From 0d8f9937bce38678f92677e92f363e8e55c1e4f7 Mon Sep 17 00:00:00 2001
From: Andrew Vancil <andrew.vancil@cchmc.org>
Date: Thu, 6 Jan 2022 10:40:12 -0500
Subject: [PATCH] don't need this on master

---
 weekly_data_report.R | 128 -------------------------------------------
 1 file changed, 128 deletions(-)
 delete mode 100644 weekly_data_report.R

diff --git a/weekly_data_report.R b/weekly_data_report.R
deleted file mode 100644
index 7d3459f..0000000
--- a/weekly_data_report.R
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/usr/local/bin/Rscript
-
-setwd('/tmp')
-
-suppressPackageStartupMessages(library(argparser))
-p <- arg_parser('create JFS Neighborhood Report')
-p <- add_argument(p,'file_name',help='name of geocoded csv file')
-args <- parse_args(p)
-
-# args <- list()
-# args$file_name <- "simulated_jfs_data_geocoded.csv"
-
-# INTAKE_ID
-# SCREENING_DECISION = SCREENED OUT or SCREENED IN
-# ALLEGATION_ADDRESS, formatted in a string without punctuation, it includes city, state, and zip code
-
-message("\nNeighborhood results with < 5 instances have been censored for privacy purposes\n")
-
-suppressPackageStartupMessages(library(readr))
-suppressPackageStartupMessages(library(tidyverse))
-suppressPackageStartupMessages(library(sf))
-suppressPackageStartupMessages(library(knitr))
-
-d <- read_csv(args$file_name,
-              col_types = cols(INTAKE_ID = col_character(),
-                               SCREENING_DECISION = col_character(),
-                               DECISION_DATE = col_character(),
-                               PERSON_ID = col_character(),
-                               RACE = col_character(),
-                               ADDRESS_START = col_date(),
-                               MANDATED_REPORTER = col_character(),
-                               #  REPORTER_PERSON_ID = col_character(),
-                               address_type = col_character(),
-                               address = col_character(),
-                               bad_address = col_logical(),
-                               PO = col_logical(),
-                               lat = col_double(),
-                               lon = col_double(),
-                               score = col_double(),
-                               precision = col_character(),
-                               precise_geocode = col_logical(),
-                               fips_tract_id = col_character(),
-                               fraction_assisted_income = col_double(),
-                               fraction_high_school_edu = col_double(),
-                               median_income = col_double(),
-                               fraction_no_health_ins = col_double(),
-                               fraction_poverty = col_double(),
-                               fraction_vacant_housing = col_double(),
-                               dep_index = col_double()
-              ))
-
-###
-d_test <- read.csv('test/simulated_jfs_data_geocoded.csv')
-d <- d_test
-###
-d <- dplyr::mutate(d, DECISION_DATE = dht::check_dates(DECISION_DATE))
-
-# rmarkdown::render(input = '/app/aggregate_data_report.rmd',
-#                   params = list(d = d),
-#                   envir = new.env())
-
-
-tract_to_neighborhood <- readRDS('/app/tract_to_neighborhood.rds')
-neighborhood_shp <- readRDS('/app/ham_neighborhoods_dep_index_shp.rds')
-
-# Overall Summary
-date_min <- min(d$DECISION_DATE)
-date_max <- max(d$DECISION_DATE)
-
-# consider 'SCREENED IN AR' same as 'SCREENED IN'
-for (i in 1:nrow(d)) {
-  if (d$SCREENING_DECISION[i] == 'SCREENED IN AR') {
-    d$SCREENING_DECISION[i] <- 'SCREENED IN'
-  }
-}
-
-d_missing_alleg_add <- d %>%
-  filter(address_type == 'ALLEGATION_ADDRESS',
-         is.na(address)) %>%
-  select(PERSON_ID:address_type)
-
-d_fill_in_address <- d %>%
-  filter(PERSON_ID %in% d_missing_alleg_add$PERSON_ID,
-         address_type == 'CHILD_ADDRESS') %>%
-  group_by(PERSON_ID) %>%
-  arrange(desc(ADDRESS_START)) %>%
-  slice(1) %>%
-  select(INTAKE_ID, address:dep_index)
-
-d_missing_alleg_add <- left_join(d_missing_alleg_add, d_fill_in_address, by = 'PERSON_ID')
-
-d <- d %>%
-  filter(address_type == 'ALLEGATION_ADDRESS' & !is.na(address) |
-           address_type == 'CHILD_ADDRESS') %>%
-  bind_rows(d_missing_alleg_add) %>%
-  filter(address_type == 'ALLEGATION_ADDRESS')
-
-### Geocoding Summary
-d <- filter(d, !duplicated(PERSON_ID)) %>%
-  select( -ADDRESS_START, -address_type) %>%
-  mutate(week = lubridate::week(DECISION_DATE))
-
-### Weekly Counts
-d_neigh <- d  %>%
-  mutate(fips_tract_id = as.character(fips_tract_id)) %>%
-  filter(!is.na(lat)) %>%
-  left_join(tract_to_neighborhood, by='fips_tract_id') %>%
-  filter(!is.na(DECISION_DATE))
-
-screen_neighborhood <- d_neigh %>%
-  group_by(neighborhood, week) %>%
-  summarize(n_screened_in = sum(SCREENING_DECISION == 'SCREENED IN',na.rm = TRUE),
-            n_calls = n(), .groups = "drop")  %>%
-  mutate(screen_in_rate = round(n_screened_in/n_calls,2)) %>%
-  mutate_at(vars(n_screened_in, n_calls),
-            ~ifelse(.x < 5, NA, .x))
-
-##
-
-d_csv <- screen_neighborhood %>%
-  select(Neighborhood = neighborhood,
-         `Number of Calls` = n_calls,
-         `Number of Calls Screened In` = n_screened_in,
-         `Screen-In Rate` = screen_in_rate)
-
-path <- "/tmp/"
-write.csv(d_csv, paste(path,"weekly_report.csv", sep = ''))
-