Click the Original, Code and Reconstruction tabs to read about the issues and how they were fixed.

Original


Source: Florida Department of Law Enforcement.


Objective

The objective of this visualisation is to deceive the audience into thinking that the number of murders have declined since Florida enacted it’s ‘Stand Your Ground’ law. The target audience would consist of American gun enthusiasts and the general Floridian community.

The visualisation chosen had the following three main issues:

  • Inverted y axis - This was a deceptive technique used to fool the audience into thinking there was a drop in gun related deaths.

  • The author fails to acknowledge the lowest point in the graph.

  • There are no clear labels for the x and y axis

Reference

Code

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

#load original data set
og_data <- read_csv("E:/RMIT/florida_murder_firearms.csv", 
    col_types = cols(Year = col_integer(), 
        `Murder Rate per 100,000` = col_double(), 
        `Total by Firearm` = col_integer(), 
        `Total Handgun` = col_integer(), 
        `Total Other Firearm` = col_integer(), 
        `Total Percent by Firearms` = col_double(), 
        `Percent by Handguns` = col_double(), 
        `Percent by Other Firearms` = col_double(), 
        `Total by Firearm % change` = col_double(), 
        `Population % Change` = col_double()))

# Get plot data from original source.
p_data <- og_data %>% select(Year, `Total by Firearm`)

#filter rows to only include years from 1990 - 2012
p_data <- p_data[-c(43:48),]
p_data <- p_data[-c(1:19),]

#Plot the data
reconstrcuted_plot <- ggplot(p_data, aes( x = Year, y = `Total by Firearm`))+ 
geom_area(fill="lightblue") + 

#show values for specific points on the graph 
geom_text_repel(min.segment.length = 0, data=subset(p_data, Year == 2005),aes(Year, `Total by Firearm`, label = `Total by Firearm`))+
  
geom_text_repel(min.segment.length = 0, data=subset(p_data, Year == 2007),aes(Year, `Total by Firearm`, label = `Total by Firearm`))+
  
geom_text_repel(min.segment.length = 0, data=subset(p_data, Year == 1990),aes(Year, `Total by Firearm`, label = `Total by Firearm`))+
  
geom_text_repel(min.segment.length = 0, data=subset(p_data, Year == 2012),aes(Year, `Total by Firearm`, label = `Total by Firearm`))+
  
geom_text_repel(min.segment.length = 0, data=subset(p_data, Year == 1999),aes(Year, `Total by Firearm`, label = `Total by Firearm`, hjust = 1))+
geom_line()+
geom_point()+

#add annotaion to specific points on the graph
annotate("label", x = 2005, y = 521, label = "2005\nFlorida enacted its\n'Stand Your Ground' law", vjust = 1.2, size = 3)+
annotate("label", x = 1999, y = 460, label = "Lowest murder count \n for 23 years", vjust = 1, hjust = 1, size = 3)+

#set y axis limits 
coord_cartesian(ylim=c(400,900))+
  
# set title and label x and y axis
theme_minimal() +
theme(axis.text.x=element_text(angle=45,hjust=1)) +
labs(title = "Total number of murders in Florida casued by firearms",
y = "Number of murders",
x = "Year")

Data Reference

Reconstruction

The following plot fixes the main issues in the original.

conclusion

  • The plunge in murders from 1990 to 1999 can be attributed to “increased imprisonment, changes in the market for crack cocaine, the aging of the population, tougher gun control laws, the strong economy and increases in the number of police.”(Levitt, 2004)

  • Since 2005, after Florida introduced the ‘Stand your ground’ law, we can see a sharp uptick in murders and it has stayed relatively high ever since.

  • In 2007, just two years after the ‘Stand you ground’ law was introduced, Florida experienced it’s second deadliest year shown in the graph.

Reference