LOADING DATA AND SEEING HEAD RECORDS

library(sqldf)
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
library(RCurl)
## Loading required package: bitops
library(ggplot2)
data<- read.csv("C:\\Users\\jkks9\\Documents\\DATA 607\\v_taxi_daily-1.csv")
head(data)


WHICH TAXI TYPE (YELLOW TAXI, FOR HIRE VEHICLES, GREEN TAXI) HAS THE MOST NUMBER OF RIDES ANNUALY FOR EACH YEAR FROM 2014-2018?


x<-sqldf('SELECT yr,taxi,sum(rides) FROM data WHERE Yr between 2014 and 2018 GROUP BY yr,taxi')
x2<-sqldf('WITH TAXI AS(SELECT yr,taxi,sum(rides) as rides, DENSE_RANK() OVER(PARTITION BY Yr ORDER BY rides desc) as Rank1 FROM data WHERE Yr between 2014 and 2018 GROUP BY yr,taxi) select * FROM TAXI WHERE Rank1=1')
x2
ggplot(data=x2, aes(x=yr, y=rides, fill=taxi)) +
    geom_bar(stat="identity")


YELLOW TAXI had the most rides from 2014-2015 with FOR HIRE VEHICLES gaining a bigger market share starting in 2016.


WHAT IS THE RIDE DISTRIBUTION FOR EACH OF THE TAXI TYPES FROM 2014 - 2018?


library(dplyr)
## 
## 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
x3<-sqldf('WITH TAXI AS(SELECT yr,taxi,sum(rides) as rides FROM data WHERE Yr between 2014 and 2018 GROUP BY yr,taxi),TAXI2 AS(select *,SUM(rides) OVER(PARTITION BY yr) as TotalRidesYr FROM TAXI) SELECT * FROM TAXI2')
x3
x4<-mutate(x3,x3$rides/x3$TotalRidesYr)
x4
ggplot(x4, aes(fill=taxi, y=x3$rides/x3$TotalRidesYr, x=yr)) + 
    geom_bar(position="fill", stat="identity")


YELLOW TAXI had the higest ride distribution early on from 2014 to 2015 but now FOR HIRE VEHICLES has the highest distribution starting in 2016.


LOADING DATA AND SEEING HEAD RECORDS


data2<- read.csv("C:\\Users\\jkks9\\Documents\\DATA 607\\v_taxi_daily-2.csv")
head(data2)


WHICH MONTH HAS THE MOST NUMBER OF RIDES FOR EACH YEAR FOR ALL TAXIS FROM 2014-2018?


xx<-sqldf('SELECT yr,mth,AVG(rides) FROM data2 WHERE Yr between 2014 and 2018 GROUP BY yr,mth')
xx2<-sqldf('WITH TAXI AS(SELECT yr,mth,sum(rides) as rides,DENSE_RANK() OVER(PARTITION BY Yr ORDER BY rides desc) as rank1 FROM data2 WHERE Yr between 2014 and 2018 GROUP BY yr,mth) SELECT * FROM TAXI WHERE rank1=1 ORDER BY Yr')
xx2
ggplot(data=xx2, aes(x=yr, y=rides, fill=mth)) +
    geom_bar(stat="identity")


The months that are the most busiest for taxi ridership is either November or December for the time period of 2014-2018.