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gigs: Assess Fetal, Newborn, and Child Growth with International Standards #626
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Thanks for submitting to rOpenSci, our editors and @ropensci-review-bot will reply soon. Type |
🚀 Editor check started 👋 |
Checks for gigs (v0.4.1)git hash: a9d9654a
Package License: GPL (>= 3) 1. rOpenSci Statistical Standards (
|
type | package | ncalls |
---|---|---|
internal | gigs | 401 |
internal | base | 271 |
imports | stats | 36 |
imports | checkmate | 13 |
imports | gamlss.dist | 1 |
imports | vctrs | 1 |
suggests | knitr | NA |
suggests | rmarkdown | NA |
suggests | testthat | NA |
suggests | units | NA |
suggests | withr | NA |
linking_to | NA | NA |
Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(<path/to/repo>)', and examining the 'external_calls' table.
gigs
validate_ig_fet (22), validate_ig_nbs (17), fn_on_subset (13), validate_who_gs (11), who_gs_lms2value (9), validate_ig_png (6), gigs_xaz_lgls (5), validate_yzp (5), ig_fet_doppler_gms (3), ig_fet_efw_lms (3), ig_fet_gwg_mu_sigma (3), ig_fet_mu_sigma (3), ig_nbs_bodycomp_mu_sigma (3), ig_nbs_msnt (3), ig_nbs_wlr_mu_sigma (3), ig_png_equations (3), ig_vpns_equations (3), mu_sigma_z2y (3), validate_parameter_lengths (3), who_gs_lms (3), classify_sfga (2), gigs_laz (2), gigs_option_get (2), gigs_waz (2), gigs_wlz (2), ig_nbs_hcfga_value2zscore (2), ig_nbs_lfga_value2zscore (2), ig_nbs_wfga_value2centile (2), ig_nbs_wfga_value2zscore (2), ig_png_hcfa_value2zscore (2), ig_png_lfa_value2zscore (2), ig_png_wfa_value2zscore (2), ig_png_wfl_value2zscore (2), inrange (2), validate_hcaz_params (2), validate_laz_params (2), validate_waz_params (2), validate_wlz_params (2), who_gs_hcfa_value2zscore (2), who_gs_lhfa_value2zscore (2), who_gs_wfa_value2zscore (2), classify_stunting (1), classify_svn (1), classify_wasting (1), classify_wfa (1), drop_null_elements (1), exponent (1), gigs_hcaz (1), gigs_option_set (1), handle_invalid_chr_options (1), handle_missing_data (1), handle_oob_centiles (1), handle_oob_xvar (1), handle_undefined_data (1), handle_var (1), hcfa_mu (1), hcfa_sigma (1), hcfga_mu (1), ig_fet_acfga_centile2value (1), ig_fet_acfga_value2centile (1), ig_fet_acfga_value2zscore (1), ig_fet_acfga_zscore2value (1), ig_fet_avfga_centile2value (1), ig_fet_avfga_value2centile (1), ig_fet_avfga_value2zscore (1), ig_fet_avfga_zscore2value (1), ig_fet_bpdfga_centile2value (1), ig_fet_bpdfga_value2centile (1), ig_fet_bpdfga_value2zscore (1), ig_fet_bpdfga_zscore2value (1), ig_fet_centile2value (1), ig_fet_cmfga_centile2value (1), ig_fet_cmfga_value2centile (1), ig_fet_cmfga_value2zscore (1), ig_fet_cmfga_zscore2value (1), ig_fet_crlfga_centile2value (1), ig_fet_crlfga_value2centile (1), ig_fet_crlfga_value2zscore (1), ig_fet_crlfga_zscore2value (1), ig_fet_doppler_y2z (1), ig_fet_doppler_z2y (1), ig_fet_efw_y2z (1), ig_fet_efw_z2y (1), ig_fet_efwfga_centile2value (1), ig_fet_efwfga_value2centile (1), ig_fet_efwfga_value2zscore (1), ig_fet_efwfga_zscore2value (1), ig_fet_estimate_fetal_weight (1), ig_fet_estimate_ga (1), ig_fet_estimate_ga_crl (1), ig_fet_estimate_ga_hc (1), ig_fet_estimate_ga_hcfl (1), ig_fet_flfga_centile2value (1), ig_fet_flfga_value2centile (1), ig_fet_flfga_value2zscore (1), ig_fet_flfga_zscore2value (1), ig_fet_gafcrl_centile2value (1), ig_fet_gafcrl_value2centile (1), ig_fet_gafcrl_value2zscore (1), ig_fet_gafcrl_zscore2value (1), ig_fet_gaftcd_centile2value (1), ig_fet_gaftcd_value2centile (1), ig_fet_gaftcd_value2zscore (1), ig_fet_gaftcd_zscore2value (1), ig_fet_gwg_y2z (1), ig_fet_gwg_z2y (1), ig_fet_gwgfga_centile2value (1), ig_fet_gwgfga_value2centile (1), ig_fet_gwgfga_value2zscore (1), ig_fet_gwgfga_zscore2value (1), ig_fet_hcfga_centile2value (1), ig_fet_hcfga_value2centile (1), ig_fet_hcfga_value2zscore (1), ig_fet_hcfga_zscore2value (1), ig_fet_mu_sigma_y2z (1), ig_fet_mu_sigma_z2y (1), ig_fet_ofdfga_centile2value (1), ig_fet_ofdfga_value2centile (1), ig_fet_ofdfga_value2zscore (1), ig_fet_ofdfga_zscore2value (1), ig_fet_pifga_centile2value (1), ig_fet_pifga_value2centile (1), ig_fet_pifga_value2zscore (1), ig_fet_pifga_zscore2value (1), ig_fet_poffga_centile2value (1), ig_fet_poffga_value2centile (1), ig_fet_poffga_value2zscore (1), ig_fet_poffga_zscore2value (1), ig_fet_pvfga_centile2value (1), ig_fet_pvfga_value2centile (1), ig_fet_pvfga_value2zscore (1), ig_fet_pvfga_zscore2value (1), ig_fet_rifga_centile2value (1), ig_fet_rifga_value2centile (1), ig_fet_rifga_value2zscore (1), ig_fet_rifga_zscore2value (1), ig_fet_sdrfga_centile2value (1), ig_fet_sdrfga_value2centile (1), ig_fet_sdrfga_value2zscore (1), ig_fet_sdrfga_zscore2value (1), ig_fet_sffga_centile2value (1), ig_fet_sffga_value2centile (1), ig_fet_sffga_value2zscore (1), ig_fet_sffga_zscore2value (1), ig_fet_sfhfga_centile2value (1), ig_fet_sfhfga_value2centile (1), ig_fet_sfhfga_value2zscore (1), ig_fet_sfhfga_zscore2value (1), ig_fet_tcdfga_centile2value (1), ig_fet_tcdfga_value2centile (1), ig_fet_tcdfga_value2zscore (1), ig_fet_tcdfga_zscore2value (1), ig_fet_v2z_internal (1), ig_fet_value2centile (1), ig_fet_value2zscore (1), ig_fet_z2v_internal (1), ig_fet_zscore2value (1), ig_nbs_bfpfga_centile2value (1), ig_nbs_bfpfga_value2centile (1), ig_nbs_bfpfga_value2zscore (1), ig_nbs_bfpfga_zscore2value (1), ig_nbs_bodycomp_p2v (1), ig_nbs_bodycomp_v2p (1), ig_nbs_c2v_internal (1), ig_nbs_centile2value (1), ig_nbs_ffmfga_centile2value (1), ig_nbs_ffmfga_value2centile (1), ig_nbs_ffmfga_value2zscore (1), ig_nbs_ffmfga_zscore2value (1), ig_nbs_fmfga_centile2value (1), ig_nbs_fmfga_value2centile (1), ig_nbs_fmfga_value2zscore (1), ig_nbs_fmfga_zscore2value (1), ig_nbs_hcfga_centile2value (1), ig_nbs_hcfga_value2centile (1), ig_nbs_hcfga_zscore2value (1), ig_nbs_lfga_centile2value (1), ig_nbs_lfga_value2centile (1), ig_nbs_lfga_zscore2value (1), ig_nbs_msnt_p2v (1), ig_nbs_msnt_v2p (1), ig_nbs_v2c_internal (1), ig_nbs_value2centile (1), ig_nbs_value2zscore (1), ig_nbs_wfga_centile2value (1), ig_nbs_wfga_zscore2value (1), ig_nbs_wlrfga_centile2value (1), ig_nbs_wlrfga_p2v (1), ig_nbs_wlrfga_v2p (1), ig_nbs_wlrfga_value2centile (1), ig_nbs_wlrfga_value2zscore (1), ig_nbs_wlrfga_zscore2value (1), ig_nbs_zscore2value (1), ig_png_centile2value (1), ig_png_hcfa_centile2value (1), ig_png_hcfa_value2centile (1), ig_png_hcfa_zscore2value (1), ig_png_lfa_centile2value (1), ig_png_lfa_value2centile (1), ig_png_lfa_zscore2value (1), ig_png_v2z_internal (1), ig_png_value2centile (1), ig_png_value2zscore (1), ig_png_wfa_centile2value (1), ig_png_wfa_value2centile (1), ig_png_wfa_zscore2value (1), ig_png_wfl_centile2value (1), ig_png_wfl_value2centile (1), ig_png_wfl_zscore2value (1), ig_png_z2v_internal (1), ig_png_zscore2value (1), ig_vpns_value2zscore (1), ig_vpns_zscore2value (1), lfa_log_mu (1), lfa_sigma (1), lfga_mu (1), msg_invalid_sex_acronym (1), msg_missing_data (1), msg_oob_centiles (1), msg_oob_xvar (1), msg_undefined_data (1), mu_sigma_y2z (1), remove_attributes (1), retrieve_coefficients (1), validate_acronym (1), validate_chr (1), validate_ig_fet_estimation_param (1), validate_ig_fet_weight_estimation_param (1), validate_numeric (1), validate_sex (1), validate_xvar (1), wfa_log_mu (1), wfa_sigma (1), wfga_logmu (1), wfl_mu (1), wfl_sigma (1), who_gs_acfa_centile2value (1), who_gs_acfa_value2centile (1), who_gs_acfa_value2zscore (1), who_gs_acfa_zscore2value (1), who_gs_bfa_centile2value (1), who_gs_bfa_value2centile (1), who_gs_bfa_value2zscore (1), who_gs_bfa_zscore2value (1), who_gs_centile2value (1), who_gs_hcfa_centile2value (1), who_gs_hcfa_value2centile (1), who_gs_hcfa_zscore2value (1), who_gs_lhfa_centile2value (1), who_gs_lhfa_value2centile (1), who_gs_lhfa_zscore2value (1), who_gs_lms_v2z (1), who_gs_lms_v2z_constrained (1), who_gs_lms_v2z_over_three (1), who_gs_lms_v2z_under_minus_three (1), who_gs_lms_z2v (1), who_gs_lms_z2v_over_three (1), who_gs_lms_z2v_under_minus_three (1), who_gs_ssfa_centile2value (1), who_gs_ssfa_value2centile (1), who_gs_ssfa_value2zscore (1), who_gs_ssfa_zscore2value (1), who_gs_tsfa_centile2value (1), who_gs_tsfa_value2centile (1), who_gs_tsfa_value2zscore (1), who_gs_tsfa_zscore2value (1), who_gs_v2z_internal (1), who_gs_value2centile (1), who_gs_value2zscore (1), who_gs_wfa_centile2value (1), who_gs_wfa_value2centile (1), who_gs_wfa_zscore2value (1), who_gs_wfh_centile2value (1), who_gs_wfh_value2centile (1), who_gs_wfh_value2zscore (1), who_gs_wfl_value2zscore (1)
base
list (63), length (32), is.null (18), c (15), rep (12), log (9), is.na (8), names (8), for (7), ifelse (6), gamma (5), lengths (5), sqrt (5), data.frame (4), logical (4), matrix (4), paste0 (4), unique (4), abs (3), character (3), get (3), if (3), lapply (3), ncol (3), nrow (3), numeric (3), vapply (3), as.data.frame (2), levels (2), options (2), parent.frame (2), rep_len (2), sum (2), with (2), deparse (1), emptyenv (1), exp (1), inherits (1), max (1), mode (1), new.env (1), paste (1), q (1), range (1), rep.int (1), replace (1), seq_along (1), sign (1), substitute (1), unlist (1), vector (1)
stats
sigma (28), complete.cases (4), var (2), approx (1), qnorm (1)
checkmate
qassert (8), assert_numeric (2), assert_subset (2), assert_character (1)
gamlss.dist
pST3C (1)
vctrs
vec_recycle_common (1)
3. Statistical Properties
This package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing.
Details of statistical properties (click to open)
The package has:
- code in R (100% in 15 files) and
- 1 authors
- 3 vignettes
- 7 internal data files
- 4 imported packages
- 185 exported functions (median 6 lines of code)
- 376 non-exported functions in R (median 8 lines of code)
Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages
The following terminology is used:
loc
= "Lines of Code"fn
= "function"exp
/not_exp
= exported / not exported
All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by the checks_to_markdown()
function
The final measure (fn_call_network_size
) is the total number of calls between functions (in R), or more abstract relationships between code objects in other languages. Values are flagged as "noteworthy" when they lie in the upper or lower 5th percentile.
measure | value | percentile | noteworthy |
---|---|---|---|
files_R | 15 | 73.0 | |
files_vignettes | 3 | 92.4 | |
files_tests | 10 | 90.7 | |
loc_R | 2689 | 89.0 | |
loc_vignettes | 877 | 89.0 | |
loc_tests | 4157 | 97.6 | TRUE |
num_vignettes | 3 | 94.2 | |
data_size_total | 1093128 | 95.5 | TRUE |
data_size_median | 11317 | 79.8 | |
n_fns_r | 561 | 98.0 | TRUE |
n_fns_r_exported | 185 | 98.3 | TRUE |
n_fns_r_not_exported | 376 | 97.5 | TRUE |
n_fns_per_file_r | 21 | 95.9 | TRUE |
num_params_per_fn | 3 | 33.6 | |
loc_per_fn_r | 6 | 12.1 | |
loc_per_fn_r_exp | 6 | 10.5 | |
loc_per_fn_r_not_exp | 8 | 22.6 | |
rel_whitespace_R | 13 | 82.6 | |
rel_whitespace_vignettes | 13 | 67.8 | |
rel_whitespace_tests | 12 | 94.8 | |
doclines_per_fn_exp | 163 | 97.4 | TRUE |
doclines_per_fn_not_exp | 0 | 0.0 | TRUE |
fn_call_network_size | 408 | 94.2 |
3a. Network visualisation
Click to see the interactive network visualisation of calls between objects in package
4. goodpractice
and other checks
Details of goodpractice checks (click to open)
3a. Continuous Integration Badges
GitHub Workflow Results
id | name | conclusion | sha | run_number | date |
---|---|---|---|---|---|
7815041175 | pages build and deployment | success | ea59a9 | 20 | 2024-02-07 |
7815003364 | pkgcheck | success | a9d965 | 5 | 2024-02-07 |
7815003368 | pkgdown | success | a9d965 | 45 | 2024-02-07 |
7814822521 | R-CMD-check | success | a9d965 | 51 | 2024-02-07 |
7814822525 | test-coverage | success | a9d965 | 51 | 2024-02-07 |
7815003373 | Update CITATION.cff | success | a9d965 | 2 | 2024-02-07 |
3b. goodpractice
results
R CMD check
with rcmdcheck
rcmdcheck found no errors, warnings, or notes
Test coverage with covr
Package coverage: 100
Cyclocomplexity with cyclocomp
No functions have cyclocomplexity >= 15
Static code analyses with lintr
lintr found the following 69 potential issues:
message | number of times |
---|---|
Avoid library() and require() calls in packages | 2 |
Lines should not be more than 80 characters. | 67 |
Package Versions
package | version |
---|---|
pkgstats | 0.1.3.9 |
pkgcheck | 0.1.2.14 |
srr | 0.0.1.194 |
Editor-in-Chief Instructions:
This package is in top shape and may be passed on to a handling editor
@ropensci-review-bot check srr |
'srr' standards compliance:
✔️ This package complies with > 50% of all standads and may be submitted. |
@ropensci-review-bot assign @rkillick as editor |
Assigned! @rkillick is now the editor |
Hi @rkillick, have you had any chance to look at this? Not meaning to be pushy if you're rained in with other things at the moment! |
Submitting Author Name: Simon Parker
Submitting Author Github Handle: @simpar1471
Repository: https://github.com/lshtm-gigs/gigs/
Version submitted: 0.4.1
Submission type: Stats
Badge grade: silver
Editor: @rkillick
Reviewers: TBD
Archive: TBD
Version accepted: TBD
Language: en
Scope
Please indicate which of our statistical package categories this package falls under. (Please check one appropriate box below):
Statistical Packages
Pre-submission Inquiry
General Information
The target audience for this package is researchers, clinicians, and policy-makers interested in nutrition and fetal/newborn/child health. The package itself is used to convert measurements, for example a baby's weight at a given age, into summary statistics such as z-scores (i.e. number of standard deviations away from mean) and percentiles (i.e. number of percentage points above/below the median, expressed as a decimal). It can also be used to classify these z-scores and percentiles into specific categories for specific facets of growth. Scientific applications of this package would therefore include using our functions to generate statistical measures of growth in individuals or populations across time or pre/post a healthcare intervention. These could then be used in further downstream analyses.
Several R packages which implement child growth charts already exist, but they differ in the range of growth charts offered, their flexibility in conversion, and in what data they make available to users.
anthro
converts measurements into z-scores in the WHO Child Growth Standards, but lacks any INTERGROWTH-21st standards and outputs tabular data. We provide an interface which takes vectors in and gives vectors out, so is more flexible (e.g. withdplyr
pipelines). This package is available on CRAN.childsds
can convert measurements to z-scores or percentiles, but cannot convert z-scores/percentiles to expected measurements. Additionally,childsds
does not contain the newborn/postnatal INTERGROWTH-21st growth standards, which we implement. Thoughchildsds
does include more growth references, growth references are not within the scope of the GIGS project. We can discuss the reasons for this if necessary. This package is available on CRAN.growthstandards
contains functions for converting between values and z-scores/percentiles, as ingigs
. It includes the INTERGROWTH-21st fetal standards, but not the newborn or postnatal standards we implement. This package makes coefficients available to end-users, but not reference growth curves. This package is not available on CRAN. It was last updated in January 2024.intergrowth
provides more fetal growth standards thangigs
, but cannot convert between z-scores/percentiles in the lacks the INTERGROWTH-21st newborn or postnatal growth standards, which we have implemented ingigs
. This package also does not make coefficients for the growth standards available to end-users, though it it does provide growth curve data to end-users. This package is not available on CRAN, and was last updated in January 2023.gigs
provides a simple interface for working with the growth standards it implements, which can be easily included indplyr
-like data wrangling pipelines. It implements all available WHO 2006/2007 and INTERGROWTH-21st growth standards, and makes published growth curves and model coefficients are available wherever possible. In our own testing, we found that it also warns end users about bad input data more comprehensively.The Other packages section of the repository README also contains links to each of these packages, and a table describing features present in gigs compared to other growth standards packages.
We have a benchmarking vignette which shows the scaling of these packages when analysing datasets containing 1 to 100,000 cases. In summary:
anthro
scales the worst (29 ms to 2.24 seconds). The other packages are then clustered together:growthstandards
is the fastest for 100,000 cases (4.62 ms to 125 ms);childsds
is next (2.40 ms to 126 ms); thengigs
(1.32 ms to 128 ms).Not applicable.
Badging
Silver
Technical checks
Confirm each of the following by checking the box.
autotest
checks on the package, and ensured no tests fail.srr_stats_pre_submit()
function confirms this package may be submitted.pkgcheck()
function confirms this package may be submitted - alternatively, please explain reasons for any checks which your package is unable to pass.This package:
Publication options
Code of conduct
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