I have multiple datasets that contain the same names of variables but different values, i am trying to create a new variable called group that will distinguish every set of datasets (i am doing this because i will put all the datasets in one and i can distinguish by group). Here is a sample for 2 datasets.
################################
### Sample data ###
################################
set.seed(8547)
a=sample(1:20,15,replace=FALSE)
a=sort(a)
f=runif(15,0,1)
f=sort(f)
trt1=data.frame(a,f)
set.seed(1452)
a=sample(1:35,22,replace=FALSE)
a=sort(a)
f=runif(22,0,1)
f=sort(f)
trt2=data.frame(a,f)
names_of_dataframes <- ls.str(mode = "list")
# I used a `for` loop because i have approximatively `10` datasets and i do not know if the `apply` family would work for this kind of treatment
for (i in length(names_of_dataframes)) {
if(names_of_dataframes[i]=="trt1"){
trt1$group=rep("trt1",nrow(trt1))
}else if (names_of_dataframes[i]=="trt2"){
trt2$group=rep("trt2",nrow(trt2))
}
}
I do not know what i am doing wrong but the group variable is only created for dataset trt2 and not trt1. Any thoughts what is wrong?
Thank you in advance for your help
We can load all of the datasets in to a list with mget and ls
lst1 <- mget(ls(pattern = '^trt\\d+$'))
lst1 <- Map(cbind, lst1, group = names(lst1))
If needed to updated the original objects, use list2env (not recommended though)
list2env(lst1, .GlobalEnv)
-check the objects
head(trt1)
# a f group
#1 1 0.03676253 trt1
#2 2 0.07212860 trt1
#3 3 0.10711856 trt1
#4 4 0.14691670 trt1
#5 5 0.33626002 trt1
#6 6 0.41223646 trt1
head(trt2)
# a f group
#1 2 0.01003053 trt2
#2 3 0.05251810 trt2
#3 4 0.08916620 trt2
#4 5 0.17498162 trt2
#5 6 0.24118046 trt2
#6 8 0.24816209 trt2
Or another option is assign
nm1 <- ls(pattern = '^trt\\d+$')
for(nm in nm1) {
assign(nm, `[[<-`(get(nm), "group", value = nm))
}
Or using map/mutate
library(dplyr)
library(purrr)
map(nm1, ~ get(.x) %>%
mutate(group = .x))