若要表达 \(f(x) = x ^ 2 + 1\)
若要表达\(f(x, y) = x ^ 2 + 3y\)
若要表达\(f(x, y, z) = x ^ 2 + 3y - 5z\)
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。
x <- list(1, 1, 1)
y <- list(10, 20, 30)
z <- list(100, 200, 300)
#map
1:10 %>% map(rnorm, n = 10)## [[1]]
## [1] 0.3061668 0.1162393 1.1704524 1.2592932 1.6338086 0.7549747 0.1918357
## [8] 1.1915237 2.6342518 0.6677879
##
## [[2]]
## [1] 3.3167577 1.1996474 1.9005974 0.3306984 0.7101021 3.4511653 2.4639469
## [8] 1.7915336 0.9570713 0.7889268
##
## [[3]]
## [1] 3.945432 4.864921 5.122253 4.520294 2.885772 2.760653 4.487460 2.904572
## [9] 4.349992 2.462967
##
## [[4]]
## [1] 3.243922 3.978584 5.461694 3.639214 3.542091 4.909262 4.195839 5.829749
## [9] 3.294302 4.541477
##
## [[5]]
## [1] 5.153825 6.478777 4.446864 5.068139 4.643089 5.365064 5.004751 5.438901
## [9] 4.538498 6.954615
##
## [[6]]
## [1] 8.320357 7.295863 7.521597 4.588422 6.710772 5.347563 5.904938 6.977774
## [9] 4.976617 6.314338
##
## [[7]]
## [1] 8.722166 7.965820 8.094866 5.501836 6.886902 5.993457 6.576535 8.330432
## [9] 6.562682 5.619906
##
## [[8]]
## [1] 7.072106 10.269568 6.966314 8.571506 8.629842 7.134706 8.519207
## [8] 7.957746 7.620457 8.512069
##
## [[9]]
## [1] 10.244631 10.314168 9.535608 6.774451 9.516094 9.774074 8.176610
## [8] 8.804441 6.962848 7.372992
##
## [[10]]
## [1] 10.222330 10.706560 12.235675 10.565482 10.987380 9.065121 8.432318
## [8] 8.197119 9.320197 10.875902#map2
map2(x, y, ~ .x + .y)## [[1]]
## [1] 11
##
## [[2]]
## [1] 21
##
## [[3]]
## [1] 31lst <- 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#install.packages("metan")
library(metan)## Warning: package 'metan' was built under R version 3.6.3## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2## |========================================================|## | Multi-Environment Trial Analysis (metan) v1.12.0 |## | Author: Tiago Olivoto |## | Type 'citation('metan')' to know how to cite metan |## | Type 'vignette('metan_start')' for a short tutorial |## | Visit 'https://bit.ly/2TIq6JE' for a complete tutorial |## |========================================================|##
## Attaching package: 'metan'## The following object is masked from 'package:forcats':
##
## as_factor## The following object is masked from 'package:tidyr':
##
## replace_na## The following objects are masked from 'package:tibble':
##
## column_to_rownames, remove_rownames, rownames_to_columnmodel = ge_stats(data_ge2, ENV, GEN, REP)
#将列表中各数据集写出到以各元素名为文件名的CSV文件
# dir.create("D:/rpubs/data1/")
names_model <- names(model) %>%
paste0("D:/rpubs/data1/", ., ".csv")
walk2(model, names_model, ~write.csv(..1, ..2, row.names = F))#将数据集中的各列数据写出到以列名为文件名的csv文件
dir.create("D:/rpubs/data2/")## Warning in dir.create("D:/rpubs/data2/"): 'D:\rpubs\data2'已存在names_df <- model$EH %>%
names() %>%
paste0("D:/rpubs/data2/", ., ".csv")
# model_list <- model$EH %>%
# as.list()
walk2(model$EH, names_df, write.csv)mpg %>%
nest_by(manufacturer, .keep = T) %>%
pwalk(
~ write_csv(..2, paste0("data/car_", ..1, ".csv") )
)##批量读取目录下同类文件
#读取工作目录下文件名以.csv结尾的文件
files <- dir(path = "data/", pattern = "\\.csv$", 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)#vector
vec <- c(1:10)
#dataframe
dfr <- iris
#list
lst <- repurrrsive::sw_filmsmap(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] 3.162278map_dbl(vec, sqrt)## [1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751 2.828427
## [9] 3.000000 3.162278map_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 104map(vec, rnorm, n = 10)## [[1]]
## [1] -0.7989356 1.1243535 -0.3716316 2.3012127 0.8922286 0.7201264
## [7] 0.8712398 0.1687882 0.8861034 0.7897579
##
## [[2]]
## [1] 0.9556122 1.8290873 0.3879282 2.2131331 1.2994987 1.7904608 2.3443855
## [8] 3.1647187 3.6722009 2.2016033
##
## [[3]]
## [1] 2.748787 3.993756 3.956900 2.171520 3.475956 4.693728 4.388541 2.234846
## [9] 2.365895 4.018333
##
## [[4]]
## [1] 2.516361 4.367344 4.469836 2.795355 4.873359 4.758895 3.578696 3.621071
## [9] 4.830140 5.041444
##
## [[5]]
## [1] 4.111856 3.681042 4.746839 5.399197 3.899355 5.570876 5.295204 5.089022
## [9] 5.065844 5.134531
##
## [[6]]
## [1] 6.231073 6.989157 5.882999 5.725695 5.355330 6.025440 5.454097 5.767258
## [9] 5.079801 5.635357
##
## [[7]]
## [1] 6.422973 6.639919 7.193007 7.844286 7.434267 6.231791 6.978896 7.449855
## [9] 7.401246 6.477827
##
## [[8]]
## [1] 6.874060 7.050052 7.115269 9.394183 6.587105 7.096720 7.942425 8.254554
## [9] 8.229789 7.217983
##
## [[9]]
## [1] 9.876408 10.026429 8.606481 9.877384 6.709892 7.638948 9.535409
## [8] 9.495972 10.495886 9.178379
##
## [[10]]
## [1] 9.887452 10.042359 10.391409 9.579592 10.176319 10.590862 9.551342
## [8] 10.299332 11.292678 9.725463dfr %>% 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] NAdfr %>% 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 NAdfr %>% 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 |
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] NAmap_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"#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#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 |
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 |
# 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 |
# 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"