Weekly Money Needed

# Load necessary packages
library(tidyverse)
library(plotly)
library(readxl)

# Load data
initial_data2019_2022 <- read_excel("Meal Gap Data exl.xlsx")

# Filter by state and county (2019-2022)
filtereddata2019_2022 <- dplyr::filter(initial_data2019_2022, `Member 1 ID` == 324 | `County, State` == "Elbert County, Georgia")

# Remove $ from data
# Turn data into a number, rename columns, keep desired columns
data2019_2022 <- filtereddata2019_2022 %>%
  mutate(`Weekly Money Needed` = str_remove_all(`Weighted weekly $ needed by FI`, "\\$")) %>%
  mutate(`Weekly $ Needed` = as.numeric(`Weekly Money Needed`), County = str_remove_all(`County, State`, ", Georgia"), Year, .keep = "none")

Research methodology changed in 2020, so data from 2018 onward should not be compared to data before 2018.