Às 08:42 de 12/09/2024, Francesca escreveu:
Dear contributors,
I need to create a set of columns, based on conditions of a dataframe as
follows.
I have managed to do the trick for one column, but I do not seem to find
any good example where the condition is extended to all the dataframe.
I have these dataframe called c10Dt:
id cp1 cp2 cp3 cp4 cp5 cp6 cp7 cp8 cp9 cp10 cp11 cp12
1 1 NA NA NA NA NA NA NA NA NA NA NA NA
2 4 8 18 15 10 12 11 9 18 8 16 15 NA
3 3 8 5 5 4 NA 5 NA 6 NA 10 10 10
4 3 5 5 4 4 3 2 1 3 2 1 1 2
5 1 NA NA NA NA NA NA NA NA NA NA NA NA
6 2 5 5 10 10 9 10 10 10 NA 10 9 10
-- Columns are id, cp1, cp2.. and so on. What I need to do is the
following, made on just one column: c10Dt <- mutate(c10Dt, exit1=
ifelse(is.na(cp1) & id!=1, 1, 0)) So, I create a new variable, called
exit1, in which the program selects cp1, checks if it is NA, and if it
is NA but also the value of the column "id" is not 1, then it gives back
a 1, otherwise 0. So, what I want is that it selects all the cases in
which the id=2,3, or 4 is not NA in the corresponding values of the
matrix. I managed to do it manually column by column, but I feel there
should be something smarter here. The problem is that I need to
replicate this over all the columns from cp2, to cp12, but keeping fixed
the id column instead. I have tried with c10Dt %>%
mutate(x=across(starts_with("cp"), ~ifelse(. == NA)) & id!=1,1,0 ) but
the problem with across is that it will implement the condition only on
cp_ columns. How do I tell R to use the column id with all the other
columns? Thanks for any help provided. Francesca
----------------------------------
Hello,
Something like this?
1. If an ifelse instruction is meant to create a binary result, coerce
the logical condition to integer instead. You can make it more clear by
substituting as.integer for the plus sign below;
2. the .names argument is used to create new columns and keeping the
original ones.
df1 <- read.table(text = "id cp1 cp2 cp3 cp4 cp5 cp6 cp7 cp8 cp9 cp10
cp11 cp12
1 1 NA NA NA NA NA NA NA NA NA NA NA NA
2 4 8 18 15 10 12 11 9 18 8 16 15 NA
3 3 8 5 5 4 NA 5 NA 6 NA 10 10 10
4 3 5 5 4 4 3 2 1 3 2 1 1 2
5 1 NA NA NA NA NA NA NA NA NA NA NA NA
6 2 5 5 10 10 9 10 10 10 NA 10 9 10", header = TRUE)
df1
library(dplyr)
df1 %>%
mutate(across(starts_with("cp"), ~ +(is.na(.) & id != 1), .names =
"{col}_new"))
Hope this helps,
Rui Barradas
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