将长数据 table 重塑为宽数据列表 tables
Reshape long data table to list of wide data tables
我的问题是这里提出的问题的扩展
How to reshape data from long to wide format 所以我会用类似的方式表达它。
区别是我想把一个长数据table重新排列成一个list宽数据tables。
dat <- data.table(
sim = rep(c(1,2), each=4),
time = rep(1:4, 2),
value1 = rnorm(8),
value2 = rnorm(8)
)
dat
sim time value1 value2
1 1 1 0.3407 0.5167
2 1 2 -0.7033 0.8416
3 1 3 -0.3795 -0.4717
4 1 4 -0.7460 0.8479
5 2 1 0.8981 -0.7163
6 2 2 -0.3347 -0.6849
7 2 3 0.5013 0.8941
8 2 4 -0.1745 0.0795
我想重塑它，以便我有一个 list 的宽数据 table 命名为 value1、value2 ... value99 等...
l = list()
l[["value1"]]
sim 1 2 3 4
1 1 0.3407 -0.7033 -0.3795 -0.7460
5 2 -0.8981 -0.3347 -0.5013 -0.1745
l[["value2"]]
sim 1 2 3 4
1 1 0.5167 0.8416 -0.4717 0.8479
5 2 -0.7163 -0.6849 0.8941 0.0795
我对这个问题的解决方案是创建结果的嵌套数据集。我已经提供了方法的简要说明，然后是 reprex。
我会使用 pivot_wider() 和 pivot_longer() 重塑数据。 pivot_longer 首先用于使每一行仅包含 1 个值，并带有时间、模拟以及值一或二的标签。然后使用 pivot_wider 每行将包含每次带有模拟标签的值以及它们是哪一组值。 （值 1 或值 2）。
最后，我们使用 nest 嵌套数据框，它将每组值的所有数据存储在数据框中。如有必要，nested_vals$data 可以将其作为数据帧数组访问，其中 nested_vals 是我们将嵌套数据帧分配给的对象。
library(tidyverse)
#Setup data
dat <- data.frame(
sim = rep(c(1,2), each=4),
time = rep(1:4, 2),
value1 = rnorm(8),
value2 = rnorm(8)
)
# Construct nested dataframe
nested_vals <- dat %>%
# Format dataset in tidy format
pivot_longer(cols = c(value1, value2)) %>%
# Move the name of the data to the beginning of the dataframe
relocate(name) %>%
# Pivot to matrix form as requested (i.e. times as columns, sims as rows)
pivot_wider(id_cols = c(name, sim), names_from = time, values_from = value) %>%
# Nest results by name
nest(-name)
#> Warning: All elements of `...` must be named.
#> Did you want `data = c(sim, `1`, `2`, `3`, `4`)`?
nested_vals
#> # A tibble: 2 x 2
#> name data
#> <chr> <list>
#> 1 value1 <tibble[,5] [2 x 5]>
#> 2 value2 <tibble[,5] [2 x 5]>
nested_vals$data[[2]]
#> # A tibble: 2 x 5
#> sim `1` `2` `3` `4`
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0.0639 0.250 -1.28 0.850
#> 2 2 -1.90 0.000421 0.704 -0.164
由 reprex package (v2.0.0)
于 2021-04-07 创建
一个 tidyverse 解决方案可以是：
library(dplyr)
library(purrr)
library(tidyr)
dat_longer <- dat %>%
tidyr::pivot_longer(starts_with("value"), names_to="col_name", values_to="values")
list_wide <- purrr::map(unique(dat_longer[["col_name"]]),
~dat_longer %>%
dplyr::filter(col_name==.x) %>%
tidyr::pivot_wider(values_from = "values", names_from="time") %>%
select(-col_name)) %>%
purrr::set_names(unique(dat_longer[["col_name"]]))
$value1
# A tibble: 2 x 5
sim `1` `2` `3` `4`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 -0.710 -0.334 -0.370 0.777
2 2 0.130 0.877 1.24 -0.202
$value2
# A tibble: 2 x 5
sim `1` `2` `3` `4`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 -0.719 -0.909 0.0821 -0.158
2 2 -0.706 1.51 0.234 1.09
两个变体。
data.table
library(data.table)
tmp <- dcast(melt(as.data.table(dat), id = c("sim", "time")), sim + variable ~ time)
tmp <- split(tmp, tmp$variable)
tmp <- lapply(tmp, set, i = NULL, j = "variable", value = NULL)
tmp
# $value1
# sim 1 2 3 4
# <num> <num> <num> <num> <num>
# 1: 1 1.0458737762 -0.4845954 0.1891288 0.05100633
# 2: 2 -0.0002406689 1.8093820 -0.8253280 1.14547045
# $value2
# sim 1 2 3 4
# <num> <num> <num> <num> <num>
# 1: 1 0.03157319 -0.8352058 -0.06876365 0.7467717
# 2: 2 -0.42551873 -0.7720822 0.15276411 0.9885968
我也经常把magrittr::%>%和data.table一起使用，所以可以转换成
library(data.table)
library(magrittr) # if %>% is not already available
as.data.table(dat) %>%
melt(., id = c("sim", "time")) %>%
dcast(., sim + variable ~ time) %>%
split(., .$variable) %>%
lapply(., set, i = NULL, j = "variable", value = NULL)
# $value1
# sim 1 2 3 4
# <num> <num> <num> <num> <num>
# 1: 1 1.0458737762 -0.4845954 0.1891288 0.05100633
# 2: 2 -0.0002406689 1.8093820 -0.8253280 1.14547045
# $value2
# sim 1 2 3 4
# <num> <num> <num> <num> <num>
# 1: 1 0.03157319 -0.8352058 -0.06876365 0.7467717
# 2: 2 -0.42551873 -0.7720822 0.15276411 0.9885968
整洁宇宙
library(dplyr)
library(tidyr) # pivot_longer, pivot_wider
dat %>%
pivot_longer(., -c(sim, time)) %>%
pivot_wider(., names_from = time, values_from = value) %>%
split(., .$name) %>%
lapply(., select, -name)
# $value1
# # A tibble: 2 x 5
# sim `1` `2` `3` `4`
# <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 1 1.05 -0.485 0.189 0.0510
# 2 2 -0.000241 1.81 -0.825 1.15
# $value2
# # A tibble: 2 x 5
# sim `1` `2` `3` `4`
# <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 1 0.0316 -0.835 -0.0688 0.747
# 2 2 -0.426 -0.772 0.153 0.989
另一种方式，使用单管道语法
library(tidyverse)
dat %>% pivot_longer(c(value1, value2)) %>%
group_split(name) %>% setNames(map(., ~.x[[3]][1])) %>%
map(~ .x %>% pivot_wider(id_cols = sim, names_from = time, values_from = value))
$value1
# A tibble: 2 x 5
sim `1` `2` `3` `4`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 -0.851 -0.0484 -0.656 -0.121
2 2 -0.645 1.59 -0.274 0.445
$value2
# A tibble: 2 x 5
sim `1` `2` `3` `4`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.46 -1.62 -0.672 1.43
2 2 1.65 0.790 0.495 0.162
另一种方法：
library(dplyr)
library(tidyr)
wide_dat <- dat %>% pivot_wider(id_cols = sim, names_from = time, values_from = starts_with('value'))
lapply(lapply(split.default(wide_dat[-1], sub('_\d','',names(wide_dat[-1]))), function(x) cbind(wide_dat[1],x)), setNames, c('sim', 1:4))
$value1
sim 1 2 3 4
1 1 -0.1704969 0.2820143 1.181898 2.2377396
2 2 2.1920534 0.8214070 0.421177 0.7601796
$value2
sim 1 2 3 4
1 1 0.1760887 0.3440053 -0.8435849 0.6729751
2 2 -0.1714095 1.5125986 -0.5739871 -0.9648294
我的问题是这里提出的问题的扩展 How to reshape data from long to wide format 所以我会用类似的方式表达它。
区别是我想把一个长数据table重新排列成一个list宽数据tables。
dat <- data.table(
sim = rep(c(1,2), each=4),
time = rep(1:4, 2),
value1 = rnorm(8),
value2 = rnorm(8)
)
dat
sim time value1 value2
1 1 1 0.3407 0.5167
2 1 2 -0.7033 0.8416
3 1 3 -0.3795 -0.4717
4 1 4 -0.7460 0.8479
5 2 1 0.8981 -0.7163
6 2 2 -0.3347 -0.6849
7 2 3 0.5013 0.8941
8 2 4 -0.1745 0.0795
我想重塑它，以便我有一个 list 的宽数据 table 命名为 value1、value2 ... value99 等...
l = list()
l[["value1"]]
sim 1 2 3 4
1 1 0.3407 -0.7033 -0.3795 -0.7460
5 2 -0.8981 -0.3347 -0.5013 -0.1745
l[["value2"]]
sim 1 2 3 4
1 1 0.5167 0.8416 -0.4717 0.8479
5 2 -0.7163 -0.6849 0.8941 0.0795
我对这个问题的解决方案是创建结果的嵌套数据集。我已经提供了方法的简要说明，然后是 reprex。
我会使用 pivot_wider() 和 pivot_longer() 重塑数据。 pivot_longer 首先用于使每一行仅包含 1 个值，并带有时间、模拟以及值一或二的标签。然后使用 pivot_wider 每行将包含每次带有模拟标签的值以及它们是哪一组值。 （值 1 或值 2）。
最后，我们使用 nest 嵌套数据框，它将每组值的所有数据存储在数据框中。如有必要，nested_vals$data 可以将其作为数据帧数组访问，其中 nested_vals 是我们将嵌套数据帧分配给的对象。
library(tidyverse)
#Setup data
dat <- data.frame(
sim = rep(c(1,2), each=4),
time = rep(1:4, 2),
value1 = rnorm(8),
value2 = rnorm(8)
)
# Construct nested dataframe
nested_vals <- dat %>%
# Format dataset in tidy format
pivot_longer(cols = c(value1, value2)) %>%
# Move the name of the data to the beginning of the dataframe
relocate(name) %>%
# Pivot to matrix form as requested (i.e. times as columns, sims as rows)
pivot_wider(id_cols = c(name, sim), names_from = time, values_from = value) %>%
# Nest results by name
nest(-name)
#> Warning: All elements of `...` must be named.
#> Did you want `data = c(sim, `1`, `2`, `3`, `4`)`?
nested_vals
#> # A tibble: 2 x 2
#> name data
#> <chr> <list>
#> 1 value1 <tibble[,5] [2 x 5]>
#> 2 value2 <tibble[,5] [2 x 5]>
nested_vals$data[[2]]
#> # A tibble: 2 x 5
#> sim `1` `2` `3` `4`
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0.0639 0.250 -1.28 0.850
#> 2 2 -1.90 0.000421 0.704 -0.164
由 reprex package (v2.0.0)
于 2021-04-07 创建一个 tidyverse 解决方案可以是：
library(dplyr)
library(purrr)
library(tidyr)
dat_longer <- dat %>%
tidyr::pivot_longer(starts_with("value"), names_to="col_name", values_to="values")
list_wide <- purrr::map(unique(dat_longer[["col_name"]]),
~dat_longer %>%
dplyr::filter(col_name==.x) %>%
tidyr::pivot_wider(values_from = "values", names_from="time") %>%
select(-col_name)) %>%
purrr::set_names(unique(dat_longer[["col_name"]]))
$value1
# A tibble: 2 x 5
sim `1` `2` `3` `4`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 -0.710 -0.334 -0.370 0.777
2 2 0.130 0.877 1.24 -0.202
$value2
# A tibble: 2 x 5
sim `1` `2` `3` `4`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 -0.719 -0.909 0.0821 -0.158
2 2 -0.706 1.51 0.234 1.09
两个变体。
data.table
library(data.table)
tmp <- dcast(melt(as.data.table(dat), id = c("sim", "time")), sim + variable ~ time)
tmp <- split(tmp, tmp$variable)
tmp <- lapply(tmp, set, i = NULL, j = "variable", value = NULL)
tmp
# $value1
# sim 1 2 3 4
# <num> <num> <num> <num> <num>
# 1: 1 1.0458737762 -0.4845954 0.1891288 0.05100633
# 2: 2 -0.0002406689 1.8093820 -0.8253280 1.14547045
# $value2
# sim 1 2 3 4
# <num> <num> <num> <num> <num>
# 1: 1 0.03157319 -0.8352058 -0.06876365 0.7467717
# 2: 2 -0.42551873 -0.7720822 0.15276411 0.9885968
我也经常把magrittr::%>%和data.table一起使用，所以可以转换成
library(data.table)
library(magrittr) # if %>% is not already available
as.data.table(dat) %>%
melt(., id = c("sim", "time")) %>%
dcast(., sim + variable ~ time) %>%
split(., .$variable) %>%
lapply(., set, i = NULL, j = "variable", value = NULL)
# $value1
# sim 1 2 3 4
# <num> <num> <num> <num> <num>
# 1: 1 1.0458737762 -0.4845954 0.1891288 0.05100633
# 2: 2 -0.0002406689 1.8093820 -0.8253280 1.14547045
# $value2
# sim 1 2 3 4
# <num> <num> <num> <num> <num>
# 1: 1 0.03157319 -0.8352058 -0.06876365 0.7467717
# 2: 2 -0.42551873 -0.7720822 0.15276411 0.9885968
整洁宇宙
library(dplyr)
library(tidyr) # pivot_longer, pivot_wider
dat %>%
pivot_longer(., -c(sim, time)) %>%
pivot_wider(., names_from = time, values_from = value) %>%
split(., .$name) %>%
lapply(., select, -name)
# $value1
# # A tibble: 2 x 5
# sim `1` `2` `3` `4`
# <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 1 1.05 -0.485 0.189 0.0510
# 2 2 -0.000241 1.81 -0.825 1.15
# $value2
# # A tibble: 2 x 5
# sim `1` `2` `3` `4`
# <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 1 0.0316 -0.835 -0.0688 0.747
# 2 2 -0.426 -0.772 0.153 0.989
另一种方式，使用单管道语法
library(tidyverse)
dat %>% pivot_longer(c(value1, value2)) %>%
group_split(name) %>% setNames(map(., ~.x[[3]][1])) %>%
map(~ .x %>% pivot_wider(id_cols = sim, names_from = time, values_from = value))
$value1
# A tibble: 2 x 5
sim `1` `2` `3` `4`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 -0.851 -0.0484 -0.656 -0.121
2 2 -0.645 1.59 -0.274 0.445
$value2
# A tibble: 2 x 5
sim `1` `2` `3` `4`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1.46 -1.62 -0.672 1.43
2 2 1.65 0.790 0.495 0.162
另一种方法：
library(dplyr)
library(tidyr)
wide_dat <- dat %>% pivot_wider(id_cols = sim, names_from = time, values_from = starts_with('value'))
lapply(lapply(split.default(wide_dat[-1], sub('_\d','',names(wide_dat[-1]))), function(x) cbind(wide_dat[1],x)), setNames, c('sim', 1:4))
$value1
sim 1 2 3 4
1 1 -0.1704969 0.2820143 1.181898 2.2377396
2 2 2.1920534 0.8214070 0.421177 0.7601796
$value2
sim 1 2 3 4
1 1 0.1760887 0.3440053 -0.8435849 0.6729751
2 2 -0.1714095 1.5125986 -0.5739871 -0.9648294