Assessment declaration checklist

Please carefully read the statements below and check each box if you agree with the declaration. If you do not check all boxes, your assignment will not be marked. If you make a false declaration on any of these points, you may be investigated for academic misconduct. Students found to have breached academic integrity may receive official warnings and/or serious academic penalties. Please read more about academic integrity here. If you are unsure about any of these points or feel your assessment might breach academic integrity, please contact your course coordinator for support. It is important that you DO NOT submit any assessment until you can complete the declaration truthfully.

By checking the boxes below, I declare the following:

I understand that:

I agree and acknowledge that:

Deconstruct

Original

The original data visualisation selected for the assignment was as follows:


Source: RMIT Centre for Urban Research.


Objective and Audience

The objective and audience of the original data visualisation chosen can be summarised as follows:

Objective To visually depict the percentage of people in different Australian cities that work locally, or in the same area as where they live. The disparity in local employment between metropolitan and regional locations is depicted in the chart, which can help guide conversations about urban planning, commute habits, and the need for better local job availability or transit infrastructure.

Audience The general public,urban commuters and city planners who are interested in issues related to urban growth and transportation efficiency, are the intended audience.

Improvements

The original data visualisation chosen could be improved in the three following ways:

  • Added data labels at ends of each bar showing the exact percentage(e.g.,“67.2%”) which enhances the readability of the graph.
  • The axes of the graph has been flip to make labeling the cities on y axis more clear compared to the original graph.
  • The cities in the graph has been sorted according to the percentage of people working locally. This shows the massive difference between capital and regional cities.

Reconstruct

Code

The following code was used to improve the original.

library(ggplot2)
library(dplyr)
library(scales)

df<-read.csv("data-uR24z.csv")

df$Percentage.of.people.working.locally<-round(
  df$Percentage.of.people.working.locally,1)

capitalCities<-c('Sydney','Melbourne','Brisbane','Perth','Adelaide','Canberra')

df[,'City type'] <- df$City %in% capitalCities
df<-df[order(df$Percentage.of.people.working.locally),]

p1<-df%>%arrange(Percentage.of.people.working.locally) %>%
  mutate(name=factor(City, levels=City)) %>%
  ggplot(aes(Percentage.of.people.working.locally,name,fill = `City type`))+
  geom_bar(stat ="identity")+
  geom_text(aes(label = percent(df$Percentage.of.people.working.locally/100)),
            hjust = 1.1)

p1<-p1+scale_fill_brewer(palette = "Set2", labels=c('Regional','Capital'))+
  labs(title = 'Percentage of people who work locally in Australia',
       x = 'Percentage of people working locally(%)',
       y='Cities')+ 
  scale_y_discrete(expand = expansion(mult = c(0, 0)))+
  theme(axis.text.y = element_text(color = "black",size = 12,margin = margin(r=-10)),
        axis.ticks = element_blank(),
        title = element_text(size = 14),
        panel.spacing.y = element_blank(),
        panel.grid.major.x = element_line(color = 'grey50'),
        panel.background = element_blank(),
        aspect.ratio = 10/13)

Reconstruction

The following plot improves the original data visualisation in the three ways previously explained.

References

The reference to the original data visualisation choose, the data source(s) used for the reconstruction and any other sources used for this assignment are as follows: