Data Wrangling with R - Assignment 7
Yash Sharma
2 December 2016
Answers in individual tabs below
Data Import
Data was imported using the RSocrata package
library(RSocrata)
#file_url <- 'https://nycopendata.socrata.com/Health/DOHMH-New-York-City-Restaurant-Inspection-Results/xx67-kt59'
#data <- read.socrata(url = file_url)
#saveRDS(data,file="nyc_restaurant_data.rds")
data <- readRDS("nyc_restaurant_data.rds")
head(data)## CAMIS DBA BORO BUILDING STREET ZIPCODE
## 1 30075445 MORRIS PARK BAKE SHOP BRONX 1007 MORRIS PARK AVE 10462
## 2 30075445 MORRIS PARK BAKE SHOP BRONX 1007 MORRIS PARK AVE 10462
## 3 30075445 MORRIS PARK BAKE SHOP BRONX 1007 MORRIS PARK AVE 10462
## 4 30075445 MORRIS PARK BAKE SHOP BRONX 1007 MORRIS PARK AVE 10462
## 5 30075445 MORRIS PARK BAKE SHOP BRONX 1007 MORRIS PARK AVE 10462
## 6 30075445 MORRIS PARK BAKE SHOP BRONX 1007 MORRIS PARK AVE 10462
## PHONE CUISINE.DESCRIPTION INSPECTION.DATE
## 1 7188924968 Bakery 2016-02-18
## 2 7188924968 Bakery 2016-02-18
## 3 7188924968 Bakery 2015-02-09
## 4 7188924968 Bakery 2014-03-03
## 5 7188924968 Bakery 2013-10-10
## 6 7188924968 Bakery 2013-09-11
## ACTION
## 1 Violations were cited in the following area(s).
## 2 Violations were cited in the following area(s).
## 3 Violations were cited in the following area(s).
## 4 Violations were cited in the following area(s).
## 5 No violations were recorded at the time of this inspection.
## 6 Violations were cited in the following area(s).
## VIOLATION.CODE
## 1 04L
## 2 08A
## 3 06C
## 4 10F
## 5
## 6 04L
## VIOLATION.DESCRIPTION
## 1 Evidence of mice or live mice present in facility's food and/or non-food areas.
## 2 Facility not vermin proof. Harborage or conditions conducive to attracting vermin to the premises and/or allowing vermin to exist.
## 3 Food not protected from potential source of contamination during storage, preparation, transportation, display or service.
## 4 Non-food contact surface improperly constructed. Unacceptable material used. Non-food contact surface or equipment improperly maintained and/or not properly sealed, raised, spaced or movable to allow accessibility for cleaning on all sides, above and underneath the unit.
## 5
## 6 Evidence of mice or live mice present in facility's food and/or non-food areas.
## CRITICAL.FLAG SCORE GRADE GRADE.DATE RECORD.DATE
## 1 Critical 10 A 2016-02-18 2016-12-01
## 2 Not Critical 10 A 2016-02-18 2016-12-01
## 3 Critical 6 A 2015-02-09 2016-12-01
## 4 Not Critical 2 A 2014-03-03 2016-12-01
## 5 Not Applicable NA <NA> 2016-12-01
## 6 Critical 6 A 2013-09-11 2016-12-01
## INSPECTION.TYPE
## 1 Cycle Inspection / Initial Inspection
## 2 Cycle Inspection / Initial Inspection
## 3 Cycle Inspection / Initial Inspection
## 4 Cycle Inspection / Initial Inspection
## 5 Trans Fat / Second Compliance Inspection
## 6 Cycle Inspection / Re-inspectionAnswer 1
Use the map function to identify the class of each variable.
library(dplyr)
library(purrr)
data %>% map(class)## $CAMIS
## [1] "integer"
##
## $DBA
## [1] "factor"
##
## $BORO
## [1] "factor"
##
## $BUILDING
## [1] "character"
##
## $STREET
## [1] "factor"
##
## $ZIPCODE
## [1] "integer"
##
## $PHONE
## [1] "character"
##
## $CUISINE.DESCRIPTION
## [1] "factor"
##
## $INSPECTION.DATE
## [1] "POSIXlt" "POSIXt"
##
## $ACTION
## [1] "factor"
##
## $VIOLATION.CODE
## [1] "factor"
##
## $VIOLATION.DESCRIPTION
## [1] "factor"
##
## $CRITICAL.FLAG
## [1] "factor"
##
## $SCORE
## [1] "integer"
##
## $GRADE
## [1] "factor"
##
## $GRADE.DATE
## [1] "POSIXlt" "POSIXt"
##
## $RECORD.DATE
## [1] "POSIXlt" "POSIXt"
##
## $INSPECTION.TYPE
## [1] "factor"Answer 2
Notice how the date variables are in POSIXlt form. Create a function that takes a single argument (âxâ) and checks if it is of POSIXlt class. If it is, have the function change the input to a simple Date class with as.Date. If not then, the function should keep the input class as is. Apply this function to each of the columns in the NY restaurant data set by using the map function. Be sure the final output is a tibble and not a list
library(lubridate)
library(tibble)
check_date <- function(x)
{
ifelse(any(class(x) == "POSIXlt"), T, F)
}
convert_date <- function(x)
{
if(!check_date(x))
{
return(x)
}
return(as.Date(x))
}
data02 <- data %>% map(convert_date) %>% as_data_frame()
head(data02)## # A tibble: 6 × 18
## CAMIS DBA BORO BUILDING STREET ZIPCODE
## <int> <fctr> <fctr> <chr> <fctr> <int>
## 1 30075445 MORRIS PARK BAKE SHOP BRONX 1007 MORRIS PARK AVE 10462
## 2 30075445 MORRIS PARK BAKE SHOP BRONX 1007 MORRIS PARK AVE 10462
## 3 30075445 MORRIS PARK BAKE SHOP BRONX 1007 MORRIS PARK AVE 10462
## 4 30075445 MORRIS PARK BAKE SHOP BRONX 1007 MORRIS PARK AVE 10462
## 5 30075445 MORRIS PARK BAKE SHOP BRONX 1007 MORRIS PARK AVE 10462
## 6 30075445 MORRIS PARK BAKE SHOP BRONX 1007 MORRIS PARK AVE 10462
## # ... with 12 more variables: PHONE <chr>, CUISINE.DESCRIPTION <fctr>,
## # INSPECTION.DATE <date>, ACTION <fctr>, VIOLATION.CODE <fctr>,
## # VIOLATION.DESCRIPTION <fctr>, CRITICAL.FLAG <fctr>, SCORE <int>,
## # GRADE <fctr>, GRADE.DATE <date>, RECORD.DATE <date>,
## # INSPECTION.TYPE <fctr>Answer 3
Using this reformatted tibble, identify how many restaurants in 2016 had a violation regarding âmiceâ? How about âhairâ? What about âsewageâ? Hint: the VIOLATION.DESCRIPTION and INSPECTION.DATE variables will be useful here
library(stringr)
library(purrr)
data02 %>%
subset(year(INSPECTION.DATE)==2016) %>%
group_by(year(INSPECTION.DATE)) %>%
subset(str_detect(tolower(VIOLATION.DESCRIPTION),"mice")) %>%
summarize(mice = n_distinct(DBA))## # A tibble: 1 × 2
## `year(INSPECTION.DATE)` mice
## <dbl> <int>
## 1 2016 5182 data02 %>%
subset(year(INSPECTION.DATE)==2016) %>%
group_by(year(INSPECTION.DATE)) %>%
subset(str_detect(tolower(VIOLATION.DESCRIPTION),"hair")) %>%
summarize(mice = n_distinct(DBA))## # A tibble: 1 × 2
## `year(INSPECTION.DATE)` mice
## <dbl> <int>
## 1 2016 1878 data02 %>%
subset(year(INSPECTION.DATE)==2016) %>%
group_by(year(INSPECTION.DATE)) %>%
subset(str_detect(tolower(VIOLATION.DESCRIPTION),"sewage")) %>%
summarize(mice = n_distinct(DBA))## # A tibble: 1 × 2
## `year(INSPECTION.DATE)` mice
## <dbl> <int>
## 1 2016 8051In 2016, the number of voilations are:
- Mice: 5,182
- Hair: 1,878
- Sewage: 8,051
Answer 4
Create a function to apply to this tibble that takes a year and a regular expression (i.e. âmiceâ) and returns a ggplot bar chart of the top 20 restaurants with the most violations. Make sure the restaurants are properly rank-ordered in the bar chart
library(ggplot2)
func4 <- function(keyword,year)
{
subset(data02,year(INSPECTION.DATE) == year) %>%
group_by(DBA) %>%
summarize(count = sum(str_detect(tolower(VIOLATION.DESCRIPTION),keyword))) %>%
arrange(desc(count)) %>%
top_n(20) %>%
ggplot()+
geom_bar(mapping=aes(x=reorder(DBA,count),y=count),stat="identity")+
coord_flip()+
ylab(paste("Number of",keyword,"voilations"))+
ggtitle(paste("Number of",keyword,"Voilations for ",year))+
xlab("")
}
func4("mice",2016)
func4("hair",2016)
func4("contamination",2016)