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Trying to melt or collapse a dataframe with multiple boolean columns into a two column database with an id column and a column for the collapsed values BUT each value results in a new row.
Example beginning:
A S1 S2 S3 S4
1 ex1 1 0 0 0
2 ex2 0 1 0 0
3 ex3 0 0 1 0
4 ex4 1 1 0 0
5 ex5 0 1 0 1
6 ex6 0 1 0 0
7 ex7 1 1 1 0
8 ex8 0 1 1 0
9 ex9 0 0 1 0
10 ex10 1 0 0 0
Desired output:
A Type
ex1 S1
ex2 S2
ex3 S3
ex4 S1
ex4 S2
ex5 S2
ex5 S4
ex6 S2
ex7 S1
ex7 S2
ex7 S3
ex8 S2
ex8 S3
ex9 S3
ex10 S1
Thanks in advance!
586
Thus, to convert columns of an R data frame into rows we can use transpose function t. For example, if we have a data frame df with five columns and five rows then we can convert the columns of the df into rows by using as. data. frame(t(df)).
To split a column into multiple columns in the R Language, We use the str_split_fixed() function of the stringr package library. The str_split_fixed() function splits up a string into a fixed number of pieces.
Rotating or transposing R objects frame so that the rows become the columns and the columns become the rows. That is, you transpose the rows and columns. You simply use the t() command.
The ncol() function in R programming R programming helps us with ncol() function by which we can get the information on the count of the columns of the object. That is, ncol() function returns the total number of columns present in the object.
in base R:
subset(cbind(A=dat[,1],stack(dat[-1])),values==1,-2)
A ind
1 ex1 S1
4 ex4 S1
7 ex7 S1
10 ex10 S1
12 ex2 S2
14 ex4 S2
15 ex5 S2
16 ex6 S2
17 ex7 S2
18 ex8 S2
23 ex3 S3
27 ex7 S3
28 ex8 S3
29 ex9 S3
35 ex5 S4
In the tidyverse:
library(tidyverse)
dat%>%
gather(Type,j,-A)%>%
filter(j==1)%>%
select(-j)
A Type
1 ex1 S1
2 ex4 S1
3 ex7 S1
4 ex10 S1
5 ex2 S2
6 ex4 S2
7 ex5 S2
8 ex6 S2
9 ex7 S2
10 ex8 S2
11 ex3 S3
12 ex7 S3
13 ex8 S3
14 ex9 S3
15 ex5 S4
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