perc for the frequency count and percent for missing data (NA)
tidy_pvalues for formatting p-values

The helper functions above are applied to single variables (vectors).

tidy_missing displays missing data of all the variables in a data frame.

Code

set.seed(1980)
data = data.frame(age = rnorm(n = 200, mean = 50, sd = 10))
data$hypertension = rbinom(n = 200, size = 1, prob = 0.20)

tab = data.frame(age = mean_sd(data$age),
                 hypertension = freq_perc(data$hypertension)
)
tab = t(tab)
colnames(tab) = "Descriptives"
tab = as.data.frame(tab)
rownames(tab) = c("age, mean (SD)", "hypertension, count (%)")
tab

	Descriptives
age, mean (SD)	50.2 (10.7)
hypertension, count (%)	57 (28.5)

Code

data$age[1:5] = NA
tidy_missing(data)

	Missing
age	5 (2.5%)
hypertension	0 (0.0%)
TOTAL	5 (2.5)

Dates

Dates are sometimes entered inconsistently. If we force the data type to be a date using the option col_types, these values are discarded and show up as NA.

We also get a warning; see below.

Code

data = as.data.frame(read_xlsx(path = "../data/dates.xlsx", col_types = "date"))

Warning: Expecting date in A4 / R4C1: got '11.03.1980 11:11:11'

Code

data

mydate
2021-08-22 16:43:01
2023-02-17 22:12:02
NA

An alternative is to import them as data type text. The dates show up as numbers and, if not recognized, as characters. The numbers are the number of days since 1899-12-30; this is an Excel convention.

Code

data = as.data.frame(read_xlsx(path = "../data/dates.xlsx", col_types = "text"))
data

mydate
44430.696539351855
44974.925023148149
11.03.1980 11:11:11

We can use the function num2date() to convert numbers into dates. There is also a built-in filter that recognizes the alternative format, which can also be modified via the option; see ?num2date.

Code

data$mydate = num2date(data$mydate, format = "%d.%m.%Y %H:%M:%OS", round = FALSE)
data

mydate
2021-08-22 16:43:01
2023-02-17 22:12:02
1980-03-11 11:11:10

One disadvantage of handling dates as numbers is when performing data cleaning. For example, manually correcting dates or adding missing data may be necessary. This has to be done in numbers before the conversion into the POSIXct data type. However, the function date2num()can be used for this purpose.

Code

data = as.data.frame(read_xlsx(path = "../data/dates.xlsx", col_types = "text"))
data$mydate[3] = date2num("1980-03-11 02:10:00")
data$mydate = num2date(data$mydate)
data

mydate
2021-08-22 16:43:01
2023-02-17 22:12:02
1980-03-11 02:10:00

Using num2date() for all date variables will also handle the time zone consistently using CET time (and CEST during summer). Note that having mixed time zones, UTC and CET, can cause offsets of 1 hour.

Computing information

Code

sessionInfo()

R version 4.4.3 (2025-02-28 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=English_Switzerland.utf8  LC_CTYPE=English_Switzerland.utf8   
[3] LC_MONETARY=English_Switzerland.utf8 LC_NUMERIC=C                        
[5] LC_TIME=English_Switzerland.utf8    

time zone: Europe/Zurich
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] readxl_1.4.5   swt_0.3        reshape2_1.4.4 gridExtra_2.3  ggplot2_3.5.1 

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      jsonlite_1.9.1    dplyr_1.1.4       compiler_4.4.3   
 [5] tidyselect_1.2.1  Rcpp_1.0.14       stringr_1.5.1     splines_4.4.3    
 [9] scales_1.3.0      yaml_2.3.10       fastmap_1.2.0     lattice_0.22-6   
[13] R6_2.6.1          plyr_1.8.9        labeling_0.4.3    generics_0.1.3   
[17] knitr_1.50        MASS_7.3-65       htmlwidgets_1.6.4 tibble_3.2.1     
[21] munsell_0.5.1     lubridate_1.9.4   pillar_1.10.1     rlang_1.1.5      
[25] stringi_1.8.4     xfun_0.51         segmented_2.1-4   timechange_0.3.0 
[29] cli_3.6.4         withr_3.0.2       magrittr_2.0.3    digest_0.6.37    
[33] grid_4.4.3        nlme_3.1-167      lifecycle_1.0.4   vctrs_0.6.5      
[37] evaluate_1.0.3    glue_1.8.0        farver_2.1.2      cellranger_1.1.0 
[41] colorspace_2.1-1  rmarkdown_2.29    tools_4.4.3       pkgconfig_2.0.3  
[45] htmltools_0.5.8.1

Introduction

Quarto report

Writing a report

Data analysis workflow

Corporate design

Colors

Color palettes

Plots with ggplot2

Scientific plots

Line plot

Barplot with lines

Tiny little helpers

Formatting

Dates

Computing information

Plots with `ggplot2`