I am trying to develop a cumsum with mutate. The challenge is that I have 10 columns to do and I know how to do one by one. Is there a way where I can do something like mutate(across(all_of(c(3:4)), ~cumsum(c(3:4)))?
cat %>%
group_by(animals) %>%
mutate(weight1 = cumsum(weight1),
weight2 = cumsum(weight2))
structure(list(animals = c("E1", "E1", "E1",
"E2", "E2", "E2"), period = structure(c(18690,
18697, 18704, 18690, 18697, 18704), class = "Date"), weight1 = c(704,
734, 653, 851, 911, 829), weight2 = c(0, 235, 325, 0, 148,
200)), row.names = c(NA, -6L), class = c("data.table", "data.frame"))
Expected output:
animals period weight1 weight2
<chr> <date> <dbl> <dbl>
1 E1 2021-03-04 704 0
2 E1 2021-03-11 1438 235
3 E1 2021-03-18 2091 560
4 E2 2021-03-04 851 0
5 E2 2021-03-11 1762 148
6 E2 2021-03-18 2591 348
try to do this
df <- structure(list(animals = c("E1", "E1", "E1",
"E2", "E2", "E2"), period = structure(c(18690,
18697, 18704, 18690, 18697, 18704), class = "Date"), weight1 = c(704,
734, 653, 851, 911, 829), weight2 = c(0, 235, 325, 0, 148,
200)), row.names = c(NA, -6L), class = c("data.table", "data.frame"))
library(dplyr)
df %>%
group_by(animals) %>%
mutate(across(starts_with("weight"), cumsum))
#> # A tibble: 6 x 4
#> # Groups: animals [2]
#> animals period weight1 weight2
#> <chr> <date> <dbl> <dbl>
#> 1 E1 2021-03-04 704 0
#> 2 E1 2021-03-11 1438 235
#> 3 E1 2021-03-18 2091 560
#> 4 E2 2021-03-04 851 0
#> 5 E2 2021-03-11 1762 148
#> 6 E2 2021-03-18 2591 348
Created on 2021-03-24 by the reprex package (v1.0.0)
or
vars <- names(df)[3:4]
df %>% group_by(animals) %>% mutate(across(all_of(vars), cumsum))