Count canreg data.
count_canreg.RdCount canreg data.
Usage
count_canreg(
x,
cutage_method = "distance",
breaks = c(0, 15, 40, 65),
length = 5,
maxage = 85,
sep_zero = TRUE,
labels = NULL,
label_tail = NULL,
cancer_type = "big"
)
# S3 method for class 'canregs'
count_canreg(x, ...)
# S3 method for class 'canreg'
count_canreg(
x,
cutage_method = "distance",
breaks = c(0, 15, 40, 65),
length = 5,
maxage = 85,
sep_zero = TRUE,
labels = NULL,
label_tail = NULL,
cancer_type = "big"
)Arguments
- x
Object data of class 'canreg' or 'canregs'.
- cutage_method
Methods for Specifying Age Groups. Options are "interval", "distance", or "quantile". Default is "distance".
- breaks
Specify the break points classify age groups when cutage_method is 'interval'. Default is 'c(0, 15, 40, 65)'.
- length
Specify the length of each age group when cutage_method is 'distance'. Default is 5.
- maxage
Specify the max age of age group when cutage_method is 'distance'. Default is 85.
- sep_zero
Logical value, TRUE or FALSE, specifying whether to treat age 0 as a separate group. Default is TRUE.
- labels
Labels for age groups. Default is NULL.
- label_tail
Tail label to be added to the labels. Default is NULL.
- cancer_type
A character string specifying the classification method used to categorize ICD-10 codes. This determines how ICD-10 codes are classified. Options include
"big"(classify ICD-10 codes into 26 cancer categories),"small"(classify ICD-10 codes into 59 cancer categories, more specific categories),"system"(classify ICD-10 codes into organ system), and"gco"(classify ICD-10 code into cancer categories same as classification published by the Global Cancer Observatory). This parameter is only available when the input data is a vector of ICD-10 codes, or object with class of'canreg'or'canregs'.- ...
Parameters.
Examples
library(canregtools)
file <- system.file("extdata", "411721.xls", package = "canregtools")
data <- read_canreg(file)
fbsw <- count_canreg(data, cutage_method = "interval")
fbsw
#> $areacode
#> [1] "411721"
#>
#> $fbswicd
#> year sex agegrp cancer fbs sws mv ub sub m8000 dco
#> <int> <int> <fctr> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1: 2016 1 0-14 岁 60 10 6 8 0 6 2 0
#> 2: 2016 1 0-14 岁 61 10 6 8 0 6 2 0
#> 3: 2016 1 0-14 岁 101 0 0 0 0 0 0 0
#> 4: 2016 1 0-14 岁 102 0 0 0 0 0 0 0
#> 5: 2016 1 0-14 岁 103 0 0 0 0 0 0 0
#> ---
#> 220: 2016 2 65+ 岁 122 17 9 1 0 6 9 2
#> 221: 2016 2 65+ 岁 123 3 0 3 0 0 0 0
#> 222: 2016 2 65+ 岁 124 11 12 6 0 8 0 0
#> 223: 2016 2 65+ 岁 125 5 6 5 0 3 0 0
#> 224: 2016 2 65+ 岁 126 14 11 13 1 3 1 0
#>
#> $sitemorp
#> year sex cancer site morp
#> <int> <int> <int> <list> <list>
#> 1: 2016 2 101 <data.frame[3x2]> <data.frame[3x2]>
#> 2: 2016 1 101 <data.frame[8x2]> <data.frame[4x2]>
#> 3: 2016 2 124 <data.frame[8x2]> <data.frame[9x2]>
#> 4: 2016 2 102 <data.frame[1x2]> <data.frame[2x2]>
#> 5: 2016 1 102 <data.frame[1x2]> <data.frame[1x2]>
#> 6: 2016 1 103 <data.frame[7x2]> <data.frame[8x2]>
#> 7: 2016 2 103 <data.frame[4x2]> <data.frame[3x2]>
#> 8: 2016 1 104 <data.frame[5x2]> <data.frame[6x2]>
#> 9: 2016 2 104 <data.frame[6x2]> <data.frame[7x2]>
#> 10: 2016 1 124 <data.frame[7x2]> <data.frame[8x2]>
#> 11: 2016 1 126 <data.frame[23x2]> <data.frame[11x2]>
#> 12: 2016 2 126 <data.frame[20x2]> <data.frame[20x2]>
#> 13: 2016 2 105 <data.frame[11x2]> <data.frame[6x2]>
#> 14: 2016 1 105 <data.frame[9x2]> <data.frame[6x2]>
#> 15: 2016 1 106 <data.frame[4x2]> <data.frame[6x2]>
#> 16: 2016 2 106 <data.frame[4x2]> <data.frame[4x2]>
#> 17: 2016 2 107 <data.frame[3x2]> <data.frame[4x2]>
#> 18: 2016 1 107 <data.frame[3x2]> <data.frame[5x2]>
#> 19: 2016 1 108 <data.frame[3x2]> <data.frame[5x2]>
#> 20: 2016 2 108 <data.frame[2x2]> <data.frame[4x2]>
#> 21: 2016 1 109 <data.frame[2x2]> <data.frame[1x2]>
#> 22: 2016 2 109 <data.frame[1x2]> <data.frame[1x2]>
#> 23: 2016 1 110 <data.frame[6x2]> <data.frame[5x2]>
#> 24: 2016 2 110 <data.frame[5x2]> <data.frame[6x2]>
#> 25: 2016 1 111 <data.frame[2x2]> <data.frame[3x2]>
#> 26: 2016 2 111 <data.frame[1x2]> <data.frame[1x2]>
#> 27: 2016 1 112 <data.frame[3x2]> <data.frame[3x2]>
#> 28: 2016 2 112 <data.frame[4x2]> <data.frame[2x2]>
#> 29: 2016 2 125 <data.frame[10x2]> <data.frame[11x2]>
#> 30: 2016 1 125 <data.frame[9x2]> <data.frame[10x2]>
#> 31: 2016 1 113 <data.frame[2x2]> <data.frame[1x2]>
#> 32: 2016 2 113 <data.frame[2x2]> <data.frame[1x2]>
#> 33: 2016 2 114 <data.frame[5x2]> <data.frame[14x2]>
#> 34: 2016 1 114 <data.frame[1x2]> <data.frame[1x2]>
#> 35: 2016 2 115 <data.frame[3x2]> <data.frame[5x2]>
#> 36: 2016 2 116 <data.frame[3x2]> <data.frame[5x2]>
#> 37: 2016 2 117 <data.frame[1x2]> <data.frame[9x2]>
#> 38: 2016 1 118 <data.frame[1x2]> <data.frame[2x2]>
#> 39: 2016 1 119 <data.frame[2x2]> <data.frame[2x2]>
#> 40: 2016 1 120 <data.frame[4x2]> <data.frame[8x2]>
#> 41: 2016 2 120 <data.frame[3x2]> <data.frame[4x2]>
#> 42: 2016 2 121 <data.frame[1x2]> <data.frame[4x2]>
#> 43: 2016 1 121 <data.frame[1x2]> <data.frame[3x2]>
#> 44: 2016 1 122 <data.frame[11x2]> <data.frame[5x2]>
#> 45: 2016 2 122 <data.frame[8x2]> <data.frame[5x2]>
#> 46: 2016 1 123 <data.frame[1x2]> <data.frame[5x2]>
#> 47: 2016 2 123 <data.frame[1x2]> <data.frame[5x2]>
#> year sex cancer site morp
#>
#> $pop
#> year sex agegrp rks
#> <int> <int> <fctr> <int>
#> 1: 2016 1 0-14 岁 80846
#> 2: 2016 1 15-39 岁 180929
#> 3: 2016 1 40-64 岁 155155
#> 4: 2016 1 65+ 岁 38723
#> 5: 2016 2 0-14 岁 70475
#> 6: 2016 2 15-39 岁 165617
#> 7: 2016 2 40-64 岁 144124
#> 8: 2016 2 65+ 岁 46148
#>
#> attr(,"class")
#> [1] "fbswicd" "list"