Extracting nth value from row vector in R - r

Question

I have been searching/thinking of a way in which I can extract the nth value (e.g. 2nd, 5th, 7th, etc.) from each row in my data frame.
For example, I have the following columns:
ID Q1-2013 Q2-2013 Q3-2013 Q4-2013 Q1-2014 Q2-2014 Q3-2014 Q4-2014
Under each column there are given values. What I would like to do is pull the nth value of each row from the quarters vector (2nd-8th columns). So for example, if I am looking for the 2nd value from each row, the formula/function I want would extract/pull the 2nd value from each row from columns 2-8 (Q1-2013 to Q4-2014). In addition, the formula/function would ignore the blanks/NA values in each row as well.

Answer 1

Maybe this is what you're after.
I first modified the iris data set with some NAs in each column:
iris[] <- lapply(iris, function(x){ x[sample(150, 30, F)] <- NA; x})
head(iris)
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#1 5.1 3.5 1.4 NA setosa
#2 NA NA 1.4 NA setosa
#3 NA NA 1.3 0.2 setosa
#4 4.6 3.1 1.5 NA setosa
#5 5.0 3.6 1.4 0.2 setosa
#6 5.4 NA 1.7 0.4 setosa
Then, to extract the second non-empty and non-NA entries per row you could use apply (I know, it's not recommended on data frames, but it does the dirty job):
apply(iris, 1, function(x) x[which(!is.na(x) & x != "")[2]])
# [1] "3.5" "setosa" "0.2" "3.1" "3.6" "1.7" "3.4" "3.4" "2.9" "3.1" "setosa"
#[12] "3.4" "1.4" "1.1" "1.2" "4.4" "3.9" "3.5" "3.8" "3.8" "0.2" "3.7"
#[23] "3.6" "1.7" "1.9" "3.0" "3.4" "1.5" "3.4" "3.2" "3.1" "3.4" "4.1"
#[34] "4.2" "3.1" "3.2" "3.5" "3.6" "setosa" "1.5" "1.3" "2.3" "1.3" "0.6"
#[45] "0.4" "3.0" "3.8" "3.2" "3.7" "3.3" "3.2" "3.2" "1.5" "2.3" "2.8"
#[56] "2.8" "3.3" "2.4" "4.6" "1.4" "2.0" "3.0" "1.0" "2.9" "2.9" "3.1"
#[67] "3.0" "2.7" "4.5" "3.9" "3.2" "4.0" "2.5" "4.7" "4.3" "3.0" "2.8"
#[78] "5.0" "2.9" "3.5" "3.8" "2.4" "2.7" "2.7" "3.0" "3.4" "3.1" "1.3"
#[89] "4.1" "1.3" "2.6" "3.0" "2.6" "2.3" "4.2" "3.0" "2.9" "2.9" "2.5"
#[100] "2.8" "3.3" "2.7" "3.0" "2.9" "3.0" "3.0" "4.5" "2.9" "5.8" "3.6"
#[111] "3.2" "1.9" "5.5" "2.0" "5.1" "3.2" "5.5" "3.8" "virginica" "1.5" "3.2"
#[122] "2.8" "2.8" "2.7" "2.1" "6.0" "2.8" "3.0" "2.8" "5.8" "2.8" "3.8"
#[133] "5.6" "1.5" "2.6" "3.0" "5.6" "5.5" "4.8" "3.1" "5.6" "5.1" "2.7"
#[144] "3.2" "3.3" "3.0" "2.5" "5.2" "5.4" "3.0"
Because apply will first convert the data frame to a matrix, all columns are covnerted to the same type which is character in this case. You can later on convert it to whatever you want (but note that you cant convert the output vector in this case directly back to numeric since it contains some character strings such as "setosa" etc).

Answer 2

You could also use a convenient function naLast from library(SOfun)
library(SOfun)
dat[dat==''] <- NA #convert all `blank` cells to `NA`
n <- 2 # the row/column index that needs to be extracted
naLast(dat, by='col')[n,] #get the 2nd non-empty/nonNA element for each columns
#V1 V2 V3 V4 V5
#"G" "B" "B" "B" "C"
which would be the same with apply
apply(dat, 2, function(x) x[which(!is.na(x) & x!='')[2]])
#V1 V2 V3 V4 V5
#"G" "B" "B" "B" "C"
You could also specify by='row'
naLast(dat, by='row')[,n] #get the 2nd non-empty/nonNA element for each row
# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#"G" "D" "B" "G" "E" "B" "J" "F" "F" "A" "H" "C" "A" "D" "H" "D" "J" "C" "A" "A"
data
set.seed(25)
dat <- as.data.frame(matrix(sample(c(NA,'',LETTERS[1:10]),
20*5, replace=TRUE), ncol=5), stringsAsFactors=FALSE)
You can install the package by
library(devtools)
install_github("mrdwab/SOfun")