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Calculate percentage status

I am new to this site and to coding. I was wondering if any of you could help me.

I need to calculate Top 5 Movies by rating distribution, calculating the percentage of ratings for each movie of 4 stars or higher.

So far I have been able to calculate the number of occurrences using dplyr.

Is it possible to compute it with dplyr (something similar to my coding)?

I'm not sure if I need to mutate to come up with a solution or if there is another way to do this.

My code is:

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

The result should be something like this:

**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'

and so far this is my result:

# 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

edit:

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 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"))
+4
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5 answers

A single-line solution using data.tableand data from the OP can be as:

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
+2

data.table 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 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
+3

dplyr movie rating.

summarise() :

  • Total_Review: Counts the total number of reviews on movie.
  • FourPlus_Rating: Counts a subset of reviews with a ratingvalue of 4 or higher.
  • Per_FourPlus_Rating: divides FourPlus_Ratingby Total_Review.

Then I sorted the date in descending order based on Per_FourPlus_Rating. Finally, I called head()to indicate what I need tibbleto return the first 5 lines.

Reproducible example

# install necessary package
install.packages( pkgs = "dplyr" )

# load necessary package
library( dplyr )


# view first six rows
head( x = df )
#   Rating                     movie
# 1      1 Star Wars IV - A New Hope
# 2      5 Star Wars IV - A New Hope
# 5      4 Star Wars IV - A New Hope
# 6      2 Star Wars IV - A New Hope
# 8      4 Star Wars IV - A New Hope
# 9      5 Star Wars IV - A New Hope

# perform calculations using 
# dplyr functions
df %>%
  group_by( movie ) %>%
  summarise( Total_Review              = n()
             , FourPlus_Rating         = length( Rating[ which( Rating >= 4 ) ] )
             , Per_FourPlus_Rating     = length( Rating[ which( Rating >= 4 ) ] ) / n() ) %>%
  arrange( desc( Per_FourPlus_Rating ) ) %>%
  head( n = 5 )
# A tibble: 5 x 4
# movie               Total_Review FourPlus_Rating Per_FourPlus_Rati…
# <fct>                      <int>           <int>              <dbl>
# 1 The Shawshank Rede…           10               7              0.700
# 2 Star Wars IV - A N…           15               8              0.533
# 3 Gladiator                     12               6              0.500
# 4 Blade Runner                   9               4              0.444
# 5 The Silence of the…           16               7              0.438

# end of script #
+1
source

this is dplyr solution:

    dfAuxhigh=filter(dfAux1,Rating>=4)%>%group_by(movie)%>%summarize(percentHigh=n())
dfAux=dfAux1%>%group_by(movie)%>%summarize(percentAll=n())
result<-merge(dfAuxhigh,dfAux,by="movie")%>%mutate(percentage=percentHigh/percentAll)
result<-result[order(result$percentage,decreasing = T)[1:5],c(1,4)]
0
source 
library(tidyverse)

df %>% 
  group_by(movie, Rating) %>% 
  summarise(n = n()) %>%           #< get freq of movies
  mutate(freq = n/sum(n)) %>%      #< find perc for each rating, by movie
  filter(Rating >=4) %>%           #< filter for desired rating (4 or above) 
  summarise(freq = sum(freq)) %>%  #< summarize again
  top_n(5) %>%                     
  arrange(desc(freq)) %>% 
  mutate(freq = paste0(round(freq*100, 2), "%"))

#>   movie                     freq  
#> 1 The Shawshank Redemption  70%  
#> 2 Star Wars IV - A New Hope 53.33%
#> 3 Gladiator                 50%   
#> 4 Blade Runner              44.44%
#> 5 The Silence of the Lambs  43.75%
0
source

Source: https://habr.com/ru/post/1693493/

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