CODEWITHSUNDEEP
rggplot2dplyrgeom-point
Ecg
Ecg

Reputation: 942

Order variables geom_point based on similar pattern across x-axis in R

How could I order the variables so they are plotted such as a heat map/where they show similar pattern, ie: at the top A and D, then B, C, and bottom E. Would want to avoid doing it manually as real data is many more variables.

Variable1 <- c(rep("A",7), rep("B",7),rep("C",7), rep("D",7), rep("E",7))
Variable2 <- c(rep(1:7, 5))
value <- c(15, 16, 11, 12, 13, 11, 12, 4, 3, 6, 5, 4, 3, 2, 3, 3, 2, 3, 3, 4, 3, 18, 17, 15, 2, 3, 4, 5, 2, 3, 4, 5, 6, 10, 18)
dff <- data.frame(Variable1, Variable2, value)

library(dplyr)
dff <- dff %>%group_by(Variable1)%>%
  mutate(scaled_val = scale(value)) %>%
  ungroup()

dff$Variable <- factor(dff$Variable1,levels=rev(unique(dff$Variable1)))

ggplot(dff, aes(x = Variable2, y = Variable1, label=NA)) +
  geom_point(aes(size = scaled_val, colour = value)) +
  geom_point(aes(size = scaled_val, colour = value), shape=21, colour="black") +
  geom_text(hjust = 1, size = 2) +
  theme_bw()+
  scale_color_gradient(low = "lightblue", high = "darkblue")+
  scale_x_discrete(expand=c(1,0))+
  coord_fixed(ratio=4)

enter image description here

And desired:

enter image description here

If you look at a heat map with clustered rows by similarity for example: https://3.bp.blogspot.com/-AI2dxe95VHk/TgTJtEkoBgI/AAAAAAAAC5w/XCyBw3qViGA/s400/heatmap_cluster2.png you see at the top you have the row whose pattern are first x-axis timepoints, then the ones higher at the last x-axis timepoints..

To do: So I wonder if using the scaled value, we can do so the top are the ones with higher mean in Variable2 (1:2), then higher mean Variable2 (3:5) then Variable2 (6:7). Let me know if I am not being clear here and can explain, better.

Upvotes: 0

Views: 106

Answers (1)

Jeff Bezos
Jeff Bezos

Reputation: 2253

It sounds like you want to arrange groups A-E based on their mean. You can do that by converting Variable1 into a factor with custom levels:

lvls <- names(sort(by(dff$value, dff$Variable1, mean)))
dff$Variable1 <- factor(dff$Variable1, levels = lvls)

Here's a solution that sorts groups by which.max:

peaks <- c(by(dff$value, dff$Variable1, which.max))
lvls <- names(sort(peaks))
dff$Variable1 <- factor(dff$Variable1, levels = lvls)

enter image description here

Upvotes: 2

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