data-raw/DATASET.R
In ready4-dev/youthvars: Describe and Validate Youth Mental Health Economic Datasets
library(ready4)
library(ready4fun)
library(ready4show)
library(ready4use)
fns_env_ls <- ready4fun::read_fns(c("data-raw/fns/","data-raw/mthds/"),
fns_env = new.env(parent = globalenv()))
x <- ready4fun::make_pkg_desc_ls(pkg_title_1L_chr = "Describe And Validate Youth Mental Health Economic Datasets",
pkg_desc_1L_chr = "Modules from the readyforwhatsnext youth mental health economic model (https://readyforwhatsnext.org/) that can be used to describe and quality assure individual human record datasets.
The main motivation for this package is to facilitate automated data integrity checks, ensure that methods are applied to the appropriate data structures and streamline reporting of descriptive statistics.
This development version of the youthvars package has been made available as part of the process of testing and documenting the package.
If you have any questions, please contact the authors (matthew.hamilton1@monash.edu).",
authors_prsn = c(utils::person(given = "Matthew",family = "Hamilton",email = "matthew.hamilton1@monash.edu", role = c("aut", "cre", "cph"),comment = c(ORCID = "0000-0001-7407-9194")),
utils::person(given = "Caroline",family = "Gao",email = "caroline.gao@orygen.org.au", role = c("aut"),comment = c(ORCID = "0000-0002-0987-2759")),
utils::person("Orygen", role = c("cph", "fnd")),
utils::person("Headspace", role = c( "fnd")),
utils::person("National Health and Medical Research Council", role = c( "fnd"))),
urls_chr = c("https://ready4-dev.github.io/youthvars/",
"https://github.com/ready4-dev/youthvars",
"https://readyforwhatsnext.org/")) %>%
ready4fun::make_manifest(addl_pkgs_ls = ready4fun::make_addl_pkgs_ls(#depends_chr = "ready4",
suggests_chr = c("rmarkdown"),
imports_chr = c("car","knitrBootstrap")),#,"car", "caret", 'lme4','quantreg',"recipes"
build_ignore_ls = ready4fun::make_build_ignore_ls(file_nms_chr = c("initial_setup.R")),
check_type_1L_chr = "ready4",
copyright_holders_chr = "Matthew Hamilton and Orygen",
custom_dmt_ls = ready4fun::make_custom_dmt_ls(user_manual_fns_chr = c(#"add_adol6d_scores",
# "add_discrete_palette",
"add_interval_var",
"add_participation_var",
"assert_ds_is_valid",
"make_corstars_tbl_xx",
"make_descv_stats_tbl",
"make_final_repln_ds_dict",
"make_formula",
"make_itm_resp_plts",
"make_sub_tot_plts",
"make_tfd_repln_ds_dict_r3",
"make_var_by_round_plt",
# "plot_descriptives",
"print_descv_stats_tbl",
"transform_ds_for_tstng",
"transform_raw_ds_for_analysis",
#"write_all_outp_dirs",
"write_desv_plots",
"write_descv_tbls")),##
dev_pkgs_chr = c(#"ready4",
"ready4use","ready4show"),
lifecycle_stage_1L_chr = "experimental",
path_to_pkg_logo_1L_chr = "../../../../../Documentation/Images/youthvars-logo/default.png",
piggyback_to_1L_chr = "ready4-dev/ready4",
ready4_type_1L_chr = "description",
zenodo_badge_1L_chr = "[](https://doi.org/10.5281/zenodo.5646550)")
y <- ready4class::ready4class_constructor() %>%
dplyr::bind_rows(tibble::tribble(
~ make_s3_lgl, ~ name_stub_chr, ~ pt_ls, ~ pt_chkr_pfx_ls, ~ pt_ns_ls, ~ vals_ls, ~ allowed_vals_ls, ~ min_max_vals_ls, ~ start_end_vals_ls, ~ class_desc_chr, ~ parent_class_chr, ~ slots_ls, ~ meaningful_nms_ls, ~ inc_clss_ls, ~ asserts_ls,
TRUE, "aqol6d_adol", list("numeric"), list("is."),list("base"), NULL, NULL,list(c(0.03, 1)), NULL, "youthvars ready4 sub-module (S3 class) for Assessment of Quality of Life Six Dimension Health Utility - Adolescent Version (AQoL6d Adolescent)", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "chu9d_adolaus", list("numeric"), list("is."),list("base"), NULL, NULL,list(c(-0.2118, 1)), NULL, "youthvars ready4 sub-module (S3 class) for Child Health Utility Nine Dimension - Australian Adolescent Scoring (CHU-9D Australian Adolescent)", NA_character_, NULL, NULL, NULL, NULL,#http://dx.doi.org/10.1016/j.socscimed.2016.03.042
TRUE, "phq9", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 27)), NULL, "youthvars ready4 sub-module (S3 class) for Patient Health Questionnaire (PHQ-9) scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "bads", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 150)), NULL, "youthvars ready4 sub-module (S3 class) for Behavioural Activation for Depression Scale (BADS) scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "gad7", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 21)), NULL, "youthvars ready4 sub-module (S3 class) for Generalised Anxiety Disorder Scale (GAD-7) scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "oasis", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 20)), NULL, "youthvars ready4 sub-module (S3 class) for Overall Anxiety Severity and Impairment Scale (OASIS) scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "scared", list("integer"), list("is."),list("base"),NULL, NULL,list(c(0, 82)), NULL, "youthvars ready4 sub-module (S3 class) for Screen for Child Anxiety Related Disorders (SCARED) scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "k6", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 24)), NULL, "youthvars ready4 sub-module (S3 class) for Kessler Psychological Distress Scale (K6) - US Scoring System scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "k6_aus", list("integer"), list("is."),list("base"), NULL, NULL,list(c(6, 30)), NULL, "youthvars ready4 sub-module (S3 class)for Kessler Psychological Distress Scale (K6) - Australian Scoring System scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "k10", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 40)), NULL, "youthvars ready4 sub-module (S3 class) for Kessler Psychological Distress Scale (K10) - US Scoring System scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "k10_aus", list("integer"), list("is."),list("base"), NULL, NULL,list(c(10, 50)), NULL, "youthvars ready4 sub-module (S3 class) for Kessler Psychological Distress Scale (K10) - Australian Scoring System scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "sofas", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 100)), NULL, "youthvars ready4 sub-module (S3 class) for Social and Occupational Functioning Assessment Scale (SOFAS)", NA_character_, NULL, NULL, NULL, NULL))
y <- dplyr::bind_rows(y,
ready4class::make_pt_ready4class_constructor(make_s3_lgl = FALSE,
name_stub_chr = "Descriptives",
slots_ls = list("descriptives_df",
"ds_tfmn_ls",
"key_var_nm_1L_chr",
"key_var_vals_chr",
"nbr_of_digits_1L_int",
"profiled_vars_chr",
"sections_as_row_1L_lgl",
"test_1L_lgl"
) %>% list(),
pt_ls = list("data.frame",
"list",
"character",
"character",
"integer",
"character",
"logical",
"logical") %>% list(),
class_desc_chr= "Metadata about descriptive statistics to be generated.",
parent_class_chr = "Ready4Module"),
ready4class::make_pt_ready4class_constructor(make_s3_lgl = FALSE,
name_stub_chr = "Profile",
slots_ls = list("a_Ready4useDyad",
"descriptives_ls",
"id_var_nm_1L_chr") %>% list(), # Change
pt_ls = list("Ready4useDyad","list",
"character") %>% list(),
class_desc_chr = "A dataset and its associated dictionary, descriptive statistics and metadata.",
parent_class_chr = "Ready4Module"),
ready4class::make_pt_ready4class_constructor(make_s3_lgl = FALSE,
name_stub_chr = "Series",
slots_ls = list("participation_var_1L_chr",
"timepoint_vals_chr",
"timepoint_var_nm_1L_chr") %>% list(), # Change
pt_ls = list("character","character",
"character") %>% list(),
class_desc_chr = "A longitudinal dataset and its associated dictionary, descriptive statistics and metadata.",
parent_class_chr = "YouthvarsProfile"))
##
replication_popl_tb <- read.csv("data-raw/csvs/fake_pop_tb.csv") %>%
dplyr::mutate(c_sofas = as.integer(round(c_sofas,0))) %>%
dplyr::mutate(round = factor(round, labels = c("Baseline",
"Follow-up"))) %>%
dplyr::mutate(d_relation_s = dplyr::case_when(d_relation_s %in% c("REPLACE_ME_1","REPLACE_ME_2") ~ "Not in a relationship",
T ~ "In a relationship")) %>%
youthu::add_dates_from_dstr(bl_start_date_dtm = Sys.Date() - lubridate::days(600),##
bl_end_date_dtm = Sys.Date() - lubridate::days(420),
duration_args_ls = list(a = 60, b = 140, mean = 90, sd = 10),
duration_fn = truncnorm::rtruncnorm,
date_var_nm_1L_chr = "d_interview_date") %>%
dplyr::select(-duration_prd) %>%
fns_env_ls$fns_env$transform_raw_ds_for_analysis() %>%
dplyr::rename(phq9_total = PHQ9,
bads_total = BADS,
gad7_total = GAD7,
oasis_total = OASIS,
scared_total = SCARED,
k6_total = K6,
c_sofas = SOFAS)
scored_data_tb <- scorz::add_adol6d_scores(replication_popl_tb,
prefix_1L_chr = "aqol6d_q",
id_var_nm_1L_chr = "fkClientID",
wtd_aqol_var_nm_1L_chr = "aqol6d_total_w",
total_aqol_var_nm_1L_chr = "aqol6d_total_c")
Hmisc::label(scored_data_tb[["aqol6d_total_c"]]) <- "AQOL-6D (unweighted total)"
Hmisc::label(scored_data_tb[["aqol6d_total_w"]]) <- "AQOL-6D (weighted total)"
dictionary_tb <- ready4use::make_pt_ready4use_dictionary(var_nm_chr = names(scored_data_tb),
var_ctg_chr = c("identifier","temporal","temporal",
rep("demographic",14),
"service provider",
rep("clinical symptom",2),
c("psychological distress",
rep("depression",2),
rep("anxiety",3)),
"functioning",
#rep("Demographic",3),
rep("multi-attribute utility instrument question",20),
rep("utility item disvalue",20),
rep("utility dimension disvalue",6),
rep("utility dimension score (adult)",6),
"utility overall score (disvalue scale)",
"utility overall score (life-death scale)",
rep("utility overall score (adolescent disutility scale)",2), # Includes Testing Duplicate
"utility overall score (instrument)",
"utility overall score (instrument - rotated)",
"utility overall score (final weighted)",
"multi-attribute utility instrument unweighted total score"
),
var_desc_chr = c("unique client identifier",
"round of data collection",
"date of data collection",
"age",
"age Group",
"gender (grouped)",
"gender",
"sex at birth",
"sexual orientation",
"Aboriginal or Torres Strait Islander",
"Culturally And Linguistically Diverse",
"country Of birth",
"speaks English at home",
"native English speaker",
"region of residence (metropolitan or regional)",
"education and employment status",
"relationship status",
"service centre name",
"primary diagnosis",
"clinical stage",
"Kessler Psychological Distress Scale (6 Dimension)",
"Patient Health Questionnaire",
"Behavioural Activation for Depression Scale",
"Generalised Anxiety Disorder Scale",
"Overall Anxiety Severity and Impairment Scale",
"Screen for Child Anxiety Related Disorders",
"Social and Occupational Functioning Assessment Scale",
paste0("Assessment of Quality of Life (6 Dimension) question ",1:20),
paste0("Assessment of Quality of Life (6 Dimension) item disvalue",1:20),
lapply(scored_data_tb, Hmisc::label) %>% purrr::flatten_chr() %>% purrr::keep(c(rep(F,67),rep(T,20)))
),
var_type_chr = names(scored_data_tb) %>% purrr::map_chr(~{
class_chr <- class(scored_data_tb %>% dplyr::pull(.x))
class_chr[class_chr!="labelled"][1]
})) %>% ready4use::ready4use_dictionary()
Hmisc::label(dictionary_tb) = as.list(c("Variable","Category", "Description", "Class"))
dictionary_tb <- dictionary_tb %>%
dplyr::arrange(var_ctg_chr,var_nm_chr)
datasets_ls <- list(
dictionary_tb %>%
dplyr::filter(var_nm_chr %in% names(replication_popl_tb)) %>%
ready4fun::make_pkg_ds_ls(db_1L_chr = "repln_ds_dict_r3",
title_1L_chr = "Data dictionary for study population dataset",
desc_1L_chr = "A data dictionary of the variables used in the source and replication (synthetic) datasets for the First Bounce transfer to utility study"),
dictionary_tb %>%
dplyr::filter((!var_nm_chr %in% names(replication_popl_tb)) | startsWith(var_nm_chr, "aqol")) %>%
ready4fun::make_pkg_ds_ls(db_1L_chr = "aqol_scrg_dict_r3",
title_1L_chr = "Data dictionary for AQoL scoring",
desc_1L_chr = "A data dictionary of the variables used in scoring AQoL 6D utility questionnaire responses."),
replication_popl_tb %>%
dplyr::select(-c(d_agegroup,Gender, CALD, Region)) %>%
#add_labels_from_dictionary(dictionary_tb = dictionary_tb) %>%
ready4fun::make_pkg_ds_ls(db_1L_chr = "replication_popl_tb",
title_1L_chr = "Synthetic population replication dataset",
desc_1L_chr = "A purely synthetic dataset, representative of the original study data, that can be used for replication runs of package algorithms.",
abbreviations_lup = tibble::tibble(short_name_chr = dictionary_tb$var_nm_chr,
long_name_chr = dictionary_tb$var_desc_chr,
plural_lgl = F),
simple_lup_1L_lgl = T),
tibble::tibble(short_name_chr = c("BADS","GAD7","K6","OASIS","PHQ9","SCARED","SOFAS"),
long_name_chr = short_name_chr %>% purrr::map_chr(~paste0(.x, " total score")),
min_val_dbl = rep(0,7),
max_val_dbl = c(150,21,24,20,27,82,100),
class_chr = "integer",
increment_dbl = rep(1,7),
class_fn_chr = paste0("youthvars::youthvars_",tolower(short_name_chr)),
mdl_scaling_dbl = 0.01,
covariate_lgl = c(rep(F,6),T)) %>%
ready4fun::make_pkg_ds_ls(db_1L_chr = "predictors_lup",
title_1L_chr = "Predictors lookup table",
desc_1L_chr = "A lookup table of the short name and long name of each predictor used in the models included with the youthu package.")
)
z <- ready4pack::make_pt_ready4pack_manifest(x,
constructor_r3 = y,
pkg_ds_ls_ls = datasets_ls) %>%
ready4pack::ready4pack_manifest()
z <- ready4::author(z)
#ready4::write_extra_pkgs_to_actions(path_to_dir_1L_chr = ".github/workflows", consent_1L_chr = "Y")
write_to_edit_workflow("pkgdown.yaml", consent_1L_chr = "Y") # In other packages, run for "test-coverage.yaml" as well.
write_to_tidy_pkg(z$x_ready4fun_manifest, build_vignettes_1L_lgl = TRUE,
clean_license_1L_lgl = TRUE, consent_1L_chr = "Y",
examples_chr = character(0), suggest_chr = "pkgload")
# readLines("_pkgdown.yml") %>%
# stringr::str_replace_all(" - text: Model", " - text: Framework & Model") %>%
# writeLines(con = "_pkgdown.yml")
# devtools::build_vignettes()
ready4-dev/youthvars documentation built on Nov. 15, 2024, 6:02 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library(ready4)
library(ready4fun)
library(ready4show)
library(ready4use)
fns_env_ls <- ready4fun::read_fns(c("data-raw/fns/","data-raw/mthds/"),
fns_env = new.env(parent = globalenv()))
x <- ready4fun::make_pkg_desc_ls(pkg_title_1L_chr = "Describe And Validate Youth Mental Health Economic Datasets",
pkg_desc_1L_chr = "Modules from the readyforwhatsnext youth mental health economic model (https://readyforwhatsnext.org/) that can be used to describe and quality assure individual human record datasets.
The main motivation for this package is to facilitate automated data integrity checks, ensure that methods are applied to the appropriate data structures and streamline reporting of descriptive statistics.
This development version of the youthvars package has been made available as part of the process of testing and documenting the package.
If you have any questions, please contact the authors (matthew.hamilton1@monash.edu).",
authors_prsn = c(utils::person(given = "Matthew",family = "Hamilton",email = "matthew.hamilton1@monash.edu", role = c("aut", "cre", "cph"),comment = c(ORCID = "0000-0001-7407-9194")),
utils::person(given = "Caroline",family = "Gao",email = "caroline.gao@orygen.org.au", role = c("aut"),comment = c(ORCID = "0000-0002-0987-2759")),
utils::person("Orygen", role = c("cph", "fnd")),
utils::person("Headspace", role = c( "fnd")),
utils::person("National Health and Medical Research Council", role = c( "fnd"))),
urls_chr = c("https://ready4-dev.github.io/youthvars/",
"https://github.com/ready4-dev/youthvars",
"https://readyforwhatsnext.org/")) %>%
ready4fun::make_manifest(addl_pkgs_ls = ready4fun::make_addl_pkgs_ls(#depends_chr = "ready4",
suggests_chr = c("rmarkdown"),
imports_chr = c("car","knitrBootstrap")),#,"car", "caret", 'lme4','quantreg',"recipes"
build_ignore_ls = ready4fun::make_build_ignore_ls(file_nms_chr = c("initial_setup.R")),
check_type_1L_chr = "ready4",
copyright_holders_chr = "Matthew Hamilton and Orygen",
custom_dmt_ls = ready4fun::make_custom_dmt_ls(user_manual_fns_chr = c(#"add_adol6d_scores",
# "add_discrete_palette",
"add_interval_var",
"add_participation_var",
"assert_ds_is_valid",
"make_corstars_tbl_xx",
"make_descv_stats_tbl",
"make_final_repln_ds_dict",
"make_formula",
"make_itm_resp_plts",
"make_sub_tot_plts",
"make_tfd_repln_ds_dict_r3",
"make_var_by_round_plt",
# "plot_descriptives",
"print_descv_stats_tbl",
"transform_ds_for_tstng",
"transform_raw_ds_for_analysis",
#"write_all_outp_dirs",
"write_desv_plots",
"write_descv_tbls")),##
dev_pkgs_chr = c(#"ready4",
"ready4use","ready4show"),
lifecycle_stage_1L_chr = "experimental",
path_to_pkg_logo_1L_chr = "../../../../../Documentation/Images/youthvars-logo/default.png",
piggyback_to_1L_chr = "ready4-dev/ready4",
ready4_type_1L_chr = "description",
zenodo_badge_1L_chr = "[](https://doi.org/10.5281/zenodo.5646550)")
y <- ready4class::ready4class_constructor() %>%
dplyr::bind_rows(tibble::tribble(
~ make_s3_lgl, ~ name_stub_chr, ~ pt_ls, ~ pt_chkr_pfx_ls, ~ pt_ns_ls, ~ vals_ls, ~ allowed_vals_ls, ~ min_max_vals_ls, ~ start_end_vals_ls, ~ class_desc_chr, ~ parent_class_chr, ~ slots_ls, ~ meaningful_nms_ls, ~ inc_clss_ls, ~ asserts_ls,
TRUE, "aqol6d_adol", list("numeric"), list("is."),list("base"), NULL, NULL,list(c(0.03, 1)), NULL, "youthvars ready4 sub-module (S3 class) for Assessment of Quality of Life Six Dimension Health Utility - Adolescent Version (AQoL6d Adolescent)", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "chu9d_adolaus", list("numeric"), list("is."),list("base"), NULL, NULL,list(c(-0.2118, 1)), NULL, "youthvars ready4 sub-module (S3 class) for Child Health Utility Nine Dimension - Australian Adolescent Scoring (CHU-9D Australian Adolescent)", NA_character_, NULL, NULL, NULL, NULL,#http://dx.doi.org/10.1016/j.socscimed.2016.03.042
TRUE, "phq9", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 27)), NULL, "youthvars ready4 sub-module (S3 class) for Patient Health Questionnaire (PHQ-9) scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "bads", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 150)), NULL, "youthvars ready4 sub-module (S3 class) for Behavioural Activation for Depression Scale (BADS) scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "gad7", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 21)), NULL, "youthvars ready4 sub-module (S3 class) for Generalised Anxiety Disorder Scale (GAD-7) scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "oasis", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 20)), NULL, "youthvars ready4 sub-module (S3 class) for Overall Anxiety Severity and Impairment Scale (OASIS) scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "scared", list("integer"), list("is."),list("base"),NULL, NULL,list(c(0, 82)), NULL, "youthvars ready4 sub-module (S3 class) for Screen for Child Anxiety Related Disorders (SCARED) scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "k6", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 24)), NULL, "youthvars ready4 sub-module (S3 class) for Kessler Psychological Distress Scale (K6) - US Scoring System scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "k6_aus", list("integer"), list("is."),list("base"), NULL, NULL,list(c(6, 30)), NULL, "youthvars ready4 sub-module (S3 class)for Kessler Psychological Distress Scale (K6) - Australian Scoring System scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "k10", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 40)), NULL, "youthvars ready4 sub-module (S3 class) for Kessler Psychological Distress Scale (K10) - US Scoring System scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "k10_aus", list("integer"), list("is."),list("base"), NULL, NULL,list(c(10, 50)), NULL, "youthvars ready4 sub-module (S3 class) for Kessler Psychological Distress Scale (K10) - Australian Scoring System scores", NA_character_, NULL, NULL, NULL, NULL,
TRUE, "sofas", list("integer"), list("is."),list("base"), NULL, NULL,list(c(0, 100)), NULL, "youthvars ready4 sub-module (S3 class) for Social and Occupational Functioning Assessment Scale (SOFAS)", NA_character_, NULL, NULL, NULL, NULL))
y <- dplyr::bind_rows(y,
ready4class::make_pt_ready4class_constructor(make_s3_lgl = FALSE,
name_stub_chr = "Descriptives",
slots_ls = list("descriptives_df",
"ds_tfmn_ls",
"key_var_nm_1L_chr",
"key_var_vals_chr",
"nbr_of_digits_1L_int",
"profiled_vars_chr",
"sections_as_row_1L_lgl",
"test_1L_lgl"
) %>% list(),
pt_ls = list("data.frame",
"list",
"character",
"character",
"integer",
"character",
"logical",
"logical") %>% list(),
class_desc_chr= "Metadata about descriptive statistics to be generated.",
parent_class_chr = "Ready4Module"),
ready4class::make_pt_ready4class_constructor(make_s3_lgl = FALSE,
name_stub_chr = "Profile",
slots_ls = list("a_Ready4useDyad",
"descriptives_ls",
"id_var_nm_1L_chr") %>% list(), # Change
pt_ls = list("Ready4useDyad","list",
"character") %>% list(),
class_desc_chr = "A dataset and its associated dictionary, descriptive statistics and metadata.",
parent_class_chr = "Ready4Module"),
ready4class::make_pt_ready4class_constructor(make_s3_lgl = FALSE,
name_stub_chr = "Series",
slots_ls = list("participation_var_1L_chr",
"timepoint_vals_chr",
"timepoint_var_nm_1L_chr") %>% list(), # Change
pt_ls = list("character","character",
"character") %>% list(),
class_desc_chr = "A longitudinal dataset and its associated dictionary, descriptive statistics and metadata.",
parent_class_chr = "YouthvarsProfile"))
##
replication_popl_tb <- read.csv("data-raw/csvs/fake_pop_tb.csv") %>%
dplyr::mutate(c_sofas = as.integer(round(c_sofas,0))) %>%
dplyr::mutate(round = factor(round, labels = c("Baseline",
"Follow-up"))) %>%
dplyr::mutate(d_relation_s = dplyr::case_when(d_relation_s %in% c("REPLACE_ME_1","REPLACE_ME_2") ~ "Not in a relationship",
T ~ "In a relationship")) %>%
youthu::add_dates_from_dstr(bl_start_date_dtm = Sys.Date() - lubridate::days(600),##
bl_end_date_dtm = Sys.Date() - lubridate::days(420),
duration_args_ls = list(a = 60, b = 140, mean = 90, sd = 10),
duration_fn = truncnorm::rtruncnorm,
date_var_nm_1L_chr = "d_interview_date") %>%
dplyr::select(-duration_prd) %>%
fns_env_ls$fns_env$transform_raw_ds_for_analysis() %>%
dplyr::rename(phq9_total = PHQ9,
bads_total = BADS,
gad7_total = GAD7,
oasis_total = OASIS,
scared_total = SCARED,
k6_total = K6,
c_sofas = SOFAS)
scored_data_tb <- scorz::add_adol6d_scores(replication_popl_tb,
prefix_1L_chr = "aqol6d_q",
id_var_nm_1L_chr = "fkClientID",
wtd_aqol_var_nm_1L_chr = "aqol6d_total_w",
total_aqol_var_nm_1L_chr = "aqol6d_total_c")
Hmisc::label(scored_data_tb[["aqol6d_total_c"]]) <- "AQOL-6D (unweighted total)"
Hmisc::label(scored_data_tb[["aqol6d_total_w"]]) <- "AQOL-6D (weighted total)"
dictionary_tb <- ready4use::make_pt_ready4use_dictionary(var_nm_chr = names(scored_data_tb),
var_ctg_chr = c("identifier","temporal","temporal",
rep("demographic",14),
"service provider",
rep("clinical symptom",2),
c("psychological distress",
rep("depression",2),
rep("anxiety",3)),
"functioning",
#rep("Demographic",3),
rep("multi-attribute utility instrument question",20),
rep("utility item disvalue",20),
rep("utility dimension disvalue",6),
rep("utility dimension score (adult)",6),
"utility overall score (disvalue scale)",
"utility overall score (life-death scale)",
rep("utility overall score (adolescent disutility scale)",2), # Includes Testing Duplicate
"utility overall score (instrument)",
"utility overall score (instrument - rotated)",
"utility overall score (final weighted)",
"multi-attribute utility instrument unweighted total score"
),
var_desc_chr = c("unique client identifier",
"round of data collection",
"date of data collection",
"age",
"age Group",
"gender (grouped)",
"gender",
"sex at birth",
"sexual orientation",
"Aboriginal or Torres Strait Islander",
"Culturally And Linguistically Diverse",
"country Of birth",
"speaks English at home",
"native English speaker",
"region of residence (metropolitan or regional)",
"education and employment status",
"relationship status",
"service centre name",
"primary diagnosis",
"clinical stage",
"Kessler Psychological Distress Scale (6 Dimension)",
"Patient Health Questionnaire",
"Behavioural Activation for Depression Scale",
"Generalised Anxiety Disorder Scale",
"Overall Anxiety Severity and Impairment Scale",
"Screen for Child Anxiety Related Disorders",
"Social and Occupational Functioning Assessment Scale",
paste0("Assessment of Quality of Life (6 Dimension) question ",1:20),
paste0("Assessment of Quality of Life (6 Dimension) item disvalue",1:20),
lapply(scored_data_tb, Hmisc::label) %>% purrr::flatten_chr() %>% purrr::keep(c(rep(F,67),rep(T,20)))
),
var_type_chr = names(scored_data_tb) %>% purrr::map_chr(~{
class_chr <- class(scored_data_tb %>% dplyr::pull(.x))
class_chr[class_chr!="labelled"][1]
})) %>% ready4use::ready4use_dictionary()
Hmisc::label(dictionary_tb) = as.list(c("Variable","Category", "Description", "Class"))
dictionary_tb <- dictionary_tb %>%
dplyr::arrange(var_ctg_chr,var_nm_chr)
datasets_ls <- list(
dictionary_tb %>%
dplyr::filter(var_nm_chr %in% names(replication_popl_tb)) %>%
ready4fun::make_pkg_ds_ls(db_1L_chr = "repln_ds_dict_r3",
title_1L_chr = "Data dictionary for study population dataset",
desc_1L_chr = "A data dictionary of the variables used in the source and replication (synthetic) datasets for the First Bounce transfer to utility study"),
dictionary_tb %>%
dplyr::filter((!var_nm_chr %in% names(replication_popl_tb)) | startsWith(var_nm_chr, "aqol")) %>%
ready4fun::make_pkg_ds_ls(db_1L_chr = "aqol_scrg_dict_r3",
title_1L_chr = "Data dictionary for AQoL scoring",
desc_1L_chr = "A data dictionary of the variables used in scoring AQoL 6D utility questionnaire responses."),
replication_popl_tb %>%
dplyr::select(-c(d_agegroup,Gender, CALD, Region)) %>%
#add_labels_from_dictionary(dictionary_tb = dictionary_tb) %>%
ready4fun::make_pkg_ds_ls(db_1L_chr = "replication_popl_tb",
title_1L_chr = "Synthetic population replication dataset",
desc_1L_chr = "A purely synthetic dataset, representative of the original study data, that can be used for replication runs of package algorithms.",
abbreviations_lup = tibble::tibble(short_name_chr = dictionary_tb$var_nm_chr,
long_name_chr = dictionary_tb$var_desc_chr,
plural_lgl = F),
simple_lup_1L_lgl = T),
tibble::tibble(short_name_chr = c("BADS","GAD7","K6","OASIS","PHQ9","SCARED","SOFAS"),
long_name_chr = short_name_chr %>% purrr::map_chr(~paste0(.x, " total score")),
min_val_dbl = rep(0,7),
max_val_dbl = c(150,21,24,20,27,82,100),
class_chr = "integer",
increment_dbl = rep(1,7),
class_fn_chr = paste0("youthvars::youthvars_",tolower(short_name_chr)),
mdl_scaling_dbl = 0.01,
covariate_lgl = c(rep(F,6),T)) %>%
ready4fun::make_pkg_ds_ls(db_1L_chr = "predictors_lup",
title_1L_chr = "Predictors lookup table",
desc_1L_chr = "A lookup table of the short name and long name of each predictor used in the models included with the youthu package.")
)
z <- ready4pack::make_pt_ready4pack_manifest(x,
constructor_r3 = y,
pkg_ds_ls_ls = datasets_ls) %>%
ready4pack::ready4pack_manifest()
z <- ready4::author(z)
#ready4::write_extra_pkgs_to_actions(path_to_dir_1L_chr = ".github/workflows", consent_1L_chr = "Y")
write_to_edit_workflow("pkgdown.yaml", consent_1L_chr = "Y") # In other packages, run for "test-coverage.yaml" as well.
write_to_tidy_pkg(z$x_ready4fun_manifest, build_vignettes_1L_lgl = TRUE,
clean_license_1L_lgl = TRUE, consent_1L_chr = "Y",
examples_chr = character(0), suggest_chr = "pkgload")
# readLines("_pkgdown.yml") %>%
# stringr::str_replace_all(" - text: Model", " - text: Framework & Model") %>%
# writeLines(con = "_pkgdown.yml")
# devtools::build_vignettes()
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