---
title: "Starting with ApexCharts"
author: "Victor Perrier"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Starting with ApexCharts}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

The objective of this vignette is to show how to quickly build data visualizations with the ApexCharts JavaScript library, as well as to give an overview of the different graphics available.

Data used are from `ggplot2` package, data manipulation will be done with the `dplyr` package.


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


## Bar charts

Simple bar charts can be created with:

```{r column}
data("mpg")
n_manufac <- count(mpg, manufacturer)

apex(data = n_manufac, type = "column", mapping = aes(x = manufacturer, y = n))
```

Flipping coordinates can be done by using `type = "bar"`:

```{r bar}
apex(data = n_manufac, type = "bar", mapping = aes(x = manufacturer, y = n))
```


To create a dodge bar charts, use aesthetic `fill` :

```{r dodge-bar}
n_manufac_year <- count(mpg, manufacturer, year)

apex(data = n_manufac_year, type = "column", mapping = aes(x = manufacturer, y = n, fill = year))
```

For stacked bar charts, specify option `stacked` in `ax_chart` :

```{r stacked-bar}
apex(data = n_manufac_year, type = "column", mapping = aes(x = manufacturer, y = n, fill = year)) %>% 
  ax_chart(stacked = TRUE)
```



## Line charts

Simple line charts can be created with (works with `character`, `Date` or `POSIXct`):

```{r line}
data("economics")
economics <- tail(economics, 100)

apex(data = economics, type = "line", mapping = aes(x = date, y = uempmed))
```


To represent several lines, use a `data.frame` in long format and the `group` aesthetic:

```{r lines}
data("economics_long")
economics_long <- economics_long %>% 
  group_by(variable) %>% 
  slice((n()-100):n())

apex(data = economics_long, type = "line", mapping = aes(x = date, y = value01, group = variable))
```


Create area charts with `type = "area"`:

```{r area}
apex(data = economics_long, type = "area", mapping = aes(x = date, y = value01, fill = variable)) %>% 
  ax_chart(stacked = TRUE)
```





## Scatter charts

Simple bar charts can be created with:

```{r scatter}
apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg))
```

Color points according to a third variable:

```{r scatter-fill}
apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg, fill = cyl)) %>%
  ax_xaxis(tickAmount = 5)
```

And change point size using `z` aesthetics:

```{r bubbles}
apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg, z = scales::rescale(qsec)))
```




## Pie charts

Simple pie charts can be created with:

```{r pie}
poll <- data.frame(
  answer = c("Yes", "No"),
  n = c(254, 238)
)

apex(data = poll, type = "pie", mapping = aes(x = answer, y = n))
```



## Radial charts

Simple radial charts can be created with (here we pass values directly in `aes`, but you can use a `data.frame`) :

```{r radial}
apex(data = NULL, type = "radialBar", mapping = aes(x = "My value", y = 65))
```


Multi radial chart (more than one value):

```{r radial-mult}
fruits <- data.frame(
  name = c('Apples', 'Oranges', 'Bananas', 'Berries'),
  value = c(44, 55, 67, 83)
)

apex(data = fruits, type = "radialBar", mapping = aes(x = name, y = value))

```



## Radar charts

Simple radar charts can be created with:

```{r radar}
mtcars$model <- rownames(mtcars)

apex(data = head(mtcars), type = "radar", mapping = aes(x = model, y = qsec))
```

With a grouping variable:

```{r radar-mult}
# extremely complicated reshaping
new_mtcars <- reshape(
  data = head(mtcars), 
  idvar = "model", 
  varying = list(c("drat", "wt")),
  times = c("drat", "wt"),
  direction = "long",
  v.names = "value",
  drop = c("mpg", "cyl", "hp", "dist", "qsec", "vs", "am", "gear", "carb")
)

apex(data = new_mtcars, type = "radar", mapping = aes(x = model, y = value, group = time))
```


## Heatmap

Create heatmap with : 

```{r heatmap}
txhousing2 <- txhousing %>% 
  filter(city %in% head(unique(city)), year %in% c(2000, 2001)) %>% 
  rename(val_med = median)

apex(data = txhousing2,
     type = "heatmap", 
     mapping = aes(x = date, y = scales::rescale(val_med), group = city)) %>% 
  ax_dataLabels(enabled = FALSE) %>% 
  ax_colors("#008FFB")
```



