How do I plot cumulative mortality in R when the x-axis is categorical?

Question

I want to plot cumulative mortality across development for individuals kept under different conditions. X1 is an ordered categorical variable representing the different life-stages of my study species (E,1,2,3,4,P) and X2 is a categorical variable representing the different conditions under which individuals were raised (five different conditions a-e). My data is as follows:

X2  X1  Cumulative.%.mortality
a   E   19.64285714
b   E   28.16901408
c   E   26.66666667
d   E   49.72972973
e   E   78.84615385
a   1   26.78571429
b   1   32.3943662
c   1   29.09090909
d   1   51.89189189
e   1   79.80769231
a   2   26.78571429
b   2   40.84507042
c   2   31.51515152
d   2   52.97297297
e   2   79.80769231
a   3   28.57142857
b   3   53.52112676
c   3   33.93939394
d   3   56.21621622
e   3   79.80769231
a   4   30.35714286
b   4   56.33802817
c   4   41.81818182
d   4   58.37837838
e   4   82.21153846
a   P   30.35714286
b   P   56.33802817
c   P   42.42424242
d   P   58.37837838
e   P   82.21153846

Each data point represents the % of individuals (added to the different conditions at the beginning of the first life stage E) that died at a particular life-stage or before e.g. the value for 1/c represents the % of individuals that died at life-stage 1 (and E) under condition c.

I would like there to be a line connecting each of the data points and for there to be separate lines for each of the different conditions of X2.

I have tried using the plot() function in R but this just brings up separate box plots for each life-stage.

Answer 1

Using matplot.

> matrix(dat[order(dat$X2), 3], length(l <- unique(dat$X1))) |> 
+   matplot(type='s', xlab='stage', ylab='cum. m.', xaxt='n') + 
+   axis(1, axTicks(1), labels=l)

---

Data:

> dput(dat)
structure(list(X2 = c("a", "b", "c", "d", "e", "a", "b", "c", 
"d", "e", "a", "b", "c", "d", "e", "a", "b", "c", "d", "e", "a", 
"b", "c", "d", "e", "a", "b", "c", "d", "e"), X1 = c("E", "E", 
"E", "E", "E", "1", "1", "1", "1", "1", "2", "2", "2", "2", "2", 
"3", "3", "3", "3", "3", "4", "4", "4", "4", "4", "P", "P", "P", 
"P", "P"), Cumulative...mortality = c(19.64285714, 28.16901408, 
26.66666667, 49.72972973, 78.84615385, 26.78571429, 32.3943662, 
29.09090909, 51.89189189, 79.80769231, 26.78571429, 40.84507042, 
31.51515152, 52.97297297, 79.80769231, 28.57142857, 53.52112676, 
33.93939394, 56.21621622, 79.80769231, 30.35714286, 56.33802817, 
41.81818182, 58.37837838, 82.21153846, 30.35714286, 56.33802817, 
42.42424242, 58.37837838, 82.21153846)), class = "data.frame", row.names = c(NA, 
-30L))

Answer 2

Convert X1 to a factor with the correct levels and draw the result using geom_step in ggplot:

library(tidyverse)

df %>%
  mutate(X1 = factor(X1, unique(X1))) %>%
  ggplot(aes(X1,`Cumulative.%.mortality`, group = X2)) +
  geom_step(aes(color = X2), linewidth = 1) +
  scale_color_brewer(palette = "Set1") +
  theme_minimal(base_size = 20) +
  scale_y_continuous("Cumulative mortality (%)", limits = c(0, 100),
                     expand = c(0, 0)) +
  labs(x = "Life stage", color = "Conditions")

---

Data from question in reproducible format

df <- structure(list(X2 = c("a", "b", "c", "d", "e", "a", "b", "c", 
"d", "e", "a", "b", "c", "d", "e", "a", "b", "c", "d", "e", "a", 
"b", "c", "d", "e", "a", "b", "c", "d", "e"), X1 = c("E", "E", 
"E", "E", "E", "1", "1", "1", "1", "1", "2", "2", "2", "2", "2", 
"3", "3", "3", "3", "3", "4", "4", "4", "4", "4", "P", "P", "P", 
"P", "P"), `Cumulative.%.mortality` = c(19.64285714, 28.16901408, 
26.66666667, 49.72972973, 78.84615385, 26.78571429, 32.3943662, 
29.09090909, 51.89189189, 79.80769231, 26.78571429, 40.84507042, 
31.51515152, 52.97297297, 79.80769231, 28.57142857, 53.52112676, 
33.93939394, 56.21621622, 79.80769231, 30.35714286, 56.33802817, 
41.81818182, 58.37837838, 82.21153846, 30.35714286, 56.33802817, 
42.42424242, 58.37837838, 82.21153846)), class = "data.frame", 
row.names = c(NA, -30L))