r - tidyr::spread() with multiple keys and values

Question

I assume this has been asked multiple times but I couldn't find the proper words to find a workable solution.

How can I spread() a data frame based on multiple keys for multiple values?

A simplified (I have many more columns to spread, but on only two keys: Id and time point of a given measurement) data I'm working with looks like this:

df <- data.frame(id = rep(seq(1:10),3), 
                 time = rep(1:3, each=10), 
                 x = rnorm(n=30), 
                 y = rnorm(n=30))

> head(df)
  id time           x           y
1  1    1 -2.62671241  0.01669755
2  2    1 -1.69862885  0.24992634
3  3    1  1.01820778 -1.04754037
4  4    1  0.97561596  0.35216040
5  5    1  0.60367158 -0.78066767
6  6    1 -0.03761868  1.08173157
> tail(df)
   id time           x          y
25  5    3  0.03621258 -1.1134368
26  6    3 -0.25900538  1.6009824
27  7    3  0.13996626  0.1359013
28  8    3 -0.60364935  1.5750232
29  9    3  0.89618748  0.0294315
30 10    3  0.14709567  0.5461084

What i'd like to have is a dataframe populated like this:

One row per Id columns for each value from the time and each measurement variable.

Answer 1

With the devel version of tidyr (tidyr_0.8.3.9000), we can use pivot_wider to reshape multiple value columns from long to wide format

library(dplyr)
library(tidyr)
library(stringr)
df %>%
   mutate(time = str_c("time", time)) %>%
   pivot_wider(names_from = time, values_from = c("x", "y"), names_sep="")
# A tibble: 10 x 7
#      id  xtime1 xtime2  xtime3  ytime1 ytime2 ytime3
#   <int>   <dbl>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>
# 1     1 -0.256   0.483 -0.254  -0.652   0.655  0.291
# 2     2  1.10   -0.596 -1.85    1.09   -0.401 -1.24 
# 3     3  0.756  -2.19  -0.0779 -0.763  -0.335 -0.456
# 4     4 -0.238  -0.675  0.969  -0.829   1.37  -0.830
# 5     5  0.987  -2.12   0.185   0.834   2.14   0.340
# 6     6  0.741  -1.27  -1.38   -0.968   0.506  1.07 
# 7     7  0.0893 -0.374 -1.44   -0.0288  0.786  1.22 
# 8     8 -0.955  -0.688  0.362   0.233  -0.902  0.736
# 9     9 -0.195  -0.872 -1.76   -0.301   0.533 -0.481
#10    10  0.926  -0.102 -0.325  -0.678  -0.646  0.563

NOTE: The numbers are different as there was no set seed while creating the sample dataset