R - group by and create new columns for grouped result

Question

I have the test data as follows

> date <- c('11:00', '12:00', '13:00', '11:00', '13:00', '13:00', '15:00')
> zone <- c('a', 'a', 'a', 'b', 'b', 'c', 'c')
> val <- c(1,2,3,4,5,6,7)
> test.data <- data.frame(date, zone, val)
> test.data
   date zone val
1 11:00    a   1
2 12:00    a   2
3 13:00    a   3
4 11:00    b   4
5 13:00    b   5
6 13:00    c   6
7 15:00    c   7

I need the result to be first grouped by date. And then for each unique zone a new column be added. The value of that column is picked from the column val, and if missing it's 0.

Desired Result:

result
   date zone_a zone_b zone_c
1 11:00      1      4      0
2 12:00      2      0      0
3 13:00      3      5      6
4 15:00      0      0      7

Try `spread` `library(tidyverse);test.data %>% spread(zone, val, fill = 0)` — akrun, May 29 '19 at 22:23

score 1 · Answer 1 · answered May 29 '19 at 22:25

1

An option is to mutate the 'zone' column by concatenating 'zone' and then spread to 'wide'

library(tidyverse)
test.data %>%
    mutate(zone = str_c("zone_", zone)) %>% 
    spread(zone, val, fill = 0)

answered May 29 '19 at 22:25

akrun

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If i don't need the prefix zone, i don't need the mutate step, correct? – mrtyormaa May 29 '19 at 22:26
1

@mrtyormaa that is right. – akrun May 29 '19 at 22:27

R - group by and create new columns for grouped result

1 Answers1