MATH 2270 Data Visualisation - Assignment 3

Source URL

Data Source

Visualisation URL

Code

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.2     v dplyr   1.0.6
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(hrbrthemes)
## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
##       Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
##       if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow
library(ggplot2)
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
BySex <- read.csv("C:/Users/IBM/Desktop/DVAssignment3/BySex-1.csv",fileEncoding="UTF-8-BOM")
ByAgeGroup <- read.csv("C:/Users/IBM/Desktop/DVAssignment3/ByAgeGroup-2.csv",fileEncoding="UTF-8-BOM")
QuaterNew <- read.csv("C:/Users/IBM/Desktop/DVAssignment3/QuaterNew-1.csv",fileEncoding="UTF-8-BOM")

Gender wise family violence in Australia 2016 - 2020

Long_BySex<-BySex %>% 
  gather(`Male`, `Female`, key = "Sex" , value = "Number")

ggplot(data=Long_BySex, aes(x=year, y=Number, fill=Sex)) +
  geom_bar(stat="identity" , position=position_dodge())+
  scale_fill_viridis_d()

Age group wise family violence in Australia 2016 - 2020

ggplot(data=ByAgeGroup, aes(x=year, y=Total, fill=Age_group)) +
  geom_bar(stat="identity" , position=position_dodge())+
  scale_fill_viridis_d()

### Family violence in Australia 2016 - 2020

QuaterNew$Year<-as.numeric(QuaterNew$Year)
QuaterNew$Month_1<-as.numeric(QuaterNew$Month)
QuaterNew$Day<-as.numeric(QuaterNew$Day)


QuaterNew$Date<-as.Date(with(QuaterNew,paste(Year,Month,Day,sep="-")),"%Y-%m-%d")


p <- ggplot(QuaterNew, aes(x=Date, y=Incident_Count)) +
  geom_line( color="steelblue") + 
  geom_point() +
  xlab("") +
  theme_ipsum() +
  theme(axis.text.x=element_text(angle=60, hjust=1)) +
  scale_x_date(date_labels = "%b/%Y")+
  ylim(5000,8000)

p

Reference