Last updated: 2024-11-11

Checks: 7 0

Knit directory: basegraphics/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20220715)

The command set.seed(20220715) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: fb3dc73

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version fb3dc73. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  data/23-G7_vs_BRCIS_GDP.tsv
    Untracked:  data/24-data_cost.tsv

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/baseplots.Rmd) and HTML (docs/baseplots.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd fb3dc73 Anand 2024-11-11 data cost
html d6a09e9 Anand 2023-12-09 Build site.
Rmd c95caf4 Anand 2023-12-09 workflowr::wflow_publish(files = "analysis/baseplots.Rmd")
html 271b17f Anand 2023-12-04 Build site.
Rmd 519daaf Anand 2023-12-04 workflowr::wflow_publish(files = "analysis/baseplots.Rmd")
html 1d87f6c Anand Mayakonda 2023-11-03 Build site.
Rmd 4970d61 Anand Mayakonda 2023-11-03 US student debt
html a6bd433 Anand 2023-10-17 Build site.
Rmd 01d61ce Anand 2023-10-17 workflowr::wflow_publish(files = "analysis/baseplots.Rmd")
Rmd 5f58119 Anand Mayakonda 2023-10-13 gold mines
html f279152 Anand 2023-05-28 Build site.
Rmd 42e68e6 Anand 2023-05-28 workflowr::wflow_publish(files = c("analysis/baseplots.Rmd",
html e3f28b2 Anand 2023-04-12 Build site.
Rmd 2da9d38 Anand 2023-04-12 workflowr::wflow_publish(files = "analysis/baseplots.Rmd")
html 553f7de Anand 2023-04-02 Build site.
Rmd 679f14c Anand 2023-04-02 workflowr::wflow_publish(files = c("analysis/baseplots.Rmd",
html fd38753 Anand 2023-03-26 Build site.
Rmd 54f4ae4 Anand 2023-03-26 brand wars
html bb723cc Anand 2022-10-16 Build site.
Rmd cdd7242 Anand 2022-10-16 browserwars
html fe9fcf4 Anand 2022-10-11 Build site.
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 uncommented code.

Data cost

Recreating the plot from visualcapitalist

data = read.delim(file = "data/24-data_cost.tsv", sep = "\t")

par(mar = c(0, 5, 0, 0), bg = "#2c3e50")
b=barplot(data$cost_per_g, horiz = TRUE, xlim = c(0, ceiling(max(data$cost_per_g))), axes = FALSE, col = ifelse(test = data$country == "Worldwide", yes = "#34495e", no = "#2ecc71"), border = "#16a085")
text(x = 0, y = b, label = toupper(data$country), adj = 0, cex = 0.6, pos = 2, xpd = TRUE, font = 2, col = "#ecf0f1")
text(x = data$cost_per_g, y = b, label = paste0("$", data$cost_per_g), adj = 0, cex = 0.6, pos = 4, xpd = TRUE, font = 2, col = "#ecf0f1")
text(x = 3, y = b[length(b)-20], 
     label = "T H E      C O S T      O F\n---------------------------------\n\n", 
     pos = 4, col = "white", cex = 1.6)
text(x = 3, y = b[length(b)-23], label = "M O B I L E\nD A T A", pos = 4, cex = 3, font = 2, col = "white")
text(x = 3, y = b[length(b)-29], label = "---------------------------------\n\n", pos = 4, col = "white", cex = 1.6)

text(x = max(data$cost_per_g)+0.2, y = b[4], label = "Average price\nfor 1GB (USD)", pos = 3, col = "white", cex = 0.6, font = 2)
arrows(x0 = max(data$cost_per_g)+0.3, y1 = b[2], x1 = max(data$cost_per_g)+0.3, y0 = b[4], length = 0.1, col = "white", lwd = 2)

india_at = subset(data, country == "India")[,2]

arrows(x1 = india_at+0.6, y1 = b[50], x0 = india_at+2, y0 = b[50], length = 0.1, col = "white", lwd = 2)
inida_txt = "Hyper-competition among major\noperators is key factor driving\nlow prices in India"
text(x = india_at+2, y = b[49], label = inida_txt, pos = 4, col = "white", cex = 0.8, font = 1)

Version Author Date
d6a09e9 Anand 2023-12-09
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 
# rect(xleft = 0, ybottom = seq(0.2, nrow(data)-1, 1), xright = data$cost_per_g, ytop = seq(0.8, nrow(data)-1, 1), col = "#16a085", border = NA)
# text(x = 0, y = seq(0.45, nrow(data)-1, 1), label = data$country, adj = 0, cex = 0.6)

# if("berryFunctions" %in% installed.packages()){
#   berryFunctions::roundedRect(xleft = 0, ybottom = seq(0.1, nrow(data)-1, 1), xright = data$cost_per_g, ytop = seq(0.9, nrow(data)-1, 1))
# }else{
#   rect(xleft = 0, ybottom = seq(0.05, nrow(data)-1, 1), xright = data$cost_per_g, ytop = seq(0.95, nrow(data)-1, 1))
# }

Marvel Box Office

Recreating the plot from visualcapitalist

data = read.delim(file = "data/23-marvel_boxoffocie.tsv")

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

#Box-office scores
par(bg = "#34495e", mar = c(8, 3, 1.2, 1)) #636e72
phase_cols = hcl.colors(n = 5, palette = "Dark 2", alpha = 0.8)

b = barplot(height = data$Box_OfficeWorldwide, col = phase_cols[data$phase], border = "#ecf0f1", axes = FALSE, ylim = c(-100, 2800))
text(x = b, y = -100, labels = data$Film, srt = 45, adj = 1, xpd = TRUE, col = "#ecf0f1", cex = 0.6)
points(x = b, y = data$Production_Budget, col = "#f1c40f", type = "p", cex = 0.3, pch = 19)
points(x = b, y = data$Opening_WeekendDomestic, col = "red", type = "p", cex = 0.3, pch = 19)

title(main = "MARVEL STUDIOS", adj = 0, col.main = "#ecf0f1", font.main = 1, line = 0.2)
title(main = "BOX OFFICE NUMBERS", adj = 0, col.main = "#ecf0f1", font.main = 1, line = -0.8, cex.main = 0.93)

rect(xleft = 0, ybottom = 1000, xright = max(b), ytop = 1000, border = "#ecf0f1", lty = 2)
text(x = 0, y = 1000, labels = "$1B", pos = 2, xpd = TRUE, col = "#ecf0f1", cex = 0.8)
rect(xleft = 0, ybottom = 2000, xright = max(b), ytop = 2000, border = "#ecf0f1", lty = 2)
text(x = 0, y = 2000, labels = "$2B", pos = 2, xpd = TRUE, col = "#ecf0f1", cex = 0.8)
text(x = b[25], y = 500, labels = "Pandemic\nreleases", pos = 3, cex = 0.6, col = "#ecf0f1")

legend(x = "topright", ncol = 5, col = phase_cols, legend = 1:5, title = "PHASE", bty = "n", pch = 15, xpd = TRUE, text.col = "#ecf0f1", pt.cex = 1.2, cex = 0.7)
text(x = 0, y = 250, labels = "Budget", pos = 2, xpd = TRUE, col = "#f1c40f", cex = 0.6)
text(x = 0, y = 100, labels = "Opening", pos = 2, xpd = TRUE, col = "red", cex = 0.6)

#Tomatometer scores
data$tomatometer = cut(data$Review_ScoreRotten_Tomatoes, breaks = c(0, 60, 75, 100), labels = c("Rotten", "Fresh", "Certified_Fresh"))
tomatometer_cols = c("#2ecc71", "#d35400", "#f39c12")
names(tomatometer_cols) = c("Rotten", "Fresh", "Certified_Fresh")

par(bg = "#34495e", mar = c(0, 3, 0, 1))
plot(x = b, y = data$Review_ScoreRotten_Tomatoes, type = "l", ylim = c(30, 100),axes = FALSE, col = "#95a5a6")
points(x = b, y = data$Review_ScoreRotten_Tomatoes, type = "p", col = tomatometer_cols[data$tomatometer], pch = 19, cex = 0.6)
rect(xleft = 0, ybottom = 60, xright = max(b), ytop = 60, border = tomatometer_cols["Rotten"], lty = 2)
text(x = 0, y = 60, labels = "Rotten", pos = 2, xpd = TRUE, col = tomatometer_cols["Rotten"], cex = 0.7)
text(x = max(b), y = 60, labels = "<60%", pos = 4, xpd = TRUE, col = tomatometer_cols["Rotten"], cex = 0.7)
rect(xleft = 0, ybottom = 75, xright = max(b), ytop = 75, border = tomatometer_cols["Certified_Fresh"], lty = 2)
text(x = 0, y = 75, labels = "Certified\nFresh", pos = 2, xpd = TRUE, col = tomatometer_cols["Certified_Fresh"], cex = 0.7)
text(x = max(b), y = 75, labels = ">75%", pos = 4, xpd = TRUE, col = tomatometer_cols["Certified_Fresh"], cex = 0.7)

text(x = 10, y = 30, labels = "ROTTEN TOMATOES CRTIC RATINGS", pos = 3, adj = 0, col = "#ecf0f1", cex = 0.8)

max_idx = which(data$Review_ScoreRotten_Tomatoes == max(data$Review_ScoreRotten_Tomatoes))
max_score = data[max_idx, "Review_ScoreRotten_Tomatoes"]
max_xpos = b[max_idx,]
text(x = max_xpos, y = max_score, label = paste0(max_score, "%\nHigh"), col = "#ecf0f1", pos = 4, cex = 0.5)

Version Author Date
d6a09e9 Anand 2023-12-09
bb723cc Anand 2022-10-16
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

BRICS vs G7 GDP

Recreating the plot from visualcapitalist

Before drawing the plot, we need to implement treemap algorithm. squarify by Uri Laserson does it in pure python. Below I have transpiled the python code into R. I have also made some modifications to make it usable in R.

# Squarified Treemap Layout
# Implements algorithm from Bruls, Huizing, van Wijk, "Squarified Treemaps"

pad_rectangle = function(rect) {
  if (rect$dx > 2) {
    rect$x = rect$x + 1
    rect$dx = rect$dx - 2
  }
  if (rect$dy > 2) {
    rect$y = rect$y + 1
    rect$dy = rect$dy - 2
  }
}

layoutrow = function(sizes, x, y, dx, dy) {
  covered_area = sum(sizes)
  width = covered_area / dy
  rects = list()
  for (size in sizes) {
    rects = c(rects, list(list(x = x, y = y, dx = width, dy = size / width)))
    y = y + size / width
  }
  return(rects)
}

layoutcol = function(sizes, x, y, dx, dy) {
  covered_area = sum(sizes)
  height = covered_area / dx
  rects = list()
  for (size in sizes) {
    rects = c(rects, list(list(x = x, y = y, dx = size / height, dy = height)))
    x = x + size / height
  }
  return(rects)
}

layoutMain = function(sizes, x, y, dx, dy) {
  if (dx >= dy) {
    return(layoutrow(sizes, x, y, dx, dy))
  } else {
    return(layoutcol(sizes, x, y, dx, dy))
  }
}

leftoverrow = function(sizes, x, y, dx, dy) {
  covered_area = sum(sizes)
  width = covered_area / dy
  leftover_x = x + width
  leftover_y = y
  leftover_dx = dx - width
  leftover_dy = dy
  return(list(leftover_x, leftover_y, leftover_dx, leftover_dy))
}

leftovercol = function(sizes, x, y, dx, dy) {
  covered_area = sum(sizes)
  height = covered_area / dx
  leftover_x = x
  leftover_y = y + height
  leftover_dx = dx
  leftover_dy = dy - height
  return(list(leftover_x, leftover_y, leftover_dx, leftover_dy))
}

leftover = function(sizes, x, y, dx, dy) {
  if (dx >= dy) {
    return(leftoverrow(sizes, x, y, dx, dy))
  } else {
    return(leftovercol(sizes, x, y, dx, dy))
  }
}

worst_ratio = function(sizes, x, y, dx, dy) {
  rects = layoutMain(sizes, x, y, dx, dy)
  return(max(sapply(rects, function(rect) {
    max(rect$dx / rect$dy, rect$dy / rect$dx)
  })))
}

squarify_main = function(sizes, x, y, dx, dy) {
  sizes = as.numeric(sizes)

  if (length(sizes) == 0) {
    return(list())
  }

  if (length(sizes) == 1) {
    return(layoutMain(sizes, x, y, dx, dy))
  }

  i = 1
  while (i < length(sizes) && worst_ratio(sizes[1:i], x, y, dx, dy) >= worst_ratio(sizes[1:(i + 1)], x, y, dx, dy)) {
    i = i + 1
  }
  current = sizes[1:i]
  remaining = sizes[(i + 1):length(sizes)]

  leftover_info = leftover(current, x, y, dx, dy)
  leftover_x = leftover_info[[1]]
  leftover_y = leftover_info[[2]]
  leftover_dx = leftover_info[[3]]
  leftover_dy = leftover_info[[4]]

  return(c(layoutMain(current, x, y, dx, dy), squarify_main(remaining, leftover_x, leftover_y, leftover_dx, leftover_dy)))
}

padded_squarify = function(sizes, x, y, dx, dy) {
  rects = squarify(sizes, x, y, dx, dy)
  for (rect in rects) {
    pad_rectangle(rect)
  }
  return(rects)
}

normalize_sizes = function(sizes, dx, dy) {
  total_size = sum(sizes)
  total_area = dx * dy
  sizes = sizes * (total_area / total_size)
  return(sizes)
}

#' Treemap in base R
#' @details Plot treemaps in base R
#' This function is a direct transpilation of python [squarify](https://github.com/laserson/squarify) package by [Uri Laserson](https://github.com/laserson)
#' @param X numeric vectors of values
#' @param labels a character vector specifying the text to be written for each value in X. Default NULL.
#' @param sub_labels a character vector specifying the sub-text to be written for each value in X. Default NULL.
#' @param fontSize numeric character expansion factor. Default 1
#' @param col colors. If colors is longer than x, the coordinates are recycled to the length of labels.
#' @param alpha Default 1
#' @param show_val Default TRUE.
#' @param show_pct Default FALSE.
#' @param text_col Default #2c3e50
#' @param sub_text_col Default #34495e
#' @param borderCol Default NULL
#' @param text_font Font type. Default 1. Can be 1, 2 (bold), 3 (italic), 4 (bold-italic)
#' @param sub_text_font Font type. Default 1. Can be 1, 2 (bold), 3 (italic), 4 (bold-italic)


squarify = function(X, labels = NULL, sub_labels = NULL, fontSize = 1, col = NULL, alpha = 1, show_val = TRUE,
                    show_pct = FALSE, text_col = "#2c3e50", sub_text_col = "#34495e",
                    borderCol = NULL, text_font = 1, sub_text_font = 1){
  x <- 0  # X-coordinate of the origin
  y <- 0  # Y-coordinate of the origin
  dx <- 100  # Full width of the treemap
  dy <- 100  # Full height of the treemap

  sizes_norm <- X / sum(X) * dx * dy  # Normalize the sizes

  if(is.null(col)){
    col = hcl.colors(n = length(X), palette = "Dark2")
  }else if(length(col) != length(X)){
    col = rep(col, length(X))
  }

  if(length(text_col) != length(X)){
    text_col = rep(text_col, length(X))
  }

  if(length(sub_text_col) != length(X)){
    sub_text_col = rep(sub_text_col, length(X))
  }

  col = grDevices::adjustcolor(col = col, alpha.f = alpha)

  # Compute the treemap rectangles
  rectangles <- squarify_main(sizes_norm, x, y, dx, dy)


  # Function to draw a rectangle
  draw_rect <- function(x, y, dx, dy, col, ...) {
    rect(x, y, x + dx, y + dy, col = col, ...)
  }

  # Function to draw text inside the rectangle
  draw_text <- function(x, y, dx, dy, label, fs = 1, ...) {
    text(x + dx / 2, y + dy / 2, label, cex = fs, xpd = TRUE, ...)
  }

  draw_text_sub <- function(x, y, dx, dy, label, fs = 1, ...) {
    text(x + dx/2, y + dy / 2, label, cex = fs * 0.8, pos = 1, xpd = TRUE, ...)
  }

  if(is.null(labels)){
    labels = seq_along(X)
  }

  plot(NA, xlim = c(0, dx), ylim = c(0, dy), xlab = "", ylab = "", xaxt = "n", yaxt = "n", frame.plot = FALSE)

  if(is.null(sub_labels)){
    sub_text = paste0(X)
  }else{
    sub_text = sub_labels
  }

  if(show_pct){
    sub_text = paste0(sub_text,  " [", round(X/sum(X) * 100, 2), "%]")
  }

  #print(rectangles)
  for (i in seq_along(rectangles)) {
    rect_info <- rectangles[[i]]
    draw_rect(rect_info$x, rect_info$y, rect_info$dx, rect_info$dy, col[i], border = borderCol)
    draw_text(rect_info$x, rect_info$y, rect_info$dx, rect_info$dy, labels[i], fs = fontSize, col = text_col[i], font = text_font) # Use original sizes
    if(show_val){
      draw_text_sub(rect_info$x, rect_info$y, rect_info$dx, rect_info$dy, sub_text[i], fs = fontSize, col = sub_text_col[i], font = sub_text_font) # Use original sizes
    }
  }
}
g7 = c(26.85, 4.41, 4.30, 3.15, 2.92, 2.17, 2.09)
g7_countries = c("U.S.", "Japan", "Germany", "UK", "France", "Italy", "Canada")

brics = c(19.37, 3.73, 2.08, 2.06, 0.39)
brics_countries = c("China", "India", "Brazil", "Russia", "SA")

layout(mat = matrix(c(1,2,3,3), nrow = 2, byrow = TRUE), heights = c(5, 1))
par(mar = c(0, 0, 1, 0), family = "mono", bg = "#ecf0f1")
squarify(
  X = g7,
  labels = g7_countries,
  col = "#273c75",
  alpha = 0.9,
  text_col = "white",
  borderCol = "white",
  sub_labels = paste0(g7, "T"),
  sub_text_col = "white",
  text_font = 2
)
title(main = "G7 $45.89T total", font = 2, col.main = "#273c75", line = 0)

squarify(
  X = brics,
  labels = brics_countries,
  col = "#c23616",
  alpha = 0.9,
  text_col = "white",
  borderCol = "white",
  sub_labels = paste0(brics, "T"),
  sub_text_col = "white",
  text_font = 2
)
title(main = "BRICS $27.63T total", font = 2, col.main = "#c23616", line = 0)

plot(x = NA, y = NA, type = "n", xlim = c(0,1), ylim = c(0,1), axes = FALSE)
title(main = "[GDP comparison]", cex.main = 2.5, font.main = 1, line = -1, col.main = "#2c3e50")
title(main = "[G7 vs BRICS]", cex.main = 3.8, font.main = 2, line = -4, col.main = "#2c3e50")

Version Author Date
271b17f Anand 2023-12-04

If you like the above plot, I have wrapped up the code in a little R package called squaRify that you might find useful.

2022 student debt

Recreating the plot from visualcapitalist

data = read.delim(file = "data/22-median_2022_student_debt.tsv")
data$Major = gsub(pattern = "\\n", replacement = "", data$Major)
#Choose top 20 entries
top = 40
d = lapply(split(data, ~degree), function(x) x[1:top,,])[[1]]

#Some heatmap colors
cols = colorRampPalette(c("#c0392b", "#273c75"))(top)
cols = adjustcolor(col = cols, 0.8)

study_facs = table(d$Study)
study_facs_cols = hcl.colors(n = length(study_facs), palette = "Dark 3")
names(study_facs_cols) = names(study_facs)

par(mar = c(1, 0, 2, 3), fig = c(0,1,0,1))
plot(NA, xlim = c(-1, max(d$median_debt)), ylim = c(0, top+1), frame.plot = FALSE, axes = FALSE, xlab = NA, ylab = NA)
#abline(v = seq(0, 50, 10), lwd = 0.6, lty = 2, col = "#bdc3c7")
rect(xleft = 0, ybottom = seq(0, top-1), xright = d$median_debt, ytop = seq(1, top), col = cols, border = "#ecf0f1")
rect(xleft = -1, ybottom = seq(0, top-1), xright = -0.2, ytop = seq(1, top), col = study_facs_cols[d$Study], border = "#ecf0f1")
text(x = 0.2, y = seq(0.5, top-0.5, 1), label = d$Major, adj = 0, col = "white", cex = 0.8, font = 2)
text(x = d$median_debt, y = seq(1, top)-0.5, labels = paste0("$", d$median_debt, "K"), pos = 4, xpd = TRUE, col = "#2c3e50", family = "serif", cex = 0.8)

mtext(text = "2022 Median Debt", side = 1, cex = 1.2, family = "serif", line = -1, font = 2, adj = 1)

title(main = "S T U D E N T - D E B T - R A N K I N G", font.main = 2, family = "mono", cex.main = 2)
title(main = "U.S. - B A C H E L O R'S - D E G R E E S", font.main = 1, family = "mono", cex.main = 1.2, line = -1)


legend(x = 0.7 * max(d$median_debt), y = nrow(d) - 1, legend = names(study_facs_cols), fill = study_facs_cols, ncol = 1, bty = "n", border = NA, title = "Study")

par(fig = c(0.7,1, 0.2, 0.7), new = T)  
debt_over_time = data.frame(year = c("2005", "2010", "2015", "2020"), public = c(25, 27, 30, 27), private = c(33, 35, 36, 33))

plot(NA, xlim = c(0, 5), ylim = c(24, 40), axes = FALSE)
abline(h = seq(25, 35, 5), lty = 2, col = "gray")
points(x = 1:nrow(debt_over_time), y = debt_over_time$public, type = 'b', col = "#27ae60")
points(x = 1:nrow(debt_over_time), y = debt_over_time$private, type = 'b', col = "#8e44ad")

text(x = 2.5, y = min(debt_over_time$public), label = "Public", font= 2, cex = 0.8, col = "#27ae60")
text(x = 2.5, y = min(debt_over_time$private), label = "Private", font= 2, cex = 0.8, col = "#8e44ad")


text(x = 1:nrow(debt_over_time), y = debt_over_time$public, labels = paste0("$", debt_over_time$public, "K"), pos = 3, cex = 0.6, font= 2)
text(x = 1:nrow(debt_over_time), y = debt_over_time$private, labels = paste0("$", debt_over_time$private, "K"), pos = 3, cex = 0.6, font= 2)
text(x = 1:nrow(debt_over_time), y = 24, labels = debt_over_time$year, cex = 0.6, srt = 45)

text(x = 0, y = 39, label = "Avg. debt over time", adj = 0, family = "mono", font = 2)
box(lty = 5)

Version Author Date
1d87f6c Anand Mayakonda 2023-11-03

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)

Version Author Date
a6bd433 Anand 2023-10-17

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
fd38753 Anand 2023-03-26
bb723cc Anand 2022-10-16
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_1.15.0      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