1  Getting started

1.1 What is tidyplots?

Jan Broder Engler (https://jbengler.de/).

  • πŸ‘‰TidyplotsπŸ“¦ is an R package to generate publication-ready plots for scientific papers, created by Jan Broder Engler (Engler 2025).

  • πŸ‘‰Tidyplots is based on ggplot2.

  • πŸ‘‰Instead of extending the ggplot2 syntax, tidyplots introduces a novel interface based on a consistent and intuitive grammar that minimizes the need of programming experience.

1.1.1 Highlights

  • πŸ‘‰Easy code-based data visualization for science.

  • πŸ‘‰Intuitive syntax to add, remove, and adjust plot components.

  • πŸ‘‰Reduced code complexity in comparison to ggplot2 (making plotting easier for beginners).

1.1.2 Citation

citation("tidyplots")
To cite package 'tidyplots' in publications use:

  Engler JB (2025). "Tidyplots empowers life scientists with easy
  code-based data visualization." _iMeta_, e70018.
  doi:10.1002/imt2.70018 <https://doi.org/10.1002/imt2.70018>.

A BibTeX entry for LaTeX users is

  @Article{,
    title = {Tidyplots empowers life scientists with easy code-based data visualization},
    author = {Jan Broder Engler},
    journal = {iMeta},
    pages = {e70018},
    year = {2025},
    doi = {10.1002/imt2.70018},
  }

1.2 Acknowledgements

The preparation of this cookbook has no direct relation to Jan Broder Engler (the developer of tidyplots), so I feel a little β€œguilty” about β€œstealing” his ideas and code.

When this cookbook was roughly ready, I wrote to Jan, and he replied:

Hi Kang,

Thank you for your email!

Feel free to offer additional resources for tidyplots. Let me know when they are available. I was also thinking about writing a book about tidyplots, but I currently do not have the time.

Therefore, I will donate 55% of any paid proceeds (if there are any) to Jan’s project: https://github.com/jbengler/tidyplots.

1.3 Prerequisites

1.3.1 Install R and its integrated development environment (IDE)

Positron IDE.

1.3.2 Install tidyplots package and other packages

install.packages("tidyplots")
# for example
install.packages("tidyverse")

Tidyverse hex logos. The tidyverse is an opinionated collection of R packages (e.g. ggplot2, tibble, and dplyr) designed for data science.

How to read your data

Your (experimental) data might be in the formats of .xlsx, .csv (comma-seperated values), or .txt (saved as tab-seperated values).

Here are the saved files with different formats from the tidyplots study dataset for illustration.

"data/" |> 
  fs::dir_tree(regexp = "^data/study")
data/
β”œβ”€β”€ study.csv
β”œβ”€β”€ study.txt
└── study.xlsx
Tip

The keyboard shortcut for the pipe operator |> is Ctrl + Shift + M (i.e. click Ctrl, Shift, and M simultaneously) on Windows and Cmd + Shift + M on the Mac.

How to read .csv file

study_csv <- "data/study.csv" |> 
  readr::read_csv()
Rows: 20 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): treatment, group, dose, participant, sex
dbl (2): age, score

β„Ή Use `spec()` to retrieve the full column specification for this data.
β„Ή Specify the column types or set `show_col_types = FALSE` to quiet this message.
# The dimension (rows by columns) of study_csv
study_csv |> dim()
[1] 20  7
Tip

The keyboard shortcut for the assignment operator <- is Alt + - (i.e. click Alt and - simultaneously) on Windows and Option + - (i.e. click Option and - simultaneously) on the Mac.

How to read .txt file

study_txt <- "data/study.txt" |> 
  readr::read_tsv()
Rows: 20 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (5): treatment, group, dose, participant, sex
dbl (2): age, score

β„Ή Use `spec()` to retrieve the full column specification for this data.
β„Ή Specify the column types or set `show_col_types = FALSE` to quiet this message.
# The dimension (rows by columns) of study_csv
study_txt |> dim()
[1] 20  7

How to read .xlsx file

study_xlsx <- "data/study.xlsx" |> 
  readxl::read_xlsx(sheet = "Sheet1")

# The dimension (rows by columns) of study_xlsx
study_xlsx |> dim()
[1] 20  7

1.3.3 Your data should be tidy data

As stated in https://tidyr.tidyverse.org/, tidy data is data where:

  1. πŸ‘‰Each variable is a column; each column is a variable.
  2. πŸ‘‰Each observation is a row; each row is an observation.
  3. πŸ‘‰Each value is a cell; each cell is a single value.

Using the tidy data tidyplots study dataset for a quick overview.

tidyplots::study |> tibble::tibble()
# A tibble: 20 Γ— 7
   treatment group     dose  participant   age sex    score
   <chr>     <chr>     <chr> <chr>       <dbl> <chr>  <dbl>
 1 A         placebo   high  p01            23 female     2
 2 A         placebo   high  p02            45 male       4
 3 A         placebo   high  p03            32 female     5
 4 A         placebo   high  p04            37 male       4
 5 A         placebo   high  p05            24 female     6
 6 B         placebo   low   p06            23 female     9
 7 B         placebo   low   p07            45 male       8
 8 B         placebo   low   p08            32 female    12
 9 B         placebo   low   p09            37 male      15
10 B         placebo   low   p10            24 female    16
11 C         treatment high  p01            23 female    32
12 C         treatment high  p02            45 male      35
13 C         treatment high  p03            32 female    24
14 C         treatment high  p04            37 male      45
15 C         treatment high  p05            24 female    56
16 D         treatment low   p06            23 female    23
17 D         treatment low   p07            45 male      25
18 D         treatment low   p08            32 female    21
19 D         treatment low   p09            37 male      22
20 D         treatment low   p10            24 female    23
Warning in rm(study): object 'study' not found

The structural dissection of the study dataset in tidyplots, with particular attention to data types, which can sometimes determine whether plotting succeeds.

The study dataset has the following features:

  • πŸ‘‰ A so-called data frame of tibble (20 rows by 7 columns).
  • πŸ‘‰ 7 columns mean 7 variables (i.e. treatment, group, dose, participant, age, sex, and score).
  • πŸ‘‰ Each column/variable has a specific data type (e.g. treatment is character (chr), age is double (dbl) etc.)
  • πŸ‘‰ 20 rows mean 20 observations.
Note

You should adjust your data to be tidy data

  1. mannually;

  2. or via code-based manipulation (e.g. R for Data Science (https://r4ds.hadley.nz/data-tidy)).

1.4 Figure formats

Opinions adapted from (Bankhead 2022).

1.4.1 Bitmaps vs. vector images

Bitmaps. These are composed of individual pixels: e.g. photographs and micrographs (e.g. in tif/tiff, jpeg, png, and pdf formats).

Vector images. These are composed of lines, curves, shapes, or text. The instructions needed to draw the image (i.e. coordinates, equations, fonts) are stored rather than pixels, and then the image is recreated from these instructions when necessary. (e.g. in pdf, svg, and eps formats)

Vector images vs. bitmaps. The points in bitmap format, especially enlarged, are not sharp compared to the points in vector format.

1.4.2 Figure format selection

  • For journals: the bitmap format of tiff (perhaps with lossless LZW compression), or the vector formats (e.g. pdf and svg).

  • For presentation: png (file size is small).

  • For website: jpeg or png (file size is small).

1.5 Color schemes and the cognate gray ones

Tidyplots comes with a number of default color schemes, as accessed via typing ?tidyplots::colors_ in R console.

Discrete schemes (for categorical variables).

Diverging schemes (for continuous variables that have a central point in the middle).

Continuous schemes (for continuous variables).

1.5.1 Discrete color schemes and the cognate gray ones

Discrete color schemes (left panel), and the cognate gray ones (right panel). The color sequence of each scheme is from left to right.

1.5.2 Discrete color schemes and the cognate gray ones

Diverging color schemes (left panel), and the cognate gray ones (right panel). The color sequence of each scheme is from left to right.

1.5.3 Continuous color schemes and the cognate gray ones

Continuous color schemes (left panel), and the cognate gray ones (right panel). The color sequence of each scheme is from left to right.

1.5.4 High/medium-contrast qualitative color schemes

Paul Tol discribed high/medium-contrast qualitative colour schemes, which are colour-blind safe, and the cognage gray ones also show appropriate contrast (https://sronpersonalpages.nl/~pault/) (Tol 2021).

Here below are the high/medium-contrast qualitative color schemes, and the cognate gray ones (except the 1st color of white β€œ#ffffff”).

High-contrast qualitative color scheme and the cognate gray one

High-contrast qualitative color scheme (left panel), and the cognate gray one (right panel). The color sequence of the scheme is from left to right (β€œ#ddaa33”, β€œ#bb5566”, β€œ#004488”, β€œ#000000”).

Medium-contrast qualitative color scheme and the cognate gray one

Medium-contrast qualitative color scheme (left panel), and the cognate gray one (right panel). The color sequence of the scheme is from left to right (β€œ#eecc66”, β€œ#ee99aa”, β€œ#6699cc”, β€œ#997700”, β€œ#994455”, β€œ#004488”, β€œ#000000”).
Note

The figures across this book use the high-contrast qualitative color scheme by default, unless a different scheme is explicitly specified in the code.

This choice ensures not only strong visual contrast and color-blind safe, but also that distinctions remain clear when the book are printed in grayscale.

Bankhead, Peter. 2022. β€œIntroduction to Bioimage Analysis.” https://bioimagebook.github.io/index.html.
Engler, Jan Broder. 2025. β€œTidyplots Empowers Life Scientists with Easy Code-Based Data Visualization.” iMeta, e70018. https://doi.org/10.1002/imt2.70018.
Tol, Paul. 2021. β€œIntroduction to Colour Schemes.” August 18. https://sronpersonalpages.nl/~pault/.