Nested for loop using paste in r

Question

I have a dataset with a number of yes/no columns indicating whether a particular record is associated with a given store number in three different regions (ie, a record will have a single yes value for one of the two stores in each of the three regions, where the variable names are formatted as 'region'_'storenumber'):

  var1_1   var1_2   var2_1   var2_2   var3_1   var3_2
1 Yes      No       No       Yes      Yes      No
2 No       Yes      Yes      No       No       Yes
3 No       Yes      Yes      No       No       Yes
4 No       Yes      No       Yes      Yes      No
5 No       Yes      No       Yes      No       Yes
6 Yes      No       No       Yes      No       Yes
7 Yes      No       Yes      No       Yes      No
8 No       Yes      Yes      No       No       Yes

I'd like to create a variable for each region called 'region_1', 'region_2', and 'region_3' that are equal to the "Yes" store number in that region for that record (1 or 2):

  region_1   region_2   region_3
1 1          2          1
2 2          1          2
3 2          1          2
4 2          2          1
5 2          2          2
6 1          2          2
7 1          1          1
8 2          1          2

I am able to create each region variable separate using for loops as follows:

for(i in 1:3) {
  df[paste("region_", toString(i), sep = "")] <- ""
}

for(i in 1:2) {
    df$region_1 <- ifelse(df[paste("var1_", toString(i), sep = "")] == "Yes" & df$region_1 == "", toString(i), df$region_1)
  }

for(i in 1:2) {
  df_1$region_2 <- ifelse(df_1[paste("var2_", toString(i), sep = "")] == "Yes" & df_1$region_2 == "", toString(i), df_1$region_2)
}

for(i in 1:2) {
  df_1$region_3 <- ifelse(df_1[paste("var3_", toString(i), sep = "")] == "Yes" & df_1$region_3 == "", toString(i), df_1$region_3)
}

My actual data has many more than 3 regions (and more than 2 stores per region), so rather than writing a separate loop for each region, I'd like to nest this loop to loop over all regions. I've attempted the following:

for(j in 1:3) {
  for(i in 1:2) {
    df[paste("region_", toString(j), sep = "")] <- ifelse(df[paste("var", toString(j), "_", toString(i), sep = "")] == "Yes" & df[paste("region_", toString(j), sep = "")] == "", toString(i), df[paste("region_", toString(j), sep = "")])
  }
}

but recieve the warning "provided #### variables to replace 1 variables" and end up with each region variable populated with the same single value for every record.

Any thoughts on where I'm going wrong with my nested loop?

Answer 1

If we convert Yes and No to logical values, we can simply do

regions = as.data.frame( sapply(seq(1, NCOL(stores), by=2),function(j) ifelse(stores[,j],1,2)))
names(regions) = c("region_1", "region_2",  "region_3")

regions
#  region_1 region_2 region_3
#1        1        2        1
#2        2        1        2
#3        2        1        2
#4        2        2        1
#5        2        2        2
#6        1        2        2
#7        1        1        1
#8        2        1        2

The data:

library(data.table)  
stores = setDF(fread(gsub("No", "FALSE", gsub("Yes", "TRUE",
"var1_1   var1_2   var2_1   var2_2   var3_1   var3_2
 Yes      No       No       Yes      Yes      No
 No       Yes      Yes      No       No       Yes
 No       Yes      Yes      No       No       Yes
 No       Yes      No       Yes      Yes      No
 No       Yes      No       Yes      No       Yes
 Yes      No       No       Yes      No       Yes
 Yes      No       Yes      No       Yes      No
 No       Yes      Yes      No       No       Yes"))))

Answer 2

For this, you may be better served converting your data into a "long" format, instead of the current "wide" format. Here are examples using dplyr and tidyr . I have tried to comment each line, but the basic idea is to generate one row per store-variable measure, and just have the presence/absence show. Then, you can group the rows by region, and count up the number of "Yes" entries.

# Data entry from @dww, without conversion to logical (though that would make it easier)

library(data.table)  
stores = setDF(fread("var1_1   var1_2   var2_1   var2_2   var3_1   var3_2
 Yes      No       No       Yes      Yes      No
 No       Yes      Yes      No       No       Yes
 No       Yes      Yes      No       No       Yes
 No       Yes      No       Yes      Yes      No
 No       Yes      No       Yes      No       Yes
 Yes      No       No       Yes      No       Yes
 Yes      No       Yes      No       Yes      No
 No       Yes      Yes      No       No       Yes"))

Change to long format, store as new variable

longStores <-
  stores %>%
  # tag for printing
  tbl_df() %>%
  # Store the variable of interest as a column, instead of row.names
  mutate(variableInterest = rownames(.)) %>%
  # Convert the data to long format
  gather(StoreID, present, -variableInterest) %>%
  # Split the store_region format
  separate(StoreID, c("Store", "Region"), sep = "_") %>%
  # Eliminate the leading "var" from store names, just for display
  mutate(Store = gsub("var", "", Store))

Summarise by region, still in long format

longRegional <-
  longStores %>%
  # Set grouping
  group_by(variableInterest, Region) %>%
  # Count the number of correct values in the region
  summarise(nStoresWithVariable = sum(present == "Yes"))

Finally, reformat to your original request

longRegional %>%
  spread(Region, nStoresWithVariable)

#   variableInterest   `1`   `2`
# *            <chr> <int> <int>
# 1                1     2     1
# 2                2     1     2
# 3                3     1     2
# 4                4     1     2
# 5                5     0     3
# 6                6     1     2
# 7                7     3     0
# 8                8     1     2

Answer 3

Here is my messy attempt with a simulation of your data as True and False:

Simulated Data - Generate data table using random Booleans

rb <- function()
{
  sample(c(T,F), size=10, replace=TRUE, prob=c(0.5, 0.5) )
}

var1_1 = rb()
var2_1 = rb()
var3_1 = rb()
df <- data.frame( var1_1, !var1_1,
                  var2_1, !var2_1,
                  var3_1, !var3_1)

colnames(df) = c('var1_1', 'var1_2', 'var2_1', 'var2_2', 'var3_1', 'var3_2')

df

    var1_1 var1_2 var2_1 var2_2 var3_1 var3_2
 1   FALSE   TRUE   TRUE  FALSE  FALSE   TRUE
 2   FALSE   TRUE  FALSE   TRUE  FALSE   TRUE
 3   FALSE   TRUE   TRUE  FALSE   TRUE  FALSE
 4   FALSE   TRUE   TRUE  FALSE  FALSE   TRUE
 5   FALSE   TRUE  FALSE   TRUE   TRUE  FALSE
 6   FALSE   TRUE  FALSE   TRUE   TRUE  FALSE
 7    TRUE  FALSE   TRUE  FALSE   TRUE  FALSE
 8    TRUE  FALSE  FALSE   TRUE   TRUE  FALSE
 9    TRUE  FALSE  FALSE   TRUE   TRUE  FALSE
 10  FALSE   TRUE  FALSE   TRUE   TRUE  FALSE

Solution

cn <- names(df)
cnprefixes <- gsub("_.*?$","",cn)
cnsuffixes <- gsub("^.*?_","",cn)

newblock<-data.frame()
bFirstTime<-T
for (prefix in unique(cnprefixes))
{
  block<-df[ , grepl( prefix , names( df ) ) ]

  theseSuffixes <- cnsuffixes[startsWith(cn, prefix)]

  j <- 1
  for(suffix in theseSuffixes)
  {
    block[,j][block[,j]==T]=as.numeric(suffix)
    j<-j+1
  }
  tempblock=data.frame(rowSums(block))
  colnames(tempblock)<- prefix
  if (bFirstTime){
    newblock <- tempblock
    bFirstTime <- F
  }
  else{
    newblock<-cbind(newblock, tempblock)
  }
}

newblock

   var1 var2 var3
1     2    1    2
2     2    2    2
3     2    1    1
4     2    1    2
5     2    2    1
6     2    2    1
7     1    1    1
8     1    2    1
9     1    2    1
10    2    2    1