From R shiny app dashboards in 2021: The Ultimate Guide for Busy People at https://yakdata.com/ultimate-guide-r-shiny-app-dashboards-2021/

feather_example_local_caching.R

# From  R shiny app dashboards in 2021: The Ultimate Guide for Busy People at 
#    https://yakdata.com/ultimate-guide-r-shiny-app-dashboards-2021/
#
#    Example for chapter 4 
#    Here is an illustration of data access and caching with feather
#
#    Stephen McDaniel at YakData
#    Released under the MIT License, 2021

library(feather)
library(readr)
library(dplyr)

file_path <- "/Development/R-code-examples/Data_Analysis/"
setwd(file_path)
feather_file <- "sales_analyze3.feather"

# check the age of our data file for this dashboard
file_age_hours <- function(filepath) {
  if(!exists(filepath)) { 
    # if it doesn't exist, infinity
    return(Inf) 
  } else {
    file_modified <- file.info(feather_file)$mtime
    file_age_hours <- floor((as.numeric(Sys.time()) - as.numeric(file_modified))/(60*60)*10)/10
    return(file_age_hours)
  }
}

if (file_age_hours(feather_file) > 24) {
    # older than 24 hours, recreate cached data
    sales_analyze <- as_tibble(readRDS("sales_analyze_geo_elegant_tray2.RDS")) %>%
        select(
            Order_Date,
            Customer_Segment,
            State,
            Category,
            Subcategory,
            Item_Total,
            State_Name,
            lat_state,
            long_state
        ) %>%
        mutate(State_Abbrev = State,
               State = State_Name,
               metric = Item_Total) %>%
        select(-State_Name) %>%
        mutate(Order_Date = as.Date(Order_Date)) %>%
        mutate(Region = ifelse(
            State_Abbrev %in% c(
                "NJ", "MA", "PA", "MD", "DE", "CT", "NY", "NH", "ME", "VT", "DC", "RI"
            ),
            "Northeast",
            ifelse(
                State_Abbrev %in% c("FL", "AL", "TN", "MS", "SC", "GA", "VA", "NC", "WV", "KY", "LA"),
                "South",
                ifelse(
                    State_Abbrev %in% c("OH", "IA", "WI", "IL", "IN", "MI", "MN", "NE"),
                    "Midwest",
                    ifelse(
                        State_Abbrev %in% c("MO", "KS", "OK", "TX", "AR"),
                        "South Central",
                        ifelse(
                            State_Abbrev %in% c("WY", "ID", "AZ", "NM", "NV",  "CO", "UT", "ND", "SD", "MT"),
                            "Mountains",
                            ifelse(
                                State_Abbrev %in% c("CA", "AK", "WA", "HI", "OR"),
                                "Pacific",
                                "Other"
                            )
                        )
                    )
                )
            )
        )
      ) 

      # larger data frames can benefit from the feather format
      save_feather(sales_analyze, "sales_analyze3.feather")
      
      chart_data <- sales_analyze %>%
        group_by(Category, Subcategory, Customer_Segment, Region) %>%
        summarise(metric = sum(metric)) %>%
        ungroup()
      
      # smaller data frames have minimal benefit from feather format
      save_rds(chart_data, "chart_data.RDS")
        
} else {
    sales_analyze <- read_feather("sales_analyze3.feather")
    
    chart_data <- read_rds(chart_data, "chart_data.RDS")
}