HTML Widgets

R plots are cool, but interactive R plots are even cooler.

HTMLwidgets is a library you may not use directly, but it enables package creators to easily port JavaScript visualisations to R for display. Since JavaScript has arguably the best visualisation libraries out there, led by https://d3js.org/, this opens up many opportunities to link R data with JavaScript interactivity.

A few notable examples are shown below using our example data:

## get the web data
web_data <- read.csv("./data/gadata_example_2.csv", stringsAsFactors = FALSE)

## remove row name row
web_data <- web_data[,-1]

library(tidyr)
library(dplyr)
## call the key column 'variable' and the value colum 'value' and 
## gather all variables apart from date, channelGrouping and deviceCategory
## get only desktop rows, and the date, channelGrouping and sessions columns
pivoted <- web_data %>% 
  filter(deviceCategory == "desktop") %>% 
  select(date, channelGrouping, sessions) %>%
  spread(channelGrouping, sessions)

## get rid of any NA's and replace with 0
pivoted[is.na(pivoted)] <- 0

library(dygraphs)
library(xts)

## dygraph likes date objects
pivoted$date <- as.Date(pivoted$date)

## dygraph likes xts objects
web_data_xts <- xts(pivoted[,-1], order.by = pivoted$date)

dygraph(web_data_xts)

library(tidyr)
library(dplyr)

## call the key column 'variable' and the value colum 'value' and 
## gather all variables apart from date, channelGrouping and deviceCategory
web_data_tidy <- web_data %>% gather(variable, value, -date, -channelGrouping, -deviceCategory)
web_data_tidy$date <- as.Date(web_data_tidy$date)

plot_data <- web_data_tidy %>% filter(variable == "sessions")

library(ggplot2)
## I don't know why, but I always call them gg
gg <- ggplot(data = plot_data, aes(x = date)) + theme_minimal()
gg <- gg + geom_area(aes(y = value, group = channelGrouping, fill = channelGrouping)) + facet_grid(deviceCategory ~ .)
## make the colours nicer
gg <- gg + scale_fill_brewer(palette = "Paired")
## add a title
gg <- gg + ggtitle("Sessions per device category")
## rename the x and y axis
gg <- gg + xlab("Date") + ylab("Sessions")
## change the legend title
gg <- gg + guides(fill = guide_legend(title = "Channel Grouping"))
## put the legend at the bottom
gg <- gg + theme(legend.position = "bottom")


library(plotly)
ggplotly(gg)

library(highcharter)
## make a time-series object
## can't include the date as its not numeric, so remove
cor_data <- pivoted[, -1]
## not including first column, so -1 subset
cor_table <- round(cor(cor_data),2)

hchart(cor_table)