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tidyplots

The goal of tidyplots is to streamline the creation of publication-ready plots for scientific papers. It allows to gradually add, remove and adjust plot components using a consistent and intuitive syntax.

Installation

You can install the released version of tidyplots from CRAN with:

install.packages("tidyplots")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("jbengler/tidyplots")

Usage

Here are some examples. Also have a look at the getting started guide and the full documentation.

library(tidyplots)

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_bar(alpha = 0.4) %>% 
  add_sem_errorbar() %>% 
  add_data_points_beeswarm()

energy %>% 
  tidyplot(x = year, y = power, color = energy_source) %>% 
  add_barstack_absolute()

energy %>% 
  dplyr::filter(year %in% c(2005, 2010, 2015, 2020)) %>% 
  tidyplot(y = power, color = energy_source) %>% 
  add_donut() %>% 
  split_plot(by = year)

energy_week %>% 
  tidyplot(x = date, y = power, color = energy_source) %>% 
  add_areastack_absolute()

energy_week %>% 
  tidyplot(x = date, y = power, color = energy_source) %>% 
  add_areastack_relative()

study %>% 
  tidyplot(x = group, y = score, color = dose) %>% 
  add_mean_bar(alpha = 0.4) %>% 
  add_mean_dash() %>% 
  add_mean_value()

time_course %>%
  tidyplot(x = day, y = score, color = treatment, dodge_width = 0) %>%
  add_mean_line() %>%
  add_mean_dot() %>%
  add_sem_ribbon()

climate %>%
  tidyplot(x = month, y = year, color = max_temperature) %>%
  add_heatmap()

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_boxplot() %>% 
  add_test_pvalue(ref.group = 1)

gene_expression %>% 
  dplyr::filter(external_gene_name %in% c("Apol6", "Col5a3", "Vgf", "Bsn")) %>% 
  tidyplot(x = condition, y = expression, color = sample_type) %>% 
  add_mean_dash() %>% 
  add_sem_errorbar() %>% 
  add_data_points_beeswarm() %>% 
  add_test_asterisks(hide_info = TRUE) %>% 
  remove_x_axis_title() %>% 
  split_plot(by = external_gene_name)

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_bar(alpha = 0.4) %>% 
  add_sem_errorbar() %>% 
  add_data_points_beeswarm() %>% 
  view_plot(title = "Default color scheme: 'friendly'") %>% 
  adjust_colors(colors_discrete_apple) %>% 
  view_plot(title = "Alternative color scheme: 'apple'")

Documentation

Acknowledgements

I would like to thank Lars Binkle-Ladisch for our insightful discussions and for consistently challenging my decisions regarding the naming of functions and their arguments.

Many thanks to the R and tidyverse communities. tidyplots is built upon their software and coding paradigms, and it would not have been possible without their contributions.

tidyplots relies on several fantastic packages that handle all the heavy lifting behind the scenes. These include cli, dplyr, forcats, ggbeeswarm, ggplot2, ggpubr, ggrastr, ggrepel, glue, Hmisc, htmltools, lifecycle, patchwork, purrr, rlang, scales, stringr, tidyr, and tidyselect.