Beginners Cookbook for Interactive Visualization in R with highcharter

Philosophy

This Post is purely aimed at helping beginners with cookbook-style code for Interactive Visualizations using highcharter package in R.

About highcharter

highcharter by Joshua Kunst R package is a wrapper for the ‘Highcharts’ library including shortcut functions to plot R objects. ‘Highcharts’ http://www.highcharts.com/ is a charting library offering numerous chart types with a simple configuration syntax.

Libraries

library(tidyverse) #data Manipulation
library(highcharter) #interactive visualization

Reading Input dataset and Summary of it

survey18 <- read_csv("~/so_survey_results_public.csv")
#glimpse(survey18)

API

highcharter lets you plot using two different functions:

• highchart()
• hchart()

highchart()

This function creates a Highchart chart using htmlwidgets. The widget can be rendered on HTML pages generated from R Markdown, Shiny, or other applications. If you are familiar with ggplot2 package, this function is very similar to ggplot() of the package where a base ggplot object is defined upon which further geometric layers can be added.

Similarly, Once the highchart() function is defined further highchart elements can be added on top of it.

hchart()

hchart() is a generic function to draw different charts on the fly. The resulting chart is a highchart object so you can keep modifying with the implmented API. If you are familiar with ggplot2, this function is similar to qplot() of it.

Let us begin our Interactive Visualization journey with the easy plots.

Icons Plot

survey18 %>% 
  select(Gender) %>% 
  filter(!is.na(Gender)) %>% 
  filter(Gender %in% c('Male','Female')) %>% 
  count(Gender) %>% 
  mutate(perc = round((n /sum(n))*100)) -> gender_icons
  hciconarray(c('Female','Male'),gender_icons$perc,icons = c('male','female'))

Bar

hchart – column

Barplot is useful when you have comparable Categorical variables (factors). Let us look at what Years of Coding experience the respondents have got.

survey18 %>% 
  count(YearsCoding) %>% 
  hchart('column', hcaes(x = 'YearsCoding', y = 'n'))

As you can see in the above code, the chart type here is column that makes a vertical bar plot. Aesthetics are given using hcaes() (similar to aes() of ggplot2).

hchart – bar

Let us look at what people are hoping for five years.

survey18 %>% 
  count(HopeFiveYears) %>% 
  hchart('column', hcaes(x = 'HopeFiveYears', y = 'n'))

while the same column plot as above does the job, it can seen the large axis label have to be rotated and also cut – which may not be something fine always. Hence we will rotate the plot to make it a horizontal bar plot so large axis label can be accomodated.

survey18 %>% 
  count(HopeFiveYears) %>% 
  hchart('bar', hcaes(x = 'HopeFiveYears', y = 'n'))

The chart type here is bar.

survey18 %>% 
  count(Employment, HopeFiveYears) %>% 
  hchart('bar', hcaes(x = 'HopeFiveYears', y = 'n', group = 'Employment'))

survey18 %>% 
  count(Employment, HopeFiveYears) %>% 
  hchart('bar', hcaes(x = 'Employment', y = 'n', group = 'HopeFiveYears'))

survey18 %>% 
  count(YearsCoding) %>% 
  hchart('line', hcaes(x = 'YearsCoding', y = 'n'))

survey18 %>% 
  count(YearsCoding, Employment) %>% 
  hchart('line', hcaes(x = 'YearsCoding', y = 'n', group = "Employment"))

survey18 %>% 
  count(YearsCoding) %>% 
  hchart('spline', hcaes(x = 'YearsCoding', y = 'n'))

survey18 %>% 
  count(YearsCoding, Employment) %>% 
  hchart('spline', hcaes(x = 'YearsCoding', y = 'n', group = "Employment"))

survey18 %>% 
  count(YearsCoding) %>% 
  hchart('area', hcaes(x = 'YearsCoding', y = 'n'))

survey18 %>% 
  count(YearsCoding, Employment) %>% 
  hchart('area', hcaes(x = 'YearsCoding', y = 'n', group = "Employment"))

survey18 %>% 
  count(YearsCoding) %>% 
  hchart('areaspline', hcaes(x = 'YearsCoding', y = 'n'))

survey18 %>% 
  count(YearsCoding, Employment) %>% 
  hchart('areaspline', hcaes(x = 'YearsCoding', y = 'n', group = "Employment"))

survey18 %>% 
  filter(!is.na(Gender),
         Gender %in% c('Male','Female')) %>% 
  filter(Country %in% 'United Kingdom') %>% 
  filter(!is.na(Age),
         !is.na(ConvertedSalary),
         ConvertedSalary > 0) %>% 
  select(Gender, Country, Age, ConvertedSalary) %>% 
  mutate(age_grp = parse_number(Age)) %>%
  hchart('scatter',hcaes('ConvertedSalary',"age_grp"))