Rcan-package {Rcan}

Cancer Registry Data Analysis and Visualisation

Description

Tools for basic and advance cancer statistics and graphics. Groups individual data, merges registry data and population data, calculates age-specific rate, age-standardized rate, cumulative risk, estimated annual percentage rate with standards error. Creates graphics across variable and time, such as age-specific trends, bar chart and period-cohort trends.

Author(s)

Mathieu Laversanne [aut, cre], Jerome Vignat [aut], Cancer Surveillance Unit [cph]

Maintainer: Mathieu Laversanne <[e-mail]>

References

https://publications.iarc.fr/_publications/media/download/3753/609d0d7711047dd76d7f3dbaa25d7f041fcd013e.pdf

See Also

csu_group_cases csu_merge_cases_pop csu_asr csu_cumrisk csu_eapc csu_ageSpecific csu_ageSpecific_top csu_bar_top csu_time_trend csu_trendCohortPeriod

Examples

data(ICD_group_GLOBOCAN)
data(data_individual_file)


#group individual data by 
#	5 year age group 
#	ICD grouping from dataframe ICD_group_GLOBOCAN
df_data_year <- csu_group_cases(data_individual_file,
  var_age="age",
  group_by=c("sex", "regcode", "reglabel"),
  df_ICD = ICD_group_GLOBOCAN,
  var_ICD  ="site",
  var_year = "doi")     

head(df_data_year)
# individual cases grouped by ICD and 5 years age group and year.
readline(prompt="Press [enter] to continue to merge cases and population)")	


data(data_population_file)	

df_data <- csu_merge_cases_pop(
	df_data_year, 
	data_population_file, 
	var_age = "age_group",
	var_cases = "cases",
	var_py = "pop",
	group_by = c("sex"))

head(df_data)
#Merge 5-years age grouped data with population by year (automatic) and sex
readline(prompt="Press [enter] to continue to merge cases and population)")	


data(csu_registry_data_2)
				  
# ASR with standard error with missing age.
df_asr <- csu_asr(csu_registry_data_2, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label", "sex", "year", "ethnic" ),
                  var_age_group = c("registry_label"), 
                  var_st_err = "st_err")

df_asr[1:4,]	



# ASR with standard error with missing age.		  
readline(prompt="Press [enter] to continue to EAPC")


# EAPC with standard error		
	  
df_eapc <- csu_eapc(df_asr,
					"asr", "year",
					group_by=c("registry", "registry_label", "sex", "ethnic" ))
					
df_eapc[1:4,]	

# EAPC with standard error				  
readline(prompt="Press [enter] to continue to age specific graph")

data(csu_registry_data_1)

# plot age specific rate for 1 population.
df_colombia <- subset(csu_registry_data_1, registry_label=="Colombia, Cali")

csu_ageSpecific(df_colombia,
			plot_title = "Colombia, Liver, male")
				
# plot age specific rate for 1 population, and comparison with CI5XI data.
csu_ageSpecific(df_colombia,
				plot_title = "Colombia, Liver, male",
				CI5_comparison = "Liver")
			
# plot age specific rate for 4 population, legend at the bottom and comparison with CI5XI data.
csu_ageSpecific(csu_registry_data_1,
				group_by="registry_label",
				legend=csu_trend_legend(position="bottom", nrow = 2),
				plot_title = "Liver, male",
				CI5_comparison = 16)