# Install required packages
install.packages("tidyverse")
## Error in install.packages : Updating loaded packages
install.packages("sf")
## Error in install.packages : Updating loaded packages
install.packages('devtools')
## Error in install.packages : Updating loaded packages
devtools::install_github("UrbanInstitute/urbnmapr")
## Skipping install of 'urbnmapr' from a github remote, the SHA1 (ef9f4488) has not changed since last install.
## Use `force = TRUE` to force installation
rm(list=ls())
# Load libraries
library(tidyverse)
library(readr)
library(ggplot2)
library(sf)
library(urbnmapr)
library(dplyr)
library(scales)
# 1. Reading the data
college_data <- read_csv("EducationDataPortal_04.03.2024_College (1).csv")
## Rows: 582 Columns: 70
## ── Column specification ────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (48): inst_name, state_abbr, inst_control, institution_level, inst_status, degre...
## dbl (22): year, unitid, student_faculty_ratio, geo_longitude, geo_latitude, county_f...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
level_of_study <- read_csv("EducationDataPortal_04.03.2024_level_of_study.csv")
## Warning: One or more parsing issues, call `problems()` on your data frame for details, e.g.:
## dat <- vroom(...)
## problems(dat)
## Rows: 1269 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): inst_name, state_abbr, level_of_study, acttype
## dbl (7): year, unitid, credit_hours, contact_hours, est_fte, rep_fte, enrollment_fall
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Convert column names to lowercase
colnames(college_data) <- tolower(make.names(colnames(college_data)))
colnames(level_of_study) <- tolower(make.names(colnames(level_of_study)))
# Filter unique institutions in Georgia based on unitid
ga_unique_institutions <- college_data %>%
distinct(unitid, .keep_all = TRUE) %>% # Get unique institutions based on unitid
filter(state_abbr == "GA") # Filter for institutions in Georgia
ga_map <- urbnmapr::counties %>%
filter(state_name == "Georgia")
total_institutions <- nrow(ga_unique_institutions)
# 1. Geographical distribution of educational institutions:
# Create a data frame with required columns, round off longitude and latitude, and remove duplicates
map_data <- college_data %>%
mutate(geo_longitude = round(geo_longitude, 2),
geo_latitude = round(geo_latitude, 2)) %>%
distinct(unitid, state_abbr, geo_longitude, geo_latitude, .keep_all = TRUE) %>%
filter(!is.na(geo_longitude) & !is.na(geo_latitude))
# Get the map data for Georgia
ga_map <- urbnmapr::counties %>%
filter(state_name == "Georgia")
# Plot the map
ggplot() +
geom_polygon(data = ga_map, mapping = aes(x = long, y = lat, group = group), fill = "lightgray", color = "white", linewidth = 0.3) +
geom_point(data = map_data, aes(x = geo_longitude, y = geo_latitude), color = "blue", size = 2, alpha = 0.8) +
coord_map(projection = "albers", lat0 = 32, lat1 = 35) +
theme_minimal() +
labs(x = "Longitude",
y = "Latitude",
caption = "Data source: Education Data Portal") +
theme(axis.text = element_text(size = 12),
axis.title = element_text(size = 14, face = "bold"),
plot.caption = element_text(size = 10, face = "italic"),
plot.margin = unit(c(1, 1, 1, 1), "cm"),
plot.title.position = "plot") +
ggtitle("Geographical Distribution of Institutions in Georgia") +
theme(plot.title = element_text(size = 18, face = "bold", hjust = 0.5))
# 2. Institution Types and Control
institution_types_control <- ga_unique_institutions %>%
group_by(institution_level, inst_control) %>%
summarise(count = n(), .groups = 'drop')
print(institution_types_control)
## # A tibble: 8 × 3
## institution_level inst_control count
## <chr> <chr> <int>
## 1 At least two but less than four years Private for-profit 19
## 2 At least two but less than four years Private not-for-profit 4
## 3 At least two but less than four years Public 24
## 4 Four or more years Private for-profit 11
## 5 Four or more years Private not-for-profit 35
## 6 Four or more years Public 27
## 7 Less than two years (below associate's) Private for-profit 32
## 8 Missing/not reported Missing/not reported 4
total_count <- sum(institution_types_control$count)
print(total_count)
## [1] 156
ggplot(institution_types_control, aes(x = inst_control, y = count, fill = institution_level)) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(aes(label = count), position = position_dodge(width = 0.9), vjust = -0.5) +
labs(title = "Institution Types and Control in Georgia", x = "Institution Control", y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.text.y = element_text(size = 8))+
annotate("text", x = Inf, y = Inf, label = paste0("Total Count: ", total_count), hjust = 1.1, vjust = 1.1, size = 3)
# 3. Degree-Granting Institutions
degree_granting <- ga_unique_institutions %>%
count(degree_granting)
print(degree_granting)
## # A tibble: 3 × 2
## degree_granting n
## <chr> <int>
## 1 Missing/not reported 4
## 2 No 37
## 3 Yes 115
ggplot(degree_granting, aes(x = degree_granting, y = n, fill = degree_granting)) +
geom_bar(stat = "identity") +
geom_text(aes(label = n), vjust = -0.5) +
labs(title = "Degree-Granting Institutions in Georgia", x = "Degree-Granting Status", y = "Count") +
theme_minimal()
# 4. Special Focus Institutions: HBCUs
hbcus_count <- sum(ga_unique_institutions$hbcu == "Yes", na.rm = TRUE)
cat("Number of HBCUs in Georgia:", hbcus_count, "\n")
## Number of HBCUs in Georgia: 9
hbcu_data <- ga_unique_institutions %>%
group_by(hbcu) %>%
summarise(count = n(), .groups = 'drop') %>%
mutate(percentage = round(count / sum(count) * 100, 2))
ggplot(hbcu_data, aes(x = "", y = count, fill = hbcu)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start = 0) +
labs(title = "Historically Black Colleges and Universities (HBCUs) in Georgia") +
geom_text(aes(label = paste0(percentage, "%")), position = position_stack(vjust = 0.5)) +
theme_minimal() +
annotate("text", x = 1.5, y = sum(hbcu_data$count) / 2, label = paste0("Total: ", sum(hbcu_data$count)), color = "black", size = 4) +
annotate("text", x = 1, y = -1, label = paste("HBCUs:", hbcus_count), hjust = 0.2, vjust = -2.5)
# 5. Top 10 Counties by Number of Institutions
top_counties <- ga_unique_institutions %>%
group_by(county_name) %>%
summarise(count = n()) %>%
arrange(desc(count)) %>%
head(10)
#ggplot(top_counties, aes(x = reorder(county_name, count), y = count, fill = county_name)) +
ggplot(top_counties, aes(x = count, y = reorder(county_name, count), fill = county_name)) +
geom_bar(stat = "identity") +
labs(title = "Top 10 Counties by Number of Institutions", x = "County", y = "Number of Institutions") +
theme_minimal() +
geom_text(aes(label = count), hjust = -0.1, vjust = 0.5, size = 3)+
scale_fill_discrete(guide = "none")
# 6. Enrollment Trends
enrollment_trends <- level_of_study %>%
filter(state_abbr == "GA") %>%
group_by(year, level_of_study) %>%
summarise(total_enrollment = sum(enrollment_fall, na.rm = TRUE), .groups = 'drop')
print(enrollment_trends)
## # A tibble: 9 × 3
## year level_of_study total_enrollment
## <dbl> <chr> <dbl>
## 1 2019 First professional 0
## 2 2019 Graduate 88272
## 3 2019 Undergraduate 464991
## 4 2020 First professional 0
## 5 2020 Graduate 88270
## 6 2020 Undergraduate 464758
## 7 2021 First professional 0
## 8 2021 Graduate 88270
## 9 2021 Undergraduate 464256
ggplot(enrollment_trends, aes(x = year, y = total_enrollment, color = level_of_study, linetype = level_of_study)) +
geom_line(size = 1.2) +
geom_point(size = 3) +
#geom_text(data = subset(enrollment_trends, year == max(year)), aes(label = total_enrollment), nudge_y = 10000, size = 3) +
geom_text(aes(label = total_enrollment), vjust = -0.6 , size = 3) +
labs(title = "Enrollment Trends by Level of Study in Georgia",
x = "Year",
y = "Total Enrollment",
caption = "Data source: Education Data Portal") +
scale_color_manual(values = c("First professional" = "firebrick", "Graduate" = "forestgreen", "Undergraduate" = "steelblue")) +
theme_minimal() +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
axis.line = element_line(color = "black", size = 0.5),
axis.ticks = element_line(color = "black", size = 0.5)) +
scale_x_continuous(breaks = seq(2019, 2021, by = 1)) +
scale_y_continuous(labels = scales::comma)
