R 自动创建数据子集
我希望对大量数据进行子集划分，使其更易于处理和绘制。我希望创建大约10个变量长的组，这样我的图就不会变得过于累赘。我从“mpg”中提取了一些虚拟数据来演示我的计划R 自动创建数据子集,r,R,我希望对大量数据进行子集划分，使其更易于处理和绘制。我希望创建大约10个变量长的组，这样我的图就不会变得过于累赘。我从“mpg”中提取了一些虚拟数据来演示我的计划 library(tidyverse) ##Creates a list of car manufacturers and how many models they offer car_types <- as.data.frame(table(mpg$manufacturer)) ##filters to only havi
library(tidyverse)
##Creates a list of car manufacturers and how many models they offer
car_types <- as.data.frame(table(mpg$manufacturer))
##filters to only having manufacturers offering at least 10 models
most_models <-filter(car_types, Freq >= 10)
##subset mpg data to only manufacturers who have 10 models
mpg_mm <- filter(mpg,mpg$manufacturer %in% most_models$Var1)
#returns mpg, three at a time and only for manufacturers offering 10 models
set_1 <- filter(mpg_mm, mpg_mm$manufacturer %in% most_models[1:3,]$Var1)
set_2 <- filter(mpg_mm, mpg_mm$manufacturer %in% most_models[4:6,]$Var1)
库（tidyverse）
##创建汽车制造商列表以及他们提供的车型数量
汽车类型我们使用map2
（从purrr
）将“df”的“start”、“end”列的相应元素循环到列表中，根据从“start”、“end”开始的索引顺序过滤“mpg\u mm”行（.x:.y
）从“most_models”中提取“Var1”，并检查它是否为%
“manufacturer”中的%，并删除列表中的元素，这些元素具有0行保留（或放弃）
库（dplyr）
图书馆（purrr）
外单位%
过滤器（大多数_型号$Var1[.x:.y]）中的制造商百分比%>%
保持（~nrow（.x）>0）
注意：如果我们需要单个数据集，请使用map2\u-dfr
以下是基本的数据集方法：
inds <- seq(nrow(most_models))
result <- by(inds, ceiling(inds/3), function(x)
subset(mpg_mm, manufacturer %in% most_models$Var1[x]))
inds简单地说，这个策略也会起作用-
1在要创建子集的字段上创建一个densite\u秩
2分组
将密集等级
分解为整数部分
，即按所需数字（如3）进行分组
无需创建包含开始和结束字段的数据框
library(tidyverse)
mpg %>% group_by(manufacturer) %>% filter(n() >=10) %>% ungroup() %>%
mutate(dummy = dense_rank(manufacturer) %/% 3) %>% group_split(dummy)
输出
祝你好运你没有接受任何答案，这些答案不符合你的目的吗？请告诉我。接受和即将发布的答案是最佳实践之一，以供将来参考。
inds <- seq(nrow(most_models))
result <- by(inds, ceiling(inds/3), function(x)
subset(mpg_mm, manufacturer %in% most_models$Var1[x]))
library(tidyverse)
mpg %>% group_by(manufacturer) %>% filter(n() >=10) %>% ungroup() %>%
mutate(dummy = dense_rank(manufacturer) %/% 3) %>% group_split(dummy)
<list_of<
tbl_df<
manufacturer: character
model : character
displ : double
year : integer
cyl : integer
trans : character
drv : character
cty : integer
hwy : integer
fl : character
class : character
dummy : double
>
>[4]>
[[1]]
# A tibble: 37 x 12
manufacturer model displ year cyl trans drv cty hwy fl class dummy
<chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> <dbl>
1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compact 0
2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compact 0
3 audi a4 2 2008 4 manual(m6) f 20 31 p compact 0
4 audi a4 2 2008 4 auto(av) f 21 30 p compact 0
5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compact 0
6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compact 0
7 audi a4 3.1 2008 6 auto(av) f 18 27 p compact 0
8 audi a4 quattro 1.8 1999 4 manual(m5) 4 18 26 p compact 0
9 audi a4 quattro 1.8 1999 4 auto(l5) 4 16 25 p compact 0
10 audi a4 quattro 2 2008 4 manual(m6) 4 20 28 p compact 0
# ... with 27 more rows
[[2]]
# A tibble: 76 x 12
manufacturer model displ year cyl trans drv cty hwy fl class dummy
<chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> <dbl>
1 dodge caravan 2wd 2.4 1999 4 auto(l3) f 18 24 r minivan 1
2 dodge caravan 2wd 3 1999 6 auto(l4) f 17 24 r minivan 1
3 dodge caravan 2wd 3.3 1999 6 auto(l4) f 16 22 r minivan 1
4 dodge caravan 2wd 3.3 1999 6 auto(l4) f 16 22 r minivan 1
5 dodge caravan 2wd 3.3 2008 6 auto(l4) f 17 24 r minivan 1
6 dodge caravan 2wd 3.3 2008 6 auto(l4) f 17 24 r minivan 1
7 dodge caravan 2wd 3.3 2008 6 auto(l4) f 11 17 e minivan 1
8 dodge caravan 2wd 3.8 1999 6 auto(l4) f 15 22 r minivan 1
9 dodge caravan 2wd 3.8 1999 6 auto(l4) f 15 21 r minivan 1
10 dodge caravan 2wd 3.8 2008 6 auto(l6) f 16 23 r minivan 1
# ... with 66 more rows
[[3]]
# A tibble: 61 x 12
manufacturer model displ year cyl trans drv cty hwy fl class dummy
<chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> <dbl>
1 nissan altima 2.4 1999 4 manual(m5) f 21 29 r compact 2
2 nissan altima 2.4 1999 4 auto(l4) f 19 27 r compact 2
3 nissan altima 2.5 2008 4 auto(av) f 23 31 r midsize 2
4 nissan altima 2.5 2008 4 manual(m6) f 23 32 r midsize 2
5 nissan altima 3.5 2008 6 manual(m6) f 19 27 p midsize 2
6 nissan altima 3.5 2008 6 auto(av) f 19 26 p midsize 2
7 nissan maxima 3 1999 6 auto(l4) f 18 26 r midsize 2
8 nissan maxima 3 1999 6 manual(m5) f 19 25 r midsize 2
9 nissan maxima 3.5 2008 6 auto(av) f 19 25 p midsize 2
10 nissan pathfinder 4wd 3.3 1999 6 auto(l4) 4 14 17 r suv 2
# ... with 51 more rows
[[4]]
# A tibble: 27 x 12
manufacturer model displ year cyl trans drv cty hwy fl class dummy
<chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> <dbl>
1 volkswagen gti 2 1999 4 manual(m5) f 21 29 r compact 3
2 volkswagen gti 2 1999 4 auto(l4) f 19 26 r compact 3
3 volkswagen gti 2 2008 4 manual(m6) f 21 29 p compact 3
4 volkswagen gti 2 2008 4 auto(s6) f 22 29 p compact 3
5 volkswagen gti 2.8 1999 6 manual(m5) f 17 24 r compact 3
6 volkswagen jetta 1.9 1999 4 manual(m5) f 33 44 d compact 3
7 volkswagen jetta 2 1999 4 manual(m5) f 21 29 r compact 3
8 volkswagen jetta 2 1999 4 auto(l4) f 19 26 r compact 3
9 volkswagen jetta 2 2008 4 auto(s6) f 22 29 p compact 3
10 volkswagen jetta 2 2008 4 manual(m6) f 21 29 p compact 3
# ... with 17 more rows
dd <- mpg %>% group_by(manufacturer) %>% filter(n() >=10) %>% ungroup() %>%
mutate(dummy = dense_rank(manufacturer) %/% 3) %>% group_split(dummy)
names(dd) <- paste("set", 1:length(dd), sep = "_")
list2env(dd, envir = .GlobalEnv)