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Stack of irregular wide-form datasets in one file; make to clean long-form data in a single pipe
How to reshape data from long to wide formatReshape Panel Data Wide Format to Long FormatLong to wide data with tidyR?How to make data in a single column (long) with multiple, nested group categories wideUndo reshape with arbitrary number of columns createdHow to cast data from long to wide format in H2O?R How to reshape data and aggregate certain columns it at the same timer wide data to widish and longTransforming long dataset to wide with a twist in R?Split, reshape, bind stacked wide data using tidyverse in single pipe
I have a stack of wide(ish) data frames provided in a single spreadsheet. The data are vote counts for various political parties in villages which are nested within districts. This is how they look:
df_in <- data.frame(
X1 = c(rep("District1", 4), rep("District2", 3)),
X2 = c("Party", "PartyA", "PartyB", "PartyC", "Party", "PartyA", "PartyB"),
X3 = c("Village1", "44", "12", "3", "Village3", "7", "88"),
X4 = c("Village2", "34", "19", "2", "Village4", "90", "65"),
X5 = c("", "", "", "", "Village5", "45", "62")
)
I want to make a long-form village/party vote count dataset that looks like this:
df_out <- data.frame(
district = c(rep("District1", 6), rep("District2", 6)),
village = c(rep("Village1", 3), rep("Village2", 3), rep("Village3", 2), rep("Village4", 2), rep("Village5", 2)),
party = c(rep(c("PartyA", "PartyB", "PartyC"), 2), rep(c("PartyA", "PartyB"), 3)),
votes = c(44, 12, 3, 34, 19, 2, 7, 88, 90, 65, 45, 62)
)
I'm looking for a way to get from df_in to df_out in a single pipe (since I have a lot of spreadsheets that look similar to this one).
r dplyr tidyr purrr
asked Mar 21 at 21:09
lethalSingerlethalSinger
714
add a comment |
I have a stack of wide(ish) data frames provided in a single spreadsheet. The data are vote counts for various political parties in villages which are nested within districts. This is how they look:
df_in <- data.frame(
X1 = c(rep("District1", 4), rep("District2", 3)),
X2 = c("Party", "PartyA", "PartyB", "PartyC", "Party", "PartyA", "PartyB"),
X3 = c("Village1", "44", "12", "3", "Village3", "7", "88"),
X4 = c("Village2", "34", "19", "2", "Village4", "90", "65"),
X5 = c("", "", "", "", "Village5", "45", "62")
)
I want to make a long-form village/party vote count dataset that looks like this:
df_out <- data.frame(
district = c(rep("District1", 6), rep("District2", 6)),
village = c(rep("Village1", 3), rep("Village2", 3), rep("Village3", 2), rep("Village4", 2), rep("Village5", 2)),
party = c(rep(c("PartyA", "PartyB", "PartyC"), 2), rep(c("PartyA", "PartyB"), 3)),
votes = c(44, 12, 3, 34, 19, 2, 7, 88, 90, 65, 45, 62)
)
I'm looking for a way to get from df_in to df_out in a single pipe (since I have a lot of spreadsheets that look similar to this one).
r dplyr tidyr purrr
asked Mar 21 at 21:09
lethalSingerlethalSinger
714
add a comment |
I have a stack of wide(ish) data frames provided in a single spreadsheet. The data are vote counts for various political parties in villages which are nested within districts. This is how they look:
df_in <- data.frame(
X1 = c(rep("District1", 4), rep("District2", 3)),
X2 = c("Party", "PartyA", "PartyB", "PartyC", "Party", "PartyA", "PartyB"),
X3 = c("Village1", "44", "12", "3", "Village3", "7", "88"),
X4 = c("Village2", "34", "19", "2", "Village4", "90", "65"),
X5 = c("", "", "", "", "Village5", "45", "62")
)
I want to make a long-form village/party vote count dataset that looks like this:
df_out <- data.frame(
district = c(rep("District1", 6), rep("District2", 6)),
village = c(rep("Village1", 3), rep("Village2", 3), rep("Village3", 2), rep("Village4", 2), rep("Village5", 2)),
party = c(rep(c("PartyA", "PartyB", "PartyC"), 2), rep(c("PartyA", "PartyB"), 3)),
votes = c(44, 12, 3, 34, 19, 2, 7, 88, 90, 65, 45, 62)
)
I'm looking for a way to get from df_in to df_out in a single pipe (since I have a lot of spreadsheets that look similar to this one).
r dplyr tidyr purrr
asked Mar 21 at 21:09
lethalSingerlethalSinger
714
I have a stack of wide(ish) data frames provided in a single spreadsheet. The data are vote counts for various political parties in villages which are nested within districts. This is how they look:
df_in <- data.frame(
X1 = c(rep("District1", 4), rep("District2", 3)),
X2 = c("Party", "PartyA", "PartyB", "PartyC", "Party", "PartyA", "PartyB"),
X3 = c("Village1", "44", "12", "3", "Village3", "7", "88"),
X4 = c("Village2", "34", "19", "2", "Village4", "90", "65"),
X5 = c("", "", "", "", "Village5", "45", "62")
)
I want to make a long-form village/party vote count dataset that looks like this:
df_out <- data.frame(
district = c(rep("District1", 6), rep("District2", 6)),
village = c(rep("Village1", 3), rep("Village2", 3), rep("Village3", 2), rep("Village4", 2), rep("Village5", 2)),
party = c(rep(c("PartyA", "PartyB", "PartyC"), 2), rep(c("PartyA", "PartyB"), 3)),
votes = c(44, 12, 3, 34, 19, 2, 7, 88, 90, 65, 45, 62)
)
I'm looking for a way to get from df_in to df_out in a single pipe (since I have a lot of spreadsheets that look similar to this one).
r dplyr tidyr purrr
r dplyr tidyr purrr
asked Mar 21 at 21:09
lethalSingerlethalSinger
714
asked Mar 21 at 21:09
lethalSingerlethalSinger
714
edited Mar 22 at 16:24
lethalSinger
asked Mar 21 at 21:09
lethalSingerlethalSinger
714
asked Mar 21 at 21:09
lethalSingerlethalSinger
714
asked Mar 21 at 21:09
lethalSingerlethalSinger
714
714
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
A solution using the tidyverse.
library(tidyverse)
dat <- df_in %>%
# Split the data frame
split(f = .$X1) %>%
# Remove columns with all ""
map(~select_if(.x, function(x) !all(x == ""))) %>%
# Use the first row as the column name
map(~set_names(.x, nm = .x %>% slice(1) %>% unlist)) %>%
# Rename the District column
map(~set_names(.x, nm = c("District", names(.x)[2:ncol(.x)]))) %>%
# Remove the first row
map(~slice(.x, 2:n())) %>%
# Gather the data frames
map(~gather(.x, village, votes, starts_with("Village"))) %>%
# Combine all results
bind_rows()
dat
# District Party village votes
# 1 District1 PartyA Village1 44
# 2 District1 PartyB Village1 12
# 3 District1 PartyC Village1 3
# 4 District1 PartyA Village2 34
# 5 District1 PartyB Village2 19
# 6 District1 PartyC Village2 2
# 7 District2 PartyA Village3 7
# 8 District2 PartyB Village3 88
# 9 District2 PartyA Village4 90
# 10 District2 PartyB Village4 65
# 11 District2 PartyA Village5 45
# 12 District2 PartyB Village5 62
answered Mar 24 at 1:40
wwwwww
28.6k112345
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
A solution using the tidyverse.
library(tidyverse)
dat <- df_in %>%
# Split the data frame
split(f = .$X1) %>%
# Remove columns with all ""
map(~select_if(.x, function(x) !all(x == ""))) %>%
# Use the first row as the column name
map(~set_names(.x, nm = .x %>% slice(1) %>% unlist)) %>%
# Rename the District column
map(~set_names(.x, nm = c("District", names(.x)[2:ncol(.x)]))) %>%
# Remove the first row
map(~slice(.x, 2:n())) %>%
# Gather the data frames
map(~gather(.x, village, votes, starts_with("Village"))) %>%
# Combine all results
bind_rows()
dat
# District Party village votes
# 1 District1 PartyA Village1 44
# 2 District1 PartyB Village1 12
# 3 District1 PartyC Village1 3
# 4 District1 PartyA Village2 34
# 5 District1 PartyB Village2 19
# 6 District1 PartyC Village2 2
# 7 District2 PartyA Village3 7
# 8 District2 PartyB Village3 88
# 9 District2 PartyA Village4 90
# 10 District2 PartyB Village4 65
# 11 District2 PartyA Village5 45
# 12 District2 PartyB Village5 62
answered Mar 24 at 1:40
wwwwww
28.6k112345
add a comment |
A solution using the tidyverse.
library(tidyverse)
dat <- df_in %>%
# Split the data frame
split(f = .$X1) %>%
# Remove columns with all ""
map(~select_if(.x, function(x) !all(x == ""))) %>%
# Use the first row as the column name
map(~set_names(.x, nm = .x %>% slice(1) %>% unlist)) %>%
# Rename the District column
map(~set_names(.x, nm = c("District", names(.x)[2:ncol(.x)]))) %>%
# Remove the first row
map(~slice(.x, 2:n())) %>%
# Gather the data frames
map(~gather(.x, village, votes, starts_with("Village"))) %>%
# Combine all results
bind_rows()
dat
# District Party village votes
# 1 District1 PartyA Village1 44
# 2 District1 PartyB Village1 12
# 3 District1 PartyC Village1 3
# 4 District1 PartyA Village2 34
# 5 District1 PartyB Village2 19
# 6 District1 PartyC Village2 2
# 7 District2 PartyA Village3 7
# 8 District2 PartyB Village3 88
# 9 District2 PartyA Village4 90
# 10 District2 PartyB Village4 65
# 11 District2 PartyA Village5 45
# 12 District2 PartyB Village5 62
answered Mar 24 at 1:40
wwwwww
28.6k112345
add a comment |
A solution using the tidyverse.
library(tidyverse)
dat <- df_in %>%
# Split the data frame
split(f = .$X1) %>%
# Remove columns with all ""
map(~select_if(.x, function(x) !all(x == ""))) %>%
# Use the first row as the column name
map(~set_names(.x, nm = .x %>% slice(1) %>% unlist)) %>%
# Rename the District column
map(~set_names(.x, nm = c("District", names(.x)[2:ncol(.x)]))) %>%
# Remove the first row
map(~slice(.x, 2:n())) %>%
# Gather the data frames
map(~gather(.x, village, votes, starts_with("Village"))) %>%
# Combine all results
bind_rows()
dat
# District Party village votes
# 1 District1 PartyA Village1 44
# 2 District1 PartyB Village1 12
# 3 District1 PartyC Village1 3
# 4 District1 PartyA Village2 34
# 5 District1 PartyB Village2 19
# 6 District1 PartyC Village2 2
# 7 District2 PartyA Village3 7
# 8 District2 PartyB Village3 88
# 9 District2 PartyA Village4 90
# 10 District2 PartyB Village4 65
# 11 District2 PartyA Village5 45
# 12 District2 PartyB Village5 62
answered Mar 24 at 1:40
wwwwww
28.6k112345
A solution using the tidyverse.
library(tidyverse)
dat <- df_in %>%
# Split the data frame
split(f = .$X1) %>%
# Remove columns with all ""
map(~select_if(.x, function(x) !all(x == ""))) %>%
# Use the first row as the column name
map(~set_names(.x, nm = .x %>% slice(1) %>% unlist)) %>%
# Rename the District column
map(~set_names(.x, nm = c("District", names(.x)[2:ncol(.x)]))) %>%
# Remove the first row
map(~slice(.x, 2:n())) %>%
# Gather the data frames
map(~gather(.x, village, votes, starts_with("Village"))) %>%
# Combine all results
bind_rows()
dat
# District Party village votes
# 1 District1 PartyA Village1 44
# 2 District1 PartyB Village1 12
# 3 District1 PartyC Village1 3
# 4 District1 PartyA Village2 34
# 5 District1 PartyB Village2 19
# 6 District1 PartyC Village2 2
# 7 District2 PartyA Village3 7
# 8 District2 PartyB Village3 88
# 9 District2 PartyA Village4 90
# 10 District2 PartyB Village4 65
# 11 District2 PartyA Village5 45
# 12 District2 PartyB Village5 62
answered Mar 24 at 1:40
wwwwww
28.6k112345
answered Mar 24 at 1:40
wwwwww
28.6k112345
answered Mar 24 at 1:40
wwwwww
28.6k112345
answered Mar 24 at 1:40
wwwwww
28.6k112345
28.6k112345
add a comment |
add a comment |
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