Introduction

We chart data, and data can come in different ways: numeric or character vectors, as time series objects, etc. but the most common object with data is a data frame. So, we’ll show how chart this type of object in highcharter.

Highcharter have two main functions to create a chart from data and another to add data to an existing highchart object.

  1. hchart(): A generic function which take an object (like vector, time series, data frames, likert object, etc) and return a highchart object (chart)
  2. hc_add_series(): A generic function which add data to a existing highchart object depending the type (class) of the data.

There are a last function will be useful to chart data from data frame. The functions is hcaes which will define the aesthetic mappings. This 3 functions are inspired in ggplot2 package. So:

The main differences with ggplot2 are here we need the data and the aesthetics explicit in every highcharts functions.

First, show some data to work with.

data("mpg", "diamonds", "economics_long", package = "ggplot2")
head(mpg)
## # A tibble: 6 x 11
##   manufacturer model displ  year   cyl trans      drv     cty   hwy fl    class 
##   <chr>        <chr> <dbl> <int> <int> <chr>      <chr> <int> <int> <chr> <chr> 
## 1 audi         a4      1.8  1999     4 auto(l5)   f        18    29 p     compa~
## 2 audi         a4      1.8  1999     4 manual(m5) f        21    29 p     compa~
## 3 audi         a4      2    2008     4 manual(m6) f        20    31 p     compa~
## 4 audi         a4      2    2008     4 auto(av)   f        21    30 p     compa~
## 5 audi         a4      2.8  1999     6 auto(l5)   f        16    26 p     compa~
## 6 audi         a4      2.8  1999     6 manual(m5) f        18    26 p     compa~

In general we’ll use this structure to get a chart:

hchart(<data.frame>, <type_of_chart>, hcaes(<aesthetics>), ...)
  • <data.frame> will be the data to chart.
  • <type_of_chart> is a string to specify the type of chart. This value can be: line, spline, area, heatmap, treemap, etc.
  • <aesthetics> is the mapping to use for plot.
  • ... are other parameters to configure the chart.

So, a basic example would be:

hchart(mpg, "point", hcaes(x = displ, y = cty, group = year))

Exercising the basics

Let’s try other charts to get familiar with the syntax.

data(economics_long, package = "ggplot2")

economics_long2 <- dplyr::filter(economics_long, variable %in% c("pop", "uempmed", "unemploy"))

head(economics_long2)
## # A tibble: 6 x 4
##   date       variable  value value01
##   <date>     <chr>     <dbl>   <dbl>
## 1 1967-07-01 pop      198712 0      
## 2 1967-08-01 pop      198911 0.00164
## 3 1967-09-01 pop      199113 0.00330
## 4 1967-10-01 pop      199311 0.00492
## 5 1967-11-01 pop      199498 0.00646
## 6 1967-12-01 pop      199657 0.00777
hchart(economics_long2, "line", hcaes(x = date, y = value01, group = variable))

Now, a heatmap and a treemap.

library(dplyr)

dfdiam <- diamonds %>% 
  group_by(cut, clarity) %>%
  summarize(price = median(price))

hchart(dfdiam, "heatmap", hcaes(x = cut, y = clarity, value = price), name = "Median Price") 
mpgman <- mpg %>% 
  group_by(manufacturer) %>% 
  summarise(n = n(),
            unique = length(unique(model))) %>% 
  arrange(-n, -unique)

hchart(mpgman, "treemap", hcaes(x = manufacturer, value = n, color = unique))

Extra parameters

You can add other parameters to add options to the data series. This options can modify the names of each series/group. These names are used in the legend as well the tooltip. You can modify the colors and a lot of properties.

mpgman2 <- count(mpg, manufacturer, year)

hchart(
  mpgman2, 
  "bar",
  hcaes(x = manufacturer, y = n, group = year),
  color = c("#7CB5EC", "#F7A35C"),
  name = c("Year 1999", "Year 2008"),
  showInLegend = c(TRUE, FALSE) # only show the first one in the legend
  )

Highcharts has a very complete API with a lot of options. You can see every option in action in https://api.highcharts.com/highcharts/plotOptions.series.

A more advanced example

Using the broom package is really nice due the you can transform models to tidy data:

library(broom)

modlss <- loess(dist ~ speed, data = cars)

fit <- arrange(augment(modlss), speed) %>% 
  mutate(.se = predict(modlss, se = TRUE)$se.fit)

head(fit)
## # A tibble: 6 x 5
##    dist speed .fitted .resid   .se
##   <dbl> <dbl>   <dbl>  <dbl> <dbl>
## 1     2     4    5.89 -3.89   9.89
## 2    10     4    5.89  4.11   9.89
## 3     4     7   12.5  -8.50   4.99
## 4    22     7   12.5   9.50   4.99
## 5    16     8   15.3   0.719  4.55
## 6    10     9   18.4  -8.45   4.31

In this case we’ll chart the original data first and store in a variable called hc (for highcharts):

hc <- hchart(
  cars,
  type = "scatter",
  hcaes(x = speed, y = dist),
  name = "Speed and Stopping Distances of Cars",
  showInLegend = TRUE
  )

hc

We can add more series to chart (like the layers in ggplot world) using the function hc_add_series. The parameters in this functions are similar to the hchart:

hc_add_series(<highcarter_object>, <data.frame>, <type_of_chart>, hcaes(<aesthetics>), ...)

# or with the pipe:

<highcarter_object> %>% hc_add_series(<data.frame>, <type_of_chart>, hcaes(<aesthetics>), ...)

In this case we will add 2 groups of series. The 1st to add will be the spline then the arearange. So we’ll use hc_add_series 2 times.

qtint <- qt(0.975, predict(modlss, se = TRUE)$df)

hc %>%
  hc_add_series(
    fit,
    type = "spline",
    hcaes(x = speed, y = .fitted),
    name = "Fit",
    id = "fit", # this is for link the arearange series to this one and have one legend
    lineWidth = 1,
    showInLegend = TRUE
    ) %>% 
  hc_add_series(
    fit,
    type = "arearange",
    name = "SE",
    hcaes(x = speed, low = .fitted - qtint*.se, high = .fitted + qtint*.se),
    linkedTo = "fit", # here we link the legends in one.
    showInLegend = FALSE,
    color = hex_to_rgba("gray", 0.2),  # put a semi transparent color
    zIndex = -3 # this is for put the series in a back so the points are showed first
    )

Example of data from https://stackoverflow.com/questions/22717930/how-to-get-the-confidence-intervals-for-lowess-fit-using-r.

What now?

Since you know the basics now it could be interesting: