CODEWITHSUNDEEP
rlapply
user1912925
user1912925

Reputation: 781

Correct implementation of lapply

In so far as I understand it, when using r it can be more elegant to use functions such as lapply rather than for loops (that are used more often than not in other object oriented languages). However I cannot get my head around the syntax and am making foolish errors when trying to implement simple tasks with the command. For example:

I have a series of dataframes loaded from csv files using a for loop.The following dummy dataframes adequately describe the data:

x <- c(0,10,11,12,13)
y <- c(1,NA,NA,NA,NA)
z <- c(2,20,21,22,23)    
a <- c(0,6,5,4,3)
b <- c(1,7,8,9,10)
c <- c(2,NA,NA,NA,NA)
df1 <- data.frame(x,y,z)
df2 <- data.frame(a,b,c)

I first generate a list of dataframe names (data_names- I do this when loading the csv files) and then simply want to sum the columns. My attempt of course does not work:

lapply(data_names, function(df) {
counts <- colSums(!is.na(data_names))
})

I could of course use lists (and I realise in the long run this maybe better) however from a pedagogical point of view I would like to understand lapply better.

Many thanks for any pointers

Upvotes: 1

Views: 393

Answers (1)

Simon O&#39;Hanlon
Simon O&#39;Hanlon

Reputation: 59970

It's really just your use of is.na and the fact you don't need to use the asignment operator <- inside the function. lapply returns a list which is the result of applying FUN to each element of the input list. You assign the output of lapply to a variable, e.g. res <- lapply( .... , FUN ).

I'm also not too sure how you made the list initially, but the below should suffice. You also don't need an anonymous function in this case, you can use the named colSums and also provide the na.rm = TRUE argument to take care of persky NAs in your data:

lapply( list( df1, df2 ) , colSums , na.rm = TRUE )
[[1]]
 x  y  z 
46  1 88 

[[2]]
 a  b  c 
18 35  2

So you can read this as:

  • For each df in the list:
  • apply colSums with the argument na.rm = TRUE

The result is a list, each element of which is the result of applying colSums to each df in the list.

Upvotes: 2

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