Combining multiple datasets in one scatterplot with R

Question

I have three datasets which I would like combine in one scatter plot. The data sets are: data 1 -

GO term	Count	Enrichment	P value
BP	163	0.008	0.37
MF	48	0.007	0.33
CC	58	0.008	0.39
KEGG	27	0.008	0.43

data 2 -

GO term	Count	Enrichment	P value
BP	167	0.01	0.31
MF	50	0.008	0.29
CC	50	0.006	0.34
KEGG	23	0.01	0.37

data 3 -

GO term	Count	Enrichment	P value
BP	123	0.009	0.22
MF	44	0.01	0.22
CC	50	0.007	0.24
KEGG	14	0.009	0.28

## to reproduce
data_1 <- structure(list(GO.term = c("BP", "MF", "CC", "KEGG"), Count = c(163L, 
48L, 58L, 27L), Enrichment = c(0.008, 0.007, 0.008, 0.008), P.value = c(0.37, 
0.33, 0.39, 0.43)), class = "data.frame", row.names = c(NA, 4L
))

data_2 <- structure(list(GO.term = c("BP", "MF", "CC", "KEGG"), Count = c(167L, 
50L, 50L, 23L), Enrichment = c(0.01, 0.008, 0.006, 0.01), P.value = c(0.31, 
0.29, 0.34, 0.37)), class = "data.frame", row.names = c(NA, 4L
))

data_3 <- structure(list(GO.term = c("BP", "MF", "CC", "KEGG"), Count = c(123L, 
44L, 50L, 14L), Enrichment = c(0.009, 0.01, 0.007, 0.009), P.value = c(0.22, 
0.22, 0.24, 0.28)), class = "data.frame", row.names = c(NA, 4L
))

For data 1, I created a scatterplot with ggplot(data, aes(x=Fold.Enrichment, y=GO.Term, color=PValue)) + geom_point(aes(size=Count))

Now I want to combine all the data sets in one plot. Is it possible with scatterplot or do I need to change the graph type?

Answer 1

Here is a way to use bind_rows to combine all the datasets. Notice that I'm using mutate to add a type column in order for you to differentiate between them, here using different shape for each type.

library(tidyverse)

data_1 <-
  structure(
    list(
      GO.term = c("BP", "MF", "CC", "KEGG"),
      Count = c(163L,
                48L, 58L, 27L),
      Enrichment = c(0.008, 0.007, 0.008, 0.008),
      P.value = c(0.37,
                  0.33, 0.39, 0.43)
    ),
    class = "data.frame",
    row.names = c(NA, 4L)
  )

data_2 <-
  structure(
    list(
      GO.term = c("BP", "MF", "CC", "KEGG"),
      Count = c(167L,
                50L, 50L, 23L),
      Enrichment = c(0.01, 0.008, 0.006, 0.01),
      P.value = c(0.31,
                  0.29, 0.34, 0.37)
    ),
    class = "data.frame",
    row.names = c(NA, 4L)
  )

data_3 <-
  structure(
    list(
      GO.term = c("BP", "MF", "CC", "KEGG"),
      Count = c(123L,
                44L, 50L, 14L),
      Enrichment = c(0.009, 0.01, 0.007, 0.009),
      P.value = c(0.22,
                  0.22, 0.24, 0.28)
    ),
    class = "data.frame",
    row.names = c(NA, 4L)
  )

bind_rows('data1' = data_1,
  'data2' = data_2,
  'data3' = data_3,
  .id = 'type') %>%
  ggplot(aes(
    x = Enrichment,
    y = GO.term,
    col = P.value,
    size = Count,
    shape = type
  )) +
  geom_point()

^{Created on 2023-06-16 with reprex v2.0.2}

Answer 2

an alternative is to facet your plot (one panel per data source):

• combine dataframes to one, adding a source column:

data_combined <- 
  paste0('data_', 1:3) |> ## the names of your single dataframes
  Map(f = \(source) cbind(source, get(source))) |>
  Reduce(f = rbind)

• plot and facet with ggplot:

library(ggplot2)

data_combined |>
  ggplot() +
  geom_point(aes(Enrichment,
                 GO.term,
                 size = Count,
                 color = P.value,
                 )
             ) +
  facet_wrap(~ source, ncol = 1)

---

aside: benchmark rbind vs bind_rows

## binds dataframes data_1:data_3 by row, using bind_function:
bind_them <- \(bind_function){
  combined <- 
    paste0('data_', 1:3) |> 
    Map(f = \(source) cbind(source, get(source))) |>
    Reduce(f = bind_function)
}


microbenchmark::microbenchmark(bind_them('rbind'), bind_them('bind_rows'),
                               control = list(warmup = 2 ))

Unit: microseconds
                   expr    min      lq     mean  median     uq    max neval cld
     bind_them("rbind")  805.1  827.75  968.666  925.55  955.7 2145.6   100  a 
 bind_them("bind_rows") 3721.6 3795.35 4251.423 4061.25 4349.8 9032.2   100   b