R: many nested loops to remove rows in multiple data frames - r

Question

I have 18 data frames called regular55, regular56, regular57, collar55, collar56, etc. In each data frame, I want to delete the first row of each nest.
Each data frame looks like this:
nest interval
1 17 -8005
2 17 183
3 17 186
4 17 221
5 17 141
6 17 30
7 17 158
8 17 23
9 17 199
10 17 51
11 17 169
12 17 176
13 31 905
14 31 478
15 31 40
16 31 488
17 31 16
18 31 203
19 31 54
20 31 341
21 31 54
22 50 -14164
23 50 98
24 50 1438
25 71 240
26 71 725
27 71 819
28 85 -13935
29 85 45
30 85 589
31 85 47
32 85 161
33 85 67
The solution I came up with to avoid writing out the function for each one of the 18 data frames includes many nested loops:
for (i in 5:7){
for (j in 5:7) {
for (k in c("regular","collar")){
for (l in c(unique(paste0(k,i,j,"$nest")))){
paste0(k,i,j)=paste0(k,i,j)[(-c(which((paste0(k,i,j,"$nest")) == l )
[1])),]
}}}}
I'm basically selecting the first value at "which" there is a "unique" value of nest. However, I get:
Error in paste0(k, i, j)[(-c(which((paste0(k, i, j, "$nest")) == l)[1])), :
incorrect number of dimensions
It might be because "paste0(k,i,j)" is only considered as a character and not recognized as the name for a data frame.
Any ideas on how to fix this? Or any other ways to delete the first rows for each nest in every data frame?

Answer 1

Thanks to help from the comments, my problem was solved.
Originally, I divided my data frame using a for loop and then grouped it into one list:
for (i in 5:7) {
for (j in 5:7) {
for (k in c("regular","collar")){
assign(paste0(k,i,j),
df[df$x == i & df$y == j & df$z == k,])
}}}
df.list=mget(ls(pattern=("[regular,collar][5-7][5-7]")))
I later found a way to split my data frame directly into a list based on multiple columns (R subsetting a data frame into multiple data frames based on multiple column values):
df.list= split(df, with(df, interaction(df$x, df$y, df$z)), drop = TRUE)
Finally, I was able to apply the function to remove the first rows of each nest:
df.list.updated = lapply(df.list, function(d) d %>% group_by(nest) %>%
slice(2:n()))
It is definitely easier to work from a list of data frames.