Update some columns conditionally with max value for all values in chosen columns (data.table, r)

Question

I have a dataset of the type 900,000 x 500, but the following shows a toy dataset apt for the question.

library(data.table)
df1 <- data.table(x = c(1,2,4,0), y = c(0,0,10,15), z = c(1,1,1,0))

I would like to do the following:

1. For columns y and z
2. select rows the value for which = 0
3. replace these with the max+1, where max is computed over the entire column

I am very new to data.table. Looking at examples of questions here at stackoverflow, I couldn't find a similar question, except this: How to replace NA values in a table *for selected columns*? data.frame, data.table

My own attempt is as follows, but this does not work:

for (col in c("x", "y")) df1[(get(col)) == 0, (col) := max(col) + 1)

Obviously, I haven't gotten accustomed to data.table , so I'm banging my head against the wall at the moment...

If anybody could provide a dplyr solution in addition to data.table , I would be thankful.

Answer 1

We can use set and assign the rows where the value is 0 with the max of that column +1.

 for(j in c("y", "z")){
    set(df1, i= which(!df1[[j]]), j=j, value= max(df1[[j]])+1)
 }

df1
#   x  y z
#1: 1 16 1
#2: 2 16 1
#3: 4 10 1
#4: 0 15 2

NOTE: The set method will be very efficient as the overhead of [.data.table is avoided

---

Or a less efficient method would be to specify the columns of interest in .SDcols , loop through the columns ( lapply(.. ), replace the value based on the logical index, and assign ( := ) the output back to the columns.

df1[, c('y', 'z') := lapply(.SD, function(x) 
         replace(x, !x, max(x)+1)), .SDcols= y:z]

Answer 2

The dplyr version is pretty simple (I think)

> library(dplyr)
# indented for clarity
> mutate(df1, 
    y= ifelse(y>0, y, max(y)+1), 
    z= ifelse(z>0, z, max(z)+1))

  x  y z
1 1 16 1
2 2 16 1
3 4 10 1
4 0 15 2

EDIT As noted by David Arenburg in comments this is helpful for the toy example but not for the data mentione dwith 500 columns. He suggests something similar to:

df1 %>% mutate_each(funs(ifelse(. > 0, ., max(.) + 1)), -1)

where -1 specifies all but the first column

Answer 3

As an alternative, ifelse(test, yes, no) might be useful

Along the lines

library(data.table)
dt <- data.table(x = c(1,2,4,0), y = c(0,0,10,15), z = c(1,1,1,0))

print(dt)

dt[, y := ifelse(!y, max(y) + 1, y)]

print(dt)