R - ggplot multiple regression lines for different columns in same chart

Question

With data like below,

text = "
R_1700,R_350,R_2950,S_1700,S_350,S_2950
,,-98.2,,,14.15
-80,-82.3,-99,,-0.7,12.4
-77.55,-80.6,-97,,,14.5
-75.55,-80.85,-96.35,,,14.4
-80.8,-81.6,-94.3,,9.95,6
-80.8,-81.8,,,4.9,
-80.8,-81.85,,,8.2,
-73.8,-77.6,-98,,6.35,
-72.8,-76.7,-96.8,3.7,4.6,
-72.65,-81.7,-94.05,2.25,,
-72.95,-80.4,-94.6,1.7,,
-72.7,-81.7,-94.35,1.6,,
-76.05,-84.25,-95.65,3.65,,
-75.5,-84.65,-95.2,1.95,,
-74.65,-83.8,-94.6,2.6,,
-74.2,-83.95,-100.65,3.25,,
-66.8,-75.65,-97.25,,6.45,
-73.7,-77.7,-97.05,,6.8,
-97.8,-100.8,-116.9,,-5.3,
,-99.7,,,-1,
,-100.2,,,-1.3,
-93.3,-94.75,-103.7,,-4.25,
-94.6,-96.55,-105,,-6.7,
-96.4,-98.45,-110.1,,-6.9,
-96.4,-101.1,-110.7,,-7.65,
-94.95,-102,,,-7.2,
-94,-102.15,,,-9.35,
-91.8,-97,-110.3,,-5.3,
"
df1 = read.table(textConnection(text), sep=",", header = T)

Need to plot regression lines for columns as below with X-axis holding R_... values and Y-axis holding S_... values

1. S_1700 vs. R_1700
2. S_350 vs. R_350
3. S_2950 vs. R_2950

For a single group of variables, I could have done something like below.

ggplot(df1, aes(x=R_1700, y=S_1700)) +
  geom_point() + 
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)

Need help to get all the three lines in a single plot as in the example below. The 3 different groups would be 1700 , 350 and 2950 .

Answer 1

If you could reorganize your data in a format like below:

# with data.table package
library(data.table)
setDT(df1)
df2 <- melt(df1, measure.vars = patterns('R_', 'S_'))
df2[, variable := factor(variable, levels = 1:3,
    labels = tstrsplit(grep('R_', names(df1), value = TRUE), '_')[[2]])]
# > df2
#     variable  value1 value2
# 1:     1700      NA     NA
# 2:     1700  -80.00     NA
# 3:     1700  -77.55     NA
# 4:     1700  -75.55     NA
# 5:     1700  -80.80     NA
# 6:     1700  -80.80     NA
# 7:     1700  -80.80     NA
# 8:     1700  -73.80     NA
# 9:     1700  -72.80   3.70


# without data.table
tmp <- split.default(df1, f = sapply(strsplit(names(df1), '_'), `[`, 2))
tmp <- lapply(tmp, function(dtf){
    names(dtf) <- c('value1', 'value2')
    return(dtf)
})
df2 <- do.call(rbind, tmp)
df2$variable <- rep(names(tmp), each = nrow(df1))

you can visualize the data as desired easily:

ggplot(df2, aes(x = value1, y = value2, color = variable)) +
    geom_point() + 
    geom_smooth(method=lm, se=FALSE, fullrange=TRUE) +
    labs(x = 'R', y = 'S')

Answer 2

tidyverse solution

library(tidyverse)

df1 %>% 
  pivot_longer(everything()) %>% #wide to long data format
  separate(name, c("key","number"), sep = "_") %>% #Separate elements like R_1700 into 2 columns 
  group_by(number, key) %>% #Group the vaules according to number, key
  mutate(row = row_number()) %>% #For creating unique IDs 
  pivot_wider(names_from = key, values_from = value) %>% #Make separate columns for R and S
  ggplot(aes(x=R, y=S, color = number, shape = number)) +
  geom_point() + 
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)