Assignment 2

Original

Visual.ly. (2019).

Objective

The original data visualisation aims to compare the accident prone nature of roads belonging to various states of Australia based on the hours of a day. It tries to help citizens of the state interpret what times of a day are most dangerous.

The visualisation chosen had the following three main issues:

• Time labels are not at sync with the traditional clock hours. This makes it difficult for audience to find the specific hour. Also presenting all 24 hours serves no purpose for the audience looking for a general idea.
• The dot plots with bins at the circumference makes it hard to interpret the state-wise difference in proportion of fatalities. Also the color usage does not consider how the visualisation appears to people with color blindness.
• The circular nature of visualisation makes it difficult to interpret the trends in the number of fatalities over the time of the day.

Reference

• Visual.ly. (2019). The most dangerous times to be on the roads of Austrlia. Retrieved September 23, 2019, from website: https://visual.ly/community/Infographics/travel/most-dangerous-times-be-roads-australia

Code

The following code was used to fix the issues identified in the original.

library(readr)
library(dplyr)
library(ggplot2)
library(tidyr)
crafatal <- read_csv("S:/shubh/STUDY MATERIAL/books/RMIT/SEM2/Data Visualisation/Assignment2/Data/CSVs/ardd_fatalities.csv")

#Renaming the columns
crafatal <- crafatal %>% rename(CrashID = `Crash ID`)
crafatal <- crafatal %>% rename(CrashType = `Crash Type`)
crafatal <- crafatal %>% rename(RoadUser = `Road User`)
crafatal <- crafatal %>% rename(AgeGroup = `Age Group`)
crafatal <- crafatal %>% rename(WeekDayvsWeekEnd = `Day of week`)
crafatal <- crafatal %>% rename(DayVsNight = `Time of day`)

#Selecting Required Columns
crafatal <- select(crafatal, CrashID, State, Time, AgeGroup, WeekDayvsWeekEnd)

#Checking unique values
crafatal$State %>% unique()

## [1] "NSW" "WA"  "SA"  "Qld" "Vic" "NT"  "Tas" "ACT"

crafatal$AgeGroup %>% unique()

## [1] "26_to_39"    "0_to_16"     "40_to_64"    "75_or_older" "17_to_25"   
## [6] "65_to_74"    "Unknown"

crafatal$WeekDayvsWeekEnd %>% unique()

## [1] "Weekend" "Weekday"

#Checking for null(-9) and missing values
crafatal %>% filter(State == -9)

## # A tibble: 0 x 5
## # ... with 5 variables: CrashID <dbl>, State <chr>, Time <time>,
## #   AgeGroup <chr>, WeekDayvsWeekEnd <chr>

crafatal %>% filter(Time == -9)

## # A tibble: 0 x 5
## # ... with 5 variables: CrashID <dbl>, State <chr>, Time <time>,
## #   AgeGroup <chr>, WeekDayvsWeekEnd <chr>

crafatal %>% filter(AgeGroup == -9)

## # A tibble: 0 x 5
## # ... with 5 variables: CrashID <dbl>, State <chr>, Time <time>,
## #   AgeGroup <chr>, WeekDayvsWeekEnd <chr>

crafatal %>% filter(WeekDayvsWeekEnd == -9)

## # A tibble: 0 x 5
## # ... with 5 variables: CrashID <dbl>, State <chr>, Time <time>,
## #   AgeGroup <chr>, WeekDayvsWeekEnd <chr>

crafatal %>% filter(is.na(State))

## # A tibble: 0 x 5
## # ... with 5 variables: CrashID <dbl>, State <chr>, Time <time>,
## #   AgeGroup <chr>, WeekDayvsWeekEnd <chr>

crafatal %>% filter(is.na(Time))

## # A tibble: 40 x 5
##     CrashID State Time   AgeGroup WeekDayvsWeekEnd
##       <dbl> <chr> <time> <chr>    <chr>           
##  1 20127002 NT       NA  26_to_39 Weekday         
##  2 20087036 NT       NA  40_to_64 Weekday         
##  3 20087041 NT       NA  17_to_25 Weekday         
##  4 20077047 NT       NA  17_to_25 Weekday         
##  5 20077047 NT       NA  Unknown  Weekday         
##  6 20057045 NT       NA  40_to_64 Weekday         
##  7 20057045 NT       NA  40_to_64 Weekday         
##  8 20057036 NT       NA  65_to_74 Weekend         
##  9 20052184 Vic      NA  17_to_25 Weekend         
## 10 20057028 NT       NA  26_to_39 Weekday         
## # ... with 30 more rows

#Removing null values from Time Column
crafatal <- crafatal[!(is.na(crafatal$Time)),]

#Splitting Time Column
crafatal <- crafatal %>% separate(Time, into = c("hour", "minute", "sec"), sep = ":")

#Changing datatype of hour column
typeof(crafatal$hour)

## [1] "character"

crafatal$hour <- as.integer(crafatal$hour)
typeof(crafatal$hour)

## [1] "integer"

#Creating a variable to distinguish times of the day.
crafatal$TimeOfDay<-ifelse(crafatal$hour>=2 & crafatal$hour<=6,"Dawn (2AM - 6AM)",
                        ifelse(crafatal$hour>=7 & crafatal$hour<=11,"Morning Peak (7AM - 11AM)",
                               ifelse(crafatal$hour>=12 & crafatal$hour<=15,"Daylight (12PM - 3PM)",
                                      ifelse(crafatal$hour>=16 & crafatal$hour<=20,"Evening Peak (4PM - 8PM)","Night (9PM - 1AM)"
                                      ))))

#Renaming values
crafatal$State[crafatal$State == "Qld"] <- "QLD"
crafatal$State[crafatal$State == "Tas"] <- "TAS"
crafatal$State[crafatal$State == "Vic"] <- "VIC"
crafatal$State <- as.factor(crafatal$State)
levels(crafatal$State)

## [1] "ACT" "NSW" "NT"  "QLD" "SA"  "TAS" "VIC" "WA"

#Changing datatypes to factors.
crafatal$TimeOfDay <- crafatal$TimeOfDay %>% factor(levels = c("Dawn (2AM - 6AM)", "Morning Peak (7AM - 11AM)", "Daylight (12PM - 3PM)", "Evening Peak (4PM - 8PM)", "Night (9PM - 1AM)"), ordered = TRUE)
levels(crafatal$TimeOfDay)

## [1] "Dawn (2AM - 6AM)"          "Morning Peak (7AM - 11AM)"
## [3] "Daylight (12PM - 3PM)"     "Evening Peak (4PM - 8PM)" 
## [5] "Night (9PM - 1AM)"

crafatal$WeekDayvsWeekEnd <-crafatal$WeekDayvsWeekEnd %>% factor(levels = c("Weekend", "Weekday"))
levels(crafatal$WeekDayvsWeekEnd)

## [1] "Weekend" "Weekday"

crafatal$AgeGroup <-crafatal$AgeGroup %>% factor(levels = c("0_to_16", "17_to_25", "26_to_39", "40_to_64", "65_to_74", "75_or_older", "Unknown"), ordered = TRUE)
levels(crafatal$AgeGroup)

## [1] "0_to_16"     "17_to_25"    "26_to_39"    "40_to_64"    "65_to_74"   
## [6] "75_or_older" "Unknown"

#Plotting 
p1 <- ggplot(crafatal, aes(State)) + 
  geom_bar(aes(fill = AgeGroup),) +
  theme_bw() +
  facet_grid(WeekDayvsWeekEnd ~ TimeOfDay) + 
  labs(title = "The most dangerous hours to be on roads of Australia.", y = "Number of fatalities", x = "State") + 
  theme(strip.text = element_text(size = 12, face = "bold.italic"),
        axis.text.x=element_text(angle=45,hjust=1),
        axis.text = element_text(size=12, face = "bold"),
        axis.title = element_text(size=14, face = "bold"),
        title = element_text(size = 16, face = "bold", colour = "black"),
        axis.line = element_line(colour = "black"))

Data Reference

• Australian Road Deaths Database Section. (2019). Australian Road Deaths Database. Retrieved September 23, 2019, from Australian Road Deaths Database website: https://www.bitre.gov.au/statistics/safety/fatal_road_crash_database.aspx

Reconstruction

The following plot fixes the main issues in the original visualisation.