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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 uncomment code.

Top Gold Mining Stocks

Recreating the plot from visualcapitalist

data = read.delim(file = "data/21-top_gold_mining_stocks.tsv")

lo = layout(mat = matrix(data = c(1,2,3), ncol = 3, byrow = FALSE), widths = c(1.7, 7, 2))

par(mar = c(3, 0, 4, 0))
plot(NA, xlim = c(0,1), ylim = c(0, 11), axes = FALSE)
for(i in seq_along(data$Performance)){
  text(x = 0, y = i, labels = paste0(i, ". ",data$Company[i]), font= 2, pos= 4, cex = 0.85, col = "#2c3e50")
}
text(y = 11.4, x = 0.5, labels = c("GDX INDEX\nRANK"), col =c("#34495e"), cex = 0.8, font = 2, xpd= TRUE)

barcols = ifelse(test = data$Performance < 0, yes = "#c0392b", no = "#40739e")

par(mar = c(3, 0, 4, 0), bg = "white")

plot(NA, xlim = c(-40, 160), ylim = c(0, 11), axes = FALSE)

if("berryFunctions" %in% installed.packages()){
  berryFunctions::roundedRect(xleft = -40, ybottom = 0, xright = 160, ytop = 11, col = "gray90", border = NA, rounding = 0.05)
}else{
  rect(xleft = -40, ybottom = 0, xright = 160, ytop = 11, col = "gray90", border = NA)
}

abline(v = seq(-40, 160, 10), lty = 1, col = "white")
rect(xleft = c(23, 25.1), ybottom = c(-0.50, -0.50), xright = c(23, 25.1), ytop = c(11, 11), border = c("#f39c12", "#d35400"))

text(y = 11.1, x = c(5, -5), labels = c("BETTER PERFORMANCE", "WORSE PERFORMANCE"), pos = c(4, 2), col =c("#40739e", "#c0392b"), cex = 0.6, font = 2)
text(y = 11.5, x = seq(-40, 160, 10), label = paste0(seq(-40, 160, 10), "%"), cex = 0.7, xpd = TRUE, font= 2, col = "#34495e")
text(x = c(23, 25.1), y = -0.3, labels = c("GDX PERFORMANCE 23%","GOLD PERFORMANCE 25.1%"), pos = c(2, 4), cex = 0.65, col =c("#f39c12", "#d35400"), font= 2)

for(i in seq_along(data$Performance)){
  rect(xleft = 0, ybottom = i - 0.4, xright = data$Performance[i], ytop = i + 0.4, col = barcols[i], border = NA)  
  text(x = data$Performance[i], y = i, labels = paste0(data$Performance[i], "%"), pos = ifelse(test = data$Performance[i] < 0, 2, 4), col = barcols[i], font = 2)
}
rect(xleft = 0, ybottom = 0, xright = 0, ytop = 11, border = "black")

title(main = "PERFORMANCE OF THE GDX", line = 3, col.main = "#2c3e50")
title(main = "AND ITS CONSTITUENT STOCKS IN 2020", cex.main = 0.8, col.main = "#34495e")

title(main = NA, sub = "Performance of the 10 companies ranked highest in the GDX index.", line = 1.5, col.sub = "#34495e", font.sub=2)

desc = "The GDX index is market-cap weighted,\nmeaning companies with a larger\nmarket capitalization
make up more of the index."
text(x = 30, y = 6.3, label = desc, pos = 4, cex = 1, font = 2)

par(mar = c(3, 0, 4, 0))
plot(NA, xlim = c(0,1), ylim = c(0, 11), axes =FALSE )
symbols(
  x = rep(0.6, nrow(data)),
  y = rev(seq_along(data$Company)),
  circles = rev(sqrt(abs(data$Market_cap / pi))),
  add = TRUE, inches = 0.25, bg = adjustcolor(col = rev(barcols), 0.6), fg = "white", xpd = TRUE)

for(i in seq_along(data$Performance)){
  text(x = 0, y = i, labels = paste0("$", data$Market_cap[i], "B"), font= 2, pos= 4, cex = 1, col = barcols[i])
}
text(y = 11.4, x = 0.5, labels = c("MARKET CAP\nUSD"), col =c("#34495e"), cex = 0.8, font = 2, xpd= TRUE)

Barcode chart: Age group in US

Recreating barcode chart from observablehq

data = read.csv(file = 'data/19-barcodechart_USagegroup.csv')
rownames(data) = data$name
data$name = NULL
data = apply(data, 1, function(x) x/sum(x))

par(mar = c(2.4, 4, 2, 1), col = '#2c3e50', family = "serif")

plot(NA, ylim = c(0, nrow(data)+0.5), xlim = c(0, 0.2), axes = FALSE, xlab = NA, ylab = NA)
abline(v = seq(0, 0.3, 0.05), col = 'gray70', lwd = 0.2)
abline(h = 1:nrow(data), col = 'gray80', lwd = 0.2, lty = 2)
tmp = lapply(1:nrow(data), function(idx){
  d = data[idx,]
  points(x = d, y = rep(idx, length(d)), pch = '|')
})
axiscol = grDevices::adjustcolor(col = '#2c3e50', alpha.f = 0.9)
axis(side = 2, at = 1:nrow(data), labels = c('<10', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79', '>80'), las = 2, tick = FALSE, col.axis = axiscol, cex.axis = 0.7)
axis(side = 1, at = seq(0, 0.3, 0.05), labels = seq(0, 0.3, 0.05) * 100, tick = FALSE, line = -1, col.axis = axiscol, cex.axis = 0.7)
mtext(text = 'Population (%)', side = 1, line = 1, adj = 1, cex = 0.7)
mtext(text = 'Age (years)', side = 2, line = 3, adj = 0, cex = 0.7, las = 2, at = nrow(data)+1.6, padj = 3)

title(main = "Percentage of each age class in every state of USA", cex.main = 0.85, adj = 0, col.main = "#2c3e50")

Version Author Date
f279152 Anand 2023-05-28

Clean energy manufactured

Recreating the plot from Visual Capitalist

lo_mat = matrix(data = c(1, 1, 2, 2, 3, 4), ncol = 2, byrow = TRUE)
lo = layout(mat = lo_mat, widths = c(4, 0.6), heights = c(1, 0.8, 4))

title1 = "WHERE ARE THE CLEAN-ENERGY TECHNOLOGIES"
title2 = "M A N U F A C T U R E D?"
title3 = "As the market for low-emission solutions expands, China dominates the production of
clean energy technologies and their components."

par(mar = c(0, 0, 0, 0), bg = "#2d3436", col = "white")
plot(NA, xlim = c(0, 1), ylim = c(0.1, 1), axes = FALSE)
text(x = 0.5, y = 0.9, label = title1, font = 2, cex = 1.2)
text(x = 0.5, y = 0.7, label = title2, font = 2, cex = 1.4)
text(x = 0.5, y = 0.3, label = title3, xpd = TRUE, cex = 1.1)

par(mar = c(0, 0, 0, 0), bg = "#2d3436", col = "white")
plot(NA, xlim = c(0, 1), ylim = c(0.1, 0.6), axes = FALSE)
country_cols = c("#c0392b", "#e67e22", "#16a085", "#3498db", "#95a5a6")
names(country_cols) = c("China", "AsiaPacific", "Europe", "NorthAmerica", "Other")

legend(x = 0, y = 0.5, legend = names(country_cols),
       col = country_cols, pch = 15, ncol = 5, cex = 1,
       border = NA, xpd = TRUE, box.col = "white", pt.cex = 1.8)

data = read.delim("data/18-cleanEnergy_manufacturer.tsv")
data$cat = paste(data$source, data$element, sep = "_")
n_cat = rev(table(data$cat)[unique(data$cat)])

y_bottoms = seq(0.1, 16.1, 1)
y_tops = seq(0.9, 16.9, 1)


par(mar = c(2, 4, 0.5, 0), bg = "#2d3436", col = "white")
plot(NA, xlim = c(0, 100), ylim = c(0, length(n_cat)), xlab = NA, ylab = NA, axes = FALSE)

for(i in seq_len(length(n_cat))){
  d = data[data$cat == names(n_cat)[i],]
  d_cumsum = cumsum(d$pct)
  x_right = cumsum(d$pct)
  x_left = c(0, d_cumsum[1:length(d_cumsum) - 1])
  rect(
    xleft = x_left,
    ybottom = y_bottoms[i],
    xright = x_right,
    ytop = y_tops[i],
    col = country_cols[d$Country], border = "#34495e"
  )
  text(x = 0, y = y_bottoms[i]+0.25, label = unique(d$element), cex = 0.7, pos = 2, xpd = NA)
}

at_lines = c()
for(s in unique(data$source)){
  at_lines = c(at_lines, length(unique(data[data$source == s, "element"])))
}

rect(xleft = -100, ybottom = cumsum(rev(at_lines))-0.03, xright = 100, ytop = cumsum(rev(at_lines)), border = NA, col = "white", xpd = TRUE)
rect(xleft = 0, ybottom = 0, xright = 0.1, ytop = 17, border = NA, col = "white", lwd = 2)
rect(xleft = -100, ybottom = 0, xright = 100, ytop = 0+0.03, border = NA, col = "white", lwd = 2, xpd = NA)

abline(v = 65, lty = 2)
text(x = 65, y = 17.5, label = "China's avergae: 65%", col = country_cols["China"], xpd = NA, cex = 0.7, adj = -0.1)
text(x = 50, y = 18.5, label = "Shares of manufacturing capacity by region, 2021", col = "white", xpd = NA, cex = 0.8, adj = 0.5, font = 2)
text(x = 0, y = 17.5, label = "Component", col = "white", xpd = NA, cex = 0.8, adj = 0.1, font = 4, pos = 2)
text(x = 105, y = 17.5, label = "Technology", col = "white", xpd = NA, cex = 0.8, adj = 0.1, font = 4, pos = 4)
axis(side = 1, at = seq(0, 100, 20), line = 0, col = "white", col.ticks = "white", col.axis = "white")

par(mar = c(2, 0, 0.5, 0), bg = "#2d3436", col = "white")
plot(NA, xlim = c(0, 1), ylim = c(0, length(n_cat)), xlab = NA, ylab = NA, axes = FALSE)

lbs = c("Electrolyzers", "Heat Pumps", "Fuel cell\nTrucks", "EV", "Solar",
        "Onshore\nWind", "Offshore\nWind")
text(x = 0, y = c(0.25, 1.5, 3, 6, 9.5, 12.5, 15.5), labels = lbs, adj = 1, xpd = NA, cex = 0.7, pos = 4, font = 2)
rect(xleft = -200, ybottom = cumsum(rev(at_lines))-0.03, xright = 100, ytop = cumsum(rev(at_lines)), border = NA, col = "white", xpd = TRUE)

Version Author Date
e3f28b2 Anand 2023-04-12
fe9fcf4 Anand 2022-10-11
4906871 Anand 2022-08-30
91aa770 Anand 2022-08-22
4f94642 PoisonAlien 2022-07-18
7d5d068 PoisonAlien 2022-07-16

Time spent by age

Recreating the plot from Visual Capitalist

data = read.delim("data/17-time-spent-with-relationships-by-age-us.csv", sep = ",")
colnames(data) = c("Entity", "Code", "Year", "alone", "frinds", "children", "parents", "partner", "coworkers")

cols = hcl.colors(n = 6, palette = "Dark 3")

par(mar = c(2, 2, 4, 3))
plot(NA, xlim = c(15, 85), ylim = c(0, 8.4), xlab = NA, ylab = NA, axes = FALSE)
grid()
for(i in 4:ncol(data)){
  points(x = data$Year, y = data[,i]/60, type = "l", col = cols[i-3])
  points(x = data$Year, y = data[,i]/60, col = cols[i-3], cex = 0.3, pch = 19)
  text(x = 86, y = data[nrow(data),i]/60, labels = colnames(data)[i], pos = 4, xpd = TRUE, cex = 0.6)
}

ttl = "Who Americans Spend Their Time With, by Age"
subttl = "\nAverage time spent with others is measured in minutes per day,and shown by \nthe age of the respondent.This is based on averages from surveys \nbetween 2009 and 2019."

title(main = ttl, adj = 0, cex.main = 1, line = 3, col.main = "#2c3e50", family = "serif")
title(main = subttl, adj = 0, cex.main = 0.7, line = 0.9, font.main = 1, col.main = "#7f8c8d", family = "serif")

mtext(text = axTicks(side = 1), side = 1, at = axTicks(side = 1), line = 0.2, col = "#636e72", family = "mono")
mtext(text = "Age in years", side = 1, line = 0.8, las = 1, at = 70, adj = 0, cex = 0.6, col= "#3c6382")

mtext(text = axTicks(side = 2), side = 2, at = axTicks(side = 2), line = 0, las = 2, col = "#636e72", family = "mono")
mtext(text = "Time spent in hours", side = 2, line = 0.8, las = 3, at = 6, adj = 0, cex = 0.6, col= "#3c6382")

Version Author Date
553f7de Anand 2023-04-02

Brand wars: Top global brands

Recreating the plot from d3

NOTE Although the plots are generated with base code, converting into a gif requires ImageMagik to be installed.

data = read.csv(file = "data/16-global_brands.csv")
single_entries = names(which(table(data$name) == 1))
data = data[!data$name %in% single_entries,]

mod_data = lapply(split(data, data$name), function(d){
  brand = unique(d$name)
  category = unique(d$category)
  d_brand = lapply(1:(nrow(d)-1), function(idx){
    val_idx = d[idx, "value"]
    val_idx_next = d[idx+1, "value"]

    date_idx = d[idx, "date"]
    date_idx_next = d[idx+1, "date"]

    seq_date = seq.Date(from = as.Date(date_idx), to = as.Date(date_idx_next), by = 'month')

    data.frame(date = seq_date, value = round(seq(from = val_idx, to = val_idx_next, length.out = length(seq_date))))
  })

  d_brand = do.call(what = "rbind", d_brand)
  d_brand$brand = brand
  d_brand$category = category
  d_brand
})

mod_data = do.call(what = "rbind", mod_data)
mod_data$date = as.Date(as.character(mod_data$date))

fnames = lapply(split(mod_data, mod_data$date), function(pd){
  pd = pd[!duplicated(pd$brand),]
  fname = NA
  if(nrow(pd) >= 12){
    pd = pd[order(pd$value, decreasing = T),][1:12,]

    pd$value = ifelse(test = is.na(pd$value), yes = 0, no = pd$value)

    fname = tempfile(pattern = "gb", fileext = ".png")
    png(filename = fname, width = 750, height = 655, bg = "white", res = 120)
    par(mar = c(0, 6, 0, 0))

    plot(NA, xlim = c(0, max(pd$value)), ylim = c(0, 13), frame.plot = FALSE, axes = FALSE, xlab =NA, ylab = NA)
    #main rect
    rect(xleft = 0, ybottom = seq(0.1, 11.1, by = 1), ytop = seq(0.9, 11.9, by = 1), xright = rev(pd$value), col = "#fdcb6e", border = "#fdcb6e")
    #add value to the end of rect
    text(x = rev(pd$value), y = seq(0.45, 11.45, by = 1), labels = rev(pd$value), adj = 1, cex = 0.6, col = "black")
    #Add category to the beginning
    text(x = 100, y = seq(0.45, 11.45, by = 1), labels = rev(pd$category), adj = 0, cex = 0.6, col = "#636e72")
    #add brand name as row names
    text(x = -0.5, y = seq(0.45, 11.45, by = 1), labels = rev(pd$brand), adj = 1.1, xpd = TRUE, cex = 0.9, col = "#34495e", family = "mono")
    #add top y axis labs
    text(x = pretty(c(0, pd$value)), y = 12, labels = pretty(c(0, pd$value)), pos = 3, cex = 0.5)
    rect(xleft = pretty(c(0, pd$value)), ybottom = 0, xright = pretty(c(0, pd$value)), ytop = 12, col = "maroon", border = "white")
    #add year in progress
    legend(x = "bottomright", legend = unique(substr(pd$date, 1, 4)), col = "#2c3e50", bty = "n", text.font = 2, cex = 1.5)
    title(main = "Top global brands", line = -1, adj = 0)
    title(main = "value in $M", line = -1.8, adj = 0, cex.main = 0.6, col.main = "#2c3e50")

    dev.off()

  }
  fname
})

pngs = unlist(fnames, use.names = FALSE)
cmd = paste0("convert -loop 0 -delay 8 ", paste(pngs, collapse = ' '), " globalbrands.gif")
sys.log = system(command = cmd, intern = TRUE)

Piechart: Browser market share

Recreating the plot from visualcapitalist. See here for data compilation code.

NOTE Although the plots are generated with base code, converting into a gif requires ImageMagik to be installed.

data = read.delim(file = "data/15-pieanimate_browserWars_compileData.tsv", header = TRUE)
data$Total = round(data$Total, 2)
data$browser = gsub(pattern = "Other", replacement = "others", x = data$browser)

#Order by months
data = lapply(split(data, data$year), function(x){
  x[order(factor(x$month, levels = month.name)),]
})
data = do.call(what = rbind, data)
data$month = as.character(data$month)

browsercols = c("#16a085", "#2980b9", "#f39c12", "#8e44ad", "#e74c3c", "gray") #hcl.colors(n = 6, palette = "Dark2")
names(browsercols) = c("chrome", "explorer", "firefox", "opera", "safari", "others")

#pie() with an added argument labcol
pie2 = function (x, labels = names(x), edges = 200, radius = 0.8, clockwise = FALSE,
          init.angle = if (clockwise) 90 else 0, density = NULL, angle = 45,
          col = NULL, border = NULL, lty = NULL, main = NULL, labcol = "black", ...)
{
  if (!is.numeric(x) || any(is.na(x) | x < 0))
    stop("'x' values must be positive.")
  if (is.null(labels))
    labels <- as.character(seq_along(x))
  else labels <- as.graphicsAnnot(labels)
  x <- c(0, cumsum(x)/sum(x))
  dx <- diff(x)
  nx <- length(dx)
  plot.new()
  pin <- par("pin")
  xlim <- ylim <- c(-1, 1)
  if (pin[1L] > pin[2L])
    xlim <- (pin[1L]/pin[2L]) * xlim
  else ylim <- (pin[2L]/pin[1L]) * ylim
  dev.hold()
  on.exit(dev.flush())
  plot.window(xlim, ylim, "", asp = 1)
  if (is.null(col))
    col <- if (is.null(density))
      c("white", "lightblue", "mistyrose", "lightcyan",
        "lavender", "cornsilk")
  else par("fg")
  if (!is.null(col))
    col <- rep_len(col, nx)
  if (!is.null(border))
    border <- rep_len(border, nx)
  if (!is.null(lty))
    lty <- rep_len(lty, nx)
  angle <- rep(angle, nx)
  if (!is.null(density))
    density <- rep_len(density, nx)
  twopi <- if (clockwise)
    -2 * pi
  else 2 * pi
  t2xy <- function(t) {
    t2p <- twopi * t + init.angle * pi/180
    list(x = radius * cos(t2p), y = radius * sin(t2p))
  }
  for (i in 1L:nx) {
    n <- max(2, floor(edges * dx[i]))
    P <- t2xy(seq.int(x[i], x[i + 1], length.out = n))
    polygon(c(P$x, 0), c(P$y, 0), density = density[i], angle = angle[i],
            border = border[i], col = col[i], lty = lty[i])
    P <- t2xy(mean(x[i + 0:1]))
    lab <- as.character(labels[i])
    if (!is.na(lab) && nzchar(lab)) {
      lines(c(1, 1.05) * P$x, c(1, 1.05) * P$y)
      text(1.1 * P$x, 1.1 * P$y, labels[i], xpd = TRUE,
           adj = ifelse(P$x < 0, 1, 0), col = labcol[i], ...)
    }
  }
  title(main = main, ...)
  invisible(NULL)
}

dataspl = split(data, data$date_obs)

pngs = lapply(dataspl, function(x){
  fname = tempfile(fileext = '.png')
  png(filename = fname, res = 110)
  par(mar = c(2, 4, 3, 4),  family = "mono") #bg = "#ecf0f1"
  pie2(
    x = x$Total,
    labcol = rep("#2c3e50", nrow(x)), #browsercols[x$browser],
    col = browsercols[x$browser],
    radius = 1,
    labels = paste0(x$browser, "\n[", x$Total, "%]"),
    xpd = TRUE,
    outer = TRUE, cex = 1, border = browsercols[x$browser], font = 2
  )
  points(x = 0, y = 0, pch = 19, cex = 18, col = "white", lwd = 0)
  text(x = 0, y = -.1, label = x$year[1], adj = 0.5, cex = 2, col = "#c0392b", font = 2)
  text(x = 0, y = .1, label = x$month[1], adj = 0.5, cex = 1.2, col = "#e74c3c", font = 2)

  title(main = "WEB BROWSERS MARKET SHARE\n2002-2022", adj = 0.5, cex.main = 1.5, line = 0, col.main = "#2c3e50")
  dev.off()
  fname
})

pngs = unlist(pngs, use.names = FALSE)
cmd = paste0("convert -loop 0 -delay 15 ", paste(pngs, collapse = ' '), " pianimate.gif")
sys.log = system(command = cmd, intern = TRUE)

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
fd38753 Anand 2023-03-26

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
fd38753 Anand 2023-03-26

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
fd38753 Anand 2023-03-26 
# 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
fd38753 Anand 2023-03-26

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
fd38753 Anand 2023-03-26

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
fd38753 Anand 2023-03-26

Heatmap: Measles

Recreating the popular maeasles vaccine heatmap from WSJ

acknowledgments:

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
fd38753 Anand 2023-03-26

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
fd38753 Anand 2023-03-26

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
fd38753 Anand 2023-03-26

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
fd38753 Anand 2023-03-26

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
fd38753 Anand 2023-03-26

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
fd38753 Anand 2023-03-26

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
fd38753 Anand 2023-03-26

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")

Version Author Date
fd38753 Anand 2023-03-26

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"
  )
}

Version Author Date
fd38753 Anand 2023-03-26 

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_1.0.10            highr_0.10             compiler_4.2.1        
 [4] pillar_1.9.0           bslib_0.4.2            later_1.3.0           
 [7] git2r_0.31.0           jquerylib_0.1.4        berryFunctions_1.21.14
[10] tools_4.2.1            getPass_0.2-2          digest_0.6.31         
[13] jsonlite_1.8.4         evaluate_0.20          lifecycle_1.0.3       
[16] tibble_3.2.1           pkgconfig_2.0.3        rlang_1.1.1           
[19] cli_3.6.0              rstudioapi_0.14        yaml_2.3.7            
[22] xfun_0.37              fastmap_1.1.1          httr_1.4.5            
[25] stringr_1.5.0          knitr_1.42             fs_1.6.1              
[28] vctrs_0.6.3            sass_0.4.5             rprojroot_2.0.3       
[31] data.table_1.14.8      glue_1.6.2             R6_2.5.1              
[34] processx_3.8.0         fansi_1.0.4            rmarkdown_2.20        
[37] callr_3.7.3            magrittr_2.0.3         whisker_0.4.1         
[40] ps_1.7.2               promises_1.2.0.1       htmltools_0.5.4       
[43] abind_1.4-5            httpuv_1.6.9           utf8_1.2.3            
[46] stringi_1.7.12         cachem_1.0.7