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"))
最佳答案
我正在使用 data.table而不是 dplyr
library(data.table)
setDT(dfAux1) # make dfAux1 as data table by reference
# calculate total number by movies, then compute percent for `Rating >= 4` by movies and then sort `tot` by descending order and also eliminating duplicates in movies using `.SD[1]` which gives the first row in each movie.
dfAux1[, .(Rating, tot = .N), by = movie ][Rating >= 4, .(percent = .N/tot, tot), by = movie ][order(-tot), .SD[1], by = movie]
# movie percent tot
# 1: Toy Story 0.35294118 17
# 2: The Silence of the Lambs 0.43750000 16
# 3: Star Wars IV - A New Hope 0.53333333 15
# 4: Star Wars VI - Return of the Jedi 0.35714286 14
# 5: Independence Day 0.30769231 13
# 6: Gladiator 0.50000000 12
# 7: Total Recall 0.08333333 12
# 8: Groundhog Day 0.41666667 12
# 9: The Matrix 0.41666667 12
# 10: Schindler's List 0.33333333 12
# 11: The Sixth Sense 0.33333333 12
# 12: Saving Private Ryan 0.36363636 11
# 13: Pulp Fiction 0.36363636 11
# 14: Stand by Me 0.36363636 11
# 15: Shakespeare in Love 0.27272727 11
# 16: Raiders of the Lost Ark 0.27272727 11
# 17: Forrest Gump 0.30000000 10
# 18: The Shawshank Redemption 0.70000000 10
# 19: Babe 0.40000000 10
# 20: Blade Runner 0.44444444 9
关于r - 计算给定条件的百分比，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/48736383/