Data Visualization and Infographic Design Elements

Exploring Los Angeles County crimes data from 2020-2024

Infographic image

Figure 1. Dangers of Los Angeles. Three main takeaways: Latinos are the top victims of crime reports, bodily forces are the top recorded “weapons” used, and hand guns are the most common firearms to encounter.

Background

This infographic on Los Angeles city crimes from 2020-2024 was created using R and Procreate. This has been a 9 week-long project that has helped me develop more practice with data visualization using R. Three main elements were considered for this project:

• theme
• contextualizing my data
• text adjustments within R

While there are many other elements that went into this project, those three main ideas has really fueled the project as a whole.

Theme

I explored on Los Angeles crime report data. This data interest me due to the darker theme surrounding this dataset. The dataset ranges from relatively benign crimes such as robbery to darker situations such as homicides. Knowing that this dataset consists of darker themes, I wanted to reflect on that accordingly. Knowing that some of the subjects are on the more delicate side, I instead wanted to focus on the general idea of the dataset: what can we take away from it? And from the data exploration I conducted, I have been most intrigued by who the victims are and will most likely deal with in the event of a crime.

Contextualizing my Data

As mentioned previously,from the data exploration I conducted, I have been most intrigued by who the victims are and will most likely deal with in the event of a crime. To put the project into context, it was somewhat simple, as I was largely interested in counts. My main goal was to find the count of each variable, and find what are the most common crimes, victims, locations, ect. And after compiling several graphs, I decided to focus on the more general ideas such as top weapons, firearms, and victims.

Text Adjustments

The most time-consuming yet eye-opening part of this project is adjusting and creating this graph in R. Not only that, but I have also learned the importance of reproducible data and the standards of tidy data. One element from ggplot2 that I have improved on significantly is learning how theme() and labs work well together. I used to prefer python more when it came to data visualization, but after taking this course, I realized that I was wrong and R is actually a great place to make graphs!

Other key data visualization aspects

• graphic form: I have explored other graphic forms using this dataset, such as a radar plot! however I realized that line charts are slightly better at visualizing trends when it comes to interpretation (and time).

• typography: I had fun figuring out the typefont and sizes I would like for my infographic. It took multiple trial and errors to figure out the right font and size.

• general design: I realized that my idea of using a detective board as my background could be considered a bit too busy or distracting of my data. To mitigate that, I darkened my plot, and highlighted my main plots using lighting.

• color: I created a color palette when creating my infographic. However due to some time constraints, my graph is not exactly ready for printing, as there are still some more cohesive color schemes to consider (and color blind friendly options too for my older 2 brothers). However I believe that by highlighting the graphs and darkening the rest of the background, it helps with the colors being a bit more cojesive. ## Process

For the libraries I used for my infogrpahic:

Show the code

# setting my chunk options
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)

#list of packages
library(tidyverse) #main use for data wrangling
library(janitor) #helps with clean names for my variables
library(lubridate) #need this for my time data. Mostly for wrangling time data
library(stringr) #this helps with dealing with strings and characters in my data
library(showtext) #choosing fonts from google fonts
library(scales) #for labeling my axis and texts

font_add_google(name = "Special Elite") #for the typewriter font
font_add_google(name = "Nosifer") #for the bloody font

showtext_auto() #to render text

Show the code

#reading in my data from my local computer
la_crimes <- read_csv("C:/Users/rosem/Documents/MEDS/Courses/EDS-240/HW/Juarez-eds240-HW4/data/Crime_Data_from_2020_to_Present_20240131.csv") %>% 
  clean_names()

Data Wrangling and Processing

Show the code

#==============================================================
#                 data wrangling
# =============================================================

#will create a new column that describes the main 5 race categories. for reference:
#c(B - Black C - Chinese D - Cambodian F - Filipino G - Guamanian H - Hispanic/Latin/Mexican I - American Indian/Alaskan Native J - Japanese K - Korean L - Laotian O - Other P - Pacific Islander S - Samoan U - Hawaiian V - Vietnamese W - White X - Unknown Z - Asian Indian)

asian_countries <-  c('A', 'C', 'D', 'F',"L", 'J', 'K', 'V', 'Z')


# Define a named vector mapping the current categories to their full names
race_names <- c(
  "A" = "Asian",
  "B" = "Black",
  "W" = "White",
  "H" = "Hispanic",
  "I" = "Native American/Alaska",
  "P" = "pacific Islander"
)

#------------------------
#   regualar wrangling
# -----------------------

#creating a cleaned-up version of la_crimes. keeping the name so that i have less names to remember

la_crimes <- la_crimes %>% 
  #removing zeros in the `vict_age` column, as 0, -1, and -2 indicated that no age was recorded.
  filter(vict_age > 0 ) %>% 
  #I want to incllude all values for victim sex, however to first test out my plots, i want to view just male and female for simplicity
  #
  #
  filter(vict_sex %in% c('M', 'F')) %>% 
  #Asian countries will be agreggated to one.
  


#-----------------------------
#   asian country aggregation
#-----------------------------

  #Asian countries will be aggregated to one. using case_when as it will help with selecting and reassigning asian countries to the letter "A" if the list of values i provided above are within asian_countries
  mutate(race_category = case_when(
    vict_descent %in% asian_countries ~ "A",
    TRUE ~ vict_descent  # Keep non-Asian races unchanged, as true will allow for the row that do not have an asian country to remain the same within the new "race_category" column.
  )) %>% 

  #filter for the top 6 race categories
  filter(race_category %in% c('B', 'H', 'W', 'I', 'P', 'A')) %>% 

# Rename the categories in the race column
  mutate(race = case_match(race_category, "B" ~ "Black",
                           "H" ~ "Hispanic",
                           "W"~ "White",
                           "I" ~ "Native American/Alaska",
                           "P" ~ "Pacific Islanders",
                           "A" ~ "asian"
                           ))


#====================================
#     new data frames for plotting
#====================================

#creating crime description
crime_desc <- la_crimes %>% 
  group_by(crm_cd_desc) %>%
  summarise(count = n()) %>%
  arrange((desc(count)), .by_group = TRUE) %>% 
  ungroup()


#crime description by sex
crime_desc_sex <- la_crimes %>% 
  group_by(crm_cd_desc, vict_sex) %>%
  summarise(count = n()) %>% 
  slice_max(order_by = count, n = 10) %>% 
  group_by(crm_cd_desc) %>% 
  ungroup()

#creating weapon description
weap_desc <- la_crimes %>% 
  group_by(weapon_desc) %>%
  summarise(count = n()) %>%
  arrange((desc(count)), .by_group = TRUE) %>% 
  na.omit() %>% 

#filtering those that are unknown or not physical

  filter(!grepl("UNKNOWN", weapon_desc)) %>% 
  filter(!grepl("OTHER", weapon_desc)) %>% 
  filter(!grepl("VERBAL", weapon_desc))

#of those weapons, which ones are guns?
weap_gun <- weap_desc %>% 
  filter(grepl("GUN", weapon_desc) | grepl("PISTOL", weapon_desc) | grepl("RIFLE", weapon_desc))

plotting the plots

Show the code

#creating the top weapons used against a victim
top_5_weap <- weap_desc %>% 
  slice(1:5)%>% 
  ggplot( aes(x = fct_reorder(weapon_desc, count), y = count)) +
  geom_col(fill = "black") +
  geom_text(aes(label = scales::comma(count)),family = "Special Elite", hjust = -.2, color = "red4", size = 9) +
  coord_flip() +
  theme_classic()+
  labs(title = "71% of All Crime Reports Site Bodily Force as the Most Common Weapon "
       
       ) +
  scale_y_continuous(limits = c(0, 200000)) +
  theme(axis.title.y = element_blank(),
        axis.line.y = element_blank(), 
        axis.ticks.y = element_blank(),
        axis.title.x = element_blank(),
        axis.line.x = element_blank(), 
        axis.ticks.x = element_blank(),
        axis.text.x = element_blank(),
        axis.text.y = element_text(size = 19, family = "Special Elite", color = "red4"),
        plot.title = element_text(size = 36, family = "Special Elite", face = "bold", color = "red4", hjust = 1.1),
        plot.background = element_blank(),
        panel.background = element_blank(),
        
        )

top_5_weap

Show the code

#I want to record the top 5 crime reports involving guns
gun_graph <- weap_gun %>% 
  slice(1:5)%>% 
  ggplot( aes(x = fct_reorder(weapon_desc, count), y = count)) +
  geom_col(fill = "black") +
  geom_text(aes(label = scales::comma(count)),family = "Special Elite", hjust = -.2, color = "red4", size = 9) +
  coord_flip() +
  theme_classic()+
  labs(title = "Handguns Account for 62% of Firearm Reported in Los Angeles"
       ) +
  scale_y_continuous(limits = c(0, 20000)) +
  theme(axis.title.y = element_blank(),
        axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        axis.line.y = element_blank(), 
        axis.line.x = element_blank(),
        axis.ticks.y = element_blank(),
        axis.ticks.x = element_blank(),
        plot.title = element_text(family = "Special Elite",
                                  size = 35, 
                                  color = "red4",
                                  hjust = 1.9),
        axis.text = element_text(family = "Special Elite", 
                                 size = 19, 
                                 color = "red4"),
        axis.title = element_text(family = "Special Elite"),
        plot.margin = margin(1,.5,.5,.5, "cm"),
        plot.background = element_blank(),
        panel.background = element_blank()
        
        )
gun_graph

Show the code

# I am reporting on the top 5 race victims in the crime data
race_pie <- la_crimes %>%
  count(race) %>%
  ggplot() +
  ggforce::geom_arc_bar(
    aes(x0 = 0, y0 = 0, r0 = 0.8, r = 1, amount = n, fill = race),
   stat = "pie") +
  theme_void()+

  scale_fill_manual(values= c("#cad2c5", "#84a98c", "#52796f", "#354f52", "#0c1113", "#2f3e46")) + 
  labs(title = "Careful if you are Latino:",
       subtitle = "you are the top victim of crime reports",
       fill = "") +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    panel.grid.major.y = element_blank(),
    axis.line = element_blank(),
    plot.background = element_blank(),
    legend.text = element_text(size=25, family = "Special Elite", color = "red4"),
    legend.position = c(0.55,0.52),
    plot.title = element_text(size = 40, family = "Special Elite", color = "red4"),
    plot.subtitle = element_text(size = 25, family = "Special Elite", color = "red4")
    
    )

race_pie