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I have a table read in R as follows:
column1 column2
A B
What is the command to be used to match two columns together as follows?
Column 3
A_B
444
To combine two columns in R data frame with comma separation, we can use paste function with sep argument.
Method 1 : Using plyr package rbind. fill() method in R is an enhancement of the rbind() method in base R, is used to combine data frames with different columns. The column names are number may be different in the input data frames. Missing columns of the corresponding data frames are filled with NA.
To join two data frames (datasets) vertically, use the rbind function. The two data frames must have the same variables, but they do not have to be in the same order. If data frameA has variables that data frameB does not, then either: Delete the extra variables in data frameA or.
I'm a bit unsure what you mean by "merge", but is this what you mean?
> DF = data.frame(A = LETTERS[1:10], B = LETTERS[11:20])
> DF$C = paste(DF$A, DF$B, sep="_")
> head(DF)
A B C
1 A K A_K
2 B L B_L
3 C M C_M
4 D N D_N
Or equivalently, as @daroczig points out:
within(DF, C <- paste(A, B, sep='_'))
My personal favourite involves making use of the unite in tidyr:
set.seed(1)
df <- data.frame(colA = sample(LETTERS, 10),
colB = sample(LETTERS, 10))
# packs: pipe + unite
require(magrittr); require(tidyr)
# Unite
df %<>%
unite(ColAandB, colA, colB, remove = FALSE)
> head(df, 3)
ColAandB colA colB
1 G_F G F
2 J_E J E
3 N_Q N Q
Personally, I find the remove = TRUE / FALSE functionality of unite very useful. In addition tidyr firs the dplyr workflow very well and plays well with separate in case you change your mind about the columns being merged. On the same lines, if NAs are the problem introducing na.omit to your workflow would enable you to conveniently drop the undesirable rows before creating the desired column.
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