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-boun...@r-project.org> On Behalf Of p...@philipsmith.ca
Sent: Monday, December 9, 2024 7:56 PM
To: R-help@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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and provide commented, minimal, self-contained, reproducible code.

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