How to name the list of the group_split output in dplyr
Lots of good answers. You can also just do:
iris %>% sample_n(size = 5) %>%
split(f = as.factor(.$Species))
Which will give you:
$setosa
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
4 5.5 3.5 1.3 0.2 setosa
5 5.3 3.7 1.5 0.2 setosa
$versicolor
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
3 5 2.3 3.3 1 versicolor
$virginica
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.7 2.6 6.9 2.3 virginica
2 7.2 3.0 5.8 1.6 virginica
Also works with your dataframe above:
df %>%
split(f = as.factor(.$Cluster))
Gives you:
$Cluster1
# A tibble: 1 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster1 Grhpr 0.00000155 4.66 0.0261 0.00000343
$Cluster11
# A tibble: 2 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster11 Vimp 3.17e-62 16.6 0.0948 1.62e-61
2 Cluster11 Fgfr1op2 2.07e- 8 5.48 0.0310 4.98e- 8
$Cluster12
# A tibble: 1 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster12 Pikfyve 0.0147 2.18 0.0120 0.0245
$Cluster6
# A tibble: 1 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster6 Zfp398 0.000354 3.39 0.0188 0.000684
$Cluster8
# A tibble: 2 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster8 Golga7 4.14e- 6 4.46 0.0251 8.96e- 6
2 Cluster8 Lars2 3.93e-184 28.9 0.165 3.48e-183
$Cluster9
# A tibble: 3 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster9 Tbc1d8 3.47e- 47 14.4 0.0815 1.58e- 46
2 Cluster9 H1f0 9.46e-131 24.3 0.139 7.00e-130
3 Cluster9 Ankrd13a 1.43e- 38 12.9 0.0737 5.96e- 38
How to name a list of a group_split in dplyr when grouped by more than one column
Use dplyr::group_keys() to get the grouping variables.
library(dplyr)
library(stringr)
# make grouped data frame
iris_group <- iris %>%
group_by(Species, Petal.Width)
# get group keys
group_name_df <- group_keys(iris_group) %>%
mutate(group_name = str_c(as.character(Species),"-",Petal.Width))
# get name for each group
group_name <- group_name_df$group_name
# assign name to each split table
df_list <- group_split(iris_group) %>%
setNames(group_name)
> group_name_df
# A tibble: 27 x 3
Species Petal.Width group_name
<fct> <dbl> <chr>
1 setosa 0.1 setosa-0.1
2 setosa 0.2 setosa-0.2
3 setosa 0.3 setosa-0.3
4 setosa 0.4 setosa-0.4
5 setosa 0.5 setosa-0.5
6 setosa 0.6 setosa-0.6
7 versicolor 1 versicolor-1
8 versicolor 1.1 versicolor-1.1
9 versicolor 1.2 versicolor-1.2
10 versicolor 1.3 versicolor-1.3
# ... with 17 more rows
> df_list
$`setosa-0.1`
# A tibble: 5 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 4.9 3.1 1.5 0.1 setosa
2 4.8 3 1.4 0.1 setosa
3 4.3 3 1.1 0.1 setosa
4 5.2 4.1 1.5 0.1 setosa
5 4.9 3.6 1.4 0.1 setosa
$`setosa-0.2`
# A tibble: 29 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
.
.
.
How to name a list created by group_split WITHOUT breaking the pipeline
You can include group_keys() in the pipe like this:
library(dplyr)
iris %>%
group_by(Species) %>%
{setNames(group_split(.), group_keys(.)[[1]])}
Output
$setosa
# A tibble: 50 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# … with 40 more rows
$versicolor
# A tibble: 50 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 7 3.2 4.7 1.4 versicolor
2 6.4 3.2 4.5 1.5 versicolor
3 6.9 3.1 4.9 1.5 versicolor
4 5.5 2.3 4 1.3 versicolor
5 6.5 2.8 4.6 1.5 versicolor
6 5.7 2.8 4.5 1.3 versicolor
7 6.3 3.3 4.7 1.6 versicolor
8 4.9 2.4 3.3 1 versicolor
9 6.6 2.9 4.6 1.3 versicolor
10 5.2 2.7 3.9 1.4 versicolor
# … with 40 more rows
$virginica
# A tibble: 50 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 6.3 3.3 6 2.5 virginica
2 5.8 2.7 5.1 1.9 virginica
3 7.1 3 5.9 2.1 virginica
4 6.3 2.9 5.6 1.8 virginica
5 6.5 3 5.8 2.2 virginica
6 7.6 3 6.6 2.1 virginica
7 4.9 2.5 4.5 1.7 virginica
8 7.3 2.9 6.3 1.8 virginica
9 6.7 2.5 5.8 1.8 virginica
10 7.2 3.6 6.1 2.5 virginica
# … with 40 more rows
output of group_split needs to be saved as separate dataframe
group_split will not return you name of the year as list name. Use base::split instead.
y <- split(year_x, year_x$Year)
for(i in seq_along(y)) {
write.csv2(y[[i]], paste0("D:/newfolder/", names(y)[i], ".csv"),row.names = FALSE)
}
You could also do this with purrr::imap
purrr::imap(y, ~write.csv2(.x, paste0("D:/newfolder/", .y, ".csv"),row.names = FALSE))
Using group_split, add a single value to each item in a list for looping and accumulating over
Perhaps, we can create a named list by looping over the list
library(purrr)
out <- map(mylist, ~ list(data = ., min_price = min(.$price)))
-checking
> out[[1]]$data
# A tibble: 163 x 10
carat cut color clarity depth table price x y z
<dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.75 Fair D SI2 64.6 57 2848 5.74 5.72 3.7
2 0.71 Fair D VS2 56.9 65 2858 5.89 5.84 3.34
3 0.9 Fair D SI2 66.9 57 2885 6.02 5.9 3.99
4 1 Fair D SI2 69.3 58 2974 5.96 5.87 4.1
5 1.01 Fair D SI2 64.6 56 3003 6.31 6.24 4.05
6 0.73 Fair D VS1 66 54 3047 5.56 5.66 3.7
7 0.71 Fair D VS2 64.7 58 3077 5.61 5.58 3.62
8 0.91 Fair D SI2 62.5 66 3079 6.08 6.01 3.78
9 0.9 Fair D SI2 65.9 59 3205 6 5.95 3.94
10 0.9 Fair D SI2 66 58 3205 6 5.97 3.95
# … with 153 more rows
> out[[1]]$min_price
[1] 536
How to split R dataframe into list based on character variable
You can use is.na() on the second column to detect where the new blocks starts. Then cumsum(is.na()) will give you an id for each block. Group on this, not the contest name.
e_pivot <- function(x) {
x %>%
select(-block_id) %>%
slice(-1) %>%
row_to_names(row_number = 1) %>%
pivot_longer(cols = 3:last_col(), names_to = "candidate", values_to = "votes") %>%
rename(contest = 1, precinct = Precinct) %>%
mutate(votes = as.numeric(votes))
}
t %>%
mutate(contest = if_else(str_detect(V1, " "), V1, NA_character_),,
block_id = cumsum(is.na(V8)),
.before = V1) %>%
fill(contest, .direction = "down") %>%
group_by(block_id) %>%
group_split() %>%
map_dfr(e_pivot)
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