library(canregtools)
library(dplyr)
files <- list.files("~/website/slides/outputs", full.names = TRUE)
file <- files[1]
data <- read_canreg(file)
class(data)[1] "canreg" "list" [1] "areacode" "FBcases" "SWcases" "POP"
Canregtools
An R package used in PBCRs
️Qiong Chen👨⚕️ Phd.
🏥Henan Cancer Center
Monday Nov 4, 2024
What we actually do in PBCR
A system designed to collect, store, and manage cancer cases within a population, which is crucial for monitoring cancer incidence, mortality, survival, and prevalence.
- Data internal consistency check
- Quality check (CI5, annual check of quality control for registry)
- Calculation of statistical indicator
- Data visualization
- Reports
Which tools are available ?
- IARCcrgTools
- JRC tools
- Canreg5
- SEER*Stat
Other software or language including R, SAS, STATA, Python
What can canregtools do ?
Canregtools is an R package developed to streamline data analysis, visualization, and reporting in cancer registration. It includes five sets of R functions that cover data reading, processing, statistical calculations, visualization, and reporting.
Data processing
- cutage()
- expand_age_pop()
- classify_icd10()
- classify-childhood()
- tidy_*()
- write_*()
Statistics calculation
- ageadjust()
- truncrate()
- cumrate()
- cumrisk()
- lt()
- expand_lifetable()
Statistics sheet
- summary()
- create_asr()
- create_quality()
- create_age_rate()
- create_sheet()
What can canregtools do ?
Canregtools is a tool designed for high-level cancer registries. It helps process data from multiple registries in batch mode, allowing users to filter data based on custom conditions and reformat or merge cancer registration data according to registry attributes.
Registry attributes
- write_registry()
- write_areacode()
- write_area_type()
- tidy_areacode()
Filter/merge object
- cr_select()
- cr_merge()
- reframe_fbswicd()
Statistics sheet
- summary()
- create_asr()
- create_quality()
- create_age_rate()
- create_sheet()
Define S3 class for generic functions
We define a set of class for different methods to carry out different functions.
S3 class for single registry
- canreg
- fbswicd
- asr
- quality
S3 class for multiple registries
- canregs
- fbswicds
- asrs
- qualities
S3 method
create_asr, create_quality, create_age_rate, create_sheet, cr_select, cr_merge, reframe_fbswicd
Internal consistency check
Internal consistency checks are a crucial step before conducting data analysis. We need to identify and address any impossible or unlikely combinations of variables to insure the data validity.
Variable
- check_topo()
- check_morp()
- check_icd10()
- check_areacode()
- check_sex()
- check_id()
Variables combination
- check_sex_morp()
- check_sex_topo()
- check_topo_morp()
- check_morp_beha()
- check_morp_grad()
- check_age_topo_morp()
Format
- check_header()
- check_followup()
- ICDO3toICD10()
How to install
- Downlaod R (Rhttps://cloud.r-project.org)
- Download Rstudio(https://posit.co/download/rstudio-desktop/)
How to install
Install it from github repository
# install the remotes package if doesn't installed
install.packages("remotes)
library(remotes)
install_github("gigu003/canregtools")Install it from compiled local source package file
Single cancer registry
Contents
Single cancer registry
Batch mode (deal with data from multiple cancer registries)
Reframe fbsws
Visualization
Raw data(call for data from NCC)
The raw data is an Excel file with three sheets named FB, SW, and POP, which store incidence data, mortality data, and population data, respectively.
Reading raw data to ‘canreg’ data
canreg object with class of ‘canreg’
‘canreg’ is a list contains four elements named ‘areacode’, ‘FBcases’, ‘SWcases’, and ‘POP’, which were read from “FB”, “SW” and “POP” sheets of raw data.
[1] "areacode" "FBcases" "SWcases" "POP" [1] "410102"# A tibble: 6 × 20
registr sex birthda addcode trib occu marri inciden topo morp
<chr> <chr> <date> <chr> <chr> <chr> <chr> <date> <chr> <chr>
1 21410500166… 1 1975-04-17 410102… 01 31 2 2021-10-26 C15.9 8010
2 21410802159… 2 1991-01-16 410102… 01 14 2 2021-10-22 C53.8 8140
3 21410102172… 2 1962-10-15 410102… 01 00 2 2021-01-19 C53.0 8070
4 21411202123… 2 1951-03-09 410102… 01 49 2 2021-01-30 C50.9 8000
5 21411624137… 1 1955-07-15 410102… 01 61 2 2021-05-13 C22.0 8000
6 23411002105… 1 1939-12-15 410102… 01 28 5 2021-12-22 C34.1 8140
# ℹ 10 more variables: beha <chr>, grad <chr>, basi <chr>, icd10 <chr>,
# autoicd10 <chr>, lastcontact <dttm>, status <chr>, caus <chr>,
# deathda <date>, deadplace <chr>canreg object with class of ‘canreg’
‘canreg’ is a list contains four elements including ‘areacode’, ‘FBcases’, ‘SWcases’, and ‘POP’
[1] "areacode" "FBcases" "SWcases" "POP" # A tibble: 6 × 19
registr sex birthda trib occu marri inciden topo morp beha grad
<chr> <chr> <date> <chr> <chr> <chr> <date> <chr> <chr> <chr> <chr>
1 1741010… 2 1921-02-18 01 39 2 2014-10-21 C44.5 8010 3 9
2 1841010… 1 1935-05-15 01 80 2 2018-07-14 C34.9 8010 3 9
3 1841010… 2 1941-02-16 01 29 2 2015-04-02 C34.9 8000 3 9
4 1841010… 1 1952-07-06 01 90 2 2018-04-17 C61.9 8140 3 3
5 1841010… 1 1947-08-21 01 85 2 2017-11-13 C16.3 8140 3 9
6 1841010… 1 1941-08-16 01 29 2 2015-12-23 C73.9 8050 3 9
# ℹ 8 more variables: basi <chr>, icd10 <chr>, autoicd10 <chr>,
# lastcontact <dttm>, status <chr>, caus <chr>, deathda <date>,
# deadplace <chr>canreg object with class of ‘canreg’
‘canreg’ is a list contains four elements including ‘areacode’, ‘FBcases’, ‘SWcases’, and ‘POP’
Counting ‘canreg’ data to ‘fbswicd’ data
[1] "fbswicd" "list" [1] "areacode" "fbswicd" "sitemorp" "pop" year sex agegrp cancer fbs sws mv ub sub m8000 dco
<int> <int> <fctr> <int> <int> <int> <int> <int> <int> <int> <int>
1: 2021 1 0 岁 60 3 0 2 1 1 1 0
2: 2021 1 0 岁 61 3 0 2 1 1 1 0 year sex cancer site morp
<int> <int> <int> <list> <list>
1: 2021 2 115 <data.frame[3x2]> <data.frame[9x2]>
2: 2021 2 114 <data.frame[8x2]> <data.frame[18x2]> year sex agegrp rks
<int> <int> <fctr> <int>
1: 2021 1 0 岁 3850
2: 2021 1 1-4 岁 14405Summary the canreg data
summary function could quickly calculate summary data of ‘canreg’ object.
[1] "summary" "list" [1] "areacode" "rks" "fbs" "inci"
[5] "sws" "mort" "mi" "mv"
[9] "dco" "rks_year" "inci_vars" "miss_r_vars_inci"
[13] "mort_vars" "miss_r_vars_mort"[1] 0.38[1] 373.15[1] 142Calculating age standardized rate
create_asr function could calculate age standardized rate, truncated rate, and cumulated rate based on provided standard population, it could also estimate the variance and 95% confidence interval of the rate.
# A tibble: 5 × 2
Vars Description
<chr> <chr>
1 cn64 Standard population in Chinese in 1964
2 cn82 Standard population in Chinese in 1982
3 cn2000 Standard population in Chinese in 2000
4 wld85 Segi's world standard population
5 wld2000 World standard population in 2000 # calculate asr using the create_asr() function
create_asr(fbsw, event = fbs, year, sex, cancer, std = c("cn2000", "wld85"))# A tibble: 56 × 11
year sex cancer no_cases cr asr_cn2000 asr_wld85 truncr_cn2000
<int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 1069 327. 266. 267. 436.
2 2021 1 61 1050 321. 262. 263. 429.
3 2021 1 101 18 5.50 4.68 4.84 10.4
4 2021 1 102 5 1.53 1.23 1.45 2.63
5 2021 1 103 47 14.4 10.8 10.3 12.0
6 2021 1 104 65 19.9 15.6 16.0 21.9
7 2021 1 105 87 26.6 21.0 21.7 32.9
8 2021 1 106 84 25.7 20.7 21.7 39.4
9 2021 1 107 21 6.41 5.12 5.57 9.64
10 2021 1 108 28 8.55 6.82 7.14 10.1
# ℹ 46 more rows
# ℹ 3 more variables: truncr_wld85 <dbl>, cumur <dbl>, prop <dbl>Calculating age standardized rate
The drop_total, drop_others, and add_labels functions can perform further processing on the ASR data, such as removing other cancers, removing total cancer, and adding labels.
create_asr(fbsw, event = fbs, year, sex, cancer) |>
drop_total() |> drop_others() |>
add_labels(lang = "en", label_type = "abbr")# A tibble: 48 × 13
year sex cancer site icd10 no_cases cr asr_cn2000 asr_wld85
<int> <fct> <int> <fct> <fct> <int> <dbl> <dbl> <dbl>
1 2021 Male 101 Oral Cavity & P… C00-… 18 5.50 4.68 4.84
2 2021 Male 102 Nasopharynx C11 5 1.53 1.23 1.45
3 2021 Male 103 Esophagus C15 47 14.4 10.8 10.3
4 2021 Male 104 Stomach C16 65 19.9 15.6 16.0
5 2021 Male 105 Colorectum C18-… 87 26.6 21.0 21.7
6 2021 Male 106 Liver C22 84 25.7 20.7 21.7
7 2021 Male 107 Gallbladder C23-… 21 6.41 5.12 5.57
8 2021 Male 108 Pancreas C25 28 8.55 6.82 7.14
9 2021 Male 109 Larynx C32 15 4.58 3.72 3.97
10 2021 Male 110 Lung C33-… 230 70.2 57.1 59.1
# ℹ 38 more rows
# ℹ 4 more variables: truncr_cn2000 <dbl>, truncr_wld85 <dbl>, cumur <dbl>,
# prop <dbl>Calculating quality indicators
create_quality function can calculate quality indicators including number of cancer cases, crude incidence, mortality, mortality:incidence ratio, proportion of morphology diagnosed cases, dco, UB%, etc based on ‘canreg’ or ‘fbswicd’ data.
# calculate quality indicators based on 'canreg' data
create_quality(data, year, sex, cancer) |> filter(!cancer == 0) |>
add_labels(lang = "en")# A tibble: 56 × 16
year sex cancer site icd10 rks fbs fbl sws swl mi mv
<int> <fct> <int> <fct> <fct> <int> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 2021 Male 60 All Ca… ALL 327403 1069 327. 559 171. 0.52 76.8
2 2021 Male 61 All Ca… ALLb… 327403 1050 321. 553 169. 0.53 76.6
3 2021 Male 101 Oral C… C00-… 327403 18 5.5 10 3.05 0.56 55.6
4 2021 Male 102 Nasoph… C11 327403 5 1.53 4 1.22 0.8 40
5 2021 Male 103 Esopha… C15 327403 47 14.4 35 10.7 0.74 87.2
6 2021 Male 104 Stomach C16 327403 65 19.8 55 16.8 0.85 73.8
7 2021 Male 105 Conlon… C18-… 327403 87 26.6 55 16.8 0.63 77.0
8 2021 Male 106 Liver C22 327403 84 25.7 77 23.5 0.92 66.7
9 2021 Male 107 Gallbl… C23-… 327403 21 6.41 16 4.89 0.76 85.7
10 2021 Male 108 Pancre… C25 327403 28 8.55 28 8.55 1 50
# ℹ 46 more rows
# ℹ 4 more variables: dco <dbl>, ub <dbl>, sub <dbl>, m8000 <dbl>Calculating quality indicators
# calculate quality indicators based on 'fbswicd' data
create_quality(fbsw, year, sex) |>
add_labels(lang = "en")# A tibble: 2 × 16
year sex cancer site icd10 rks fbs fbl sws swl mi mv
<int> <fct> <dbl> <fct> <fct> <int> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 2021 Male 60 All Canc… ALL 327403 1069 327. 559 171. 0.52 76.8
2 2021 Female 60 All Canc… ALL 355707 1415 398. 405 114. 0.29 84.8
# ℹ 4 more variables: dco <dbl>, ub <dbl>, sub <dbl>, m8000 <dbl># A tibble: 28 × 16
year sex cancer site icd10 rks fbs fbl sws swl mi mv
<dbl> <fct> <int> <fct> <fct> <int> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 9000 Total 60 All Ca… ALL 683110 2484 364. 964 141. 0.39 81.4
2 9000 Total 61 All Ca… ALLb… 683110 2451 359. 950 139. 0.39 81.2
3 9000 Total 101 Oral C… C00-… 683110 26 3.81 13 1.9 0.5 57.7
4 9000 Total 102 Nasoph… C11 683110 7 1.02 5 0.73 0.71 57.1
5 9000 Total 103 Esopha… C15 683110 66 9.66 46 6.73 0.7 86.4
6 9000 Total 104 Stomach C16 683110 101 14.8 79 11.6 0.78 71.3
7 9000 Total 105 Conlon… C18-… 683110 181 26.5 103 15.1 0.57 81.2
8 9000 Total 106 Liver C22 683110 125 18.3 100 14.6 0.8 68
9 9000 Total 107 Gallbl… C23-… 683110 44 6.44 31 4.54 0.7 86.4
10 9000 Total 108 Pancre… C25 683110 46 6.73 57 8.34 1.24 43.5
# ℹ 18 more rows
# ℹ 4 more variables: dco <dbl>, ub <dbl>, sub <dbl>, m8000 <dbl>Calculating age specific rate
create_age_rate function could calculate age specific rate based on ‘canreg’ or ‘fbswicd’ data.
# calculate age specific rate from 'canreg' data.
create_age_rate(data, year, sex, cancer, format = "long") |>
filter(!cancer == 0) |>
arrange(year, sex, cancer, agegrp)# A tibble: 1,064 × 6
year sex cancer agegrp cases rate
<int> <int> <int> <fct> <int> <dbl>
1 2021 1 60 0 岁 3 77.9
2 2021 1 60 1-4 岁 3 20.8
3 2021 1 60 5-9 岁 5 32.9
4 2021 1 60 10-14 岁 0 0
5 2021 1 60 15-19 岁 2 10.7
6 2021 1 60 20-24 岁 5 18.0
7 2021 1 60 25-29 岁 14 38.7
8 2021 1 60 30-34 岁 38 130.
9 2021 1 60 35-39 岁 36 121.
10 2021 1 60 40-44 岁 60 196.
# ℹ 1,054 more rowsCalculating age specific rate
# calculate age specific rate from 'fbswicd' data.
create_age_rate(fbsw, year, sex, cancer, format = "wide") |>
filter(!cancer == 0) |>
add_labels(lang = "en")# A tibble: 56 × 45
year sex cancer site icd10 f0 f1 f2 f3 f4 f5 f6
<int> <fct> <int> <fct> <fct> <int> <int> <int> <int> <int> <int> <int>
1 2021 Male 60 All Cance… ALL 1069 3 3 5 0 2 5
2 2021 Male 61 All Cance… ALLb… 1050 3 3 5 0 2 5
3 2021 Male 101 Oral Cavi… C00-… 18 0 0 0 0 0 0
4 2021 Male 102 Nasophary… C11 5 0 0 0 0 0 0
5 2021 Male 103 Esophagus C15 47 0 0 0 0 0 0
6 2021 Male 104 Stomach C16 65 0 0 0 0 0 0
7 2021 Male 105 Conlon, R… C18-… 87 0 0 0 0 0 0
8 2021 Male 106 Liver C22 84 0 1 0 0 0 0
9 2021 Male 107 Gallbladd… C23-… 21 0 0 0 0 0 0
10 2021 Male 108 Pancreas C25 28 0 0 0 0 0 0
# ℹ 46 more rows
# ℹ 33 more variables: f7 <int>, f8 <int>, f9 <int>, f10 <int>, f11 <int>,
# f12 <int>, f13 <int>, f14 <int>, f15 <int>, f16 <int>, f17 <int>,
# f18 <int>, f19 <int>, r0 <dbl>, r1 <dbl>, r2 <dbl>, r3 <dbl>, r4 <dbl>,
# r5 <dbl>, r6 <dbl>, r7 <dbl>, r8 <dbl>, r9 <dbl>, r10 <dbl>, r11 <dbl>,
# r12 <dbl>, r13 <dbl>, r14 <dbl>, r15 <dbl>, r16 <dbl>, r17 <dbl>,
# r18 <dbl>, r19 <dbl>Batch mode (deal with data from multiple cancer registries)
Contents
Single cancer registry
Batch mode (deal with data from multiple cancer registries)
Reframe fbsws
Visualization
Reading data from multiple raw data files
Object with class of ‘canregs’ is a list with elements of object with class of ‘canreg’, it could be read using the ‘read_canreg’ function.
Summary the canregs data
summary function could quickly calculate summary data of ‘canreg’ or ‘canregs’ object.
[1] "410102" "410103" "410104" "410105" "410123" "410185" "410224" "410302"
[9] "410303" "410304"[1] "410302" "410303" "410304"[1] "410102" "410103" "410104" "410105" "410302" "410303" "410304"[1] "410102" "410303"Filter the canregs data
cr_select function can filter ‘carengs’, ‘fbswicds’, ‘asrs’, and, ‘summaries’ based on input conditions.
Counting ‘canregs’ data
count_canreg function can count ‘canregs’ data into ‘fbswicds’ data, which is a list of elements of ‘fbswicd’ data that could used as input data for create_asr, create_quality, create_sheet, etc.
Calculate age standardized rate
create_asr function can also calculate age standard rate based on ‘fbswicds’ data.
[1] "asrs" "list"$`410102`
# A tibble: 2 × 11
year sex cancer no_cases cr asr_cn2000 asr_wld85 truncr_cn2000
<int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 1069 327. 266. 267. 436.
2 2021 2 60 1415 398. 319. 299. 565.
# ℹ 3 more variables: truncr_wld85 <dbl>, cumur <dbl>, prop <dbl>
$`410103`
# A tibble: 2 × 11
year sex cancer no_cases cr asr_cn2000 asr_wld85 truncr_cn2000
<int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 1190 362. 242. 237. 380.
2 2021 2 60 1469 409. 289. 272. 539.
# ℹ 3 more variables: truncr_wld85 <dbl>, cumur <dbl>, prop <dbl>
$`410104`
# A tibble: 2 × 11
year sex cancer no_cases cr asr_cn2000 asr_wld85 truncr_cn2000
<int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 720 316. 244. 239. 398.
2 2021 2 60 862 341. 272. 258. 500.
# ℹ 3 more variables: truncr_wld85 <dbl>, cumur <dbl>, prop <dbl>
$`410105`
# A tibble: 2 × 11
year sex cancer no_cases cr asr_cn2000 asr_wld85 truncr_cn2000
<int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 1816 392. 286. 273. 429.
2 2021 2 60 2334 461. 344. 319. 610.
# ℹ 3 more variables: truncr_wld85 <dbl>, cumur <dbl>, prop <dbl>
$`410302`
# A tibble: 2 × 11
year sex cancer no_cases cr asr_cn2000 asr_wld85 truncr_cn2000
<int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 289 348. 225. 222. 345.
2 2021 2 60 272 315. 188. 182. 343.
# ℹ 3 more variables: truncr_wld85 <dbl>, cumur <dbl>, prop <dbl>
$`410303`
# A tibble: 2 × 11
year sex cancer no_cases cr asr_cn2000 asr_wld85 truncr_cn2000
<int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 625 383. 278. 310. 280.
2 2021 2 60 596 369. 230. 222. 354.
# ℹ 3 more variables: truncr_wld85 <dbl>, cumur <dbl>, prop <dbl>Merge age standardized rate(asrs)
cr_merge function cancer merge ‘carengs’, ‘fbswicds’, ‘asrs’, ‘qualities’ into ‘canreg’, ‘fbswicd’, ‘asr’, and ‘quality’ data.
# A tibble: 6 × 8
areacode year sex cancer no_cases cr asr_cn2000 asr_wld85
<int> <int> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 410102 2021 1 60 1069 327. 266. 267.
2 410102 2021 2 60 1415 398. 319. 299.
3 410103 2021 1 60 1190 362. 242. 237.
4 410103 2021 2 60 1469 409. 289. 272.
5 410104 2021 1 60 720 316. 244. 239.
6 410104 2021 2 60 862 341. 272. 258. [1] "areacode" "year" "sex" "cancer"
[5] "no_cases" "cr" "asr_cn2000" "asr_wld85"
[9] "truncr_cn2000" "truncr_wld85" "cumur" "prop" Creating quality indicators
$`410102`
# A tibble: 2 × 14
year sex cancer rks fbs fbl sws swl mi mv dco ub
<int> <int> <dbl> <int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 327403 1069 327. 559 171. 0.52 76.8 1.5 1.78
2 2021 2 60 355707 1415 398. 405 114. 0.29 84.8 0.85 1.98
# ℹ 2 more variables: sub <dbl>, m8000 <dbl>
$`410103`
# A tibble: 2 × 14
year sex cancer rks fbs fbl sws swl mi mv dco ub
<int> <int> <dbl> <int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 328415 1190 362. 672 205. 0.56 73.9 0.67 2.18
2 2021 2 60 358941 1469 409. 408 114. 0.28 77.9 1.09 2.11
# ℹ 2 more variables: sub <dbl>, m8000 <dbl>
$`410104`
# A tibble: 2 × 14
year sex cancer rks fbs fbl sws swl mi mv dco ub
<int> <int> <dbl> <int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 227511 720 316. 369 162. 0.51 82.2 0.56 1.81
2 2021 2 60 253077 862 341. 252 99.6 0.29 82.2 0.35 1.28
# ℹ 2 more variables: sub <dbl>, m8000 <dbl>
$`410105`
# A tibble: 2 × 14
year sex cancer rks fbs fbl sws swl mi mv dco ub
<int> <int> <dbl> <int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 462738 1816 392. 792 171. 0.44 77.8 0.72 1.6
2 2021 2 60 506456 2334 461. 553 109. 0.24 80.9 0.21 1.63
# ℹ 2 more variables: sub <dbl>, m8000 <dbl>
$`410302`
# A tibble: 2 × 14
year sex cancer rks fbs fbl sws swl mi mv dco ub sub
<int> <int> <dbl> <int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 83010 289 348. 153 184. 0.53 69.9 0 1.04 65.7
2 2021 2 60 86359 272 315. 103 119. 0.38 75.4 0.74 0.74 72.1
# ℹ 1 more variable: m8000 <dbl>
$`410303`
# A tibble: 2 × 14
year sex cancer rks fbs fbl sws swl mi mv dco ub
<int> <int> <dbl> <int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 163005 625 383. 412 253. 0.66 76.8 1.12 0.96
2 2021 2 60 161724 596 369. 252 156. 0.42 84.4 1.51 2.52
# ℹ 2 more variables: sub <dbl>, m8000 <dbl>
$`410304`
# A tibble: 2 × 14
year sex cancer rks fbs fbl sws swl mi mv dco ub
<int> <int> <dbl> <int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2021 1 60 99735 427 428. 232 233. 0.54 71.4 2.81 2.81
2 2021 2 60 100963 353 350. 147 146. 0.42 79.3 2.55 1.13
# ℹ 2 more variables: sub <dbl>, m8000 <dbl>
attr(,"class")
[1] "qualities" "list" Merge quality indicators
# A tibble: 6 × 12
areacode year sex cancer rks fbs fbl sws swl mi mv dco
<int> <int> <int> <dbl> <int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
1 410102 2021 1 60 327403 1069 327. 559 171. 0.52 76.8 1.5
2 410102 2021 2 60 355707 1415 398. 405 114. 0.29 84.8 0.85
3 410103 2021 1 60 328415 1190 362. 672 205. 0.56 73.9 0.67
4 410103 2021 2 60 358941 1469 409. 408 114. 0.28 77.9 1.09
5 410104 2021 1 60 227511 720 316. 369 162. 0.51 82.2 0.56
6 410104 2021 2 60 253077 862 341. 252 99.6 0.29 82.2 0.35 [1] "areacode" "year" "sex" "cancer" "rks" "fbs"
[7] "fbl" "sws" "swl" "mi" "mv" "dco"
[13] "ub" "sub" "m8000" Reframe fbsws
Contents
Single cancer registry
Batch mode (deal with data from multiple cancer registries)
Reframe fbsws
Visualization
show attributes of cancer registry affiliated with areacode
tidy_areacode function show attributes of cancer registry affiliated with areacode.
[1] "areacode" "registry" "province" "city" "area_type" "region" You cancer use write_registry, or write_area_type function to modify the attributes of the registry.
Reframe ‘fbswicds’ to ‘fbswicd’
You can reframe the ‘fbswicds’ according to the attribute name of registry like ‘area_type’, ‘registry’, ‘province’, etc.
Ceating asr based on reframed fbsws
# A tibble: 4 × 8
areacode year sex cancer no_cases cr asr_cn2000 asr_wld85
<int> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 910000 9000 1 60 6136 363. 253. 248.
2 910000 9000 2 60 7301 400. 288. 270.
3 920000 9000 1 60 2676 233. 172. 168.
4 920000 9000 2 60 2817 254. 186. 176. [1] "areacode" "year" "sex" "cancer"
[5] "no_cases" "cr" "asr_cn2000" "asr_wld85"
[9] "truncr_cn2000" "truncr_wld85" "cumur" "prop" Visualization
Contents
Single cancer registry
Batch mode (deal with data from multiple cancer registries)
Reframe fbsws
Visualization
Draw pyramid plot
Draw bar chart
Draw bar chart
library(dplyr)
asr1 <- create_asr(fbsw,year,sex,cancer,event = fbs) |> mutate(type="incidence")
asr2 <- create_asr(fbsw,year,sex,cancer,event = sws) |> mutate(type="mortality")
asr <- bind_rows(asr1, asr2) |> drop_others() |> drop_total() |>
add_labels(label_type = "abbr",lang = "en")
draw_barchart(asr, plot_var =cr, cate_var = site,group_var = type,
side_label = c("Male","Female"))
Draw line chart
https://slides.chenq.site
Canregtools An R package used in PBCRs ️Qiong Chen👨⚕️ Phd. 🏥Henan Cancer Center Monday Nov 4, 2024