Pretty base plots

Last updated: 2022-10-11

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Knit directory: basegraphics/

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File	Version	Author	Date	Message
Rmd	d4fe512	Anand	2022-10-11	marathon finish times
html	4906871	Anand	2022-08-30	Build site.
Rmd	23befe6	Anand	2022-08-30	job change
html	d8b5266	Anand	2022-08-22	Build site.
html	91aa770	Anand	2022-08-22	workflowr::wflow_publish(files = c("analysis/baseplots.Rmd",
Rmd	848d905	Anand	2022-08-22	blood donuts
html	9c9607b	PoisonAlien	2022-07-18	Build site.
Rmd	4b6aa1e	PoisonAlien	2022-07-18	workflowr::wflow_publish(files = "analysis/baseplots.Rmd")
html	4f94642	PoisonAlien	2022-07-18	Build site.
Rmd	dc7d6c5	PoisonAlien	2022-07-18	workflowr::wflow_publish(files = "analysis/baseplots.Rmd")
html	7d5d068	PoisonAlien	2022-07-16	Build site.
Rmd	4cd02d0	PoisonAlien	2022-07-16	workflowr

Introduction

This document demonstrates the usage of base R graphics to generate visually appealing plots, without any dependencies.

Most of the plots are recreated as is from several sources. My gratitude to them for all the hard work and making it accessible/open

Acknowledgments:

Following are the major sources of all the recreated plots.

Finally, I apologize in advance for the uncommentated code. Lets go..

Barplot: Berlin Marathon 2022 Finish times

2022 Berlin marathon set the new record for fastest marathon finish by Eliud Kipchoge. Lets celebrate this by recreating the STRAVA inspired Berlin marathon finish times. Of course the plot is not nearly identical to the one on strava and I suspect there might have been issues with data scraping.

This is how got the raw data:

Max 100 results per page. There are 356 pages on the official site

#!/usr/bin/env bash

for (( i = 1; i < 356; i++ )); do
        echo ${i}
        url="https://berlin.r.mikatiming.com/2022/?page=${i}&event=BML&num_results=100&pid=search&search%5Bage_class%5D=%25&search%5Bsex%5D=%25&search%5Bnation%5D=%25&search_sort=name"
        wget -O berlin.html ${url}
        grep type-fullname berlin.html | tr '>' '\t' | cut -f 3 | tr '<' '\t' | cut -f1 | uniq > runner_${i}.tsv
        grep Finish berlin.html | tr '>' '\t' | grep -v 'pan class' | cut -f 4 | tr '<' '\t' | cut -f1 > times_${i}.tsv
done

cat times_* | grep -v Finish | grep -v DSQ > 14-barplot_BerlinMarathon2022.tsv

data = read.delim(file = "data/14-barplot_BerlinMarathon2022.tsv", header = FALSE)

data$secs = apply(X = data, MARGIN = 1, FUN = function(x){
  sapply(strsplit(x = x, split = ":"), function(x) {
    as.numeric(x[1]) * 3600 + as.numeric(x[2]) * 60 + as.numeric(x[3])
  })
})
data$mins = data$secs / 60
data$cut = cut(x = data$mins, breaks = seq(90, 480, 1))
hcol = adjustcolor(col = '#d35400', alpha.f = 0.6)

par(mar = c(3, 2, 3, 1), family = "mono")
plot.new()
plot.window(xlim = c(0, 390), ylim = range(table(data$cut)))
abline(v = seq(0, 390, 30), col = "#ecf0f1")
points(table(data$cut), type = 'h', col = hcol)
#axis(side = 1, at = seq(0, 390, 30), labels = seq(90, 480, 30)/60, las = 2, col.axis = "#34495e", tick = FALSE, )
text(
  x = seq(0, 390, 30),
  y = -45,
  labels = c(
    "1:30",
    "2:00",
    "2:30",
    "3:00",
    "3:30",
    "4:00",
    "4:30",
    "5:00",
    "5:30",
    "6:00",
    "6:30",
    "7:00",
    "7:30",
    "8:00"
  ),
  srt = -45,
  xpd = TRUE,
  cex = 0.8, col = "#2c3e50"
)

kipchoge_slot_idx = which(names(table(data$cut)) == "(121,122]")
assefa_slot_idx = which(names(table(data$cut)) == "(135,136]")

median_time = median(data$secs) #245.7 i.e: 04:05:42
median_time_idx = which(names(table(data$cut)) == "(245,246]")

rect(xleft = kipchoge_slot_idx, ybottom = 0, xright = kipchoge_slot_idx, ytop = 100, border = "#34495e", lwd = 1.2)
text(x = kipchoge_slot_idx, y = 90, labels = "KIPCHOGE\n2:01:09", cex = 0.7, pos = 3, font = 2)

rect(xleft = assefa_slot_idx, ybottom = 0, xright = assefa_slot_idx, ytop = 200, border = "#95a5a6", lwd = 1.2)
text(x = assefa_slot_idx, y = 190, labels = "ASSEFA\n2:15:37", cex = 0.7, pos = 3, font = 2)

rect(xleft = median_time_idx, ybottom = 0, xright = median_time_idx, ytop = 90, border = "#34495e", lwd = 1.2)
text(x = median_time_idx, y = 90, labels = "MEDIAN\n04:05:42", cex = 0.7, pos = 3, font = 2)

mtext(text = "TIME [H:MM]", side = 1, line = 2)
mtext(text = "NUMBER OF ATHLETES", side = 2, line = 0.5)

title(main = "BMW BERLIN-MARATHON 2022: FINISH TIMES", col.main = "#2c3e50")

Version	Author	Date
4906871	Anand	2022-08-30
91aa770	Anand	2022-08-22
4f94642	PoisonAlien	2022-07-18
7d5d068	PoisonAlien	2022-07-16

Bubble chart: Fastest growing/declining jobs

Recreating the plot from visualcapitalist. Data is provided as a table in the corresponding article.

data = read.delim(file = "data/11-bubblechart_furturejobs2030.tsv")

cols = c("#16a085", "#2980b9", "#f39c12", "#8e44ad", "#e74c3c")
cols = adjustcolor(col = cols, alpha.f = 0.6)
names(cols) = names(table(data$categ))

par(mar = c(1, 4, 4, 4), family = "sans", bg = "#ecf0f1")
plot(NA, xlim = range(pretty(data$median_ann_wage)), ylim = c(-40, 80), axes = FALSE, xlab = NA, ylab = NA)
grid(col = "gray80")

symbols(
  x = data$median_ann_wage,
  y = data$pct_empl_change,
  circles = sqrt(abs(data$num_empl_change / pi)),
  add = TRUE, inches = 0.9, bg = cols[data$categ], fg = "#ecf0f1"
)

#rect(xleft = seq(45, 105, 15)*1000, ybottom = 75, xright = seq(60, 120, 15)*1000, ytop = 80, col = cols, border = cols)
rect(xleft = seq(70, 110, 10)*1000, ybottom = 75, xright = seq(80, 120, 10)*1000, ytop = 80, col = cols, border = cols)
text(x = seq(75, 115, 10)*1000, y = 85, labels = c("Computer\n&math", "Health\nrerlated", "Office\nsupport", "Other", "Production"), xpd = TRUE, adj = 0.5, family = "sans", cex = 0.65)

if("berryFunctions" %in% installed.packages()){
  berryFunctions::roundedRect(xleft = 68*1000, ybottom = 72, xright = 122*1000, ytop = 90, xpd = FALSE, lwd = 1.4)
}else{
  rect(xleft = 40*1000, ybottom = 72, xright = 122*1000, ytop = 90, xpd = FALSE, lwd = 1.4)
}

axis(side = 2, at = seq(0, 80, 20), tick = FALSE, las = 2, labels = paste0(seq(0, 80, 20), "%"), col.axis = "#34495e", line = -0.75, cex.axis = 0.8)
mtext(text = "Employment change %\n2020-2030", side = 2, at = 60, line = 2, col = "#2c3e50", cex = 0.9)

axis(side = 4, at = seq(-20, -40, -20), tick = FALSE, las = 2, labels = paste0(seq(-20, -40, -20), "%"), col.axis = "#34495e", cex.axis = 0.8, line = -0.75)
rect(xleft = 20000, ybottom = 0, xright = 120000, ytop = 0)
text(x = pretty(data$median_ann_wage), y = 0, labels = paste0(pretty(data$median_ann_wage)/1000, "K"), pos = 3, col = "#34495e", xpd = TRUE, cex = 0.8)
text(x = 120000, y = -3, label = "Median annual wage", family = "mono", cex = 0.8, font = 2, adj = 1, col = "#2c3e50")


#Annotate some bubbles manually
text(x = 30000,  y = 50, label = ("Home health &\npersonal care aids"), cex = 0.6, col = "#34495e")
text(x = 118000,  y = 65, label = ("Nurse\npractitioners"), cex = 0.6, col = "#34495e")
text(x = 95000,  y = 40, label = ("Statisticians"), cex = 0.6, adj = 1, col = "#34495e")
text(x = 60000,  y = 42, label = ("PT assistants"), cex = 0.6, col = "#34495e")
text(x = 60000,  y = 42, label = ("PT assistants"), cex = 0.6, col = "#34495e")
text(x = 56230,  y = 63, label = ("Wind turbine &\nservice technicians"), cex = 0.6, col = "#34495e")

rect(xleft = 85700, ybottom = 18, xright = 85700, ytop = 25.5, border = "#2980B999")
text(x = 85700,  y = 14, label = ("Genetic\ncounselors"), cex = 0.6, col = "#34495e")

rect(xleft = 70000, ybottom = 20, xright = 70000, ytop = 29.6, border = "#2980B999")
rect(xleft = 70000, ybottom = 29.6, xright = 74560, ytop = 29.6, border = "#2980B999")
text(x = 70000,  y = 17, label = ("Epidemiologists"), cex = 0.6, col = "#34495e")

text(x = 65110,  y = -30, label = ("Executive secretaries &\n admin assistants"), cex = 0.6, col = "#34495e")
text(x = 46500,  y = -29, label = ("Watch&\nclock repairers"), cex = 0.6, col = "#34495e")
text(x = 105000,  y = -38, label = ("Nuclear power\nreactor operator"), cex = 0.6, col = "#34495e")

rect(xleft = 22140, ybottom = -20.1, xright = 29140, ytop = -20.1, border = "#8E44AD99")
text(x = 22140,  y = -22, label = ("Florists"), cex = 0.6, col = "#34495e")

text(x = 31630,  y = -34, label = ("Cutters&\ntrimmers"), cex = 0.6, col = "#34495e")

title(main = "The 20 Fastest Growing and Declining Jobs", adj = 0, line = 2.5, cex.main = 1.4, col.main = "#2c3e50")
title(main = "OVER THE NEXT DECADE", adj = 0, line = 1.5, cex.main = 0.9, col.main = "#2c3e50")

Version	Author	Date
4906871	Anand	2022-08-30
91aa770	Anand	2022-08-22
4f94642	PoisonAlien	2022-07-18
7d5d068	PoisonAlien	2022-07-16

Donut chart: Blood group distrubution

Recreating plot from visualcapitalist

# Blood group distribution across the world
# MIT License
# Copyright (c) 2022 Anand Mayakonda <anandmt3@gmail.com>
# Recreating plot from https://www.visualcapitalist.com/visualizing-the-most-widespread-blood-types-in-every-country/

data = read.delim(file = "data/10-donut_BloodGroup.tsv")
data2 = read.delim(file = "data/10-donut_BloodGroupCountry.tsv")


cols = hcl.colors(n = 8, palette = "Viridis", alpha = 0.8)
cols = c("#c23616", "#f0932b", "#f9ca24", "#6ab04c", "#40739e", "#192a56", "#ff9ff3", "purple")

# lomat = matrix(data = c(1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,
#                         6,6,6,7,7,7,8,8,8,9,9,9,10,10,10,
#                         11,11,11,0,12,12,12,0,13,13,13,0,14,14,14,
#                         0,0,15,15,15,0,16,16,16,0,17,17,17,0,0),
#                         nrow = 4, byrow = TRUE)

lomat = matrix(data = c(rep(1, 5), 2:11, rep(12, 5)), nrow = 4, ncol = 5, byrow = TRUE)

lo = layout(mat = lomat, heights = c(0.5, 3, 3, 1))

par(mar = c(0, 0, 0, 0), bg = "#ecf0f1", family = "mono")
plot(NA, xlim = c(0, 1), ylim = c(0, 1), axes = FALSE)
title(
  main = "Distribution of Blood types by country",
  line = -3,
  cex.main = 2.3,
  col.main = "white",
  xpd = TRUE,
  outer = TRUE,
  family = "mono", col.main = "#222f3e"
)
# text(
#   x = 0.5,
#   y = 0.3,
#   label = "Distribution of Blood types by country",
#   cex = 2,
#   font = 2,
#   col = "white", xpd = TRUE, family = "Helvetica"
# )


par(mar = c(1, 1, 1, 1))

for(i in 1:nrow(data2)){
  pie(
    as.numeric(data2[i, 2:9]),
    clockwise = TRUE,
    col = cols,
    border = cols,
    labels = paste0(as.numeric(data2[i, 2:ncol(data)])[1:4], "%"), radius = 1.05, col.lab = "white", xpd = TRUE, cex = 0.8
  ) #
  points(x = 0, y = 0, pch = 19, cex = 8, col = "#ecf0f1", lwd = 0)
  #legend(x = "bottomright", legend = data[i,"Region"], bty = "n", cex.title = 1.2, adj = 0)
  text(
    x = 0,
    y = -1.6,
    label = paste0(data2[i, "Country"], "\n", data2[i, "Pop"]),
    family = "mono",
    cex = 1.4,
    xpd = TRUE,
    font = 2,
    col = "#535c68"
  )
}

plot(NA, xlim = c(0, 1), ylim = c(0, 1), axes = FALSE)
legend(
  x = "center",
  legend = c("O+", "A+", "B+", "AB+", "O-", "A-", "B-", "AB-"),
  col = cols,
  ncol = 8,
  pch = 15, bty = "n", cex = 1.7
)

Version	Author	Date
4906871	Anand	2022-08-30
91aa770	Anand	2022-08-22
4f94642	PoisonAlien	2022-07-18
7d5d068	PoisonAlien	2022-07-16

# for(i in 1:nrow(data)){
#   pie(
#     as.numeric(data[i, 2:ncol(data)]),
#     clockwise = TRUE,
#     col = cols,
#     border = cols,
#     labels = paste0(as.numeric(data[i, 2:ncol(data)]), "%"), radius = 1.05, col.font = cols, xpd = TRUE, cex = 0.8
#   ) #
#   points(x = 0, y = 0, pch = 19, cex = 8, col = "white", lwd = 0)
#   #legend(x = "bottomright", legend = data[i,"Region"], bty = "n", cex.title = 1.2, adj = 0)
#   text(x = 0, y = -2, label = data[i,"Region"], family = "mono", cex= 1.4, xpd= TRUE, font = 2)
# }

Bubble chart: California fires

Recreating plot from buzzfeed

Original ggplot source

#download.file(url = "https://raw.githubusercontent.com/BuzzFeedNews/2018-07-wildfire-trends/master/data/calfire_frap.csv", destfile = "data/09-calfire_frap.csv")
data = read.delim(file = "data/09-calfire_frap.csv", sep = ",")

date_lvls = seq.Date(as.Date("1950/01/01"), as.Date("2017/12/31"), 1)
md = names(table(substr(date_lvls, 6, 10)))

data$reporting_date = factor(data$alarm_date, levels = as.character.Date(date_lvls), ordered = TRUE)
data$reporting_date = as.Date(data$reporting_date)
data = data[order(data$reporting_date),]
data$md = substr(x = data$reporting_date, 6, 10)
data$md = factor(x = data$md, levels = md, ordered = TRUE)

par(mar = c(1, 3, 3, 1), family = "mono")
plot(NA, pch = NA, axes = FALSE, ylim = c(1950,2017), xlim = c(1, 366))
abline(h = 1950:2017, v = c(0, cumsum(table(substr(md, 1, 2)))), col = "gray90")
abline(h = seq(1950, 2010, 10), col = adjustcolor("#34495e", 0.5), lwd = 1)
symb_cols = adjustcolor(col = "#e74c3c", alpha.f = 0.6)
symbols(
  x = data$md,
  y = data$year_, add = TRUE,
  circles = sqrt(data$gis_acres / pi),
  inches = 0.12, bty = "n", pch = 19, bg = symb_cols, fg = symb_cols
)
axis(side = 1, at = cumsum(table(substr(md, 1, 2))), labels = month.abb, tick = FALSE, col.axis = "#34495e", line = -1.2, cex.axis = 0.7)
axis(side = 2, at = seq(1950, 2010, 10), las = 2, tick = FALSE, line = -1, col.axis = "#34495e")

title("Big fires have gotten more common in California", adj = 0, line = 2, family = "Helvetica", col.main = "#2C3A47")
title("Each fire is a dot that is scaled by the area that ultimately burned, centered on the date on which\nthe alarm was sounded", adj = 0, line = 0, font.main = 1, cex.main = 0.8, family = "Helvetica", col.main = "#2C3A47")

Version	Author	Date
4906871	Anand	2022-08-30
91aa770	Anand	2022-08-22
4f94642	PoisonAlien	2022-07-18
7d5d068	PoisonAlien	2022-07-16

Area chart: US migration

Data source

data = read.table(file = "data/07-areachart_USmigration.csv", header = TRUE, sep = ",")

#Maximum
max_pop = max(rowSums(data[,2:ncol(data)]))

layout(mat = matrix(data = c(1:2), nrow = 2), heights = c(0.75, 6))

par(family = "mono")
par(mar = c(0, 0, 0, 0))
plot(NA, xlim = c(0, 1), ylim = c(0, 1), frame.plot = FALSE, axes = FALSE, xlab = NA, ylab = NA)
title_main = "Migration to the US by world region, 1820-2009"
title_main2 = "The numbers are recorded by decade\nFor example, the numbers recorded for 1905 tell us the number of immigrants between 1900 and 1910."

title(main = title_main, cex.main = 1, line = -1, family = "Helvetica", col.main = "#2C3A47", adj = 0)
title(main = title_main2, cex.main = .75, line = -2.5, family = "Helvetica", col.main = "#2C3A47", adj = 0, font.main = 1)


par(mar = c(1, 1, 1, 3))
plot(
  NA,
  xlim = range(data$Region.and.country.of.last.residence.1),
  ylim = c(0, max_pop),
  xlab = NA,
  ylab = NA,
  frame.plot = FALSE,
  axes = FALSE
)

abline(h = pretty(c(0, max_pop)), col = "gray90")
axis(side = 4, at = pretty(c(0, max_pop)), labels = paste0(seq(0, 12, 2), "M"), las = 2, tick = FALSE, line = .1, col.axis = "#34495e", cex.axis = 0.8)
axis(side = 1, at = seq(1820, 2000, 10), tick = FALSE, cex.axis = 0.8, col.axis = "#34495e", line = -1.5)

rect(xleft = 1914, ybottom = 0, xright = 1918, ytop = max_pop, col = "#95a5a6", border = NA)
text(x = 1916, y = max_pop, labels = "WW1", cex = 0.75, pos = 3, xpd = TRUE, font = 4, adj = 1)
rect(xleft = 1939, ybottom = 0, xright = 1945, ytop = max_pop, col = "#95a5a6", border = NA)
text(x = 1942, y = max_pop, labels = "WW2", cex = 0.75, pos = 3, xpd = TRUE, font = 4, adj = 1)

cols = c("white", "#1d81a2", "#004765", "#3a96b8", "#48adc0", "#329a9b", "#2b8589", 
         "#257085", "#005d71", 
         "#b4241c", "#cd3d2e", "#dc464b", "#e65340", 
         "#ffa126", "#ffca76", "#ffe59c", "#fffbb1", "#ffdc6b", "#ffbb7f", 
         "#009a69", "#003f65", "#181818", "black")
#cols = adjustcolor(col = cols, alpha.f = 0.7)

for(i in 22:2){
  if(i > 2){
    polygon(
      c(
        data[1, "Region.and.country.of.last.residence.1"],
        data$Region.and.country.of.last.residence.1,
        data[nrow(data), "Region.and.country.of.last.residence.1"]
      ),
      c(0, rowSums(data[, 2:i, drop = FALSE]), 0),
      col = cols[i], border = cols[i]
    )
    
  }else{
    polygon(data$Region.and.country.of.last.residence.1,
            data[,i],
            col = cols[i],  border = cols[i])
  }
}

to_hghlt = c("Germany", "United.Kingdom", "Austria.Hungary","Ireland", "Philippines", "Italy", "Russia", "India", "China", "Mexico", "Central.America")

for(h in to_hghlt){
  i = which(colnames(data) == h)
  max_idx = which(data[,i] == max(data[,i]))
  y_point = data[,i] + rowSums(data[,2:(i-1), drop = FALSE])
  text(x = data[max_idx,1], y = y_point[max_idx], labels = colnames(data)[i], cex = 0.55, xpd = TRUE, font = 2, family = "mono", col = "#192a56")
}

text(x = 1900, y = 1e6, label = "EUROPE", col = "white", cex = 1.2, font = 2)
text(x = 1990, y = 2e6, label = "ASIA", col = "white", cex = 1.2, adj = 0.8, font = 2)
text(x = 1990, y = 6e6, label = "AMERICA", col = "#2c3e50", cex = 1.2, adj = 0.7, font = 2)

Version	Author	Date
4906871	Anand	2022-08-30
91aa770	Anand	2022-08-22
4f94642	PoisonAlien	2022-07-18
7d5d068	PoisonAlien	2022-07-16

Parliament plot: German Bundestag

Data source: https://www.tatsachen-ueber-deutschland.de/en/politics-germany/parliament-parties

Acknowledgments: https://www.datawrapper.de/charts

nseats = 736
nrows = 14

seat_distr = c(4, 80, 45, 152, 92, 118, 206, 39)
names(seat_distr) = c("Independent", "AfD", "CSU", "CDU", "FDP", "The Greens", "SPD", "The Left")

seat_cols = c("#7f8c8d", "#f368e0", "#2980b9", "#2c3e50", "#D6A2E8", "#27ae60", "#d35400", "#8e44ad")
names(seat_cols) = names(seat_distr)

#Simple estimate for how many seats (dots) in concentric arcs
n_seats_per_row = ((pi * nrows:1)/sum(pi * nrows:1)) * nseats
n_seats_per_row = ceiling(n_seats_per_row)

x <- seq(0, pi, length.out = n_seats_per_row[1])
xlims = range(cos(x) * nrows)
ylims = range(sin(x) * nrows)
ylims = c(-max(ylims), max(ylims))

plot_data = data.frame()
for(i in nrows:1){
  x <- seq(0, pi, length.out = rev(n_seats_per_row)[i])
  plot_data = rbind(plot_data, data.frame(cos(x) * i, sin(x) * i, i))
}

colnames(plot_data) = c("x", "y", "row")
plot_data = plot_data[1:nseats,]

#--Comment out the below section to avoid spiral ordering
plot_data_spl = split(plot_data, ~row)
max_row = max(unlist(lapply(plot_data_spl, nrow)))
data_ordered = data.frame()
for(i in seq_len(max_row)){
  data_i = lapply(plot_data_spl, function(dat){
    dat[i,]
  })
  data_i = do.call("rbind", data_i)
  data_ordered = rbind(data_ordered, data_i)
}
#plot_data = data_ordered[!is.na(data_ordered$x),]
#--

plot_data$party = rep(x = names(seat_distr), seat_distr)

layout(mat = matrix(data = c(1:3), nrow = 3), heights = c(1, 6, 0.75))
par(family = "mono")
par(mar = c(0, 0, 1, 0))
plot(NA, xlim = c(0, 1), ylim = c(0, 1), frame.plot = FALSE, axes = FALSE, xlab = NA, ylab = NA)
title_main = "The 20th Bundestag"
title_main2 = "Parliament & Parties of the German Bundestag"
title_sub = "736 members of parliament are represented"

title(main = title_main, cex.main = 1.8, line = -0.5, family = "Helvetica", col.main = "#2C3A47")
title(main = title_main2, cex.main = 1.5, line = -2, family = "Helvetica", col.main = "#2C3A47")
title(main = title_sub, cex.main = 1.2, font = 3, line = -3.25,family = "Helvetica", col.main = "#2C3A47")

par(mar = c(0.75, 0, 0.75, 0))
plot(NA, xlim = xlims, ylim = ylims , "", asp = 1,  xlab = NA, ylab = NA, frame.plot = FALSE, axes = FALSE)
points(x = plot_data$x, y = plot_data$y, pch = 19, col = seat_cols[plot_data$party], cex = 0.65)

x <- seq(0, pi, length.out = 500)

#Outer ring
points(cos(x) * (nrows+0.5), sin(x) * (nrows+0.5), type = "l", lwd = 3, col = "#ea8685")
points(cos(x) * -(nrows+0.5), sin(x) * -(nrows+0.5), type = "l", lwd = 3, col = "#ea8685")
#Outer ring 2
rng_col2 = adjustcolor("#ea8685", 0.4)
points(cos(x) * (nrows+1), sin(x) * (nrows+1), type = "l", lwd = 3, col = rng_col2)
points(cos(x) * -(nrows+1), sin(x) * -(nrows+1), type = "l", lwd = 3, col = rng_col2)

text(x = 0, -8, label = "736\nSEATS", font = 2, col  = "#34495e", cex = 2.5)

par(mar = c(0, 0, 0, 0), family = "Helvetica")
plot(NA, xlim = c(0, 1), ylim = c(0, 1), frame.plot = FALSE, axes = FALSE, xlab = NA, ylab = NA)
legend(
  x = "bottom",
  legend = paste0(names(seat_distr), " (", seat_distr, ")"),
  ncol = 4,
  text.col = seat_cols,
  bty = "n", xpd = TRUE, cex = 1, text.font = 2
)

Version	Author	Date
4906871	Anand	2022-08-30
91aa770	Anand	2022-08-22
4f94642	PoisonAlien	2022-07-18
7d5d068	PoisonAlien	2022-07-16

Heatmap: Measles

Recreating the popular maeasles vaccine heatmap from WSJ

acknowledgments:

Initial attempt in R by Mick Watson in base R using heatmp and gplots
ggplot version
ComplexHeatmap by Zuguang Gu

Raw data matrix obtained as a part of ComplexHeatmap package.

NOTE: Counts are raw and not normalized for per 100K as in original WSJ plot. Color grading are according to the breaks in the number of cases rather than being continuous.

#mat = readRDS(system.file("extdata", "measles.rds", package = "ComplexHeatmap"))
mat = readRDS("data/measles.Rds")
#Get rid of some of the entries
rem_states = c(
  "AMERICAN.SAMOA",
  "GUAM",
  "NEW.YORK.CITY",
  "NORTHERN.MARIANA.ISLANDS",
  "PAC.TRUST.TERR",
  "PUERTO.RICO",
  "RHODE.ISLAND",
  "UPSTATE.NEW.YORK",
  "VIRGIN.ISLANDS"
)

#Since image() flips the data, sort it before plotting
us_stats = sort(setdiff(x = rownames(mat), rem_states), decreasing = TRUE)
mat = mat[us_stats,]

colpal = c(
  "white", "#ecf0f1",
  "#e7f0fa",
  "#c9e2f6",
  "#95cbee",
  "#0099dc",
  "#4ab04a",
  "#ffd73e",
  "#eec73a",
  "#e29421",
  "#e29421",
  "#f05336",
  "#ce472e"
)

break_pnts = c(0, 50, 100, 250, 500, 1000, 2000, 4000, 5000, 10000, 25000, 50000, 100000, 125000)

layout(mat = matrix(data = c(1:2), nrow = 2), heights = c(6, 0.75))

par(mar = c(1, 6, 3, 0.5),  family = "mono")
image(y = 1:nrow(mat), x = 1:ncol(mat),
  z = t(mat),
  axes = FALSE,
  xaxt = "n",
  yaxt = "n",
  xlab = "",
  ylab = "", col = colpal, breaks = break_pnts
)
#
#contour(t(mat), add = TRUE, drawlabels = FALSE)

abline(h = (1:nrow(mat)) + 0.5, v = (1:ncol(mat)) + 0.5, col = "white")

mtext(
  text = colnames(t(mat)),
  side = 2,
  at = 1:nrow(mat),
  font = 2,
  line = 0.4,
  las = 2,
  cex = 0.5,
  col = "#34495e"
)

vac_idx = which(colnames(x = mat) == "1963")
abline(v = vac_idx-0.5, col = "black", lwd = 1.2)


lab_at = seq(1930, 2000, 10)
for(lab in lab_at){
  lab_idx = which(colnames(mat) == lab)
  axis(side = 1, at = lab_idx, labels = lab, col.axis = "#34495e", tick = F, line = -1, cex.axis = 0.6, gap.axis = 0.1, xpd = TRUE)
}

title_main = "Impact of vacccines on Measles in the US states (1930-2001)"
title_sub = "Vaccine introduced in 1963"

title(main = title_main, adj = 0, cex.main = 0.95, line = 2)
title(main = title_sub, adj = 0.5, cex.main = 0.8, line = 1, font = 3, adj = 0)

par(mar = c(0, 0, 0, 0), family = "mono")
plot(NA, xlim = c(0, 1), ylim = c(0, 1), frame.plot = FALSE, axes = FALSE, xlab = NA, ylab = NA)
legend(x = "center", legend = break_pnts, col = colpal, pch = 15, ncol = 7, bty = "n", cex = 0.65, title = "no. of cases, upto")

Version	Author	Date
4906871	Anand	2022-08-30
91aa770	Anand	2022-08-22
4f94642	PoisonAlien	2022-07-18
7d5d068	PoisonAlien	2022-07-16

Bubble charts: Rich vs Health

Data obtained from datawrapper.

data = data.table::fread(input = "data/06-scatterplot_RichGDP.csv", data.table = FALSE)

data$population = as.numeric(data$population)
#data$bub_size = data$population/max(data$population, na.rm = TRUE)
cols = c("#34495e", "#ff5252", "#34ace0", "#ffb142", "#218c74", "#84817a")
cols = adjustcolor(col = cols, alpha.f = 0.6)
names(cols) = names(table(data$regions))

title_main = "The richer, the healthier"
title_sub = "GDP per person adjusted for differences in purchasing power (in 2011 international dollars) and life expectancy in years for selected countries, 2018.\nThe bigger a circle, the more people live in a country."

layout(mat = matrix(data = c(1:2), nrow = 2), heights = c(2, 6))

par(mar = c(0, 0, 0, 0))
plot(NA, xlim = c(0, 1), ylim = c(0, 1), frame.plot = FALSE, axes = FALSE, xlab = NA, ylab = NA)
text(x = 0, y = 0.8, labels = title_main, cex = 1, font = 2, adj = 0)
text(x = 0, y = 0.6, labels = title_sub, cex = 0.6, font = 3, adj = 0)

legend(x = .1, y = 0.4, legend = names(cols), col = cols, pch = 19, ncol = 3, bty = "n", cex = 0.7)

data$logGDP = log10(data$`GDP per capita`)

par(mar = c(2, 2, 0, 1))
plot(NA, xlim = range(pretty(data$logGDP)), ylim = c(50, 90), xlab = NA, ylab = NA, frame.plot = FALSE, axes = FALSE)
axl_lb = round(10^pretty(data$logGDP))
axl_lb = ifelse(test = axl_lb > 1000, yes = paste0(round(axl_lb/1000), "k"), no = round(axl_lb))

axis(side = 1, at = pretty(data$logGDP), labels = axl_lb, col.axis = "#7f8c8d", tick = FALSE, col = "gray", cex.axis = 0.8, line = -.5)
axis(side = 2, at = seq(50, 100, 10), col.axis = "#7f8c8d", tick = FALSE, col = "gray", cex.axis = 0.8, las = 2, line = -0.5)
abline(h = seq(50, 100, 10), v = pretty(data$logGDP), col = "gray90")

text(x = rev(pretty(data$logGDP))[1], y = 50, labels = "GDP per capita", col = "#34495e", adj = 1, cex = 0.7, font = 2)
text(x = pretty(data$logGDP)[1], y = 90, labels = "Life Expectancy", col = "#34495e", adj = 0, cex = 0.7, font = 2)

symbols(
  x = data$logGDP,
  y = data$`Life expectancy`,
  circles  = sqrt(data$population / pi),
  inches = 0.18, add = TRUE, bg = cols[data$regions], fg = cols[data$regions]
)

bot_cntrs = data[data$country %in% c("South Africa", "China", "Afghanistan", "Lesotho", "United Arab Emirates"),]
text(x = bot_cntrs$logGDP, y = bot_cntrs$`Life expectancy`, labels = bot_cntrs$country, pos = 1, cex = 0.6, col = "#2c2c54")
top_cntrs = data[data$country %in% c("Liberia", "Timor-Leste", "Nicaragua", "Costa Rica"),]
text(x = top_cntrs$logGDP, y = top_cntrs$`Life expectancy`, labels = top_cntrs$country, pos = 3, cex = 0.6, col = "#2c2c54")

Version	Author	Date
4906871	Anand	2022-08-30
91aa770	Anand	2022-08-22
4f94642	PoisonAlien	2022-07-18
7d5d068	PoisonAlien	2022-07-16

Donut charts: Wealthiset percentage

Data obtained from datawrapper.

data = read.table("data/08-donutplot_TopWealth.csv", sep = ",", header = TRUE)

Warning in read.table("data/08-donutplot_TopWealth.csv", sep = ",", header
= TRUE): incomplete final line found by readTableHeader on 'data/08-
donutplot_TopWealth.csv'

lo =layout(mat = matrix(data = c(1, 1, 1, 2:7), nrow = 3, ncol = 3, byrow = TRUE), heights = c(1.7, 3, 3))

par(mar = c(0, 0, 0, 0))
title_main = "Who gets which slice of the pie?"
title_sub = "How much of the wealth of a country do the poorest 50% own?\nHow much do the top 10% own?"

plot(NA, xlim = c(0, 1), ylim = c(0, 1), frame.plot = FALSE, axes = FALSE, xlab = NA, ylab = NA)
text(x = 0, y = 0.9, labels = title_main, cex = 1.5, font = 2, adj = 0)
text(x = 0, y = 0.6, labels = title_sub, cex = 1.2, font = 3, adj = 0)
legend(x = .1, y = 0.4, legend = c("bottom 50%", "next 40%", "top 10%"), col = c("#b33939", "#f7f1e3", "#40407a"), pch = 15, ncol = 3, bty = "n", cex = 1.4)

par(mar = c(2, 2, 2, 2))

for (i in 2:ncol(data)) {
  pie(
    data[, i],
    radius = 1,
    clockwise = TRUE,
    labels = paste0(round(data[, i]), "%"), border = "white", 
    col = c("#b33939", "#f7f1e3", "#40407a")
  )
  points(x = 0, y = 0, pch = 19, cex = 10, col = "white", lwd = 0)
  title(main = colnames(data)[i], adj = 0)
}

Version	Author	Date
4906871	Anand	2022-08-30
91aa770	Anand	2022-08-22
4f94642	PoisonAlien	2022-07-18
7d5d068	PoisonAlien	2022-07-16

Line chart: COVID19 preprints

Number of COVID19 articles in arxiv preprints

Data obtained from biorxiv API

arxiv_data = read.delim(file = "data/05-linechart_COVIDrxiv.tsv")
arxiv_data$rel_date = as.Date.character(arxiv_data$rel_date)
date_lvls = seq.Date(as.Date(arxiv_data[1,"rel_date"]), as.Date(arxiv_data[nrow(arxiv_data),"rel_date"]), 1)
arxiv_data$date = factor(x = arxiv_data$rel_date, levels = as.character.Date(date_lvls), ordered = TRUE )

biorxiv = data.frame(n_articles = unlist(lapply(split(arxiv_data[arxiv_data$rel_site %in% "bioRxiv",], ~date), nrow)))
medrxiv = data.frame(n_articles = unlist(lapply(split(arxiv_data[arxiv_data$rel_site %in% "medRxiv",], ~date), nrow)))

data = merge(biorxiv, medrxiv, by = "row.names", suffixes = c("_biorxiv", "_medrxiv"))
data$n_articles = rowSums(data[,c("n_articles_biorxiv", "n_articles_medrxiv")])
rownames(data) = data$Row.names
#data = data.frame(n_articles = unlist(lapply(split(arxiv_data, ~date), nrow)))
data$ym = substr(x = rownames(data), start = 1, stop = 7)
data$yr = substr(x = rownames(data), start = 1, stop = 4)

month_lines = cumsum(lapply(split(data, ~ym), nrow))
year_lines = cumsum(lapply(split(data, ~yr), nrow))
#
heatcols = colorRampPalette(c("#EFF3FF", "#C6DBEF", "#9ECAE1", "#6BAED6", "#4292C6", "#2171B5", "#084594"))(180)

par(mar = c(3, 3, 4, 1), family = "mono")
plot(NA, xlim = c(0, nrow(data)), ylim = c(0, 170), frame.plot = FALSE, axes = FALSE, xlab = NA, ylab = NA)
abline(v = month_lines, col = "gray90", lty = 1, lwd = 0.6)
abline(v = year_lines[c("2020", "2021")], col = "#2c3e50", lwd = 0.9)
abline(h = seq(0, 150, 25), col = "gray90", lwd = 0.9, lty = 2)

axis(side = 1, at = month_lines, labels = substr(x = names(month_lines), 6, 7), las = 2, tick = FALSE, cex.axis = 0.6, line = -0.75, font.axis = 3, col.axis = "#95a5a6")
axis(side = 2, at = seq(0, 150, 25), labels = seq(0, 150, 25), las = 2, tick = FALSE, line = -0.5, col.axis = "#95a5a6")
axis(side = 3, at = year_lines[c("2020", "2021")], labels = c("2020", "2021"), tick = FALSE, line = -1, col.axis = "#8e44ad")

pnt_col = "#95a5a6"
# points(
#   x = 1:nrow(data),
#   y = data$n_articles,
#   pch = 20,
#   col = adjustcolor(col = pnt_col, alpha.f = .5), cex = 0.9
# )
#lines(smooth.spline(x = 1:nrow(data), y = data$n_articles), col = pnt_col, lwd = 3)

#e67e22
biorxiv_col = "#e67e22"
points(
  x = 1:nrow(data),
  y = data$n_articles_biorxiv,
  pch = 20,
  col = adjustcolor(col = biorxiv_col, alpha.f = 0.2), cex = 0.9
)
lines(smooth.spline(x = 1:nrow(data), y = data$n_articles_biorxiv), col = biorxiv_col, lwd = 3)

medrxiv_col = "#27ae60"
points(
  x = 1:nrow(data),
  y = data$n_articles_medrxiv,
  pch = 20,
  col = adjustcolor(col = medrxiv_col, alpha.f = 0.2), cex = 0.9
)
lines(smooth.spline(x = 1:nrow(data), y = data$n_articles_medrxiv), col = medrxiv_col, lwd = 3)

#mtext(text = "Month", side = 1, line = 1.5, font = 2)
#mtext(text = "# of articles", side = 2, line = 2, font = 2)

title(main = "Frequency of COVID19 articles in bioRxiv and medRxiv", adj = 0, line = 2, col.main = "#34495e", cex.main = 1)

legend(x = "topleft", legend = c("bioRxiv", "medRxiv"), col = c(biorxiv_col, medrxiv_col), bty = "n", lwd = 2)

Version	Author	Date
4906871	Anand	2022-08-30
91aa770	Anand	2022-08-22
4f94642	PoisonAlien	2022-07-18
7d5d068	PoisonAlien	2022-07-16

Line chart: Cigerate consumption

Data obtained from datawrapper.

data = read.table("data/05-linechart_CigConsumption.csv", sep = ",", header = TRUE)
data[data == "null"] = NA
data = apply(data, 2, as.numeric)
data = as.data.frame(data)
rownames(data) = data$country

max_count = max(data[,2:ncol(data)], na.rm = TRUE)

title_main = "The rise and fall of cigarette consumption in developed countries"
title_sub = "Sales of cigarettes per adult per day, in selected countries.\nFigures include manufactured cigarettes, as well as an estimated\nnumber of hand-rolled cigarettes,per adult (ages 15+) per day."

layout(mat = matrix(data = c(1:2), nrow = 2), heights = c(2, 6))

par(mar = c(0, 0, 0, 0))
plot(NA, xlim = c(0, 1), ylim = c(0, 1), frame.plot = FALSE, axes = FALSE, xlab = NA, ylab = NA)
text(x = 0, y = 0.8, labels = title_main, cex = 1, font = 2, adj = 0)
text(x = 0, y = 0.35, labels = title_sub, cex = 1, font = 3, adj = 0)

par(mar = c(3, 2, 0, 1))
plot(NA, xlim = range(data$country), ylim = c(0, max_count), xlab = NA, ylab = NA, frame.plot = FALSE, axes = FALSE)
axis(side = 2, at = seq(0, 12, 2), labels = seq(0, 12, 2), line = 0, col.axis = "gray", cex.axis = 1, las = 2, tick = FALSE)
abline(h = seq(0, 12, 2), lwd = 1, col = "gray90")

text(x = data$country[1], y = 10, labels = "Sold cigarettes\nper day per adult", cex = 0.7, xpd = TRUE, adj = 0)

for(i in 2:ncol(data)){
  points(x = data$country, y = data[,i], pch = "", type = "l", col = "#7f8c8d")  
}

hghlt = c("United.States", "Germany", "France")
hghlt_cols = c("#c0392b", "#16a085", "#2980b9")
for(hghlt_idx in seq_along(hghlt)){
  hghlt_col_idx = which(colnames(data) == hghlt[hghlt_idx])
  points(x = data$country, y = data[,hghlt_col_idx], pch = "", type = "l", col = hghlt_cols[hghlt_idx], lwd = 2)
}

text(x = data$country[1], y = c(6:4), labels = hghlt, col = hghlt_cols, adj = 0)

axis(side = 1, at = pretty(data$country), labels = pretty(data$country), line = 0, tick = TRUE, col.axis = "#7f8c8d", col = "#7f8c8d")

Version	Author	Date
4906871	Anand	2022-08-30
91aa770	Anand	2022-08-22
4f94642	PoisonAlien	2022-07-18

Arrow plot: Women in Parliment

Data obtained from datawrapper.

data = read.table("data/04-arrowplot_WomenInParliment.csv", sep = ",", header = TRUE)
colnames(data) = c("Country", "y2000", "y2020", "group")
data$diff = data$y2020 - data$y2000

pan_ratio = unname(unlist(lapply(split(data, ~group), nrow)))

asia = split(data, ~group)[[1]]
eu = split(data, ~group)[[2]]

title_txt = "Most countries have a higher share of women in their national parliaments than twenty years ago"
subtitle_txt = "Proportion of seats held by women in national parliaments, 2000 and 2020,\nin selected countries in Europe, Central Asia, East Asia and the Pacific"

lo = layout(matrix(1:3, ncol = 1), heights = c(2, pan_ratio))
par(mar = c(0, 0, 0, 0))
plot(NA, xlim = c(0, 1), ylim = c(0, 1), frame.plot = FALSE, axes = FALSE, xlab = NA, ylab = NA)
text(x = 0, y = 0.8, labels = title_txt, cex = 1, font = 2, adj = 0)
text(x = 0, y = 0.35, labels = subtitle_txt, cex = 1, font = 3, adj = 0)

par(mar = c(2, 7, 1, 1))

temp = lapply(split(data, ~group), function(dat){
  plot(
    NA,
    xlim = c(0, 50),
    ylim = c(1, nrow(dat)),
    frame.plot = FALSE,
    axes = FALSE,
    xlab = NA,
    ylab = NA
  )
  abline(h = 1:nrow(dat), v = c(0, 25, 50), col = "gray90", lwd = 0.6)
  abline(v = 0, col = "gray")
  rect(xleft = dat$y2000, ybottom = 1:nrow(dat), xright = dat$y2020, ytop = 1:nrow(dat), lwd = 1.5, col = ifelse(test = dat$diff > 0, yes = "#3498db", no = "#eb2f06"), border = ifelse(test = dat$diff > 0, yes = "#3498db", no = "#eb2f06"))
  for(i in 1:nrow(dat)){
    if(dat$diff[i] > 0){
      points(x = dat$y2020[i], y = i, pch = ">", col = "#3498db")
      text(x = dat$y2020[i], y = i, labels = paste0(dat$y2020[i], "%"), col = "#0c2461", pos = 4, cex = 0.9, xpd = TRUE)
    }else{
      points(x = dat$y2020[i], y = i, pch = "<", col = "#eb2f06")
      text(x = dat$y2020[i], y = i, labels = paste0(dat$y2000[i], "%"), pos = 2, col = "#eb2f06", cex = 0.9, xpd = TRUE)
    }
  }
  
  axis(side = 2, at = 1:nrow(dat), labels = dat$Country, las = 2, tick = FALSE, line = -0.75, col = "#2c3e50", cex.axis = 1)
  axis(side = 1, at = c(0, 25, 50), labels = paste0(c(0, 25, 50), "%"), tick = FALSE, line = -1, col.axis = "gray50", cex.axis = 1)
  
  title(x = "topright", main = dat$group[1], adj = 1, col.main = "#2c3e50", cex.main = 0.9)
})

bottom_anno = split(data, ~group)[[2]]
bottom_anno = unlist(bottom_anno[nrow(bottom_anno), c(2, 3)])
axis(side = 3, at = bottom_anno, labels = c("2000", "2020"), tick = FALSE, line = -1, col.axis = "#7f8c8d", cex.axis = 0.75)

Version	Author	Date
4906871	Anand	2022-08-30
d8b5266	Anand	2022-08-22

Dot plot: Oldest country

Data obtained from datawrapper.

data = read.table("data/03-dotplot_OldestCountry.csv", sep = ",", header = TRUE)
data$Country[1:3] = c("Monaco", "Japan", "Germany")
xlims = range(data[,2:4])

par(mar = c(3, 5, 4, 1))
plot(
  NA,
  xlim = xlims,
  ylim = c(1, nrow(data)),
  frame.plot = FALSE,
  axes = FALSE,
  xlab = NA,
  ylab = NA
)

abline(h = 1:nrow(data), col = "gray90")
abline(v = pretty(xlims), col = "gray90")
axis(side = 3, at = pretty(xlims), labels = pretty(xlims), col = "gray90", tick = FALSE, line = -1, cex.axis = 0.75, col.axis = "gray50")

for(i in 1:nrow(data)){
  combined = rev(data[,"Combined"])[i]
  male = rev(data[,"Male"])[i]
  female = rev(data[,"Female"])[i]
  
  rect(xleft = male, ybottom = i-0.2, xright = female, ytop = i+0.2, col = "#bdc3c7", border = NA)
  points(x = combined, y = i, col = "#34495e", pch = 19, cex = 1.5)
  points(x = male, y = i, col = "#60a3bc", pch = 19, cex = 1.5)
  points(x = female, y = i, col = "#e58e26", pch = 19, cex = 1.5)
}

legend(
  x = 32, y = -0.5,
  legend = c("Combined", "Male", "Female"),
  border = NA,
  ncol = 3,
  col = c("#34495e", "#60a3bc", "#e58e26"),
  pch = 19,
  bty = "n", xpd = TRUE, cex = 0.9, adj = 0)

axis(
  side = 2,
  at = 1:nrow(data),
  labels = rev(data$Country),
  tick = FALSE,
  cex.axis = 0.8, las = 2, line = -1, col = "#34495e"
)

title(
  main = "Germany is the third-oldest country in the world",
  line = 3,
  adj = 0,
  xpd = TRUE,
  cex.main = 1,
  col.main = "#2c3e50"
)

title(
  main = "Median age in the three countries with the oldest population and selected other countries, in years",
  line = 2,
  adj = 0,
  xpd = TRUE,
  cex.main = 0.6,
  col.main = "#2c3e50"
)

Version	Author	Date
4906871	Anand	2022-08-30

Split barplot: Population Pyramid

Data obtained from datawrapper.

data = read.table("data/02-splitbarplot_PopGermany.csv", sep = ",", header = TRUE)
max_count = max(data[,c("male", "female")]) #Maximum value

par(mar = c(2, 3, 2, 1))
plot(
  NA,
  xlim = c(-max_count, max_count),
  ylim = c(1, nrow(data)),
  frame.plot = FALSE,
  axes = FALSE,
  xlab = NA,
  ylab = NA
)

axis(
  side = 2,
  at = 1:nrow(data)+0.4,
  labels = rev(data$age.span),
  tick = FALSE,
  cex.axis = 0.8, las = 2, line = -1, col = "#34495e"
)

for(i in 1:nrow(data)){
  row_idx = rev(1:nrow(data))[i] #Plot the data from bottom to top
  
  rect(xleft = -max_count, ybottom = i, xright = -10, ytop = i+0.9, col = "#ecf0f1", border = NA)
  
  rect(
    xleft = -data[row_idx, "male"],
    xright = -10,
    ybottom = i,
    ytop = i + 0.9, col = "#2980b9", border = NA,
  )
  pretty_pop_male = ifelse(test = data[row_idx, 2] < 1e6,
                             yes = paste0(round(data[row_idx, 2] / 1000, 1), "k"),
                             no = paste0(round(data[row_idx, 2] / 1e6, 1), "m"))
  
  if(data[row_idx, 2] < 1e6){
    text(
      x = -data[row_idx, "male"],
      y = i + 0.4,
      labels = pretty_pop_male,
      pos = 2,
      xpd = TRUE, cex = 0.7, col = "black"
    )
  }else{
    text(
      x = -500,
      y = i + 0.4,
      labels = pretty_pop_male,
      pos = 2,
      xpd = TRUE, cex = 0.7, col = "white"
    )
  }
  
  
  
  rect(xleft = -10, ybottom = i, xright = max_count, ytop = i+0.9, col = "#ecf0f1", border = NA)
  rect(
    xleft = data[row_idx, "female"],
    xright = 10,
    ybottom = i,
    ytop = i + 0.9, col = "#c0392b", border = NA
  )
  pretty_pop_female = ifelse(test = data[row_idx, 3] < 1e6,
                           yes = paste0(round(data[row_idx, 3] / 1000, 1), "k"),
                           no = paste0(round(data[row_idx, 3] / 1e6, 1), "m"))
  
  if(data[row_idx, 2] < 1e6){
    text(
      x = data[row_idx, "female"],
      y = i + 0.4,
      labels = pretty_pop_male,
      pos = 4,
      xpd = TRUE, cex = 0.7, col = "black"
    )
  }else{
    text(
      x = 500,
      y = i + 0.4,
      labels = pretty_pop_female,
      pos = 4,
      xpd = TRUE, cex = 0.7, col = "white"
    )
  }
}

text(x = -max_count, y = nrow(data)+0.45, labels = "male", adj = 0, cex = 0.8, font = 1)
text(x = max_count, y = nrow(data)+0.45, labels = "female", adj = 1, cex = 0.8, font = 1)

title(main = "Population projection for Germany, 2020", line = 1, adj = 0, col.main = "#2c3e50")
title(main = NA, sub = "Projection from 2015\nbased on continued trend with higher immigration
", line = 1, font.sub = 3, cex.sub = 0.6, adj = 1, col.sub = "#7f8c8d")

Bar plot: Bechdel Test

Data obtained from datawrapper.

data = read.table(file = "data/01-barplot_BechdelTest.csv", sep = ",", header = TRUE)
colnames(data) = c("Decade", "3 of 3", "2 of 3", "1 of 3", "0 of 3")
rownames(data) = data$Decade
data$Decade = NULL
data = t(data)

## Choose some color codes
colorcodes = c("#1abc9c", "#2980b9", "#d35400", "#c0392b")

#Adjust the margins (bottom, left, top, and right)
par(mar = c(4, 3, 3, 1))

b_idx = barplot(
  data,
  horiz = TRUE,
  las = 2,
  col = colorcodes,
  border = NA,
  axes = FALSE,
  names.arg = rep(NA, ncol(data))
)
mtext(text = colnames(data), side = 2, at = b_idx, las = 2, cex = 0.85, col = "#2c3e50")
abline(v = seq(0, 100, 50), col = "gray90", lty = 1)
axis(
  side = 3,
  at = seq(0, 100, 50),
  labels = paste0(seq(0, 100, 50), "%"),
  tick = FALSE,
  col.axis = "gray",
  line = -1,
  lwd = 0, cex.axis = 0.75
)

mainttl = "Percentage of films that pass the Bechdel test"
subttl = "Dataset includes 7,924 films.\nDataset has many more ratings for films released in recent decades, so earlier decades are likely to be less accurately represented.\nSome ratings given to films have been contested and may not be accurate."
subttl2 = "Source: Bechdel Test Website"
title(main = mainttl, line = 2, cex.main = 0.9, col = "#2c3e50")
title(
  main = NA,
  sub = subttl,
  line = 3,
  col.sub = "#7f8c8d",
  adj = 1,
  font = 3, cex = 0.3, cex.sub = 0.5
)
title(
  main = NA,
  sub = subttl2,
  line = 1.5,
  col.sub = "#7f8c8d",
  adj = 1,
  font = 3, cex = 0.3, cex.sub = 0.5
)

legend(
  x = -1, y = -0.5,
  legend = rownames(data),
  border = NA,
  ncol = nrow(data),
  col = colorcodes,
  pch = 15,
  bty = "n", xpd = TRUE, title = "criteria passed", cex = 0.9, adj = 0)

for(i in 1:ncol(data)){
  text(
    y = b_idx[i],
    x = cumsum(data[, i]),
    labels = paste0(data[, i], "%"),
    cex = .6, adj = 1, col = "#ecf0f1"
  )
}

sessionInfo()

R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.9             highr_0.9              compiler_4.2.1        
 [4] pillar_1.8.1           bslib_0.4.0            later_1.3.0           
 [7] git2r_0.30.1           jquerylib_0.1.4        berryFunctions_1.21.14
[10] tools_4.2.1            getPass_0.2-2          digest_0.6.29         
[13] jsonlite_1.8.0         evaluate_0.17          tibble_3.1.8          
[16] lifecycle_1.0.2        pkgconfig_2.0.3        rlang_1.0.6           
[19] cli_3.4.1              rstudioapi_0.14        yaml_2.3.5            
[22] xfun_0.33              fastmap_1.1.0          httr_1.4.4            
[25] stringr_1.4.1          knitr_1.40             fs_1.5.2              
[28] vctrs_0.4.1            sass_0.4.2             rprojroot_2.0.3       
[31] data.table_1.14.2      glue_1.6.2             R6_2.5.1              
[34] processx_3.7.0         fansi_1.0.3            rmarkdown_2.17        
[37] callr_3.7.2            magrittr_2.0.3         whisker_0.4           
[40] ps_1.7.1               promises_1.2.0.1       htmltools_0.5.3       
[43] abind_1.4-5            httpuv_1.6.6           utf8_1.2.2            
[46] stringi_1.7.8          cachem_1.0.6