csu_CI5XI_data {Rcan}

Cancer in five continent volume XI.

Description

CI5 XI summary database.

Usage

data("csu_CI5XI_data")

Format

A data frame with 1113210 observations on the following 10 variables.

registry_code

Registry code (integer)

registry_label

Name of the registry (character)

sex

sex (integer)

cancer_code

cancer code (integer)

cancer_label

cancer label (character)

age

age variable from 1 to 19. 1 is 0-4 years, 2 is 5-9, etc..., 17 is 80-84, 18 is 85+, 19 represents missing age. (integer)

cases

Number of cases (incidence) (integer)

py

Population-year: Reference population of the registry (integer)

CI5_continent

Continent code (integer)

ethnic_group

ethnic code (integer)

Details

All information are available here: http://ci5.iarc.fr/CI5-XI/Default.aspx

Source

http://ci5.iarc.fr/CI5-XI/Default.aspx

See Also

csu_asr csu_cumrisk csu_eapc csu_ageSpecific

Examples

data("csu_CI5XI_data")

#get the registry code asssociate to registry_label
#print(unique(csu_CI5XI_data[,c("registry_code", "registry_label" )]),nrows = 1000)

#get the cancer code asssociate to cancer_label
#print(unique(csu_CI5XI_data[,c("cancer_label", "cancer_code")]),nrows = 1000)

#remove all cancers:
df_data <- subset(csu_CI5XI_data ,cancer_code < 62)

#change group_by option to factor
df_data$sex <- factor(df_data$sex, levels=c(1,2), labels=c("Male", "Female"))


#select Thailand changmai
df_data_1 <- subset(df_data, registry_code==764001)



dt_result_1 <- csu_ageSpecific_top(df_data_1,
                    var_age="age", 
                    var_cases="cases", 
                    var_py="py",
                    var_top="cancer_label", 
                    group_by="sex", 
                    plot_title= "Thailand, Chiangmai",
                    plot_subtitle = "Top 5 cancer",
                    missing_age = 19)



					
#select USA
df_data_2 <- subset(df_data,registry_code== 840000 & ethnic_group == 99)

dt_result_2 <- csu_ageSpecific_top(df_data_2,
                                   var_age="age", 
                                   var_cases="cases", 
                                   var_py="py",
                                   var_top="cancer_label", 
                                   group_by="sex", 
                                   plot_title= "USA",
                                   plot_subtitle = "Top 5 cancer",
                                   missing_age = 19)