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Analyses
- 1: Decision aids
- 2: Code to reproduce and/or replicate scientific studies
- 2.1: Model youth choices
- 2.1.1: Design a Discrete Choice Experiment
- 2.1.2: Analyse the results of a Discrete Choice Experiment
- 2.2: Create synthetic populations
- 2.3: Model health utility
- 3: Subroutine templates for authoring analysis reports
- 3.1: Make a catalogue of utility mapping models
- 3.2: Author a template scientific manuscript
- 3.3: Author a draft scientific manuscript for a utility mapping study
- 3.4: Make results summary for a Discrete Choice Experiment
- 4: Authoring reproducible analyses
1 - Decision aids
1.1 - Predicting the spatial epidemiology of emerging mental disorders
The Springtides app reproduced below is currently deprecated, pending a new version to be released in 2023. We don’t encourage use of this app to inform decision making as the input data has become dated and the current web based version often fails if generating large / or customised geometries. The app is reproduced below purely for illustrative purposes. If you try it out, we recommend that you only select the default type of geometry (“Select from a menu of existing options”) as the web version is not configured to render all bar the simplest custom geometries. To use the app you need to first confirm your selections in the “Where” tab, before confirming selections from the “What” box, then from the “Who” box and finally the “When” box before an orange box appears that gives you the option to generate a report. When trying this app out, we recommend keeping the number of simulations low (e.g. 10) as it takes several minutes for even small numbers of runs to execute.
2 - Code to reproduce and/or replicate scientific studies
2.1 - Model youth choices
2.1.1 - Design a Discrete Choice Experiment
This below section embeds a PDF version of an R Markdown program. The following alternative options may provide improved viewing experience, more contextual information and access to more useful code formats:
- download the PDF (recommended for enhanced readibility);
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- view the PDF along with the current release of its R Markdown code (useful if you plan to run the code) and
- view the PDF along with the current development version of its R Markdown code (useful if you wish to copy or edit the code)
2.1.2 - Analyse the results of a Discrete Choice Experiment
This below section embeds a PDF version of an R Markdown program. The following alternative options may provide improved viewing experience, more contextual information and access to more useful code formats:
- download the PDF (recommended for enhanced readibility);
- view the PDF in its study dataset (includes contextual information) from that article; and;
- view the PDF along with the current release of its R Markdown code (useful if you plan to run the code) and
- view the PDF along with the current development version of its R Markdown code (useful if you wish to copy or edit the code)
2.2 - Create synthetic populations
2.2.1 - Create a synthetic population of young people attending primary mental health services
This below section renders an R Markdown program. The following alternative options may provide improved viewing experience, more contextual information and access to more useful code formats:
- view the HTML in the dataset containing its outputs (includes contextual information) from that article; and;
- view the HTML along with the current release of its R Markdown code (useful if you plan to run the code) and
- view the HTML along with the current development version of its R Markdown code (useful if you wish to copy or edit the code)
Introduction
This program generates a purely synthetic (i.e. fake - no trace of any real records) population that is reasonably representative of the input data we used for the utility mapping study described in the article https://doi.org/10.1101/2021.07.07.21260129.
No access to the real data is required in order to use this program - it is based on summary statistics (e.g. means and standard deviations of variables, correlation matrices). It should be noted however, that a different (and simpler) workflow can be implemented when you do have access to the source dataset (for example, by using the syn function from the synthpop package).
The output of this program is very similar but not identical to a fake dataset created by an earlier version of this program and which is saved in the “ymh_clinical_dict_r3.RDS” file from the https://doi.org/10.7910/DVN/HJXYKQ data repository.
Install required R packages
If you do not have the following packages already installed, uncomment and run the following lines.
# install.packages("faux")
# devtools::install_github("ready4-dev/ready4)
# devtools::install_github("ready4-dev/youthvars)
# devtools::install_github("ready4-dev/scorz)
# devtools::install_github("ready4-dev/specific")
# devtools::install_github("ready4-dev/TTU")
# devtools::install_github("ready4-dev/youthu")Load the ready4 framework package.
Specify parameters to generate outcome fake data
AQoL item response parameters
The first set of input data are the proportions for each allowed response for each of the twenty AQOL-6D questions at both baseline and followup.
aqol_items_prpns_tbs_ls <- list(bl_answer_props_tb = tibble::tribble(
~Question, ~Answer_1, ~Answer_2, ~Answer_3, ~Answer_4, ~Answer_5, ~Answer_6,
"Q1", 0.35, 0.38, 0.16, 0.03, NA_real_,100, # Check item 5 in real data.
"Q2", 0.28, 0.38, 0.18, 0.08, 0.04,100,
"Q3", 0.78, 0.18, 0.03, 0.01, 0.0, 100,
"Q4", 0.64, 0.23, 0.09, 0.0, 100, NA_real_,
"Q5", 0.3, 0.48, 0.12, 0.05, 100, NA_real_,
"Q6", 0.33, 0.48, 0.15, 100, NA_real_,NA_real_,
"Q7", 0.44, 0.27, 0.11, 100, NA_real_, NA_real_,
"Q8", 0.18, 0.29, 0.23, 0.21, 100, NA_real_,
"Q9", 0.07, 0.27, 0.19, 0.37, 100, NA_real_,
"Q10", 0.04, 0.15, 0.4, 0.25, 100, NA_real_,
"Q11", 0.03, 0.13, 0.52, 0.25, 100, NA_real_,
"Q12", 0.06, 0.21, 0.25, 0.34, 100, NA_real_,
"Q13", 0.05, 0.25, 0.31, 0.28, 100, NA_real_,
"Q14", 0.05, 0.3, 0.34, 0.25, 100, NA_real_,
"Q15", 0.57, 0.25, 0.12, 100, NA_real_,NA_real_,
"Q16", 0.48, 0.42, 0.06, 100, NA_real_, NA_real_,
"Q17", 0.44, 0.3, 0.16, 0.07, 100, NA_real_,
"Q18", 0.33, 0.38, 0.25, 0.04, 0.0, 100,
"Q19", 0.33, 0.49, 0.16, 0.02, 0.0, 100,
"Q20", 0.67, 0.21, 0.02, 100, NA_real_,NA_real_),
fup_answer_props_tb = tibble::tribble(
~Question, ~Answer_1, ~Answer_2, ~Answer_3, ~Answer_4, ~Answer_5, ~Answer_6,
"Q1", 0.51, 0.33, 0.12, 0.02, NA_real_, 100,
"Q2", 0.36, 0.38, 0.16, 0.06, 0.02,100,
"Q3", 0.81, 0.15, 0.04, 0.00, 0.0, 100,
"Q4", 0.73, 0.18, 0.09, 0.0, 100, NA_real_,
"Q5", 0.36, 0.42, 0.12, 0.05, 100, NA_real_,
"Q6", 0.48, 0.40, 0.11, 100, NA_real_,NA_real_,
"Q7", 0.57, 0.25, 0.09, 100, NA_real_, NA_real_,
"Q8", 0.31, 0.33, 0.17, 0.12, 100, NA_real_,
"Q9", 0.13, 0.35, 0.19, 0.23, 100, NA_real_,
"Q10", 0.1, 0.21, 0.43, 0.16, 100, NA_real_,
"Q11", 0.06, 0.25, 0.48, 0.18, 100, NA_real_,
"Q12", 0.08, 0.27, 0.26, 0.25, 100, NA_real_,
"Q13", 0.07, 0.37, 0.31, 0.19, 100, NA_real_,
"Q14", 0.08, 0.37, 0.34, 0.15, 100, NA_real_,
"Q15", 0.62, 0.23, 0.09, 100, NA_real_,NA_real_,
"Q16", 0.52, 0.40, 0.06, 100, NA_real_, NA_real_,
"Q17", 0.51, 0.28, 0.15, 0.06, 100, NA_real_,
"Q18", 0.37, 0.35, 0.25, 0.03, 0.0, 100,
"Q19", 0.43, 0.40, 0.16, 0.01, 0.0, 100,
"Q20", 0.77, 0.21, 0.02, 100, NA_real_,NA_real_)) %>%
youthvars::make_complete_prpns_tbs_ls()Outcome variable correlation parameters
First we specify the names of variables we will be creating as outcome variables.
var_names_chr <- c("aqol6d_total_w","phq9_total","bads_total",
"gad7_total","oasis_total","scared_total","k6_total")The next step is to specify the correlations between outcome variables (variables assumed to be ordered as in previous step) at baseline and follow-up timepoints.
cor_mat_ls <- list(matrix(c(1,-0.78,0.72,-0.67,-0.71,-0.65,-0.67,
NA,1,-0.73,0.69,0.66,0.63,0.71,
NA,NA,1,-.57,-0.64,-0.57,-0.65,
NA,NA,NA,1,0.74,0.70,0.63,
NA,NA,NA,NA,1,0.7,0.59,
NA,NA,NA,NA,NA,1,0.55,
NA,NA,NA,NA,NA,NA,1),7,7),
matrix(c(1,-0.81,0.72,-0.71,-0.73,-0.64,-0.68,
NA,1,-0.72,0.69,0.68,0.61,0.68,
NA,NA,1,-0.59,-0.61,-0.51,-0.61,
NA,NA,NA,1,0.75,0.71,0.6,
NA,NA,NA,NA,1,0.68,0.59,
NA,NA,NA,NA,NA,1,0.52,
NA,NA,NA,NA,NA,NA,1),7,7)) We now specify the univariate distribution parameters for each of the outcome variables.
synth_data_spine_ls <- list(cor_mat_ls = cor_mat_ls,
nbr_obs_dbl = c(1068,643),
timepoint_nms_chr = c("BL","FUP"),
means_ls = list(c(0.6,12.8,78.2, 10.4,8.1,34.2,12.2),
c(0.7,9.8,89.4, 7.9,6.3,28.8,9.8)),
sds_ls = list(c(0.2,6.6,24.8,5.7,4.7,17.9,5.8),
c(0.2,6.5,24.4,5.5,4.3,17.8,5.9)),
missing_ls = list(c(0,4,10,6,7,7,4),
c(0,5,2,2,1,2,2)),
min_max_ls = list(c(0.03,1),
c(0,27),
c(0,150),
c(0,21),
c(0,20),
c(0,82),
c(0,24)),
discrete_lgl = c(F,rep(T,6)),
var_names_chr = var_names_chr,
aqol_tots_var_nms_chr = c(cumulative = "aqol6d_total_c",
weighted = "aqol6d_total_w")) Generate fake data
Create fake outcome variable datasets
We now use the parameters we have just specified to create baseline and follow-up datasets with fake data for our nominated outcome variables.
aqol_scores_pars_ls <- list(means_dbl = c(44.5,40.6),
sds_dbl = c(9.9,9.8),
corr_dbl = -0.95)
aqol6d_adol_pop_tbs_ls <- aqol_items_prpns_tbs_ls %>%
scorz::make_aqol6d_adol_pop_tbs_ls(aqol_scores_pars_ls = aqol_scores_pars_ls,
series_names_chr = c("bl_outcomes_tb",
"fup_outcomes_tb"),
synth_data_spine_ls = synth_data_spine_ls,
temporal_cors_ls = list(aqol6d_total_w = 0.85))
#> Joining, by = c("id", "match_var_chr")
#> Joining, by = "id"
#> Joining, by = c("id", "match_var_chr")
#> Joining, by = "id"Create fake descriptive variables
We now specify the names and statistical parameters of the variables we will be using in descriptive statistics. For this analysis we are not interested in capturing the joint distribution between these variables, so we only use univariate parameters.
descriptives_BL_tb <- tibble::tibble(fkClientID = aqol6d_adol_pop_tbs_ls$bl_outcomes_tb$fkClientID,
round = c(1) ,
d_age = rnorm(1068,18.1,3.3) %>%
purrr::map_dbl(~min(.x,25) %>%
max(12)),
d_gender = c(rep(1,653),
rep(2,359),
rep(3,39),
rep(NA_real_,17)) %>%
specific::scramble_xx() %>%
factor(labels = c("Female","Male","Other")),
d_sexual_ori_s = c(rep(1,738),
rep(2,289),
rep(NA_real_,41)) %>%
specific::scramble_xx() %>%
factor(labels = c("Straight","Other")),
Region = c(rep(1,671),
rep(2,397)) %>%
specific::scramble_xx() %>%
factor(labels = c("Metro","Regional")),
CALD = c(rep(T,759),
rep(F,169),
rep(NA,140)) %>%
specific::scramble_xx(),
d_studying_working = c(rep(1,405),
rep(2,167),
rep(3,305),
rep(4,159),
rep(NA_real_,32)) %>%
specific::scramble_xx() %>%
factor(labels = c("Studying only",
"Working only",
"Studying and working",
"Not studying or working")),
c_p_diag_s = c(rep(1,182),
rep(2,264),
rep(3,332),
rep(4,237),
rep(NA_real_,53)) %>%
specific::scramble_xx() %>%
factor(labels = c("Depression", "Anxiety","Depression and Anxiety", "Other")),
c_clinical_staging_s = c(rep(1,625),
rep(2,326),
rep(3,86),
rep(NA_real_,31)) %>%
specific::scramble_xx() %>%
factor(labels = c("0-1a","1b","2-4")),
c_sofas = c(rnorm(1068-30,65.2,9.5),
rep(NA_real_,30)) %>%
purrr::map_dbl(~min(.x,100) %>%
max(0)) %>%
specific::scramble_xx(),
s_centre = NA_character_,
d_agegroup = NA_character_,
d_sex_birth_s = NA_character_,
d_country_bir_s = NA_character_,
d_ATSI = NA_character_,
d_english_home = NA,
d_english_native = NA,
d_relation_s = c(rep(1,325),
rep(2,426),
rep(3,286),
rep(NA_real_,31)) %>%
specific::scramble_xx() %>%
factor(labels = c("REPLACE_ME_1",
"REPLACE_ME_2",
"REPLACE_ME_3"))) %>%
dplyr::mutate(d_sex_birth_s = dplyr::case_when(is.na(d_gender) ~ NA_integer_,
as.integer(d_gender) %in%
c(1L,2L) &
runif(1068)>0.995 ~ as.integer(d_gender) %>%
purrr::map_int(~ ifelse(is.na(.x),
.x,
switch(.x,2L,1L,3L))),
as.integer(d_gender) == 3 ~ sample(c(1L,2L),
1068,
replace = T),
TRUE ~ as.integer(d_gender)
) %>%
factor(labels = c("Female","Male")))
#> Registered S3 methods overwritten by 'ggalt':
#> method from
#> grid.draw.absoluteGrob ggplot2
#> grobHeight.absoluteGrob ggplot2
#> grobWidth.absoluteGrob ggplot2
#> grobX.absoluteGrob ggplot2
#> grobY.absoluteGrob ggplot2#> Registered S3 methods overwritten by 'rmutil':
#> method from
#> plot.residuals psych
#> print.response httrdescriptives_FUP_tb <- descriptives_BL_tb %>%
dplyr::filter(fkClientID %in%
aqol6d_adol_pop_tbs_ls$fup_outcomes_tb$fkClientID) %>%
dplyr::mutate(round = 2,
d_age = d_age + 0.25,
Region = Region %>%
specific::randomise_changes_in_fct_lvls(0.98),
d_studying_working = d_studying_working %>%
specific::randomise_changes_in_fct_lvls(0.9),
c_p_diag_s = c_p_diag_s %>%
specific::randomise_changes_in_fct_lvls(0.90),
c_clinical_staging_s = c_clinical_staging_s %>%
specific::randomise_changes_in_fct_lvls(0.8),
c_sofas = c_sofas + rnorm(643,4.7,10) %>%
purrr::map_dbl(~min(.x,100) %>% max(0)))
bl_tb <- dplyr::inner_join(descriptives_BL_tb,
aqol6d_adol_pop_tbs_ls$bl_outcomes_tb)
#> Joining, by = "fkClientID"fup_tb <- dplyr::inner_join(descriptives_FUP_tb,
aqol6d_adol_pop_tbs_ls$fup_outcomes_tb)
#> Joining, by = "fkClientID"We make some adjustments to ensure that the c_sofas variable is correlated with our aqol6d_total_w variable at both baseline and follow-up.
bl_tb <- bl_tb %>%
dplyr::mutate(c_sofas = faux::rnorm_pre(bl_tb$aqol6d_total_w %>%
as.vector(),
mu = 65.2,
sd = 9.5,
r = 0.5,
empirical = T) %>%
purrr::map_dbl(~min(.x,100) %>% max(0)))
fup_tb <- fup_tb %>%
dplyr::mutate(c_sofas = faux::rnorm_pre(fup_tb$aqol6d_total_w %>%
as.vector(),
mu = 69.9,
sd = 10,
r = 0.5,
empirical = T) %>%
purrr::map_dbl(~min(.x,100) %>% max(0)))Combine datasets
We now add the fake outcome variables dataset to the fake descriptive variables dataset.
composite_tb <- dplyr::bind_rows(bl_tb, fup_tb) %>%
dplyr::mutate(d_age = floor(d_age)) %>%
dplyr::mutate(d_gender = d_gender %>% as.character() %>%
purrr::map_chr(~ifelse(.x=="Other",
sample(c("Genderqueer/gender nonconforming/agender",
"Transgender"),1),
.x)),
s_centre = Region %>% as.character() %>%
purrr::map_chr(~ifelse(.x=="Metro",
sample(c("Canberra","Southport","Knox"),1),
"Regional Centre")),
d_country_bir_s = CALD %>%
purrr::map_chr(~ifelse(.x,
"Other",
"Australia")),
d_ATSI = CALD %>%
purrr::map_chr(~ifelse(.x,
"Yes",
"No")),
d_english_home = CALD %>%
purrr::map_chr(~ifelse(.x,
"No",
"Yes")),
d_english_native = CALD %>%
purrr::map_chr(~ifelse(.x,
"No",
"Yes"))
) %>%
dplyr::select(-CALD) %>%
dplyr::select(-Region)
composite_tb <- composite_tb %>%
dplyr::select(-setdiff(names(composite_tb)[startsWith(names(composite_tb),
"aqol6d_")],
names(composite_tb)[startsWith(names(composite_tb),
"aqol6d_q")]))
composite_tb <- composite_tb %>%
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) %>%
youthvars::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) %>%
dplyr::select(-c("d_agegroup","Gender", "CALD", "Region"))2.3 - Model health utility
2.3.1 - Develop health utility mapping algorithms
This below section embeds a PDF version of an R Markdown program. The following alternative options may provide improved viewing experience, more contextual information and access to more useful code formats:
- download the PDF (recommended for enhanced readibility);
- view the PDF in its study dataset (includes contextual information) from that article; and;
- view the PDF along with the current release of its R Markdown code (useful if you plan to run the code) and
- view the PDF along with the current development version of its R Markdown code (useful if you wish to copy or edit the code)
2.3.2 - Predict health utility
This below section embeds a PDF version of an R Markdown program. The following alternative options may provide improved viewing experience, more contextual information and access to more useful code formats:
- download the PDF (recommended for enhanced readibility);
- view the PDF in its study dataset (includes contextual information) from that article; and;
- view the PDF along with the current release of its R Markdown code (useful if you plan to run the code) and
- view the PDF along with the current development version of its R Markdown code (useful if you wish to copy or edit the code)
3 - Subroutine templates for authoring analysis reports
3.1 - Make a catalogue of utility mapping models
This below section reproduces the README file of an R Markdown sub-routine. The following alternative options may provide more contextual information and access to more useful code formats:
- view examples of the catalogues produced by this subroutine in a study dataset (the names of the relevant files all begin with “AAA_TTU_MDL_CTG”);
- view the README along with the current release of its R Markdown code (useful if you plan to run the code); and
- view the README along with the current development version of its R Markdown code (useful if you wish to copy or edit the code)
ttu_mdl_ctlg
Markdown files to create utility mapping (transfer to utility) model catalogues
3.2 - Author a template scientific manuscript
This below section reproduces the README file of an R Markdown sub-routine. The following alternative options may provide more contextual information and access to more useful code formats:
- view an illustrative example of a program to call this subroutine (useful to understand how to use it);
- view the README along with the current release of its R Markdown code (useful if you plan to run the code); and
- view the README along with the current development version of its R Markdown code (useful if you wish to copy or edit the code)
ms_tmpl: Generate a template scientific manuscript
3.3 - Author a draft scientific manuscript for a utility mapping study
This below section reproduces the README file of an R Markdown sub-routine. The following alternative options may provide more contextual information and access to more useful code formats:
Create a Draft Scientific Manuscript For A Utility Mapping Study
This sub-routine program extends the R package TTU (https://ready4-dev.github.io/TTU/index.html) by providing a toolkit for automatically authoring a first draft of a scientific manuscript from results generated by TTU functions.
The program is intended for use and as the last component of TTU’s reporting workflow for longitudinal modelling projects. An example of this workflow is available at: https://doi.org/10.5281/zenodo.6116077 . This program generalises a program that produced the manuscript for a real world study (https://www.medrxiv.org/content/10.1101/2021.07.07.21260129v2.full).
The program can produce manuscripts in PDF / LaTex (example - https://dataverse.harvard.edu/api/access/datafile/4957407) and Word (example - https://dataverse.harvard.edu/api/access/datafile/4957416). It should be noted that the Word output requires some manual editing to adapt section numbering, modify table headers and resize tables to page boundaries.
There are two suggested workflows to further develop the algorithm-authored first draft into something that could be submitted for publication:
Suggested citation (bibTeX):
@software{hamilton_matthew_2022_6931146, author = {Hamilton, Matthew and Gao, Caroline}, title = {{ttu_lng_ss: Create a Draft Scientific Manuscript For A Utility Mapping Study}}, month = jul, year = 2022, note = {{Matthew Hamilton and Caroline Gao (2022). ttu_lng_ss: Create a Draft Scientific Manuscript For A Utility Mapping Study. Zenodo. https://doi.org/10.5281/zenodo.5976987. Version 0.8.0.0}}, publisher = {Zenodo}, version = {0.8.0.0}, doi = {10.5281/zenodo.5976987}, url = {https://doi.org/10.5281/zenodo.5976987} }
3.4 - Make results summary for a Discrete Choice Experiment
This below section reproduces the README file of an R Markdown sub-routine. The following alternative options may provide more contextual information and access to more useful code formats:
Report results from a Discrete Choice Experiment implemented with the mychoice R package.
Suggested citation (bibTeX):
@software{hamilton_matthew_2022_6931146, author = {Hamilton, Matthew}, title = {{mychoice_results: Report results from a Discrete Choice Experiment implemented with the mychoice R package}}, month = nov, year = 2022, note = {{Matthew Hamilton (2022). mychoice_results: Report results from a Discrete Choice Experiment implemented with the mychoice R package. Zenodo. https://doi.org/10.5281/zenodo.7297904. Version 0.1.0.0}}, version = {0.1.0.0}, doi = {10.5281/zenodo.7297904}, url = {https://doi.org/10.5281/zenodo.7297904} }
4 - Authoring reproducible analyses
4.1 - Authoring scientific manuscripts
This below section renders a vignette article from the ready4show library. You can use the following links to:
- view the vignette on the library website (adds useful hyperlinks to code blocks)
- view the source file from that article, and;
- edit its contents (requires a GitHub account).
Motivation
Open science workflows should ideally span an unbroken chain between data-ingest to production of a scientific summary such as a manuscript. Such extensive workflows provide an explicit means of linking all content in a scientific summary with the analysis that it reports.
Implementation
ready4show includes a number of classes and methods that help integrate manuscript authoring into a reproducible workflow. These tools are principally intended for use with the ready4 youth mental health system model.
Create a synopsis of the manuscript to be authored
To start with we create X, an instance of Ready4showSynopsis, a ready4 module (S4 class). We can use X to record metadata about the manuscript to be authored (including details about the study being summarised and the title and format of the intended output).
X <- Ready4showSynopsis(background_1L_chr = "Our study is entirely fictional.",
coi_1L_chr = "None declared.",
conclusion_1L_chr = "These fake results are not interesting.",
digits_int = 3L,
ethics_1L_chr = "The study was reviewed and granted approval by Awesome University's Human Research Ethics Committee (1111111.1).",
funding_1L_chr = "The study was funded by Generous Benefactor.",
interval_chr = "three months",
keywords_chr = c("entirely","fake","do", "not","cite"),
outp_formats_chr = "PDF",
sample_desc_1L_chr = "The study sample is fake data that pretends to be young people aged 12 to 25 years who attended Australian primary care services for mental health related needs between November 2019 to August 2020.",
title_1L_chr = "A hypothetical study using fake data")Add authorship details
Authorship details can be added to slots of X that contain ready4show_authors and ready4show_instututes ready4 sub-modules.
As we can see from the below call to exhibitSlot, X was created with no authorship information.
exhibitSlot(X,
"authors_r3",
scroll_box_args_ls = list(width = "100%"))
| First-name | Middle-name | Last-name | Title | Qualifications | Institutes | Sequence Position | Corresponding | Joint-first | |
|---|---|---|---|---|---|---|---|---|---|
We can add details on each author by repeated calls to the renewSlot method.
X <- renewSlot(X,
"authors_r3",
first_nm_chr = "Alejandra",
middle_nm_chr = "Rocio",
last_nm_chr = "Scienceace",
title_chr = "Dr",
qualifications_chr = "MD, PhD",
institute_chr = "Institute_A, Institute_B",
sequence_int = 1,
is_corresponding_lgl = T,
email_chr = "fake_email@fake_institute.com") %>%
renewSlot("authors_r3",
first_nm_chr = "Fionn",
middle_nm_chr = "Seamus",
last_nm_chr = "Researchchamp",
title_chr = "Prof",
qualifications_chr = "MSc, PhD",
institute_chr = "Institute_C, Institute_B",
sequence_int = 2,
email_chr = "fake_email@unreal_institute.com") The updated authorship table can now be inspected.
X %>%
exhibitSlot("authors_r3",
scroll_box_args_ls = list(width = "100%"))
| First-name | Middle-name | Last-name | Title | Qualifications | Institutes | Sequence Position | Corresponding | Joint-first | |
|---|---|---|---|---|---|---|---|---|---|
| Alejandra | Rocio | Scienceace | Dr | MD, PhD | Institute_A, Institute_B | 1 | TRUE |
|
NA |
| Fionn | Seamus | Researchchamp | Prof | MSc, PhD | Institute_C, Institute_B | 2 | NA |
|
NA |
We now need to add additional information for each author institute.
X <- renewSlot(X,
"institutes_r3",
short_name_chr = "Institute_A",
long_name_chr = "Awesome University, Shanghai") %>%
renewSlot("institutes_r3",
short_name_chr = "Institute_B",
long_name_chr = "August Institution, London") %>%
renewSlot("institutes_r3",
new_val_xx = "use_renew_mthd",
short_name_chr = "Institute_C",
long_name_chr = "Highly Ranked Uni, Montreal")The updated institutes table can now be inspected.
X %>%
exhibitSlot("institutes_r3",
scroll_box_args_ls = list(width = "100%"))
| Reference | Name |
|---|---|
| Institute_A | Awesome University, Shanghai |
| Institute_B | August Institution, London |
| Institute_C | Highly Ranked Uni, Montreal |
Add correspondences
We can also add a look-up table about any changes we wish to make from the analysis code of how names of variables / parameters are presented in the manuscript text.
X <- renewSlot(X,
"correspondences_r3",
old_nms_chr = c("PHQ9", "GAD7"),
new_nms_chr = c("PHQ-9", "GAD-7"))These edits can now be inspected with a call to exhibitSlot.
X %>%
exhibitSlot("correspondences_r3",
scroll_box_args_ls = list(width = "100%")) # Add Exhibit Method
| Old name | New name |
|---|---|
| PHQ9 | PHQ-9 |
| GAD7 | GAD-7 |
Specify output directory
We now update X with details of the directory to which we wish to write the manuscript we are authoring and all its supporting files.
Create dataset of literate programming files
Our next step is to copy a dataset of files that can implement a literate program to generate our manuscript. If you have a template you wish to work with, you can specify its local path using the a_Ready4showPaths@mkdn_source_dir_1L_chr slot of the X. Skip this step if you wish to use the default markdown dataset, which leverages popular rmarkdown toolkits such as bookdown and rticles.
## Not run
# procureSlot(X,
# "a_Ready4showPaths@mkdn_source_dir_1L_chr",
# new_val_xx = "PATH TO MARKDOWN DATASET")We create the dataset copy with the authorData method.
authorData(X)Having created a local copy of the template literate program files dataset, it is now possible to manually edit the markdown files to author the manuscript. However, in this example we are skipping this step and will continue to use the unedited template in conjunction with the metadata we have specified in X. We combine the two to author a manuscript using the authorReport method.
authorReport(X)If we wish, we can now ammend X and then rerun the authorReport method to generate Word and HTML versions of the manuscript.
renewSlot(X,
"outp_formats_chr",
new_val_xx = "Word") %>%
authorReport()renewSlot(X,
"outp_formats_chr",
new_val_xx = "HTML") %>%
authorReport()The outputed files are as follows: