Overlapping many plots with ggplot2 in R

Question

I have the following data frames:

df1<-data.frame(id_1=c(1,2,3,4,5),
                value1=c(0,0.2,0.5,0.8,0),
                value2=c(0.1,0.3,0.5,0.7,0.8),
                value3=c(0.5,0.6,0.3,0.2,0.1))

df2<-data.frame(id_2=c(1,2,3,4,5),
                value1=c(0,0.2,0.5,0.8,0),
                value2=c(0.1,0.1,0.5,0.6,0.7),
                value3=c(0.4,0.4,0.8,0.9,0.2))

I want to make the following plot:

ggplot(data.frame(x=df1$value1, y=df2$value1), aes(x=x, y=y)) + 
       geom_point() + 
       geom_point(data.frame(x=df1$value2, y=df2$value2), aes(x=x, y=y)) + 
       geom_point(data.frame(x=df1$value3, y=df2$value3), aes(x=x, y=y))

How can I make that plot without having to copy paste geom_point() for each value column? And afterwards, how can I find the correlation coefficient for the variables in final overlapped plot?

Any help would be much appreciated, thanks!

Like this? Or this or this? Once you've bound the data frames, just use cor to get the coefficient — camille
– camille, Commented Jan 18, 2022 at 15:50

Gregor Thomas · Accepted Answer · 2022-01-18 15:30:37Z

You need to combine your data into one data frame. Here's one way:

## make column names the same
## and add columns indicating the data frame source
df1$var = "x"
df2$var = "y"
names(df1)[1] = "id"
names(df2)[1] = "id"

## put the data together
df = rbind(df1, df2)

## reshaped the data
library(tidyr) 
df = pivot_longer(df, starts_with("value"))
df = pivot_wider(df, names_from = "var", values_from = "value")
df
# # A tibble: 15 × 4
#       id name       x     y
#    <dbl> <chr>  <dbl> <dbl>
#  1     1 value1   0     0  
#  2     1 value2   0.1   0.1
#  3     1 value3   0.5   0.4
#  4     2 value1   0.2   0.2
#  5     2 value2   0.3   0.1
#  6     2 value3   0.6   0.4
#  7     3 value1   0.5   0.5
#  8     3 value2   0.5   0.5
#  9     3 value3   0.3   0.8
# 10     4 value1   0.8   0.8
# 11     4 value2   0.7   0.6
# 12     4 value3   0.2   0.9
# 13     5 value1   0     0  
# 14     5 value2   0.8   0.7
# 15     5 value3   0.1   0.2

Once your data is in a tidy format, plotting is simple. You could further customize your plot using shape or color aesthetics to identify the data source.

ggplot(df, aes(x = x, y = y)) +
  geom_point()

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