How to merge variables looping through by variable number in R - r

Question

I have a dataframe with a lot of variables seen in multiple conditions. I'd like to merge each variable by condition.
The example data frame is a simplified version of what I have (3 variables over 2 conditions).
VAR.B_1 <- c(1, 2, 3, 4, 5, 'NA', 'NA', 'NA', 'NA', 'NA')
VAR.B_2 <- c(2, 2, 3, 4, 5,'NA', 'NA', 'NA', 'NA', 'NA')
VAR.B_3 <- c(1, 1, 1, 1, 1,'NA', 'NA', 'NA', 'NA', 'NA')
VAR.E_1 <- c(NA, NA, NA, NA, NA, 1, 1, 1, 1, 1)
VAR.E_2 <- c(NA, NA, NA, NA, NA, 1, 2, 3, 4, 5)
VAR.E_3 <- c(NA, NA, NA, NA, NA, 1, 1, 1, 1, 1)
Condition <- c("B", "B","B","B","B","E","E","E","E","E")
#Example dataset
data<-as.data.frame(cbind(VAR.B_1,VAR.B_2,VAR.B_3, VAR.E_1,VAR.E_2, VAR.E_3, Condition))
I want to end up with this, appended to the original data frame:
VAR_1 VAR_2 VAR_3
1 2 1
2 2 1
3 3 1
4 4 1
5 5 1
1 1 1
1 2 1
1 3 1
1 4 1
1 5 1
I understand that R won't work with i inside the variable name, but I have an example of the kind of for loop I was trying to do. I would rather not call variables by column location, since there will be a lot of variables.
##Example of how I want to merge - this code does not work
for(i in 1:3) {
data$VAR_[,i] <-ifelse(data$Condition == "B", VAR.B_[,i],
ifelse(data$Condition == "E", VAR.E_[,i], NA))
}

Answer 1

This might work for your situation:
library(tidyverse)
library(stringr)
data %>%
mutate_all(as.character) %>%
gather(key, value, -Condition) %>%
filter(!is.na(value), value != "NA") %>%
mutate(key = str_replace(key, paste0("\\.", Condition), "")) %>%
group_by(Condition, key) %>%
mutate(rowid = 1:n()) %>%
spread(key, value) %>%
bind_cols(data)
#> # A tibble: 10 x 12
#> # Groups: Condition [2]
#> Condition rowid VAR_1 VAR_2 VAR_3 VAR.B_1 VAR.B_2 VAR.B_3 VAR.E_1
#> <chr> <int> <chr> <chr> <chr> <fctr> <fctr> <fctr> <fctr>
#> 1 B 1 1 2 1 1 2 1 NA
#> 2 B 2 2 2 1 2 2 1 NA
#> 3 B 3 3 3 1 3 3 1 NA
#> 4 B 4 4 4 1 4 4 1 NA
#> 5 B 5 5 5 1 5 5 1 NA
#> 6 E 1 1 1 1 NA NA NA 1
#> 7 E 2 1 2 1 NA NA NA 1
#> 8 E 3 1 3 1 NA NA NA 1
#> 9 E 4 1 4 1 NA NA NA 1
#> 10 E 5 1 5 1 NA NA NA 1
#> # ... with 3 more variables: VAR.E_2 <fctr>, VAR.E_3 <fctr>,
#> # Condition1 <fctr>

Answer 2

data.frame(lapply(split.default(data[-NCOL(data)], gsub("\\D+", "", head(names(data), -1))),
function(a){
a = sapply(a, function(x) as.numeric(as.character(x)))
rowSums(a, na.rm = TRUE)
}))
# X1 X2 X3
#1 1 2 1
#2 2 2 1
#3 3 3 1
#4 4 4 1
#5 5 5 1
#6 1 1 1
#7 1 2 1
#8 1 3 1
#9 1 4 1
#10 1 5 1
#Warning messages:
#1: In FUN(X[[i]], ...) : NAs introduced by coercion
#2: In FUN(X[[i]], ...) : NAs introduced by coercion
#3: In FUN(X[[i]], ...) : NAs introduced by coercion

Answer 3

Your data appears to have two kinds of NA values in it. It has NA, or R's NA value, and it also has the string 'NA'. In my solution below, I replace both with zero, cast each column in the data frame to numeric, and then just sum together like-numbered VAR columns. Then, drop the original columns which you don't want anymore.
data <- as.data.frame(cbind(VAR.B_1,VAR.B_2,VAR.B_3, VAR.E_1,VAR.E_2, VAR.E_3),
stringsAsFactors=FALSE)
data[is.na(data)] <- 0
data[data == 'NA'] <- 0
data <- as.data.frame(lapply(data, as.numeric))
data$VAR_1 <- data$VAR.B_1 + data$VAR.E_1
data$VAR_2 <- data$VAR.B_2 + data$VAR.E_2
data$VAR_3 <- data$VAR.B_3 + data$VAR.E_3
data <- data[c("VAR_1", "VAR_2", "VAR_3")]
Demo