Homework2

Analysis In the 2nd and 3rd tables: 1.The restaurants were displayed in the order they were because they are ordered by their count from the largest to the lowest;

2.The overall column represents mean value of customer rate by sex/race;

3.The overall column was added to the table in the position it was because it is added as the margin of average value : c3= addmargins(c2, FUN = overall);

4.The sex/race columns were grouped together because they belong to the same dimension_;

require(readxl)

## Loading required package: readxl

## Warning: package 'readxl' was built under R version 3.5.3

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.5.3

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(data.table)

## Warning: package 'data.table' was built under R version 3.5.3

## 
## Attaching package: 'data.table'

## The following objects are masked from 'package:dplyr':
## 
##     between, first, last

library(vcd)

## Loading required package: grid

data= read.csv("C:/Users/zjoyi/Downloads/data.csv")
dft= data.table(data)

# total cases
totalcase= nrow(dft)
totalcase

## [1] 1192

1.The restaurants were displayed in the order they were because they are ordered by their Count from the largest to the lowest, as shown in the following code:setorder(a,-Count);

 # we can get Burger king 309, KFC= 273, McDonald = 296, Taco Bell = 314
options(digits = 4)
a= dft %>% group_by(restaurant) %>% summarise( Count = n()) %>% mutate (Percentage = (Count/totalcase)*100)
colnames(a)[1] <- "Restaurant"
# order table by Count
setorder(a,-Count)
a

## # A tibble: 4 x 3
##   Restaurant  Count Percentage
## * <fct>       <int>      <dbl>
## 1 Taco Bell     314       26.3
## 2 Burger King   309       25.9
## 3 McDonald      296       24.8
## 4 KFC           273       22.9

For each restaurant, find the average star rating by Sex.Average Customer Rating by Sex;

2.The overall column represents mean value of customer rate by sex/race;

#Average star rating by Sex
b= dft %>% group_by(restaurant, sex) %>%  summarise(val = mean(stars))
colnames(b)[1] <- "Restaurant"
b2= xtabs(val ~ Restaurant+ sex, data = b)
b2

##              sex
## Restaurant    Female  Male
##   Burger King  3.058 2.954
##   KFC          2.890 3.247
##   McDonald     2.830 2.920
##   Taco Bell    3.144 3.170

#Average Customer Rating by Sex
overall<-mean
b3= addmargins(b2, FUN = overall)

## Margins computed over dimensions
## in the following order:
## 1: Restaurant
## 2: sex

b3<-b3[-nrow(b3),]
b4<-b3[order(-b3[,3]),]
round(b4, 2) 

##              sex
## Restaurant    Female Male overall
##   Taco Bell     3.14 3.17    3.16
##   KFC           2.89 3.25    3.07
##   Burger King   3.06 2.95    3.01
##   McDonald      2.83 2.92    2.87

For each restaurant, find the average star rating by race.Average Customer Rating by race;

3.The overall column was added to the table in the position it was because it is added as the margin of average value : c3= addmargins(c2, FUN = overall);

4.The sex/race columns were grouped together because they belong to the same dimension.

#For each restaurant, find the average star rating by race.
c= dft %>% group_by(restaurant, race) %>%  summarise(val = mean(stars))
colnames(c)[1] <- "Restaurant"

c2= xtabs(val ~ Restaurant+ race, data = c)
c2

##              race
## Restaurant    Black Hispanic Other White
##   Burger King 2.926    2.877 3.176 3.011
##   KFC         2.889    3.194 2.875 3.409
##   McDonald    2.805    2.694 3.014 2.962
##   Taco Bell   3.167    3.108 3.203 3.152

#Average Customer Rating by race
overall<-mean
c3= addmargins(c2, FUN = overall)

## Margins computed over dimensions
## in the following order:
## 1: Restaurant
## 2: race

c3<-c3[-nrow(c3),]
c4<-c3[order(-c3[,5]),]

round(c4, 2) 

##              race
## Restaurant    Black Hispanic Other White overall
##   Taco Bell    3.17     3.11  3.20  3.15    3.16
##   KFC          2.89     3.19  2.88  3.41    3.09
##   Burger King  2.93     2.88  3.18  3.01    3.00
##   McDonald     2.80     2.69  3.01  2.96    2.87