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combine_med_data.R
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combine_med_data.R
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## Import MED excel doc reports & combine them into one dataset
# Author: Mark Barrett - mbarrett@rand.org
## This is a somewhat complicated process because we have 2011-2019 data which have been downloaded as PDFs from the
## Colorado Gov't website - https://www.colorado.gov/pacific/cdphe/medical-marijuana-statistics-and-data
## These PDFs were then exported to Excel spreadsheets using Adobe Acrobat
## Given the rapid changes in Marijuana legality and the subsequent creation or allocation of government entities
## to watch over the substance, the data changes quite often.
## Data changes (new information) occur at:
# - July 2014
# - April 2015
# - April 2016
# - January 2017
# - May 2018
## However, the structure of the file changes at different intervals:
# - April 2015
# - January 2017
# This creates 3 distinct versions that need to be pulled in.
# This file creates a set of functions that pull in information from different tables for each version (as necessary)
# and then uses these functions to create a dataframe
# Libraries
library(tidyverse)
library(readxl)
library(furrr)
library(haven)
library(pipeR)
library(lubridate)
plan(multiprocess)
folders = str_subset(list.dirs(getwd()), "20[0-9]{2}$")
files = map(folders, list.files, pattern = "\\.xlsx$", full.names=T)
test = files[[1]]
# Functions ---------------------------------------------------------------
pull_county_info_v1 = function(list){
map_dfr(list,
function(y)
read_excel(y, skip = if_else(str_detect(y, "0[4-6]_2013|12_2014|02_2015"), 20,19),
col_names = c("county", "num_patients", "percent_patients"),
col_types = c("text", "numeric", "skip", "skip", "text"),
n_max = 64) %>%
mutate(year = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/)201[0-9]"),
month = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/201[0-9]/CHED_MMR_)[0-9]{2}"),
percent_patients = as.numeric(case_when(percent_patients == "<1%" ~ "1",
T ~ str_remove_all(percent_patients, "%")))/100)
) %>>%
(~ cat("Each file contains Adams?",(count(., county) %>% filter(county == "Adams") %>% pull(n) == sum(map_int(list, length))),"\n",
"Each file contains Yuma?",(count(., county) %>% filter(county == "Yuma") %>% pull(n) == sum(map_int(list, length))),"\n"))
}
pull_county_info_v2 = function(list){
map_dfr(list,
function(y) {
excel_df = tryCatch(
error = function(cnd) {
read_excel(y) %>% print(n=Inf)
stop(paste("Problem reading in file for county_info:", y), call. = F)
},
read_excel(y, skip = case_when(str_detect(y, "0[4-8]_2015") ~ 33,
str_detect(y, "12_201[56]") ~ 36,
T ~ 35),
n_max = if_else(str_detect(y, "(04|1[01])_2016"), 66, 65))
) %>%
select_if(negate(is_logical)) %>%
mutate_if(is_numeric, as.character) %>%
set_names("county", "num_patients1", "num_patients2", "percent_patients1", "percent_patients2")
tryCatch(
error = function(cnd) stop(paste("Problem cleaning file:", y), call. = F),
excel_df %>%
filter(!str_detect(county, "County")) %>%
mutate(num_patients = as.numeric(if_else(is.na(num_patients1), num_patients2, num_patients1)),
percent_patients = if_else(is.na(percent_patients1), percent_patients2, percent_patients1)) %>%
select(county, num_patients, percent_patients) %>%
mutate(year = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/)201[0-9]"),
month = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/201[0-9]/CHED_MMR_)[0-9]{2}"),
percent_patients = as.numeric(case_when(percent_patients == "<1%" ~ "1",
T ~ str_remove_all(percent_patients, "%")))/100)
)
}
)
}
pull_county_info_v3 = function(list){
map_dfr(list,
function(y) {
excel_df = tryCatch(
error = function(cnd) {
read_excel(y) %>% print(n=Inf)
stop(paste("Problem reading in file for county_info:", y), call. = F)
},
read_excel(y, skip = case_when(str_detect(y, "0[1-3]_2018|(0[7-9]|1[0-2])_2017") ~ 40,
str_detect(y, "04_2018") ~ 39,
str_detect(y, "0[1-6]_2017") ~ 38,
T ~ 41),
n_max = 65)
) %>%
select_if(negate(is_logical)) %>%
mutate_if(is_numeric, as.character)
named_excel_df = tryCatch(
error = function(cnd) {
excel_df %>% print(n=Inf)
stop(paste("Problem naming the file:", y), call.=F)
},
excel_df %>%
# Percent has two columns
when(str_detect(y, "0[1-6]_2017") ~ (.) %>%
set_names("county", "num_patients", "percent_patients1", "percent_patients2") %>%
mutate(percent_patients = if_else(is.na(percent_patients1), percent_patients2, percent_patients1)) %>%
select(county, num_patients, percent_patients),
# Both have two columns
str_detect(y, "(1[0-2]|07)_2017|201[89]") ~ (.) %>%
set_names("county", "num_patients1", "num_patients2", "percent_patients1", "percent_patients2") %>%
mutate(num_patients = as.numeric(if_else(is.na(num_patients1), num_patients2, num_patients1)),
percent_patients = if_else(is.na(percent_patients1), percent_patients2, percent_patients1)) %>%
select(county, num_patients, percent_patients),
T ~ (.) %>%
set_names("county", "num_patients", "percent_patients")) %>%
filter(!str_detect(county, "County")) %>%
mutate(num_patients = as.numeric(num_patients),
county = Hmisc::capitalize(tolower(county)),
county = str_replace_all(county, "\\b.{1}", Hmisc::capitalize))
)
tryCatch(
error = function(cnd) stop(paste("Problem cleaning file:", y), call. = F),
named_excel_df %>%
mutate(year = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/)201[0-9]"),
month = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/201[0-9]/CHED_MMR_)[0-9]{2}"),
percent_patients = as.numeric(case_when(percent_patients == "<1%" ~ "1",
T ~ str_remove_all(percent_patients, "%")))/100)
)
}
)
}
pull_condition_info = function(list){
cond_df = map_dfr(list,
function(y){
excel_df = tryCatch(
error = function(cnd) {
read_excel(y) %>% print(n=Inf)
stop(paste("Problem reading in file for condition_info:", y), call. = F)
},
read_excel(y,
# Lines change in April 2013
skip = case_when(str_detect(y, "0[4-6]_2013|12_2014|02_2015") ~ 87,
str_detect(y, "0[4-8]_2015") ~ 22,
str_detect(y, "(09|1[01])_2015") ~ 24,
str_detect(y, "12_2015") ~ 25,
T ~ 86),
col_names = c("condition", "num_patients", "percent_patients"),
col_types = c("text", "skip", "numeric", "skip", "skip", "text"),
n_max = 8)
)
tryCatch(
error = function(cnd) { excel_df %>% print(n = Inf)
stop(paste("Problem cleaning year, month, and patients:", y), call. = F)
},
excel_df %>%
mutate(year = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/)201[0-9]"),
month = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/201[0-9]/CHED_MMR_)[0-9]{2}"),
percent_patients = as.numeric(case_when(percent_patients == "<1%" ~ "1",
T ~ str_remove_all(percent_patients, "%")))/100)
)}
)
tryCatch(
error = function(cnd) {cond_df %>% print(n = Inf)
stop(paste("Problem in user info gather-unite-spread:", y), call. = F)
},
cond_df %>%
gather(measure, value, num_patients:percent_patients) %>%
unite(col_name, condition, measure) %>%
mutate(col_name = str_remove_all(col_name, "_patients$")) %>%
spread(col_name, value)
)
}
pull_condition_info_v2 = function(list){
cond_df = map_dfr(list,
function(y){
excel_df = tryCatch(
error = function(cnd) {
read_excel(y) %>% print(n=Inf)
stop(paste("Problem reading in file for condition_info:", y), call. = F)
},
read_excel(y,
# Lines change in April 2013
skip = case_when(str_detect(y, "0[4-8]_2015") ~ 21,
str_detect(y, "12_2015|(04|1[0-2])_2016") ~ 24,
str_detect(y, "(0[5-9]|1[0-2])_2018|0[1-3]_2019") ~ 29,
str_detect(y, "(0[1-4])_2018|2017") ~ 27,
T ~ 23),
n_max = 8) %>%
select(-contains("..")) %>%
set_names("condition", "num_patients", "percent_patients")
)
tryCatch(
error = function(cnd) { excel_df %>% print(n = Inf)
stop(paste("Problem cleaning year, month, and patients:", y), call. = F)
},
excel_df %>%
mutate(year = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/)201[0-9]"),
month = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/201[0-9]/CHED_MMR_)[0-9]{2}"),
percent_patients = as.numeric(case_when(percent_patients == "<1%" ~ "1",
T ~ str_remove_all(percent_patients, "%")))/100)
)}
)
tryCatch(
error = function(cnd) {cond_df %>% print(n = Inf)
stop(paste("Problem in user info gather-unite-spread:", y), call. = F)
},
cond_df %>%
gather(measure, value, num_patients:percent_patients) %>%
unite(col_name, condition, measure) %>%
mutate(col_name = str_remove_all(col_name, "_patients$")) %>%
spread(col_name, value)
)
}
pull_user_info = function(list){
cond_df = map_dfr(list,
function(y){
excel_df = tryCatch(
error = function(cnd) {
read_excel(y) %>% print(n=Inf)
stop(paste("Problem reading in file for user info:", y), call. = F) },
read_excel(y, skip = if_else(str_detect(y, "0[4-6]_2013|12_2014|02_2015"), 99, 98),
col_names = c("sex", "registry_percent", "avg_age"),
col_types = c("text", "skip", "skip", "text", "skip", "skip", "skip", "numeric"),
n_max = 3)
)
tryCatch(
error = function(cnd) { excel_df %>% print(n = Inf)
stop(paste("Problem cleaning user info:", y), call. = F) },
excel_df %>%
mutate(year = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/)201[0-9]"),
month = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/201[0-9]/CHED_MMR_)[0-9]{2}"),
avg_age = if_else(is.na(avg_age), as.numeric(str_extract(sex, "[0-9]{2}")), avg_age),
sex = if_else(str_detect(sex, "overall"), "Overall", sex),
registry_percent = as.numeric(case_when(registry_percent == "<1%" ~ "1",
T ~ str_remove_all(registry_percent, "%")))/100)
)}
)
tryCatch(
error = function(cnd) {cond_df %>% print(n = Inf)
stop(paste("Problem in user info gather-unite-spread:", y), call. = F)
},
cond_df %>%
gather(measure, value, registry_percent:avg_age) %>%
unite(col_name, sex, measure) %>%
spread(col_name, value) %>%
select(-Overall_registry_percent)
)
}
pull_user_info_v2 = function(list){
cond_df = map_dfr(list,
function(y){
excel_df = tryCatch(
error = function(cnd) {
read_excel(y) %>% print(n=Inf)
stop(paste("Problem reading in file for user info:", y), call. = F) },
read_excel(y, skip = case_when(str_detect(y, "0[4-8]_2015") ~ 7,
str_detect(y, "0[1-3|5-9]_2016|(09|1[01])_2015") ~ 9,
str_detect(y, "(0[5-9]|1[0-2])_2018|0[1-3]_2019") ~ 13,
str_detect(y, "(0[1-4])_2018|2017") ~ 11,
T ~ 10),
col_types = "text",
n_max = 3) %>%
select(-contains("..")) %>%
set_names("sex", "avg_age", "registry_percent")
)
tryCatch(
error = function(cnd) { excel_df %>% print(n = Inf)
stop(paste("Problem cleaning user info:", y), call. = F) },
excel_df %>%
mutate(year = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/)201[0-9]"),
month = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/201[0-9]/CHED_MMR_)[0-9]{2}"),
avg_age = as.numeric(if_else(is.na(avg_age), str_extract(sex, "[0-9]{2}"), avg_age)),
sex = if_else(str_detect(sex, "all patients|average"), "Overall", sex),
registry_percent = as.numeric(case_when(registry_percent == "<1%" ~ "1",
T ~ str_remove_all(registry_percent, "%")))/100)
)}
)
tryCatch(
error = function(cnd) {cond_df %>% print(n = Inf)
stop(paste("Problem in user info gather-unite-spread"), call. = F)
},
cond_df %>%
select(sex, registry_percent, avg_age, year, month) %>%
gather(measure, value, registry_percent:avg_age) %>%
unite(col_name, sex, measure) %>%
spread(col_name, value) %>%
select(-Overall_registry_percent)
)
}
pull_agegroups = function(list){
map_dfr(list,
function(y){
if(str_detect(y, "(0[7-9]|1[0-2])_2014|_2015_")){
excel_df = tryCatch(
error = function(cnd) {
read_excel(y) %>% print(n=Inf)
stop(paste("Problem reading in file for age groups:", y), call. = F)
},
read_excel(y, skip = if_else(str_detect(y, "12_2014|02_2015"), 103, 102),
n_max = 8)
)
tryCatch(
error = function(cnd) { excel_df %>% print(n = Inf)
stop(paste("Problem cleaning age groups info:", y), call. = F)
},
excel_df %>%
select(-contains("...")) %>%
set_names(c("age_group", "num", "percent")) %>%
mutate(year = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/)201[0-9]"),
month = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/201[0-9]/CHED_MMR_)[0-9]{2}"),
age_group = str_replace_all(age_group, c("\\-" = "_", "\\+" = "plus", "71 and Older" = "71plus")),
percent = as.numeric(case_when(percent == "<1%" ~ "1",
T ~ str_remove_all(percent, "%")))/100) %>%
gather(measure, value, num:percent) %>%
unite(col_name, measure, age_group) %>%
spread(col_name, value)
)
} else {
tibble()
}
}
)
}
pull_agegroups_v2 = function(list){
map_dfr(list,
function(y){
excel_df = tryCatch(
error = function(cnd) {
read_excel(y) %>% print(n=Inf)
stop(paste("Problem reading in file for age groups:", y), call. = F)
},
read_excel(y, skip = case_when(str_detect(y, "0[4-8]_2015") ~ 11,
str_detect(y, "12_2015|(1[0-2]|04)_2016") ~ 14,
str_detect(y, "(0[5-9]|1[0-2])_2018|0[1-3]_2019") ~ 18,
str_detect(y, "(0[1-4])_2018|2017") ~ 16,
T ~ 13),
# case_when(str_detect(y, "0[4-8]_2015") ~ 8,
# str_detect(y, "0[1-3|5-9]_2016") ~ 9,
# str_detect(y, "12_2015") ~ 11,
# T ~ 10)
n_max = 9)
)
tryCatch(
error = function(cnd) { excel_df %>% print(n = Inf)
stop(paste("Problem cleaning age groups info:", y), call. = F)
},
excel_df %>%
select(-contains("...")) %>%
set_names(c("age_group", "num", "percent", "top_condition")) %>%
mutate(year = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/)201[0-9]"),
month = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/201[0-9]/CHED_MMR_)[0-9]{2}"),
age_group = str_replace_all(age_group, c("\\-" = "_", "\\+" = "plus", "71 and Older" = "71plus")),
percent = as.numeric(case_when(percent == "<1%" ~ "1",
T ~ str_remove_all(percent, "%")))/100) %>%
gather(measure, value, num:top_condition) %>%
unite(col_name, measure, age_group) %>%
spread(col_name, value) %>%
mutate_at(vars(matches("num|percent")), as.numeric)
)
}
)
}
pull_caregiver_distribution_v2 = function(list){
cond_df = map_dfr(list,
function(y){
excel_df = tryCatch(
error = function(cnd) {
read_excel(y) %>% print(n=Inf)
stop(paste("Problem reading in file for condition_info:", y), call. = F)
},
read_excel(y,
# Lines change in April 2013
skip = case_when(str_detect(y, "0[4-8]_2015") ~ 101,
str_detect(y, "12_2015") ~ 25,
T ~ 24),
col_names = c("patients_served", "num_caregivers", "percent_patients"),
col_types = c("text", "skip", "skip", "skip", "numeric", "skip", "skip", "skip", "skip", "skip", "skip", "skip", "skip", "text"),
n_max = 8)
)
tryCatch(
error = function(cnd) { excel_df %>% print(n = Inf)
stop(paste("Problem cleaning year, month, and patients:", y), call. = F)
},
excel_df %>%
mutate(year = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/)201[0-9]"),
month = str_extract(y, "(?<=/Users/mbarrett/Documents/Projects/Drug Policy/MED Data/201[0-9]/CHED_MMR_)[0-9]{2}"),
percent_patients = as.numeric(case_when(percent_patients == "<1%" ~ "1",
T ~ str_remove_all(percent_patients, "%")))/100)
)}
)
tryCatch(
error = function(cnd) {cond_df %>% print(n = Inf)
stop(paste("Problem in user info gather-unite-spread:", y), call. = F)
},
cond_df %>%
gather(measure, value, num_patients:percent_patients) %>%
unite(col_name, condition, measure) %>%
mutate(col_name = str_remove_all(col_name, "_patients$")) %>%
spread(col_name, value)
)
}
# V1 data extraction ------------------------------------------------------
# January 2011 - March 2015
# Table I: County Information
folders_v1 = folders[1:5]
files_v1 = future_map(folders_v1, list.files, pattern = "^[A-Z].*xlsx$", full.names=T)
# files_v1[[4]] = files_v1[[4]][-7:-12]
files_v1[[5]] = files_v1[[5]][-4:-12]
future_map_dfr(files_v1, function(x) pull_county_info_v1(x))# %>% View()
# County Long
county_long_v1 = future_map_dfr(files_v1,
function(x) pull_county_info_v1(x)) %>%
mutate(agency = "county")
# State-level data
state_v1 = future_map_dfr(files_v1,
function(x) {
condition_info = pull_condition_info(x)
user_info = pull_user_info(x)
age_groups = pull_agegroups(x)
total = left_join(condition_info, user_info, by = c("year", "month")) %>%
when(nrow(age_groups) > 0 ~ (.) %>%
left_join(age_groups, by = c("year","month")),
T ~ (.))
return(total)
}
)
# V2 data extraction ------------------------------------------------------
# April 2015 - December 2016
# Lines to skip for county
# - 04_2015 - 08_2015: 34
# - 09_2015 - 11_2015: 36
# - 12_2015: 37
folders_v2 = folders[5:6]
files_v2 = future_map(folders_v2, list.files, pattern = "^[A-Z].*xlsx$", full.names=T)
files_v2[[1]] = files_v2[[1]][4:12] # 2015: April - December
county_long_v2 = future_map_dfr(files_v2,
function(x) pull_county_info_v2(x)) %>%
mutate(agency = "county")
# State-level data
state_v2 = future_map_dfr(files_v2,
function(x) {
condition_info = pull_condition_info_v2(x)
user_info = pull_user_info_v2(x)
age_groups = pull_agegroups_v2(x)
total = left_join(condition_info, user_info, by = c("year", "month")) %>%
when(nrow(age_groups) > 0 ~ (.) %>%
left_join(age_groups, by = c("year","month")),
T ~ (.))
return(total)
}
)
# Lines to skip for gender
# - 04_2015 - 08_2015: 8
# - 09_2015 - 11_2015: 10
# - 12_2015: 11
# V3 data extraction ------------------------------------------------------
# January 2017 - March 2019
# Lines to skip for county
# - 04_2015 - 08_2015: 34
# - 09_2015 - 11_2015: 36
# - 12_2015: 37
folders_v3 = folders[7:9]
files_v3 = future_map(folders_v3, list.files, pattern = "^[A-Z].*xlsx$", full.names=T)
county_long_v3 = future_map_dfr(files_v3,
function(x) pull_county_info_v3(x)) %>%
mutate(agency = "county")
# State-level data
state_v3 = future_map_dfr(files_v3,
function(x) {
condition_info = pull_condition_info_v2(x)
user_info = pull_user_info_v2(x)
age_groups = pull_agegroups_v2(x)
total = left_join(condition_info, user_info, by = c("year", "month")) %>%
when(nrow(age_groups) > 0 ~ (.) %>%
left_join(age_groups, by = c("year","month")),
T ~ (.))
return(total)
}
)
# Create sample -----------------------------------------------------------
# med2011_14 = future_map_dfr(files_v1,
# function(x) {
# condition_info = pull_condition_info(x)
#
# user_info = pull_user_info(x)
#
# total = left_join(condition_info, user_info, by = c("year", "month"))
#
# return(total)
# }
# ) %>%
# mutate(agency = "state") %>%
# full_join(county_long, by = c("agency", "year", "month")) %>%
# arrange(county, year, month) %>%
# select(year, month, agency, county, num_patients, percent_patients, everything())
# colnames(med2011_14) = tolower(str_replace_all(colnames(med2011_14), c("\\s" = "","\\/" = "_")))
#
# write_dta(med2011_14, "med2011_14.dta")
# Join county-level variables ---------------------------------------------
med_full_county = bind_rows(county_long_v1, county_long_v2, county_long_v3)
# write_dta(med_full_county, "med2011_19_county.dta")
wide_vars = do.call(paste0, expand.grid(c("num_", "percent_"), do.call(paste0, expand.grid(c(paste0("0",1:9), 10:12), paste0("_", 2011:2019)))))
wide_vars = wide_vars[-199:-length(wide_vars)]
med_full_county_wide = med_full_county %>%
arrange(year, month) %>%
gather(measure, value, num_patients:percent_patients) %>%
mutate(measure = str_remove_all(measure, "_patients$")) %>%
unite(mmYYYY, month, year) %>%
unite(varname, measure, mmYYYY) %>%
spread(varname, value) %>%
select(agency, county, wide_vars)
# write_dta(med_full_county_wide, "med2011_19_county_wide.dta")
# Join state-level variables ----------------------------------------------
med_full_state = bind_rows(state_v1, state_v2, state_v3)
colnames(med_full_state) = tolower(str_replace_all(colnames(med_full_state), c("\\s" = "","\\/" = "_")))
# write_dta(med_full_state, "med2011_19_state.dta")
med_full_state %>%
mutate(num_11_20 = if_else(is.na(num_11_20) & !is.na(num_11_17), num_11_17+num_18_20, num_11_20),
percent_11_20 = if_else(is.na(percent_11_20) & !is.na(percent_11_17), percent_11_17+percent_18_20, percent_11_20)) %>%
select(year, month, num_11_17, num_18_20, num_11_20, percent_11_17, percent_18_20, percent_11_20) %>%
View()
#med_full_state = read_dta("med2011_19_state.dta")
# Make a datetime var for graphing
med_full_state = med_full_state %>%
mutate(datetime = str_c(year, month, "01", sep = "-"),
datetime = lubridate::ymd(datetime),
num_11_20 = if_else(is.na(num_11_20) & !is.na(num_11_17), num_11_17+num_18_20, num_11_20),
percent_11_20 = if_else(is.na(percent_11_20) & !is.na(percent_11_17), percent_11_17+percent_18_20, percent_11_20))
## Make codebook
med_full_state %>%
filter_all(all_vars(!is.na(.))) %>%
slice(1) %>%
gather(variable, example_value) %>%
select(variable) %>%
View()
# Visualization -----------------------------------------------------------
# Conditions reported by patients
med_full_state %>%
gather(condition, num, cachexia_num:severepain_num, -contains("percent")) %>%
mutate(condition = Hmisc::capitalize(str_remove_all(condition, "_num")),
condition = str_replace_all(condition, c("Hiv_aids" = "HIV/AIDS", "spasms" = " Spasms", "nausea" = " Nausea", "pain" = " Pain"))) %>%
ggplot(., aes(x = datetime, y = num, colour = condition))+
geom_line()+
scale_colour_brewer(type = "qual", palette = 6, guide = guide_legend(title = NULL))+
scale_y_continuous(labels = scales::comma)+
scale_x_date(limits = c(ymd("2011-01-01"), ymd("2019-04-01")))+
labs(x = "", y = "Number of Patients Reporting Condition", caption = "Source: Colorado Department of Public Health & Environment")+
ggtitle("Conditions reported by medical marijuana patients in Colorado (2011-2019)")+
theme_minimal()+
theme(plot.caption = element_text(colour = "grey47"),
plot.margin = margin(l = 5, t = 15, b = 15, r = 5),
aspect.ratio = .65)
ggsave("conditionsReported2011-19.png", dpi = 320, height = 6, width = 10, units = "in")
# Sex of patients
med_full_state %>%
select(matches("(fe)*male_reg"), datetime) %>%
gather(sex, percent, -datetime) %>%
mutate(sex = Hmisc::capitalize(str_remove_all(sex, "_registry_percent"))) %>%
ggplot(., aes(x = datetime, y = percent, colour = sex))+
geom_line()+
scale_colour_brewer(type = "qual", palette = 6, guide = guide_legend(title = NULL))+
scale_y_continuous(labels = scales::percent_format(accuracy = 1))+
scale_x_date()+
labs(x = "", y = "% of Patients", caption = "Source: Colorado Department of Public Health & Environment")+
ggtitle("Gender distribution of medical marijuana patients in Colorado (2011-2019)")+
theme_minimal()+
theme(plot.caption = element_text(colour = "grey47"),
plot.margin = margin(l = 5, t = 15, b = 15, r = 5),
aspect.ratio = .6)
ggsave("gender2011-19.png", dpi = 320, height = 6, width = 10, units = "in")
# Age groups of patients
med_full_state %>%
select(matches("num_[0-9]"), datetime) %>%
filter_at(vars(-datetime), any_vars(!is.na(.))) %>%
mutate(num_11_20 = if_else(is.na(num_11_20) & !is.na(num_11_17), num_11_17+num_18_20, num_11_20)) %>%
select(-num_11_17, -num_18_20) %>%
gather(age_group, num, num_0_10:num_71plus) %>%
mutate(age_group = str_remove_all(age_group, "num_"),
age_group = str_replace_all(age_group, c("_" = "-", "plus" = "+")),
age_group = fct_rev(factor(age_group, levels = c("0-10", "11-20", "21-30", "31-40", "41-50", "51-60", "61-70", "71+"), ordered=T))) %>%
ggplot(., aes(x = datetime, y = num, fill = age_group))+
geom_area()+
scale_fill_brewer(type = "seq", palette = 1, direction = -1, guide = guide_legend(title = NULL, reverse = F))+
scale_y_continuous(labels = scales::comma)+
scale_x_date(limits = c(ymd("2011-01-01"), ymd("2019-04-01")))+
labs(x = "", y = "% of Patients", caption = "Source: Colorado Department of Public Health & Environment")+
ggtitle("Number of medical marijuana patients in Colorado, by age group (2014-2019)")+
theme_minimal()+
annotate("label", x = ymd("2011-01-01"), y = 97500, label = "Note:\nData on age groups was not\nreported prior to July 2014", hjust = 0, size = 5)+
theme(plot.caption = element_text(colour = "grey47"),
plot.margin = margin(l = 5, t = 15, b = 15, r = 5),
aspect.ratio = .6)
ggsave("ageGroupsLabel2011-19.png", dpi = 320, height = 6, width = 10, units = "in")