CODEWITHSUNDEEP
r
StLouisO
StLouisO

Reputation: 71

Get a Proportion table from survey data

I have survey data for 30+ questions on a 5-point Likert scale (Strongly Agree to Strongly Disagree)

Here's some sample data:

survey_df <- data.frame("ID" = c(1,2,3,4,5),
                        "Q1" = c("Strongly Agree", "Strongly Agree", "Agree", "Agree", "Neither"),
                        "Q2" = c("Agree", "Strongly Disagree", "Disagree", "Agree", "Neither"),
                        "Q3" = c("Neither", "Neither", "Disagree", "Agree", "Neither"))

So basically I want to go from this:

ID Q1             Q2                Q3
1  Strongly Agree Agree             Neither
2  Strongly Agree Strongly Disagree Neither
3  Agree          Disagree          Disagree
4  Agree          Agree             Agree
5  Neither        Neither           Neither

To this:

Question Strongly.Agree Agree Neither Disagree Strongly.Disagree N.Count
Q1       0.4            0.4   0.2     0.0      0.0               5
Q2       0.0            0.4   0.2     0.2      0.2               5
Q3       0.0            0.2   0.6     0.2      0.0               5

Upvotes: 0

Views: 211

Answers (1)

demarsylvain
demarsylvain

Reputation: 2185

You need to use the function gather() and spread() from the librairie tidyr in order to transpose your dataframe, and then use mutate() and mutate_at() (from dplyr) to calculate the N.Count and the proportion by answer.

library(tidyverse)

survey_df %>% 
  gather(-ID, key = 'Question', value = 'Answer') %>% 
  count(Question, Answer) %>% 
  spread(key = Answer, value = n, fill = 0) %>% 
  mutate(N.Count = Agree + Disagree + Neither + `Strongly Agree` + `Strongly Disagree`) %>% 
  mutate_at(vars(-Question, -N.Count), funs(. / N.Count))

#   Question Agree Disagree Neither `Strongly Agree` `Strongly Disagree` N.Count
#   <chr>    <dbl>    <dbl>   <dbl>            <dbl>               <dbl>   <dbl>
# 1 Q1         0.4      0       0.2              0.4                 0         5
# 2 Q2         0.4      0.2     0.2              0                   0.2       5
# 3 Q3         0.2      0.2     0.6              0                   0         5

Upvotes: 2

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