Project Part 2

Interactive and static plots for Agricultural Value added per Worker from 1991 to 2017.

Packages I will use to read in and plot the data

library(tidyverse)
library(echarts4r)

Read the data in from part 1

regional_value <- read_csv(here::here("_posts/2022-05-10-project-part-1/regional_value.csv"))

Interactive Graph

Start with the data
Group_by region so there will be a “country” for each region
Use mutate to round regionalvalue so only 2 digits will be displayed when you hover over it
Use mutate to change Year so it will be displayed as end of year instead of beginning of year
Use e_charts to create an e_charts object with year on the x-axis
Use e_country to build “countries” that contain agricultural value by region.
Use e_tooltip to add a tooltip that will display based on the axis values
Use e_title to add a title, subtitle, and link to subtitle
Use e_theme to change the theme to roma

regional_value %>%
  group_by(Region) %>%
  mutate(Valueaddedperworker = round(Valueaddedperworker, 2),
         Year = paste(Year, "12", "31", sep="-")) %>%
  e_charts(x = Year) %>%
  e_river(serie = Valueaddedperworker, legend = FALSE) %>%
  e_tooltip(trigger = "axis") %>%
  e_title(text = "Annual Aggriculture Value Added per Worker",
          subtext = "Source: Our World in Data",
          sublink = "https://ourworldindata.org/grapher/agriculture-value-added-per-worker-wdi",
          left = "center") %>%
  e_theme("roma")

Static graph

Start with the data
Use ggplot to create a new ggplot object. Use aes to indicate that Year will be mapped to the x axis ; value added per worker will be mapped to the y axis Region will be the fill variable
geom_area will display Valueaddedperworker
scale_fill_discrete_divergingx is a function in the colorspace package. It sets the color palette to roma and selects a maximum of 12 colors for the different regions.
theme_classic sets the theme
theme(legend.position = “bottom”) puts the legend at the bottom of the plot
labs sets the y axis label, fill = NULL indicates that the fill variable will not have the labelled Region.

regional_value %>%
  ggplot(aes(x = Year, y = Valueaddedperworker,
             fill = Region)) +
  geom_area() +
  colorspace::scale_fill_discrete_divergingx(palette = "roma", nmax = 12) +
  theme_classic() +
  theme(legend.position = "bottom") +
  labs( y = "Value Added per Worker (in dollars)",
        fill = NULL)

These plots show a steady increase in value added per worker (besides in Liberia where it has stayed pretty stagnent) since 2000. The value added per worker has continued to increase.

ggsave(filename = here::here("_posts/2022-05-14-project-part-2/preview.png"))