206 lines
4.2 KiB
Plaintext
206 lines
4.2 KiB
Plaintext
---
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title: "Starting with ApexCharts"
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author: "Victor Perrier"
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date: "`r Sys.Date()`"
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output: rmarkdown::html_vignette
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vignette: >
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%\VignetteIndexEntry{Starting with ApexCharts}
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%\VignetteEngine{knitr::rmarkdown}
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%\VignetteEncoding{UTF-8}
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---
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```{r setup, include = FALSE}
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knitr::opts_chunk$set(
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collapse = TRUE,
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comment = "#>"
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)
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```
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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.
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Data used are from `ggplot2` package, data manipulation will be done with the `dplyr` package.
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```{r}
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library(ggplot2)
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library(dplyr)
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library(apexcharter)
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```
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## Bar charts
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Simple bar charts can be created with:
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```{r}
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data("mpg")
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n_manufac <- count(mpg, manufacturer)
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apex(data = n_manufac, type = "column", mapping = aes(x = manufacturer, y = n))
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```
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Flipping coordinates can be done by using `type = "bar"`:
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```{r}
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apex(data = n_manufac, type = "bar", mapping = aes(x = manufacturer, y = n))
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```
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To create a dodge bar charts, use aesthetic `fill` :
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```{r}
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n_manufac_year <- count(mpg, manufacturer, year)
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apex(data = n_manufac_year, type = "column", mapping = aes(x = manufacturer, y = n, fill = year))
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```
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For stacked bar charts, specify option `stacked` in `ax_chart` :
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```{r}
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apex(data = n_manufac_year, type = "column", mapping = aes(x = manufacturer, y = n, fill = year)) %>%
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ax_chart(stacked = TRUE)
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```
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## Line charts
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Simple line charts can be created with (works with `character`, `Date` or `POSIXct`):
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```{r}
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data("economics")
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economics <- head(economics, 100)
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apex(data = economics, type = "line", mapping = aes(x = date, y = uempmed))
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```
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To represent several lines, use a `data.frame` in long format and the `group` aesthetic:
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```{r}
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economics_long <- economics_long %>%
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group_by(variable) %>%
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slice(1:100)
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apex(data = economics_long, type = "line", mapping = aes(x = date, y = value01, group = variable))
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```
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Create area charts with `type = "area"`:
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```{r}
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apex(data = economics_long, type = "area", mapping = aes(x = date, y = value01, fill = variable)) %>%
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ax_chart(stacked = TRUE) %>%
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ax_dataLabels(enabled = FALSE)
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```
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## Scatter charts
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Simple bar charts can be created with:
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```{r}
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apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg))
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```
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Color points according to a third variable:
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```{r}
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apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg, fill = cyl))
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```
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And change point size using `z` aesthetics:
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```{r}
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apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg, z = scales::rescale(qsec)))
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```
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## Pie charts
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Simple pie charts can be created with:
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```{r}
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poll <- data.frame(
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answer = c("Yes", "No"),
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n = c(254, 238)
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)
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apex(data = poll, type = "pie", mapping = aes(x = answer, y = n))
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```
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## Radial charts
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Simple radial charts can be created with (here we pass values directly in `aes`, but you can use a `data.frame`) :
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```{r}
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apex(data = NULL, type = "radialBar", mapping = aes(x = "My value", y = 65))
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```
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Multi radial chart (more than one value):
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```{r}
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fruits <- data.frame(
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name = c('Apples', 'Oranges', 'Bananas', 'Berries'),
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value = c(44, 55, 67, 83)
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)
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apex(data = fruits, type = "radialBar", mapping = aes(x = name, y = value))
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```
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## Radar charts
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Simple radar charts can be created with:
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```{r}
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mtcars$model <- rownames(mtcars)
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apex(data = head(mtcars), type = "radar", mapping = aes(x = model, y = qsec))
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```
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With a grouping variable:
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```{r}
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# extremely complicated reshaping
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new_mtcars <- reshape(
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data = head(mtcars),
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idvar = "model",
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varying = list(c("drat", "wt")),
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times = c("drat", "wt"),
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direction = "long",
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v.names = "value",
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drop = c("mpg", "cyl", "hp", "dist", "qsec", "vs", "am", "gear", "carb")
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)
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apex(data = new_mtcars, type = "radar", mapping = aes(x = model, y = value, group = time))
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```
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## Heatmap
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Create heatmap with :
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```{r}
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txhousing2 <- txhousing %>%
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filter(city %in% head(unique(city)), year %in% c(2000, 2001)) %>%
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rename(val_med = median)
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apex(data = txhousing2, type = "heatmap", mapping = aes(x = date, y = scales::rescale(val_med), group = city)) %>%
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ax_dataLabels(enabled = FALSE) %>%
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ax_colors("#008FFB")
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```
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