I am trying to figure out how to sum values belonging to category a and b by factor file, but also keep the original data.
library(dplyr)
df <- data.frame(ID = 1:20, values = runif(20), category = rep(letters[1:5], 4), file = as.factor(sort(rep(1:5, 4))))
ID values category file
1 1 0.65699229 a 1
2 2 0.70506478 b 1
3 3 0.45774178 c 1
4 4 0.71911225 d 1
5 5 0.93467225 e 1
6 6 0.25542882 a 2
7 7 0.46229282 b 2
8 8 0.94001452 c 2
9 9 0.97822643 d 2
10 10 0.11748736 e 2
11 11 0.47499708 a 3
12 12 0.56033275 b 3
13 13 0.90403139 c 3
14 14 0.13871017 d 3
15 15 0.98889173 e 3
16 16 0.94666823 a 4
17 17 0.08243756 b 4
18 18 0.51421178 c 4
19 19 0.39020347 d 4
20 20 0.90573813 e 4
so that
df[1,2] will be added to df[2,2] to category 'ab' for file 1df[6,2] will be added to df[7,2] to category 'ab' for file 2 - etc.
So far I have this:
df %>%
filter(category %in% c('a' , 'b')) %>%
group_by(file) %>%
summarise(values = sum(values))
Problem
I would like to change the category of the summed values to "ab" and append it to the original data frame in the same pipeline.
Desired output:
ID values category file
1 1 0.65699229 a 1
2 2 0.70506478 b 1
3 3 0.45774178 c 1
4 4 0.71911225 d 1
5 5 0.93467225 e 1
6 6 0.25542882 a 2
7 7 0.46229282 b 2
8 8 0.94001452 c 2
9 9 0.97822643 d 2
10 10 0.11748736 e 2
11 11 0.47499708 a 3
12 12 0.56033275 b 3
13 13 0.90403139 c 3
14 14 0.13871017 d 3
15 15 0.98889173 e 3
16 16 0.94666823 a 4
17 17 0.08243756 b 4
18 18 0.51421178 c 4
19 19 0.39020347 d 4
20 20 0.90573813 e 4
21 21 1.25486225 ab 1
22 22 1.87216325 ab 2
23 23 1.36548126 ab 3
This will get you the result
df %>% bind_rows(
df %>%
filter(category %in% c('a' , 'b')) %>%
group_by(file) %>%
mutate(values = sum(values), category = paste0(category,collapse='')) %>%
filter(row_number() == 1 & n() > 1)
) %>% mutate(ID = row_number())
BTW the code pro produce the dataframe in the example is this one:
df <- data.frame(ID = 1:20, values = runif(20), category = rep(letters[1:5], 4), file = as.factor(sort(rep(1:4, 5))))
now lets say you want to sum multiple columns, you need to provide the list in a vector:
cols = c("values") # columns to be sum
df %>% bind_rows(
df %>%
filter(category %in% c('a' , 'b')) %>%
group_by(file) %>%
mutate_at(vars(cols), sum) %>%
mutate(category = paste0(category,collapse='')) %>%
filter(row_number() == 1 & n() > 1)
) %>% mutate(ID = row_number())
library(dplyr)
df1 %>%
filter(category %in% c('a' , 'b')) %>%
group_by(file) %>%
filter(n_distinct(category) > 1) %>%
summarise(values = sum(values)) %>%
mutate(category="ab",
ID=max(df1$ID)+1:n()) %>%
bind_rows(df1, .)
#> Warning in bind_rows_(x, .id): binding factor and character vector,
#> coercing into character vector
#> Warning in bind_rows_(x, .id): binding character and factor vector,
#> coercing into character vector
#> ID values category file
#> 1 1 0.62585921 a 1
#> 2 2 0.61865851 b 1
#> 3 3 0.05274456 c 1
#> 4 4 0.68156961 d 1
.
.
.
#> 19 19 0.43239411 d 5
#> 20 20 0.85886314 e 5
#> 21 21 1.24451773 ab 1
#> 22 22 0.99001810 ab 2
#> 23 23 1.25331943 ab 3
This data.table approach uses a self-join to get all of the possible two-character combinations.
library(data.table)
setDT(df)
df_self_join <- df[df, on = .(file), allow.cartesian = T
][category != i.category,
.(category = paste0(i.category, category), values = values + i.values, file)
][order(category), .(ID = .I + nrow(df), values, category, file)]
rbindlist(list(df, df_self_join))
ID values category file
1: 1 0.76984382 a 1
2: 2 0.54311583 b 1
3: 3 0.23462016 c 1
4: 4 0.60179043 d 1
...
20: 20 0.03534223 e 5
21: 21 1.31295965 ab 1
22: 22 0.51666175 ab 2
23: 23 1.02305754 ab 3
24: 24 1.00446399 ac 1
25: 25 0.96910373 ac 2
26: 26 0.87795389 ac 4
#total of 80 rows
Here is pretty close dplyr translation:
library(dplyr)
tib <- as_tibble(df)
inner_join(tib, tib, by = 'file')%>%
filter(ID.x != ID.y)%>%
transmute(category = paste0(category.x, category.y)
, values = values.x + values.y
, file)%>%
arrange(category)%>%
bind_rows(tib, .)%>%
mutate(ID = row_number())%>%
filter(category == 'ab') #filter added to show the "ab" files
# A tibble: 3 x 4
ID values category file
<int> <dbl> <chr> <fct>
1 21 1.31 ab 1
2 22 0.517 ab 2
3 23 1.02 ab 3
