library(tidyverse)
library(here)
library(tidyepi)
seer_pop <-
c(3795, 15192, 19920, 20057, 19820, 18257, 17722, 19511, 22180,
22479, 19806, 17224, 13307, 10654, 9410, 8726, 7415, 4900, 4259)
cancer_us <-
read_tsv(here("data-raw",
"US cancer incidence, all sites, by year and agegroup.txt"),
n_max = 323) %>%
select(Year, age_group = `Age Groups Code`, Count, Population) %>%
mutate_at("age_group", fct_inorder) %>%
mutate_at("age_group", fct_recode, `0` = "1") %>%
group_by(Year) %>%
do(direct_adjust(., age_group, Count, Population, seer_pop))
# `1-4` = "01-4",`5-9` = "05-09")
# by Census division
cancer_us <-
# year 2015 for subregion method
read_tsv(here("data-raw",
"US cancer incidence, all sites, by year, division.txt"),
n_max = 2907) %>%
select(Year, Division, age_group = `Age Groups Code`, Count, Population) %>%
mutate_at("Year", as.character) %>%
mutate_at("age_group", fct_inorder) %>%
mutate_at("age_group", fct_recode, `0` = "1")
# group_by(Year, Division) %>%
cancer_by_division <- cancer_us %>%
filter(Year == "2015") %>%
group_by(Division, age_group) %>%
summarize(Count = sum(Count),
Pop = sum(Population)) %>%
mutate(Count_c = sum(Count) - Count,
Pop_c = sum(Pop) - Pop,
prop = Pop_c / (Pop + Pop_c))
# adjusted rate each division
step2 <- step1 %>%
group_by(age_group) %>%
summarize(n = sum(Count),
pop = sum(Pop)) %>%
direct_adjust(age_group, n, pop, seer_pop)
cancer_us <-
read_tsv(here("data-raw",
"US cancer incidence, all sites, by year, division.txt"),
n_max = 2907) %>%
select(Year, Division, age_group = `Age Groups Code`, Count, Population) %>%
filter(Year == "2015") %>%
mutate_at("age_group", fct_inorder) %>%
mutate_at("age_group", fct_recode, `0` = "1") %>%
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