For each row in an R dataframe

Question

I have a dataframe, and for each row in that dataframe I have to do some complicated lookups and append some data to a file.

The dataFrame contains scientific results for selected wells from 96 well plates used in biological research so I want to do something like:

for (well in dataFrame) {
  wellName <- well$name    # string like "H1"
  plateName <- well$plate  # string like "plate67"
  wellID <- getWellID(wellName, plateName)
  cat(paste(wellID, well$value1, well$value2, sep=","), file=outputFile)
}

In my procedural world, I'd do something like:

for (row in dataFrame) {
    #look up stuff using data from the row
    #write stuff to the file
}

What is the "R way" to do this?

Answer 1

You can try this, using apply() function

> d
  name plate value1 value2
1    A    P1      1    100
2    B    P2      2    200
3    C    P3      3    300

> f <- function(x, output) {
 wellName <- x[1]
 plateName <- x[2]
 wellID <- 1
 print(paste(wellID, x[3], x[4], sep=","))
 cat(paste(wellID, x[3], x[4], sep=","), file= output, append = T, fill = T)
}

> apply(d, 1, f, output = 'outputfile')

Answer 2

You can use the by() function:

by(dataFrame, 1:nrow(dataFrame), function(row) dostuff)

But iterating over the rows directly like this is rarely what you want to; you should try to vectorize instead. Can I ask what the actual work in the loop is doing?

Answer 3

First, Jonathan's point about vectorizing is correct. If your getWellID() function is vectorized, then you can skip the loop and just use cat or write.csv:

write.csv(data.frame(wellid=getWellID(well$name, well$plate), 
         value1=well$value1, value2=well$value2), file=outputFile)

If getWellID() isn't vectorized, then Jonathan's recommendation of using by or knguyen's suggestion of apply should work.

Otherwise, if you really want to use for, you can do something like this:

for(i in 1:nrow(dataFrame)) {
    row <- dataFrame[i,]
    # do stuff with row
}

You can also try to use the foreach package, although it requires you to become familiar with that syntax. Here's a simple example:

library(foreach)
d <- data.frame(x=1:10, y=rnorm(10))
s <- foreach(d=iter(d, by='row'), .combine=rbind) %dopar% d

A final option is to use a function out of the plyr package, in which case the convention will be very similar to the apply function.

library(plyr)
ddply(dataFrame, .(x), function(x) { # do stuff })

Answer 4

I use this simple utility function:

rows = function(tab) lapply(
  seq_len(nrow(tab)),
  function(i) unclass(tab[i,,drop=F])
)

Or a faster, less clear form:

rows = function(x) lapply(seq_len(nrow(x)), function(i) lapply(x,"[",i))

This function just splits a data.frame to a list of rows. Then you can make a normal "for" over this list:

tab = data.frame(x = 1:3, y=2:4, z=3:5)
for (A in rows(tab)) {
    print(A$x + A$y * A$z)
}        

Your code from the question will work with a minimal modification:

for (well in rows(dataFrame)) {
  wellName <- well$name    # string like "H1"
  plateName <- well$plate  # string like "plate67"
  wellID <- getWellID(wellName, plateName)
  cat(paste(wellID, well$value1, well$value2, sep=","), file=outputFile)
}

Answer 5

I think the best way to do this with basic R is:

for( i in rownames(df) )
   print(df[i, "column1"])

The advantage over the for( i in 1:nrow(df))-approach is that you do not get into trouble if df is empty and nrow(df)=0.

Answer 6

I was curious about the time performance of the non-vectorised options. For this purpose, I have used the function f defined by knguyen

f <- function(x, output) {
  wellName <- x[1]
  plateName <- x[2]
  wellID <- 1
  print(paste(wellID, x[3], x[4], sep=","))
  cat(paste(wellID, x[3], x[4], sep=","), file= output, append = T, fill = T)
}

and a dataframe like the one in his example:

n = 100; #number of rows for the data frame
d <- data.frame( name = LETTERS[ sample.int( 25, n, replace=T ) ],
                  plate = paste0( "P", 1:n ),
                  value1 = 1:n,
                  value2 = (1:n)*10 )

I included two vectorised functions (for sure quicker than the others) in order to compare the cat() approach with a write.table() one...

library("ggplot2")
library( "microbenchmark" )
library( foreach )
library( iterators )

tm <- microbenchmark(S1 =
                       apply(d, 1, f, output = 'outputfile1'),
                     S2 = 
                       for(i in 1:nrow(d)) {
                         row <- d[i,]
                         # do stuff with row
                         f(row, 'outputfile2')
                       },
                     S3 = 
                       foreach(d1=iter(d, by='row'), .combine=rbind) %dopar% f(d1,"outputfile3"),
                     S4= {
                       print( paste(wellID=rep(1,n), d[,3], d[,4], sep=",") )
                       cat( paste(wellID=rep(1,n), d[,3], d[,4], sep=","), file= 'outputfile4', sep='\n',append=T, fill = F)                           
                     },
                     S5 = {
                       print( (paste(wellID=rep(1,n), d[,3], d[,4], sep=",")) )
                       write.table(data.frame(rep(1,n), d[,3], d[,4]), file='outputfile5', row.names=F, col.names=F, sep=",", append=T )
                     },
                     times=100L)
autoplot(tm)

The resulting image shows that apply gives the best performance for a non-vectorised version, whereas write.table() seems to outperform cat().

Answer 7

You can use the by_row function from the package purrrlyr for this:

myfn <- function(row) {
  #row is a tibble with one row, and the same 
  #number of columns as the original df
  #If you'd rather it be a list, you can use as.list(row)
}

purrrlyr::by_row(df, myfn)

By default, the returned value from myfn is put into a new list column in the df called .out.

If this is the only output you desire, you could write purrrlyr::by_row(df, myfn)$.out

Answer 8

Well, since you asked for R equivalent to other languages, I tried to do this. Seems to work though I haven't really looked at which technique is more efficient in R.

> myDf <- head(iris)
> myDf
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa
> nRowsDf <- nrow(myDf)
> for(i in 1:nRowsDf){
+ print(myDf[i,4])
+ }
[1] 0.2
[1] 0.2
[1] 0.2
[1] 0.2
[1] 0.2
[1] 0.4

For the categorical columns though, it would fetch you a Data Frame which you could typecast using as.character() if needed.