How to sum variables by groups in R - r

Question

I have a data frame named "SpatialKey" with three columns. First column contains 5 categories representing population quintile. The second column has 4 kind of data: 0, 400, 800 and 1200. The third column represents population.
For example
quintile
isocrona
total
4
1200
1674
1
400
1676
4
400
1723
5
800
1567
3
0
1531
3
1200
1370
2
1200
1925
1
400
1916
5
0
1776
2
800
1896
3
800
2143
5
400
2098
4
400
1496
1
0
961
4
800
1684
I want to clasify the data by quintile and sum the population by the 4 kind of data I have in the second column. For example:
0
400
800
1200
1
961
3592
0
0
2
0
0
1896
1925
3
1531
0
2143
1370
4
0
3219
1684
1674
5
1776
2098
1567
0
And here is my code.
po <- SpatialKey %>%
group_by(quintile, isocrona) %>%
summarise_at(vars(contains("total")), sum)
final_df <- as.data.frame(t(po))
But R give me the following table:
V1
V2
V3
V4
V5
V6
V7
V8
V9
V10
V11
V12
V13
V14
V15
V16
V17
V18
V19
V20
quintile
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
isocrona
0
400
800
1200
0
400
800
1200
0
400
800
1200
0
400
800
1200
0
400
800
1200
total
961
3592
0
0
0
0
1896
1925
1531
0
2143
1370
0
3219
1684
1674
1776
2098
1567
0
How would I do the second table in R?

Answer 1

Use xtabs. Put the variable to be summed on the left hand side of the formula and the others on the right hand side. We can use dot to mean all the rest. No packages are used.
xtabs(total ~., SpatialKey)
giving this xtabs table:
isocrona
quintile 0 400 800 1200
1 961 3592 0 0
2 0 0 1896 1925
3 1531 0 2143 1370
4 0 3219 1684 1674
5 1776 2098 1567 0
Note
The input in reproducible form is:
SpatialKey <- structure(list(quintile = c(4L, 1L, 4L, 5L, 3L, 3L, 2L, 1L, 5L,
2L, 3L, 5L, 4L, 1L, 4L), isocrona = c(1200L, 400L, 400L, 800L,
0L, 1200L, 1200L, 400L, 0L, 800L, 800L, 400L, 400L, 0L, 800L),
total = c(1674L, 1676L, 1723L, 1567L, 1531L, 1370L, 1925L,
1916L, 1776L, 1896L, 2143L, 2098L, 1496L, 961L, 1684L)),
class = "data.frame", row.names = c(NA, -15L))

Answer 2

Here we need a pivot_wider to reshape into 'wide' format while doing the sum
library(dplyr)
library(tidyr)
SpatialKey %>%
arrange(quintile, isocrona) %>%
pivot_wider(names_from = isocrona, values_from = total,
values_fn = sum, values_fill = 0)
-output
# A tibble: 5 x 5
# quintile `0` `400` `800` `1200`
# <int> <int> <int> <int> <int>
#1 1 961 3592 0 0
#2 2 0 0 1896 1925
#3 3 1531 0 2143 1370
#4 4 0 3219 1684 1674
#5 5 1776 2098 1567 0
Or use xtabs from base R
xtabs(total ~ quintile + isocrona, SpatialKey)
data
SpatialKey <- structure(list(quintile = c(4L, 1L, 4L, 5L, 3L, 3L, 2L, 1L, 5L,
2L, 3L, 5L, 4L, 1L, 4L), isocrona = c(1200L, 400L, 400L, 800L,
0L, 1200L, 1200L, 400L, 0L, 800L, 800L, 400L, 400L, 0L, 800L),
total = c(1674L, 1676L, 1723L, 1567L, 1531L, 1370L, 1925L,
1916L, 1776L, 1896L, 2143L, 2098L, 1496L, 961L, 1684L)),
class = "data.frame", row.names = c(NA,
-15L))

Answer 3

A method based on the idea of group. The benefit is the result is still the dataframe format.
Result of long format:
library(data.table)
dt.long <- setDT(SpatialKey)[,sum(total),keyby = .(quintile,isocrona)]
dt.long
quintile isocrona V1
1: 1 0 961
2: 1 400 3592
3: 2 800 1896
4: 2 1200 1925
5: 3 0 1531
6: 3 800 2143
7: 3 1200 1370
8: 4 400 3219
9: 4 800 1684
10: 4 1200 1674
11: 5 0 1776
12: 5 400 2098
13: 5 800 1567
Result of wide format:
dcast(dt.long,quintile ~ isocrona,fill = 0,value.var = "V1")
quintile 0 400 800 1200
1: 1 961 3592 0 0
2: 2 0 0 1896 1925
3: 3 1531 0 2143 1370
4: 4 0 3219 1684 1674
5: 5 1776 2098 1567 0
Data:
SpatialKey <- structure(list(quintile = c(4L, 1L, 4L, 5L, 3L, 3L, 2L, 1L, 5L,
2L, 3L, 5L, 4L, 1L, 4L), isocrona = c(1200L, 400L, 400L, 800L,
0L, 1200L, 1200L, 400L, 0L, 800L, 800L, 400L, 400L, 0L, 800L),
total = c(1674L, 1676L, 1723L, 1567L, 1531L, 1370L, 1925L,
1916L, 1776L, 1896L, 2143L, 2098L, 1496L, 961L, 1684L)),
class = "data.frame", row.names = c(NA,
-15L))