Melting an R data.table with a factor column

3
votes

I have the following R data.table (though this should scale with a data.frame too). The goal is to reshape this data.table to plot as a scatterplot in ggplot2. I therefore need to reshape this data.table to have one "factor" column to color the points:

> library(data.table)
> dt
   ID                   x_A               y_A        x_B       y_B                                                                                                                                                                                                  
   1:   05AC            0.81               3          0.92      2.05                                                                                                                                                                                                   
   2:   01BA            0.41               5          0.63      1.8                                                                                                                                                                                                   
   3:   Z1AC            0.41               5          0.58      1.8                                                                                                                                                                                                   
   4:   B2BA            0.21             6.5          1.00      1.8   
   ....

I believe the correct output needs to be of the form:

ID     type   x      y
05AC   A      0.81   3       
05AC   B      0.92   2.05
01BA   A      0.41   5 
01BA   B      0.63   1.8
Z1AC   A      0.41   5 
Z1AC   B      0.58   1.8
B2BA   A      0.21   6.5 
B2BA   B      1.00   1.8

Is there a standard way to "unfold" data.tables in this fashion? I'm happy for how to use dplyr in this case, but I suspect there should be a data.table method.

melt() would work, if I could figure out how to create the column type, e.g. 

melt(dt, id.vars=c("ID"))

will only melt based on the one column ID

I'm especially confused how one "scrapes" the A and B type from columns 2-3 and columns 4-5 respectively...

2
rdataframedata.tablemelt

2 Answers

4
votes

Staying within data.table, after your suggested approach of using melt, you can tstrsplit to split the variable based on the "_" character. 

## use tstrsplit to split a column on a regular expression
dt[, c("xy", "type") := tstrsplit(variable, "_")]
dt 
#       ID variable value xy type
#  1: 05AC      x_A  0.81  x    A
#  2: 01BA      x_A  0.41  x    A
#  3: Z1AC      x_A  0.41  x    A
#  4: B2BA      x_A  0.21  x    A
#  5: 05AC      y_A  3.00  y    A
#  6: 01BA      y_A  5.00  y    A
#  7: Z1AC      y_A  5.00  y    A
#  8: B2BA      y_A  6.50  y    A
#  9: 05AC      x_B  0.92  x    B
# 10: 01BA      x_B  0.63  x    B
# 11: Z1AC      x_B  0.58  x    B
# 12: B2BA      x_B  1.00  x    B
# 13: 05AC      y_B  2.05  y    B
# 14: 01BA      y_B  1.80  y    B
# 15: Z1AC      y_B  1.80  y    B
# 16: B2BA      y_B  1.80  y    B

This gives you the long-form of your required solution. You can then use dcast to widen it

dcast(dt, formula = ID + type ~ xy)

#      ID type    x    y
# 1: 01BA    A 0.41 5.00
# 2: 01BA    B 0.63 1.80
# 3: 05AC    A 0.81 3.00
# 4: 05AC    B 0.92 2.05
# 5: B2BA    A 0.21 6.50
# 6: B2BA    B 1.00 1.80
# 7: Z1AC    A 0.41 5.00
# 8: Z1AC    B 0.58 1.80

The logic of this answer is the same as the suggested dplyr approach of gather %>% separate %>% spread, but using data.table.

2
votes

A combination of dplyr and tidyr can produce your desired result. This is untested, due to the lack of a reproducible example.

library(tidyr)
library(dplyr)

dt %>% 
  gather(variable, value, -ID) %>% 
  separate(variable, c("group", "type"), sep = "\\_") %>% 
  spread(group, value, na.rm = TRUE)

What this does:

  1. gathers all columns except the ID column into a key-value rows, variable and value.
  2. separates the variable column into group and type, using _ as a separator.
  3. spread the contents of the group rows into columns and populate them with the value column, removing any NA combinations.