revdep/checks.noindex/msSPChelpR/old/msSPChelpR.Rcheck/msSPChelpR-Ex.R
In mtfairbanks/gdt: Tidy Interface to 'data.table'
pkgname <- "msSPChelpR"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('msSPChelpR')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("asir")
### * asir
flush(stderr()); flush(stdout())
### Name: asir
### Title: Calculate age-standardized incidence rates
### Aliases: asir
### ** Examples
#load sample data
data("us_second_cancer")
data("standard_population")
data("population_us")
#make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
#only use sample
dplyr::filter(as.numeric(fake_id) < 200000) %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 2)
#create count variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#remove cases for which no reference population exists
usdata_wide <- usdata_wide %>%
dplyr::filter(t_yeardiag.2 %in% c("1990 - 1994", "1995 - 1999", "2000 - 2004",
"2005 - 2009", "2010 - 2014"))
#now we can run the function
msSPChelpR::asir(usdata_wide,
dattype = "seer",
std_pop = "ESP2013",
truncate_std_pop = FALSE,
futime_src = "refpop",
summarize_groups = "none",
count_var = "count_spc",
refpop_df = population_us,
region_var = "registry.1",
age_var = "fc_agegroup.1",
sex_var = "sex.1",
year_var = "t_yeardiag.2",
site_var = "t_site_icd.2",
pyar_var = "population_pyar")
cleanEx()
nameEx("calc_futime")
### * calc_futime
flush(stderr()); flush(stdout())
### Name: calc_futime
### Title: Calculate follow-up time per case until end of follow-up
### depending on pat_status - tidyverse version
### Aliases: calc_futime
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
status_var = "p_status", life_var = "p_alive.1",
birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#now we can run the function
msSPChelpR::calc_futime(usdata_wide,
futime_var_new = "p_futimeyrs",
fu_end = "2017-12-31",
dattype = "seer",
time_unit = "years",
status_var = "p_status",
lifedat_var = "datedeath.1",
fcdat_var = "t_datediag.1",
spcdat_var = "t_datediag.2")
cleanEx()
nameEx("calc_futime_tt")
### * calc_futime_tt
flush(stderr()); flush(stdout())
### Name: calc_futime_tt
### Title: Calculate follow-up time per case until end of follow-up
### depending on pat_status - tidytable version
### Aliases: calc_futime_tt
### ** Examples
#load sample data
data("us_second_cancer")
#make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
status_var = "p_status", life_var = "p_alive.1",
birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#now we can run the function
msSPChelpR::calc_futime_tt(usdata_wide,
futime_var_new = "p_futimeyrs",
fu_end = "2017-12-31",
dattype = "seer",
time_unit = "years",
status_var = "p_status",
lifedat_var = "datedeath.1",
fcdat_var = "t_datediag.1",
spcdat_var = "t_datediag.2")
cleanEx()
nameEx("calc_refrates")
### * calc_refrates
flush(stderr()); flush(stdout())
### Name: calc_refrates
### Title: Calculate age-, sex-, cohort-, region-specific incidence rates
### from a cohort
### Aliases: calc_refrates
### ** Examples
#load sample data
data("us_second_cancer")
data("population_us")
us_second_cancer %>%
#create variable to indicate to be counted as case
dplyr::mutate(is_case = 1) %>%
#calculate refrates - warning: these are not realistic numbers, just showing functionality
calc_refrates(dattype = "seer", , count_var = "is_case", refpop_df = population_us,
region_var = "registry", age_var = "fc_agegroup", sex_var = "sex",
site_var = "t_site_icd")
cleanEx()
nameEx("histgroup_iarc")
### * histgroup_iarc
flush(stderr()); flush(stdout())
### Name: histgroup_iarc
### Title: Create variable for groups of malignant neoplasms considered to
### be histologically 'different' for the purpose of defining multiple
### tumors, ICD-O-3
### Aliases: histgroup_iarc
### ** Examples
#load sample data
data("us_second_cancer")
us_second_cancer %>%
msSPChelpR::histgroup_iarc(., hist_var = t_hist) %>%
dplyr::select(fake_id, t_hist, t_histgroupiarc)
cleanEx()
nameEx("ir_crosstab")
### * ir_crosstab
flush(stderr()); flush(stdout())
### Name: ir_crosstab
### Title: Calculate crude incidence rates and crosstabulate results by
### break variables
### Aliases: ir_crosstab
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
status_var = "p_status", life_var = "p_alive.1",
birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#now we can run the function
usdata_wide <- usdata_wide %>%
msSPChelpR::calc_futime(.,
futime_var_new = "p_futimeyrs",
fu_end = "2017-12-31",
dattype = "seer",
time_unit = "years",
status_var = "p_status",
lifedat_var = "datedeath.1",
fcdat_var = "t_datediag.1",
spcdat_var = "t_datediag.2")
#for example, you can calculate incidence and summarize by sex and registry
msSPChelpR::ir_crosstab(usdata_wide,
dattype = "seer",
count_var = "count_spc",
xbreak_var = "none",
ybreak_vars = c("sex.1", "registry.1"),
collapse_ci = FALSE,
add_total = "no",
add_n_percentages = FALSE,
futime_var = "p_futimeyrs",
alpha = 0.05)
cleanEx()
nameEx("ir_crosstab_byfutime")
### * ir_crosstab_byfutime
flush(stderr()); flush(stdout())
### Name: ir_crosstab_byfutime
### Title: Calculate crude incidence rates and cross-tabulate results by
### break variables; cumulative FU-times as are used as xbreak_var
### Aliases: ir_crosstab_byfutime
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
#only use sample
dplyr::filter(as.numeric(fake_id) < 200000) %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 2)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
status_var = "p_status", life_var = "p_alive.1",
birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#now we can run the function
usdata_wide <- usdata_wide %>%
msSPChelpR::calc_futime(.,
futime_var_new = "p_futimeyrs",
fu_end = "2017-12-31",
dattype = "seer",
time_unit = "years",
status_var = "p_status",
lifedat_var = "datedeath.1",
fcdat_var = "t_datediag.1",
spcdat_var = "t_datediag.2")
#for example, you can calculate incidence and summarize by sex and registry
msSPChelpR::ir_crosstab_byfutime(usdata_wide,
dattype = "seer",
count_var = "count_spc",
futime_breaks = c(0, .5, 1, 5, 10, Inf),
ybreak_vars = c("sex.1", "registry.1"),
collapse_ci = FALSE,
add_total = "no",
futime_var = "p_futimeyrs",
alpha = 0.05)
cleanEx()
nameEx("pat_status")
### * pat_status
flush(stderr()); flush(stdout())
### Name: pat_status
### Title: Determine patient status at specific end of follow-up -
### tidyverse version
### Aliases: pat_status
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#now we can run the function
msSPChelpR::pat_status(usdata_wide,
fu_end = "2017-12-31",
dattype = "seer",
status_var = "p_status",
life_var = "p_alive.1",
spc_var = NULL,
birthdat_var = "datebirth.1",
lifedat_var = "datedeath.1",
use_lifedatmin = FALSE,
check = TRUE,
as_labelled_factor = FALSE)
cleanEx()
nameEx("pat_status_tt")
### * pat_status_tt
flush(stderr()); flush(stdout())
### Name: pat_status_tt
### Title: Determine patient status at specific end of follow-up -
### tidytable version
### Aliases: pat_status_tt
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#now we can run the function
msSPChelpR::pat_status_tt(usdata_wide,
fu_end = "2017-12-31",
dattype = "seer",
status_var = "p_status",
life_var = "p_alive.1",
spc_var = NULL,
birthdat_var = "datebirth.1",
lifedat_var = "datedeath.1",
use_lifedatmin = FALSE,
check = TRUE,
as_labelled_factor = FALSE)
cleanEx()
nameEx("renumber_time_id")
### * renumber_time_id
flush(stderr()); flush(stdout())
### Name: renumber_time_id
### Title: Renumber the time ID per case (i.e. Tumor sequence)
### Aliases: renumber_time_id
### ** Examples
data(us_second_cancer)
us_second_cancer %>%
#only select first 10000 rows so example runs faster
dplyr::slice(1:10000) %>%
msSPChelpR::renumber_time_id(new_time_id_var = "t_tumid",
dattype = "seer",
case_id_var = "fake_id")
cleanEx()
nameEx("renumber_time_id_tt")
### * renumber_time_id_tt
flush(stderr()); flush(stdout())
### Name: renumber_time_id_tt
### Title: Renumber the time ID per case (i.e. Tumor sequence) - tidytable
### version
### Aliases: renumber_time_id_tt
### ** Examples
data(us_second_cancer)
us_second_cancer %>%
#only select first 10000 rows so example runs faster
dplyr::slice(1:10000) %>%
msSPChelpR::renumber_time_id_tt(new_time_id_var = "t_tumid",
dattype = "seer",
case_id_var = "fake_id")
cleanEx()
nameEx("reshape_long")
### * reshape_long
flush(stderr()); flush(stdout())
### Name: reshape_long
### Title: Reshape dataset to long format - stats::reshape version
### Aliases: reshape_long
### ** Examples
data(us_second_cancer)
#prep step - reshape wide a sample of 10000 rows from us_second_cancer
usdata_wide_sample <- msSPChelpR::reshape_wide(us_second_cancer,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM",
timevar_max = 2,
datsize = 10000)
#now we can reshape long again
msSPChelpR::reshape_long(usdata_wide_sample,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM")
cleanEx()
nameEx("reshape_long_tidyr")
### * reshape_long_tidyr
flush(stderr()); flush(stdout())
### Name: reshape_long_tidyr
### Title: Reshape dataset to wide format - tidyr version
### Aliases: reshape_long_tidyr
### ** Examples
data(us_second_cancer)
#prep step - reshape wide a sample of 10000 rows from us_second_cancer
usdata_wide_sample <- msSPChelpR::reshape_wide(us_second_cancer,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM",
timevar_max = 2,
datsize = 10000)
#now we can reshape long again
msSPChelpR::reshape_long_tidyr(usdata_wide_sample,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM")
cleanEx()
nameEx("reshape_long_tt")
### * reshape_long_tt
flush(stderr()); flush(stdout())
### Name: reshape_long_tt
### Title: Reshape dataset to wide format - tidytable version
### Aliases: reshape_long_tt
### ** Examples
data(us_second_cancer)
#prep step - reshape wide a sample of 10000 rows from us_second_cancer
usdata_wide_sample <- msSPChelpR::reshape_wide(us_second_cancer,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM",
timevar_max = 2,
datsize = 10000)
#now we can reshape long again
msSPChelpR::reshape_long_tt(usdata_wide_sample,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM")
cleanEx()
nameEx("reshape_wide")
### * reshape_wide
flush(stderr()); flush(stdout())
### Name: reshape_wide
### Title: Reshape dataset to wide format
### Aliases: reshape_wide
### ** Examples
data(us_second_cancer)
msSPChelpR::reshape_wide(us_second_cancer,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM",
timevar_max = 2,
datsize = 10000)
cleanEx()
nameEx("reshape_wide_tidyr")
### * reshape_wide_tidyr
flush(stderr()); flush(stdout())
### Name: reshape_wide_tidyr
### Title: Reshape dataset to wide format - tidyr version
### Aliases: reshape_wide_tidyr
### ** Examples
data(us_second_cancer)
msSPChelpR::reshape_wide_tidyr(us_second_cancer,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM",
timevar_max = 2,
datsize = 10000)
cleanEx()
nameEx("reshape_wide_tt")
### * reshape_wide_tt
flush(stderr()); flush(stdout())
### Name: reshape_wide_tt
### Title: Reshape dataset to wide format - tidytable version
### Aliases: reshape_wide_tt
### ** Examples
data(us_second_cancer)
msSPChelpR::reshape_wide_tt(us_second_cancer,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM",
timevar_max = 2,
datsize = 10000)
cleanEx()
nameEx("sir_byfutime")
### * sir_byfutime
flush(stderr()); flush(stdout())
### Name: sir_byfutime
### Title: Calculate standardized incidence ratios with custom grouping
### variables stratified by follow-up time
### Aliases: sir_byfutime
### ** Examples
#There are various preparation steps required, before you can run this function.
#Please refer to the Introduction vignette to see how to prepare your data
## Not run:
##D usdata_wide %>%
##D sir_byfutime(
##D dattype = "seer",
##D ybreak_vars = c("race.1", "t_dco.1"),
##D xbreak_var = "none",
##D futime_breaks = c(0, 1/12, 2/12, 1, 5, 10, Inf),
##D count_var = "count_spc",
##D refrates_df = us_refrates_icd2,
##D calc_total_row = TRUE,
##D calc_total_fu = TRUE,
##D region_var = "registry.1",
##D age_var = "fc_agegroup.1",
##D sex_var = "sex.1",
##D year_var = "t_yeardiag.1",
##D site_var = "t_site_icd.1", #using grouping by second cancer incidence
##D futime_var = "p_futimeyrs",
##D alpha = 0.05)
##D
## End(Not run)
cleanEx()
nameEx("sir_ratio")
### * sir_ratio
flush(stderr()); flush(stdout())
### Name: sir_ratio
### Title: Calculate Ratio of two SIRs or SMRs
### Aliases: sir_ratio sir_ratio_lci sir_ratio_uci
### ** Examples
#provide the two expected and observed count to get the ratio of SIRs/SMRs
msSPChelpR::sir_ratio(o1 = 2140, o2 = 3158, e1 = 1993, e2 = 2123)
#calculate lower confidence limit
msSPChelpR::sir_ratio_lci(o1 = 2140, o2 = 3158, e1 = 1993, e2 = 2123, alpha = 0.05)
#calculate upper confidence limit
msSPChelpR::sir_ratio_uci(o1 = 2140, o2 = 3158, e1 = 1993, e2 = 2123, alpha = 0.05)
#functions can be easily used inside dplyr::mutate function
library(dplyr)
test_df <- data.frame(sir_oth = c(1.07, 1.36, 0.96),
sir_smo = c(1.49, 1.81, 1.41),
observed_oth = c(2140, 748, 1392),
expected_oth = c(1993, 550, 1443),
observed_smo = c(3158, 744, 2414),
expected_smo = c(2123, 412, 1711))
test_df %>%
mutate(smo_ratio = sir_ratio(observed_oth, observed_smo, expected_oth, expected_smo),
smo_ratio_lci = sir_ratio_lci(observed_oth, observed_smo, expected_oth, expected_smo),
smo_ratio_uci = sir_ratio_uci(observed_oth, observed_smo, expected_oth, expected_smo))
cleanEx()
nameEx("summarize_sir_results")
### * summarize_sir_results
flush(stderr()); flush(stdout())
### Name: summarize_sir_results
### Title: Summarize detailed SIR results
### Aliases: summarize_sir_results
### ** Examples
#There are various preparation steps required, before you can run this function.
#Please refer to the Introduction vignette to see how to prepare your data
## Not run:
##D summarize_sir_results(.,
##D summarize_groups = c("region", "age", "year", "race"),
##D summarize_site = TRUE,
##D output = "long", output_information = "minimal",
##D add_total_row = "only", add_total_fu = "no",
##D collapse_ci = FALSE, shorten_total_cols = TRUE,
##D fubreak_var_name = "fu_time", ybreak_var_name = "yvar_name",
##D xbreak_var_name = "none", site_var_name = "t_site",
##D alpha = 0.05
##D )
##D
## End(Not run)
cleanEx()
nameEx("vital_status")
### * vital_status
flush(stderr()); flush(stdout())
### Name: vital_status
### Title: Determine vital status at end of follow-up depending on
### pat_status - tidyverse version
### Aliases: vital_status
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
status_var = "p_status", life_var = "p_alive.1",
birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#now we can run the function
msSPChelpR::vital_status(usdata_wide,
status_var = "p_status",
life_var_new = "p_alive_new",
check = TRUE,
as_labelled_factor = FALSE)
cleanEx()
nameEx("vital_status_tt")
### * vital_status_tt
flush(stderr()); flush(stdout())
### Name: vital_status_tt
### Title: Determine vital status at end of follow-up depending on
### pat_status - tidytable version
### Aliases: vital_status_tt
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
status_var = "p_status", life_var = "p_alive.1",
birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#now we can run the function
msSPChelpR::vital_status_tt(usdata_wide,
status_var = "p_status",
life_var_new = "p_alive_new",
check = TRUE,
as_labelled_factor = FALSE)
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
mtfairbanks/gdt documentation built on April 12, 2024, 6:51 p.m.
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pkgname <- "msSPChelpR"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('msSPChelpR')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("asir")
### * asir
flush(stderr()); flush(stdout())
### Name: asir
### Title: Calculate age-standardized incidence rates
### Aliases: asir
### ** Examples
#load sample data
data("us_second_cancer")
data("standard_population")
data("population_us")
#make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
#only use sample
dplyr::filter(as.numeric(fake_id) < 200000) %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 2)
#create count variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#remove cases for which no reference population exists
usdata_wide <- usdata_wide %>%
dplyr::filter(t_yeardiag.2 %in% c("1990 - 1994", "1995 - 1999", "2000 - 2004",
"2005 - 2009", "2010 - 2014"))
#now we can run the function
msSPChelpR::asir(usdata_wide,
dattype = "seer",
std_pop = "ESP2013",
truncate_std_pop = FALSE,
futime_src = "refpop",
summarize_groups = "none",
count_var = "count_spc",
refpop_df = population_us,
region_var = "registry.1",
age_var = "fc_agegroup.1",
sex_var = "sex.1",
year_var = "t_yeardiag.2",
site_var = "t_site_icd.2",
pyar_var = "population_pyar")
cleanEx()
nameEx("calc_futime")
### * calc_futime
flush(stderr()); flush(stdout())
### Name: calc_futime
### Title: Calculate follow-up time per case until end of follow-up
### depending on pat_status - tidyverse version
### Aliases: calc_futime
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
status_var = "p_status", life_var = "p_alive.1",
birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#now we can run the function
msSPChelpR::calc_futime(usdata_wide,
futime_var_new = "p_futimeyrs",
fu_end = "2017-12-31",
dattype = "seer",
time_unit = "years",
status_var = "p_status",
lifedat_var = "datedeath.1",
fcdat_var = "t_datediag.1",
spcdat_var = "t_datediag.2")
cleanEx()
nameEx("calc_futime_tt")
### * calc_futime_tt
flush(stderr()); flush(stdout())
### Name: calc_futime_tt
### Title: Calculate follow-up time per case until end of follow-up
### depending on pat_status - tidytable version
### Aliases: calc_futime_tt
### ** Examples
#load sample data
data("us_second_cancer")
#make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
status_var = "p_status", life_var = "p_alive.1",
birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#now we can run the function
msSPChelpR::calc_futime_tt(usdata_wide,
futime_var_new = "p_futimeyrs",
fu_end = "2017-12-31",
dattype = "seer",
time_unit = "years",
status_var = "p_status",
lifedat_var = "datedeath.1",
fcdat_var = "t_datediag.1",
spcdat_var = "t_datediag.2")
cleanEx()
nameEx("calc_refrates")
### * calc_refrates
flush(stderr()); flush(stdout())
### Name: calc_refrates
### Title: Calculate age-, sex-, cohort-, region-specific incidence rates
### from a cohort
### Aliases: calc_refrates
### ** Examples
#load sample data
data("us_second_cancer")
data("population_us")
us_second_cancer %>%
#create variable to indicate to be counted as case
dplyr::mutate(is_case = 1) %>%
#calculate refrates - warning: these are not realistic numbers, just showing functionality
calc_refrates(dattype = "seer", , count_var = "is_case", refpop_df = population_us,
region_var = "registry", age_var = "fc_agegroup", sex_var = "sex",
site_var = "t_site_icd")
cleanEx()
nameEx("histgroup_iarc")
### * histgroup_iarc
flush(stderr()); flush(stdout())
### Name: histgroup_iarc
### Title: Create variable for groups of malignant neoplasms considered to
### be histologically 'different' for the purpose of defining multiple
### tumors, ICD-O-3
### Aliases: histgroup_iarc
### ** Examples
#load sample data
data("us_second_cancer")
us_second_cancer %>%
msSPChelpR::histgroup_iarc(., hist_var = t_hist) %>%
dplyr::select(fake_id, t_hist, t_histgroupiarc)
cleanEx()
nameEx("ir_crosstab")
### * ir_crosstab
flush(stderr()); flush(stdout())
### Name: ir_crosstab
### Title: Calculate crude incidence rates and crosstabulate results by
### break variables
### Aliases: ir_crosstab
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
status_var = "p_status", life_var = "p_alive.1",
birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#now we can run the function
usdata_wide <- usdata_wide %>%
msSPChelpR::calc_futime(.,
futime_var_new = "p_futimeyrs",
fu_end = "2017-12-31",
dattype = "seer",
time_unit = "years",
status_var = "p_status",
lifedat_var = "datedeath.1",
fcdat_var = "t_datediag.1",
spcdat_var = "t_datediag.2")
#for example, you can calculate incidence and summarize by sex and registry
msSPChelpR::ir_crosstab(usdata_wide,
dattype = "seer",
count_var = "count_spc",
xbreak_var = "none",
ybreak_vars = c("sex.1", "registry.1"),
collapse_ci = FALSE,
add_total = "no",
add_n_percentages = FALSE,
futime_var = "p_futimeyrs",
alpha = 0.05)
cleanEx()
nameEx("ir_crosstab_byfutime")
### * ir_crosstab_byfutime
flush(stderr()); flush(stdout())
### Name: ir_crosstab_byfutime
### Title: Calculate crude incidence rates and cross-tabulate results by
### break variables; cumulative FU-times as are used as xbreak_var
### Aliases: ir_crosstab_byfutime
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
#only use sample
dplyr::filter(as.numeric(fake_id) < 200000) %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 2)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
status_var = "p_status", life_var = "p_alive.1",
birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#now we can run the function
usdata_wide <- usdata_wide %>%
msSPChelpR::calc_futime(.,
futime_var_new = "p_futimeyrs",
fu_end = "2017-12-31",
dattype = "seer",
time_unit = "years",
status_var = "p_status",
lifedat_var = "datedeath.1",
fcdat_var = "t_datediag.1",
spcdat_var = "t_datediag.2")
#for example, you can calculate incidence and summarize by sex and registry
msSPChelpR::ir_crosstab_byfutime(usdata_wide,
dattype = "seer",
count_var = "count_spc",
futime_breaks = c(0, .5, 1, 5, 10, Inf),
ybreak_vars = c("sex.1", "registry.1"),
collapse_ci = FALSE,
add_total = "no",
futime_var = "p_futimeyrs",
alpha = 0.05)
cleanEx()
nameEx("pat_status")
### * pat_status
flush(stderr()); flush(stdout())
### Name: pat_status
### Title: Determine patient status at specific end of follow-up -
### tidyverse version
### Aliases: pat_status
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#now we can run the function
msSPChelpR::pat_status(usdata_wide,
fu_end = "2017-12-31",
dattype = "seer",
status_var = "p_status",
life_var = "p_alive.1",
spc_var = NULL,
birthdat_var = "datebirth.1",
lifedat_var = "datedeath.1",
use_lifedatmin = FALSE,
check = TRUE,
as_labelled_factor = FALSE)
cleanEx()
nameEx("pat_status_tt")
### * pat_status_tt
flush(stderr()); flush(stdout())
### Name: pat_status_tt
### Title: Determine patient status at specific end of follow-up -
### tidytable version
### Aliases: pat_status_tt
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#now we can run the function
msSPChelpR::pat_status_tt(usdata_wide,
fu_end = "2017-12-31",
dattype = "seer",
status_var = "p_status",
life_var = "p_alive.1",
spc_var = NULL,
birthdat_var = "datebirth.1",
lifedat_var = "datedeath.1",
use_lifedatmin = FALSE,
check = TRUE,
as_labelled_factor = FALSE)
cleanEx()
nameEx("renumber_time_id")
### * renumber_time_id
flush(stderr()); flush(stdout())
### Name: renumber_time_id
### Title: Renumber the time ID per case (i.e. Tumor sequence)
### Aliases: renumber_time_id
### ** Examples
data(us_second_cancer)
us_second_cancer %>%
#only select first 10000 rows so example runs faster
dplyr::slice(1:10000) %>%
msSPChelpR::renumber_time_id(new_time_id_var = "t_tumid",
dattype = "seer",
case_id_var = "fake_id")
cleanEx()
nameEx("renumber_time_id_tt")
### * renumber_time_id_tt
flush(stderr()); flush(stdout())
### Name: renumber_time_id_tt
### Title: Renumber the time ID per case (i.e. Tumor sequence) - tidytable
### version
### Aliases: renumber_time_id_tt
### ** Examples
data(us_second_cancer)
us_second_cancer %>%
#only select first 10000 rows so example runs faster
dplyr::slice(1:10000) %>%
msSPChelpR::renumber_time_id_tt(new_time_id_var = "t_tumid",
dattype = "seer",
case_id_var = "fake_id")
cleanEx()
nameEx("reshape_long")
### * reshape_long
flush(stderr()); flush(stdout())
### Name: reshape_long
### Title: Reshape dataset to long format - stats::reshape version
### Aliases: reshape_long
### ** Examples
data(us_second_cancer)
#prep step - reshape wide a sample of 10000 rows from us_second_cancer
usdata_wide_sample <- msSPChelpR::reshape_wide(us_second_cancer,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM",
timevar_max = 2,
datsize = 10000)
#now we can reshape long again
msSPChelpR::reshape_long(usdata_wide_sample,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM")
cleanEx()
nameEx("reshape_long_tidyr")
### * reshape_long_tidyr
flush(stderr()); flush(stdout())
### Name: reshape_long_tidyr
### Title: Reshape dataset to wide format - tidyr version
### Aliases: reshape_long_tidyr
### ** Examples
data(us_second_cancer)
#prep step - reshape wide a sample of 10000 rows from us_second_cancer
usdata_wide_sample <- msSPChelpR::reshape_wide(us_second_cancer,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM",
timevar_max = 2,
datsize = 10000)
#now we can reshape long again
msSPChelpR::reshape_long_tidyr(usdata_wide_sample,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM")
cleanEx()
nameEx("reshape_long_tt")
### * reshape_long_tt
flush(stderr()); flush(stdout())
### Name: reshape_long_tt
### Title: Reshape dataset to wide format - tidytable version
### Aliases: reshape_long_tt
### ** Examples
data(us_second_cancer)
#prep step - reshape wide a sample of 10000 rows from us_second_cancer
usdata_wide_sample <- msSPChelpR::reshape_wide(us_second_cancer,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM",
timevar_max = 2,
datsize = 10000)
#now we can reshape long again
msSPChelpR::reshape_long_tt(usdata_wide_sample,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM")
cleanEx()
nameEx("reshape_wide")
### * reshape_wide
flush(stderr()); flush(stdout())
### Name: reshape_wide
### Title: Reshape dataset to wide format
### Aliases: reshape_wide
### ** Examples
data(us_second_cancer)
msSPChelpR::reshape_wide(us_second_cancer,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM",
timevar_max = 2,
datsize = 10000)
cleanEx()
nameEx("reshape_wide_tidyr")
### * reshape_wide_tidyr
flush(stderr()); flush(stdout())
### Name: reshape_wide_tidyr
### Title: Reshape dataset to wide format - tidyr version
### Aliases: reshape_wide_tidyr
### ** Examples
data(us_second_cancer)
msSPChelpR::reshape_wide_tidyr(us_second_cancer,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM",
timevar_max = 2,
datsize = 10000)
cleanEx()
nameEx("reshape_wide_tt")
### * reshape_wide_tt
flush(stderr()); flush(stdout())
### Name: reshape_wide_tt
### Title: Reshape dataset to wide format - tidytable version
### Aliases: reshape_wide_tt
### ** Examples
data(us_second_cancer)
msSPChelpR::reshape_wide_tt(us_second_cancer,
case_id_var = "fake_id",
time_id_var = "SEQ_NUM",
timevar_max = 2,
datsize = 10000)
cleanEx()
nameEx("sir_byfutime")
### * sir_byfutime
flush(stderr()); flush(stdout())
### Name: sir_byfutime
### Title: Calculate standardized incidence ratios with custom grouping
### variables stratified by follow-up time
### Aliases: sir_byfutime
### ** Examples
#There are various preparation steps required, before you can run this function.
#Please refer to the Introduction vignette to see how to prepare your data
## Not run:
##D usdata_wide %>%
##D sir_byfutime(
##D dattype = "seer",
##D ybreak_vars = c("race.1", "t_dco.1"),
##D xbreak_var = "none",
##D futime_breaks = c(0, 1/12, 2/12, 1, 5, 10, Inf),
##D count_var = "count_spc",
##D refrates_df = us_refrates_icd2,
##D calc_total_row = TRUE,
##D calc_total_fu = TRUE,
##D region_var = "registry.1",
##D age_var = "fc_agegroup.1",
##D sex_var = "sex.1",
##D year_var = "t_yeardiag.1",
##D site_var = "t_site_icd.1", #using grouping by second cancer incidence
##D futime_var = "p_futimeyrs",
##D alpha = 0.05)
##D
## End(Not run)
cleanEx()
nameEx("sir_ratio")
### * sir_ratio
flush(stderr()); flush(stdout())
### Name: sir_ratio
### Title: Calculate Ratio of two SIRs or SMRs
### Aliases: sir_ratio sir_ratio_lci sir_ratio_uci
### ** Examples
#provide the two expected and observed count to get the ratio of SIRs/SMRs
msSPChelpR::sir_ratio(o1 = 2140, o2 = 3158, e1 = 1993, e2 = 2123)
#calculate lower confidence limit
msSPChelpR::sir_ratio_lci(o1 = 2140, o2 = 3158, e1 = 1993, e2 = 2123, alpha = 0.05)
#calculate upper confidence limit
msSPChelpR::sir_ratio_uci(o1 = 2140, o2 = 3158, e1 = 1993, e2 = 2123, alpha = 0.05)
#functions can be easily used inside dplyr::mutate function
library(dplyr)
test_df <- data.frame(sir_oth = c(1.07, 1.36, 0.96),
sir_smo = c(1.49, 1.81, 1.41),
observed_oth = c(2140, 748, 1392),
expected_oth = c(1993, 550, 1443),
observed_smo = c(3158, 744, 2414),
expected_smo = c(2123, 412, 1711))
test_df %>%
mutate(smo_ratio = sir_ratio(observed_oth, observed_smo, expected_oth, expected_smo),
smo_ratio_lci = sir_ratio_lci(observed_oth, observed_smo, expected_oth, expected_smo),
smo_ratio_uci = sir_ratio_uci(observed_oth, observed_smo, expected_oth, expected_smo))
cleanEx()
nameEx("summarize_sir_results")
### * summarize_sir_results
flush(stderr()); flush(stdout())
### Name: summarize_sir_results
### Title: Summarize detailed SIR results
### Aliases: summarize_sir_results
### ** Examples
#There are various preparation steps required, before you can run this function.
#Please refer to the Introduction vignette to see how to prepare your data
## Not run:
##D summarize_sir_results(.,
##D summarize_groups = c("region", "age", "year", "race"),
##D summarize_site = TRUE,
##D output = "long", output_information = "minimal",
##D add_total_row = "only", add_total_fu = "no",
##D collapse_ci = FALSE, shorten_total_cols = TRUE,
##D fubreak_var_name = "fu_time", ybreak_var_name = "yvar_name",
##D xbreak_var_name = "none", site_var_name = "t_site",
##D alpha = 0.05
##D )
##D
## End(Not run)
cleanEx()
nameEx("vital_status")
### * vital_status
flush(stderr()); flush(stdout())
### Name: vital_status
### Title: Determine vital status at end of follow-up depending on
### pat_status - tidyverse version
### Aliases: vital_status
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
status_var = "p_status", life_var = "p_alive.1",
birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#now we can run the function
msSPChelpR::vital_status(usdata_wide,
status_var = "p_status",
life_var_new = "p_alive_new",
check = TRUE,
as_labelled_factor = FALSE)
cleanEx()
nameEx("vital_status_tt")
### * vital_status_tt
flush(stderr()); flush(stdout())
### Name: vital_status_tt
### Title: Determine vital status at end of follow-up depending on
### pat_status - tidytable version
### Aliases: vital_status_tt
### ** Examples
#load sample data
data("us_second_cancer")
#prep step - make wide data as this is the required format
usdata_wide <- us_second_cancer %>%
msSPChelpR::reshape_wide_tidyr(case_id_var = "fake_id",
time_id_var = "SEQ_NUM", timevar_max = 10)
#prep step - calculate p_spc variable
usdata_wide <- usdata_wide %>%
dplyr::mutate(p_spc = dplyr::case_when(is.na(t_site_icd.2) ~ "No SPC",
!is.na(t_site_icd.2) ~ "SPC developed",
TRUE ~ NA_character_)) %>%
dplyr::mutate(count_spc = dplyr::case_when(is.na(t_site_icd.2) ~ 1,
TRUE ~ 0))
#prep step - create patient status variable
usdata_wide <- usdata_wide %>%
msSPChelpR::pat_status(., fu_end = "2017-12-31", dattype = "seer",
status_var = "p_status", life_var = "p_alive.1",
birthdat_var = "datebirth.1", lifedat_var = "datedeath.1")
#now we can run the function
msSPChelpR::vital_status_tt(usdata_wide,
status_var = "p_status",
life_var_new = "p_alive_new",
check = TRUE,
as_labelled_factor = FALSE)
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
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