conclusion The Highcharter package by Joshua Kunst allows you to access the Highcharts JavaScript visualization library and can seamlessly fit into your workflow as it uses %>% to connect functions. For some examples check this link out. To find out more about the myriad of options available to build your chart, check out the Highcharts API reference here. For the more advanced customizations you might need to know JS and/or HTML but Stack Overflow posts can help you out (tags: highcharts & r-highcharter)! Another great tutorial to learn Highcharter from is here, another page out of Peter Aldhous' lecture notes!
What I did here was to show across 2 different y-axes, the total amount of Japanese textile exports and the percentage of those exports heading to the USA. Instead of having them on one graph, I moved the axes labels to opposite sides and had each time series split up the y-axis space between them using the hc_yAxis_multiples() function. To make things easier to understand, I had the tooltip show the data for both time series using hc_tooltip() and customizing the labels for each series inside the tooltip with the tooltip = argument inside hc_add_series() function.
library(dplyr) # pipes and dplyr verbs##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(readr) # reading in .csv files
library(ggplot2) # plotting
library(ggthemes) # ggplot2 themes
library(scales) ##
## Attaching package: 'scales'
## The following object is masked from 'package:readr':
##
## col_factor
Japan_Textiles <- read_csv("Data/JapanCottonTextileExports(1946-1960).csv",
col_names = c("Year", "total_exports", "exports_to_USA",
"percentage_exports_USA"),
skip = 1)## Parsed with column specification:
## cols(
## Year = col_integer(),
## total_exports = col_integer(),
## exports_to_USA = col_integer(),
## percentage_exports_USA = col_double()
## )
library(htmlwidgets)
library(highcharter)## Highcharts (www.highcharts.com) is a Highsoft software product which is
## not free for commercial and Governmental use
colors <- RColorBrewer::brewer.pal(2, "Set1")## Warning in RColorBrewer::brewer.pal(2, "Set1"): minimal value for n is 3, returning requested palette with 3 different levels
textiles_chart <- highchart() %>%
hc_colors(colors) %>%
hc_yAxis_multiples(
list(top = "0%", height = "30%", lineWidth = 3, opposite = TRUE,
title = list(text = "% of Total Exports")),
list(top = "30%", height = "70%",
title = list(text = "Total Exports (Millions sq. yards)"))
) %>%
hc_xAxis(title = list(text = "Year"),
categories = Japan_Textiles$Year,
tickInterval = 1) %>%
hc_add_series(name = "Total Exports",
data = Japan_Textiles, hcaes(Year, total_exports),
type = "spline", yAxis = 1,
tooltip = list(valueSuffix = " M.Sq.Yards")) %>%
hc_add_series(name = "Percentage (%) of Exports to USA",
data = Japan_Textiles, hcaes(Year, percentage_exports_USA),
type = "spline", yAxis = 0,
tooltip = list(valueSuffix = "%", valueDecimals = "1")) %>%
hc_tooltip(shared = TRUE,
borderColor = "black",
headerFormat = '<span style = "font-size: 14px"><b>{point.key}</b></span><br/>'
) %>%
hc_title(text = "Japan's Exports of Cotton Textiles (1946-1960)")
saveWidget(textiles_chart, "textiles_chart.html", selfcontained = TRUE, background = "white")You can even export what you've made as a standalone web page using the saveWidget() function from the htmlwidgets package!
Despite the devastation of the Allied bombing campaigns and the initial SCAP policies, the growing tensions of the Cold War and the postwar needs of America became the catalyst for a dramatic heel-turn in economic policy that laid the foundations for a miraculous Japanese recovery. By studying the causes and impacts of external/internal events and policies we can gain clues to tackle the future challenges in rebuilding economies in other war-devastated areas of the world.
Voilá