Exercise On Data Visualization
Syarah Hashim [2017210184]
10/18/2019
Exercise on Data Visualization
Questions :
using “Charm_City_Circulator_Ridership.csv”
construct simple barchart for orangeBoardings, purpleBoardings, greenBoardings, bannerBoardings
construct boxplot by group of day for orangeBoardings, PurpleBoardings, greenBoardings, bannerBoardings.
construct line chart for orangeBoardings, purpleBoardings, greenBoardings and BannerBoardings by date for month October 2010
Answer :
#using "Charm_City_Circulator_Ridership.csv"
data1 <- read.csv("Charm_City_Circulator_Ridership.csv")
dim(data1)## [1] 1146 15names(data1)## [1] "day" "date" "orangeBoardings"
## [4] "orangeAlightings" "orangeAverage" "purpleBoardings"
## [7] "purpleAlightings" "purpleAverage" "greenBoardings"
## [10] "greenAlightings" "greenAverage" "bannerBoardings"
## [13] "bannerAlightings" "bannerAverage" "daily"head(data1[1:5])## day date orangeBoardings orangeAlightings orangeAverage
## 1 Monday 01/11/2010 877 1027 952.0
## 2 Tuesday 01/12/2010 777 815 796.0
## 3 Wednesday 01/13/2010 1203 1220 1211.5
## 4 Thursday 01/14/2010 1194 1233 1213.5
## 5 Friday 01/15/2010 1645 1643 1644.0
## 6 Saturday 01/16/2010 1457 1524 1490.5#Editing data on orange
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, unionorange <- data1 %>%
filter(!is.na(orangeBoardings)) %>%
select(orangeBoardings)
orange$boarding <- "orange"
head(orange)## orangeBoardings boarding
## 1 877 orange
## 2 777 orange
## 3 1203 orange
## 4 1194 orange
## 5 1645 orange
## 6 1457 orangeorange2 <- orange$boarding
orange2 <- as.data.frame(orange2)
library(dplyr)
orange2 <- rename(orange2, "boarding" = "orange2")
names(orange2)## [1] "boarding"head(orange2)## boarding
## 1 orange
## 2 orange
## 3 orange
## 4 orange
## 5 orange
## 6 orange#Editing data on purple
purple <- data1 %>%
filter(!is.na(purpleBoardings)) %>%
select(purpleBoardings)
purple$boarding <- "purple"
head(purple)## purpleBoardings boarding
## 1 1028 purple
## 2 1327 purple
## 3 1726 purple
## 4 2044 purple
## 5 2230 purple
## 6 1852 purplepurple2 <- purple$boarding
purple2 <- as.data.frame(purple2)
library(dplyr)
purple2 <- rename(purple2, "boarding" = "purple2")
names(purple2)## [1] "boarding"head(purple2)## boarding
## 1 purple
## 2 purple
## 3 purple
## 4 purple
## 5 purple
## 6 purple#Editing data on green
green <- data1 %>%
filter(!is.na(greenBoardings)) %>%
select(greenBoardings)
green$boarding <- "green"
head(green)## greenBoardings boarding
## 1 887 green
## 2 1057 green
## 3 1142 green
## 4 1357 green
## 5 760 green
## 6 654 greengreen2 <- green$boarding
green2 <- as.data.frame(green2)
library(dplyr)
green2 <- rename(green2, "boarding" = "green2")
names(green2)## [1] "boarding"head(green2)## boarding
## 1 green
## 2 green
## 3 green
## 4 green
## 5 green
## 6 green#Editing data on banner
banner <- data1 %>%
filter(!is.na(bannerBoardings)) %>%
select(bannerBoardings)
banner$boarding <- "banner"
head(banner)## bannerBoardings boarding
## 1 520 banner
## 2 574 banner
## 3 630 banner
## 4 670 banner
## 5 847 banner
## 6 987 bannerbanner2 <- banner$boarding
banner2 <- as.data.frame(banner2)
library(dplyr)
banner2 <- rename(banner2, "boarding" = "banner2")
names(banner2)## [1] "boarding"head(banner2)## boarding
## 1 banner
## 2 banner
## 3 banner
## 4 banner
## 5 banner
## 6 banner#Combine the data
cc <- rbind(orange2,
green2,
purple2,
banner2)
# 1) Construct simple barchart for orangeBoardings, purpleBoardings,
# greenBoardings, bannerBoardings
barplot(table(cc$boarding),
main = "Bar chart showing the frequency of Boarding",
xlab = " category of boarding",
ylab = "frequency of boarding",
border = NA,
col = c("lightblue", "lightgreen", "#FFFD96", "#FFFDC8"))
# 2) construct boxplot by group of day for orangeBoardings, PurpleBoardings,
# greenBoardings, bannerBoardings.
# boxplot for orangeBoardings
library(dplyr)
new <- data1 %>%
group_by(day) %>%
summarise(orange = mean(orangeBoardings, na.rm=T),
purple = mean(purpleBoardings, na.rm= T),
green = mean(greenBoardings, na.rm=T),
banner = mean(bannerBoardings, na.rm=T))
new2 <- data1 %>%
group_by(day) %>%
select(day, orangeBoardings)
boxplot(new2$orangeBoardings~new2$day)
#boxplot for purpleBoardings
new3 <- data1 %>%
group_by(day) %>%
select(day, purpleBoardings)
boxplot(new3$purpleBoardings~new3$day)
#boxplot for greenBoardings
new4 <- data1 %>%
group_by(day) %>%
select(day, greenBoardings)
boxplot(new4$greenBoardings~new4$day)
#boxplot for bannerBoardings
new5 <- data1 %>%
group_by(day) %>%
select(day, bannerBoardings)
boxplot(new5$bannerBoardings~new5$day)
#3) construct line chart for orangeBoardings, purpleBoardings,
# greenBoardings and BannerBoardings by date for month October 2010
new22 <- data1 %>%
group_by(date) %>%
summarise(orange = mean(orangeBoardings, na.rm=T),
purple = mean(purpleBoardings, na.rm= T),
green = mean(greenBoardings, na.rm=T),
banner = mean(bannerBoardings, na.rm=T))
# Line chart for orangeBoardings
new6 <- data1 %>%
group_by(date) %>%
select(date, orangeBoardings)
boxplot(new6$orangeBoardings~new6$date)
# Line chart for purpleBoardings
new7 <- data1 %>%
group_by(date) %>%
select(date, purpleBoardings)
boxplot(new7$purpleBoardings~new7$date)
# Line chart for greenBoardings
new8 <- data1 %>%
group_by(date) %>%
select(date, greenBoardings)
boxplot(new8$greenBoardings~new8$date)
# Line chart for bannerBoardings
new9 <- data1 %>%
group_by(date) %>%
select(date, bannerBoardings)
boxplot(new9$bannerBoardings~new9$date)