how to split a dataframe by specific rows in r - r

Question

I have a data look like this:
data <- structure(list(A = c("1", "1", "1", "A", "10", "10", "B", "200"), B = c("2", "2", "2", "B", "20", "20", "C", "300"), C = c("3","3", "3", "C", "30", "30", "D", "400"), D = c("4", "4", "4", "D", "40", "40", NA, NA)), row.names = c(NA, -8L), class = c("tbl_df","tbl", "data.frame"))
data
> data
# A tibble: 8 x 4
A B C D
<chr> <chr> <chr> <chr>
1 1 2 3 4
2 1 2 3 4
3 1 2 3 4
4 A B C D
5 10 20 30 40
6 10 20 30 40
7 B C D NA
8 200 300 400 NA
It was wrong bind by rows and I wanted to split the data into 3 sub data(d1, d2 and d3) such like this:
NOTE: In my real situation, d1, d2 and d3 have different nrow(). I set nrow(d1) = 3, nrow(d2) = 2 and nrow(d3) = 1 just for simplify the question in this example.
d1 <- data.frame(A = rep(1,3), B = rep(2,3), C = rep(3,3), D = rep(4,3))
d2 <- data.frame(A = rep(10,2), B = rep(20,2), C = rep(30,2), D = rep(40,2))
d3 <- data.frame( B = 200, C = 300, D = 400)
> d1
A B C D
1 1 2 3 4
2 1 2 3 4
3 1 2 3 4
> d2
A B C D
1 10 20 30 40
2 10 20 30 40
> d3
B C D
1 200 300 400
And then I could bind them correctly using bind_rows from dplyr
bind_rows(d1, d2, d3) %>% as_tibble()
# A tibble: 6 x 4
A B C D
<dbl> <dbl> <dbl> <dbl>
1 1 2 3 4
2 1 2 3 4
3 1 2 3 4
4 10 20 30 40
5 10 20 30 40
6 NA 200 300 400
The problem is that I am troubled by how to get the d1, d2 and d3 from data.
Any help will be highly appreciated!

Answer 1

Here is a tidyverse solution.
process_df takes a data frame and sets the column names and removes the first row.
process_df <- function(df, ...) {
df %>%
set_names(slice(., 1)) %>%
select(which(!is.na(names(.)))) %>%
slice(-1)
}
Add a header row that just contains the column names.
Use rowwise() and c_across() to get the values of all columns by row. Use this to identify which rows are header rows.
group_map will apply a function over each group and bind_rows will combine the results.
data %>%
add_row(!!!set_names(names(.)), .before = 1) %>%
rowwise() %>%
mutate(
group = all(is.na(c_across()) | c_across() %in% names(.))
) %>%
ungroup() %>%
mutate(group = cumsum(group)) %>%
group_by(group) %>%
group_map(process_df) %>%
bind_rows()
#> # A tibble: 6 x 4
#> A B C D
#> <chr> <chr> <chr> <chr>
#> 1 1 2 3 4
#> 2 1 2 3 4
#> 3 1 2 3 4
#> 4 10 20 30 40
#> 5 10 20 30 40
#> 6 NA 200 300 400
Explanation of the usage of !!! in new_row
set_names(names(.)) creates a named vector that represents the row we want to add. However, add_row doesn't accept a named vector - it wants the values to be specified as arguments.
Here is a simplified example.
new_row <- c(speed = 1, dist = 2)
add_row doesn't accept a named vector, so this doesn't work.
cars %>% add_row(new_row, .before = TRUE)
# (Error)
!!! will unpack the vector as arguments to the function.
cars %>% add_row(!!!new_row, .before = TRUE)
# (Works)
!!! above essentially results in this:
cars %>% add_row(speed = 1, dist = 2, .before = TRUE)

Answer 2

Does this work:
data
# A tibble: 5 x 4
A B C D
<chr> <chr> <chr> <chr>
1 1 2 3 4
2 A B C D
3 10 20 30 40
4 B C D NA
5 200 300 400 NA
data <- rbind(LETTERS[1:4],data)
data
# A tibble: 6 x 4
A B C D
<chr> <chr> <chr> <chr>
1 A B C D
2 1 2 3 4
3 A B C D
4 10 20 30 40
5 B C D NA
6 200 300 400 NA
split(data, rep(1:ceiling(nrow(data)/2), each = 2))
$`1`
# A tibble: 2 x 4
A B C D
<chr> <chr> <chr> <chr>
1 A B C D
2 1 2 3 4
$`2`
# A tibble: 2 x 4
A B C D
<chr> <chr> <chr> <chr>
1 A B C D
2 10 20 30 40
$`3`
# A tibble: 2 x 4
A B C D
<chr> <chr> <chr> <chr>
1 B C D NA
2 200 300 400 NA

Answer 3

Base R solution:
Map(function(x){setNames(data.frame(t(x[,2, drop = FALSE])), x[,1])[,!is.na(x[,1])]},
split.default(cbind(X0 = names(df), data.frame(t(df))), c(0, seq_len(nrow(df)) %/% 2)))
Including pushing separate data.frames to Global Environment:
list2env(setNames(Map(function(x){setNames(data.frame(t(x[,2, drop = FALSE])), x[,1])[,!is.na(x[,1])]},
split.default(cbind(X0 = names(df), data.frame(t(df))), c(0, seq_len(nrow(df)) %/% 2))),
paste0('d', seq_len(ceiling(nrow(df) / 2)))), .GlobalEnv)
Tidyverse Solution:
library(tidyverse)
df %>%
rbind(names(df), .) %>%
split(cumsum(seq_len(nrow(.)) %% 2)) %>%
Map(function(x){setNames(x[2,], x[1,])[,complete.cases(t(x))]}, .) %>%
set_names(str_c('d', names(.))) %>%
list2env(., .GlobalEnv)
Note solution adjusted to reflect edit to the question:
rdf <- type.convert(data.frame(t(rbind(names(df), df))))
Map(function(x){
y <- setNames(t(x[,-1, drop = FALSE]), x[,1]); y[,!is.na(colSums(y))]
}, split.default(rdf, cumsum(!sapply(rdf, is.integer))))
New solution including push to Global Env:
rdf <- type.convert(data.frame(t(rbind(names(df), df))))
dflist <- Map(function(x) {
y <-
setNames(t(x[, -1, drop = FALSE]), x[, 1])
y[, !is.na(colSums(y))]
}, split.default(rdf, cumsum(!sapply(rdf, is.integer))))
list2env(setNames(dflist, paste0('d', names(dflist))), .GlobalEnv)
Adjusted Tidyverse solution:
df %>%
rbind(names(.), .) %>%
t() %>%
data.frame() %>%
type.convert() %>%
split.default(cumsum(!sapply(., is.integer))) %>%
Map(function(x){
y <- setNames(t(x[,-1, drop = FALSE]), x[,1])
data.frame(y[,!is.na(colSums(y)), drop = FALSE])}, .) %>%
set_names(str_c('d', names(.))) %>%
list2env(., .GlobalEnv)
Data:
df <- structure(list(A = c("1", "A", "10", "B", "200"), B = c("2", "B", "20", "C", "300"), C = c("3", "C", "30", "D", "400"), D = c("4","D", "40", NA, NA)), row.names = c(NA, -5L), class = c("tbl_df", "tbl", "data.frame"))
Updated Data:
df <- structure(list(A = c("1", "1", "1", "A", "10", "10", "B", "200"), B = c("2", "2", "2", "B", "20", "20", "C", "300"), C = c("3","3", "3", "C", "30", "30", "D", "400"), D = c("4", "4", "4", "D", "40", "40", NA, NA)), row.names = c(NA, -8L), class = c("tbl_df","tbl", "data.frame"))