Changes in country level life expectancy 1870-2016 based on data from Gapminder.org

LE_1870_2016.R

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# This is a small piece of R-Code to support a recent blog-post of mine on country-level inequalities in life expectancy.            #
# If you are so inclined, you can read the blog-post here: http://wp.me/p8fOE1-13                                                    #
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# For the purposes of re-use, the code is released under an MIT License (https://choosealicense.com/licenses/mit/)                   #
# The code is provided "as is" and without warranties.                                                                               #
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####### Notes                                                                                                                        #
# 1. For the "How To" on accessing Gapminder data, look at David Comfort's post: http://bit.ly/2jXkclu                               #
# 2. I use the googlesheets package of Bryan and Zhao (2017) to extract the data                                                     #
# 3. For a great discussion of changes in countries' life expectancy over time see https://ourworldindata.org/life-expectancy/       #
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# Daniel D Reidpath, 21 January 2017                                                                                                 #
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library(googlesheets)
library(dplyr)


# indicator life_expectancy_at_birth -- Public Sheet
le_data_url <- "https://docs.google.com/spreadsheets/d/1H3nzTwbn8z4lJ5gJ_WfDgCeGEXK3PVGcNjQ_U5og8eo/pub?gid=0#"

# Access a Gapminder Google Sheet by the URL and get some information about the Google Sheet:
gs_url(le_data_url, lookup = FALSE)

le_data_key <- extract_key_from_url(le_data_url)

# Download the liofe expectancy data (quite slow)
data <- gs_key(le_data_key, verbose=FALSE, lookup = FALSE) %>% gs_read(ws = "Data", range = "A1:HJ261")

# Extract the life expectancy data into a data matrix.  It simplifies the manipulation (at least in my head)
mat_data <- as.matrix(data[, 72:218])  # only take the data from 1870 to 2016

# Calculate the deciles of country life expectancy in each year 
quantile_data <- apply(mat_data, 2, quantile, probs = seq(0.1, 0.9, 0.1), na.rm=T)
# Set up difference colors for each decile's line
quantile_col <- c("#0000FF", "#8989FF", "#BCBCFF", "#E1E1FF", "#FFFFFF", "#FFE1E1", "#FFBCBC", "#FF8989", "#FF0000")


### Plot the deciles of life expectancy
# Create an empty plot of the right dimensions, with appropriate labels
plot(x = 1870:2016, y = rep(1, length(1870:2016)), type = "n",
     ylim = c(0, 100),
     ylab = "Life Expectancy at Birth",
     xlab = "Year",
     main = "Deciles of countries' national life expectancy at birth\n(1870 \u2013 2016)")
# Plot the 9 deciles of of life expectancy as different line
for(i in 1:9){
  lines(1870:2016, quantile_data[i, ],
        lwd = 2,
        col = quantile_col[i])
}


### Plot the 90/10 decile difference in life expectancy
decile_diff <- quantile_data[9, ] - quantile_data[1, ]  # Calculate the 90/10 decile difference
plot(x = 1870:2016, y = decile_diff, 
     type = "l", lwd = 2, col = "purple",
     ylim = c(10, 40),
     ylab = "Difference",
     xlab = "Year",
     main = "The 9th/1st decile difference in national life expectancy at birth\n(1870 \u2013 2016)")


### Plot the 90/10 decile ratio in life expectancy
decile_ratio <- quantile_data[9, ] / quantile_data[1, ]
plot(x = 1870:2016, y = decile_ratio, 
     type = "l", lwd = 2, col = "Brown",
     ylim = c(1, 3.5),
     ylab = "Decile Ratio",
     xlab = "Year",
     main = "The 9th/1st decile ration in national life expectancy at birth\n(1870 \u2013 2016)")