Original


Source: Kajstura and Sawyer (March 1, 2023) Nearly 10% of confined girls are held for status offenses, prisonpolicy website, accessed 1 May 2023. https://www.prisonpolicy.org/reports/pie2023women.html


Objective

Objective of the original data: Show an overview of the distribution of confined girls locked in youth facilities in the United States in 2023 to show that 10% of confined girls are held for status offenses such as running away, truancy, and incorrigibility and identify areas where reform is needed and to make more informed decisions about how to allocate resources within the criminal justice system.

Targetted audience: policymakers, researchers, journalists, the general public who are interested in criminal justice and the rates of incarceration for different types of crimes.

The visualisation chosen had the following three main issues:

1- The topic has so many issues, it is impossible to determine the region and time period depicted in this visual. Due to the lack of specific details, each person may interpret the visual differently.”

2- In the visual, the subcategories for “Drug”, “Person”, “Property” and “public order” are included as part of the categories. This suggests that these subcategories are occupying a certain portion of the categories, which is not accurate and misleading.

3- In the visual, we observe that the numerical values of certain categories are not equal to the sum of their respective subcategories, indicating a discrepancy that makes the visual unreliable. Also, To improve the visual, it is more effective to display the percentage of each category, since percentages are being used in the visual text(near 10%) and the explaination in the topic on the website, not the number.

Reference

Kajstura and Sawyer (March 1, 2023) Nearly 10% of confined girls are held for status offenses, prisonpolicy website, accessed 1 May 2023. https://www.prisonpolicy.org/reports/pie2023women.html*

Code

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

library(ggplot2)
data <- read.csv("YouthCrimeAmerica.CSV")

data$Population <- data$Population /100

data$Subcategory <- factor(data$Subcategory, levels = c("Trafficking","Other drug","Homicide","Robbery","Simple assault","Aggravated assault","Sexual assault","Other person","Burglary","Theft","Auto theft","Arson","Other property","Weapons","Other public order","Status", "Technical violations"))

p <-ggplot(data, aes(x = Category, y = Population, fill = Subcategory)) +
  geom_bar(position = "stack", stat = "identity") +
  scale_fill_manual(values = c("#CC6600", "#FFB266", "#000033", "#000066", "#0000CC", "#3333FF", "#66B2FF", "#CCE5FF", "#660000", "#CC0000", "#FF6666", "#FFCCCC", "#FFCCCC", "#4C0099", "#9933FF", "#404040", "#A0A0A0")) +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(title = "Distribution of confined girls locked in youth facilities in the U.S. in 2023", x = "Crimes", y = "Percentage of the population", fill = "Crime Subcategories")

Data Reference I created my own dataset from the original visual since I couldn’t find the data from web, I guess it’s because it is kind of sensitive data.

Kajstura and Sawyer (March 1, 2023) Nearly 10% of confined girls are held for status offenses, prisonpolicy website, accessed 1 May 2023. https://www.prisonpolicy.org/reports/pie2023women.html

Reconstruction

The following plot fixes the main issues in the original.