1 purrr风格函数（匿名函数）

1.1 一元函数

若要表达 \(f(x) = x ^ 2 + 1\)

purrr风格
- ~ .x ^ 2 + 1 #其中.x是序列参数
Base R风格
- function(x) x^2+1

1.2 二元函数

若要表达\(f(x, y) = x ^ 2 + 3y\)

purrr风格
- ~ .x ^ 2 + 3 * .y # 其中.x，.y是序列参数
Base R风格
- function(x, y) x ^ 2 + 3 * y

1.3 多元函数

若要表达\(f(x, y, z) = x ^ 2 + 3y - 5z\)

purrr风格
- ~ ..1 ^ 2 + 3 * ..2 - 5 * ..3 # 其中..1，..2, ..3是序列参数
Base R风格
- function(x, y) x ^ 2 + 3 * y

2 map函数

2.1 主要函数

map(.x, .f, ...)
map2(.x, .y, .f(x,y))
map_chr(.x, .f): #返回字符型向量
map_lgl(.x, .f): #返回逻辑型向量
map_dbl(.x, .f): #返回实数型向量
map_int(.x, .f): #返回整数型向量
map_dfr(.x, .f): #返回数据框列表，再 bind_rows 按行合并为一个数据框
map_dfc(.x, .f): #返回数据框列表，再 bind_cols 按列合并为一个数据框
map_raw(.x, .f, ...)
walk(.x, .f, ...)
pwalk(.l, .f, ...)
pmap(.l, .f, ...)
reduce(.x, .f, ..., .init =  , .dir = c("forward", "backwark"))

其操作是把第一个向量参数分别代入，第二个参数函数中运算。

2.2 各函数示例

x <- list(1, 1, 1)
y <- list(10, 20, 30)
z <- list(100, 200, 300)

#map

1:10 %>% map(rnorm, n = 10)

## [[1]]
##  [1] -0.2312546  0.2170350  1.2166008  1.0589713  1.2363006  0.9974856
##  [7]  1.5858387  0.2143689  1.1256797  0.4967441
## 
## [[2]]
##  [1] 2.937753 2.143710 1.327630 2.400958 2.298002 1.965064 0.773323 1.030068
##  [9] 3.411796 1.225296
## 
## [[3]]
##  [1] 3.195378 3.659540 4.388030 4.526718 3.453919 1.069416 4.342772 4.366289
##  [9] 2.436807 3.555973
## 
## [[4]]
##  [1] 3.724134 4.135160 4.622716 4.658719 4.857236 3.766783 2.957374 3.937567
##  [9] 5.357020 2.684989
## 
## [[5]]
##  [1] 3.646279 7.617171 4.558268 4.589027 5.132298 6.311953 5.028724 6.016986
##  [9] 3.996754 4.415504
## 
## [[6]]
##  [1] 6.183010 5.517258 7.369602 5.326449 6.701092 5.733973 5.371256 6.546578
##  [9] 6.141289 5.130093
## 
## [[7]]
##  [1] 5.961460 7.986753 4.672181 8.709407 4.975756 6.850655 6.737405 6.476136
##  [9] 8.886857 6.348038
## 
## [[8]]
##  [1] 9.203260 9.057937 7.954667 7.335570 5.881353 9.408116 6.319427 7.111108
##  [9] 9.629299 9.089298
## 
## [[9]]
##  [1] 7.544799 7.663894 8.252694 8.909437 7.462485 9.146917 9.122637 6.880307
##  [9] 8.917760 9.958957
## 
## [[10]]
##  [1]  9.915833 10.273913  8.959154  8.903351 10.086287  9.273798 10.332931
##  [8]  7.967636 10.347668  9.185299

#map2



map2(x, y, ~ .x + .y)

## [[1]]
## [1] 11
## 
## [[2]]
## [1] 21
## 
## [[3]]
## [1] 31

lst <- tibble(x = c(1, 3, 5),
               y = c(2, 4, 6),
               z = c(3, 6, 9)) 

# pmap

pmap(lst,  ~ ..1 ^ 2 + 3 * ..2 - 5 * ..3)

## [[1]]
## [1] -8
## 
## [[2]]
## [1] -9
## 
## [[3]]
## [1] -2

3 解决问题

3.1 批量保存数据文件

mpg %>% 
  nest_by(manufacturer, .keep = T) %>% 
  pwalk(
    ~ write_csv(..2, paste0("data/car_", ..1, ".csv") )
  )

3.2 批量合并文件

##批量读取目录下同类文件
#读取工作目录下文件名以.csv结尾的文件
files <- dir(path = "data/", pattern = "\\.csv$", full.names = T)
#读取工作目录下文件名以df开头的文件
# files <- dir(pattern = "^df", full.names = T)

files

##  [1] "data/car_audi.csv"       "data/car_chevrolet.csv" 
##  [3] "data/car_dodge.csv"      "data/car_ford.csv"      
##  [5] "data/car_honda.csv"      "data/car_hyundai.csv"   
##  [7] "data/car_jeep.csv"       "data/car_land rover.csv"
##  [9] "data/car_lincoln.csv"    "data/car_mercury.csv"   
## [11] "data/car_nissan.csv"     "data/car_pontiac.csv"   
## [13] "data/car_subaru.csv"     "data/car_toyota.csv"    
## [15] "data/car_volkswagen.csv"

names(files) <- basename(files)
# 批量合并
df  <-  map(files, read_csv, col_types = cols(drv = col_character())) %>% 
  #批量左合并为一个数据集
  reduce(left_join)

## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")
## Joining, by = c("manufacturer", "model", "displ", "year", "cyl", "trans", "drv", "cty", "hwy", "fl", "class")

  #按某个id进行交集合并
  # reduce(inner_join)
df

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact
audi	a4	2.8	1999	6	manual(m5)	f	18	26	p	compact
audi	a4	3.1	2008	6	auto(av)	f	18	27	p	compact
audi	a4 quattro	1.8	1999	4	manual(m5)	4	18	26	p	compact
audi	a4 quattro	1.8	1999	4	auto(l5)	4	16	25	p	compact
audi	a4 quattro	2.0	2008	4	manual(m6)	4	20	28	p	compact
audi	a4 quattro	2.0	2008	4	auto(s6)	4	19	27	p	compact
audi	a4 quattro	2.8	1999	6	auto(l5)	4	15	25	p	compact
audi	a4 quattro	2.8	1999	6	manual(m5)	4	17	25	p	compact
audi	a4 quattro	3.1	2008	6	auto(s6)	4	17	25	p	compact
audi	a4 quattro	3.1	2008	6	manual(m6)	4	15	25	p	compact
audi	a6 quattro	2.8	1999	6	auto(l5)	4	15	24	p	midsize
audi	a6 quattro	3.1	2008	6	auto(s6)	4	17	25	p	midsize
audi	a6 quattro	4.2	2008	8	auto(s6)	4	16	23	p	midsize

## 批量拼结
#按行直接追加()
map_dfr(files,
        #读取CSV
        read_csv,
        #read_csv的参数，可以有多个。
        col_types = cols(drv = col_character()),
        #map_dfr的参数，为读入的数据添加一个文件名列变量
        .id = "filename")

filename	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
car_audi.csv	audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
car_audi.csv	audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
car_audi.csv	audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
car_audi.csv	audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
car_audi.csv	audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact
car_audi.csv	audi	a4	2.8	1999	6	manual(m5)	f	18	26	p	compact
car_audi.csv	audi	a4	3.1	2008	6	auto(av)	f	18	27	p	compact
car_audi.csv	audi	a4 quattro	1.8	1999	4	manual(m5)	4	18	26	p	compact
car_audi.csv	audi	a4 quattro	1.8	1999	4	auto(l5)	4	16	25	p	compact
car_audi.csv	audi	a4 quattro	2.0	2008	4	manual(m6)	4	20	28	p	compact
car_audi.csv	audi	a4 quattro	2.0	2008	4	auto(s6)	4	19	27	p	compact
car_audi.csv	audi	a4 quattro	2.8	1999	6	auto(l5)	4	15	25	p	compact
car_audi.csv	audi	a4 quattro	2.8	1999	6	manual(m5)	4	17	25	p	compact
car_audi.csv	audi	a4 quattro	3.1	2008	6	auto(s6)	4	17	25	p	compact
car_audi.csv	audi	a4 quattro	3.1	2008	6	manual(m6)	4	15	25	p	compact
car_audi.csv	audi	a6 quattro	2.8	1999	6	auto(l5)	4	15	24	p	midsize
car_audi.csv	audi	a6 quattro	3.1	2008	6	auto(s6)	4	17	25	p	midsize
car_audi.csv	audi	a6 quattro	4.2	2008	8	auto(s6)	4	16	23	p	midsize
car_chevrolet.csv	chevrolet	c1500 suburban 2wd	5.3	2008	8	auto(l4)	r	14	20	r	suv
car_chevrolet.csv	chevrolet	c1500 suburban 2wd	5.3	2008	8	auto(l4)	r	11	15	e	suv
car_chevrolet.csv	chevrolet	c1500 suburban 2wd	5.3	2008	8	auto(l4)	r	14	20	r	suv
car_chevrolet.csv	chevrolet	c1500 suburban 2wd	5.7	1999	8	auto(l4)	r	13	17	r	suv
car_chevrolet.csv	chevrolet	c1500 suburban 2wd	6.0	2008	8	auto(l4)	r	12	17	r	suv
car_chevrolet.csv	chevrolet	corvette	5.7	1999	8	manual(m6)	r	16	26	p	2seater
car_chevrolet.csv	chevrolet	corvette	5.7	1999	8	auto(l4)	r	15	23	p	2seater
car_chevrolet.csv	chevrolet	corvette	6.2	2008	8	manual(m6)	r	16	26	p	2seater
car_chevrolet.csv	chevrolet	corvette	6.2	2008	8	auto(s6)	r	15	25	p	2seater
car_chevrolet.csv	chevrolet	corvette	7.0	2008	8	manual(m6)	r	15	24	p	2seater
car_chevrolet.csv	chevrolet	k1500 tahoe 4wd	5.3	2008	8	auto(l4)	4	14	19	r	suv
car_chevrolet.csv	chevrolet	k1500 tahoe 4wd	5.3	2008	8	auto(l4)	4	11	14	e	suv
car_chevrolet.csv	chevrolet	k1500 tahoe 4wd	5.7	1999	8	auto(l4)	4	11	15	r	suv
car_chevrolet.csv	chevrolet	k1500 tahoe 4wd	6.5	1999	8	auto(l4)	4	14	17	d	suv
car_chevrolet.csv	chevrolet	malibu	2.4	1999	4	auto(l4)	f	19	27	r	midsize
car_chevrolet.csv	chevrolet	malibu	2.4	2008	4	auto(l4)	f	22	30	r	midsize
car_chevrolet.csv	chevrolet	malibu	3.1	1999	6	auto(l4)	f	18	26	r	midsize
car_chevrolet.csv	chevrolet	malibu	3.5	2008	6	auto(l4)	f	18	29	r	midsize
car_chevrolet.csv	chevrolet	malibu	3.6	2008	6	auto(s6)	f	17	26	r	midsize
car_dodge.csv	dodge	caravan 2wd	2.4	1999	4	auto(l3)	f	18	24	r	minivan
car_dodge.csv	dodge	caravan 2wd	3.0	1999	6	auto(l4)	f	17	24	r	minivan
car_dodge.csv	dodge	caravan 2wd	3.3	1999	6	auto(l4)	f	16	22	r	minivan
car_dodge.csv	dodge	caravan 2wd	3.3	1999	6	auto(l4)	f	16	22	r	minivan
car_dodge.csv	dodge	caravan 2wd	3.3	2008	6	auto(l4)	f	17	24	r	minivan
car_dodge.csv	dodge	caravan 2wd	3.3	2008	6	auto(l4)	f	17	24	r	minivan
car_dodge.csv	dodge	caravan 2wd	3.3	2008	6	auto(l4)	f	11	17	e	minivan
car_dodge.csv	dodge	caravan 2wd	3.8	1999	6	auto(l4)	f	15	22	r	minivan
car_dodge.csv	dodge	caravan 2wd	3.8	1999	6	auto(l4)	f	15	21	r	minivan
car_dodge.csv	dodge	caravan 2wd	3.8	2008	6	auto(l6)	f	16	23	r	minivan
car_dodge.csv	dodge	caravan 2wd	4.0	2008	6	auto(l6)	f	16	23	r	minivan
car_dodge.csv	dodge	dakota pickup 4wd	3.7	2008	6	manual(m6)	4	15	19	r	pickup
car_dodge.csv	dodge	dakota pickup 4wd	3.7	2008	6	auto(l4)	4	14	18	r	pickup
car_dodge.csv	dodge	dakota pickup 4wd	3.9	1999	6	auto(l4)	4	13	17	r	pickup
car_dodge.csv	dodge	dakota pickup 4wd	3.9	1999	6	manual(m5)	4	14	17	r	pickup
car_dodge.csv	dodge	dakota pickup 4wd	4.7	2008	8	auto(l5)	4	14	19	r	pickup
car_dodge.csv	dodge	dakota pickup 4wd	4.7	2008	8	auto(l5)	4	14	19	r	pickup
car_dodge.csv	dodge	dakota pickup 4wd	4.7	2008	8	auto(l5)	4	9	12	e	pickup
car_dodge.csv	dodge	dakota pickup 4wd	5.2	1999	8	manual(m5)	4	11	17	r	pickup
car_dodge.csv	dodge	dakota pickup 4wd	5.2	1999	8	auto(l4)	4	11	15	r	pickup
car_dodge.csv	dodge	durango 4wd	3.9	1999	6	auto(l4)	4	13	17	r	suv
car_dodge.csv	dodge	durango 4wd	4.7	2008	8	auto(l5)	4	13	17	r	suv
car_dodge.csv	dodge	durango 4wd	4.7	2008	8	auto(l5)	4	9	12	e	suv
car_dodge.csv	dodge	durango 4wd	4.7	2008	8	auto(l5)	4	13	17	r	suv
car_dodge.csv	dodge	durango 4wd	5.2	1999	8	auto(l4)	4	11	16	r	suv
car_dodge.csv	dodge	durango 4wd	5.7	2008	8	auto(l5)	4	13	18	r	suv
car_dodge.csv	dodge	durango 4wd	5.9	1999	8	auto(l4)	4	11	15	r	suv
car_dodge.csv	dodge	ram 1500 pickup 4wd	4.7	2008	8	manual(m6)	4	12	16	r	pickup
car_dodge.csv	dodge	ram 1500 pickup 4wd	4.7	2008	8	auto(l5)	4	9	12	e	pickup
car_dodge.csv	dodge	ram 1500 pickup 4wd	4.7	2008	8	auto(l5)	4	13	17	r	pickup
car_dodge.csv	dodge	ram 1500 pickup 4wd	4.7	2008	8	auto(l5)	4	13	17	r	pickup
car_dodge.csv	dodge	ram 1500 pickup 4wd	4.7	2008	8	manual(m6)	4	12	16	r	pickup
car_dodge.csv	dodge	ram 1500 pickup 4wd	4.7	2008	8	manual(m6)	4	9	12	e	pickup
car_dodge.csv	dodge	ram 1500 pickup 4wd	5.2	1999	8	auto(l4)	4	11	15	r	pickup
car_dodge.csv	dodge	ram 1500 pickup 4wd	5.2	1999	8	manual(m5)	4	11	16	r	pickup
car_dodge.csv	dodge	ram 1500 pickup 4wd	5.7	2008	8	auto(l5)	4	13	17	r	pickup
car_dodge.csv	dodge	ram 1500 pickup 4wd	5.9	1999	8	auto(l4)	4	11	15	r	pickup
car_ford.csv	ford	expedition 2wd	4.6	1999	8	auto(l4)	r	11	17	r	suv
car_ford.csv	ford	expedition 2wd	5.4	1999	8	auto(l4)	r	11	17	r	suv
car_ford.csv	ford	expedition 2wd	5.4	2008	8	auto(l6)	r	12	18	r	suv
car_ford.csv	ford	explorer 4wd	4.0	1999	6	auto(l5)	4	14	17	r	suv
car_ford.csv	ford	explorer 4wd	4.0	1999	6	manual(m5)	4	15	19	r	suv
car_ford.csv	ford	explorer 4wd	4.0	1999	6	auto(l5)	4	14	17	r	suv
car_ford.csv	ford	explorer 4wd	4.0	2008	6	auto(l5)	4	13	19	r	suv
car_ford.csv	ford	explorer 4wd	4.6	2008	8	auto(l6)	4	13	19	r	suv
car_ford.csv	ford	explorer 4wd	5.0	1999	8	auto(l4)	4	13	17	r	suv
car_ford.csv	ford	f150 pickup 4wd	4.2	1999	6	auto(l4)	4	14	17	r	pickup
car_ford.csv	ford	f150 pickup 4wd	4.2	1999	6	manual(m5)	4	14	17	r	pickup
car_ford.csv	ford	f150 pickup 4wd	4.6	1999	8	manual(m5)	4	13	16	r	pickup
car_ford.csv	ford	f150 pickup 4wd	4.6	1999	8	auto(l4)	4	13	16	r	pickup
car_ford.csv	ford	f150 pickup 4wd	4.6	2008	8	auto(l4)	4	13	17	r	pickup
car_ford.csv	ford	f150 pickup 4wd	5.4	1999	8	auto(l4)	4	11	15	r	pickup
car_ford.csv	ford	f150 pickup 4wd	5.4	2008	8	auto(l4)	4	13	17	r	pickup
car_ford.csv	ford	mustang	3.8	1999	6	manual(m5)	r	18	26	r	subcompact
car_ford.csv	ford	mustang	3.8	1999	6	auto(l4)	r	18	25	r	subcompact
car_ford.csv	ford	mustang	4.0	2008	6	manual(m5)	r	17	26	r	subcompact
car_ford.csv	ford	mustang	4.0	2008	6	auto(l5)	r	16	24	r	subcompact
car_ford.csv	ford	mustang	4.6	1999	8	auto(l4)	r	15	21	r	subcompact
car_ford.csv	ford	mustang	4.6	1999	8	manual(m5)	r	15	22	r	subcompact
car_ford.csv	ford	mustang	4.6	2008	8	manual(m5)	r	15	23	r	subcompact
car_ford.csv	ford	mustang	4.6	2008	8	auto(l5)	r	15	22	r	subcompact
car_ford.csv	ford	mustang	5.4	2008	8	manual(m6)	r	14	20	p	subcompact
car_honda.csv	honda	civic	1.6	1999	4	manual(m5)	f	28	33	r	subcompact
car_honda.csv	honda	civic	1.6	1999	4	auto(l4)	f	24	32	r	subcompact
car_honda.csv	honda	civic	1.6	1999	4	manual(m5)	f	25	32	r	subcompact
car_honda.csv	honda	civic	1.6	1999	4	manual(m5)	f	23	29	p	subcompact
car_honda.csv	honda	civic	1.6	1999	4	auto(l4)	f	24	32	r	subcompact
car_honda.csv	honda	civic	1.8	2008	4	manual(m5)	f	26	34	r	subcompact
car_honda.csv	honda	civic	1.8	2008	4	auto(l5)	f	25	36	r	subcompact
car_honda.csv	honda	civic	1.8	2008	4	auto(l5)	f	24	36	c	subcompact
car_honda.csv	honda	civic	2.0	2008	4	manual(m6)	f	21	29	p	subcompact
car_hyundai.csv	hyundai	sonata	2.4	1999	4	auto(l4)	f	18	26	r	midsize
car_hyundai.csv	hyundai	sonata	2.4	1999	4	manual(m5)	f	18	27	r	midsize
car_hyundai.csv	hyundai	sonata	2.4	2008	4	auto(l4)	f	21	30	r	midsize
car_hyundai.csv	hyundai	sonata	2.4	2008	4	manual(m5)	f	21	31	r	midsize
car_hyundai.csv	hyundai	sonata	2.5	1999	6	auto(l4)	f	18	26	r	midsize
car_hyundai.csv	hyundai	sonata	2.5	1999	6	manual(m5)	f	18	26	r	midsize
car_hyundai.csv	hyundai	sonata	3.3	2008	6	auto(l5)	f	19	28	r	midsize
car_hyundai.csv	hyundai	tiburon	2.0	1999	4	auto(l4)	f	19	26	r	subcompact
car_hyundai.csv	hyundai	tiburon	2.0	1999	4	manual(m5)	f	19	29	r	subcompact
car_hyundai.csv	hyundai	tiburon	2.0	2008	4	manual(m5)	f	20	28	r	subcompact
car_hyundai.csv	hyundai	tiburon	2.0	2008	4	auto(l4)	f	20	27	r	subcompact
car_hyundai.csv	hyundai	tiburon	2.7	2008	6	auto(l4)	f	17	24	r	subcompact
car_hyundai.csv	hyundai	tiburon	2.7	2008	6	manual(m6)	f	16	24	r	subcompact
car_hyundai.csv	hyundai	tiburon	2.7	2008	6	manual(m5)	f	17	24	r	subcompact
car_jeep.csv	jeep	grand cherokee 4wd	3.0	2008	6	auto(l5)	4	17	22	d	suv
car_jeep.csv	jeep	grand cherokee 4wd	3.7	2008	6	auto(l5)	4	15	19	r	suv
car_jeep.csv	jeep	grand cherokee 4wd	4.0	1999	6	auto(l4)	4	15	20	r	suv
car_jeep.csv	jeep	grand cherokee 4wd	4.7	1999	8	auto(l4)	4	14	17	r	suv
car_jeep.csv	jeep	grand cherokee 4wd	4.7	2008	8	auto(l5)	4	9	12	e	suv
car_jeep.csv	jeep	grand cherokee 4wd	4.7	2008	8	auto(l5)	4	14	19	r	suv
car_jeep.csv	jeep	grand cherokee 4wd	5.7	2008	8	auto(l5)	4	13	18	r	suv
car_jeep.csv	jeep	grand cherokee 4wd	6.1	2008	8	auto(l5)	4	11	14	p	suv
car_land rover.csv	land rover	range rover	4.0	1999	8	auto(l4)	4	11	15	p	suv
car_land rover.csv	land rover	range rover	4.2	2008	8	auto(s6)	4	12	18	r	suv
car_land rover.csv	land rover	range rover	4.4	2008	8	auto(s6)	4	12	18	r	suv
car_land rover.csv	land rover	range rover	4.6	1999	8	auto(l4)	4	11	15	p	suv
car_lincoln.csv	lincoln	navigator 2wd	5.4	1999	8	auto(l4)	r	11	17	r	suv
car_lincoln.csv	lincoln	navigator 2wd	5.4	1999	8	auto(l4)	r	11	16	p	suv
car_lincoln.csv	lincoln	navigator 2wd	5.4	2008	8	auto(l6)	r	12	18	r	suv
car_mercury.csv	mercury	mountaineer 4wd	4.0	1999	6	auto(l5)	4	14	17	r	suv
car_mercury.csv	mercury	mountaineer 4wd	4.0	2008	6	auto(l5)	4	13	19	r	suv
car_mercury.csv	mercury	mountaineer 4wd	4.6	2008	8	auto(l6)	4	13	19	r	suv
car_mercury.csv	mercury	mountaineer 4wd	5.0	1999	8	auto(l4)	4	13	17	r	suv
car_nissan.csv	nissan	altima	2.4	1999	4	manual(m5)	f	21	29	r	compact
car_nissan.csv	nissan	altima	2.4	1999	4	auto(l4)	f	19	27	r	compact
car_nissan.csv	nissan	altima	2.5	2008	4	auto(av)	f	23	31	r	midsize
car_nissan.csv	nissan	altima	2.5	2008	4	manual(m6)	f	23	32	r	midsize
car_nissan.csv	nissan	altima	3.5	2008	6	manual(m6)	f	19	27	p	midsize
car_nissan.csv	nissan	altima	3.5	2008	6	auto(av)	f	19	26	p	midsize
car_nissan.csv	nissan	maxima	3.0	1999	6	auto(l4)	f	18	26	r	midsize
car_nissan.csv	nissan	maxima	3.0	1999	6	manual(m5)	f	19	25	r	midsize
car_nissan.csv	nissan	maxima	3.5	2008	6	auto(av)	f	19	25	p	midsize
car_nissan.csv	nissan	pathfinder 4wd	3.3	1999	6	auto(l4)	4	14	17	r	suv
car_nissan.csv	nissan	pathfinder 4wd	3.3	1999	6	manual(m5)	4	15	17	r	suv
car_nissan.csv	nissan	pathfinder 4wd	4.0	2008	6	auto(l5)	4	14	20	p	suv
car_nissan.csv	nissan	pathfinder 4wd	5.6	2008	8	auto(s5)	4	12	18	p	suv
car_pontiac.csv	pontiac	grand prix	3.1	1999	6	auto(l4)	f	18	26	r	midsize
car_pontiac.csv	pontiac	grand prix	3.8	1999	6	auto(l4)	f	16	26	p	midsize
car_pontiac.csv	pontiac	grand prix	3.8	1999	6	auto(l4)	f	17	27	r	midsize
car_pontiac.csv	pontiac	grand prix	3.8	2008	6	auto(l4)	f	18	28	r	midsize
car_pontiac.csv	pontiac	grand prix	5.3	2008	8	auto(s4)	f	16	25	p	midsize
car_subaru.csv	subaru	forester awd	2.5	1999	4	manual(m5)	4	18	25	r	suv
car_subaru.csv	subaru	forester awd	2.5	1999	4	auto(l4)	4	18	24	r	suv
car_subaru.csv	subaru	forester awd	2.5	2008	4	manual(m5)	4	20	27	r	suv
car_subaru.csv	subaru	forester awd	2.5	2008	4	manual(m5)	4	19	25	p	suv
car_subaru.csv	subaru	forester awd	2.5	2008	4	auto(l4)	4	20	26	r	suv
car_subaru.csv	subaru	forester awd	2.5	2008	4	auto(l4)	4	18	23	p	suv
car_subaru.csv	subaru	impreza awd	2.2	1999	4	auto(l4)	4	21	26	r	subcompact
car_subaru.csv	subaru	impreza awd	2.2	1999	4	manual(m5)	4	19	26	r	subcompact
car_subaru.csv	subaru	impreza awd	2.5	1999	4	manual(m5)	4	19	26	r	subcompact
car_subaru.csv	subaru	impreza awd	2.5	1999	4	auto(l4)	4	19	26	r	subcompact
car_subaru.csv	subaru	impreza awd	2.5	2008	4	auto(s4)	4	20	25	p	compact
car_subaru.csv	subaru	impreza awd	2.5	2008	4	auto(s4)	4	20	27	r	compact
car_subaru.csv	subaru	impreza awd	2.5	2008	4	manual(m5)	4	19	25	p	compact
car_subaru.csv	subaru	impreza awd	2.5	2008	4	manual(m5)	4	20	27	r	compact
car_toyota.csv	toyota	4runner 4wd	2.7	1999	4	manual(m5)	4	15	20	r	suv
car_toyota.csv	toyota	4runner 4wd	2.7	1999	4	auto(l4)	4	16	20	r	suv
car_toyota.csv	toyota	4runner 4wd	3.4	1999	6	auto(l4)	4	15	19	r	suv
car_toyota.csv	toyota	4runner 4wd	3.4	1999	6	manual(m5)	4	15	17	r	suv
car_toyota.csv	toyota	4runner 4wd	4.0	2008	6	auto(l5)	4	16	20	r	suv
car_toyota.csv	toyota	4runner 4wd	4.7	2008	8	auto(l5)	4	14	17	r	suv
car_toyota.csv	toyota	camry	2.2	1999	4	manual(m5)	f	21	29	r	midsize
car_toyota.csv	toyota	camry	2.2	1999	4	auto(l4)	f	21	27	r	midsize
car_toyota.csv	toyota	camry	2.4	2008	4	manual(m5)	f	21	31	r	midsize
car_toyota.csv	toyota	camry	2.4	2008	4	auto(l5)	f	21	31	r	midsize
car_toyota.csv	toyota	camry	3.0	1999	6	auto(l4)	f	18	26	r	midsize
car_toyota.csv	toyota	camry	3.0	1999	6	manual(m5)	f	18	26	r	midsize
car_toyota.csv	toyota	camry	3.5	2008	6	auto(s6)	f	19	28	r	midsize
car_toyota.csv	toyota	camry solara	2.2	1999	4	auto(l4)	f	21	27	r	compact
car_toyota.csv	toyota	camry solara	2.2	1999	4	manual(m5)	f	21	29	r	compact
car_toyota.csv	toyota	camry solara	2.4	2008	4	manual(m5)	f	21	31	r	compact
car_toyota.csv	toyota	camry solara	2.4	2008	4	auto(s5)	f	22	31	r	compact
car_toyota.csv	toyota	camry solara	3.0	1999	6	auto(l4)	f	18	26	r	compact
car_toyota.csv	toyota	camry solara	3.0	1999	6	manual(m5)	f	18	26	r	compact
car_toyota.csv	toyota	camry solara	3.3	2008	6	auto(s5)	f	18	27	r	compact
car_toyota.csv	toyota	corolla	1.8	1999	4	auto(l3)	f	24	30	r	compact
car_toyota.csv	toyota	corolla	1.8	1999	4	auto(l4)	f	24	33	r	compact
car_toyota.csv	toyota	corolla	1.8	1999	4	manual(m5)	f	26	35	r	compact
car_toyota.csv	toyota	corolla	1.8	2008	4	manual(m5)	f	28	37	r	compact
car_toyota.csv	toyota	corolla	1.8	2008	4	auto(l4)	f	26	35	r	compact
car_toyota.csv	toyota	land cruiser wagon 4wd	4.7	1999	8	auto(l4)	4	11	15	r	suv
car_toyota.csv	toyota	land cruiser wagon 4wd	5.7	2008	8	auto(s6)	4	13	18	r	suv
car_toyota.csv	toyota	toyota tacoma 4wd	2.7	1999	4	manual(m5)	4	15	20	r	pickup
car_toyota.csv	toyota	toyota tacoma 4wd	2.7	1999	4	auto(l4)	4	16	20	r	pickup
car_toyota.csv	toyota	toyota tacoma 4wd	2.7	2008	4	manual(m5)	4	17	22	r	pickup
car_toyota.csv	toyota	toyota tacoma 4wd	3.4	1999	6	manual(m5)	4	15	17	r	pickup
car_toyota.csv	toyota	toyota tacoma 4wd	3.4	1999	6	auto(l4)	4	15	19	r	pickup
car_toyota.csv	toyota	toyota tacoma 4wd	4.0	2008	6	manual(m6)	4	15	18	r	pickup
car_toyota.csv	toyota	toyota tacoma 4wd	4.0	2008	6	auto(l5)	4	16	20	r	pickup
car_volkswagen.csv	volkswagen	gti	2.0	1999	4	manual(m5)	f	21	29	r	compact
car_volkswagen.csv	volkswagen	gti	2.0	1999	4	auto(l4)	f	19	26	r	compact
car_volkswagen.csv	volkswagen	gti	2.0	2008	4	manual(m6)	f	21	29	p	compact
car_volkswagen.csv	volkswagen	gti	2.0	2008	4	auto(s6)	f	22	29	p	compact
car_volkswagen.csv	volkswagen	gti	2.8	1999	6	manual(m5)	f	17	24	r	compact
car_volkswagen.csv	volkswagen	jetta	1.9	1999	4	manual(m5)	f	33	44	d	compact
car_volkswagen.csv	volkswagen	jetta	2.0	1999	4	manual(m5)	f	21	29	r	compact
car_volkswagen.csv	volkswagen	jetta	2.0	1999	4	auto(l4)	f	19	26	r	compact
car_volkswagen.csv	volkswagen	jetta	2.0	2008	4	auto(s6)	f	22	29	p	compact
car_volkswagen.csv	volkswagen	jetta	2.0	2008	4	manual(m6)	f	21	29	p	compact
car_volkswagen.csv	volkswagen	jetta	2.5	2008	5	auto(s6)	f	21	29	r	compact
car_volkswagen.csv	volkswagen	jetta	2.5	2008	5	manual(m5)	f	21	29	r	compact
car_volkswagen.csv	volkswagen	jetta	2.8	1999	6	auto(l4)	f	16	23	r	compact
car_volkswagen.csv	volkswagen	jetta	2.8	1999	6	manual(m5)	f	17	24	r	compact
car_volkswagen.csv	volkswagen	new beetle	1.9	1999	4	manual(m5)	f	35	44	d	subcompact
car_volkswagen.csv	volkswagen	new beetle	1.9	1999	4	auto(l4)	f	29	41	d	subcompact
car_volkswagen.csv	volkswagen	new beetle	2.0	1999	4	manual(m5)	f	21	29	r	subcompact
car_volkswagen.csv	volkswagen	new beetle	2.0	1999	4	auto(l4)	f	19	26	r	subcompact
car_volkswagen.csv	volkswagen	new beetle	2.5	2008	5	manual(m5)	f	20	28	r	subcompact
car_volkswagen.csv	volkswagen	new beetle	2.5	2008	5	auto(s6)	f	20	29	r	subcompact
car_volkswagen.csv	volkswagen	passat	1.8	1999	4	manual(m5)	f	21	29	p	midsize
car_volkswagen.csv	volkswagen	passat	1.8	1999	4	auto(l5)	f	18	29	p	midsize
car_volkswagen.csv	volkswagen	passat	2.0	2008	4	auto(s6)	f	19	28	p	midsize
car_volkswagen.csv	volkswagen	passat	2.0	2008	4	manual(m6)	f	21	29	p	midsize
car_volkswagen.csv	volkswagen	passat	2.8	1999	6	auto(l5)	f	16	26	p	midsize
car_volkswagen.csv	volkswagen	passat	2.8	1999	6	manual(m5)	f	18	26	p	midsize
car_volkswagen.csv	volkswagen	passat	3.6	2008	6	auto(s6)	f	17	26	p	midsize

#按列直接拼结
# df <- map(files, read_csv) %>% 
#   reduce(cbind)

3.3 计算

#vector
vec <- c(1:10)
#dataframe
dfr <- iris
#list

lst <- repurrrsive::sw_films

3.3.1 向量对象

map(vec, sqrt)

## [[1]]
## [1] 1
## 
## [[2]]
## [1] 1.414214
## 
## [[3]]
## [1] 1.732051
## 
## [[4]]
## [1] 2
## 
## [[5]]
## [1] 2.236068
## 
## [[6]]
## [1] 2.44949
## 
## [[7]]
## [1] 2.645751
## 
## [[8]]
## [1] 2.828427
## 
## [[9]]
## [1] 3
## 
## [[10]]
## [1] 3.162278

map_dbl(vec, sqrt)

##  [1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751 2.828427
##  [9] 3.000000 3.162278

map_dfc(vec, sqrt)

## New names:
## * NA -> ...1
## * NA -> ...2
## * NA -> ...3
## * NA -> ...4
## * NA -> ...5
## * ...

…1	…2	…3	…4	…5	…6	…7	…8	…9	…10
1	1.414214	1.732051	2	2.236068	2.44949	2.645751	2.828427	3	3.162278

#purrr类函数
map_dfc(vec, ~ .x ^ 2 + 4)

## New names:
## * NA -> ...1
## * NA -> ...2
## * NA -> ...3
## * NA -> ...4
## * NA -> ...5
## * ...

…1	…2	…3	…4	…5	…6	…7	…8	…9	…10
5	8	13	20	29	40	53	68	85	104

#等价于
vec ^ 2 + 4

##  [1]   5   8  13  20  29  40  53  68  85 104

map(vec, rnorm, n = 10)

## [[1]]
##  [1]  2.0392546  1.3833731  0.4503958 -0.5615083  2.0277010  0.5757787
##  [7]  0.9926362  1.5242704  1.2500704  1.1980228
## 
## [[2]]
##  [1] 2.044542 2.095702 1.291732 4.656221 2.240324 3.452830 4.441055 2.507604
##  [9] 2.026001 2.189133
## 
## [[3]]
##  [1] 1.450793 4.194082 3.755393 2.597831 3.438772 2.078394 2.043497 5.120745
##  [9] 4.302572 2.042154
## 
## [[4]]
##  [1] 6.009749 5.738427 2.305999 5.015468 5.821130 1.691180 4.610653 3.699768
##  [9] 2.665870 3.088170
## 
## [[5]]
##  [1] 6.608808 3.899080 5.015784 4.157291 4.775482 5.789290 8.200197 4.025838
##  [9] 4.741420 6.474132
## 
## [[6]]
##  [1] 5.639423 6.737363 6.568461 5.977975 5.111751 4.954088 6.080819 5.986260
##  [9] 5.646351 6.196224
## 
## [[7]]
##  [1] 8.145603 6.020278 7.336183 7.762939 6.579403 6.457833 7.164466 5.463148
##  [9] 5.721114 7.058252
## 
## [[8]]
##  [1]  8.790745  8.424280  9.587739  8.827018  8.166971 10.048593 10.183723
##  [8]  7.790411  8.118954  7.177301
## 
## [[9]]
##  [1]  9.705561  8.966128  8.987250  7.228433  9.520450  9.657124 10.595664
##  [8]  9.697270  9.118447  7.669219
## 
## [[10]]
##  [1]  8.600549 10.054728 10.159342 10.190705 10.988159  8.999603 10.084820
##  [8] 10.440464  9.347736 10.228124

3.3.2 数据框对象

dfr %>% map(mean)

## Warning in mean.default(.x[[i]], ...): argument is not numeric or logical:
## returning NA

## $Sepal.Length
## [1] 5.843333
## 
## $Sepal.Width
## [1] 3.057333
## 
## $Petal.Length
## [1] 3.758
## 
## $Petal.Width
## [1] 1.199333
## 
## $Species
## [1] NA

dfr %>% map_dbl(mean)

## Warning in mean.default(.x[[i]], ...): argument is not numeric or logical:
## returning NA

## Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species 
##     5.843333     3.057333     3.758000     1.199333           NA

dfr %>% map_dfc(mean)

## Warning in mean.default(.x[[i]], ...): argument is not numeric or logical:
## returning NA

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.843333	3.057333	3.758	1.199333	NA

#分组汇总
dfr %>% 
  nest_by(Species) %>% 
  dplyr::summarise(map_dfc(data, mean))

## `summarise()` regrouping output by 'Species' (override with `.groups` argument)

Species	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width
setosa	5.006	3.428	1.462	0.246
versicolor	5.936	2.770	4.260	1.326
virginica	6.588	2.974	5.552	2.026

#或
dfr %>% 
  group_by(Species) %>% 
  group_modify(~ map_dfc(.x, mean))

Species	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width
setosa	5.006	3.428	1.462	0.246
versicolor	5.936	2.770	4.260	1.326
virginica	6.588	2.974	5.552	2.026

3.3.3 列表对象

3.3.3.1 列表计算1

library(repurrrsive)

## Warning: package 'repurrrsive' was built under R version 3.6.3

# What's each character's name?
map(got_chars, ~.x[["name"]])

## [[1]]
## [1] "Theon Greyjoy"
## 
## [[2]]
## [1] "Tyrion Lannister"
## 
## [[3]]
## [1] "Victarion Greyjoy"
## 
## [[4]]
## [1] "Will"
## 
## [[5]]
## [1] "Areo Hotah"
## 
## [[6]]
## [1] "Chett"
## 
## [[7]]
## [1] "Cressen"
## 
## [[8]]
## [1] "Arianne Martell"
## 
## [[9]]
## [1] "Daenerys Targaryen"
## 
## [[10]]
## [1] "Davos Seaworth"
## 
## [[11]]
## [1] "Arya Stark"
## 
## [[12]]
## [1] "Arys Oakheart"
## 
## [[13]]
## [1] "Asha Greyjoy"
## 
## [[14]]
## [1] "Barristan Selmy"
## 
## [[15]]
## [1] "Varamyr"
## 
## [[16]]
## [1] "Brandon Stark"
## 
## [[17]]
## [1] "Brienne of Tarth"
## 
## [[18]]
## [1] "Catelyn Stark"
## 
## [[19]]
## [1] "Cersei Lannister"
## 
## [[20]]
## [1] "Eddard Stark"
## 
## [[21]]
## [1] "Jaime Lannister"
## 
## [[22]]
## [1] "Jon Connington"
## 
## [[23]]
## [1] "Jon Snow"
## 
## [[24]]
## [1] "Aeron Greyjoy"
## 
## [[25]]
## [1] "Kevan Lannister"
## 
## [[26]]
## [1] "Melisandre"
## 
## [[27]]
## [1] "Merrett Frey"
## 
## [[28]]
## [1] "Quentyn Martell"
## 
## [[29]]
## [1] "Samwell Tarly"
## 
## [[30]]
## [1] "Sansa Stark"

map(sw_people, ~.x[["name"]])

## [[1]]
## [1] "Luke Skywalker"
## 
## [[2]]
## [1] "C-3PO"
## 
## [[3]]
## [1] "R2-D2"
## 
## [[4]]
## [1] "Darth Vader"
## 
## [[5]]
## [1] "Leia Organa"
## 
## [[6]]
## [1] "Owen Lars"
## 
## [[7]]
## [1] "Beru Whitesun lars"
## 
## [[8]]
## [1] "R5-D4"
## 
## [[9]]
## [1] "Biggs Darklighter"
## 
## [[10]]
## [1] "Obi-Wan Kenobi"
## 
## [[11]]
## [1] "Anakin Skywalker"
## 
## [[12]]
## [1] "Wilhuff Tarkin"
## 
## [[13]]
## [1] "Chewbacca"
## 
## [[14]]
## [1] "Han Solo"
## 
## [[15]]
## [1] "Greedo"
## 
## [[16]]
## [1] "Jabba Desilijic Tiure"
## 
## [[17]]
## [1] "Wedge Antilles"
## 
## [[18]]
## [1] "Jek Tono Porkins"
## 
## [[19]]
## [1] "Yoda"
## 
## [[20]]
## [1] "Palpatine"
## 
## [[21]]
## [1] "Boba Fett"
## 
## [[22]]
## [1] "IG-88"
## 
## [[23]]
## [1] "Bossk"
## 
## [[24]]
## [1] "Lando Calrissian"
## 
## [[25]]
## [1] "Lobot"
## 
## [[26]]
## [1] "Ackbar"
## 
## [[27]]
## [1] "Mon Mothma"
## 
## [[28]]
## [1] "Arvel Crynyd"
## 
## [[29]]
## [1] "Wicket Systri Warrick"
## 
## [[30]]
## [1] "Nien Nunb"
## 
## [[31]]
## [1] "Qui-Gon Jinn"
## 
## [[32]]
## [1] "Nute Gunray"
## 
## [[33]]
## [1] "Finis Valorum"
## 
## [[34]]
## [1] "Jar Jar Binks"
## 
## [[35]]
## [1] "Roos Tarpals"
## 
## [[36]]
## [1] "Rugor Nass"
## 
## [[37]]
## [1] "Ric Olié"
## 
## [[38]]
## [1] "Watto"
## 
## [[39]]
## [1] "Sebulba"
## 
## [[40]]
## [1] "Quarsh Panaka"
## 
## [[41]]
## [1] "Shmi Skywalker"
## 
## [[42]]
## [1] "Darth Maul"
## 
## [[43]]
## [1] "Bib Fortuna"
## 
## [[44]]
## [1] "Ayla Secura"
## 
## [[45]]
## [1] "Dud Bolt"
## 
## [[46]]
## [1] "Gasgano"
## 
## [[47]]
## [1] "Ben Quadinaros"
## 
## [[48]]
## [1] "Mace Windu"
## 
## [[49]]
## [1] "Ki-Adi-Mundi"
## 
## [[50]]
## [1] "Kit Fisto"
## 
## [[51]]
## [1] "Eeth Koth"
## 
## [[52]]
## [1] "Adi Gallia"
## 
## [[53]]
## [1] "Saesee Tiin"
## 
## [[54]]
## [1] "Yarael Poof"
## 
## [[55]]
## [1] "Plo Koon"
## 
## [[56]]
## [1] "Mas Amedda"
## 
## [[57]]
## [1] "Gregar Typho"
## 
## [[58]]
## [1] "Cordé"
## 
## [[59]]
## [1] "Cliegg Lars"
## 
## [[60]]
## [1] "Poggle the Lesser"
## 
## [[61]]
## [1] "Luminara Unduli"
## 
## [[62]]
## [1] "Barriss Offee"
## 
## [[63]]
## [1] "Dormé"
## 
## [[64]]
## [1] "Dooku"
## 
## [[65]]
## [1] "Bail Prestor Organa"
## 
## [[66]]
## [1] "Jango Fett"
## 
## [[67]]
## [1] "Zam Wesell"
## 
## [[68]]
## [1] "Dexter Jettster"
## 
## [[69]]
## [1] "Lama Su"
## 
## [[70]]
## [1] "Taun We"
## 
## [[71]]
## [1] "Jocasta Nu"
## 
## [[72]]
## [1] "Ratts Tyerell"
## 
## [[73]]
## [1] "R4-P17"
## 
## [[74]]
## [1] "Wat Tambor"
## 
## [[75]]
## [1] "San Hill"
## 
## [[76]]
## [1] "Shaak Ti"
## 
## [[77]]
## [1] "Grievous"
## 
## [[78]]
## [1] "Tarfful"
## 
## [[79]]
## [1] "Raymus Antilles"
## 
## [[80]]
## [1] "Sly Moore"
## 
## [[81]]
## [1] "Tion Medon"
## 
## [[82]]
## [1] "Finn"
## 
## [[83]]
## [1] "Rey"
## 
## [[84]]
## [1] "Poe Dameron"
## 
## [[85]]
## [1] "BB8"
## 
## [[86]]
## [1] "Captain Phasma"
## 
## [[87]]
## [1] "Padmé Amidala"

# What color is each SW character's hair?
map(sw_people, ~ .x[["hair_color"]])

## [[1]]
## [1] "blond"
## 
## [[2]]
## [1] "n/a"
## 
## [[3]]
## [1] "n/a"
## 
## [[4]]
## [1] "none"
## 
## [[5]]
## [1] "brown"
## 
## [[6]]
## [1] "brown, grey"
## 
## [[7]]
## [1] "brown"
## 
## [[8]]
## [1] "n/a"
## 
## [[9]]
## [1] "black"
## 
## [[10]]
## [1] "auburn, white"
## 
## [[11]]
## [1] "blond"
## 
## [[12]]
## [1] "auburn, grey"
## 
## [[13]]
## [1] "brown"
## 
## [[14]]
## [1] "brown"
## 
## [[15]]
## [1] "n/a"
## 
## [[16]]
## [1] "n/a"
## 
## [[17]]
## [1] "brown"
## 
## [[18]]
## [1] "brown"
## 
## [[19]]
## [1] "white"
## 
## [[20]]
## [1] "grey"
## 
## [[21]]
## [1] "black"
## 
## [[22]]
## [1] "none"
## 
## [[23]]
## [1] "none"
## 
## [[24]]
## [1] "black"
## 
## [[25]]
## [1] "none"
## 
## [[26]]
## [1] "none"
## 
## [[27]]
## [1] "auburn"
## 
## [[28]]
## [1] "brown"
## 
## [[29]]
## [1] "brown"
## 
## [[30]]
## [1] "none"
## 
## [[31]]
## [1] "brown"
## 
## [[32]]
## [1] "none"
## 
## [[33]]
## [1] "blond"
## 
## [[34]]
## [1] "none"
## 
## [[35]]
## [1] "none"
## 
## [[36]]
## [1] "none"
## 
## [[37]]
## [1] "brown"
## 
## [[38]]
## [1] "black"
## 
## [[39]]
## [1] "none"
## 
## [[40]]
## [1] "black"
## 
## [[41]]
## [1] "black"
## 
## [[42]]
## [1] "none"
## 
## [[43]]
## [1] "none"
## 
## [[44]]
## [1] "none"
## 
## [[45]]
## [1] "none"
## 
## [[46]]
## [1] "none"
## 
## [[47]]
## [1] "none"
## 
## [[48]]
## [1] "none"
## 
## [[49]]
## [1] "white"
## 
## [[50]]
## [1] "none"
## 
## [[51]]
## [1] "black"
## 
## [[52]]
## [1] "none"
## 
## [[53]]
## [1] "none"
## 
## [[54]]
## [1] "none"
## 
## [[55]]
## [1] "none"
## 
## [[56]]
## [1] "none"
## 
## [[57]]
## [1] "black"
## 
## [[58]]
## [1] "brown"
## 
## [[59]]
## [1] "brown"
## 
## [[60]]
## [1] "none"
## 
## [[61]]
## [1] "black"
## 
## [[62]]
## [1] "black"
## 
## [[63]]
## [1] "brown"
## 
## [[64]]
## [1] "white"
## 
## [[65]]
## [1] "black"
## 
## [[66]]
## [1] "black"
## 
## [[67]]
## [1] "blonde"
## 
## [[68]]
## [1] "none"
## 
## [[69]]
## [1] "none"
## 
## [[70]]
## [1] "none"
## 
## [[71]]
## [1] "white"
## 
## [[72]]
## [1] "none"
## 
## [[73]]
## [1] "none"
## 
## [[74]]
## [1] "none"
## 
## [[75]]
## [1] "none"
## 
## [[76]]
## [1] "none"
## 
## [[77]]
## [1] "none"
## 
## [[78]]
## [1] "brown"
## 
## [[79]]
## [1] "brown"
## 
## [[80]]
## [1] "none"
## 
## [[81]]
## [1] "none"
## 
## [[82]]
## [1] "black"
## 
## [[83]]
## [1] "brown"
## 
## [[84]]
## [1] "brown"
## 
## [[85]]
## [1] "none"
## 
## [[86]]
## [1] "unknown"
## 
## [[87]]
## [1] "brown"

# Is the GoT character alive?
map(got_chars, ~ .x[["alive"]])

## [[1]]
## [1] TRUE
## 
## [[2]]
## [1] TRUE
## 
## [[3]]
## [1] TRUE
## 
## [[4]]
## [1] FALSE
## 
## [[5]]
## [1] TRUE
## 
## [[6]]
## [1] FALSE
## 
## [[7]]
## [1] FALSE
## 
## [[8]]
## [1] TRUE
## 
## [[9]]
## [1] TRUE
## 
## [[10]]
## [1] TRUE
## 
## [[11]]
## [1] TRUE
## 
## [[12]]
## [1] FALSE
## 
## [[13]]
## [1] TRUE
## 
## [[14]]
## [1] TRUE
## 
## [[15]]
## [1] FALSE
## 
## [[16]]
## [1] TRUE
## 
## [[17]]
## [1] TRUE
## 
## [[18]]
## [1] FALSE
## 
## [[19]]
## [1] TRUE
## 
## [[20]]
## [1] FALSE
## 
## [[21]]
## [1] TRUE
## 
## [[22]]
## [1] TRUE
## 
## [[23]]
## [1] TRUE
## 
## [[24]]
## [1] TRUE
## 
## [[25]]
## [1] FALSE
## 
## [[26]]
## [1] TRUE
## 
## [[27]]
## [1] FALSE
## 
## [[28]]
## [1] FALSE
## 
## [[29]]
## [1] TRUE
## 
## [[30]]
## [1] TRUE

# Is the SW character female?
map(sw_people, ~ .x[["gender"]] == "female")

## [[1]]
## [1] FALSE
## 
## [[2]]
## [1] FALSE
## 
## [[3]]
## [1] FALSE
## 
## [[4]]
## [1] FALSE
## 
## [[5]]
## [1] TRUE
## 
## [[6]]
## [1] FALSE
## 
## [[7]]
## [1] TRUE
## 
## [[8]]
## [1] FALSE
## 
## [[9]]
## [1] FALSE
## 
## [[10]]
## [1] FALSE
## 
## [[11]]
## [1] FALSE
## 
## [[12]]
## [1] FALSE
## 
## [[13]]
## [1] FALSE
## 
## [[14]]
## [1] FALSE
## 
## [[15]]
## [1] FALSE
## 
## [[16]]
## [1] FALSE
## 
## [[17]]
## [1] FALSE
## 
## [[18]]
## [1] FALSE
## 
## [[19]]
## [1] FALSE
## 
## [[20]]
## [1] FALSE
## 
## [[21]]
## [1] FALSE
## 
## [[22]]
## [1] FALSE
## 
## [[23]]
## [1] FALSE
## 
## [[24]]
## [1] FALSE
## 
## [[25]]
## [1] FALSE
## 
## [[26]]
## [1] FALSE
## 
## [[27]]
## [1] TRUE
## 
## [[28]]
## [1] FALSE
## 
## [[29]]
## [1] FALSE
## 
## [[30]]
## [1] FALSE
## 
## [[31]]
## [1] FALSE
## 
## [[32]]
## [1] FALSE
## 
## [[33]]
## [1] FALSE
## 
## [[34]]
## [1] FALSE
## 
## [[35]]
## [1] FALSE
## 
## [[36]]
## [1] FALSE
## 
## [[37]]
## [1] FALSE
## 
## [[38]]
## [1] FALSE
## 
## [[39]]
## [1] FALSE
## 
## [[40]]
## [1] FALSE
## 
## [[41]]
## [1] TRUE
## 
## [[42]]
## [1] FALSE
## 
## [[43]]
## [1] FALSE
## 
## [[44]]
## [1] TRUE
## 
## [[45]]
## [1] FALSE
## 
## [[46]]
## [1] FALSE
## 
## [[47]]
## [1] FALSE
## 
## [[48]]
## [1] FALSE
## 
## [[49]]
## [1] FALSE
## 
## [[50]]
## [1] FALSE
## 
## [[51]]
## [1] FALSE
## 
## [[52]]
## [1] TRUE
## 
## [[53]]
## [1] FALSE
## 
## [[54]]
## [1] FALSE
## 
## [[55]]
## [1] FALSE
## 
## [[56]]
## [1] FALSE
## 
## [[57]]
## [1] FALSE
## 
## [[58]]
## [1] TRUE
## 
## [[59]]
## [1] FALSE
## 
## [[60]]
## [1] FALSE
## 
## [[61]]
## [1] TRUE
## 
## [[62]]
## [1] TRUE
## 
## [[63]]
## [1] TRUE
## 
## [[64]]
## [1] FALSE
## 
## [[65]]
## [1] FALSE
## 
## [[66]]
## [1] FALSE
## 
## [[67]]
## [1] TRUE
## 
## [[68]]
## [1] FALSE
## 
## [[69]]
## [1] FALSE
## 
## [[70]]
## [1] TRUE
## 
## [[71]]
## [1] TRUE
## 
## [[72]]
## [1] FALSE
## 
## [[73]]
## [1] TRUE
## 
## [[74]]
## [1] FALSE
## 
## [[75]]
## [1] FALSE
## 
## [[76]]
## [1] TRUE
## 
## [[77]]
## [1] FALSE
## 
## [[78]]
## [1] FALSE
## 
## [[79]]
## [1] FALSE
## 
## [[80]]
## [1] TRUE
## 
## [[81]]
## [1] FALSE
## 
## [[82]]
## [1] FALSE
## 
## [[83]]
## [1] TRUE
## 
## [[84]]
## [1] FALSE
## 
## [[85]]
## [1] FALSE
## 
## [[86]]
## [1] TRUE
## 
## [[87]]
## [1] TRUE

# How heavy is each SW character?
map(sw_people, ~ .x[["mass"]])

## [[1]]
## [1] "77"
## 
## [[2]]
## [1] "75"
## 
## [[3]]
## [1] "32"
## 
## [[4]]
## [1] "136"
## 
## [[5]]
## [1] "49"
## 
## [[6]]
## [1] "120"
## 
## [[7]]
## [1] "75"
## 
## [[8]]
## [1] "32"
## 
## [[9]]
## [1] "84"
## 
## [[10]]
## [1] "77"
## 
## [[11]]
## [1] "84"
## 
## [[12]]
## [1] "unknown"
## 
## [[13]]
## [1] "112"
## 
## [[14]]
## [1] "80"
## 
## [[15]]
## [1] "74"
## 
## [[16]]
## [1] "1,358"
## 
## [[17]]
## [1] "77"
## 
## [[18]]
## [1] "110"
## 
## [[19]]
## [1] "17"
## 
## [[20]]
## [1] "75"
## 
## [[21]]
## [1] "78.2"
## 
## [[22]]
## [1] "140"
## 
## [[23]]
## [1] "113"
## 
## [[24]]
## [1] "79"
## 
## [[25]]
## [1] "79"
## 
## [[26]]
## [1] "83"
## 
## [[27]]
## [1] "unknown"
## 
## [[28]]
## [1] "unknown"
## 
## [[29]]
## [1] "20"
## 
## [[30]]
## [1] "68"
## 
## [[31]]
## [1] "89"
## 
## [[32]]
## [1] "90"
## 
## [[33]]
## [1] "unknown"
## 
## [[34]]
## [1] "66"
## 
## [[35]]
## [1] "82"
## 
## [[36]]
## [1] "unknown"
## 
## [[37]]
## [1] "unknown"
## 
## [[38]]
## [1] "unknown"
## 
## [[39]]
## [1] "40"
## 
## [[40]]
## [1] "unknown"
## 
## [[41]]
## [1] "unknown"
## 
## [[42]]
## [1] "80"
## 
## [[43]]
## [1] "unknown"
## 
## [[44]]
## [1] "55"
## 
## [[45]]
## [1] "45"
## 
## [[46]]
## [1] "unknown"
## 
## [[47]]
## [1] "65"
## 
## [[48]]
## [1] "84"
## 
## [[49]]
## [1] "82"
## 
## [[50]]
## [1] "87"
## 
## [[51]]
## [1] "unknown"
## 
## [[52]]
## [1] "50"
## 
## [[53]]
## [1] "unknown"
## 
## [[54]]
## [1] "unknown"
## 
## [[55]]
## [1] "80"
## 
## [[56]]
## [1] "unknown"
## 
## [[57]]
## [1] "85"
## 
## [[58]]
## [1] "unknown"
## 
## [[59]]
## [1] "unknown"
## 
## [[60]]
## [1] "80"
## 
## [[61]]
## [1] "56.2"
## 
## [[62]]
## [1] "50"
## 
## [[63]]
## [1] "unknown"
## 
## [[64]]
## [1] "80"
## 
## [[65]]
## [1] "unknown"
## 
## [[66]]
## [1] "79"
## 
## [[67]]
## [1] "55"
## 
## [[68]]
## [1] "102"
## 
## [[69]]
## [1] "88"
## 
## [[70]]
## [1] "unknown"
## 
## [[71]]
## [1] "unknown"
## 
## [[72]]
## [1] "15"
## 
## [[73]]
## [1] "unknown"
## 
## [[74]]
## [1] "48"
## 
## [[75]]
## [1] "unknown"
## 
## [[76]]
## [1] "57"
## 
## [[77]]
## [1] "159"
## 
## [[78]]
## [1] "136"
## 
## [[79]]
## [1] "79"
## 
## [[80]]
## [1] "48"
## 
## [[81]]
## [1] "80"
## 
## [[82]]
## [1] "unknown"
## 
## [[83]]
## [1] "unknown"
## 
## [[84]]
## [1] "unknown"
## 
## [[85]]
## [1] "unknown"
## 
## [[86]]
## [1] "unknown"
## 
## [[87]]
## [1] "45"

map(sw_people, "mass")

## [[1]]
## [1] "77"
## 
## [[2]]
## [1] "75"
## 
## [[3]]
## [1] "32"
## 
## [[4]]
## [1] "136"
## 
## [[5]]
## [1] "49"
## 
## [[6]]
## [1] "120"
## 
## [[7]]
## [1] "75"
## 
## [[8]]
## [1] "32"
## 
## [[9]]
## [1] "84"
## 
## [[10]]
## [1] "77"
## 
## [[11]]
## [1] "84"
## 
## [[12]]
## [1] "unknown"
## 
## [[13]]
## [1] "112"
## 
## [[14]]
## [1] "80"
## 
## [[15]]
## [1] "74"
## 
## [[16]]
## [1] "1,358"
## 
## [[17]]
## [1] "77"
## 
## [[18]]
## [1] "110"
## 
## [[19]]
## [1] "17"
## 
## [[20]]
## [1] "75"
## 
## [[21]]
## [1] "78.2"
## 
## [[22]]
## [1] "140"
## 
## [[23]]
## [1] "113"
## 
## [[24]]
## [1] "79"
## 
## [[25]]
## [1] "79"
## 
## [[26]]
## [1] "83"
## 
## [[27]]
## [1] "unknown"
## 
## [[28]]
## [1] "unknown"
## 
## [[29]]
## [1] "20"
## 
## [[30]]
## [1] "68"
## 
## [[31]]
## [1] "89"
## 
## [[32]]
## [1] "90"
## 
## [[33]]
## [1] "unknown"
## 
## [[34]]
## [1] "66"
## 
## [[35]]
## [1] "82"
## 
## [[36]]
## [1] "unknown"
## 
## [[37]]
## [1] "unknown"
## 
## [[38]]
## [1] "unknown"
## 
## [[39]]
## [1] "40"
## 
## [[40]]
## [1] "unknown"
## 
## [[41]]
## [1] "unknown"
## 
## [[42]]
## [1] "80"
## 
## [[43]]
## [1] "unknown"
## 
## [[44]]
## [1] "55"
## 
## [[45]]
## [1] "45"
## 
## [[46]]
## [1] "unknown"
## 
## [[47]]
## [1] "65"
## 
## [[48]]
## [1] "84"
## 
## [[49]]
## [1] "82"
## 
## [[50]]
## [1] "87"
## 
## [[51]]
## [1] "unknown"
## 
## [[52]]
## [1] "50"
## 
## [[53]]
## [1] "unknown"
## 
## [[54]]
## [1] "unknown"
## 
## [[55]]
## [1] "80"
## 
## [[56]]
## [1] "unknown"
## 
## [[57]]
## [1] "85"
## 
## [[58]]
## [1] "unknown"
## 
## [[59]]
## [1] "unknown"
## 
## [[60]]
## [1] "80"
## 
## [[61]]
## [1] "56.2"
## 
## [[62]]
## [1] "50"
## 
## [[63]]
## [1] "unknown"
## 
## [[64]]
## [1] "80"
## 
## [[65]]
## [1] "unknown"
## 
## [[66]]
## [1] "79"
## 
## [[67]]
## [1] "55"
## 
## [[68]]
## [1] "102"
## 
## [[69]]
## [1] "88"
## 
## [[70]]
## [1] "unknown"
## 
## [[71]]
## [1] "unknown"
## 
## [[72]]
## [1] "15"
## 
## [[73]]
## [1] "unknown"
## 
## [[74]]
## [1] "48"
## 
## [[75]]
## [1] "unknown"
## 
## [[76]]
## [1] "57"
## 
## [[77]]
## [1] "159"
## 
## [[78]]
## [1] "136"
## 
## [[79]]
## [1] "79"
## 
## [[80]]
## [1] "48"
## 
## [[81]]
## [1] "80"
## 
## [[82]]
## [1] "unknown"
## 
## [[83]]
## [1] "unknown"
## 
## [[84]]
## [1] "unknown"
## 
## [[85]]
## [1] "unknown"
## 
## [[86]]
## [1] "unknown"
## 
## [[87]]
## [1] "45"

map(sw_vehicles, "pilots", .default = NA)

## [[1]]
## [1] NA
## 
## [[2]]
## [1] NA
## 
## [[3]]
## [1] NA
## 
## [[4]]
## [1] NA
## 
## [[5]]
## [1] "http://swapi.co/api/people/1/"  "http://swapi.co/api/people/18/"
## 
## [[6]]
## [1] NA
## 
## [[7]]
## [1] NA
## 
## [[8]]
## [1] "http://swapi.co/api/people/13/"
## 
## [[9]]
## [1] NA
## 
## [[10]]
## [1] NA
## 
## [[11]]
## [1] NA
## 
## [[12]]
## [1] NA
## 
## [[13]]
## [1] "http://swapi.co/api/people/1/" "http://swapi.co/api/people/5/"
## 
## [[14]]
## [1] NA
## 
## [[15]]
## [1] NA
## 
## [[16]]
## [1] NA
## 
## [[17]]
## [1] NA
## 
## [[18]]
## [1] NA
## 
## [[19]]
## [1] "http://swapi.co/api/people/10/" "http://swapi.co/api/people/32/"
## 
## [[20]]
## [1] "http://swapi.co/api/people/44/"
## 
## [[21]]
## [1] "http://swapi.co/api/people/11/"
## 
## [[22]]
## [1] "http://swapi.co/api/people/70/"
## 
## [[23]]
## [1] "http://swapi.co/api/people/11/"
## 
## [[24]]
## [1] NA
## 
## [[25]]
## [1] NA
## 
## [[26]]
## [1] "http://swapi.co/api/people/79/"
## 
## [[27]]
## [1] NA
## 
## [[28]]
## [1] NA
## 
## [[29]]
## [1] NA
## 
## [[30]]
## [1] NA
## 
## [[31]]
## [1] NA
## 
## [[32]]
## [1] NA
## 
## [[33]]
## [1] NA
## 
## [[34]]
## [1] NA
## 
## [[35]]
## [1] NA
## 
## [[36]]
## [1] NA
## 
## [[37]]
## [1] "http://swapi.co/api/people/67/"
## 
## [[38]]
## [1] NA
## 
## [[39]]
## [1] NA

map_chr(sw_vehicles, 1)

##  [1] "Sand Crawler"                   "T-16 skyhopper"                
##  [3] "X-34 landspeeder"               "TIE/LN starfighter"            
##  [5] "Snowspeeder"                    "TIE bomber"                    
##  [7] "AT-AT"                          "AT-ST"                         
##  [9] "Storm IV Twin-Pod cloud car"    "Sail barge"                    
## [11] "Bantha-II cargo skiff"          "TIE/IN interceptor"            
## [13] "Imperial Speeder Bike"          "Vulture Droid"                 
## [15] "Multi-Troop Transport"          "Armored Assault Tank"          
## [17] "Single Trooper Aerial Platform" "C-9979 landing craft"          
## [19] "Tribubble bongo"                "Sith speeder"                  
## [21] "Zephyr-G swoop bike"            "Koro-2 Exodrive airspeeder"    
## [23] "XJ-6 airspeeder"                "LAAT/i"                        
## [25] "LAAT/c"                         "Tsmeu-6 personal wheel bike"   
## [27] "Emergency Firespeeder"          "Droid tri-fighter"             
## [29] "Oevvaor jet catamaran"          "Raddaugh Gnasp fluttercraft"   
## [31] "Clone turbo tank"               "Corporate Alliance tank droid" 
## [33] "Droid gunship"                  "AT-RT"                         
## [35] "AT-TE"                          "SPHA"                          
## [37] "Flitknot speeder"               "Neimoidian shuttle"            
## [39] "Geonosian starfighter"

map(got_chars, names)

## [[1]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[2]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[3]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[4]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[5]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[6]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[7]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[8]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[9]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[10]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[11]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[12]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[13]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[14]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[15]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[16]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[17]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[18]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[19]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[20]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[21]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[22]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[23]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[24]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[25]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[26]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[27]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[28]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[29]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"   
## 
## [[30]]
##  [1] "url"         "id"          "name"        "gender"      "culture"    
##  [6] "born"        "died"        "alive"       "titles"      "aliases"    
## [11] "father"      "mother"      "spouse"      "allegiances" "books"      
## [16] "povBooks"    "tvSeries"    "playedBy"

3.3.3.2 列表转数据框

#list转dataframe
people_tbl <- tibble(
  name = sw_people %>% map_chr("name"),
  films = sw_people %>% map("films"),
  height = sw_people %>% map_chr("height") %>%
    readr::parse_number(na = "unknown"),
  species = sw_people %>% map_chr("species", .null = NA_character_)
) %>% 
  unnest(cols = c(films))
people_tbl

name	films	height	species
Luke Skywalker	http://swapi.co/api/films/6/	172	http://swapi.co/api/species/1/
Luke Skywalker	http://swapi.co/api/films/3/	172	http://swapi.co/api/species/1/
Luke Skywalker	http://swapi.co/api/films/2/	172	http://swapi.co/api/species/1/
Luke Skywalker	http://swapi.co/api/films/1/	172	http://swapi.co/api/species/1/
Luke Skywalker	http://swapi.co/api/films/7/	172	http://swapi.co/api/species/1/
C-3PO	http://swapi.co/api/films/5/	167	http://swapi.co/api/species/2/
C-3PO	http://swapi.co/api/films/4/	167	http://swapi.co/api/species/2/
C-3PO	http://swapi.co/api/films/6/	167	http://swapi.co/api/species/2/
C-3PO	http://swapi.co/api/films/3/	167	http://swapi.co/api/species/2/
C-3PO	http://swapi.co/api/films/2/	167	http://swapi.co/api/species/2/
C-3PO	http://swapi.co/api/films/1/	167	http://swapi.co/api/species/2/
R2-D2	http://swapi.co/api/films/5/	96	http://swapi.co/api/species/2/
R2-D2	http://swapi.co/api/films/4/	96	http://swapi.co/api/species/2/
R2-D2	http://swapi.co/api/films/6/	96	http://swapi.co/api/species/2/
R2-D2	http://swapi.co/api/films/3/	96	http://swapi.co/api/species/2/
R2-D2	http://swapi.co/api/films/2/	96	http://swapi.co/api/species/2/
R2-D2	http://swapi.co/api/films/1/	96	http://swapi.co/api/species/2/
R2-D2	http://swapi.co/api/films/7/	96	http://swapi.co/api/species/2/
Darth Vader	http://swapi.co/api/films/6/	202	http://swapi.co/api/species/1/
Darth Vader	http://swapi.co/api/films/3/	202	http://swapi.co/api/species/1/
Darth Vader	http://swapi.co/api/films/2/	202	http://swapi.co/api/species/1/
Darth Vader	http://swapi.co/api/films/1/	202	http://swapi.co/api/species/1/
Leia Organa	http://swapi.co/api/films/6/	150	http://swapi.co/api/species/1/
Leia Organa	http://swapi.co/api/films/3/	150	http://swapi.co/api/species/1/
Leia Organa	http://swapi.co/api/films/2/	150	http://swapi.co/api/species/1/
Leia Organa	http://swapi.co/api/films/1/	150	http://swapi.co/api/species/1/
Leia Organa	http://swapi.co/api/films/7/	150	http://swapi.co/api/species/1/
Owen Lars	http://swapi.co/api/films/5/	178	http://swapi.co/api/species/1/
Owen Lars	http://swapi.co/api/films/6/	178	http://swapi.co/api/species/1/
Owen Lars	http://swapi.co/api/films/1/	178	http://swapi.co/api/species/1/
Beru Whitesun lars	http://swapi.co/api/films/5/	165	http://swapi.co/api/species/1/
Beru Whitesun lars	http://swapi.co/api/films/6/	165	http://swapi.co/api/species/1/
Beru Whitesun lars	http://swapi.co/api/films/1/	165	http://swapi.co/api/species/1/
R5-D4	http://swapi.co/api/films/1/	97	http://swapi.co/api/species/2/
Biggs Darklighter	http://swapi.co/api/films/1/	183	http://swapi.co/api/species/1/
Obi-Wan Kenobi	http://swapi.co/api/films/5/	182	http://swapi.co/api/species/1/
Obi-Wan Kenobi	http://swapi.co/api/films/4/	182	http://swapi.co/api/species/1/
Obi-Wan Kenobi	http://swapi.co/api/films/6/	182	http://swapi.co/api/species/1/
Obi-Wan Kenobi	http://swapi.co/api/films/3/	182	http://swapi.co/api/species/1/
Obi-Wan Kenobi	http://swapi.co/api/films/2/	182	http://swapi.co/api/species/1/
Obi-Wan Kenobi	http://swapi.co/api/films/1/	182	http://swapi.co/api/species/1/
Anakin Skywalker	http://swapi.co/api/films/5/	188	http://swapi.co/api/species/1/
Anakin Skywalker	http://swapi.co/api/films/4/	188	http://swapi.co/api/species/1/
Anakin Skywalker	http://swapi.co/api/films/6/	188	http://swapi.co/api/species/1/
Wilhuff Tarkin	http://swapi.co/api/films/6/	180	http://swapi.co/api/species/1/
Wilhuff Tarkin	http://swapi.co/api/films/1/	180	http://swapi.co/api/species/1/
Chewbacca	http://swapi.co/api/films/6/	228	http://swapi.co/api/species/3/
Chewbacca	http://swapi.co/api/films/3/	228	http://swapi.co/api/species/3/
Chewbacca	http://swapi.co/api/films/2/	228	http://swapi.co/api/species/3/
Chewbacca	http://swapi.co/api/films/1/	228	http://swapi.co/api/species/3/
Chewbacca	http://swapi.co/api/films/7/	228	http://swapi.co/api/species/3/
Han Solo	http://swapi.co/api/films/3/	180	http://swapi.co/api/species/1/
Han Solo	http://swapi.co/api/films/2/	180	http://swapi.co/api/species/1/
Han Solo	http://swapi.co/api/films/1/	180	http://swapi.co/api/species/1/
Han Solo	http://swapi.co/api/films/7/	180	http://swapi.co/api/species/1/
Greedo	http://swapi.co/api/films/1/	173	http://swapi.co/api/species/4/
Jabba Desilijic Tiure	http://swapi.co/api/films/4/	175	http://swapi.co/api/species/5/
Jabba Desilijic Tiure	http://swapi.co/api/films/3/	175	http://swapi.co/api/species/5/
Jabba Desilijic Tiure	http://swapi.co/api/films/1/	175	http://swapi.co/api/species/5/
Wedge Antilles	http://swapi.co/api/films/3/	170	http://swapi.co/api/species/1/
Wedge Antilles	http://swapi.co/api/films/2/	170	http://swapi.co/api/species/1/
Wedge Antilles	http://swapi.co/api/films/1/	170	http://swapi.co/api/species/1/
Jek Tono Porkins	http://swapi.co/api/films/1/	180	http://swapi.co/api/species/1/
Yoda	http://swapi.co/api/films/5/	66	http://swapi.co/api/species/6/
Yoda	http://swapi.co/api/films/4/	66	http://swapi.co/api/species/6/
Yoda	http://swapi.co/api/films/6/	66	http://swapi.co/api/species/6/
Yoda	http://swapi.co/api/films/3/	66	http://swapi.co/api/species/6/
Yoda	http://swapi.co/api/films/2/	66	http://swapi.co/api/species/6/
Palpatine	http://swapi.co/api/films/5/	170	http://swapi.co/api/species/1/
Palpatine	http://swapi.co/api/films/4/	170	http://swapi.co/api/species/1/
Palpatine	http://swapi.co/api/films/6/	170	http://swapi.co/api/species/1/
Palpatine	http://swapi.co/api/films/3/	170	http://swapi.co/api/species/1/
Palpatine	http://swapi.co/api/films/2/	170	http://swapi.co/api/species/1/
Boba Fett	http://swapi.co/api/films/5/	183	http://swapi.co/api/species/1/
Boba Fett	http://swapi.co/api/films/3/	183	http://swapi.co/api/species/1/
Boba Fett	http://swapi.co/api/films/2/	183	http://swapi.co/api/species/1/
IG-88	http://swapi.co/api/films/2/	200	http://swapi.co/api/species/2/
Bossk	http://swapi.co/api/films/2/	190	http://swapi.co/api/species/7/
Lando Calrissian	http://swapi.co/api/films/3/	177	http://swapi.co/api/species/1/
Lando Calrissian	http://swapi.co/api/films/2/	177	http://swapi.co/api/species/1/
Lobot	http://swapi.co/api/films/2/	175	http://swapi.co/api/species/1/
Ackbar	http://swapi.co/api/films/3/	180	http://swapi.co/api/species/8/
Ackbar	http://swapi.co/api/films/7/	180	http://swapi.co/api/species/8/
Mon Mothma	http://swapi.co/api/films/3/	150	http://swapi.co/api/species/1/
Arvel Crynyd	http://swapi.co/api/films/3/	NA	http://swapi.co/api/species/1/
Wicket Systri Warrick	http://swapi.co/api/films/3/	88	http://swapi.co/api/species/9/
Nien Nunb	http://swapi.co/api/films/3/	160	http://swapi.co/api/species/10/
Qui-Gon Jinn	http://swapi.co/api/films/4/	193	http://swapi.co/api/species/1/
Nute Gunray	http://swapi.co/api/films/5/	191	http://swapi.co/api/species/11/
Nute Gunray	http://swapi.co/api/films/4/	191	http://swapi.co/api/species/11/
Nute Gunray	http://swapi.co/api/films/6/	191	http://swapi.co/api/species/11/
Finis Valorum	http://swapi.co/api/films/4/	170	http://swapi.co/api/species/1/
Jar Jar Binks	http://swapi.co/api/films/5/	196	http://swapi.co/api/species/12/
Jar Jar Binks	http://swapi.co/api/films/4/	196	http://swapi.co/api/species/12/
Roos Tarpals	http://swapi.co/api/films/4/	224	http://swapi.co/api/species/12/
Rugor Nass	http://swapi.co/api/films/4/	206	http://swapi.co/api/species/12/
Ric Olié	http://swapi.co/api/films/4/	183	NA
Watto	http://swapi.co/api/films/5/	137	http://swapi.co/api/species/13/
Watto	http://swapi.co/api/films/4/	137	http://swapi.co/api/species/13/
Sebulba	http://swapi.co/api/films/4/	112	http://swapi.co/api/species/14/
Quarsh Panaka	http://swapi.co/api/films/4/	183	NA
Shmi Skywalker	http://swapi.co/api/films/5/	163	http://swapi.co/api/species/1/
Shmi Skywalker	http://swapi.co/api/films/4/	163	http://swapi.co/api/species/1/
Darth Maul	http://swapi.co/api/films/4/	175	http://swapi.co/api/species/22/
Bib Fortuna	http://swapi.co/api/films/3/	180	http://swapi.co/api/species/15/
Ayla Secura	http://swapi.co/api/films/5/	178	http://swapi.co/api/species/15/
Ayla Secura	http://swapi.co/api/films/4/	178	http://swapi.co/api/species/15/
Ayla Secura	http://swapi.co/api/films/6/	178	http://swapi.co/api/species/15/
Dud Bolt	http://swapi.co/api/films/4/	94	http://swapi.co/api/species/17/
Gasgano	http://swapi.co/api/films/4/	122	http://swapi.co/api/species/18/
Ben Quadinaros	http://swapi.co/api/films/4/	163	http://swapi.co/api/species/19/
Mace Windu	http://swapi.co/api/films/5/	188	http://swapi.co/api/species/1/
Mace Windu	http://swapi.co/api/films/4/	188	http://swapi.co/api/species/1/
Mace Windu	http://swapi.co/api/films/6/	188	http://swapi.co/api/species/1/
Ki-Adi-Mundi	http://swapi.co/api/films/5/	198	http://swapi.co/api/species/20/
Ki-Adi-Mundi	http://swapi.co/api/films/4/	198	http://swapi.co/api/species/20/
Ki-Adi-Mundi	http://swapi.co/api/films/6/	198	http://swapi.co/api/species/20/
Kit Fisto	http://swapi.co/api/films/5/	196	http://swapi.co/api/species/21/
Kit Fisto	http://swapi.co/api/films/4/	196	http://swapi.co/api/species/21/
Kit Fisto	http://swapi.co/api/films/6/	196	http://swapi.co/api/species/21/
Eeth Koth	http://swapi.co/api/films/4/	171	http://swapi.co/api/species/22/
Eeth Koth	http://swapi.co/api/films/6/	171	http://swapi.co/api/species/22/
Adi Gallia	http://swapi.co/api/films/4/	184	http://swapi.co/api/species/23/
Adi Gallia	http://swapi.co/api/films/6/	184	http://swapi.co/api/species/23/
Saesee Tiin	http://swapi.co/api/films/4/	188	http://swapi.co/api/species/24/
Saesee Tiin	http://swapi.co/api/films/6/	188	http://swapi.co/api/species/24/
Yarael Poof	http://swapi.co/api/films/4/	264	http://swapi.co/api/species/25/
Plo Koon	http://swapi.co/api/films/5/	188	http://swapi.co/api/species/26/
Plo Koon	http://swapi.co/api/films/4/	188	http://swapi.co/api/species/26/
Plo Koon	http://swapi.co/api/films/6/	188	http://swapi.co/api/species/26/
Mas Amedda	http://swapi.co/api/films/5/	196	http://swapi.co/api/species/27/
Mas Amedda	http://swapi.co/api/films/4/	196	http://swapi.co/api/species/27/
Gregar Typho	http://swapi.co/api/films/5/	185	http://swapi.co/api/species/1/
Cordé	http://swapi.co/api/films/5/	157	http://swapi.co/api/species/1/
Cliegg Lars	http://swapi.co/api/films/5/	183	http://swapi.co/api/species/1/
Poggle the Lesser	http://swapi.co/api/films/5/	183	http://swapi.co/api/species/28/
Poggle the Lesser	http://swapi.co/api/films/6/	183	http://swapi.co/api/species/28/
Luminara Unduli	http://swapi.co/api/films/5/	170	http://swapi.co/api/species/29/
Luminara Unduli	http://swapi.co/api/films/6/	170	http://swapi.co/api/species/29/
Barriss Offee	http://swapi.co/api/films/5/	166	http://swapi.co/api/species/29/
Dormé	http://swapi.co/api/films/5/	165	http://swapi.co/api/species/1/
Dooku	http://swapi.co/api/films/5/	193	http://swapi.co/api/species/1/
Dooku	http://swapi.co/api/films/6/	193	http://swapi.co/api/species/1/
Bail Prestor Organa	http://swapi.co/api/films/5/	191	http://swapi.co/api/species/1/
Bail Prestor Organa	http://swapi.co/api/films/6/	191	http://swapi.co/api/species/1/
Jango Fett	http://swapi.co/api/films/5/	183	http://swapi.co/api/species/1/
Zam Wesell	http://swapi.co/api/films/5/	168	http://swapi.co/api/species/30/
Dexter Jettster	http://swapi.co/api/films/5/	198	http://swapi.co/api/species/31/
Lama Su	http://swapi.co/api/films/5/	229	http://swapi.co/api/species/32/
Taun We	http://swapi.co/api/films/5/	213	http://swapi.co/api/species/32/
Jocasta Nu	http://swapi.co/api/films/5/	167	http://swapi.co/api/species/1/
Ratts Tyerell	http://swapi.co/api/films/4/	79	http://swapi.co/api/species/16/
R4-P17	http://swapi.co/api/films/5/	96	NA
R4-P17	http://swapi.co/api/films/6/	96	NA
Wat Tambor	http://swapi.co/api/films/5/	193	http://swapi.co/api/species/33/
San Hill	http://swapi.co/api/films/5/	191	http://swapi.co/api/species/34/
Shaak Ti	http://swapi.co/api/films/5/	178	http://swapi.co/api/species/35/
Shaak Ti	http://swapi.co/api/films/6/	178	http://swapi.co/api/species/35/
Grievous	http://swapi.co/api/films/6/	216	http://swapi.co/api/species/36/
Tarfful	http://swapi.co/api/films/6/	234	http://swapi.co/api/species/3/
Raymus Antilles	http://swapi.co/api/films/6/	188	http://swapi.co/api/species/1/
Raymus Antilles	http://swapi.co/api/films/1/	188	http://swapi.co/api/species/1/
Sly Moore	http://swapi.co/api/films/5/	178	NA
Sly Moore	http://swapi.co/api/films/6/	178	NA
Tion Medon	http://swapi.co/api/films/6/	206	http://swapi.co/api/species/37/
Finn	http://swapi.co/api/films/7/	NA	http://swapi.co/api/species/1/
Rey	http://swapi.co/api/films/7/	NA	http://swapi.co/api/species/1/
Poe Dameron	http://swapi.co/api/films/7/	NA	http://swapi.co/api/species/1/
BB8	http://swapi.co/api/films/7/	NA	http://swapi.co/api/species/2/
Captain Phasma	http://swapi.co/api/films/7/	NA	NA
Padmé Amidala	http://swapi.co/api/films/5/	165	http://swapi.co/api/species/1/
Padmé Amidala	http://swapi.co/api/films/4/	165	http://swapi.co/api/species/1/
Padmé Amidala	http://swapi.co/api/films/6/	165	http://swapi.co/api/species/1/

#list中的单值变量可以直接用以下方法转换成数据框
map_dfr(sw_people, `[`, c("name", "mass", "height"))

name	mass	height
Luke Skywalker	77	172
C-3PO	75	167
R2-D2	32	96
Darth Vader	136	202
Leia Organa	49	150
Owen Lars	120	178
Beru Whitesun lars	75	165
R5-D4	32	97
Biggs Darklighter	84	183
Obi-Wan Kenobi	77	182
Anakin Skywalker	84	188
Wilhuff Tarkin	unknown	180
Chewbacca	112	228
Han Solo	80	180
Greedo	74	173
Jabba Desilijic Tiure	1,358	175
Wedge Antilles	77	170
Jek Tono Porkins	110	180
Yoda	17	66
Palpatine	75	170
Boba Fett	78.2	183
IG-88	140	200
Bossk	113	190
Lando Calrissian	79	177
Lobot	79	175
Ackbar	83	180
Mon Mothma	unknown	150
Arvel Crynyd	unknown	unknown
Wicket Systri Warrick	20	88
Nien Nunb	68	160
Qui-Gon Jinn	89	193
Nute Gunray	90	191
Finis Valorum	unknown	170
Jar Jar Binks	66	196
Roos Tarpals	82	224
Rugor Nass	unknown	206
Ric Olié	unknown	183
Watto	unknown	137
Sebulba	40	112
Quarsh Panaka	unknown	183
Shmi Skywalker	unknown	163
Darth Maul	80	175
Bib Fortuna	unknown	180
Ayla Secura	55	178
Dud Bolt	45	94
Gasgano	unknown	122
Ben Quadinaros	65	163
Mace Windu	84	188
Ki-Adi-Mundi	82	198
Kit Fisto	87	196
Eeth Koth	unknown	171
Adi Gallia	50	184
Saesee Tiin	unknown	188
Yarael Poof	unknown	264
Plo Koon	80	188
Mas Amedda	unknown	196
Gregar Typho	85	185
Cordé	unknown	157
Cliegg Lars	unknown	183
Poggle the Lesser	80	183
Luminara Unduli	56.2	170
Barriss Offee	50	166
Dormé	unknown	165
Dooku	80	193
Bail Prestor Organa	unknown	191
Jango Fett	79	183
Zam Wesell	55	168
Dexter Jettster	102	198
Lama Su	88	229
Taun We	unknown	213
Jocasta Nu	unknown	167
Ratts Tyerell	15	79
R4-P17	unknown	96
Wat Tambor	48	193
San Hill	unknown	191
Shaak Ti	57	178
Grievous	159	216
Tarfful	136	234
Raymus Antilles	79	188
Sly Moore	48	178
Tion Medon	80	206
Finn	unknown	unknown
Rey	unknown	unknown
Poe Dameron	unknown	unknown
BB8	unknown	unknown
Captain Phasma	unknown	unknown
Padmé Amidala	45	165

3.3.3.3 列表计算2

sw_species %>% 
  #用数据集内数据命名list各元素
  set_names(map(., 'name')) %>% 
  #提取某个变量
  map(~ str_extract_all(.x$"eye_colors", "[[:alpha:]]+")) %>%
  #生成数据框列表
  enframe() %>%
  #展开数据框内各级列表
  unnest() %>% 
  unnest() %>% 
  group_by(name) %>% 
  tally(sort = T)

## Warning: `cols` is now required when using unnest().
## Please use `cols = c(value)`

## Warning: `cols` is now required when using unnest().
## Please use `cols = c(value)`

name	n
Human	6
Mirialan	6
Togruta	6
Wookiee	6
Twi’lek	4
Yoda’s species	3
Droid	2
Dug	2
Ewok	2
Geonosian	2
Hutt	2
Kel Dor	2
Neimodian	2
Tholothian	2
Trandoshan	2
Zabrak	2
Aleena	1
Besalisk	1
Cerean	1
Chagrian	1
Clawdite	1
Gungan	1
Iktotchi	1
Kaleesh	1
Kaminoan	1
Mon Calamari	1
Muun	1
Nautolan	1
Pau’an	1
Quermian	1
Rodian	1
Skakoan	1
Sullustan	1
Toong	1
Toydarian	1
Vulptereen	1
Xexto	1

3.3.3.4 批量建模

# 分组建模预测
by_cyl <- mtcars %>% 
  split(.$cyl) 
mod <- by_cyl%>% 
  map(~ lm(mpg ~ wt, data = .))

mod %>% 
  map2(by_cyl, predict)

## $`4`
##     Datsun 710      Merc 240D       Merc 230       Fiat 128    Honda Civic 
##       26.47010       21.55719       21.78307       27.14774       30.45125 
## Toyota Corolla  Toyota Corona      Fiat X1-9  Porsche 914-2   Lotus Europa 
##       29.20890       25.65128       28.64420       27.48656       31.02725 
##     Volvo 142E 
##       23.87247 
## 
## $`6`
##      Mazda RX4  Mazda RX4 Wag Hornet 4 Drive        Valiant       Merc 280 
##       21.12497       20.41604       19.47080       18.78968       18.84528 
##      Merc 280C   Ferrari Dino 
##       18.84528       20.70795 
## 
## $`8`
##   Hornet Sportabout          Duster 360          Merc 450SE          Merc 450SL 
##            16.32604            16.04103            14.94481            15.69024 
##         Merc 450SLC  Cadillac Fleetwood Lincoln Continental   Chrysler Imperial 
##            15.58061            12.35773            11.97625            12.14945 
##    Dodge Challenger         AMC Javelin          Camaro Z28    Pontiac Firebird 
##            16.15065            16.33700            15.44907            15.43811 
##      Ford Pantera L       Maserati Bora 
##            16.91800            16.04103

#等价于
mtcars %>% 
  nest_by(cyl, .keep = T) %>% 
  mutate(mod = list(lm(mpg ~ wt, data = data))) %>% 
  mutate(pred = list(predict(mod, data))) %>% 
  unnest(pred)

cyl	data	mod	pred
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	26.47010
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	21.55719
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	21.78307
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	27.14774
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	30.45125
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	29.20890
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	25.65128
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	28.64420
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	27.48656
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	31.02725
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	23.87247
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	21.12497
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	20.41604
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	19.47080
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	18.78968
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	18.84528
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	18.84528
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 6.000, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	20.70795
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	16.32604
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	16.04103
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	14.94481
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	15.69024
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	15.58061
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	12.35773
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	11.97625
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	12.14945
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	16.15065
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	16.33700
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	15.44907
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	15.43811
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	16.91800
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 8.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = data), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	16.04103

mtcars %>%
  group_nest(cyl) %>%
  mutate(model = map(data, ~ lm(mpg ~ wt, data = .x))) %>% 
  mutate(pred = map(model, predict)) %>% 
  unnest(pred)

cyl	data	model	pred
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	26.47010
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	21.55719
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	21.78307
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	27.14774
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	30.45125
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	29.20890
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	25.65128
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	28.64420
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	27.48656
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	31.02725
4	22.800, 24.400, 22.800, 32.400, 30.400, 33.900, 21.500, 27.300, 26.000, 30.400, 21.400, 108.000, 146.700, 140.800, 78.700, 75.700, 71.100, 120.100, 79.000, 120.300, 95.100, 121.000, 93.000, 62.000, 95.000, 66.000, 52.000, 65.000, 97.000, 66.000, 91.000, 113.000, 109.000, 3.850, 3.690, 3.920, 4.080, 4.930, 4.220, 3.700, 4.080, 4.430, 3.770, 4.110, 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.513, 2.780, 18.610, 20.000, 22.900, 19.470, 18.520, 19.900, 20.010, 18.900, 16.700, 16.900, 18.600, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 0.000, 0.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 1.000, 1.000, 4.000, 4.000, 4.000, 4.000, 4.000, 4.000, 3.000, 4.000, 5.000, 5.000, 4.000, 1.000, 2.000, 2.000, 1.000, 2.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000	39.5712, -5.647025, -3.670097, 2.842815, 1.016934, 5.25226, -0.05125022, 4.691095, -4.151279, -1.344202, -1.486562, -0.6272468, -2.472466, -88.43328, 10.17096, 0.6947654, 6.230721, 1.728126, 6.169273, -3.535624, -0.00293297, -0.4259551, 1.291776, -2.288073, 2, 26.4701, 21.55719, 21.78307, 27.14774, 30.45125, 29.2089, 25.65128, 28.6442, 27.48656, 31.02725, 23.87247, 0, 1, -3.316625, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, 0.3015113, -7.5809, -1.801119, 0.4754451, -0.05200489, -0.376803, -0.2546567, 0.0951259, -0.1991357, -0.08531752, -0.4334345, 0.2700172, 1.301511, 1.497654, 1, 2, 1e-07, 2, 9, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26, 30.4, 21.4, 2.32, 3.19, 3.15, 2.2, 1.615, 1.835, 2.465, 1.935, 2.14, 1.513, 2.78	23.87247
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	21.12497
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	20.41604
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	19.47080
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	18.78968
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	18.84528
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	18.84528
6	21.000, 21.000, 21.400, 18.100, 19.200, 17.800, 19.700, 160.000, 160.000, 258.000, 225.000, 167.600, 167.600, 145.000, 110.000, 110.000, 110.000, 105.000, 123.000, 123.000, 175.000, 3.900, 3.900, 3.080, 2.760, 3.920, 3.920, 3.620, 2.620, 2.875, 3.215, 3.460, 3.440, 3.440, 2.770, 16.460, 17.020, 19.440, 20.220, 18.300, 18.900, 15.500, 0.000, 0.000, 1.000, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 1.000, 4.000, 4.000, 3.000, 3.000, 4.000, 4.000, 5.000, 4.000, 4.000, 1.000, 1.000, 4.000, 4.000, 6.000	28.40884, -2.780106, -0.124967, 0.5839601, 1.929196, -0.689678, 0.3547199, -1.04528, -1.007951, -52.23469, -2.426656, 2.111436, -0.3526643, 0.679099, -0.720901, -1.10683, 2, 21.12497, 20.41604, 19.4708, 18.78968, 18.84528, 18.84528, 20.70795, 0, 1, -2.645751, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, 0.3779645, -8.247185, 0.8728647, -0.2683341, -0.5490191, -0.526106, -0.526106, 0.2414814, 1.377964, 1.121188, 1, 2, 1e-07, 2, 5, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 21, 21, 21.4, 18.1, 19.2, 17.8, 19.7, 2.62, 2.875, 3.215, 3.46, 3.44, 3.44, 2.77	20.70795
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	16.32604
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	16.04103
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	14.94481
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	15.69024
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	15.58061
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	12.35773
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	11.97625
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	12.14945
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	16.15065
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	16.33700
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	15.44907
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	15.43811
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	16.91800
8	18.700, 14.300, 16.400, 17.300, 15.200, 10.400, 10.400, 14.700, 15.500, 15.200, 13.300, 19.200, 15.800, 15.000, 360.000, 360.000, 275.800, 275.800, 275.800, 472.000, 460.000, 440.000, 318.000, 304.000, 350.000, 400.000, 351.000, 301.000, 175.000, 245.000, 180.000, 180.000, 180.000, 205.000, 215.000, 230.000, 150.000, 150.000, 245.000, 175.000, 264.000, 335.000, 3.150, 3.210, 3.070, 3.070, 3.070, 2.930, 3.000, 3.230, 2.760, 3.150, 3.730, 3.080, 4.220, 3.540, 3.440, 3.570, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 3.520, 3.435, 3.840, 3.845, 3.170, 3.570, 17.020, 15.840, 17.400, 17.600, 18.000, 17.980, 17.820, 17.420, 16.870, 17.300, 15.410, 17.050, 14.500, 14.600, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 1.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 3.000, 5.000, 5.000, 2.000, 4.000, 3.000, 3.000, 3.000, 4.000, 4.000, 4.000, 2.000, 2.000, 4.000, 2.000, 4.000, 8.000	23.86803, -2.192438, 2.373957, -1.741026, 1.455193, 1.609764, -0.3806137, -1.95773, -1.576246, 2.550552, -0.6506476, -1.137005, -2.149067, 3.761895, -1.118001, -1.041026, -56.49903, -6.003055, 0.8157971, 1.220314, -0.8068206, -3.464586, -3.211015, 0.9738578, -0.8857193, -1.30959, -2.619382, 3.287904, -1.095775, -1.312854, 2, 16.32604, 16.04103, 14.94481, 15.69024, 15.58061, 12.35773, 11.97625, 12.14945, 16.15065, 16.337, 15.44907, 15.43811, 16.918, 16.04103, 0, 1, -3.741657, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, 0.2672612, -14.96369, 2.738073, -0.06892519, 0.05524975, 0.03698873, -0.4998852, -0.5634336, -0.5345812, 0.131946, 0.1629898, 0.0150755, 0.0132494, 0.2597732, 0.113685, 1.267261, 1.113685, 1, 2, 1e-07, 2, 12, lm(formula = mpg ~ wt, data = .x), mpg ~ wt, 18.7, 14.3, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 15.5, 15.2, 13.3, 19.2, 15.8, 15, 3.44, 3.57, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 3.52, 3.435, 3.84, 3.845, 3.17, 3.57	16.04103

3.3.4 批量替换

# Vectorizing a function over multiple arguments
df1 <- data.frame(
  x = c("apple", "banana", "cherry"),
  pattern = c("p", "n", "h"),
  replacement = c("P", "N", "H"),
  stringsAsFactors = FALSE
)
pmap(df1, gsub)

## [[1]]
## [1] "aPPle"
## 
## [[2]]
## [1] "baNaNa"
## 
## [[3]]
## [1] "cHerry"

pmap_chr(df1, gsub)

## [1] "aPPle"  "baNaNa" "cHerry"

purrr包应用

Tony

2020-12-03

1 purrr风格函数（匿名函数）

1.1 一元函数

1.2 二元函数

1.3 多元函数

2 map函数

2.1 主要函数

2.2 各函数示例

3 解决问题

3.1 批量保存数据文件

3.2 批量合并文件

3.3 计算

3.3.1 向量对象

3.3.2 数据框对象

3.3.3 列表对象

3.3.3.1 列表计算1

3.3.3.2 列表转数据框

3.3.3.3 列表计算2

3.3.3.4 批量建模

3.3.4 批量替换

4 参考文献