Как рассчитать групповые средние из построчных данных? - PullRequest
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Как рассчитать групповые средние из построчных данных?

0 голосов
/ 27 марта 2019

[Мне нужно решить некоторые вопросы со следующими данными: 

c(ages)
  [1] 29 29 19 25 22 29 24 23 28 33 30 21 22 27 32 25 25 23 33 22 31 32 26 27 23 27 16 21 18 17 27 23 29 26 25 27 26 29
 [39] 25 26 22 31 21 22 19 25 29 21 21 25 24 33 25 28 23 26 23 23 28 26 22 26 26 28 23 29 31 28 23 23 21 27 20 24 27 20
 [77] 30 27 21 29 21 24 27 23 30 24 26 29 24 30 24 23 28 25 33 26 24 25 26 32

c(genders)
  [1] "male"   "female" "female" "female" "male"   "male"   "male"   "male"   "female" "female" "female" "male"  
 [13] "male"   "male"   "male"   "male"   "female" "female" "male"   "male"   "female" "female" "female" "female"
 [25] "female" "female" "female" "female" "female" "female" "female" "female" "female" "female" "male"   "male"  
 [37] "female" "female" "male"   "male"   "female" "male"   "female" "male"   "male"   "male"   "male"   "male"  
 [49] "female" "male"   "male"   "male"   "male"   "female" "male"   "male"   "male"   "male"   "male"   "male"  
 [61] "female" "male"   "male"   "female" "male"   "female" "male"   "male"   "female" "female" "male"   "male"  
 [73] "female" "male"   "female" "male"   "male"   "female" "male"   "female" "female" "female" "female" "male"  
 [85] "male"   "male"   "female" "female" "male"   "male"   "female" "male"   "female" "male"   "female" "female"
 [97] "male"   "female" "male"   "male"

Я думал, что если я использую mean(ages[genders=male]), я могу получить средний возраст для мужчин, но я получил следующее сообщение об ошибке

Ошибка в среднем (возраст [пол = мужчина]): объект «мужчина» не найден

Есть ли дополнительные шаги, которые я должен был сделать? что я делаю не так ??

Для справки: полный список вопросов, которые мне нужно решить, таков:

  1. Мужчины в среднем моложе женщин или наоборот?
  2. Сколько женщин в выборке?
  3. Сколько мужчин в выборке?
  4. Сколько лет самому молодому человеку в выборке?
  5. Сколько лет самой молодой женщине в выборке?

1 Ответ

0 голосов
/ 27 марта 2019

Добро пожаловать на SO.

Во-первых, ваши данные выглядят по строкам. Я не настолько знаком с этой структурой. Итак, после чтения данных в строки, как показано ниже: 

df = read.csv("give the complete path of your file", header = F)

dput (df): 

structure(list(V1 = structure(1:2, .Label = c("ages", "genders"), class = "factor"), V2 = structure(1:2, .Label = c("29", "male"), class = "factor"), V3 = structure(1:2, .Label = c("29", "female"), class = "factor"), V4 = structure(1:2, .Label = c("19", "female"), class = "factor"), V5 = structure(1:2, .Label = c("25", "female"), class = "factor"), V6 = structure(1:2, .Label = c("22", "male"), class = "factor"), V7 = structure(1:2, .Label = c("29", "male"), class = "factor"), V8 = structure(1:2, .Label = c("24", "female"), class = "factor"), V9 = structure(1:2, .Label = c("23", "female"), class = "factor"), V10 = structure(1:2, .Label = c("28", "female"), class = "factor"), V11 = structure(1:2, .Label = c("33", "male"), class = "factor"), V12 = structure(1:2, .Label = c("30", "male"), class = "factor"), V13 = structure(1:2, .Label = c("21", "male"), class = "factor"), V14 = structure(1:2, .Label = c("22", "male"), class = "factor"), V15 = structure(1:2, .Label = c("27", "female"), class = "factor"), V16 = structure(1:2, .Label = c("32", "female"), class = "factor"), V17 = structure(1:2, .Label = c("25", "female"), class = "factor"), V18 = structure(1:2, .Label = c("25", "male"), class = "factor"), V19 = structure(1:2, .Label = c("23", "male"), class = "factor"), V20 = structure(1:2, .Label = c("33", "male"), class = "factor"), V21 = structure(1:2, .Label = c("22", "male"), class = "factor"), V22 = structure(1:2, .Label = c("31", "male"), class = "factor"), V23 = structure(1:2, .Label = c("32", "female"), class = "factor"), V24 = structure(1:2, .Label = c("26", "female"), class = "factor"), V25 = structure(1:2, .Label = c("27", "male"), class = "factor"), V26 = structure(1:2, .Label = c("23", "male"), class = "factor"), V27 = structure(1:2, .Label = c("27", "female"), class = "factor"), V28 = structure(1:2, .Label = c("16", "female"), class = "factor"), V29 = structure(1:2, .Label = c("21", "female"), class = "factor"), V30 = structure(1:2, .Label = c("18", "female"), class = "factor"), V31 = structure(1:2, .Label = c("17", "female"), class = "factor"), V32 = structure(1:2, .Label = c("27", "female"), class = "factor"), V33 = structure(1:2, .Label = c("23", "female"), class = "factor"), V34 = structure(1:2, .Label = c("29", "female"), class = "factor"), V35 = structure(1:2, .Label = c("26", "female"), class = "factor"), V36 = structure(1:2, .Label = c("25", "female"), class = "factor"), V37 = structure(1:2, .Label = c("27", "female"), class = "factor"), V38 = structure(1:2, .Label = c("26", "female"), class = "factor"), V39 = structure(1:2, .Label = c("29", "female"), class = "factor"), V40 = structure(1:2, .Label = c("25", "female"), class = "factor"), V41 = structure(1:2, .Label = c("26", "male"), class = "factor"), V42 = structure(1:2, .Label = c("22", "male"), class = "factor"), V43 = structure(1:2, .Label = c("31", "female"), class = "factor"), V44 = structure(1:2, .Label = c("21", "female"), class = "factor"), V45 = structure(1:2, .Label = c("22", "male"), class = "factor"), V46 = structure(1:2, .Label = c("19", "male"), class = "factor"), V47 = structure(1:2, .Label = c("25", "female"), class = "factor"), V48 = structure(1:2, .Label = c("29", "male"), class = "factor"), V49 = structure(1:2, .Label = c("21", "female"), class = "factor"), V50 = structure(1:2, .Label = c("21", "male"), class = "factor"), V51 = structure(1:2, .Label = c("25", "male"), class = "factor"), V52 = structure(1:2, .Label = c("24", "male"), class = "factor"), V53 = structure(1:2, .Label = c("33", "male"), class = "factor"), V54 = structure(1:2, .Label = c("25", "male"), class = "factor"), V55 = structure(1:2, .Label = c("28", "female"), class = "factor"), V56 = structure(1:2, .Label = c("23", "male"), class = "factor"), V57 = structure(1:2, .Label = c("26", "male"), class = "factor"), V58 = structure(1:2, .Label = c("23", "male"), class = "factor"), V59 = structure(1:2, .Label = c("23", "male"), class = "factor"), V60 = structure(1:2, .Label = c("28", "female"), class = "factor"), V61 = structure(1:2, .Label = c("26", "male"), class = "factor"), V62 = structure(1:2, .Label = c("22", "male"), class = "factor"), V63 = structure(1:2, .Label = c("26", "male"), class = "factor"), V64 = structure(1:2, .Label = c("26", "male"), class = "factor"), V65 = structure(1:2, .Label = c("28", "male"), class = "factor"), V66 = structure(1:2, .Label = c("23", "male"), class = "factor"), V67 = structure(1:2, .Label = c("29", "female"), class = "factor"), V68 = structure(1:2, .Label = c("31", "male"), class = "factor"), V69 = structure(1:2, .Label = c("28", "male"), class = "factor"), V70 = structure(1:2, .Label = c("23", "female"), class = "factor"), V71 = structure(1:2, .Label = c("23", "male"), class = "factor"), V72 = structure(1:2, .Label = c("21", "female"), class = "factor"), V73 = structure(1:2, .Label = c("27", "male"), class = "factor"), V74 = structure(1:2, .Label = c("20", "male"), class = "factor"), V75 = structure(1:2, .Label = c("24", "female"), class = "factor"), V76 = structure(1:2, .Label = c("27", "female"), class = "factor"), V77 = structure(1:2, .Label = c("20", "male"), class = "factor"), V78 = structure(1:2, .Label = c("30", "male"), class = "factor"), V79 = structure(1:2, .Label = c("27", "female"), class = "factor"), V80 = structure(1:2, .Label = c("21", "male"), class = "factor"), V81 = structure(1:2, .Label = c("29", "female"), class = "factor"), V82 = structure(1:2, .Label = c("21", "male"), class = "factor"), V83 = structure(1:2, .Label = c("24", "male"), class = "factor"), V84 = structure(1:2, .Label = c("27", "female"), class = "factor"), V85 = structure(1:2, .Label = c("23", "male"), class = "factor"), V86 = structure(1:2, .Label = c("30", "female"), class = "factor"), V87 = structure(1:2, .Label = c("24", "female"), class = "factor"), V88 = structure(1:2, .Label = c("26", "female"), class = "factor"), V89 = structure(1:2, .Label = c("29", "female"), class = "factor"), V90 = structure(1:2, .Label = c("24", "male"), class = "factor"), V91 = structure(1:2, .Label = c("30", "male"), class = "factor"), V92 = structure(1:2, .Label = c("24", "male"), class = "factor"), V93 = structure(1:2, .Label = c("23", "female"), class = "factor"), V94 = structure(1:2, .Label = c("28", "female"), class = "factor"), V95 = structure(1:2, .Label = c("25", "male"), class = "factor"), V96 = structure(1:2, .Label = c("33", "male"), class = "factor"), V97 = structure(1:2, .Label = c("26", "female"), class = "factor"), V98 = structure(1:2, .Label = c("24", "male"), class = "factor"), V99 = structure(1:2, .Label = c("25", "female"), class = "factor"), V100 = structure(1:2, .Label = c("26", "male"), class = "factor"), V101 = structure(1:2, .Label = c("32", "female"), class = "factor")), class = "data.frame", row.names = c(NA, -2L))

Я попытался упростить ситуацию, преобразовав эти данные по строкам в кадр данных по столбцам. Это преобразование основано на коде, совместно используемом @Ricardo Oliveros-Ramos здесь: Чтение CSV-файла, расположенного горизонтально

Для удобства скопирую сюда: 

read.tcsv = function(file, header=TRUE, sep=",", ...) 
{

   n = max(count.fields(file, sep=sep), na.rm=TRUE)
   x = readLines(file)

   .splitvar = function(x, sep, n) {
   var = unlist(strsplit(x, split=sep))
   length(var) = n
   return(var)
 }

x = do.call(cbind, lapply(x, .splitvar, sep=sep, n=n))
x = apply(x, 1, paste, collapse=sep) 
out = read.csv(text=x, sep=sep, header=header, ...)
return(out)

}

Затем вы просто запускаете вышеупомянутую функцию для своего файла CSV: 

df2 = read.tcsv("give the complete path of your file")

Для справки, dput этого как показано ниже: 

dput(df2)

structure(list(ages = c(29L, 29L, 19L, 25L, 22L, 29L, 24L, 23L, 28L, 33L, 30L, 21L, 22L, 27L, 32L, 25L, 25L, 23L, 33L, 22L, 31L, 32L, 26L, 27L, 23L, 27L, 16L, 21L, 18L, 17L, 27L, 23L, 29L, 26L, 25L, 27L, 26L, 29L, 25L, 26L, 22L, 31L, 21L, 22L, 19L, 25L, 29L, 21L, 21L, 25L, 24L, 33L, 25L, 28L, 23L, 26L, 23L, 23L, 28L, 26L, 22L, 26L, 26L, 28L, 23L, 29L, 31L, 28L, 23L, 23L, 21L, 27L, 20L, 24L, 27L, 20L, 30L, 27L, 21L, 29L, 21L, 24L, 27L, 23L, 30L, 24L, 26L, 29L, 24L, 30L, 24L, 23L, 28L, 25L, 33L, 26L, 24L, 25L, 26L, 32L), genders = structure(c(2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L), .Label = c("female", "male"), class = "factor")), class = "data.frame", row.names = c(NA, -100L))

Следующим и более важным шагом является функциональность, которую вы хотите достичь: 

# for mean by gender using the base R functionality
mean(df2$ages[which(df2$genders == 'male')])
mean(df2$ages[which(df2$genders == 'female')])

Обратите внимание на знак == здесь, что означает сравнение а также не '' вокруг мужчины, который определяет эти данные как тип символов. 

# for finding the youngest by gender
min(df2$ages[which(df2$genders == 'male')])
min(df2$ages[which(df2$genders == 'female')])

И, наконец, посчитать, сколько мужчин или сколько женщин в группе: 

sum(df2$genders == 'male')
sum(df2$genders == 'female')

Проверяет равенство и суммирует или подсчитывает ИСТИННЫЕ случаи.

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