R: Summing of the column values by ranged values of another column - r

Question

Good day!
I’ve got a table of two columns. In the first column (x) there are values which I want to divide in into categories according to the specified range of values (in my instance – 300). And then using these categories I want to sum values in anther column (v). For instance, using my test data: The first category is from 65100 to 65400 (65100
The result: there is a table of two columns. The first one is the categories of x; the second column is the sum of according values of v.
Thank you!!!
# data
set.seed(1)
x <- sample(seq(65100, 67900, by=5), 100, replace = TRUE)
v <- sample(seq(1000, 8000), 100, replace = TRUE)
tabl <- data.frame(x=c(x), v=c(v))
attach(tabl)
#categories
seq(((min(x) - min(x)%%300) + 300), ((max(x) - max(x)%%300) + 300), by =300)

Answer 1

I understood you want to:
Cut vector x,
Using pre-calculated cut-off thresholds
Compute sums over vector v using those groupings
This is one line of code with data.table and chaining. Your data are in data.table named DT.
DT[, CUT := cut(x, breaks)][, sum(v), by=CUT]
Explanation:
First, assign cut-offs to variable breaks like so.
breaks <- seq(((min(x) - min(x) %% 300) + 300), ((max(x) - max(x) %% 300) + 300), by =300)
Second, compute a new column CUT to group rows by the data in breaks.
DT[, CUT := cut(x, breaks)]
Third, sum on column v in groups, using by=. I have chained this operation with the previous.
DT[, CUT := cut(x, breaks)][, sum(v), by=CUT]
Convert your data.frame to data.table like so.
library(data.table)
DT <- as.data.table(tabl)
This is the final result:
CUT V1
1: (6.57e+04,6.6e+04] 45493
2: (6.6e+04,6.63e+04] 77865
3: (6.66e+04,6.69e+04] 22893
4: (6.75e+04,6.78e+04] 61738
5: (6.54e+04,6.57e+04] 44805
6: (6.69e+04,6.72e+04] 64079
7: NA 33234
8: (6.72e+04,6.75e+04] 66517
9: (6.63e+04,6.66e+04] 43887
10: (6.78e+04,6.81e+04] 172
You can dress this up to improve aesthetics. For example, you can reset the factor levels for ease of reading.

Answer 2

When I use dplyr I am used to do it like this. Although I like the cut solution too.
# data
set.seed(1)
x <- sample(seq(65100, 67900, by=5), 100, replace = TRUE)
v <- sample(seq(1000, 8000), 100, replace = TRUE)
tabl <- data.frame(group=c(x), value=c(v))
attach(tabl)
#categories
s <- seq(((min(x) - min(x)%%300) + 300), ((max(x) - max(x)%%300) + 300), by =300)
tabl %>% rowwise() %>% mutate(g = s[min(which(group < s), na.rm=T)]) %>% ungroup() %>%
group_by(g) %>% summarise(sumvalue = sum(value))
result:
g sumvalue
<dbl> <int>
65400 28552
65700 49487
66000 45493
66300 77865
66600 43887
66900 21187
67200 65785
67500 66517
67800 61738
68100 1722

Answer 3

Try this (no package needed):
s <- seq(65100, max(tabl$x)+300, 300)
tabl$col = as.vector(cut(tabl$x, breaks = s, labels = 1:10))
df <- aggregate(v~col, tabl, sum)
# col v
# 1 1 33234
# 2 2 44805
# 3 3 45493
# 4 4 77865
# 5 5 43887
# 6 6 22893
# 7 7 64079
# 8 8 66517
# 9 9 61738
# 10 10 1722