计算给定条件的百分比

Question

我是这个网站的新手，也是编码的新手。我想知道你们中是否有人可以帮助我

我需要通过评分分布计算前 5 部电影，计算每部电影 4 星或更高评分的百分比。

到目前为止，我只能使用 dplyr 计算出现次数。

是否可以使用 dplyr （类似于我的编码）来计算它？

我不确定我是否需要变异来提出解决方案，或者是否有另一种方法可以这样做。

到目前为止我的代码：

dfAux1 <- na.omit(dfAux)
dfAux1 %>%
  group_by(movie) %>%
  summarise(tot = n()) %>%
  arrange(desc(tot))%>%
  head(5)

结果应该是这样的：

**Expected result**:
0.7000000, 'The Shawshank Redemption'
0.5333333, 'Star Wars IV - A New Hope'
0.5000000, 'Gladiator'
0.4444444, 'Blade Runner'
0.4375000, 'The Silence of the Lambs'

到目前为止，这是我的结果：

# A tibble: 5 x 2
                              movie   tot
                             <fctr> <int>
1                         Toy Story    17
2          The Silence of the Lambs    16
3         Star Wars IV - A New Hope    15
4 Star Wars VI - Return of the Jedi    14
5                  Independence Day    13

编辑：

str(dfAux1)
'data.frame':   241 obs. of  2 variables:
 $ Rating: int  1 5 4 2 4 5 4 2 3 2 ...
 $ movie : Factor w/ 20 levels "Star Wars IV - A New Hope",..: 1 1 1 1 1 1 1 1 1 1 ...
 - attr(*, "na.action")=Class 'omit'  Named int [1:159] 3 4 7 16 17 23 27 28 34 36 ...
  .. ..- attr(*, "names")= chr [1:159] "3" "4" "7" "16" ...

dput(dfAux1)
structure(list(Rating = c(1L, 5L, 4L, 2L, 4L, 5L, 4L, 2L, 3L, 
2L, 3L, 4L, 4L, 5L, 1L, 5L, 3L, 3L, 3L, 4L, 1L, 2L, 1L, 5L, 3L, 
4L, 5L, 1L, 2L, 2L, 4L, 4L, 3L, 5L, 2L, 3L, 1L, 1L, 2L, 2L, 5L, 
1L, 4L, 1L, 4L, 5L, 5L, 5L, 4L, 4L, 4L, 2L, 4L, 1L, 3L, 2L, 3L, 
2L, 4L, 2L, 5L, 3L, 4L, 1L, 5L, 4L, 2L, 1L, 1L, 4L, 2L, 4L, 5L, 
5L, 2L, 1L, 4L, 2L, 1L, 4L, 2L, 3L, 2L, 4L, 4L, 5L, 2L, 4L, 3L, 
2L, 2L, 4L, 2L, 2L, 2L, 3L, 4L, 1L, 5L, 4L, 3L, 5L, 2L, 1L, 3L, 
4L, 4L, 2L, 3L, 4L, 1L, 3L, 2L, 5L, 3L, 2L, 3L, 4L, 1L, 1L, 4L, 
1L, 4L, 5L, 1L, 3L, 2L, 2L, 3L, 5L, 5L, 1L, 2L, 3L, 5L, 2L, 3L, 
1L, 2L, 1L, 4L, 1L, 2L, 2L, 3L, 3L, 2L, 1L, 1L, 1L, 5L, 2L, 4L, 
1L, 4L, 3L, 1L, 2L, 2L, 3L, 4L, 2L, 3L, 2L, 4L, 3L, 4L, 3L, 2L, 
2L, 4L, 5L, 2L, 1L, 5L, 1L, 4L, 5L, 2L, 3L, 3L, 2L, 5L, 5L, 4L, 
1L, 3L, 1L, 2L, 1L, 5L, 5L, 2L, 4L, 2L, 4L, 2L, 5L, 2L, 5L, 5L, 
1L, 5L, 1L, 3L, 2L, 2L, 3L, 5L, 1L, 3L, 1L, 5L, 3L, 3L, 1L, 2L, 
4L, 1L, 5L, 3L, 1L, 1L, 5L, 5L, 1L, 5L, 3L, 3L, 2L, 3L, 3L, 2L, 
2L, 2L, 5L, 4L, 2L, 1L, 4L, 5L), movie = structure(c(1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 
14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L), .Label = c("Star Wars IV - A New Hope", 
"Star Wars VI - Return of the Jedi", "Forrest Gump", "The Shawshank Redemption", 
"The Silence of the Lambs", "Gladiator", "Toy Story", "Saving Private Ryan", 
"Pulp Fiction", "Stand by Me", "Shakespeare in Love", "Total Recall", 
"Independence Day", "Blade Runner", "Groundhog Day", "The Matrix", 
"Schindler's List", "The Sixth Sense", "Raiders of the Lost Ark", 
"Babe"), class = "factor")), .Names = c("Rating", "movie"), row.names = c(1L, 
2L, 5L, 6L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 18L, 19L, 20L, 
21L, 22L, 24L, 25L, 26L, 29L, 30L, 31L, 32L, 33L, 35L, 38L, 39L, 
40L, 41L, 45L, 46L, 47L, 51L, 52L, 54L, 56L, 58L, 60L, 62L, 63L, 
65L, 66L, 67L, 69L, 70L, 73L, 78L, 80L, 81L, 82L, 83L, 85L, 87L, 
88L, 89L, 90L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 100L, 101L, 
102L, 104L, 105L, 107L, 108L, 109L, 111L, 115L, 116L, 118L, 119L, 
121L, 122L, 123L, 124L, 126L, 128L, 129L, 130L, 131L, 132L, 133L, 
134L, 135L, 137L, 138L, 139L, 140L, 141L, 144L, 145L, 146L, 147L, 
149L, 150L, 153L, 156L, 159L, 160L, 164L, 166L, 167L, 168L, 170L, 
172L, 175L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 185L, 186L, 
189L, 194L, 195L, 196L, 199L, 200L, 201L, 202L, 205L, 206L, 207L, 
209L, 212L, 216L, 217L, 219L, 220L, 222L, 223L, 224L, 225L, 226L, 
228L, 229L, 231L, 233L, 234L, 235L, 239L, 241L, 242L, 243L, 244L, 
246L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 255L, 261L, 263L, 
264L, 265L, 267L, 268L, 274L, 278L, 280L, 282L, 283L, 284L, 286L, 
288L, 289L, 292L, 293L, 294L, 295L, 296L, 300L, 301L, 303L, 305L, 
307L, 310L, 311L, 312L, 314L, 316L, 317L, 319L, 320L, 321L, 322L, 
323L, 324L, 325L, 328L, 330L, 334L, 335L, 336L, 338L, 340L, 341L, 
342L, 343L, 344L, 345L, 346L, 348L, 350L, 351L, 356L, 358L, 360L, 
362L, 363L, 364L, 367L, 368L, 371L, 373L, 375L, 376L, 378L, 380L, 
383L, 384L, 386L, 387L, 389L, 391L, 392L, 395L, 396L, 398L), class = "data.frame", na.action = structure(c(3L, 
4L, 7L, 16L, 17L, 23L, 27L, 28L, 34L, 36L, 37L, 42L, 43L, 44L, 
48L, 49L, 50L, 53L, 55L, 57L, 59L, 61L, 64L, 68L, 71L, 72L, 74L, 
75L, 76L, 77L, 79L, 84L, 86L, 91L, 99L, 103L, 106L, 110L, 112L, 
113L, 114L, 117L, 120L, 125L, 127L, 136L, 142L, 143L, 148L, 151L, 
152L, 154L, 155L, 157L, 158L, 161L, 162L, 163L, 165L, 169L, 171L, 
173L, 174L, 176L, 184L, 187L, 188L, 190L, 191L, 192L, 193L, 197L, 
198L, 203L, 204L, 208L, 210L, 211L, 213L, 214L, 215L, 218L, 221L, 
227L, 230L, 232L, 236L, 237L, 238L, 240L, 245L, 247L, 256L, 257L, 
258L, 259L, 260L, 262L, 266L, 269L, 270L, 271L, 272L, 273L, 275L, 
276L, 277L, 279L, 281L, 285L, 287L, 290L, 291L, 297L, 298L, 299L, 
302L, 304L, 306L, 308L, 309L, 313L, 315L, 318L, 326L, 327L, 329L, 
331L, 332L, 333L, 337L, 339L, 347L, 349L, 352L, 353L, 354L, 355L, 
357L, 359L, 361L, 365L, 366L, 369L, 370L, 372L, 374L, 377L, 379L, 
381L, 382L, 385L, 388L, 390L, 393L, 394L, 397L, 399L, 400L), .Names = c("3", 
"4", "7", "16", "17", "23", "27", "28", "34", "36", "37", "42", 
"43", "44", "48", "49", "50", "53", "55", "57", "59", "61", "64", 
"68", "71", "72", "74", "75", "76", "77", "79", "84", "86", "91", 
"99", "103", "106", "110", "112", "113", "114", "117", "120", 
"125", "127", "136", "142", "143", "148", "151", "152", "154", 
"155", "157", "158", "161", "162", "163", "165", "169", "171", 
"173", "174", "176", "184", "187", "188", "190", "191", "192", 
"193", "197", "198", "203", "204", "208", "210", "211", "213", 
"214", "215", "218", "221", "227", "230", "232", "236", "237", 
"238", "240", "245", "247", "256", "257", "258", "259", "260", 
"262", "266", "269", "270", "271", "272", "273", "275", "276", 
"277", "279", "281", "285", "287", "290", "291", "297", "298", 
"299", "302", "304", "306", "308", "309", "313", "315", "318", 
"326", "327", "329", "331", "332", "333", "337", "339", "347", 
"349", "352", "353", "354", "355", "357", "359", "361", "365", 
"366", "369", "370", "372", "374", "377", "379", "381", "382", 
"385", "388", "390", "393", "394", "397", "399", "400"), class = "omit"))

Answer 1

使用来自 OP 的数据的单行解决方案data.table可能如下：

library(data.table)
setDT(dfAux1)[, .(pct = sum(Rating>=4)/.N), by=movie][order(-pct)][1:5]
                  movie        pct
1:  The Shawshank Redemption 0.7000000
2: Star Wars IV - A New Hope 0.5333333
3:                 Gladiator 0.5000000
4:              Blade Runner 0.4444444
5:  The Silence of the Lambs 0.4375000