flights |>
group_by(origin) |>#不同机场分类
summarise(n=n(),dly=mean(dep_delay,na.rm=T))#n=航班数# A tibble: 3 × 3
origin n dly
<chr> <int> <dbl>
1 EWR 120835 15.1
2 JFK 111279 12.1
3 LGA 104662 10.3tidyverse初认识
1 第一题 编写代码
利用nycflights13包的flights数据集是2013年从纽约三大机场（JFK、LGA、EWR）起飞的所有航班的准点数据，共336776条记录。
计算纽约三大机场2013起飞航班数和平均延误时间（可使用group_by, summarise函数）
计算不同航空公司2013从纽约起飞航班数和平均延误时间
flights |> group_by(carrier) |>#不同航空公司分类 summarise(n=n(),dly=mean(dep_delay,na.rm=T)) |> arrange(desc(n))#按按航班数降序排列# A tibble: 16 × 3 carrier n dly <chr> <int> <dbl> 1 UA 58665 12.1 2 B6 54635 13.0 3 EV 54173 20.0 4 DL 48110 9.26 5 AA 32729 8.59 6 MQ 26397 10.6 7 US 20536 3.78 8 9E 18460 16.7 9 WN 12275 17.7 10 VX 5162 12.9 11 FL 3260 18.7 12 AS 714 5.80 13 F9 685 20.2 14 YV 601 19.0 15 HA 342 4.90 16 OO 32 12.6计算纽约三大机场排名前三个目的地和平均飞行距离(可使用group_by, summarise, arrange, slice_max函数)
flights |> group_by(origin,dest) |> summarise(n=n(),dist=mean(distance)) |> slice_max(n,n=3)`summarise()` has grouped output by 'origin'. You can override using the `.groups` argument.# A tibble: 9 × 4 # Groups: origin [3] origin dest n dist <chr> <chr> <int> <dbl> 1 EWR ORD 6100 719 2 EWR BOS 5327 200 3 EWR SFO 5127 2565 4 JFK LAX 11262 2475 5 JFK SFO 8204 2586 6 JFK BOS 5898 187 7 LGA ATL 10263 762 8 LGA ORD 8857 733 9 LGA CLT 6168 544
2 第二题 解释代码
代码含义：利用tibble函数给iris（鸢尾花）增加数据框，arrange将数据进行排序，Species鸢尾花物种，提取以Sepal开头的列，desc进行降序排序
tibble(iris) %>% arrange(Species,across(starts_with("Sepal"), desc))# A tibble: 150 × 5 Sepal.Length Sepal.Width Petal.Length Petal.Width Species <dbl> <dbl> <dbl> <dbl> <fct> 1 5.8 4 1.2 0.2 setosa 2 5.7 4.4 1.5 0.4 setosa 3 5.7 3.8 1.7 0.3 setosa 4 5.5 4.2 1.4 0.2 setosa 5 5.5 3.5 1.3 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa 7 5.4 3.9 1.3 0.4 setosa 8 5.4 3.7 1.5 0.2 setosa 9 5.4 3.4 1.7 0.2 setosa 10 5.4 3.4 1.5 0.4 setosa # ℹ 140 more rows代码含义：group_by(gender)按照性别对数据starwars进行分组，利用filter函数筛选出质量大于平均质量的数值，na.rm = TRUE忽略缺失值
starwars %>% group_by(gender) %>% filter(mass > mean(mass, na.rm = TRUE))# A tibble: 15 × 14 # Groups: gender [3] name height mass hair_color skin_color eye_color birth_year sex gender <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> 1 Darth … 202 136 none white yellow 41.9 male mascu… 2 Owen L… 178 120 brown, gr… light blue 52 male mascu… 3 Beru W… 165 75 brown light blue 47 fema… femin… 4 Chewba… 228 112 brown unknown blue 200 male mascu… 5 Jabba … 175 1358 <NA> green-tan… orange 600 herm… mascu… 6 Jek To… 180 110 brown fair blue NA <NA> <NA> 7 IG-88 200 140 none metal red 15 none mascu… 8 Bossk 190 113 none green red 53 male mascu… 9 Ayla S… 178 55 none blue hazel 48 fema… femin… 10 Gregar… 185 85 black dark brown NA <NA> <NA> 11 Lumina… 170 56.2 black yellow blue 58 fema… femin… 12 Zam We… 168 55 blonde fair, gre… yellow NA fema… femin… 13 Shaak … 178 57 none red, blue… black NA fema… femin… 14 Grievo… 216 159 none brown, wh… green, y… NA male mascu… 15 Tarfful 234 136 brown brown blue NA male mascu… # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>, # vehicles <list>, starships <list>代码含义：选择名字、家乡、物种列，使用mutate across函数将name列转化为因子类型
starwars %>% select(name, homeworld, species) %>% mutate(across(!name, as.factor))# A tibble: 87 × 3 name homeworld species <chr> <fct> <fct> 1 Luke Skywalker Tatooine Human 2 C-3PO Tatooine Droid 3 R2-D2 Naboo Droid 4 Darth Vader Tatooine Human 5 Leia Organa Alderaan Human 6 Owen Lars Tatooine Human 7 Beru Whitesun Lars Tatooine Human 8 R5-D4 Tatooine Droid 9 Biggs Darklighter Tatooine Human 10 Obi-Wan Kenobi Stewjon Human # ℹ 77 more rows代码含义：利用tibble函数给增加mtcars数据框，group_by(vs)按vs（发动机气缸是否为V型）进行分类，mutate函数：修改现有变量，cut函数：分割连续变量，将hp（马力）切割成3组，group_by(hp_cut)按hp_cut进行分组
tibble(mtcars) %>% group_by(vs) %>% mutate(hp_cut = cut(hp, 3)) %>% group_by(hp_cut)# A tibble: 32 × 12 # Groups: hp_cut [6] mpg cyl disp hp drat wt qsec vs am gear carb hp_cut <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 (90.8,172] 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 (90.8,172] 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 (75.7,99.3] 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 (99.3,123] 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 (172,254] 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 (99.3,123] 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 (172,254] 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 (51.9,75.7] 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 (75.7,99.3] 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 (99.3,123] # ℹ 22 more rows
3 第三题 查找帮助理解函数
阅读 https://dplyr.tidyverse.org/reference/mutate-joins.html 内容，说明4个数据集链接函数函数的作用。分别举一个实际例子演示并解释其输出结果。
inner_join()：保留匹配的行：只有当
x中的某行在y中有对应的匹配键值时，该行才会出现在结果中。丢弃不匹配的行：如果
x或y中的某些行没有匹配的键值，则这些行不会出现在结果中。合并列：结果数据框包含
x的所有列和y中非键列的列。如果x和y中有相同名称的非键列，则会通过suffix参数添加后缀以区分。
# 加载 dplyr 包 library(dplyr) # 查看 mtcars 数据集 head(mtcars)mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1# 创建 df1：包含 mpg、cyl 和 disp 列 df1 <- mtcars %>% select(mpg, cyl, disp) # 创建 df2：包含 cyl 和 hp 列 df2 <- mtcars %>% select(cyl, hp) # 使用 inner_join() 将 df1 和 df2 基于 cyl 列合并 result <- inner_join(df1, df2, by = "cyl")Warning in inner_join(df1, df2, by = "cyl"): Detected an unexpected many-to-many relationship between `x` and `y`. ℹ Row 1 of `x` matches multiple rows in `y`. ℹ Row 1 of `y` matches multiple rows in `x`. ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.# 查看结果 print(result)mpg cyl disp hp 1 21.0 6 160.0 110 2 21.0 6 160.0 110 3 21.0 6 160.0 110 4 21.0 6 160.0 105 5 21.0 6 160.0 123 6 21.0 6 160.0 123 7 21.0 6 160.0 175 8 21.0 6 160.0 110 9 21.0 6 160.0 110 10 21.0 6 160.0 110 11 21.0 6 160.0 105 12 21.0 6 160.0 123 13 21.0 6 160.0 123 14 21.0 6 160.0 175 15 22.8 4 108.0 93 16 22.8 4 108.0 62 17 22.8 4 108.0 95 18 22.8 4 108.0 66 19 22.8 4 108.0 52 20 22.8 4 108.0 65 21 22.8 4 108.0 97 22 22.8 4 108.0 66 23 22.8 4 108.0 91 24 22.8 4 108.0 113 25 22.8 4 108.0 109 26 21.4 6 258.0 110 27 21.4 6 258.0 110 28 21.4 6 258.0 110 29 21.4 6 258.0 105 30 21.4 6 258.0 123 31 21.4 6 258.0 123 32 21.4 6 258.0 175 33 18.7 8 360.0 175 34 18.7 8 360.0 245 35 18.7 8 360.0 180 36 18.7 8 360.0 180 37 18.7 8 360.0 180 38 18.7 8 360.0 205 39 18.7 8 360.0 215 40 18.7 8 360.0 230 41 18.7 8 360.0 150 42 18.7 8 360.0 150 43 18.7 8 360.0 245 44 18.7 8 360.0 175 45 18.7 8 360.0 264 46 18.7 8 360.0 335 47 18.1 6 225.0 110 48 18.1 6 225.0 110 49 18.1 6 225.0 110 50 18.1 6 225.0 105 51 18.1 6 225.0 123 52 18.1 6 225.0 123 53 18.1 6 225.0 175 54 14.3 8 360.0 175 55 14.3 8 360.0 245 56 14.3 8 360.0 180 57 14.3 8 360.0 180 58 14.3 8 360.0 180 59 14.3 8 360.0 205 60 14.3 8 360.0 215 61 14.3 8 360.0 230 62 14.3 8 360.0 150 63 14.3 8 360.0 150 64 14.3 8 360.0 245 65 14.3 8 360.0 175 66 14.3 8 360.0 264 67 14.3 8 360.0 335 68 24.4 4 146.7 93 69 24.4 4 146.7 62 70 24.4 4 146.7 95 71 24.4 4 146.7 66 72 24.4 4 146.7 52 73 24.4 4 146.7 65 74 24.4 4 146.7 97 75 24.4 4 146.7 66 76 24.4 4 146.7 91 77 24.4 4 146.7 113 78 24.4 4 146.7 109 79 22.8 4 140.8 93 80 22.8 4 140.8 62 81 22.8 4 140.8 95 82 22.8 4 140.8 66 83 22.8 4 140.8 52 84 22.8 4 140.8 65 85 22.8 4 140.8 97 86 22.8 4 140.8 66 87 22.8 4 140.8 91 88 22.8 4 140.8 113 89 22.8 4 140.8 109 90 19.2 6 167.6 110 91 19.2 6 167.6 110 92 19.2 6 167.6 110 93 19.2 6 167.6 105 94 19.2 6 167.6 123 95 19.2 6 167.6 123 96 19.2 6 167.6 175 97 17.8 6 167.6 110 98 17.8 6 167.6 110 99 17.8 6 167.6 110 100 17.8 6 167.6 105 101 17.8 6 167.6 123 102 17.8 6 167.6 123 103 17.8 6 167.6 175 104 16.4 8 275.8 175 105 16.4 8 275.8 245 106 16.4 8 275.8 180 107 16.4 8 275.8 180 108 16.4 8 275.8 180 109 16.4 8 275.8 205 110 16.4 8 275.8 215 111 16.4 8 275.8 230 112 16.4 8 275.8 150 113 16.4 8 275.8 150 114 16.4 8 275.8 245 115 16.4 8 275.8 175 116 16.4 8 275.8 264 117 16.4 8 275.8 335 118 17.3 8 275.8 175 119 17.3 8 275.8 245 120 17.3 8 275.8 180 121 17.3 8 275.8 180 122 17.3 8 275.8 180 123 17.3 8 275.8 205 124 17.3 8 275.8 215 125 17.3 8 275.8 230 126 17.3 8 275.8 150 127 17.3 8 275.8 150 128 17.3 8 275.8 245 129 17.3 8 275.8 175 130 17.3 8 275.8 264 131 17.3 8 275.8 335 132 15.2 8 275.8 175 133 15.2 8 275.8 245 134 15.2 8 275.8 180 135 15.2 8 275.8 180 136 15.2 8 275.8 180 137 15.2 8 275.8 205 138 15.2 8 275.8 215 139 15.2 8 275.8 230 140 15.2 8 275.8 150 141 15.2 8 275.8 150 142 15.2 8 275.8 245 143 15.2 8 275.8 175 144 15.2 8 275.8 264 145 15.2 8 275.8 335 146 10.4 8 472.0 175 147 10.4 8 472.0 245 148 10.4 8 472.0 180 149 10.4 8 472.0 180 150 10.4 8 472.0 180 151 10.4 8 472.0 205 152 10.4 8 472.0 215 153 10.4 8 472.0 230 154 10.4 8 472.0 150 155 10.4 8 472.0 150 156 10.4 8 472.0 245 157 10.4 8 472.0 175 158 10.4 8 472.0 264 159 10.4 8 472.0 335 160 10.4 8 460.0 175 161 10.4 8 460.0 245 162 10.4 8 460.0 180 163 10.4 8 460.0 180 164 10.4 8 460.0 180 165 10.4 8 460.0 205 166 10.4 8 460.0 215 167 10.4 8 460.0 230 168 10.4 8 460.0 150 169 10.4 8 460.0 150 170 10.4 8 460.0 245 171 10.4 8 460.0 175 172 10.4 8 460.0 264 173 10.4 8 460.0 335 174 14.7 8 440.0 175 175 14.7 8 440.0 245 176 14.7 8 440.0 180 177 14.7 8 440.0 180 178 14.7 8 440.0 180 179 14.7 8 440.0 205 180 14.7 8 440.0 215 181 14.7 8 440.0 230 182 14.7 8 440.0 150 183 14.7 8 440.0 150 184 14.7 8 440.0 245 185 14.7 8 440.0 175 186 14.7 8 440.0 264 187 14.7 8 440.0 335 188 32.4 4 78.7 93 189 32.4 4 78.7 62 190 32.4 4 78.7 95 191 32.4 4 78.7 66 192 32.4 4 78.7 52 193 32.4 4 78.7 65 194 32.4 4 78.7 97 195 32.4 4 78.7 66 196 32.4 4 78.7 91 197 32.4 4 78.7 113 198 32.4 4 78.7 109 199 30.4 4 75.7 93 200 30.4 4 75.7 62 201 30.4 4 75.7 95 202 30.4 4 75.7 66 203 30.4 4 75.7 52 204 30.4 4 75.7 65 205 30.4 4 75.7 97 206 30.4 4 75.7 66 207 30.4 4 75.7 91 208 30.4 4 75.7 113 209 30.4 4 75.7 109 210 33.9 4 71.1 93 211 33.9 4 71.1 62 212 33.9 4 71.1 95 213 33.9 4 71.1 66 214 33.9 4 71.1 52 215 33.9 4 71.1 65 216 33.9 4 71.1 97 217 33.9 4 71.1 66 218 33.9 4 71.1 91 219 33.9 4 71.1 113 220 33.9 4 71.1 109 221 21.5 4 120.1 93 222 21.5 4 120.1 62 223 21.5 4 120.1 95 224 21.5 4 120.1 66 225 21.5 4 120.1 52 226 21.5 4 120.1 65 227 21.5 4 120.1 97 228 21.5 4 120.1 66 229 21.5 4 120.1 91 230 21.5 4 120.1 113 231 21.5 4 120.1 109 232 15.5 8 318.0 175 233 15.5 8 318.0 245 234 15.5 8 318.0 180 235 15.5 8 318.0 180 236 15.5 8 318.0 180 237 15.5 8 318.0 205 238 15.5 8 318.0 215 239 15.5 8 318.0 230 240 15.5 8 318.0 150 241 15.5 8 318.0 150 242 15.5 8 318.0 245 243 15.5 8 318.0 175 244 15.5 8 318.0 264 245 15.5 8 318.0 335 246 15.2 8 304.0 175 247 15.2 8 304.0 245 248 15.2 8 304.0 180 249 15.2 8 304.0 180 250 15.2 8 304.0 180 251 15.2 8 304.0 205 252 15.2 8 304.0 215 253 15.2 8 304.0 230 254 15.2 8 304.0 150 255 15.2 8 304.0 150 256 15.2 8 304.0 245 257 15.2 8 304.0 175 258 15.2 8 304.0 264 259 15.2 8 304.0 335 260 13.3 8 350.0 175 261 13.3 8 350.0 245 262 13.3 8 350.0 180 263 13.3 8 350.0 180 264 13.3 8 350.0 180 265 13.3 8 350.0 205 266 13.3 8 350.0 215 267 13.3 8 350.0 230 268 13.3 8 350.0 150 269 13.3 8 350.0 150 270 13.3 8 350.0 245 271 13.3 8 350.0 175 272 13.3 8 350.0 264 273 13.3 8 350.0 335 274 19.2 8 400.0 175 275 19.2 8 400.0 245 276 19.2 8 400.0 180 277 19.2 8 400.0 180 278 19.2 8 400.0 180 279 19.2 8 400.0 205 280 19.2 8 400.0 215 281 19.2 8 400.0 230 282 19.2 8 400.0 150 283 19.2 8 400.0 150 284 19.2 8 400.0 245 285 19.2 8 400.0 175 286 19.2 8 400.0 264 287 19.2 8 400.0 335 288 27.3 4 79.0 93 289 27.3 4 79.0 62 290 27.3 4 79.0 95 291 27.3 4 79.0 66 292 27.3 4 79.0 52 293 27.3 4 79.0 65 294 27.3 4 79.0 97 295 27.3 4 79.0 66 296 27.3 4 79.0 91 297 27.3 4 79.0 113 298 27.3 4 79.0 109 299 26.0 4 120.3 93 300 26.0 4 120.3 62 301 26.0 4 120.3 95 302 26.0 4 120.3 66 303 26.0 4 120.3 52 304 26.0 4 120.3 65 305 26.0 4 120.3 97 306 26.0 4 120.3 66 307 26.0 4 120.3 91 308 26.0 4 120.3 113 309 26.0 4 120.3 109 310 30.4 4 95.1 93 311 30.4 4 95.1 62 312 30.4 4 95.1 95 313 30.4 4 95.1 66 314 30.4 4 95.1 52 315 30.4 4 95.1 65 316 30.4 4 95.1 97 317 30.4 4 95.1 66 318 30.4 4 95.1 91 319 30.4 4 95.1 113 320 30.4 4 95.1 109 321 15.8 8 351.0 175 322 15.8 8 351.0 245 323 15.8 8 351.0 180 324 15.8 8 351.0 180 325 15.8 8 351.0 180 326 15.8 8 351.0 205 327 15.8 8 351.0 215 328 15.8 8 351.0 230 329 15.8 8 351.0 150 330 15.8 8 351.0 150 331 15.8 8 351.0 245 332 15.8 8 351.0 175 333 15.8 8 351.0 264 334 15.8 8 351.0 335 335 19.7 6 145.0 110 336 19.7 6 145.0 110 337 19.7 6 145.0 110 338 19.7 6 145.0 105 339 19.7 6 145.0 123 340 19.7 6 145.0 123 341 19.7 6 145.0 175 342 15.0 8 301.0 175 343 15.0 8 301.0 245 344 15.0 8 301.0 180 345 15.0 8 301.0 180 346 15.0 8 301.0 180 347 15.0 8 301.0 205 348 15.0 8 301.0 215 349 15.0 8 301.0 230 350 15.0 8 301.0 150 351 15.0 8 301.0 150 352 15.0 8 301.0 245 353 15.0 8 301.0 175 354 15.0 8 301.0 264 355 15.0 8 301.0 335 356 21.4 4 121.0 93 357 21.4 4 121.0 62 358 21.4 4 121.0 95 359 21.4 4 121.0 66 360 21.4 4 121.0 52 361 21.4 4 121.0 65 362 21.4 4 121.0 97 363 21.4 4 121.0 66 364 21.4 4 121.0 91 365 21.4 4 121.0 113 366 21.4 4 121.0 109#输出结果：结果数据框包含 df1 的所有列（mpg、cyl、disp）和 df2 的非键列（hp）。每行表示在两个数据框中 cyl 值匹配的汽车数据。left_join()：保留所有
x的行：无论x中的行是否在y中有匹配的键值，x的所有行都会被保留。添加
y的列：将y中匹配的列添加到结果中。如果x中的某些行在y中没有匹配的键值，则这些行对应的列值为NA。基于键匹配：通过指定的键（
by参数）进行匹配。默认情况下，left_join()会自动匹配两个数据框中同名的列。
# 加载 dplyr 包 library(dplyr) # 创建 df1：包含 Sepal.Length、Sepal.Width 和 Species df1 <- iris %>% select(Sepal.Length, Sepal.Width, Species) # 创建 df2：包含 Species 和额外的统计信息（平均 Petal.Length 和 Petal.Width） df2 <- iris %>% group_by(Species) %>% summarise( Avg_Petal_Length = mean(Petal.Length), Avg_Petal_Width = mean(Petal.Width) ) # 查看 df1 和 df2 的结构 head(df1)Sepal.Length Sepal.Width Species 1 5.1 3.5 setosa 2 4.9 3.0 setosa 3 4.7 3.2 setosa 4 4.6 3.1 setosa 5 5.0 3.6 setosa 6 5.4 3.9 setosahead(df2)# A tibble: 3 × 3 Species Avg_Petal_Length Avg_Petal_Width <fct> <dbl> <dbl> 1 setosa 1.46 0.246 2 versicolor 4.26 1.33 3 virginica 5.55 2.03# 使用 left_join() 将 df1 和 df2 基于 Species 列合并 result <- left_join(df1, df2, by = "Species") # 查看结果 print(result)Sepal.Length Sepal.Width Species Avg_Petal_Length Avg_Petal_Width 1 5.1 3.5 setosa 1.462 0.246 2 4.9 3.0 setosa 1.462 0.246 3 4.7 3.2 setosa 1.462 0.246 4 4.6 3.1 setosa 1.462 0.246 5 5.0 3.6 setosa 1.462 0.246 6 5.4 3.9 setosa 1.462 0.246 7 4.6 3.4 setosa 1.462 0.246 8 5.0 3.4 setosa 1.462 0.246 9 4.4 2.9 setosa 1.462 0.246 10 4.9 3.1 setosa 1.462 0.246 11 5.4 3.7 setosa 1.462 0.246 12 4.8 3.4 setosa 1.462 0.246 13 4.8 3.0 setosa 1.462 0.246 14 4.3 3.0 setosa 1.462 0.246 15 5.8 4.0 setosa 1.462 0.246 16 5.7 4.4 setosa 1.462 0.246 17 5.4 3.9 setosa 1.462 0.246 18 5.1 3.5 setosa 1.462 0.246 19 5.7 3.8 setosa 1.462 0.246 20 5.1 3.8 setosa 1.462 0.246 21 5.4 3.4 setosa 1.462 0.246 22 5.1 3.7 setosa 1.462 0.246 23 4.6 3.6 setosa 1.462 0.246 24 5.1 3.3 setosa 1.462 0.246 25 4.8 3.4 setosa 1.462 0.246 26 5.0 3.0 setosa 1.462 0.246 27 5.0 3.4 setosa 1.462 0.246 28 5.2 3.5 setosa 1.462 0.246 29 5.2 3.4 setosa 1.462 0.246 30 4.7 3.2 setosa 1.462 0.246 31 4.8 3.1 setosa 1.462 0.246 32 5.4 3.4 setosa 1.462 0.246 33 5.2 4.1 setosa 1.462 0.246 34 5.5 4.2 setosa 1.462 0.246 35 4.9 3.1 setosa 1.462 0.246 36 5.0 3.2 setosa 1.462 0.246 37 5.5 3.5 setosa 1.462 0.246 38 4.9 3.6 setosa 1.462 0.246 39 4.4 3.0 setosa 1.462 0.246 40 5.1 3.4 setosa 1.462 0.246 41 5.0 3.5 setosa 1.462 0.246 42 4.5 2.3 setosa 1.462 0.246 43 4.4 3.2 setosa 1.462 0.246 44 5.0 3.5 setosa 1.462 0.246 45 5.1 3.8 setosa 1.462 0.246 46 4.8 3.0 setosa 1.462 0.246 47 5.1 3.8 setosa 1.462 0.246 48 4.6 3.2 setosa 1.462 0.246 49 5.3 3.7 setosa 1.462 0.246 50 5.0 3.3 setosa 1.462 0.246 51 7.0 3.2 versicolor 4.260 1.326 52 6.4 3.2 versicolor 4.260 1.326 53 6.9 3.1 versicolor 4.260 1.326 54 5.5 2.3 versicolor 4.260 1.326 55 6.5 2.8 versicolor 4.260 1.326 56 5.7 2.8 versicolor 4.260 1.326 57 6.3 3.3 versicolor 4.260 1.326 58 4.9 2.4 versicolor 4.260 1.326 59 6.6 2.9 versicolor 4.260 1.326 60 5.2 2.7 versicolor 4.260 1.326 61 5.0 2.0 versicolor 4.260 1.326 62 5.9 3.0 versicolor 4.260 1.326 63 6.0 2.2 versicolor 4.260 1.326 64 6.1 2.9 versicolor 4.260 1.326 65 5.6 2.9 versicolor 4.260 1.326 66 6.7 3.1 versicolor 4.260 1.326 67 5.6 3.0 versicolor 4.260 1.326 68 5.8 2.7 versicolor 4.260 1.326 69 6.2 2.2 versicolor 4.260 1.326 70 5.6 2.5 versicolor 4.260 1.326 71 5.9 3.2 versicolor 4.260 1.326 72 6.1 2.8 versicolor 4.260 1.326 73 6.3 2.5 versicolor 4.260 1.326 74 6.1 2.8 versicolor 4.260 1.326 75 6.4 2.9 versicolor 4.260 1.326 76 6.6 3.0 versicolor 4.260 1.326 77 6.8 2.8 versicolor 4.260 1.326 78 6.7 3.0 versicolor 4.260 1.326 79 6.0 2.9 versicolor 4.260 1.326 80 5.7 2.6 versicolor 4.260 1.326 81 5.5 2.4 versicolor 4.260 1.326 82 5.5 2.4 versicolor 4.260 1.326 83 5.8 2.7 versicolor 4.260 1.326 84 6.0 2.7 versicolor 4.260 1.326 85 5.4 3.0 versicolor 4.260 1.326 86 6.0 3.4 versicolor 4.260 1.326 87 6.7 3.1 versicolor 4.260 1.326 88 6.3 2.3 versicolor 4.260 1.326 89 5.6 3.0 versicolor 4.260 1.326 90 5.5 2.5 versicolor 4.260 1.326 91 5.5 2.6 versicolor 4.260 1.326 92 6.1 3.0 versicolor 4.260 1.326 93 5.8 2.6 versicolor 4.260 1.326 94 5.0 2.3 versicolor 4.260 1.326 95 5.6 2.7 versicolor 4.260 1.326 96 5.7 3.0 versicolor 4.260 1.326 97 5.7 2.9 versicolor 4.260 1.326 98 6.2 2.9 versicolor 4.260 1.326 99 5.1 2.5 versicolor 4.260 1.326 100 5.7 2.8 versicolor 4.260 1.326 101 6.3 3.3 virginica 5.552 2.026 102 5.8 2.7 virginica 5.552 2.026 103 7.1 3.0 virginica 5.552 2.026 104 6.3 2.9 virginica 5.552 2.026 105 6.5 3.0 virginica 5.552 2.026 106 7.6 3.0 virginica 5.552 2.026 107 4.9 2.5 virginica 5.552 2.026 108 7.3 2.9 virginica 5.552 2.026 109 6.7 2.5 virginica 5.552 2.026 110 7.2 3.6 virginica 5.552 2.026 111 6.5 3.2 virginica 5.552 2.026 112 6.4 2.7 virginica 5.552 2.026 113 6.8 3.0 virginica 5.552 2.026 114 5.7 2.5 virginica 5.552 2.026 115 5.8 2.8 virginica 5.552 2.026 116 6.4 3.2 virginica 5.552 2.026 117 6.5 3.0 virginica 5.552 2.026 118 7.7 3.8 virginica 5.552 2.026 119 7.7 2.6 virginica 5.552 2.026 120 6.0 2.2 virginica 5.552 2.026 121 6.9 3.2 virginica 5.552 2.026 122 5.6 2.8 virginica 5.552 2.026 123 7.7 2.8 virginica 5.552 2.026 124 6.3 2.7 virginica 5.552 2.026 125 6.7 3.3 virginica 5.552 2.026 126 7.2 3.2 virginica 5.552 2.026 127 6.2 2.8 virginica 5.552 2.026 128 6.1 3.0 virginica 5.552 2.026 129 6.4 2.8 virginica 5.552 2.026 130 7.2 3.0 virginica 5.552 2.026 131 7.4 2.8 virginica 5.552 2.026 132 7.9 3.8 virginica 5.552 2.026 133 6.4 2.8 virginica 5.552 2.026 134 6.3 2.8 virginica 5.552 2.026 135 6.1 2.6 virginica 5.552 2.026 136 7.7 3.0 virginica 5.552 2.026 137 6.3 3.4 virginica 5.552 2.026 138 6.4 3.1 virginica 5.552 2.026 139 6.0 3.0 virginica 5.552 2.026 140 6.9 3.1 virginica 5.552 2.026 141 6.7 3.1 virginica 5.552 2.026 142 6.9 3.1 virginica 5.552 2.026 143 5.8 2.7 virginica 5.552 2.026 144 6.8 3.2 virginica 5.552 2.026 145 6.7 3.3 virginica 5.552 2.026 146 6.7 3.0 virginica 5.552 2.026 147 6.3 2.5 virginica 5.552 2.026 148 6.5 3.0 virginica 5.552 2.026 149 6.2 3.4 virginica 5.552 2.026 150 5.9 3.0 virginica 5.552 2.026#结果解释：每行包含原始的 Sepal.Length 和 Sepal.Width，以及对应的物种平均花瓣长度和宽度。由于 Species 列在两个数据框中完全匹配，因此没有 NA 值出现。right_join()：保留所有
y的行：无论y中的行是否在x中有匹配的键值，y的所有行都会被保留。添加
x的列：将x中匹配的列添加到结果中。如果y中的某些行在x中没有匹配的键值，则这些行对应的列值为NA。基于键匹配：通过指定的键（
by参数）进行匹配。
# 加载 dplyr 包 library(dplyr) # 查看 ToothGrowth 数据集 head(ToothGrowth)len supp dose 1 4.2 VC 0.5 2 11.5 VC 0.5 3 7.3 VC 0.5 4 5.8 VC 0.5 5 6.4 VC 0.5 6 10.0 VC 0.5# 创建 df1：包含 len 和 supp 列 df1 <- ToothGrowth %>% select(len, supp) # 创建 df2：包含 supp 和 dose 列，但只保留部分行 df2 <- ToothGrowth %>% select(supp, dose) %>% filter(dose == 0.5 | dose == 1) # 使用 right_join() 将 df1 和 df2 基于 supp 列合并 result <- right_join(df1, df2, by = "supp")Warning in right_join(df1, df2, by = "supp"): Detected an unexpected many-to-many relationship between `x` and `y`. ℹ Row 1 of `x` matches multiple rows in `y`. ℹ Row 1 of `y` matches multiple rows in `x`. ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.# 查看结果 print(result)len supp dose 1 4.2 VC 0.5 2 4.2 VC 0.5 3 4.2 VC 0.5 4 4.2 VC 0.5 5 4.2 VC 0.5 6 4.2 VC 0.5 7 4.2 VC 0.5 8 4.2 VC 0.5 9 4.2 VC 0.5 10 4.2 VC 0.5 11 4.2 VC 1.0 12 4.2 VC 1.0 13 4.2 VC 1.0 14 4.2 VC 1.0 15 4.2 VC 1.0 16 4.2 VC 1.0 17 4.2 VC 1.0 18 4.2 VC 1.0 19 4.2 VC 1.0 20 4.2 VC 1.0 21 11.5 VC 0.5 22 11.5 VC 0.5 23 11.5 VC 0.5 24 11.5 VC 0.5 25 11.5 VC 0.5 26 11.5 VC 0.5 27 11.5 VC 0.5 28 11.5 VC 0.5 29 11.5 VC 0.5 30 11.5 VC 0.5 31 11.5 VC 1.0 32 11.5 VC 1.0 33 11.5 VC 1.0 34 11.5 VC 1.0 35 11.5 VC 1.0 36 11.5 VC 1.0 37 11.5 VC 1.0 38 11.5 VC 1.0 39 11.5 VC 1.0 40 11.5 VC 1.0 41 7.3 VC 0.5 42 7.3 VC 0.5 43 7.3 VC 0.5 44 7.3 VC 0.5 45 7.3 VC 0.5 46 7.3 VC 0.5 47 7.3 VC 0.5 48 7.3 VC 0.5 49 7.3 VC 0.5 50 7.3 VC 0.5 51 7.3 VC 1.0 52 7.3 VC 1.0 53 7.3 VC 1.0 54 7.3 VC 1.0 55 7.3 VC 1.0 56 7.3 VC 1.0 57 7.3 VC 1.0 58 7.3 VC 1.0 59 7.3 VC 1.0 60 7.3 VC 1.0 61 5.8 VC 0.5 62 5.8 VC 0.5 63 5.8 VC 0.5 64 5.8 VC 0.5 65 5.8 VC 0.5 66 5.8 VC 0.5 67 5.8 VC 0.5 68 5.8 VC 0.5 69 5.8 VC 0.5 70 5.8 VC 0.5 71 5.8 VC 1.0 72 5.8 VC 1.0 73 5.8 VC 1.0 74 5.8 VC 1.0 75 5.8 VC 1.0 76 5.8 VC 1.0 77 5.8 VC 1.0 78 5.8 VC 1.0 79 5.8 VC 1.0 80 5.8 VC 1.0 81 6.4 VC 0.5 82 6.4 VC 0.5 83 6.4 VC 0.5 84 6.4 VC 0.5 85 6.4 VC 0.5 86 6.4 VC 0.5 87 6.4 VC 0.5 88 6.4 VC 0.5 89 6.4 VC 0.5 90 6.4 VC 0.5 91 6.4 VC 1.0 92 6.4 VC 1.0 93 6.4 VC 1.0 94 6.4 VC 1.0 95 6.4 VC 1.0 96 6.4 VC 1.0 97 6.4 VC 1.0 98 6.4 VC 1.0 99 6.4 VC 1.0 100 6.4 VC 1.0 101 10.0 VC 0.5 102 10.0 VC 0.5 103 10.0 VC 0.5 104 10.0 VC 0.5 105 10.0 VC 0.5 106 10.0 VC 0.5 107 10.0 VC 0.5 108 10.0 VC 0.5 109 10.0 VC 0.5 110 10.0 VC 0.5 111 10.0 VC 1.0 112 10.0 VC 1.0 113 10.0 VC 1.0 114 10.0 VC 1.0 115 10.0 VC 1.0 116 10.0 VC 1.0 117 10.0 VC 1.0 118 10.0 VC 1.0 119 10.0 VC 1.0 120 10.0 VC 1.0 121 11.2 VC 0.5 122 11.2 VC 0.5 123 11.2 VC 0.5 124 11.2 VC 0.5 125 11.2 VC 0.5 126 11.2 VC 0.5 127 11.2 VC 0.5 128 11.2 VC 0.5 129 11.2 VC 0.5 130 11.2 VC 0.5 131 11.2 VC 1.0 132 11.2 VC 1.0 133 11.2 VC 1.0 134 11.2 VC 1.0 135 11.2 VC 1.0 136 11.2 VC 1.0 137 11.2 VC 1.0 138 11.2 VC 1.0 139 11.2 VC 1.0 140 11.2 VC 1.0 141 11.2 VC 0.5 142 11.2 VC 0.5 143 11.2 VC 0.5 144 11.2 VC 0.5 145 11.2 VC 0.5 146 11.2 VC 0.5 147 11.2 VC 0.5 148 11.2 VC 0.5 149 11.2 VC 0.5 150 11.2 VC 0.5 151 11.2 VC 1.0 152 11.2 VC 1.0 153 11.2 VC 1.0 154 11.2 VC 1.0 155 11.2 VC 1.0 156 11.2 VC 1.0 157 11.2 VC 1.0 158 11.2 VC 1.0 159 11.2 VC 1.0 160 11.2 VC 1.0 161 5.2 VC 0.5 162 5.2 VC 0.5 163 5.2 VC 0.5 164 5.2 VC 0.5 165 5.2 VC 0.5 166 5.2 VC 0.5 167 5.2 VC 0.5 168 5.2 VC 0.5 169 5.2 VC 0.5 170 5.2 VC 0.5 171 5.2 VC 1.0 172 5.2 VC 1.0 173 5.2 VC 1.0 174 5.2 VC 1.0 175 5.2 VC 1.0 176 5.2 VC 1.0 177 5.2 VC 1.0 178 5.2 VC 1.0 179 5.2 VC 1.0 180 5.2 VC 1.0 181 7.0 VC 0.5 182 7.0 VC 0.5 183 7.0 VC 0.5 184 7.0 VC 0.5 185 7.0 VC 0.5 186 7.0 VC 0.5 187 7.0 VC 0.5 188 7.0 VC 0.5 189 7.0 VC 0.5 190 7.0 VC 0.5 191 7.0 VC 1.0 192 7.0 VC 1.0 193 7.0 VC 1.0 194 7.0 VC 1.0 195 7.0 VC 1.0 196 7.0 VC 1.0 197 7.0 VC 1.0 198 7.0 VC 1.0 199 7.0 VC 1.0 200 7.0 VC 1.0 201 16.5 VC 0.5 202 16.5 VC 0.5 203 16.5 VC 0.5 204 16.5 VC 0.5 205 16.5 VC 0.5 206 16.5 VC 0.5 207 16.5 VC 0.5 208 16.5 VC 0.5 209 16.5 VC 0.5 210 16.5 VC 0.5 211 16.5 VC 1.0 212 16.5 VC 1.0 213 16.5 VC 1.0 214 16.5 VC 1.0 215 16.5 VC 1.0 216 16.5 VC 1.0 217 16.5 VC 1.0 218 16.5 VC 1.0 219 16.5 VC 1.0 220 16.5 VC 1.0 221 16.5 VC 0.5 222 16.5 VC 0.5 223 16.5 VC 0.5 224 16.5 VC 0.5 225 16.5 VC 0.5 226 16.5 VC 0.5 227 16.5 VC 0.5 228 16.5 VC 0.5 229 16.5 VC 0.5 230 16.5 VC 0.5 231 16.5 VC 1.0 232 16.5 VC 1.0 233 16.5 VC 1.0 234 16.5 VC 1.0 235 16.5 VC 1.0 236 16.5 VC 1.0 237 16.5 VC 1.0 238 16.5 VC 1.0 239 16.5 VC 1.0 240 16.5 VC 1.0 241 15.2 VC 0.5 242 15.2 VC 0.5 243 15.2 VC 0.5 244 15.2 VC 0.5 245 15.2 VC 0.5 246 15.2 VC 0.5 247 15.2 VC 0.5 248 15.2 VC 0.5 249 15.2 VC 0.5 250 15.2 VC 0.5 251 15.2 VC 1.0 252 15.2 VC 1.0 253 15.2 VC 1.0 254 15.2 VC 1.0 255 15.2 VC 1.0 256 15.2 VC 1.0 257 15.2 VC 1.0 258 15.2 VC 1.0 259 15.2 VC 1.0 260 15.2 VC 1.0 261 17.3 VC 0.5 262 17.3 VC 0.5 263 17.3 VC 0.5 264 17.3 VC 0.5 265 17.3 VC 0.5 266 17.3 VC 0.5 267 17.3 VC 0.5 268 17.3 VC 0.5 269 17.3 VC 0.5 270 17.3 VC 0.5 271 17.3 VC 1.0 272 17.3 VC 1.0 273 17.3 VC 1.0 274 17.3 VC 1.0 275 17.3 VC 1.0 276 17.3 VC 1.0 277 17.3 VC 1.0 278 17.3 VC 1.0 279 17.3 VC 1.0 280 17.3 VC 1.0 281 22.5 VC 0.5 282 22.5 VC 0.5 283 22.5 VC 0.5 284 22.5 VC 0.5 285 22.5 VC 0.5 286 22.5 VC 0.5 287 22.5 VC 0.5 288 22.5 VC 0.5 289 22.5 VC 0.5 290 22.5 VC 0.5 291 22.5 VC 1.0 292 22.5 VC 1.0 293 22.5 VC 1.0 294 22.5 VC 1.0 295 22.5 VC 1.0 296 22.5 VC 1.0 297 22.5 VC 1.0 298 22.5 VC 1.0 299 22.5 VC 1.0 300 22.5 VC 1.0 301 17.3 VC 0.5 302 17.3 VC 0.5 303 17.3 VC 0.5 304 17.3 VC 0.5 305 17.3 VC 0.5 306 17.3 VC 0.5 307 17.3 VC 0.5 308 17.3 VC 0.5 309 17.3 VC 0.5 310 17.3 VC 0.5 311 17.3 VC 1.0 312 17.3 VC 1.0 313 17.3 VC 1.0 314 17.3 VC 1.0 315 17.3 VC 1.0 316 17.3 VC 1.0 317 17.3 VC 1.0 318 17.3 VC 1.0 319 17.3 VC 1.0 320 17.3 VC 1.0 321 13.6 VC 0.5 322 13.6 VC 0.5 323 13.6 VC 0.5 324 13.6 VC 0.5 325 13.6 VC 0.5 326 13.6 VC 0.5 327 13.6 VC 0.5 328 13.6 VC 0.5 329 13.6 VC 0.5 330 13.6 VC 0.5 331 13.6 VC 1.0 332 13.6 VC 1.0 333 13.6 VC 1.0 334 13.6 VC 1.0 335 13.6 VC 1.0 336 13.6 VC 1.0 337 13.6 VC 1.0 338 13.6 VC 1.0 339 13.6 VC 1.0 340 13.6 VC 1.0 341 14.5 VC 0.5 342 14.5 VC 0.5 343 14.5 VC 0.5 344 14.5 VC 0.5 345 14.5 VC 0.5 346 14.5 VC 0.5 347 14.5 VC 0.5 348 14.5 VC 0.5 349 14.5 VC 0.5 350 14.5 VC 0.5 351 14.5 VC 1.0 352 14.5 VC 1.0 353 14.5 VC 1.0 354 14.5 VC 1.0 355 14.5 VC 1.0 356 14.5 VC 1.0 357 14.5 VC 1.0 358 14.5 VC 1.0 359 14.5 VC 1.0 360 14.5 VC 1.0 361 18.8 VC 0.5 362 18.8 VC 0.5 363 18.8 VC 0.5 364 18.8 VC 0.5 365 18.8 VC 0.5 366 18.8 VC 0.5 367 18.8 VC 0.5 368 18.8 VC 0.5 369 18.8 VC 0.5 370 18.8 VC 0.5 371 18.8 VC 1.0 372 18.8 VC 1.0 373 18.8 VC 1.0 374 18.8 VC 1.0 375 18.8 VC 1.0 376 18.8 VC 1.0 377 18.8 VC 1.0 378 18.8 VC 1.0 379 18.8 VC 1.0 380 18.8 VC 1.0 381 15.5 VC 0.5 382 15.5 VC 0.5 383 15.5 VC 0.5 384 15.5 VC 0.5 385 15.5 VC 0.5 386 15.5 VC 0.5 387 15.5 VC 0.5 388 15.5 VC 0.5 389 15.5 VC 0.5 390 15.5 VC 0.5 391 15.5 VC 1.0 392 15.5 VC 1.0 393 15.5 VC 1.0 394 15.5 VC 1.0 395 15.5 VC 1.0 396 15.5 VC 1.0 397 15.5 VC 1.0 398 15.5 VC 1.0 399 15.5 VC 1.0 400 15.5 VC 1.0 401 23.6 VC 0.5 402 23.6 VC 0.5 403 23.6 VC 0.5 404 23.6 VC 0.5 405 23.6 VC 0.5 406 23.6 VC 0.5 407 23.6 VC 0.5 408 23.6 VC 0.5 409 23.6 VC 0.5 410 23.6 VC 0.5 411 23.6 VC 1.0 412 23.6 VC 1.0 413 23.6 VC 1.0 414 23.6 VC 1.0 415 23.6 VC 1.0 416 23.6 VC 1.0 417 23.6 VC 1.0 418 23.6 VC 1.0 419 23.6 VC 1.0 420 23.6 VC 1.0 421 18.5 VC 0.5 422 18.5 VC 0.5 423 18.5 VC 0.5 424 18.5 VC 0.5 425 18.5 VC 0.5 426 18.5 VC 0.5 427 18.5 VC 0.5 428 18.5 VC 0.5 429 18.5 VC 0.5 430 18.5 VC 0.5 431 18.5 VC 1.0 432 18.5 VC 1.0 433 18.5 VC 1.0 434 18.5 VC 1.0 435 18.5 VC 1.0 436 18.5 VC 1.0 437 18.5 VC 1.0 438 18.5 VC 1.0 439 18.5 VC 1.0 440 18.5 VC 1.0 441 33.9 VC 0.5 442 33.9 VC 0.5 443 33.9 VC 0.5 444 33.9 VC 0.5 445 33.9 VC 0.5 446 33.9 VC 0.5 447 33.9 VC 0.5 448 33.9 VC 0.5 449 33.9 VC 0.5 450 33.9 VC 0.5 451 33.9 VC 1.0 452 33.9 VC 1.0 453 33.9 VC 1.0 454 33.9 VC 1.0 455 33.9 VC 1.0 456 33.9 VC 1.0 457 33.9 VC 1.0 458 33.9 VC 1.0 459 33.9 VC 1.0 460 33.9 VC 1.0 461 25.5 VC 0.5 462 25.5 VC 0.5 463 25.5 VC 0.5 464 25.5 VC 0.5 465 25.5 VC 0.5 466 25.5 VC 0.5 467 25.5 VC 0.5 468 25.5 VC 0.5 469 25.5 VC 0.5 470 25.5 VC 0.5 471 25.5 VC 1.0 472 25.5 VC 1.0 473 25.5 VC 1.0 474 25.5 VC 1.0 475 25.5 VC 1.0 476 25.5 VC 1.0 477 25.5 VC 1.0 478 25.5 VC 1.0 479 25.5 VC 1.0 480 25.5 VC 1.0 481 26.4 VC 0.5 482 26.4 VC 0.5 483 26.4 VC 0.5 484 26.4 VC 0.5 485 26.4 VC 0.5 486 26.4 VC 0.5 487 26.4 VC 0.5 488 26.4 VC 0.5 489 26.4 VC 0.5 490 26.4 VC 0.5 491 26.4 VC 1.0 492 26.4 VC 1.0 493 26.4 VC 1.0 494 26.4 VC 1.0 495 26.4 VC 1.0 496 26.4 VC 1.0 497 26.4 VC 1.0 498 26.4 VC 1.0 499 26.4 VC 1.0 500 26.4 VC 1.0 501 32.5 VC 0.5 502 32.5 VC 0.5 503 32.5 VC 0.5 504 32.5 VC 0.5 505 32.5 VC 0.5 506 32.5 VC 0.5 507 32.5 VC 0.5 508 32.5 VC 0.5 509 32.5 VC 0.5 510 32.5 VC 0.5 511 32.5 VC 1.0 512 32.5 VC 1.0 513 32.5 VC 1.0 514 32.5 VC 1.0 515 32.5 VC 1.0 516 32.5 VC 1.0 517 32.5 VC 1.0 518 32.5 VC 1.0 519 32.5 VC 1.0 520 32.5 VC 1.0 521 26.7 VC 0.5 522 26.7 VC 0.5 523 26.7 VC 0.5 524 26.7 VC 0.5 525 26.7 VC 0.5 526 26.7 VC 0.5 527 26.7 VC 0.5 528 26.7 VC 0.5 529 26.7 VC 0.5 530 26.7 VC 0.5 531 26.7 VC 1.0 532 26.7 VC 1.0 533 26.7 VC 1.0 534 26.7 VC 1.0 535 26.7 VC 1.0 536 26.7 VC 1.0 537 26.7 VC 1.0 538 26.7 VC 1.0 539 26.7 VC 1.0 540 26.7 VC 1.0 541 21.5 VC 0.5 542 21.5 VC 0.5 543 21.5 VC 0.5 544 21.5 VC 0.5 545 21.5 VC 0.5 546 21.5 VC 0.5 547 21.5 VC 0.5 548 21.5 VC 0.5 549 21.5 VC 0.5 550 21.5 VC 0.5 551 21.5 VC 1.0 552 21.5 VC 1.0 553 21.5 VC 1.0 554 21.5 VC 1.0 555 21.5 VC 1.0 556 21.5 VC 1.0 557 21.5 VC 1.0 558 21.5 VC 1.0 559 21.5 VC 1.0 560 21.5 VC 1.0 561 23.3 VC 0.5 562 23.3 VC 0.5 563 23.3 VC 0.5 564 23.3 VC 0.5 565 23.3 VC 0.5 566 23.3 VC 0.5 567 23.3 VC 0.5 568 23.3 VC 0.5 569 23.3 VC 0.5 570 23.3 VC 0.5 571 23.3 VC 1.0 572 23.3 VC 1.0 573 23.3 VC 1.0 574 23.3 VC 1.0 575 23.3 VC 1.0 576 23.3 VC 1.0 577 23.3 VC 1.0 578 23.3 VC 1.0 579 23.3 VC 1.0 580 23.3 VC 1.0 581 29.5 VC 0.5 582 29.5 VC 0.5 583 29.5 VC 0.5 584 29.5 VC 0.5 585 29.5 VC 0.5 586 29.5 VC 0.5 587 29.5 VC 0.5 588 29.5 VC 0.5 589 29.5 VC 0.5 590 29.5 VC 0.5 591 29.5 VC 1.0 592 29.5 VC 1.0 593 29.5 VC 1.0 594 29.5 VC 1.0 595 29.5 VC 1.0 596 29.5 VC 1.0 597 29.5 VC 1.0 598 29.5 VC 1.0 599 29.5 VC 1.0 600 29.5 VC 1.0 601 15.2 OJ 0.5 602 15.2 OJ 0.5 603 15.2 OJ 0.5 604 15.2 OJ 0.5 605 15.2 OJ 0.5 606 15.2 OJ 0.5 607 15.2 OJ 0.5 608 15.2 OJ 0.5 609 15.2 OJ 0.5 610 15.2 OJ 0.5 611 15.2 OJ 1.0 612 15.2 OJ 1.0 613 15.2 OJ 1.0 614 15.2 OJ 1.0 615 15.2 OJ 1.0 616 15.2 OJ 1.0 617 15.2 OJ 1.0 618 15.2 OJ 1.0 619 15.2 OJ 1.0 620 15.2 OJ 1.0 621 21.5 OJ 0.5 622 21.5 OJ 0.5 623 21.5 OJ 0.5 624 21.5 OJ 0.5 625 21.5 OJ 0.5 626 21.5 OJ 0.5 627 21.5 OJ 0.5 628 21.5 OJ 0.5 629 21.5 OJ 0.5 630 21.5 OJ 0.5 631 21.5 OJ 1.0 632 21.5 OJ 1.0 633 21.5 OJ 1.0 634 21.5 OJ 1.0 635 21.5 OJ 1.0 636 21.5 OJ 1.0 637 21.5 OJ 1.0 638 21.5 OJ 1.0 639 21.5 OJ 1.0 640 21.5 OJ 1.0 641 17.6 OJ 0.5 642 17.6 OJ 0.5 643 17.6 OJ 0.5 644 17.6 OJ 0.5 645 17.6 OJ 0.5 646 17.6 OJ 0.5 647 17.6 OJ 0.5 648 17.6 OJ 0.5 649 17.6 OJ 0.5 650 17.6 OJ 0.5 651 17.6 OJ 1.0 652 17.6 OJ 1.0 653 17.6 OJ 1.0 654 17.6 OJ 1.0 655 17.6 OJ 1.0 656 17.6 OJ 1.0 657 17.6 OJ 1.0 658 17.6 OJ 1.0 659 17.6 OJ 1.0 660 17.6 OJ 1.0 661 9.7 OJ 0.5 662 9.7 OJ 0.5 663 9.7 OJ 0.5 664 9.7 OJ 0.5 665 9.7 OJ 0.5 666 9.7 OJ 0.5 667 9.7 OJ 0.5 668 9.7 OJ 0.5 669 9.7 OJ 0.5 670 9.7 OJ 0.5 671 9.7 OJ 1.0 672 9.7 OJ 1.0 673 9.7 OJ 1.0 674 9.7 OJ 1.0 675 9.7 OJ 1.0 676 9.7 OJ 1.0 677 9.7 OJ 1.0 678 9.7 OJ 1.0 679 9.7 OJ 1.0 680 9.7 OJ 1.0 681 14.5 OJ 0.5 682 14.5 OJ 0.5 683 14.5 OJ 0.5 684 14.5 OJ 0.5 685 14.5 OJ 0.5 686 14.5 OJ 0.5 687 14.5 OJ 0.5 688 14.5 OJ 0.5 689 14.5 OJ 0.5 690 14.5 OJ 0.5 691 14.5 OJ 1.0 692 14.5 OJ 1.0 693 14.5 OJ 1.0 694 14.5 OJ 1.0 695 14.5 OJ 1.0 696 14.5 OJ 1.0 697 14.5 OJ 1.0 698 14.5 OJ 1.0 699 14.5 OJ 1.0 700 14.5 OJ 1.0 701 10.0 OJ 0.5 702 10.0 OJ 0.5 703 10.0 OJ 0.5 704 10.0 OJ 0.5 705 10.0 OJ 0.5 706 10.0 OJ 0.5 707 10.0 OJ 0.5 708 10.0 OJ 0.5 709 10.0 OJ 0.5 710 10.0 OJ 0.5 711 10.0 OJ 1.0 712 10.0 OJ 1.0 713 10.0 OJ 1.0 714 10.0 OJ 1.0 715 10.0 OJ 1.0 716 10.0 OJ 1.0 717 10.0 OJ 1.0 718 10.0 OJ 1.0 719 10.0 OJ 1.0 720 10.0 OJ 1.0 721 8.2 OJ 0.5 722 8.2 OJ 0.5 723 8.2 OJ 0.5 724 8.2 OJ 0.5 725 8.2 OJ 0.5 726 8.2 OJ 0.5 727 8.2 OJ 0.5 728 8.2 OJ 0.5 729 8.2 OJ 0.5 730 8.2 OJ 0.5 731 8.2 OJ 1.0 732 8.2 OJ 1.0 733 8.2 OJ 1.0 734 8.2 OJ 1.0 735 8.2 OJ 1.0 736 8.2 OJ 1.0 737 8.2 OJ 1.0 738 8.2 OJ 1.0 739 8.2 OJ 1.0 740 8.2 OJ 1.0 741 9.4 OJ 0.5 742 9.4 OJ 0.5 743 9.4 OJ 0.5 744 9.4 OJ 0.5 745 9.4 OJ 0.5 746 9.4 OJ 0.5 747 9.4 OJ 0.5 748 9.4 OJ 0.5 749 9.4 OJ 0.5 750 9.4 OJ 0.5 751 9.4 OJ 1.0 752 9.4 OJ 1.0 753 9.4 OJ 1.0 754 9.4 OJ 1.0 755 9.4 OJ 1.0 756 9.4 OJ 1.0 757 9.4 OJ 1.0 758 9.4 OJ 1.0 759 9.4 OJ 1.0 760 9.4 OJ 1.0 761 16.5 OJ 0.5 762 16.5 OJ 0.5 763 16.5 OJ 0.5 764 16.5 OJ 0.5 765 16.5 OJ 0.5 766 16.5 OJ 0.5 767 16.5 OJ 0.5 768 16.5 OJ 0.5 769 16.5 OJ 0.5 770 16.5 OJ 0.5 771 16.5 OJ 1.0 772 16.5 OJ 1.0 773 16.5 OJ 1.0 774 16.5 OJ 1.0 775 16.5 OJ 1.0 776 16.5 OJ 1.0 777 16.5 OJ 1.0 778 16.5 OJ 1.0 779 16.5 OJ 1.0 780 16.5 OJ 1.0 781 9.7 OJ 0.5 782 9.7 OJ 0.5 783 9.7 OJ 0.5 784 9.7 OJ 0.5 785 9.7 OJ 0.5 786 9.7 OJ 0.5 787 9.7 OJ 0.5 788 9.7 OJ 0.5 789 9.7 OJ 0.5 790 9.7 OJ 0.5 791 9.7 OJ 1.0 792 9.7 OJ 1.0 793 9.7 OJ 1.0 794 9.7 OJ 1.0 795 9.7 OJ 1.0 796 9.7 OJ 1.0 797 9.7 OJ 1.0 798 9.7 OJ 1.0 799 9.7 OJ 1.0 800 9.7 OJ 1.0 801 19.7 OJ 0.5 802 19.7 OJ 0.5 803 19.7 OJ 0.5 804 19.7 OJ 0.5 805 19.7 OJ 0.5 806 19.7 OJ 0.5 807 19.7 OJ 0.5 808 19.7 OJ 0.5 809 19.7 OJ 0.5 810 19.7 OJ 0.5 811 19.7 OJ 1.0 812 19.7 OJ 1.0 813 19.7 OJ 1.0 814 19.7 OJ 1.0 815 19.7 OJ 1.0 816 19.7 OJ 1.0 817 19.7 OJ 1.0 818 19.7 OJ 1.0 819 19.7 OJ 1.0 820 19.7 OJ 1.0 821 23.3 OJ 0.5 822 23.3 OJ 0.5 823 23.3 OJ 0.5 824 23.3 OJ 0.5 825 23.3 OJ 0.5 826 23.3 OJ 0.5 827 23.3 OJ 0.5 828 23.3 OJ 0.5 829 23.3 OJ 0.5 830 23.3 OJ 0.5 831 23.3 OJ 1.0 832 23.3 OJ 1.0 833 23.3 OJ 1.0 834 23.3 OJ 1.0 835 23.3 OJ 1.0 836 23.3 OJ 1.0 837 23.3 OJ 1.0 838 23.3 OJ 1.0 839 23.3 OJ 1.0 840 23.3 OJ 1.0 841 23.6 OJ 0.5 842 23.6 OJ 0.5 843 23.6 OJ 0.5 844 23.6 OJ 0.5 845 23.6 OJ 0.5 846 23.6 OJ 0.5 847 23.6 OJ 0.5 848 23.6 OJ 0.5 849 23.6 OJ 0.5 850 23.6 OJ 0.5 851 23.6 OJ 1.0 852 23.6 OJ 1.0 853 23.6 OJ 1.0 854 23.6 OJ 1.0 855 23.6 OJ 1.0 856 23.6 OJ 1.0 857 23.6 OJ 1.0 858 23.6 OJ 1.0 859 23.6 OJ 1.0 860 23.6 OJ 1.0 861 26.4 OJ 0.5 862 26.4 OJ 0.5 863 26.4 OJ 0.5 864 26.4 OJ 0.5 865 26.4 OJ 0.5 866 26.4 OJ 0.5 867 26.4 OJ 0.5 868 26.4 OJ 0.5 869 26.4 OJ 0.5 870 26.4 OJ 0.5 871 26.4 OJ 1.0 872 26.4 OJ 1.0 873 26.4 OJ 1.0 874 26.4 OJ 1.0 875 26.4 OJ 1.0 876 26.4 OJ 1.0 877 26.4 OJ 1.0 878 26.4 OJ 1.0 879 26.4 OJ 1.0 880 26.4 OJ 1.0 881 20.0 OJ 0.5 882 20.0 OJ 0.5 883 20.0 OJ 0.5 884 20.0 OJ 0.5 885 20.0 OJ 0.5 886 20.0 OJ 0.5 887 20.0 OJ 0.5 888 20.0 OJ 0.5 889 20.0 OJ 0.5 890 20.0 OJ 0.5 891 20.0 OJ 1.0 892 20.0 OJ 1.0 893 20.0 OJ 1.0 894 20.0 OJ 1.0 895 20.0 OJ 1.0 896 20.0 OJ 1.0 897 20.0 OJ 1.0 898 20.0 OJ 1.0 899 20.0 OJ 1.0 900 20.0 OJ 1.0 901 25.2 OJ 0.5 902 25.2 OJ 0.5 903 25.2 OJ 0.5 904 25.2 OJ 0.5 905 25.2 OJ 0.5 906 25.2 OJ 0.5 907 25.2 OJ 0.5 908 25.2 OJ 0.5 909 25.2 OJ 0.5 910 25.2 OJ 0.5 911 25.2 OJ 1.0 912 25.2 OJ 1.0 913 25.2 OJ 1.0 914 25.2 OJ 1.0 915 25.2 OJ 1.0 916 25.2 OJ 1.0 917 25.2 OJ 1.0 918 25.2 OJ 1.0 919 25.2 OJ 1.0 920 25.2 OJ 1.0 921 25.8 OJ 0.5 922 25.8 OJ 0.5 923 25.8 OJ 0.5 924 25.8 OJ 0.5 925 25.8 OJ 0.5 926 25.8 OJ 0.5 927 25.8 OJ 0.5 928 25.8 OJ 0.5 929 25.8 OJ 0.5 930 25.8 OJ 0.5 931 25.8 OJ 1.0 932 25.8 OJ 1.0 933 25.8 OJ 1.0 934 25.8 OJ 1.0 935 25.8 OJ 1.0 936 25.8 OJ 1.0 937 25.8 OJ 1.0 938 25.8 OJ 1.0 939 25.8 OJ 1.0 940 25.8 OJ 1.0 941 21.2 OJ 0.5 942 21.2 OJ 0.5 943 21.2 OJ 0.5 944 21.2 OJ 0.5 945 21.2 OJ 0.5 946 21.2 OJ 0.5 947 21.2 OJ 0.5 948 21.2 OJ 0.5 949 21.2 OJ 0.5 950 21.2 OJ 0.5 951 21.2 OJ 1.0 952 21.2 OJ 1.0 953 21.2 OJ 1.0 954 21.2 OJ 1.0 955 21.2 OJ 1.0 956 21.2 OJ 1.0 957 21.2 OJ 1.0 958 21.2 OJ 1.0 959 21.2 OJ 1.0 960 21.2 OJ 1.0 961 14.5 OJ 0.5 962 14.5 OJ 0.5 963 14.5 OJ 0.5 964 14.5 OJ 0.5 965 14.5 OJ 0.5 966 14.5 OJ 0.5 967 14.5 OJ 0.5 968 14.5 OJ 0.5 969 14.5 OJ 0.5 970 14.5 OJ 0.5 971 14.5 OJ 1.0 972 14.5 OJ 1.0 973 14.5 OJ 1.0 974 14.5 OJ 1.0 975 14.5 OJ 1.0 976 14.5 OJ 1.0 977 14.5 OJ 1.0 978 14.5 OJ 1.0 979 14.5 OJ 1.0 980 14.5 OJ 1.0 981 27.3 OJ 0.5 982 27.3 OJ 0.5 983 27.3 OJ 0.5 984 27.3 OJ 0.5 985 27.3 OJ 0.5 986 27.3 OJ 0.5 987 27.3 OJ 0.5 988 27.3 OJ 0.5 989 27.3 OJ 0.5 990 27.3 OJ 0.5 991 27.3 OJ 1.0 992 27.3 OJ 1.0 993 27.3 OJ 1.0 994 27.3 OJ 1.0 995 27.3 OJ 1.0 996 27.3 OJ 1.0 997 27.3 OJ 1.0 998 27.3 OJ 1.0 999 27.3 OJ 1.0 1000 27.3 OJ 1.0 1001 25.5 OJ 0.5 1002 25.5 OJ 0.5 1003 25.5 OJ 0.5 1004 25.5 OJ 0.5 1005 25.5 OJ 0.5 1006 25.5 OJ 0.5 1007 25.5 OJ 0.5 1008 25.5 OJ 0.5 1009 25.5 OJ 0.5 1010 25.5 OJ 0.5 1011 25.5 OJ 1.0 1012 25.5 OJ 1.0 1013 25.5 OJ 1.0 1014 25.5 OJ 1.0 1015 25.5 OJ 1.0 1016 25.5 OJ 1.0 1017 25.5 OJ 1.0 1018 25.5 OJ 1.0 1019 25.5 OJ 1.0 1020 25.5 OJ 1.0 1021 26.4 OJ 0.5 1022 26.4 OJ 0.5 1023 26.4 OJ 0.5 1024 26.4 OJ 0.5 1025 26.4 OJ 0.5 1026 26.4 OJ 0.5 1027 26.4 OJ 0.5 1028 26.4 OJ 0.5 1029 26.4 OJ 0.5 1030 26.4 OJ 0.5 1031 26.4 OJ 1.0 1032 26.4 OJ 1.0 1033 26.4 OJ 1.0 1034 26.4 OJ 1.0 1035 26.4 OJ 1.0 1036 26.4 OJ 1.0 1037 26.4 OJ 1.0 1038 26.4 OJ 1.0 1039 26.4 OJ 1.0 1040 26.4 OJ 1.0 1041 22.4 OJ 0.5 1042 22.4 OJ 0.5 1043 22.4 OJ 0.5 1044 22.4 OJ 0.5 1045 22.4 OJ 0.5 1046 22.4 OJ 0.5 1047 22.4 OJ 0.5 1048 22.4 OJ 0.5 1049 22.4 OJ 0.5 1050 22.4 OJ 0.5 1051 22.4 OJ 1.0 1052 22.4 OJ 1.0 1053 22.4 OJ 1.0 1054 22.4 OJ 1.0 1055 22.4 OJ 1.0 1056 22.4 OJ 1.0 1057 22.4 OJ 1.0 1058 22.4 OJ 1.0 1059 22.4 OJ 1.0 1060 22.4 OJ 1.0 1061 24.5 OJ 0.5 1062 24.5 OJ 0.5 1063 24.5 OJ 0.5 1064 24.5 OJ 0.5 1065 24.5 OJ 0.5 1066 24.5 OJ 0.5 1067 24.5 OJ 0.5 1068 24.5 OJ 0.5 1069 24.5 OJ 0.5 1070 24.5 OJ 0.5 1071 24.5 OJ 1.0 1072 24.5 OJ 1.0 1073 24.5 OJ 1.0 1074 24.5 OJ 1.0 1075 24.5 OJ 1.0 1076 24.5 OJ 1.0 1077 24.5 OJ 1.0 1078 24.5 OJ 1.0 1079 24.5 OJ 1.0 1080 24.5 OJ 1.0 1081 24.8 OJ 0.5 1082 24.8 OJ 0.5 1083 24.8 OJ 0.5 1084 24.8 OJ 0.5 1085 24.8 OJ 0.5 1086 24.8 OJ 0.5 1087 24.8 OJ 0.5 1088 24.8 OJ 0.5 1089 24.8 OJ 0.5 1090 24.8 OJ 0.5 1091 24.8 OJ 1.0 1092 24.8 OJ 1.0 1093 24.8 OJ 1.0 1094 24.8 OJ 1.0 1095 24.8 OJ 1.0 1096 24.8 OJ 1.0 1097 24.8 OJ 1.0 1098 24.8 OJ 1.0 1099 24.8 OJ 1.0 1100 24.8 OJ 1.0 1101 30.9 OJ 0.5 1102 30.9 OJ 0.5 1103 30.9 OJ 0.5 1104 30.9 OJ 0.5 1105 30.9 OJ 0.5 1106 30.9 OJ 0.5 1107 30.9 OJ 0.5 1108 30.9 OJ 0.5 1109 30.9 OJ 0.5 1110 30.9 OJ 0.5 1111 30.9 OJ 1.0 1112 30.9 OJ 1.0 1113 30.9 OJ 1.0 1114 30.9 OJ 1.0 1115 30.9 OJ 1.0 1116 30.9 OJ 1.0 1117 30.9 OJ 1.0 1118 30.9 OJ 1.0 1119 30.9 OJ 1.0 1120 30.9 OJ 1.0 1121 26.4 OJ 0.5 1122 26.4 OJ 0.5 1123 26.4 OJ 0.5 1124 26.4 OJ 0.5 1125 26.4 OJ 0.5 1126 26.4 OJ 0.5 1127 26.4 OJ 0.5 1128 26.4 OJ 0.5 1129 26.4 OJ 0.5 1130 26.4 OJ 0.5 1131 26.4 OJ 1.0 1132 26.4 OJ 1.0 1133 26.4 OJ 1.0 1134 26.4 OJ 1.0 1135 26.4 OJ 1.0 1136 26.4 OJ 1.0 1137 26.4 OJ 1.0 1138 26.4 OJ 1.0 1139 26.4 OJ 1.0 1140 26.4 OJ 1.0 1141 27.3 OJ 0.5 1142 27.3 OJ 0.5 1143 27.3 OJ 0.5 1144 27.3 OJ 0.5 1145 27.3 OJ 0.5 1146 27.3 OJ 0.5 1147 27.3 OJ 0.5 1148 27.3 OJ 0.5 1149 27.3 OJ 0.5 1150 27.3 OJ 0.5 1151 27.3 OJ 1.0 1152 27.3 OJ 1.0 1153 27.3 OJ 1.0 1154 27.3 OJ 1.0 1155 27.3 OJ 1.0 1156 27.3 OJ 1.0 1157 27.3 OJ 1.0 1158 27.3 OJ 1.0 1159 27.3 OJ 1.0 1160 27.3 OJ 1.0 1161 29.4 OJ 0.5 1162 29.4 OJ 0.5 1163 29.4 OJ 0.5 1164 29.4 OJ 0.5 1165 29.4 OJ 0.5 1166 29.4 OJ 0.5 1167 29.4 OJ 0.5 1168 29.4 OJ 0.5 1169 29.4 OJ 0.5 1170 29.4 OJ 0.5 1171 29.4 OJ 1.0 1172 29.4 OJ 1.0 1173 29.4 OJ 1.0 1174 29.4 OJ 1.0 1175 29.4 OJ 1.0 1176 29.4 OJ 1.0 1177 29.4 OJ 1.0 1178 29.4 OJ 1.0 1179 29.4 OJ 1.0 1180 29.4 OJ 1.0 1181 23.0 OJ 0.5 1182 23.0 OJ 0.5 1183 23.0 OJ 0.5 1184 23.0 OJ 0.5 1185 23.0 OJ 0.5 1186 23.0 OJ 0.5 1187 23.0 OJ 0.5 1188 23.0 OJ 0.5 1189 23.0 OJ 0.5 1190 23.0 OJ 0.5 1191 23.0 OJ 1.0 1192 23.0 OJ 1.0 1193 23.0 OJ 1.0 1194 23.0 OJ 1.0 1195 23.0 OJ 1.0 1196 23.0 OJ 1.0 1197 23.0 OJ 1.0 1198 23.0 OJ 1.0 1199 23.0 OJ 1.0 1200 23.0 OJ 1.0#输出结果中，df2 的所有行都被保留，而 df1 中的 len 列根据 supp 的匹配情况被添加到结果中full_join()：保留所有行：无论
x和y中的行是否匹配，所有行都会被保留。合并列：将
x和y中的所有列合并到结果中。基于键匹配：通过指定的键（
by参数）进行匹配。处理不匹配的行：如果某个数据框中的行在另一个数据框中没有匹配的键值，则结果中对应的列将填充为
NA。
# 加载 dplyr 包 library(dplyr) # 创建 df1：包含 mpg、cyl 和 disp 列 df1 <- mtcars %>% select(mpg, cyl, disp) # 创建 df2：包含 cyl、hp 和 wt 列，但只选择部分行，并添加额外的行 df2 <- mtcars %>% select(cyl, hp, wt) %>% slice(1:10) %>% # 只选择前 10 行 bind_rows(data.frame(cyl = c(4, 6, 8), hp = c(NA, NA, NA), wt = c(NA, NA, NA))) # 添加额外的行 # 使用 full_join() 将 df1 和 df2 基于 cyl 列合并 result <- full_join(df1, df2, by = "cyl")Warning in full_join(df1, df2, by = "cyl"): Detected an unexpected many-to-many relationship between `x` and `y`. ℹ Row 1 of `x` matches multiple rows in `y`. ℹ Row 1 of `y` matches multiple rows in `x`. ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.# 查看结果 print(result)mpg cyl disp hp wt 1 21.0 6 160.0 110 2.620 2 21.0 6 160.0 110 2.875 3 21.0 6 160.0 110 3.215 4 21.0 6 160.0 105 3.460 5 21.0 6 160.0 123 3.440 6 21.0 6 160.0 NA NA 7 21.0 6 160.0 110 2.620 8 21.0 6 160.0 110 2.875 9 21.0 6 160.0 110 3.215 10 21.0 6 160.0 105 3.460 11 21.0 6 160.0 123 3.440 12 21.0 6 160.0 NA NA 13 22.8 4 108.0 93 2.320 14 22.8 4 108.0 62 3.190 15 22.8 4 108.0 95 3.150 16 22.8 4 108.0 NA NA 17 21.4 6 258.0 110 2.620 18 21.4 6 258.0 110 2.875 19 21.4 6 258.0 110 3.215 20 21.4 6 258.0 105 3.460 21 21.4 6 258.0 123 3.440 22 21.4 6 258.0 NA NA 23 18.7 8 360.0 175 3.440 24 18.7 8 360.0 245 3.570 25 18.7 8 360.0 NA NA 26 18.1 6 225.0 110 2.620 27 18.1 6 225.0 110 2.875 28 18.1 6 225.0 110 3.215 29 18.1 6 225.0 105 3.460 30 18.1 6 225.0 123 3.440 31 18.1 6 225.0 NA NA 32 14.3 8 360.0 175 3.440 33 14.3 8 360.0 245 3.570 34 14.3 8 360.0 NA NA 35 24.4 4 146.7 93 2.320 36 24.4 4 146.7 62 3.190 37 24.4 4 146.7 95 3.150 38 24.4 4 146.7 NA NA 39 22.8 4 140.8 93 2.320 40 22.8 4 140.8 62 3.190 41 22.8 4 140.8 95 3.150 42 22.8 4 140.8 NA NA 43 19.2 6 167.6 110 2.620 44 19.2 6 167.6 110 2.875 45 19.2 6 167.6 110 3.215 46 19.2 6 167.6 105 3.460 47 19.2 6 167.6 123 3.440 48 19.2 6 167.6 NA NA 49 17.8 6 167.6 110 2.620 50 17.8 6 167.6 110 2.875 51 17.8 6 167.6 110 3.215 52 17.8 6 167.6 105 3.460 53 17.8 6 167.6 123 3.440 54 17.8 6 167.6 NA NA 55 16.4 8 275.8 175 3.440 56 16.4 8 275.8 245 3.570 57 16.4 8 275.8 NA NA 58 17.3 8 275.8 175 3.440 59 17.3 8 275.8 245 3.570 60 17.3 8 275.8 NA NA 61 15.2 8 275.8 175 3.440 62 15.2 8 275.8 245 3.570 63 15.2 8 275.8 NA NA 64 10.4 8 472.0 175 3.440 65 10.4 8 472.0 245 3.570 66 10.4 8 472.0 NA NA 67 10.4 8 460.0 175 3.440 68 10.4 8 460.0 245 3.570 69 10.4 8 460.0 NA NA 70 14.7 8 440.0 175 3.440 71 14.7 8 440.0 245 3.570 72 14.7 8 440.0 NA NA 73 32.4 4 78.7 93 2.320 74 32.4 4 78.7 62 3.190 75 32.4 4 78.7 95 3.150 76 32.4 4 78.7 NA NA 77 30.4 4 75.7 93 2.320 78 30.4 4 75.7 62 3.190 79 30.4 4 75.7 95 3.150 80 30.4 4 75.7 NA NA 81 33.9 4 71.1 93 2.320 82 33.9 4 71.1 62 3.190 83 33.9 4 71.1 95 3.150 84 33.9 4 71.1 NA NA 85 21.5 4 120.1 93 2.320 86 21.5 4 120.1 62 3.190 87 21.5 4 120.1 95 3.150 88 21.5 4 120.1 NA NA 89 15.5 8 318.0 175 3.440 90 15.5 8 318.0 245 3.570 91 15.5 8 318.0 NA NA 92 15.2 8 304.0 175 3.440 93 15.2 8 304.0 245 3.570 94 15.2 8 304.0 NA NA 95 13.3 8 350.0 175 3.440 96 13.3 8 350.0 245 3.570 97 13.3 8 350.0 NA NA 98 19.2 8 400.0 175 3.440 99 19.2 8 400.0 245 3.570 100 19.2 8 400.0 NA NA 101 27.3 4 79.0 93 2.320 102 27.3 4 79.0 62 3.190 103 27.3 4 79.0 95 3.150 104 27.3 4 79.0 NA NA 105 26.0 4 120.3 93 2.320 106 26.0 4 120.3 62 3.190 107 26.0 4 120.3 95 3.150 108 26.0 4 120.3 NA NA 109 30.4 4 95.1 93 2.320 110 30.4 4 95.1 62 3.190 111 30.4 4 95.1 95 3.150 112 30.4 4 95.1 NA NA 113 15.8 8 351.0 175 3.440 114 15.8 8 351.0 245 3.570 115 15.8 8 351.0 NA NA 116 19.7 6 145.0 110 2.620 117 19.7 6 145.0 110 2.875 118 19.7 6 145.0 110 3.215 119 19.7 6 145.0 105 3.460 120 19.7 6 145.0 123 3.440 121 19.7 6 145.0 NA NA 122 15.0 8 301.0 175 3.440 123 15.0 8 301.0 245 3.570 124 15.0 8 301.0 NA NA 125 21.4 4 121.0 93 2.320 126 21.4 4 121.0 62 3.190 127 21.4 4 121.0 95 3.150 128 21.4 4 121.0 NA NA#full_join() 保留了df1和df2中的所有行，无论它们是否在另一个数据框中有匹配的键值。对于 df1 中没有匹配的行（即 df2 中不存在的行），hp 和 wt 列的值将填充为 NA。对于 df2 中没有匹配的行（即额外添加的 3 行），mpg 和 disp 列的值将填充为 NA