highchart_test.rmd

---
title: "highchart"
author: "RN7"
date: "January 12, 2018"
output: 
  md_document:
    variant: markdown_github
always_allow_html: yes
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Highcharter
[conclusion](#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](http://jkunst.com/highcharter/) out. To find out more about the myriad of options available to build your chart, check out the Highcharts API reference [here](https://api.highcharts.com/highcharts/). 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](http://paldhous.github.io/ucb/2016/dataviz/week13.html), 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.


```{r textiles static}
library(dplyr)    # pipes and dplyr verbs
library(readr)    # reading in .csv files

library(ggplot2)  # plotting
library(ggthemes) # ggplot2 themes
library(scales)  

Japan_Textiles <- read_csv("Data/JapanCottonTextileExports(1946-1960).csv",
                           col_names = c("Year", "total_exports", "exports_to_USA",
                                         "percentage_exports_USA"),
                           skip = 1)

library(htmlwidgets)
library(highcharter)

colors <- RColorBrewer::brewer.pal(2, "Set1")

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!

## Conclusion

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.