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monthly_data_report.R
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monthly_data_report.R
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# # # #test file
# d <- read_csv('test/sim_test_data_new_prepped_geocoder_3.3.0_score_threshold_0.5_census_block_group_0.6.0_2010.csv',
# col_types = cols(INTAKE_ID = col_character(),
# SCREENING_DECISION = col_character(),
# DECISION_DATE = col_date(format = "%m/%d/%y"),#format = "%m/%d/%Y"),
# BIRTH_DATE = col_date(format = "%m/%d/%y"),#format = "%m/%d/%Y"),
# PERSON_ID = col_character(),
# RACE = col_character(),
# ADDRESS_START = col_date(format = "%m/%d/%y"),
# REPORTER_PERSON_ID = col_character(),
# MANDATED_REPORTER = col_character(),
# address_type = col_character(),
# address = col_character(),
# bad_address = col_logical(),
# PO = col_logical(),
# matched_street = col_character(),
# matched_zip = col_character(),
# matched_city = col_character(),
# matched_state = col_character(),
# lat = col_double(),
# lon = col_double(),
# score = col_double(),
# precision = col_character(),
# geocode_result = col_character(),
# census_block_group_id_2010 = col_character(),
# census_tract_id_2010 = col_character()
# ))
library(tidyr, warn.conflicts = FALSE)
library(readr, warn.conflicts = FALSE)
library(dplyr, warn.conflicts = FALSE)
library(stringr, warn.conflicts = FALSE)
options(dplyr.summarise.inform = FALSE)
##special test dataset to check for multiple entries per week
# d <- read_csv('test/simulated_jfs_data_concentrated.csv')
# d$DECISION_DATE <- as.Date(d$DECISION_DATE, format = "%m/%d/%Y")
# d$BIRTH_DATE <- as.Date(d$BIRTH_DATE, format = "%m/%d/%Y")
##
# consider 'SCREENED IN AR' same as 'SCREENED IN'
d <- d |>
mutate(screened_in = SCREENING_DECISION %in% c("SCREENED IN", "SCREENED IN AR"))
#if two different addresses, keep allegation; if one address, keep whichever it is
d1 <- d |>
group_by(INTAKE_ID) |>
filter(n() == 1) |>
ungroup()
d2 <- d |>
group_by(INTAKE_ID) |>
filter(n() == 2) |>
filter(address_type == 'ALLEGATION_ADDRESS') |>
ungroup()
d <- rbind(d1, d2)
##
d <- d |>
filter(!duplicated(INTAKE_ID)) |>
mutate(month = lubridate::month(DECISION_DATE),
year = lubridate::year(DECISION_DATE))
#get age, sort into groups
d <- d |>
mutate(BIRTH_DATE = replace_na(BIRTH_DATE, as.Date('1900-01-01'))) |>
mutate(age = as.numeric(difftime(DECISION_DATE, BIRTH_DATE, units = "weeks")/52.25)) |>
mutate(age_grp = case_when(age <= 5.0 ~ "zero_five",
age < 19 ~ "five_plus",
age >= 19 ~ "no_birth_date"))
##Annual counts
d_yearly <- d |>
group_by(year) |>
summarize(num_year = n(), .groups = "keep")
print(knitr::kable(d_yearly))
##
### Monthly Counts
d_neigh <- d |>
mutate(fips_tract_id = as.character(census_tract_id_2010)) |> #comment these out when testing with concentrated data
left_join(tract_to_neighborhood, by='fips_tract_id') |>
filter(!is.na(DECISION_DATE))
d_neigh <- d_neigh |>
mutate(neighborhood = ifelse(is.na(lat), 'Missing', neighborhood))
screen_neighborhood_age <- d_neigh |>
group_by(neighborhood, year, month, age_grp) |>
summarise(n_screened_in = sum(screened_in == TRUE, na.rm = TRUE),
n_calls = n(),
.groups = "keep"
) |>
ungroup()
screen_neighborhood_age_2 <- screen_neighborhood_age |>
group_by(year, month, age_grp) |>
summarise(neighborhood = "Total",
n_calls = sum(n_calls),
n_screened_in = sum(n_screened_in)) %>%
bind_rows(screen_neighborhood_age, .)
screen_neighborhood_age_2 <- screen_neighborhood_age_2 |>
rename(group = "age_grp")
screen_neighborhood_MR <- d_neigh |>
group_by(neighborhood, year, month, MANDATED_REPORTER) |>
summarise(n_screened_in = sum(screened_in == TRUE, na.rm = TRUE),
n_calls = n(),
.groups = "keep"
) |>
ungroup()
screen_neighborhood_MR_2 <- screen_neighborhood_MR |>
group_by(year, month, MANDATED_REPORTER) |>
summarise(neighborhood = "Total",
n_calls = sum(n_calls),
n_screened_in = sum(n_screened_in)) %>%
bind_rows(screen_neighborhood_MR, .)
screen_neighborhood_MR_2 <- screen_neighborhood_MR_2 |>
mutate(MANDATED_REPORTER = recode_factor(MANDATED_REPORTER, Yes = "MR", No = "Non_MR")) |>
rename(group = "MANDATED_REPORTER")
screen_neighborhood <- rbind(screen_neighborhood_age_2, screen_neighborhood_MR_2) |>
arrange(neighborhood, year, month)
screen_neighborhood_2 <- screen_neighborhood |>
group_by(neighborhood, year, month) |>
summarise(group = "Total",
n_calls = sum(n_calls),
n_screened_in = sum(n_screened_in)) %>%
bind_rows(screen_neighborhood, .) |>
arrange(neighborhood, year, month)
###---- Produces wide data format
# summarise(n_screened_in_all = sum(screened_in == TRUE, na.rm = TRUE),
# n_calls_all = n(),
# n_screened_in_less_5 = sum(screened_in == TRUE & age_grp == 'zero_five', na.rm = TRUE),
# n_calls_less_5 = length(INTAKE_ID[age_grp == 'zero_five']),
# n_screened_in_greater_5 = sum(screened_in == TRUE & age_grp == 'five_plus', na.rm = TRUE),
# n_calls_greater_5 = length(INTAKE_ID[age_grp == 'five_plus']),
# n_screened_in_no_bd = sum(screened_in == TRUE & age_grp == 'no_birth_date', na.rm = TRUE),
# n_calls_no_bd = length(INTAKE_ID[age_grp == 'no_birth_date']),
# n_screened_in_MR = sum(screened_in == TRUE & MANDATED_REPORTER == 'Yes', na.rm = TRUE),
# n_calls_MR = length(INTAKE_ID[MANDATED_REPORTER == 'Yes']),
# n_screened_in_NON_MR = sum(screened_in == TRUE & MANDATED_REPORTER == 'No', na.rm = TRUE),
# n_calls_NON_MR = length(INTAKE_ID[MANDATED_REPORTER == 'No']),
# .groups = "keep"
# ) |>
# ungroup() |>
# group_by(year, two_week) |>
# bind_rows(summarize(., across(where(is.numeric), sum),
# across(where(is.character), ~"Total"), .groups = "keep"))
#
#
# screen_neighborhood_rate <- screen_neighborhood |>
# group_by(neighborhood, year, two_week) |>
# mutate(screen_in_rate_all = round(n_screened_in_all/n_calls_all,2),
# screen_in_rate_less_5 = round(n_screened_in_less_5/n_calls_less_5,2),
# screen_in_rate_greater_5 = round(n_screened_in_greater_5/n_calls_greater_5,2),
# screen_in_rate_no_bd = round(n_screened_in_no_bd/n_calls_no_bd,2),
# screen_in_rate_MR = round(n_screened_in_MR/n_calls_MR,2),
# screen_in_rate_NON_MR = round(n_screened_in_NON_MR/n_calls_NON_MR,2)
# ) |>
# mutate(across(starts_with('n_'),
# ~ifelse(.x < 5, NA, .x)))
##---
screen_neighborhood_rate <- screen_neighborhood_2 |>
rowwise() |>
mutate(screen_in_rate = round(n_screened_in / n_calls, 2)) |>
mutate(across(starts_with('n_'),
~ifelse(.x < 5, NA, .x)))
##
d_csv <- screen_neighborhood_rate
# d_csv <- screen_neighborhood_rate |>
# select(neighborhood, year, two_week,
# 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, "monthly_report_v5.0.1.csv", sep = ''))