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https://github.com/thomas-neitmann/ggcharts

Get You to Your Desired Plot Faster
https://github.com/thomas-neitmann/ggcharts

data-visualization ggplot2 plots r

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Get You to Your Desired Plot Faster

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README 

        
---

output: github_document

---



```{r, include=FALSE}

knitr::opts_chunk$set(

  fig.path = "man/figures/README-",

  message = FALSE,

  warning = FALSE,

  dev = "svglite",

  fig.showtext = TRUE,

  out.width = "80%"

)

```



# ggcharts 



[![R build status](https://github.com/thomas-neitmann/ggcharts/workflows/R-CMD-check/badge.svg)](https://github.com/thomas-neitmann/ggcharts/actions)

[![CRAN Version](https://www.r-pkg.org/badges/version/ggcharts?color=green)](https://cran.r-project.org/package=ggcharts)

[![Total Downloads](http://cranlogs.r-pkg.org/badges/grand-total/ggcharts?color=green)](https://cran.r-project.org/package=ggcharts)

[![Lifecycle Status](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)



## Overview



`{ggcharts}` provides a high-level `{ggplot2}` interface for creating common charts. Its aim is both simple and ambitious: to get you from your data visualization idea to an actual plot faster. How so? By taking care of a lot of data preprocessing, obscure `{ggplot2}` details and plot styling for you. The resulting plots are `ggplot` objects and can be further customized using any `{ggplot2}` function.



## Installation



The package is available from CRAN.



```{r install_cran, eval=FALSE}

install.packages("ggcharts")

```



Alternatively, you can install the latest development version from GitHub.



```{r install_github, eval=FALSE}

if (!"remotes" %in% installed.packages()) {

  install.packages("remotes")

}

remotes::install_github("thomas-neitmann/ggcharts", upgrade = "never")

```



If you get an error when trying to install from GitHub, run this code and then try to install once again.



```{r no_errors_from_warnings, eval=FALSE}

Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS = "true")

```



If the installation still fails please open an [issue](https://github.com/thomas-neitmann/ggcharts/issues).



## Why ggcharts?



Thanks to `{ggplot2}` you can create beautiful plots in `R`. However, it can often take quite a bit of effort to get from a data visualization idea to an actual plot. As an example, let's say you want to create a faceted bar chart displaying the top 10 within each facet ordered from highest to lowest. What sounds simple is actually pretty hard to achieve. Have a look:



```{r motivation, fig.height = 3, fig.width = 8, out.width = "100%"}

library(dplyr)

library(ggplot2)

library(ggcharts)

data("biomedicalrevenue")



biomedicalrevenue %>%

  filter(year %in% c(2012, 2015, 2018)) %>%

  group_by(year) %>%

  top_n(10, revenue) %>%

  ungroup() %>%

  mutate(company = tidytext::reorder_within(company, revenue, year)) %>%

  ggplot(aes(company, revenue)) +

  geom_col() +

  coord_flip() +

  tidytext::scale_x_reordered() +

  facet_wrap(vars(year), scales = "free_y")

```



That's a lot of code! And you likely never heard of some of the functions involved. With `{ggcharts}` you can create the same plot (actually an even better looking one) in almost a single line of code.



```{r motivation_continued, fig.height = 3, fig.width = 8, out.width = "100%"}

biomedicalrevenue %>%

  filter(year %in% c(2012, 2015, 2018)) %>%

  bar_chart(x = company, y = revenue, facet = year, top_n = 10)

```



## Gallery



### Charts



```{r ggcharts_line_chart}

data("revenue_wide")

line_chart(data = revenue_wide, x = year, y = Roche:Bayer) +

  labs(x = "Year", y = "Revenue (Billion USD)")

```



```{r ggcharts_lollipop_chart}

biomedicalrevenue %>%

  filter(year == 2018) %>%

  lollipop_chart(x = company, y = revenue, threshold = 30) +

  labs(

    x = NULL,

    y = "Revenue",

    title = "Biomedical Companies with Revenue > $30Bn."

  ) +

  scale_y_continuous(

    labels = function(x) paste0("$", x, "Bn."),

    expand = expansion(mult = c(0, .05))

  )

```



```{r ggcharts_dumbbell_chart}

data("popeurope")

dumbbell_chart(

  data = popeurope,

  x = country,

  y1 = pop1952,

  y2 = pop2007,

  top_n = 10,

  point_colors = c("lightgray", "#494F5C")

) +

  labs(

    x = NULL,

    y = "Population",

    title = "Europe's Largest Countries by Population in 2007"

  ) +

  scale_y_continuous(

    limits = c(0, NA),

    labels = function(x) paste(x, "Mn.")

  )

```



```{r ggcharts_diverging_bar_chart}

data(mtcars)

mtcars_z <- dplyr::transmute(

  .data = mtcars,

  model = row.names(mtcars),

  hpz = scale(hp)

)



diverging_bar_chart(data = mtcars_z, x = model, y = hpz)

```



```{r ggcharts_diverging_lollipop_chart}

diverging_lollipop_chart(

  data = mtcars_z,

  x = model,

  y = hpz,

  lollipop_colors = c("#006400", "#b32134"),

  text_color = c("#006400", "#b32134")

)

```



```{r}

data("popch")

pyramid_chart(data = popch, x = age, y = pop, group = sex)

```



### Themes



```{r ggcharts_theme_hermit}

ggcharts_set_theme("theme_hermit")

bar_chart(data = diamonds, x = cut)

```



```{r ggcharts_theme_ng}

ggcharts_set_theme("theme_ng")

bar_chart(data = diamonds, x = cut)

```



```{r ggcharts_theme_nightblue}

ggcharts_set_theme("theme_nightblue")

bar_chart(data = diamonds, x = cut)

```