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: Howmuch.net(2019).


Objective and Audience

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

Objective

The visualisation informs the audience about how much money the US government allocate the federal expenditure on children in detail for each categories and each programs that are available in the categories.

Audience

The general public especially families with children who are interested in the monetary allocation of US government expense on children

Critique

The visualisation chosen had the following three main issues:

  • Visual bombardment

The visualisation has too much data included. This causes the viewers hardly see some of the categories and values that are included in the chart as the chart is too cramped. For example, the training category that is hardly to be seen in the chart due to too many data included.

  • The usage of pie chart

By using pie chart, area become the main element to compare each of the categories that are presented. However, it causes viewers hard to compare the area and value of each of the categories without seeing the label of it especially for the categories that have similar size to each other. For instance, the income security and nutrition security have very similar area compare to each other. Without the label it’s very hard to determine which of the category has higher value.

  • Colour usage

The visualisation uses many shades of green to differentiate categories. For example, medicaid, housing, social services, early education and care, and education, these five categories use different shades of green to differentiate them. This can be problematic especially if there’s no label in the pie chart since pie chart rely heavily on colour to differentiate categories and it’s also especially challenging for the red-green color blind viewers as it’s difficult for them to differentiate many shades of green.

Reconstruct

Code

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

library(ggplot2)
library(readxl)
library(magrittr)
library(dplyr)

# Importing data
expenditure <- read_excel("~/Data visualisation/Exepnditure.xlsx")

# Changing the data type 
expenditure$`Expenditure value` <- as.numeric(expenditure$`Expenditure value`)
expenditure$Category %>% unique()
##  [1] "Health"                            "Tax Reductions"                   
##  [3] "Refundable Portion of Tax Credits" "Nutrition"                        
##  [5] "Income security"                   "Education"                        
##  [7] "Early Education and care"          "Social services"                  
##  [9] "Housing"                           "Training"
expenditure $Category<- as.factor(expenditure$Category) %>% 
  factor(levels=c("Health","Tax Reductions","Refundable Portion of Tax Credits",
                  "Nutrition","Income security","Education","Early Education and care","Social services","Housing","Training"))

# Creating the plot
p <- ggplot(expenditure,aes(x=reorder(Program, `Expenditure value`),y=`Expenditure value`,fill=Category))+ geom_col(stat="identity")+
scale_fill_manual(values = c("#F3C300","#875692","#F38400","#A1CAF1","#882D17","#C2B280","#848482","#008856","#E68FAC","#0067A5"))+
facet_wrap(~Category,ncol = 1,nrow = 10,scales ="free_y")+
geom_text( aes( label =paste0( "$",`Expenditure value`,"B" )), position = position_dodge(width = 1),
           hjust = 0.065, size = 2.5, color = "black")+
labs(x= "Category", y="Cost of Expenditures",title="Federal Expenditure on Children 2018 ", subtitle = "For Each Categories' Programs")+
   theme(legend.position = "none")+coord_flip()

Reconstruction

The following plot fixes the main issues in the original.

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: