How to analyase ranking data in R please?

General
4

Here is an example of working with your data. It uses the functions select(), gather(), group_by() and summarize()

  • select() chooses columns.
  • gather() combines many columns into two, one column to label the data and one to store the value
  • group_by() makes subsets of the data, one subset for each value of the chosen column(s).
  • summarize() does calculations on the subsets created by group_by()

Look at the intermediate data frames at each calculation step and see if it makes sense. This is a lot to digest at one time, so do not be surprised if you are confused.

library(dplyr)
library(tidyr)
Dat <- data.frame(
  Progress = c(100L, 100L, 100L, 100L, 100L),
  Duration.in.seconds. = c(1770L, 1030L, 644L, 3988L, 1292L),
  Id = c(1L, 2L, 3L, 4L, 5L),
  model = c(4L, 2L, 1L, 3L, 2L),
  location = c(1L, 3L, 2L, 2L, 3L),
  education = c(3L, 1L, 3L, 1L, 1L),
  fee = c(2L, 5L, 4L, 5L, 4L),
  income = c(5L, 4L, 5L, 4L, 5L),
  red = c(4L, 1L, 2L, 2L, 4L),
  blue = c(3L, 2L, 1L, 1L, 3L),
  green = c(1L, 4L, 4L, 3L, 2L),
  yellow = c(2L, 5L, 3L, 4L, 1L),
  black = c(5L, 3L, 5L, 5L, 5L),
  Age = c(47L, 47L, 51L, 50L, 38L),
  Recorded.Date = as.factor(c("15/06/2018 21:29",
                              "16/06/2018 15:47",
                              "18/06/2018 19:07", "19/06/2018 20:29",
                              "20/06/2018 13:59")),
  RID = as.factor(c("R_Djkev4OH9F3RuIp",
                    "R_2vY3qfyS8vNWvCH",
                    "R_1Rr1Eh9iCI3wznj", "R_T1rPDENUBBntTCF",
                    "R_3inja17CkIpsjHr")),
  Distribution = as.factor(c("anonymous", "anonymous",
                             "anonymous", "anonymous",
                             "anonymous")),
  Block = as.factor(c("A", "C", "A", "C", "B")),
  Difficulty.of.choice.questions = as.factor(c("Moderately easy",
                                               "Moderately easy",
                                               "Extremely difficult", "Extremely difficult",
                                               "Extremely difficult")),
  Gender = as.factor(c("Female", "Male", "Female",
                       "Female", "Female")),
  Ethnicity = as.factor(c("Other", "European",
                          "European", "Other",
                          "European")),
  Current.job.satisfaction4 = as.factor(c("Somewhat dissatisfied",
                                          "Extremely satisfied",
                                          "Somewhat satisfied",
                                          "Somewhat satisfied", "Somewhat satisfied")),
  Current.job.satisfaction2 = as.factor(c("Dissatisfied", "Satisfied",
                                          "Satisfied", "Satisfied",
                                          "Satisfied"))
  )

#Calculate means
Columns1 <- Dat %>% select(model:income)
Col1_tall <- Columns1 %>% gather(key = Feature, value = Rank, model:income)
Stats1 <- Col1_tall %>% group_by(Feature) %>% summarize(Avg = mean(Rank)) 
Stats1
#> # A tibble: 5 x 2
#>   Feature     Avg
#>   <chr>     <dbl>
#> 1 education   1.8
#> 2 fee         4  
#> 3 income      4.6
#> 4 location    2.2
#> 5 model       2.4

Colors <- Dat %>% select(red:black)
Colors_tall <- Colors %>% gather(key = Color, value = Rank, red:black)
ColorStats <- Colors_tall %>% group_by(Color) %>% summarize(Avg = mean(Rank)) 
ColorStats
#> # A tibble: 5 x 2
#>   Color    Avg
#>   <chr>  <dbl>
#> 1 black    4.6
#> 2 blue     2  
#> 3 green    2.8
#> 4 red      2.6
#> 5 yellow   3

Created on 2019-09-17 by the reprex package (v0.2.1)

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