---
title: "Customizing ggcharts Plots"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{customize}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
knitr::opts_chunk$set(
  message = FALSE,
  warning = FALSE,
  fig.width = 7,
  fig.height = 5
)
```


`ggcharts` aims to shorten the distance from data visualization idea to actual plot. That means it will do much of the work for you but *not* everything. Some things just cannot be automated, e.g. setting titles and footnotes. This vignette will show you how you can further customize a `ggcharts` plot using `ggplot2` functions.

The data I will use comes from the [2019 Stackoverflow Developer Survey](https://insights.stackoverflow.com/survey/2019#most-loved-dreaded-and-wanted).

```{r}
library(dplyr)
library(ggplot2)
library(ggcharts)

dreaded_lang <- tibble::tribble(
  ~language, ~pct,
  "VBA", 75.2,
  "Objective-C", 68.7,
  "Assembly", 64.4,
  "C", 57.5,
  "PHP", 54.2,
  "Erlang", 52.6,
  "Ruby", 49.7,
  "R", 48.3,
  "C++", 48.0,
  "Java", 46.6
)

(chart <- lollipop_chart(dreaded_lang, language, pct, highlight = "R"))
```

First of all, a good data visualization needs text. I use the `labs()` function here but you might as well use `ggtitle()`, `xlab()` and `ylab()` depending on your needs.

```{r}
chart <- chart +
  labs(
    x = NULL,
    y = "Developers Who are Developing with the Language but\nHave not Expressed Interest in Continuing to Do so",
    title = "Top 10 Most Dreaded Programming Languages",
    subtitle = "R Placed 8th",
    caption = "Source: Stackoverflow Developer Survey 2019"
  )
print(chart)
```

Given that the plot displays percentages it is best practice to have the axis labels include the percentages sign. That can be achieved by adding a `scale`. Note that I use the `scale_y_continuous()` function here even though I am changing the x axis. That's because in order to create a horizontal plot `ggcharts` uses `coord_flip()` internally which - as the name - suggests flips the axes.

```{r}
chart <- chart +
  scale_y_continuous(
    labels = scales::percent_format(scale = 1),
    expand = expand_scale(mult = c(0, .05)),
    limits = c(0, 80)
  )
print(chart)
```

If you only want to change the axis limits then there's no need to use `scale_y_continuous()` instead you can use `ylim()`.

The styling of the plot is probably the customization where you will spend most of your time. Just as with any 'usual' `ggplot` you can add any of the built-in themes to a `ggcharts` plot and tweak it further using the `theme()` function.

```{r}
chart <- chart +
  theme_classic(base_size = 15) +
  theme(
    plot.title = element_text(face = "bold"),
    plot.subtitle = element_text(face = "italic"),
    axis.text = element_text(color = "black")
  )
print(chart)
```