4
The GLEASON variable in the database is 0 to 10. And I wanted to turn this variable into three categories, for example: 0-4: not aggressive, 5-7: aggressive intermediate and 8-10: very aggressive. In programming R. Thank you
4
The GLEASON variable in the database is 0 to 10. And I wanted to turn this variable into three categories, for example: 0-4: not aggressive, 5-7: aggressive intermediate and 8-10: very aggressive. In programming R. Thank you
4
Another option is to use the function cut
. Using the data.frame created by @Daniel:
dados <- data.frame(GLEASON = sample(0:10, 50, replace = TRUE))
dados$categorias <- cut(dados$GLEASON, c(0,4,7,10),
include.lowest = T, labels = c("pouco agressivo",
"agressivo intermedio","muito agressivo"))
The first argument is the numerical vector, the second is the cut vector, the third is to indicate whether it includes the lower value, 0, and last are the categories you want.
3
A simple and easy to understand way is by using the logical vectors of data analysis. Vectors generate TRUE/FALSE values that validate the next execution. In this example, the data is in a data.frame
and when doing the first check we automatically create the variable categoria
.
dados <- data.frame(GLEASON = sample(0:10, 50, replace = TRUE))
dados$categorias[dados$GLEASON <= 4] <- 'pouco agressivo'
dados$categorias[dados$GLEASON >= 5 & dados$GLEASON <= 7] <- 'agressivo intermedio'
dados$categorias[dados$GLEASON >= 8] <- 'muito agressivo'
head(dados)
GLEASON categorias
9 muito agressivo
1 pouco agressivo
8 muito agressivo
10 muito agressivo
10 muito agressivo
6 agressivo intermedio
Thanks, I tried to do "e" with "&" but I was doing it wrong. :)
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The advantage here is that in this the column type
categorias
will be Factor instead ofCharacter
. Factors are more economical in terms of memory.– Anthony Accioly
You can also add the ordered_result=TRUE argument to sort the categories, depending on the analysis you want to do.
– José