CODEWITHSUNDEEP
rif-statementfor-loopdataset
user2900280
user2900280

Reputation: 39

Recoding hierarchial data using for loop in R

I have a problem that I encounter regularly, and I need a more efficient way of dealing with. I have a messy solution that is posted below.

First, I'll generate some example data that is similar to my datasets.

a <- c(1, 2, 2, 2, 3, 3)
b <- c("10/12", "10/12", "10/12", "10/13", "10/12", "10/12")
c <- c("c", "c", "pv", "c", "c", "c")
data <- matrix(NA, nrow = 6, ncol = 3)
data[,1] <- a
data[,2] <- b
data[,3] <- c

data

        [,1]    [,2]    [,3]
[1,]    1       10/12   c
[2,]    2       10/12   c
[3,]    2       10/12   pv
[4,]    2       10/13   c
[5,]    3       10/12   c
[6,]    3       10/12   c
# [,1] is a unique identifier, [,2] is a date, and [,3] is a type of occurrance

What I need to do is generate a table that includes only one entry for each ID for each day with a column showing whether that entry corresponds to 'c' only, 'pv' only, 'c & pv', or 'multiple c'. Multiple pvs are not possible in the data

The way I have done this is using a nested for loop:

# I generate an object to post the data to
output.temp <- matrix(NA, nrow = 1, ncol = 4)

# Then I define the outer loop that subsets the data over each ID  
ids <- unique(data[,1])
n.ids <- length(ids)

for(i in 1:n.ids){
  temp.data <- subset(data, data[,1] == ids[i])

  dates <- unique(temp.data[,2])
  n.dates <- length(dates)  

# Then I define the inner loop that subsets the data for each ID over each date
  for(j in 1: n.dates){
    date.data <- subset(temp.data, temp.data[,2] == dates[j])

    # Then I apply the logic of what to write out
    if(nrow(date.data) == 1){
      if(date.data[,3] == 'c'){
      new.row <- cbind(date.data, "c only")
      output.temp <- rbind(output.temp, new.row)
      }
      if(date.data[,3] == 'pv'){
      new.row <- cbind(date.data, "pv only")
      output.temp <- rbind(output.temp, new.row)
      }
    }

    if(nrow(date.data) > 1){
      if('pv' %in% date.data[,3]){
      new.row <- cbind(matrix(date.data[1,], nrow = 1), c("c & pv"))
      output.temp <- rbind(output.temp, new.row)
    }
    else{
      new.row <- cbind(matrix(date.data[1,], nrow = 1), " multiple c only")
      output.temp <- rbind(output.temp, new.row)
    }
   }
  }
 }

# Finally, I drop the unnecessary row and column from the output object
output.final <- output.temp[-1,-3]

This works, but it is terribly inefficient. As my datasets become larger (approaching 1 million rows), it becomes more and more of a problem.

Since I am really new to R and have little experience with programming, any advice on an alternate strategy would be greatly appreciated.

Upvotes: 3

Views: 103

Answers (2)

TheComeOnMan
TheComeOnMan

Reputation: 12905

You should be able to use the code below to get to the exact format of output you require.

dataset <- data.table(dataset)
setnames(dataset, c('id','day','occurrence'))

dataset[,list(noofc = table(occurrence)['c'], noofpv = table(occurrence)['pv']), by = c('id','day')]

data.tables are very efficient data frames and should help with your data size problem as well

Upvotes: 1

David
David

Reputation: 9405

I think this ddply() solution should work for you:

library(plyr)
data <- data.frame(data)
names(data) <- c("id","date","type")
get.type <- function(x) ifelse("c" %in% x & "pv" %in% x, "c & pv",
                               ifelse(sum("c" == x) > 1,"multiple c",
                                      ifelse("c" %in% x,"c",
                                             ifelse("pv" %in% x,"pv","other"))))
ddply(data,.(id,date),summarize,type=get.type(type))

  id  date       type
1  1 10/12          c
2  2 10/12     c & pv
3  2 10/13          c
4  3 10/12 multiple c

Upvotes: 0

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