Question

I am working on a large dataset, an example of which is shown below:

Df1 <- data.frame(ID = c(1:7),                    
              home_pc = c("VB2 4RF","CB4 2DT", "NE5 7TH", "BY5 8IB", "DH4 6PB","MP9 7GH","KN4 5GH"),
              start_pc = c(NA,"Home", "FC5 7YH","Home", "CB3 5TH", "BV6 5PB",NA),
              end_pc = c(NA,"CB5 4FG","Home","Home","Home","GH6 8HG",NA))

I want to do two things:

1. Firstly, delete rows which have an NA in the columns “start_pc” and “end_pc”.
2. When “Home” is written in either the “start_pc” or “end_pc” columns, I want to be able to replace this with the postcode in “home_pc”.

How is best to tackle this problem – could anyone give me any ideas how best to do this?

Many thanks.

Answer 1

okay here’s one starting point – others will surely give you more elaborate answers.

First, getting rid of NA values:

  Df1 <-  na.omit(Df1)

this will do the job for all columns in the data.frame object

Second, replacing the start and end columns. try the ifelse() function which is vectorised:

Df1 <- within(Df1, 
{
  start_pc <- ifelse(start_pc == 'Home', home_pc, start_pc)
  end_pc <- ifelse(end_pc == 'Home', home_pc, end_pc)
})

hope i understood your question correctly! Some additional comments: if you want to prove if something is NA (e.g. within the ifelse() function) use is.na() the opposite is !is.na(). You may also build subsets of the dataframe with this: subset(Df1, !is.na(home_pc)) should work for example. Of course check out the help file for all these functions if you need some more hints: ?ifelse or ?subset etc.