TT - Childcare Costs

library(tidyverse)

childcare_costs <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-05-09/childcare_costs.csv')
counties <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-05-09/counties.csv')

colnames(childcare_costs)

data_ny <- childcare_costs %>%
  filter(county_fips_code > 36000 & county_fips_code < 37000)

head(data_ny, 25)

data_ny %>%
  ggplot(aes(x = study_year, y = unr_16)) +
  geom_line() +
  labs(x = "", y = "") +
  facet_wrap(~ as.factor(county_fips_code)) +
  theme_minimal() +
  theme(legend.position = "none") +
  ggtitle("Unemployment Rate (2008 - 2018)") +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank())

head(counties)

# Joining the ny_data dataframe with the counties dataframe by county_fips_code and removing "County" from every county_name
data_ny <- data_ny %>%
  inner_join(counties, by = 'county_fips_code') 

data_ny <- data_ny %>%
			mutate(county_name = gsub(" County", "", county_name, fixed = TRUE))

head(data_ny, 25)

data_ny %>%
  ggplot(aes(x = study_year, y = unr_16)) +
  geom_line() +
  labs(x = "Year", y = "") +
  facet_wrap(~ as.factor(county_name)) +
  theme_minimal() +
  theme(legend.position = "none") +
  ggtitle("Unemployment Rate (2008 - 2018)") +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank())

data_nyc <- data_ny %>%
			filter(county_name %in% c('Bronx', 'New York', 'Kings', 'Richmond', 'Queens'))

data_nyc <- data_nyc %>%
  mutate(county_name = case_when(
    county_fips_code == 36047 ~ "Brooklyn",
    county_fips_code == 36061 ~ "Manhattan",
    county_fips_code == 36085 ~ "Staten Island",
    TRUE ~ county_name
  ))

head(data_nyc)

nyc_2018 <- data_nyc %>%
				filter(study_year == 2018)

head(nyc_2018)

library(viridis)