Rcan (version 1.3.70)

csu_CI5X_data: Cancer in five continent volume X.

Description

CI5 X summary database.

Usage

data("csu_CI5X_data")

Format

A data frame with 1015056 observations on the following 11 variables.

registry_code

Registry code (integer)

registry_label

Name of the registry (factor)

ethnic_group

ethnic code (integer)

sex

sex (factor)

cancer_code

cancer code (integer)

cancer_label

cancer label (factor)

mv

Microscopic Verification (%) (integer)

dco

Death Certificate Only (%) (integer)

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)

Details

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

See Also

csu_asr csu_eapc csu_ageSpecific

Examples

# NOT RUN {
data("csu_CI5X_data")

#get the registry code asssociate to registry_label
print(unique(csu_CI5X_data[,c("registry_label", "registry_code")]),nrows = 1000)

#get the cancer code asssociate to cancer_label
print(unique(csu_CI5X_data[,c("cancer_label", "cancer_code")]),nrows = 1000)

#remove all cancers:
df_data <- csu_CI5X_data[csu_CI5X_data$cancer_code < 62,]


#select Thailand changmai
df_data_1 <- df_data[df_data$registry_code==76401,]






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)



# }
# NOT RUN {
#select USAm NPCR
df_data_2 <- df_data[df_data$registry_code== 84080,]

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, NPCR",
                                   plot_subtitle = "Top 5 cancer",
                                   missing_age = 19)


				  
# }