Creating a dplyr summarised table - r

Question

I have a dataset that I'd like to be summarised. My data looks like this looks like this.
The table in Sheet1 refers to the original table.
The table in Sheet2 is the result I'd like to get, using dplyr.
Basically, for each variable (Our Website, Friendliness of Staff, and Food Quality), I'd like a sum of 'Satisfied' + 'Very Satsified', expressed as a percentage of the total number of respondents for the Parameter. For example, the 80% for the Internet Column is 4 (Satisfied+V.Satisfied)/5 (total number of respondents whose moed of reservation is Internet) * 100 = 80%.
I used this code but I'm not getting the desired result:
test %>%
group_by(Parameter.1..Mode.of.reservation,Our.Website) %>%
select(Our.Website,Friendliness.of.Staff,Food.Quality) %>%
summarise_each(funs(freq = n()))
Any help would be appreciated.

Answer 1

#ira's solution can be streamlined if you gather the data prior to summarizing. This way you skip the multiple assignments.
library(tidyverse)
library(googlesheets)
library(scales)
# Authorize with google.
gs_auth()
# Register the sheet
gs_data <- gs_url("https://docs.google.com/spreadsheets/d/1zljXN7oxUvij2mXHiyuRVG3xp5063chEFW_QERgHegg/")
# Read in the first worksheet
data <- gs_read(gs_data, ws = 1)
# Summarize using tidyr/dplyr
data %>%
gather(item, response, -1:-2) %>%
filter(!is.na(response)) %>%
group_by(`Parameter 1: Mode of reservation`, item) %>%
summarise(percentage = percent(sum(response %in% c("Satisfied","Very Satisfied"))/n())) %>%
spread(`Parameter 1: Mode of reservation`, percentage)

Answer 2

After using dplyr to summarise the data, you can use tidyr to transpose the dataset so that you have the columns and rows just as you asked in the question.
# read in the data
data <- read.csv("C:/RSnips/My Dataset - Sheet1.csv")
# load libraries
library(dplyr)
library(tidyr)
# take the loaded data
data2 <- data %>%
# group it by mode of reservation
group_by(Parameter.1..Mode.of.reservation) %>%
# summarise
summarise(
# count how many times website column takes values sat or very sat and divide by number of observations in each group given by group_by
OurWeb = sum(Our.Website == "Satisfied" |
Our.Website == "Very Satisfied")/n(),
# do the same for Staff and food
Staff = sum(Friendliness.of.Staff == "Satisfied" |
Friendliness.of.Staff == "Very Satisfied")/n(),
Food = sum(Food.Quality == "Satisfied" |
Food.Quality == "Very Satisfied")/n()) %>%
# If you want to have email, internet and phone in columns
# use tidyr package to transpose the dataset
# first turn it into a long format, where mode of the original columns are your key
gather(categories, val, 2:(ncol(data)-1)) %>%
# then turn it back to wide format, but mode of reservation will be in columns
spread(Parameter.1..Mode.of.reservation, val)

Answer 3

How about:
data %>% data
mutate(OurWebsite2 = ifelse(Our.Website == "Very Satisfied" | Our.Website == "Satisfied", 1, 0),
Friendlinessofstaff2 = ifelse(Friendlinessofstaff == "Very Satisfied" | Friendlinessofstaff == "Satisfied", 1, 0),
FoodQuality2 = ifelse(FoodQuality== "Very Satisfied" | FoodQuality== "Satisfied", 1, 0) %>%
group_by(Parameter1) %>%
summarise(OurWebsiteSatisfaction = mean(OurWebsite2),
FriendlinessofstaffSatisfaction = mean(Friendlinessofstaff2),
FoodQualitySatisfaction = mean(FoodQuality2))