csu_merge_cases_pop {Rcan}

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) 
	

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).

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

Value

Return a dataframe.

Author(s)

Mathieu Laversanne

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")