如何将数据框中的列按数字和字母分为两列

Question

EDIT for clarity: I have a dataframe called "dat" with 3 columns "Trial.Type", "Affect", and "Reaction.Time"

The first three rows:

Trial.Type Affect Reaction.Time

Aa, 1 0 1231

Aa, 2 1 1241

Ha, 1 1 1112

I am wondering if there is a way to split the column "Trial.Type" so that "Aa" and "1" are two columns; Trial.Type and Intensity respectively. Resulting in a data frame with 4 columns.

Much thanks for any help. I had trouble finding an answer to this question, my apologies if it is a repeat!

Answer 1

You can just use read.csv on your "column".

> dat <- c("Aa, 1", "Aa, 2", "Ha, 1", "Hpa, 8")
> read.csv(text = dat, header=FALSE, col.names = c("Trial.Type", "Intensity"))
  Trial.Type Intensity
1         Aa         1
2         Aa         2
3         Ha         1
4        Hpa         8

Replace "dat" with your column name (for example, mydf$Trial.Type , or possibly as.character(mydf$Trial.Type) ).

You can also check out my "splitstackshape" package, particularly the concat.split group of functions.

---

For the benefit of the OP, here's a reproducible example and a solution using my "splitstackshape" package. Of course, this can be done with base R too, using the approach I described above (or using one of the strsplit approaches mentioned here).

First, some sample data with a column that is character and another that is factor :

mydf <- data.frame(A = factor(c("1, Z", "2, Y", "3, X", "4, W")),
                   B = c("11, ZZ", "22, YY", "33, XX", "44, WW"),
                   C = c(123, 234, 345, 456), stringsAsFactors = FALSE)
mydf
#      A      B   C
# 1 1, Z 11, ZZ 123
# 2 2, Y 22, YY 234
# 3 3, X 33, XX 345
# 4 4, W 44, WW 456
str(mydf)
# 'data.frame':  4 obs. of  3 variables:
#  $ A: Factor w/ 4 levels "1, Z","2, Y",..: 1 2 3 4
#  $ B: chr  "11, ZZ" "22, YY" "33, XX" "44, WW"
#  $ C: num  123 234 345 456

Second, load the package and explore the options:

library(splitstackshape)
## Split a factor column
concat.split(mydf, split.col = "A", sep = ",")
#      A      B   C A_1 A_2
# 1 1, Z 11, ZZ 123   1   Z
# 2 2, Y 22, YY 234   2   Y
# 3 3, X 33, XX 345   3   X
# 4 4, W 44, WW 456   4   W

## Split a character column
concat.split(mydf, split.col = "B", sep = ",")
#      A      B   C B_1 B_2
# 1 1, Z 11, ZZ 123  11  ZZ
# 2 2, Y 22, YY 234  22  YY
# 3 3, X 33, XX 345  33  XX
# 4 4, W 44, WW 456  44  WW

## Split two columns in one go
concat.split.multiple(mydf, split.cols = c("A", "B"), seps = ",")
#     C A_1 A_2 B_1 B_2
# 1 123   1   Z  11  ZZ
# 2 234   2   Y  22  YY
# 3 345   3   X  33  XX
# 4 456   4   W  44  WW

Answer 2

You can do this with strsplit :

dat = c("Aa, 1", "Aa, 2", "Ha, 1", "Hpa, 8")
spl = strsplit(dat, ", ")
data.frame(Trial.Type = unlist(lapply(spl, "[", 1)),
           Intensity = as.numeric(unlist(lapply(spl, "[", 2))))
#   Trial.Type Intensity
# 1         Aa         1
# 2         Aa         2
# 3         Ha         1
# 4        Hpa         8

Answer 3

You could use these instructions:

datNames <- names(dat)
dat <- cbind(t(matrix(unlist(strsplit(dat$Trial.type, ", ")),ncol=dim(dat)[1])))
names(dat) <- c(datNames,"Trial.type2","Intensity")