Analysis of multiple response

Question

df1 <-
  data.frame(c("male", "female", "male"),
             c("1", "2", "3", "4", "5", "6"),
             seq(141, 170))

names(df1) = c("gender", "age", "height")

df1$age <- factor(
  df1$age,
  levels = c(1, 2, 3, 4, 5, 6),
  labels = c("16-24", "25-34", "35-44", "45-54", "55-64", "65+")
)

q1a = c(1, 0, 1, 0, 0, 1)
q1b = c(0, 0, 2, 2, 2, 0)
q1c = c(0, 0, 3, 3, 0, 3)
# 1,2 and 3 used to be compatible with existing datasets. 
# Could change all to 1 if necessary.

df2 <- data.frame(q1a = q1a, q1b = q1b, q1c = q1c)
df1 <- cbind(df1, df2)

rm(q1a, q1b, q1c, df2)

I am looking to replicate the analysis of multiple response questions from SPSS in R.

At the moment I am using this code:

#creating function for analysing questions with grouped data
multfreqtable <- function(a, b, c) {
  # number of respondents (for percent of cases)
  totrep = sum(a == 1 | b == 2 | c == 3)
  
  #creating frequency table
  table_a = data.frame("a", sum(a == 1))
  names(table_a) = c("question", "freq")
  table_b = data.frame("b", sum(b == 2))
  names(table_b) = c("question", "freq")
  table_c = data.frame("c", sum(c == 3))
  names(table_c) = c("question", "freq")
  table_question <- rbind(table_a, table_b, table_c)
  
  #remove individual question tables
  rm(table_a, table_b, table_c)
  
  #adding total
  total = as.data.frame("Total")
  totalsum = (sum(table_question$freq, na.rm = TRUE))
  totalrow = cbind(total, totalsum)
  names(totalrow) = c("question", "freq")
  table_question = rbind(table_question, totalrow)
  
  #adding percentage column to frequency table
  percentcalc = as.numeric(table_question$freq)
  percent = (percentcalc / totalsum) * 100
  table_question <- cbind(table_question, percent)
  
  #adding percent of cases column to frequency table
  poccalc = as.numeric(table_question$freq)
  percentofcases = (poccalc / totrep) * 100
  table_question <- cbind(table_question, percentofcases)
  
  #print percent of cases value
  total_respondents <<- data.frame(totrep)
  
  #remove all unnecessary data and values
  rm(
    total,
    totalsum,
    totalrow,
    b,
    c,
    percent,
    percentcalc,
    percentofcases,
    totrep,
    poccalc
  )
  
  return(table_question)
}

#calling function - must tie to data.frame using $ !!!
q1_frequency <- multfreqtable(df1$q1a, df1$q1b, df1$q1c)

#renaming percent of cases - This is very important while using current method
total_respondents_q1 <- total_respondents
rm(total_respondents)

Producing this table as a result:

Output table

I am looking for a more efficient method of doing this that ideally would not require the function to be edited if there were more or less multiple choice questions.

This seems like a duplicate of your earlier question. What has changed? Please post your expected results, since I am not going to try and decipher your code again, but can most likely point you to an existing function once I know what you want. — Andrie, Feb 13 '12 at 18:23
@Andrie This is the same code as with my earlier post, it is only the focus of the question that has changed. The image above shows the output table from my code. Sorry if that is not the best way of demonstrating my expected results. — BuckyOH, Feb 14 '12 at 09:22
@RomanLuštrik. Yes, ideally I would like to specify a range of questions to analyse in this way. As mentioned if it is easier to use 1s for every question then that would be fine. — BuckyOH, Feb 14 '12 at 11:02

A5C1D2H2I1M1N2O1R2T1 · Accepted Answer · 2012-04-23T07:01:11.707

Your function is actually far too complicated for what you need to do. I think a function like this should work and be more flexible.

multfreqtable = function(data, question.prefix) {
  # Find the columns with the questions
  a = grep(question.prefix, names(data))
  # Find the total number of responses
  b = sum(data[, a] != 0)
  # Find the totals for each question
  d = colSums(data[, a] != 0)
  # Find the number of respondents
  e = sum(rowSums(data[,a]) !=0)
  # d + b as a vector. This is your overfall frequency 
  f = as.numeric(c(d, b))
  data.frame(question = c(names(d), "Total"),
             freq = f,
             percent = (f/b)*100,
             percentofcases = (f/e)*100 )
}

Add another question to your example dataset:

set.seed(1); df1$q2a = sample(c(0, 1), 30, replace=T)
set.seed(2); df1$q2b = sample(c(0, 2), 30, replace=T)
set.seed(3); df1$q2c = sample(c(0, 3), 30, replace=T)

Make a table for "q1" responses:

> multfreqtable(df1, "q1")
  question freq   percent percentofcases
1      q1a   15  33.33333             60
2      q1b   15  33.33333             60
3      q1c   15  33.33333             60
4    Total   45 100.00000            180

Make a table for "q2" responses:

> multfreqtable(df1, "q2")
  question freq   percent percentofcases
1      q2a   14  31.11111       53.84615
2      q2b   13  28.88889       50.00000
3      q2c   18  40.00000       69.23077
4    Total   45 100.00000      173.07692

Tables for multiple questions

Here's a modified version of the function that allows you to create a list of tables for multiple questions at once:

multfreqtable = function(data, question.prefix) {
  z = length(question.prefix)
  temp = vector("list", z)

  for (i in 1:z) {
    a = grep(question.prefix[i], names(data))
    b = sum(data[, a] != 0)
    d = colSums(data[, a] != 0)
    e = sum(rowSums(data[,a]) !=0)
    f = as.numeric(c(d, b))
    temp[[i]] = data.frame(question = c(sub(question.prefix[i], 
                                            "", names(d)), "Total"),
                           freq = f,
                           percent = (f/b)*100,
                           percentofcases = (f/e)*100 )
    names(temp)[i] = question.prefix[i]
  }
  temp
}

Examples:

> multfreqtable(df1, "q1")
$q1
  question freq   percent percentofcases
1        a   15  33.33333             60
2        b   15  33.33333             60
3        c   15  33.33333             60
4    Total   45 100.00000            180

> test1 = multfreqtable(df1, c("q1", "q2"))
> test1
$q1
  question freq   percent percentofcases
1        a   15  33.33333             60
2        b   15  33.33333             60
3        c   15  33.33333             60
4    Total   45 100.00000            180

$q2
  question freq   percent percentofcases
1        a   14  31.11111       53.84615
2        b   13  28.88889       50.00000
3        c   18  40.00000       69.23077
4    Total   45 100.00000      173.07692

> test1$q1
  question freq   percent percentofcases
1        a   15  33.33333             60
2        b   15  33.33333             60
3        c   15  33.33333             60
4    Total   45 100.00000            180

Hello, this seems to do exactly what I want. Having the option of multiple outputs from the same command is also something that could come in very handy. — BuckyOH, Apr 23 '12 at 12:04
Will do, thanks. I've tried it with zero responses and all answers coded as 1 which were two of the main things I had issues with and haven't seen any problems so far. — BuckyOH, Apr 23 '12 at 15:49
I've been trying to use adapt this code for a weighted data set and am having no luck. In my original code I achieved this by using `xtabs` instead of `table`. `colSums` seems to count the cases in each column regardless of their value. Is it possible to apply a weighting here? — BuckyOH, Apr 25 '12 at 09:43
Thank you. Haven't yet had time to look at this again since, I will think about whether it is best to update or add a new question. — BuckyOH, May 02 '12 at 08:26
This is really cool--but it does not work if the cols are character/factor vars huh? — NewBee, Jan 14 '22 at 21:54

score 0 · Answer 2 · answered Jul 07 '19 at 11:33

I've noticed that this is post is quite old, however I couldn’t find a more up to date solution. Here's my version based on dplyr/tidyverse approach.

mult_resp = function(df1, mv_q = c("q1a", "q1b", "q1c")){

  df2 = df1 %>%
    mutate(id = rownames(.)) %>%  #row id for counting n_cases
    select(id, everything()) %>% 
    mutate_at(mv_q, ~ ifelse(. != 0, 1, 0)) %>%
    gather(question, resp,-id, -gender,-age,-height) 

  #count number of cases excluding "all zeros" cases
  n_cases = df2 %>% group_by(id) %>%
    summarise(n = sum(resp)) %>% 
    summarise(sum(n > 0))

  #output table
  res = df2 %>% 
    group_by(question) %>%
    summarise(freq = sum(resp)) %>%
    mutate(
      percent = freq/sum(freq) *100,
      percent_of_cases = freq/as.numeric(n_cases)*100
      ) %>% 
    rbind(., 
          data.frame(question ="Total", 
                     freq =sum(.$freq, na.rm=TRUE),
                     percent =sum(.$percent, na.rm=TRUE),
                     percent_of_cases = sum(.$percent_of_cases, na.rm=TRUE)
                     )
          )
    res
}

Example:

> mult_resp(df1, mv_q = c("q1a", "q1b", "q1c"))
# A tibble: 4 x 4
  question  freq percent percent_of_cases
  <chr>    <dbl>   <dbl>            <dbl>
1 q1a         15    33.3               60
2 q1b         15    33.3               60
3 q1c         15    33.3               60
4 Total       45   100.               180

score 0 · Answer 3 · answered Dec 02 '20 at 17:44

0

It's an old question. However, you can use userfriendlyscience package to analyze multiple responses survey data very easily.

library(userfriendlyscience)
multiResponse (data, c('v1', 'v2', 'v3'))

answered Dec 02 '20 at 17:44

Shakil Ahmed Shaon

109
1
10

1

Actually that function in *userfriendlyscience* is inspired by the accepted answer. Check `?multiResponse` – BroVic Jan 20 '21 at 16:30
1

There is also the expss R package. – CK7 Feb 02 '22 at 21:58

Analysis of multiple response

3 Answers3

Tables for multiple questions

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