[R] Heat maps containing two types of variables

[Ebert,Timothy Aaron]

What happens if you switch the colors in this line:
   scale_fill_gradient2(low = "#E94A26", mid = "white", high = "#A1D385", midpoint = 0.5) +
to be the following
   scale_fill_gradient2(low = "# A1D385", mid = "white", high = "# E94A26", midpoint = 0.5) +

That said, a red-green heat map may be unhelpful to color blind people.

So then you need two ggplot statements, one with each scale_fill_gradient2 and then specify which version to plot for each variable.

Tim

-----Original Message-----
From: R-help <r-help-bounces using r-project.org> On Behalf Of phil using philipsmith.ca
Sent: Monday, December 9, 2024 7:56 PM
To: R-help using r-project.org
Subject: [R] Heat maps containing two types of variables

[External Email]

I am working with a heat map, as in the REPREX below. The code works fine as long as "bigger numbers imply greener and smaller numbers imply redder". These are time series where bigger numbers are "better", like total employment for example. But I also have cases within the heat map where "bigger numbers imply redder and smaller numbers imply greener".
These are time series where bigger numbers are "worse", like total unemployment for example. So suppose column B in dat is of the second type, i.e. "bigger numbers imply redder and smaller numbers imply greener". I would like the colour coding to be the reverse of what it is for columns A and C. How can I modify the code to accomplish this? I have tried different approaches with no success. Thanks for your help.
Philip

# REPREX
library(ggplot2)
library(tidyr)
library(dplyr)
dat <- data.frame(
   date=seq.Date(as.Date("2024-01-01"),as.Date("2024-06-01"),by="month"),
   A=c(1,3,3,4,2,6),
   B=c(3,5,6,4,8,9),
   C=c(10,8,17,19,26,22)
)
dat_long <- pivot_longer(dat,2:4,names_to="variable",values_to="value")
normalize <- function(x) { y <- (x-min(x))/(max(x)-min(x)) } dat_norm <- mutate(dat,across(2:4,normalize)) dat_long_norm <-
pivot_longer(dat_norm,2:4,names_to="variable",values_to="norm_value")
dat_long <- inner_join(dat_long,dat_long_norm,by=c("date","variable"))
heatmap <- ggplot(dat_long, aes(x = date, y = variable,fill=norm_value))
+
   geom_tile() +
   geom_text(aes(label = as.character(value)),
     color = "black", size = 2.5) +
   labs(title="REPREX",x="",y="")+
   scale_fill_gradient2(low = "#E94A26", mid = "white", high = "#A1D385", midpoint = 0.5) +
   scale_x_continuous(breaks=seq.Date(as.Date("2024-01-01"),
     as.Date("2024-06-01"),by="month"),
     labels=function(x) format(x,"%b\n%Y"),position="top")+
   theme(legend.position="none")
heatmap
ggsave("REPREXHeatmap.png",heatmap,height=3.5,width=4.9,dpi=200)

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