[R] DPLYR Multiple Mutate Statements On Same DataFrame
Rui Barradas
ru|pb@rr@d@@ @end|ng |rom @@po@pt
Fri Oct 18 09:27:56 CEST 2024
Às 22:50 de 17/10/2024, Sparks, John escreveu:
> Hi R Helpers,
>
> I have been looking for an example of how to execute different dplyr mutate statements on the same dataframe in a single step. I show how to do what I want to do by going from df0 to df1 to df2 to df3 by applying a mutate statement to each dataframe in sequence, but I would like to know if there is a way to execute this in a single step; so simply go from df0 to df1 while executing all the transformations. See example below.
>
> Guidance would be appreciated.
> --John J. Sparks, Ph.D.
>
> library(dplyr)
> df0<-structure(list(SeqNum = c(1L, 2L, 3L, 4L, 5L, 6L, 8L, 9L, 10L,
> 11L, 12L, 13L, 14L, 15L, 16L, 18L, 19L, 21L, 22L, 23L), MOSTYP = c(37L,
> 41L, 41L, 13L, 3L, 27L, 37L, 37L, 15L, 14L, 13L, 37L, 4L, 27L,
> 37L, 26L, 17L, 37L, 37L, 17L), MGEMOM = c(1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L),
> MGODRK = c(3L, 2L, 2L, 3L, 4L, 2L, 2L, 2L, 3L, 4L, 3L, 2L,
> 3L, 1L, 2L, 3L, 4L, 4L, 3L, 3L), MOSHOO = c(7L, 7L, 7L, 2L,
> 9L, 4L, 7L, 7L, 2L, 2L, 2L, 7L, 9L, 4L, 7L, 4L, 2L, 7L, 7L,
> 2L), MRELGE = c(0L, 1L, 0L, 2L, 1L, 0L, 0L, 0L, 3L, 1L, 1L,
> 1L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 1L), MSKB2 = c(5L, 4L, 4L,
> 3L, 4L, 5L, 7L, 1L, 5L, 4L, 3L, 4L, 5L, 6L, 7L, 5L, 4L, 6L,
> 4L, 7L), MFWEKI = c(1L, 1L, 2L, 2L, 1L, 0L, 0L, 3L, 0L, 1L,
> 2L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 2L, 0L), MAANTH = c(3L, 4L,
> 4L, 4L, 4L, 5L, 2L, 6L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 4L, 3L,
> 3L, 3L, 2L), MHHUUR = c(2L, 2L, 4L, 2L, 2L, 3L, 0L, 3L, 2L,
> 2L, 2L, 3L, 1L, 6L, 0L, 2L, 2L, 0L, 2L, 2L), MSKA = c(1L,
> 0L, 4L, 2L, 2L, 3L, 0L, 3L, 2L, 0L, 2L, 3L, 1L, 5L, 0L, 0L,
> 1L, 0L, 0L, 1L), MAUT2 = c(2L, 4L, 4L, 3L, 4L, 5L, 5L, 3L,
> 2L, 3L, 3L, 4L, 4L, 3L, 5L, 2L, 3L, 3L, 2L, 3L), MFALLE = c(1L,
> 0L, 0L, 3L, 5L, 0L, 0L, 0L, 0L, 4L, 1L, 1L, 2L, 2L, 0L, 2L,
> 5L, 0L, 0L, 3L), MGEMLE = c(1L, 0L, 0L, 0L, 4L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 3L, 2L, 0L), MAUT1 = c(2L,
> 5L, 7L, 3L, 0L, 4L, 2L, 1L, 3L, 9L, 5L, 3L, 2L, 4L, 2L, 1L,
> 3L, 0L, 4L, 2L), MINKGE = c(2L, 4L, 2L, 2L, 0L, 2L, 2L, 1L,
> 3L, 0L, 1L, 4L, 2L, 2L, 2L, 5L, 1L, 0L, 3L, 1L), MOPLHO = c(1L,
> 0L, 0L, 0L, 0L, 2L, 2L, 1L, 2L, 0L, 0L, 1L, 0L, 0L, 2L, 0L,
> 0L, 0L, 0L, 0L), MGODPR = c(1L, 2L, 2L, 0L, 1L, 3L, 2L, 3L,
> 2L, 1L, 2L, 3L, 0L, 3L, 2L, 2L, 2L, 0L, 2L, 1L), MAUT0 = c(8L,
> 6L, 9L, 7L, 5L, 9L, 6L, 7L, 6L, 5L, 4L, 7L, 8L, 5L, 6L, 7L,
> 5L, 9L, 9L, 5L), MSKB1 = c(0L, 2L, 4L, 1L, 0L, 5L, 2L, 7L,
> 2L, 0L, 3L, 3L, 3L, 4L, 2L, 0L, 2L, 3L, 3L, 1L), MSKC = c(4L,
> 5L, 3L, 4L, 6L, 3L, 3L, 2L, 4L, 8L, 3L, 3L, 4L, 3L, 3L, 4L,
> 4L, 3L, 3L, 5L), PAANHA = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), PWAPAR = c(0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L), PPERSA = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), AMOTSC = c(0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L), APERSA = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), AWAPAR = c(1L,
> 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L,
> 1L, 0L, 1L, 1L), Resp = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), row.names = c(NA,
> 20L), class = "data.frame")
>
>
> df1<-df0 %>%
> mutate(across(starts_with('P'),~ifelse(.x==0, 0,
> ifelse(.x==1, 25,
> ifelse(.x==2, 75,
> ifelse(.x==3, 150,
> ifelse(.x==4, 350,
> ifelse(.x==5, 750,
> ifelse(.x==6, 3000,
> ifelse(.x==7, 7500,
> ifelse(.x==8,15000,
> ifelse(.x==9,30000,
> -99))))))))))))
>
> df2<-df1 %>%
> mutate_at(vars(MRELGE:MSKC),~ifelse(.x==0, 0,
> ifelse(.x==1, 5,
> -99)))
> df3<-df2 %>%
> mutate_at(vars(MGODRK),~ifelse(.x==0, 0,
> ifelse(.x==1, 5,
> -99)))
>
>
>
>
> [[alternative HTML version deleted]]
>
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> and provide commented, minimal, self-contained, reproducible code.
Hello,
Use chained mutate() %>% mutate(). In the 2nd mutate I don't even have
to pipe a third time, the final variable is changed in the same
instruction.
Also use mutate(across(...)), mutate_at is deprecated.
And use ?case_when instead of nested ifelse's. It's much cleaner.
As you can see, the result is identical to your code's result.
library(dplyr)
df3b <- df0 %>%
mutate(
across(starts_with('P'), ~case_when(
.x == 0 ~ 0,
.x == 1 ~ 25,
.x == 2 ~ 75,
.x == 3 ~ 150,
.x == 4 ~ 350,
.x == 5 ~ 750,
.x == 6 ~ 3000,
.x == 7 ~ 7500,
.x == 8 ~ 15000,
.x == 9 ~ 30000,
TRUE ~ -99
))
) %>%
mutate(
across(MRELGE:MSKC, ~case_when(
.x == 0 ~ 0,
.x == 1 ~ 5,
TRUE ~ -99
)),
MGODRK = case_when(
MGODRK == 0 ~ 0,
MGODRK == 1 ~ 5,
TRUE ~ -99
))
identical(df3, df3b)
# [1] TRUE
And you can have just one mutate, as long as you respect the order the
variables are changed.
df3c <- df0 %>%
mutate(
across(starts_with('P'), ~case_when(
.x == 0 ~ 0,
.x == 1 ~ 25,
.x == 2 ~ 75,
.x == 3 ~ 150,
.x == 4 ~ 350,
.x == 5 ~ 750,
.x == 6 ~ 3000,
.x == 7 ~ 7500,
.x == 8 ~ 15000,
.x == 9 ~ 30000,
TRUE ~ -99
)),
across(MRELGE:MSKC, ~case_when(
.x == 0 ~ 0,
.x == 1 ~ 5,
TRUE ~ -99
)),
MGODRK = case_when(
MGODRK == 0 ~ 0,
MGODRK == 1 ~ 5,
TRUE ~ -99
)
)
identical(df3, df3c)
# [1] TRUE
Hope this helps,
Rui Barradas
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