Beautiful interactive visualizations

The package highcharacter allows to build nice interactive plots and may be viewed as the best solution for displayed some changes in time.

In this case, the gapminder dataset is used and changes in population size are shown.

The example is based on the following book chapter.

Rob Kabacoff, Data Visualization with R. 2020.

Uploading data

## Uploading gapminder dataset (if necessary)
## install.packages("gapminder")

library(gapminder)
 
df <- gapminder

What it is about

##      country      
## [1,] "Afghanistan"
## [2,] "Albania"    
## [3,] "Algeria"    
## [4,] "Angola"     
## [5,] "Argentina"  
## [6,] "Australia"

##      continent 
## [1,] "Africa"  
## [2,] "Americas"
## [3,] "Asia"    
## [4,] "Europe"  
## [5,] "Oceania"

## 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 
##  142  142  142  142  142  142  142  142  142  142  142  142

library(psych)
library(kableExtra)
library(tidyverse)

describe(df) %>%
  round(digits = 0) %>%
  knitr::kable() %>%
  kable_styling()

## The following data contains information about 142 countries in five continents
## The period of observations is between 1952 and 2007 (with five years interval)
## It includes such indexes as population, life expectancy, and GDP 

Interactive plots

The highcharter package allows to create nicely formatted interactive plots. In this case, the graph is about population changes in six Asian countries from 1952 to 2007.

## install.packages("highcharter")

library(highcharter)

## Remaining only population size in Asian countries
asia <- filter(df, continent == "Asia")
asia <- select(asia, year, country, pop)

## Convert to long to wide format

library(tidyr)
sprasia <- spread(asia, country, pop)

## Define six Asian countries and plot draft plot

## Years are on the X-axis, population - Y-axis, and countries are represented with six different lines from blue to red

graph <- highchart() %>% 
  hc_xAxis(categories = sprasia$year) %>% 
  hc_add_series(name = "China", 
                data = sprasia$China) %>% 
  hc_add_series(name = "India", 
                data = sprasia$India) %>%
  hc_add_series(name = "Japan", 
                data = sprasia$Japan) %>%
  hc_add_series(name = "Iran", 
                data = sprasia$Iran) %>%
  hc_add_series(name = "Syria", 
                data = sprasia$Syria) %>%
  hc_add_series(name = "Pakistan", 
                data = sprasia$Pakistan)

graph

## By moving a cursor to a certain point on a line, one may see the data in the following format - year on the top and country together with population size in a given year on the bottom

Improved version:

graph <- graph %>%
  ## assinging title to the plot and determing its style
  hc_title(text = "Population changes by Country",
           margin = 20, 
           align = "left",
           style = list(color = "steelblue")) %>% 
  ## the same with subtitle
  hc_subtitle(text = "1952 to 2007",
              align = "left",
              style = list(color = "#2b908f", 
                           fontWeight = "bold")) %>% 
  ## add link to gapminder dataset in the caption section 
  hc_credits(enabled = TRUE, # add credits
             text = "Gapminder Data",
             href = "http://gapminder.com") %>% 
  ## legend is put on the upper left corner
  hc_legend(align = "left", 
            verticalAlign = "top",
            layout = "vertical", 
            x = 0, 
            y = 100) %>%
  ## this line adds the interactivity - joint small legend for any point on the chart
  hc_tooltip(crosshairs = TRUE, 
             backgroundColor = "#FCFFC5",
             shared = TRUE, 
             borderWidth = 4) %>% 
  ## just enables to download the chart, may be avoided
  hc_exporting(enabled = TRUE)

graph

## Moreover, by clicking on the legend in the upper left corner, it is possible to specify countries or remain only one and examine it in detail.

## In sum, it seems that population in all of the given countries was on the increase - but in some it was stable, while in some more of a fluctuating nature (e.g. Iran).