Visual Analytics Tasks 2022

Visual Analytics
Student Name:
Instructor Name:
Course Number:
24th September 2019

Task 1:
In this task, we use the data contained in the file ‘aushealth.csv’ to create a visualization that:
i) A visualization graph/chart that is able to show how the proportion of the population
with each health condition has changed over time from 2001 to 2018.
ii) A visualization graph/chart that is able to highlight the health condition that has
increased the most and the health condition that has deceased the most in the data set.
We begin with a visualization graph/chart that is able to show how the proportion of the
population with each health condition has changed over time from 2001 to 2018.
The code that was used to generate the graph is given below;
The graph output is given in figure 1 below. From the plot, it can
be observed that in 2001 back problems was the major health
condition affecting the population and in 2018, the major health
condition was the mental and behavioural problems. Arthritis
was the second highest proportion of health condition
experienced in the year 2001. In 2018, the second highest health condition was the back
raw_data<-
read.csv("C:\\Users\\
310187796\\
Downloads\\
aushealth.csv")
attach(raw_data)
str(raw_data)
library(ggplot2)
raw_data[,'year']<-
factor(raw_data[,'year'
])
ggplot(raw_data,
aes(fill=health_cond,
y=prop_population,
x=year)) +
geom_bar(position="
dodge",
stat="identity")

problems followed by arthritis. Kidney disease was very low in 2001 but increased significantly
in 2018.
Figure 1: Trend visualizations of the health conditions over the years
In the second part of task 1, we present a visualization graph/chart that is able to highlight the
health condition that has increased the most and the health condition that has deceased the most
in the data set.
library(tidyverse)
wide_data = raw_data
%>%
spread(year,
prop_population)
str(wide_data)
colnames(wide_data)<
-
c("Health_condition","Y
2001","Y2018")
wide_data_new<-
wide_data %>%
mutate(Change =
((Y2018-Y2001)/Y2001)
*100)
str(wide_data_new)
dim(wide_data_new)

Figure 2: Visualizations on the disease changes from 2001 and 2018
From the above plot (figure 2), it can be seen that back problems is the health condition that has
decreased the most and kidney disease is the health condition that has increased the most in the
data set.
Task 2:
In this task, we use the data contained in the file ‘bigstocks.csv’ to create a visualization that:
i) A visualization graph/chart that is able to compare the shares performance of the big
four companies (Apple, Amazon, Google, and Facebook) over time based the closing
price.
ii) A visualization graph/chart that is able to show the distribution of share volume
traded between 2013 and 2015 for the big four companies that highlights which
company has the highest median value.