R 将多行折叠为 1 行 - 相同的列

Question

This is piggy backing on a question I answered last night as I am reconsidering how I'd like to format my data. I did search but couldn't find up with any applicable answer; I may be searching with wrong terms.

I have a data table with many rows that I'd like to combine:

record_numb <- c(1,1,1,2,2,2)
col_a <- c(123,'','',987,'','')
col_b <- c('','234','','','765','')
col_c <- c('','','543','','','543')
df <- data.frame(record_numb,col_a,col_b,col_c)
library(data.table)
setDT(df)

record_numb    col_a    col_b     col_c
1               123
1                       234
1                                 345
2               987
2                       765
2                               543

Each row will always have either col_a, col_b, or col_c populated. It will never have more than 1 of those 3 populated. I'd like to pivot(?) these into a single row per record so it appears like this:

record_numb     col_a   col_b   col_c
1               123     234     345
2               987     765     543

I played with melt/cast a bit, but I'm such a novice at R that half of my issue is knowing what is available to use. There is just so much to use that I'm hoping one of you can point me to a package or function off the top of your head. My searches I performed pointed me to melt and cast and such, but I was unable to apply it to this case. I'm open to using any function or package.

Answer 1

As you suggested that you would like a data.table solution in your comment, you could use

library(data.table)
df <- data.table(record_numb,col_a,col_b,col_c)

df[, lapply(.SD, paste0, collapse=""), by=record_numb]
   record_numb col_a col_b col_c
1:           1   123   234   543
2:           2   987   765   543

.SD basically says, "take all the variables in my data.table" except those in the by argument. In @Frank's answer, he reduces the set of the variables using .SDcols . If you want to cast the variables into numeric, you can still do this in one line. Here is a chaining method.

df[, lapply(.SD, paste0, collapse=""), by=record_numb][, lapply(.SD, as.integer)]

The second "chain" casts all the variables as integers.

Answer 2

You can reshape to long format, drop the blank entries and then go back to wide:

res <- dcast(melt(df, id.vars = "record_numb")[ value != "" ], record_numb ~ variable)

   record_numb col_a col_b col_c
1:           1   123   234   543
2:           2   987   765   543

You may find it more readable at first using magrittr:

library(magrittr)
res = df %>% 
  melt(id.vars = "record_numb") %>% 
  .[ value != "" ] %>% 
  dcast(record_numb ~ variable)

The numbers are still formatted as strings, but you can convert them with...

cols = setdiff(names(res), "record_numb")
res[, (cols) := lapply(.SD, type.convert), .SDcols = cols]

Type conversion will change each column to whatever class it looks like it should be (numeric, integer, whatever). See ?type.convert .

Answer 3

Just do this :

df = df %>% group_by(record_numb) %>%
    summarise(col_a = sum(col_a, na.rm = T),
    col_b = sum(col_b, na.rm = T), 
    col_c = sum(col_c, na.rm = T))

.... inplace of 'sum' you could use min, max or whatever.