I try to execute the following function:
result<-lapply(mylist,function(x)cbind(x,var=tapply(x[,c(3)],x[,c(1)],sum)))
But, this error appears:
Error in data.frame(..., check.names = FALSE) :
arguments imply differing number of rows: 340623, 63073
I need to return the sum within the dataframes for my list. What is the problem with the formula?
Unfortunately, I can not share the data to help answer
Without the data it becomes very complicated to replicate the problem you are encountering. Using the structure you have already posted on other questions
mylist
[[1]]
number group sexo
1 26.12186 a Masculino
2 40.39104 a Masculino
3 29.29426 a Masculino
4 45.11651 b Feminino
5 26.72512 b Masculino
6 45.95550 b Masculino
7 47.56538 c Feminino
8 43.14062 c Feminino
9 47.42608 c Masculino
10 23.57519 c Feminino
[[2]]
number group sexo
1 47.64770 a Masculino
2 22.61412 a Feminino
3 48.37883 a Masculino
4 48.44754 b Masculino
5 41.67047 b Feminino
6 23.74823 b Masculino
7 28.82786 c Masculino
8 30.12309 c Feminino
9 27.12305 c Masculino
10 49.58259 c Feminino
11 40.21284 d Masculino
12 40.57279 d Feminino
13 48.33335 d Masculino
14 22.92160 d Masculino
15 25.07216 e Masculino
I've been doing what you want step-by-step. When running the tapply function you will get a array with two columns and a row, which will be the sum of the values by sex:
tapply(mylist[[1]][,1], mylist[[1]][,3], sum)
Feminino Masculino
159.3977 215.9139
This is why the error is appearing, when executing the command cbind it tries to concatenate a data.frame with a number of lines different from the result of tapply .
To work around this problem and understanding that what you want is to put the value of the sum of the sexes as a new variable, you can base it on the following code:
teste <- mylist[[1]]
teste1 <- tapply(teste[,1], teste[,3], sum)
teste2 <- tidyr::gather(data.frame(teste1), key = "sexo")
teste2$sexo <- names(teste1)
dplyr::left_join(teste, teste2)
Joining, by = "sexo"
number group sexo value
1 26.12186 a Masculino 215.9139
2 40.39104 a Masculino 215.9139
3 29.29426 a Masculino 215.9139
4 45.11651 b Feminino 159.3977
5 26.72512 b Masculino 215.9139
6 45.95550 b Masculino 215.9139
7 47.56538 c Feminino 159.3977
8 43.14062 c Feminino 159.3977
9 47.42608 c Masculino 215.9139
10 23.57519 c Feminino 159.3977
I've only routed the first data.frame of the list just to try to understand the problem.