Frank Hänel

library(tidyverse)            # For data manipulation and visualization

library(tidytuesdayR)        # For accessing TidyTuesday datasets
library(showtext)            # For working with fonts

library(glue)                # For text formatting
library(ggtext)              # For enhanced text formatting in ggplot2

# Load Data
tuesdata <- tidytuesdayR::tt_load(2023, week = 40)

grant_opportunity_details <- tuesdata$grant_opportunity_details

# Load Fonts and Define Colors
# Fonts are loaded and colors are defined for text and symbols in visualizations.
font_add_google("Poppins", "poppins")
font_add('fa-reg', 'c:/Users/info/OneDrive/Dokumente/fonts/Font Awesome 6 Free-Regular-400.otf')
font_add('fa-brands', 'c:/Users/info/OneDrive/Dokumente/fonts/Font Awesome 6 Brands-Regular-400.otf')
font_add('fa-solid', 'c:/Users/info/OneDrive/Dokumente/fonts/Font Awesome 6 Free-Solid-900.otf')
showtext_auto()
bg <- "#e9c46a" # background color
col1 <- "#264653" # for text
col2 <- "#2a9d8f" # for text
col3 <- "#e76f51"

# Define Symbols
# Symbols are defined using HTML-style code with appropriate colors and fonts.
twitter <- glue("<span style='color:{col3};font-family:fa-brands;'>&#xf099;</span>")
mastodon <- glue("<span style='color:{col3};font-family:fa-brands;'>&#xf4f6;</span>")
link <- glue("<span style='color:{col3};font-family:fa-solid;'>&#xf0c1;</span>")
data <- glue("<span style='color:{col3};font-family:fa-solid;'>&#xf1c0;</span>")
quote <- glue("<span style='color:{col3};font-family:fa-solid;'>&#xf10d;</span>")
space <- glue("<span style='color:{bg}'>-</span>")
space2 <- glue("<span style='color:{bg}'>--</span>") # This creates horizontal lines for formatting.

# Define Title
t <- "Distribution of Posted Grant Opportunities by Eligibility Criteria"
s <- "Analysis of Eligibility Criteria for Grant Opportunities"
cap <- glue("{twitter}{space2}@web_design_fh{space2} 
	{space2}{mastodon}{space2}@frankhaenel @fosstodon.org{space2}
	{space2}{link}{space}{space2}www.frankhaenel.de<br>
	{data}{space2}grants.gov")


eligible_groups <- c(
  "Individuals",
  "State Goverments",
  "County Goverments",
  "Independent School Districts",
  "City Or Township Governments",
  "Special District Governments",
  "Native American Tribal Governments",
  "Native American Tribal Governments",
  "Nonprofits",
  "Nonprofits",
  "Profit",
  "Small Businesses",
  "Private Institutions Of Higher Education",
  "Public Institutions Of Higher Education",
  "Public Indian Housing Authorities",
  "Others"
)

column_name <- c(
  "eligibility_individuals",
  "eligibility_state_governments",
  "eligibility_county_governments",
  "eligibility_independent_school_districts",
  "eligibility_city_or_township_governments",
  "eligibility_special_district_governments",
  "eligibility_native_american_tribal_governments_federally_recognized",
  "eligibility_native_american_tribal_organizations_other",
  "eligibility_nonprofits_501c3",
  "eligibility_nonprofits_non_501c3",
  "eligibility_for_profit",
  "eligibility_small_businesses",
  "eligibility_private_institutions_of_higher_education",
  "eligibility_public_institutions_of_higher_education",
  "eligibility_public_indian_housing_authorities",
  "eligibility_others"
)

# Create a dataframe with eligible_groups and column_name
df <- data.frame(eligible_groups, column_name)

# Calculate the count of grant opportunities for each eligible group
df %>%
  mutate(count = sapply(column_name, function(col) sum(grant_opportunity_details[[col]]))) %>%
  group_by(eligible_groups) %>%
  summarize(opportunities = sum(count))  %>%
  ggplot(aes(x = eligible_groups, y = opportunities)) +
  geom_col(color = col2, fill = col2) +
  geom_text(aes(x = eligible_groups, y = 10, label = eligible_groups),color = "white", hjust = 0) +
  labs(title=t, subtitle = s, caption = cap) +
  coord_flip() +
  theme_void() +
  theme(
                plot.margin = margin(30, 30, 30, 30),
                panel.background = element_rect(fill=bg, colour = bg),
                plot.background = element_rect(fill=bg),
                plot.title = element_markdown(size = 15, hjust = 0.5, lineheight = 1.3, family = "poppins", color = col1),
                plot.subtitle = element_markdown(size = 12, hjust = 0.5, lineheight = 1.3, family = "poppins", color = col1),
                plot.caption = element_markdown(size = 10, hjust = 0, lineheight = 1.3, family = "poppins", color = col1),
                axis.text.y = element_blank(),
                axis.text.x = element_markdown(size = 10, hjust = 0, lineheight = 1.3, family = "poppins", color = col1),
                panel.grid.major.x = element_line(color = col1, size = 0.5)
                )