R Notebook

Establish the location in which the csv and R file is located

Bring in packages

#setwd("~/Binghamton/harp325")
data <- read.csv("occupation_gender_race.csv", stringsAsFactors = F, fileEncoding="UTF-8-BOM")

library(dplyr)
library(ggplot2)
library(grafify)

Data that highlights job type, description of job, year, and the different races

data

Data filters by specific job types and the average percent of Women, Asian, and Black Individuals for those Jobs

data_summary <- data%>% 
  filter(job_type %in% c("computer_all", "professional", "total")) %>%
  group_by(job_type) %>% 
  summarise(Average_Women_Percent = mean(Women),
            Average_Asian_Percent = mean(Asian),
            Average_Black_Percent = mean(Black))
print(data_summary)

Bar Plot of Women Professions Over Years 2005-2020

install.packages("grafify")

Error in install.packages : Updating loaded packages

library(grafify)
library(ggplot2)


ggplot(data_summary, aes(x = job_type, y = Average_Women_Percent, fill = job_type))+
  geom_bar(stat = "Identity")+
  theme_minimal()+
  labs(title = "Women Professions Over Years 2005-2020")+
  scale_fill_grafify(palette = "bright")

Bar Plot of Asian Professions Over Years 2005-2020

install.packages("grafify")

Error in install.packages : Updating loaded packages

library(grafify)
library(ggplot2)

ggplot(data_summary, aes(x = job_type, y = Average_Asian_Percent, fill = job_type))+
  geom_bar(stat = "Identity")+
  theme_minimal()+
  labs(title = "Asian Professions Over Years 2005-2020")+
  scale_fill_grafify(palette = "muted")

Bar Plot of Black Professions Over Years 2005-2020

install.packages("grafify")

Error in install.packages : Updating loaded packages

library(grafify)
library(ggplot2)

ggplot(data_summary, aes(x = job_type, y = Average_Black_Percent, fill = job_type))+
  geom_bar(stat = "Identity")+
  theme_minimal()+
  labs(title = "Black Professions Over Years 2005-2020")+
  scale_fill_grafify(palette = "kelly")

Breaks down and summarizes each of the code chunks

#Summary Paragraphs
  #The codes above conduct an analysis on occupation data amongst assorted genders and races in three different job type categories; "computer_all", "professional", and "total." It calculates the average percentage of Women, Asians, and Black individuals in each job type and summarizes the findings in a summary table. The table includes columns such as "job_type," "Average_Women_Percent," "Average_Asian_Percent," and "Average_Black_Percent." The bar charts include essentially the same information as the summary table but it organizes it into a visual code by using the 'graftify' package and 'ggplot2' package to visualize average percentages of Women, Asians, and Black individuals across assorted job types within the time-span of 16 year, or from 2005-2020. 
  #The bar charts provide a visual representation of the demographic distribution within the selected job type categories over the years 2005-2020. Each chart is labeled with a corresponded title, such as "Women Professions Over Years 2005-2020" which has women in professional jobs as the highest job type category, "Asian Professions Over Years 2005-2020" which has Asians in computer_all jobs as the highest job type category, and and "Black Professions Over Years 2005-2020" which has the total job types as the highest job type category. I chose different color palettes for each of the three bar charts, such as 'bright', 'muted', and 'kelly' to enhance the visual and legible appeal in addition to providing differentiation between the job types for each of the three gender/race categories. Overall this analysis and visualization gives insights into the diversity trends within the specified job categories over the given time period.