第5讲

第5讲 数据管理

代码

教学视频

函数select()和mutate()

函数if_else()对变量值进行条件转换

函数filter()筛选个案

如何删除含有缺失值的个案？

本章习题

第1题

第1题：数据 ggplot2::mpg

提示：加载包tidyverse，运行data(mpg), 即可调用数据框mpg。

1.1 用select()函数从mpg提取5个变量⽣成新的数据框。

1.2 用mutate()函数在数据框中追加新的变量，将油耗变量cty和hwy（miles per gallon）转换成转公里/升(kilometers per liter)的油耗指标。

1.3 任选mpg中的某个变量，用if_else()函数对该变量的数值进⾏条件转换。

1.4 设置三个筛选条件，用filter()函数从mpg筛选出满足条件的个案, 保存到新的数据框。

提交R script代码。

第2题

习题2 :数据nycflights13::flights

提示：加载包nycflights13，运行data(flights), 即可调用数据框flights。

2.1 根据distance(单位：英里)的数值，将航班分为短途、中途和长途三个类别。创建新变量distance_group,其值为”short”, “medium”, “long”。统计每个类别的航班数量。

• 短途飞行（Short-haul flight）：飞行距离少于500英里的航班。

• 中途飞行（Medium-haul flight）：飞行距离在500到1,550英里之间的航班。

• 长途飞行（Long-haul flight）：飞行距离超过1,550英里的航班

2.2 如果你想尽可能避免延误，你应该选择哪个时间段起飞的航班？

2.3 哪个航空公司(carrier)的平均延误时间最长？

习题答案

第1题

第1题

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(gt)

1.1 用select()函数从mpg提取5个变量⽣成新的数据框。

#library(gt)
data(mpg)

mpg %>% 
  select(manufacturer:cyl) %>% 
  head() #查看前6行

# A tibble: 6 × 5
  manufacturer model displ  year   cyl
  <chr>        <chr> <dbl> <int> <int>
1 audi         a4      1.8  1999     4
2 audi         a4      1.8  1999     4
3 audi         a4      2    2008     4
4 audi         a4      2    2008     4
5 audi         a4      2.8  1999     6
6 audi         a4      2.8  1999     6

mpg %>% 
  select(1:5) %>% 
  head()

# A tibble: 6 × 5
  manufacturer model displ  year   cyl
  <chr>        <chr> <dbl> <int> <int>
1 audi         a4      1.8  1999     4
2 audi         a4      1.8  1999     4
3 audi         a4      2    2008     4
4 audi         a4      2    2008     4
5 audi         a4      2.8  1999     6
6 audi         a4      2.8  1999     6

mpg %>% 
  select(4,5,7:9) %>% 
  head()

# A tibble: 6 × 5
   year   cyl drv     cty   hwy
  <int> <int> <chr> <int> <int>
1  1999     4 f        18    29
2  1999     4 f        21    29
3  2008     4 f        20    31
4  2008     4 f        21    30
5  1999     6 f        16    26
6  1999     6 f        18    26

mpg %>% 
  select(year,cyl,drv:hwy) %>% 
  head()

# A tibble: 6 × 5
   year   cyl drv     cty   hwy
  <int> <int> <chr> <int> <int>
1  1999     4 f        18    29
2  1999     4 f        21    29
3  2008     4 f        20    31
4  2008     4 f        21    30
5  1999     6 f        16    26
6  1999     6 f        18    26

1.2 用mutate()函数在数据框中追加新的变量，将油耗变量cty和hwy（miles per gallon）转换成转公里/升(kilometers per liter)的油耗指标。(1 miles per gallon = 0.425 kilometers per liter)

mpgnew <- mpg %>% mutate(cty.kpl = 0.425*cty, 
               hwy.kpl = 0.425*hwy)

head(mpgnew) %>% gt()

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class	cty.kpl	hwy.kpl
audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact	7.650	12.325
audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact	8.925	12.325
audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact	8.500	13.175
audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact	8.925	12.750
audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact	6.800	11.050
audi	a4	2.8	1999	6	manual(m5)	f	18	26	p	compact	7.650	11.050

mpg$hwy.kpl <- 0.425*mpg$hwy
mpg$cty.kpl <- 0.425*mpg$cty

head(mpg) %>% gt()

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class	hwy.kpl	cty.kpl
audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact	12.325	7.650
audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact	12.325	8.925
audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact	13.175	8.500
audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact	12.750	8.925
audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact	11.050	6.800
audi	a4	2.8	1999	6	manual(m5)	f	18	26	p	compact	11.050	7.650

1.3 任选mpg中的某个变量，用if_else()函数对该变量的数值进⾏条件转换。

mpgnew <- mpg %>% 
  mutate(transmission = 
           if_else(substring(trans, 1,4) == "auto", 
                   "auto","manual"))

head(mpgnew) %>% gt()

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class	hwy.kpl	cty.kpl	transmission
audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact	12.325	7.650	auto
audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact	12.325	8.925	manual
audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact	13.175	8.500	manual
audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact	12.750	8.925	auto
audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact	11.050	6.800	auto
audi	a4	2.8	1999	6	manual(m5)	f	18	26	p	compact	11.050	7.650	manual

#二值转换 if_else

mpg$transmission <- if_else(
  substring(mpg$trans, 1,4) == "auto", 
  "auto","manual") 

#多值转换 case_when
mpg$drive <- case_when(
  mpg$drv == "f" ~ "front-wheel",
  mpg$drv == "r" ~ "rear-wheel",
  mpg$drv == "4" ~ "four-wheel")


head(mpg) %>% gt()

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class	hwy.kpl	cty.kpl	transmission	drive
audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact	12.325	7.650	auto	front-wheel
audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact	12.325	8.925	manual	front-wheel
audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact	13.175	8.500	manual	front-wheel
audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact	12.750	8.925	auto	front-wheel
audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact	11.050	6.800	auto	front-wheel
audi	a4	2.8	1999	6	manual(m5)	f	18	26	p	compact	11.050	7.650	manual	front-wheel

1.4 设置三个筛选条件，用filter()函数从mpg筛选个案⽣成新的数据框。

# the & operator is used to combine two conditions, where both conditions must be true for a row to be included in the filtered data frame.

mpg %>% 
  filter(class == "suv" & 
           cyl == 8 & 
          year == 2008) %>%
  head() %>%
  gt()

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class	hwy.kpl	cty.kpl	transmission	drive
chevrolet	c1500 suburban 2wd	5.3	2008	8	auto(l4)	r	14	20	r	suv	8.500	5.950	auto	rear-wheel
chevrolet	c1500 suburban 2wd	5.3	2008	8	auto(l4)	r	11	15	e	suv	6.375	4.675	auto	rear-wheel
chevrolet	c1500 suburban 2wd	5.3	2008	8	auto(l4)	r	14	20	r	suv	8.500	5.950	auto	rear-wheel
chevrolet	c1500 suburban 2wd	6.0	2008	8	auto(l4)	r	12	17	r	suv	7.225	5.100	auto	rear-wheel
chevrolet	k1500 tahoe 4wd	5.3	2008	8	auto(l4)	4	14	19	r	suv	8.075	5.950	auto	four-wheel
chevrolet	k1500 tahoe 4wd	5.3	2008	8	auto(l4)	4	11	14	e	suv	5.950	4.675	auto	four-wheel

mpg %>% 
  filter(substr(trans, 1, 4) == "auto" & 
        class %in% c("compact", "subcompact") &
          !fl %in% c("c" ,"d" ,"e")) %>%
  head() %>%
  gt()

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class	hwy.kpl	cty.kpl	transmission	drive
audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact	12.325	7.650	auto	front-wheel
audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact	12.750	8.925	auto	front-wheel
audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact	11.050	6.800	auto	front-wheel
audi	a4	3.1	2008	6	auto(av)	f	18	27	p	compact	11.475	7.650	auto	front-wheel
audi	a4 quattro	1.8	1999	4	auto(l5)	4	16	25	p	compact	10.625	6.800	auto	four-wheel
audi	a4 quattro	2.0	2008	4	auto(s6)	4	19	27	p	compact	11.475	8.075	auto	four-wheel

# the | operator combines the two conditions, where only one of the conditions needs to be true for a row to be included in the filtered data frame.

mpg %>% 
  filter(class == "suv" | 
           cyl == 8 |
          year == 2008)  %>% 
  head() %>% 
  gt()

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class	hwy.kpl	cty.kpl	transmission	drive
audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact	13.175	8.500	manual	front-wheel
audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact	12.750	8.925	auto	front-wheel
audi	a4	3.1	2008	6	auto(av)	f	18	27	p	compact	11.475	7.650	auto	front-wheel
audi	a4 quattro	2.0	2008	4	manual(m6)	4	20	28	p	compact	11.900	8.500	manual	four-wheel
audi	a4 quattro	2.0	2008	4	auto(s6)	4	19	27	p	compact	11.475	8.075	auto	four-wheel
audi	a4 quattro	3.1	2008	6	auto(s6)	4	17	25	p	compact	10.625	7.225	auto	four-wheel

拓展资源

R for Data Science 2nd edition https://r4ds.hadley.nz/data-tidy

R代码风格

R如何快速预览变量分布？summarytools::dfSummary()

R代码风格：如何写出美观易读的代码?