Equal sign changes rendering of legend labels in autoplot of a survfit object-CodePudding

I am using the survival package to make Kaplan-Mayer estimates of survival curves by group and then I plot out the said curves using packages ggfortify and survminer. All works fine except the legend labels for plotting. I want to present N sizes of groups in the legend labels. I thought that adding the N size to the grouping variable itself using paste0 was a good way to go. In my case it is easier than to use something like scale_fill_discrete("", labels = legend_labeller_for_plot).

library(dplyr)
library(ggplot2)
library(survival)
library(survminer)
library(ggfortify)


set.seed = 100
data <- data.frame(
  time = rlnorm(20),
  event = as.integer(runif(20) < 0.5),
  group = ifelse(runif(20) > 0.5,
                 "group A",
                 "group B")
)

# Plotting survival curves without N sizes in the legend
fit <- survfit(
  with(data, Surv(time, event)) ~ group,
  data)

autoplot(fit)

# Adding N sizes to the data and plotting
data_new <- data %>% 
  group_by(group) %>% mutate(N = n()) %>% 
  ungroup() %>% 
  mutate(group_with_N = paste0(group, ", N = ", N))

fit_new <- survfit(
  with(data, Surv(time, event)) ~ group_with_N,
  data_new)

autoplot(fit_new)

When I try to add N sizes to the groups variable, the part with "N =" in the grouping variable disappears, i.e. the group variable isn't displayed on the legend labels as expected.

For comparison, what I expect is something like the following using Iris data:

What is more, I found that that the culprit is the equali sign =. When I remove the = sign, the legend labels correspond to the grouping variable values. My question is, why does the equal sign cause this?

CodePudding user response：

An option could be using ggsurvplot where you can specify the legend.labs so you can show your size in the legend like this:

library(dplyr)
library(ggplot2)
library(survival)
library(survminer)
library(ggfortify)

set.seed = 100
data <- data.frame(
  time = rlnorm(20),
  event = as.integer(runif(20) < 0.5),
  group = ifelse(runif(20) > 0.5,
                 "group A",
                 "group B")
)

# Adding N sizes to the data and plotting
data_new <- data %>% 
  group_by(group) %>% mutate(N = n()) %>% 
  ungroup() %>% 
  mutate(group_with_N = paste0(group, ", N = ", N))

fit_new <- survfit(
  with(data, Surv(time, event)) ~ group_with_N,
  data_new)

p <- autoplot(fit_new)
p

# ggsurvplot
ggsurvplot(fit_new, data_new, 
           legend.labs = unique(sort(data_new$group_with_N)),
           conf.int = TRUE)

^{Created on 2022-08-18 with reprex v2.0.2}