In R, I am trying to make a for loop that will cycle through variable names and perform functions on them

Question

I have variables that are named team.1, team.2, team.3, and so forth.

First of all, I would like to know how to go through each of these and assign a data frame to each one. So team.1 would have data from one team, then team.2 would have data from a second team. I am trying to do this for about 30 teams, so instead of typing the code out 30 times, is there a way to cycle through each with a counter or something similar?

I have tried things like

    vars = list(sprintf("team.x%s", 1:33)))

to create my variables, but then I have no luck assigning anything to them.

Along those same lines, I would like to be able to run a function I made for cleaning and sorting the individual data sets on all of them at once.

For this, I have tried a for loop

    for (j in 1:33) {
      assign(paste("team.",j, sep = ""), cleaning1(paste("team.",j, sep =""), j))
    }

where cleaning1 is my function, with two calls.

    cleaning1(team.1, 1)

This produces the error message

Error in who[, -1] : incorrect number of dimensions

So obviously I am hoping the loop would count through my data sets, and also input my function calls and reassign my datasets with the newly cleaned data.

Is something like this possible? I am a complete newbie, so the more basic, the better.

Edit:

cleaning1:

cleaning1 = function (who, year) {
  who[,-1]
  who$SeasonEnd = rep(year, nrow(who))
  who = (who[-nrow(who),])
  who = tbl_df(who)
  for (i in 1:nrow(who)) {
    if ((str_sub(who$Team[i], -1)) == "*") {
      who$Playoffs[i] = 1
      } else {
      who$Playoffs[i] = 0
      }
     }
  who$Team = gsub("[[:punct:]]",'',who$Team)
  who = who[c(27:28,2:26)]
  return(who)
  } 

This works just fine when I run it on the data sets I have compiled myself.

To run it though, I have to go through and reassign each data set, like this:

team.1 = cleaning1(team.1, 1)   

team.2 = cleaning1(team.2, 2)   

So, I'm trying to find a way to automate that part of it.

Answer 1

I think your problem would be better solved by using a list of data frames instead of many variables containing one data frame each.

You do not say where you get your data from, so I am not sure how you would create the list. But assuming you have your data frames already stored in the variables team.1 etc., you could generate the list with

team.list <- list(team.1, team.2, ...,team.33)

where the dots stand for the variables that I did not write explicitly (you will have to do that). This is tedious, of course, and could be simplified as follows

team.list <- do.call(list,mget(paste0("team.",1:33)))

The paste0 command creates the variable names as strings, mget converts them to the actual objects, and do.call applies the list command to these objects.

Now that you have all your data in a list, it is much easier to apply a function on all of them. I am not quite sure how the year argument should be used, but from your example, I assume that it just runs from 1 to 33 (let me know, if this is not true and I'll change the code). So the following should work:

team.list.cleaned <- mapply(cleaning1,team.list,1:33)

It will go through all elements of team.list and 1:33 and apply the function cleaning1 with the elements as its arguments. The result will again be a list containing the output of each call, i.e.,

list( cleaning1(team.list[[1]],1), cleaning1(team.list[[2]],2), ...)

Since you are now to R I strongly recommend that you read the help on the apply commands (apply, lapply, tapply, mapply). There are very useful and once you got used to them, you will use them all the time...

There is probably also a simple way to directly generate the list of data frames using lapply. As an example: if the data frames are read in from files and you have the file names stored in a character vector file.names, then something along the lines of

team.list <- lapply(file.names,read.table)

might work.