MATH2270/MATH2237 Assignment 1

Turning Tables

Student Details

• Anh Phan (s3258110)

Data Source

• Baidawi S et al. (2011) Older prisoners: A challenge for Australian corrections. Retrieved from https://aic.gov.au/publications/tandi/tandi426

Code and Visualisation

Pre-visualisation: Preparing the dataset

# Load the packages
library(readr)
library(ggplot2)
library(dplyr)
library(XML)
library(RCurl)

# Scrape the dataset from the website
aic <- "https://aic.gov.au/publications/tandi/tandi426"
htmlContent <- getURL(aic)
result <- readHTMLTable(htmlContent)
prisoner <- readHTMLTable(htmlContent, which = 4, stringsAsFactors = FALSE,
                         Header = TRUE)

# Read the dataset
print(prisoner)

##          V1                        V2                   V3
## 1 Age (yrs) Indigenous male prisoners Increase 2000–10 (%)
## 2      2000                      2010                 2000
## 3     45–49                        95                  363
## 4     50–54                        51                  179
## 5     55–59                        14                   65
## 6     60–64                         7                   27
## 7       65+                        10                   23
##                            V4                   V5   V6    V7
## 1 Indigenous female prisoners Increase 2000–10 (%) <NA>  <NA>
## 2                        2010                 <NA> <NA>  <NA>
## 3                       282.1                    6   36 500.0
## 4                       251.0                    3   12 300.0
## 5                       364.3                    0    0   0.0
## 6                       285.7                    1    3 200.0
## 7                       130.0                    0    0   0.0

#Subset the dataset
prisoner_male_2000 <- prisoner[(3:7),(1:2)]
prisoner_male_2010 <- prisoner[(3:7), c(1,3)]
prisoner_female_2000 <- prisoner[(3:7), c(1,5)]
prisoner_female_2010 <- prisoner[(3:7), c(1,6)]

#Add two variables 'Year' and 'Prisoner Gender' to the sub-datasets
prisoner_male_2000 <- cbind(prisoner_male_2000, strrep('2000',1), strrep("Male",1))
prisoner_male_2010 <- cbind(prisoner_male_2010, strrep('2010',1),strrep("Male",1))
prisoner_female_2000 <- cbind(prisoner_female_2000 , strrep('2000',1),strrep("Female",1))
prisoner_female_2010 <- cbind(prisoner_female_2010 , strrep('2010',1),strrep("Female",1))

#Rename the columns of the sub-datasets
colnames(prisoner_male_2000) <- c("Age", "Number","Year", "Prisoner Gender")
colnames(prisoner_male_2010) <- c("Age", "Number","Year", "Prisoner Gender")
colnames(prisoner_female_2000) <- c("Age", "Number","Year", "Prisoner Gender")
colnames(prisoner_female_2010) <- c("Age", "Number","Year", "Prisoner Gender")

#Unite the sub-datasets into a complete big one
prisoner_official <- bind_rows(prisoner_male_2000,prisoner_male_2010,prisoner_female_2000,prisoner_female_2010)

#Summary of the final dataset
str(prisoner_official)

## 'data.frame':    20 obs. of  4 variables:
##  $ Age            : chr  "45–49" "50–54" "55–59" "60–64" ...
##  $ Number         : chr  "95" "51" "14" "7" ...
##  $ Year           : chr  "2000" "2000" "2000" "2000" ...
##  $ Prisoner Gender: chr  "Male" "Male" "Male" "Male" ...

Visualisation

# Assign an object p to the ggplot, specifying the dataset and two main variables
p <- ggplot(data = prisoner_official, aes(x= Year, y = as.integer(Number), fill = Age))

# Create two layers for the plot, using geom_ bar 
p <- p + geom_bar(stat="identity", position ="dodge") +  theme_minimal(base_size = 16)

# Break the dataset into two subsets of "Female", and "Male"
p <- p +  facet_grid(~`Prisoner Gender`)

# Add title, make main and legend titles bold for better clarity
p <- p + ggtitle("Is The Number of Older Indigenous Australian\n Prisoners On The Rise?") +
  theme(plot.title = element_text(lineheight=.8, face="bold"), legend.title = element_text(lineheight=.8, face="bold"))

# Add label for x and y axises
p <- p + xlab("Year") + ylab ("Count")

# Visualise the dataset
p

Post Visualisation

The question: Is the number of older indigenous Australian prisoners on the rise?

The answer: Yes, it is, by almost all age categories and both genders from 2000 to 2010.

Other comments:

• The age category ‘45- 49’ was seen to have the most significant rise in both genders from 2000 to 2010.

• Australia had more male prisoners than female in 2000 and 2010.