Rcan (version 1.3.91)

csu_merge_cases_pop: csu_merge_cases_pop

Description

csu_merge_cases_pop merges registry data and population data, group by year and other user defined variable (sex, registry, etc...).

Usage

csu_merge_cases_pop(df_cases, 
	df_pop,
	var_age,
	var_cases="cases",
	var_py=NULL,
	group_by=NULL)

Value

Return a dataframe.

Arguments

df_cases

Registry data group by 5 years-age group (need to be R data.frame format, see examples to import csv file).

df_pop

Population data group by 5-years age group (need to be R data.frame format, see examples to import csv file).

var_age

Age variable. Several format are accepted

1	0-4	0
2	5-9	5
3	10-14	10
...	...	...
17	80-84	80
18	85+	85

This variable must be a variable with the same column name in both dataset (df_cases and df_pop).
Age >= 85 in the df_pop dataset will be aggregated as 85+.

var_cases

Cases variable in the df_cases dataset.

var_py

(Optional) If population is "long format", name of the population variable in the df_pop dataset.
If population data is wide format (see details), var_py must be NULL.

group_by

(Optional) A vector of variables to create the different population (sex, country, etc...).
Each variable must be a variable with the same column name in both dataset (df_cases and df_pop).
Do not include the "year" variable since it is automatically detected (see details).

Author

Mathieu Laversanne

Details

This function merges registry data and population for further analysis.
Both datasets must be group by 5-years age group.
If present, the year information in format "yyyy" will be detected automatically.
2 formats are accepted for population data:.
Long format: (year and population are 2 variables)

sex	age	pop	year
1	1	116128	2005
1	2	130995	2005
1	3	137556	2005
...	...	...	...
2	16	27171	2007
2	17	13585	2007
2	18	13585	2007

Wide format: (One column per year and no population variable, "yyyy" year format must be included in columns name)

sex	age	Y2013	Y2014	Y2015
1	0-4	215607	237346	247166
1	5-9	160498	152190	152113
1	10-14	175676	171794	165406
...	...	...	...	...
2	75-79	20625	20868	23434
2	80-84	7187	7276	7620
2	85+	2551	2597	2617

See Also

csu_group_cases csu_asr csu_cumrisk csu_eapc csu_ageSpecific csu_ageSpecific_top csu_bar_top csu_time_trend csu_trendCohortPeriod

Examples

# you can import your data from csv file using read.csv:
# mydata <-  read.csv("mydata.csv", sep=",")

data(ICD_group_GLOBOCAN)
data(data_individual_file)
data(data_population_file)

#group individual data by 
# 5 year age group 
# ICD grouping from dataframe ICD_group_GLOBOCAN
# year (extract from date of incidence)

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")     

#Merge 5-years age grouped data with population by year (automatic) and sex

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"))


# you can export your result as csv file using write.csv:
# write.csv(result, file="result.csv")