Homework.R

library(here) #Load all the necessary packages
library(dplyr)
library(janitor)
library(stringr)
library(sf)
library(tidyverse)

#Read in the csv file containing country names and geographical data
#"Here" removes the need for me to specify the full path, as it will start from the project folder

CountryData <- read_csv(here("World_Countries_(Generalized).csv")) %>%
  clean_names() #Neatens the column names, removing the capitalization

#Repeat this process for the data with country names and inequality indices 
#'skip = 5' removes the first five rows, which are all header material
#'na = ".." removes na values which are stored as ".." in this data set
#This is necessary as we need the values in the index column to be recognized as numeric
#remove_empty() removes the blank columns in this data set, making it neater
#'quiet = True' stops the program declaring the columns it's removed
#'#clean_names also adds an "x" to columns with numeric titles, making later calculations possible

InequalityData <- read_csv(here("Gender Inequality Index (GII).csv"),
                           skip = 5, na = "..",
                           locale = locale(encoding = "latin1")) %>%
  remove_empty(which = "cols", quiet = TRUE) %>%
  clean_names()

#The country names from the two dataframes cannot be merged as it stands
#This is because InequaliyData has a blank space " " before the country name
#This block of code fixes this problem

CountryList <- dplyr::select(InequalityData, country) #Extracts countries as a list
CountryListTrimmed <- as.list(trimws(CountryList$country, "l")) #Removes the blank spaces
#Adds this fixed list back into the dataframe
CleanInequalityData <- mutate(InequalityData, CountryListTrimmed)

#This block merges the two dataframes together using their common country names columns
#**Some data is lost because some country names do not match perfectly
#The code also produces a new column with the change of index from columns "x2010" and "x2019"

JoinedDataFrame <- merge(CountryData, CleanInequalityData,
                         #The titles of the columns with the common value names
                         by.x = "country", by.y = "CountryListTrimmed") %>%
  #Creates the new comparison column
  mutate(., InequalityDifference2010s = x2010 - x2019) %>%
  #Reduces the number of columns to just the important ones we want
  select(country, iso, hdi_rank, shape_length,
         shape_area, InequalityDifference2010s)

#This calculates and prints a mean value for the change in inequality index across the world
#"na.rm = TRUE" removes the na values, which prevents "na" from being returned as our output
MeanChange <- mean(JoinedDataFrame$InequalityDifference2010s, na.rm = TRUE)
print(MeanChange)

CountriesShapefile <- st_read("World_Countries_(Generalized)")