README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  results = FALSE,
  message = FALSE,
  warning = FALSE,
  fig.align = "center",
  dpi = 300,
  fig.width = 5,
  fig.height = 2.5,
  fig.path = "man/figures/README-"
)
```

# tidyplots <a href="https://jbengler.github.io/tidyplots/"><img src="man/figures/logo.svg" align="right" height="139" alt="tidyplots website" /></a>

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[![CRAN status](https://www.r-pkg.org/badges/version/tidyplots)](https://CRAN.R-project.org/package=tidyplots)
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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](https://cran.r-project.org/) with:
``` r
install.packages("tidyplots")
```

And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("jbengler/tidyplots")
```

## Usage

Here are some examples. Also have a look at the [getting started guide](https://jbengler.github.io/tidyplots/articles/tidyplots.html) and the [full documentation](https://jbengler.github.io/tidyplots/reference/).

```{r}
library(tidyplots)

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

```{r}
energy %>% 
  tidyplot(x = year, y = energy, color = energy_source) %>% 
  add_barstack_absolute()
```

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

```{r}
energy_week %>% 
  tidyplot(x = date, y = power, color = energy_source) %>% 
  add_areastack_absolute()
```

```{r}
energy_week %>% 
  tidyplot(x = date, y = power, color = energy_source) %>% 
  add_areastack_relative()
```

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

```{r}
time_course %>%
  tidyplot(x = day, y = score, color = treatment) %>%
  add_mean_line() %>%
  add_mean_dot() %>%
  add_sem_ribbon()
```

```{r}
climate %>%
  tidyplot(x = month, y = year, color = max_temperature) %>%
  add_heatmap()
```

```{r, fig.height=3}
study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_boxplot() %>% 
  add_test_pvalue(ref.group = 1)
```

```{r, fig.height=3.2}
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)
```

```{r}
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

- [Package index](https://jbengler.github.io/tidyplots/reference/)  
Overview of all tidyplots functions

- [Get started](https://jbengler.github.io/tidyplots/articles/tidyplots.html)  
Getting started guide

- [Visualizing data](https://jbengler.github.io/tidyplots/articles/Visualizing-data.html)  
Article with examples for common data visualizations

- [Advanced plotting](https://jbengler.github.io/tidyplots/articles/Advanced-plotting.html)  
Article about advanced plotting techniques and workflows

- [Color schemes](https://jbengler.github.io/tidyplots/articles/Color-schemes.html)  
Article about the use of color schemes

## 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.