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rural_urban.R
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rural_urban.R
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library(tidyverse)
library(RODBC)
library(scales)
library(usmap)
library(readxl)
ch = odbcConnect('vertica',
uid = 'XXXXX',
pwd = 'XXX')
cbp <- c('#e69f00', '#0072b2', '#009e73',
'#d55e00', '#f0e442', '#56b4e9')
rural_urban = read_excel('RUCA2010zipcode_1.xlsx', sheet = 'Data')
rural_urban_def = read_excel('RUCA2010zipcode_1.xlsx', sheet = 'description')
rural_urban = rural_urban %>% left_join(rural_urban_def)
key = read_excel('keys.xlsx', sheet = 'disease')
patient_list = read.csv('reports/prevalence/overall/patient_list.csv')
zip_members = sqlQuery(ch, paste("SELECT mbr_zip_5_cd,
count(distinct z_patid) as members
from HCCI_2.MBR_SDDV2
where yr >'2015'
group by 1;"), stringsAsFactors = F, as.is = c(T,T))
zip_members$members = as.numeric(zip_members$members)
zip_members$mbr_zip_5_cd = zip_members$mbr_zip_5_cd %>% sprintf(fmt = '%05d')
patient_list$mbr_zip_5_cd = patient_list$mbr_zip_5_cd %>% sprintf(fmt = '%05d')
zip_members = zip_members %>%
left_join(rural_urban, by = c('mbr_zip_5_cd' = 'ZIP_CODE'))
zip_members = zip_members %>% drop_na()
diseases = unique(patient_list$disease)
for (i in 1:length(diseases)) {
if(i==1){
prev_table = zip_members %>% mutate(disease = diseases[i])
}else{
prev_table = prev_table %>% bind_rows(
zip_members %>% mutate(disease = diseases[i])
)
}
}
df_patient = patient_list %>%
group_by(disease, mbr_zip_5_cd) %>%
summarise(patients= n())
prev_table = prev_table %>%
left_join(df_patient)
prev_table$prevalence = prev_table$patients*100000/prev_table$members
prev_table$prevalence[is.na(prev_table$prevalence)] = 0
prev_table$patients[is.na(prev_table$patients)] = 0
prev_table = prev_table %>% left_join(key)
# do the same plot without dividing
# us heat map based on the zip codes
prev_table$short_description = factor(prev_table$short_description,
levels = c('Metropolitan', 'Micropolitan',
'Small town', 'Rural area'))
ggplot(prev_table %>% filter(short_description !='Unknown'),
aes(x=short_description, y = log(prevalence+1), fill = short_description))+
geom_boxplot(outlier.shape = NA)+
geom_point(shape = 1, aes(color = short_description),
alpha = .15, position = position_jitter())+
facet_wrap(~short_disease, nrow = 2, ncol = 7)+
ylab(label = 'Prevalence - 100,000 per members log()')+
xlab(label = "")+
theme_bw()+
theme(legend.position = 'none',
strip.background = element_blank(),
strip.text = element_text(size = 8, face = 'bold'),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))+
scale_fill_manual(values = cbp[1:4])+
scale_color_manual(values = cbp[1:4])
ggsave('./reports/prevalence/overall/rural_urban_all_areas.png',
dpi = 600, width = 7.2, height = 5.5, units = 'in')
for (i in 1:length(diseases)) {
tmp = prev_table %>% filter(disease==diseases[i])
kt = kruskal.test(tmp$prevalence~tmp$short_description)
wl = pairwise.wilcox.test(tmp$prevalence,tmp$short_description)
tmp = tmp %>%
group_by(short_description) %>%
summarise(med = mean(prevalence)) %>%
drop_na() %>%
spread(short_description, med) %>%
mutate(p_value = kt$p.value,
disease = diseases[i])
tmp2 = wl$p.value %>%
data.frame() %>%
mutate(gr1 = rownames(.)) %>%
gather('gr2', 'pvalue', -'gr1') %>%
drop_na() %>%
mutate(diseas = diseases[i])
if(i==1){
ks_test = tmp
wl_test = tmp2
}else{
ks_test = ks_test %>%
bind_rows(tmp)
wl_test = wl_test %>%
bind_rows(tmp2)
}
}
write.csv(ks_test, './reports/prevalence/overall/kruskall_all.csv', row.names = FALSE)
write.csv(wl_test, './reports/prevalence/overall/wil_pairwise_all.csv', row.names = FALSE)
ggplot(prev_table %>%
filter(short_description !='Unknown') %>%
filter(prevalence>0),
aes(x=short_description, y = log(prevalence+1), fill = short_description))+
geom_boxplot(outlier.shape = NA)+
geom_point(shape = 1, aes(color = short_description),
alpha = .15, position = position_jitter())+
facet_wrap(~short_disease, nrow = 2, ncol = 7)+
ylab(label = 'Prevalence - 100,000 per members log()')+
xlab(label = "")+
theme_bw()+
theme(legend.position = 'none',
strip.background = element_blank(),
strip.text = element_text(size = 8, face = 'bold'),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))+
scale_fill_manual(values = cbp[1:4])+
scale_color_manual(values = cbp[1:4])
ggsave('./reports/prevalence/overall/rural_urban_only_with_patient.png',
dpi = 600, width = 7.2, height = 5.5, units = 'in')
for (i in 1:length(diseases)) {
tmp = prev_table %>%
filter(disease==diseases[i]) %>%
filter(prevalence>0)
kt = kruskal.test(tmp$prevalence~tmp$short_description)
tmp = tmp %>%
group_by(short_description) %>%
summarise(med = median(prevalence)) %>%
drop_na() %>%
spread(short_description, med) %>%
mutate(p_value = kt$p.value,
disease = diseases[i])
tmp2 = wl$p.value %>%
data.frame() %>%
mutate(gr1 = rownames(.)) %>%
gather('gr2', 'pvalue', -'gr1') %>%
drop_na() %>%
mutate(diseas = diseases[i])
if(i==1){
ks_test = tmp
wl_test = tmp2
}else{
ks_test = ks_test %>%
bind_rows(tmp)
wl_test = wl_test %>%
bind_rows(tmp2)
}
}
write.csv(ks_test, './reports/prevalence/overall/kruskall_only_with_patients.csv', row.names = FALSE)
write.csv(wl_test, './reports/prevalence/overall/wil_pairwise_only_with_patients.csv', row.names = FALSE)