Calculate summed data set in r [duplicate] - r

Question

I am trying to obtain counts of each combination of levels of two variables, "week" and "id". I'd like the result to have "id" as rows, and "week" as columns, and the counts as the values.
Example of what I've tried so far (tried a bunch of other things, including adding a dummy variable = 1 and then fun.aggregate = sum over that):
library(plyr)
ddply(data, .(id), dcast, id ~ week, value_var = "id",
fun.aggregate = length, fill = 0, .parallel = TRUE)
However, I must be doing something wrong because this function is not finishing. Is there a better way to do this?
Input:
id week
1 1
1 2
1 3
1 1
2 3
Output:
1 2 3
1 2 1 1
2 0 0 1

Answer 1

You could just use the table command:
table(data$id,data$week)
1 2 3
1 2 1 1
2 0 0 1
If "id" and "week" are the only columns in your data frame, you can simply use:
table(data)
# week
# id 1 2 3
# 1 2 1 1
# 2 0 0 1

Answer 2

You don't need ddply for this. The dcast from reshape2 is sufficient:
dat <- data.frame(
id = c(rep(1, 4), 2),
week = c(1:3, 1, 3)
)
library(reshape2)
dcast(dat, id~week, fun.aggregate=length)
id 1 2 3
1 1 2 1 1
2 2 0 0 1
Edit : For a base R solution (other than table - as posted by Joshua Uhlrich), try xtabs:
xtabs(~id+week, data=dat)
week
id 1 2 3
1 2 1 1
2 0 0 1

Answer 3

The reason ddply is taking so long is that the splitting by group is not run in parallel (only the computations on the 'splits'), therefore with a large number of groups it will be slow (and .parallel = T) will not help.
An approach using data.table::dcast (data.table version >= 1.9.2) should be extremely efficient in time and memory. In this case, we can rely on default argument values and simply use:
library(data.table)
dcast(setDT(data), id ~ week)
# Using 'week' as value column. Use 'value.var' to override
# Aggregate function missing, defaulting to 'length'
# id 1 2 3
# 1: 1 2 1 1
# 2: 2 0 0 1
Or setting the arguments explicitly:
dcast(setDT(data), id ~ week, value.var = "week", fun = length)
# id 1 2 3
# 1: 1 2 1 1
# 2: 2 0 0 1
For pre-data.table 1.9.2 alternatives, see edits.

Answer 4

A tidyverse option could be :
library(dplyr)
library(tidyr)
df %>%
count(id, week) %>%
pivot_wider(names_from = week, values_from = n, values_fill = list(n = 0))
#spread(week, n, fill = 0) #In older version of tidyr
# id `1` `2` `3`
# <dbl> <dbl> <dbl> <dbl>
#1 1 2 1 1
#2 2 0 0 1
Using only pivot_wider -
tidyr::pivot_wider(df, names_from = week,
values_from = week, values_fn = length, values_fill = 0)
Or using tabyl from janitor :
janitor::tabyl(df, id, week)
# id 1 2 3
# 1 2 1 1
# 2 0 0 1
data
df <- structure(list(id = c(1L, 1L, 1L, 1L, 2L), week = c(1L, 2L, 3L,
1L, 3L)), class = "data.frame", row.names = c(NA, -5L))