extra numbers - deletion needed

[parkerth]

ManyAnalysts/index.qmd

Line 3508 in 91ca56a

For the *Eucalyptus* dataset, the meta-analytic mean shifted from `r filter(effectsparams, dataset == "eucalyptus", publishable_subset == "All") %>% round_pluck("estimate")` to `r filter(effectsparams, dataset == "eucalyptus", publishable_subset == "data_flawed") %>% round_pluck("estimate")` when effects from analyses rated as unpublishable were removed, and to `r filter(effectsparams, dataset == "eucalyptus", publishable_subset == "data_flawed_major") %>% round_pluck("estimate")` when effects from analyses rated, at least once, as unpublishable or requiring major revisions were removed.

In the render, this currently reads as:

"For the Eucalyptus dataset, the meta-analytic mean shifted from -0.09, -0.17, -0.03 to -0.02 when effects from analyses rated as unpublishable were removed, and to -0.04 when effects from analyses rated, at least once, as unpublishable or requiring major revisions were removed."

There are too many numbers in that first list of numbers.

[hannahsfraser]

I think I need the packages to run to fix this. I suspect we just need to add an extra filter clause in because several results are being called by the current one... but I need to see the object it's pulling from to know how to do that

[egouldo]

Thanks @hannahsfraser The relevant chunk is:

ManyAnalysts/index.qmd

Lines 3443 to 3510 in 31412e0

	#| label: tbl-effects-params
	#| tbl-cap: 'Estimated mean value of the standardised correlation coefficient, $Z_r$, along with its standard error and 95$\\%$confidence intervals. We re-computed the meta-analysis for different post-hoc subsets of the data: All eligible effects, removal of effects from blue tit analyses that contained a pair of highly collinear predictor variables, removal of effects from analysis teams that received at least one peer rating of "deeply flawed and unpublishable", removal of any effects from analysis teams that received at least one peer rating of either "deeply flawed and unpublishable" or "publishable with major revisions", inclusion of only effects from analysis teams that included at least one member who rated themselves as "highly proficient" or "expert" at conducting statistical analyses in their research area.'
	#| echo: false
	effectsparams <- ManyEcoEvo_viz %>%
	dplyr::filter(estimate_type == "Zr",
	model_name == "MA_mod",
	exclusion_set == "complete") %>%
	bind_rows(ManyEcoEvo_viz %>%
	dplyr::filter(exclusion_set == "complete-rm_outliers",
	estimate_type == "Zr",
	model_name == "MA_mod",
	publishable_subset == "All")) %>%
	hoist(tidy_mod_summary) %>%
	unnest(tidy_mod_summary)
	effectsparams %>%
	select(dataset,
	exclusion_set,
	publishable_subset,
	expertise_subset,
	collinearity_subset,
	estimate,
	std.error,
	statistic,
	p.value,
	starts_with("conf")) %>%
	mutate(publishable_subset =
	case_when(publishable_subset == "All" & expertise_subset == "All" ~
	"All analyses",
	publishable_subset == "data_flawed" & expertise_subset == "All" ~
	"Analyses receiving at least one 'Unpublishable' rating removed",
	publishable_subset == "data_flawed_major" & expertise_subset == "All" ~
	"Analyses receiving at least one 'Unpublishable' and or 'Major Revisions' rating removed",
	expertise_subset == "expert" ~ "Analyses from teams with highly proficient or expert data analysts",
	collinearity_subset == "collinearity_removed" ~ "Blue tit analyses containing highly collinear predictors removed",
	TRUE ~ ""),
	exclusion_set =
	case_when(exclusion_set == "complete" ~ "",
	TRUE ~ ", outliers removed"),
	dataset = case_when(str_detect(dataset, "euc") ~ "Eucalyptus",
	TRUE ~ dataset),
	collinearity_subset = dplyr::na_if(collinearity_subset, "All")) %>%
	select(-expertise_subset) %>%
	unite("data_subset", publishable_subset, exclusion_set, sep = "", remove = TRUE) %>%
	group_by(data_subset) %>%
	arrange(data_subset, dataset) %>%
	gt::gt() %>%
	gt::opt_stylize(style = 6, color = "gray") %>%
	gt::fmt(columns = "p.value",
	fns = function(x) gtsummary::style_pvalue(x)) %>%
	gt::fmt_number(columns = c(-p.value, -dataset)) %>%
	gt::sub_missing(columns = "collinearity_subset", rows = everything(), "") %>%
	gt::fmt("collinearity_subset", fns = function(x) str_replace(x, "_", " ") %>%
	str_c(", ", .)) %>%
	gt::cols_label(dataset = "Dataset",
	estimate = "$$\\hat\\mu$$",
	std.error = gt::md("$$\\text{SE}[\\hat\\mu]$$"),
	conf.low = gt::md("95\\%CI"),
	p.value = "p-value") %>%
	gt::cols_merge(columns = c(dataset, collinearity_subset),
	hide_columns =c(collinearity_subset)) %>%
	gt::cols_merge(columns = starts_with("conf"),
	pattern = "[{1},{2}]") %>%
	gt::cols_move(columns = conf.low, after = std.error) %>%
	gt::tab_style(locations = cells_body(rows = str_detect(dataset, "Eucalyptus"),
	columns = dataset),
	style = cell_text(style = "italic")) %>%
	gt::as_raw_html()

on branch 42-mv-fns-2-pkg.

I think if you:

1. check out 42-mv-fns-2-pkg locally
2. pull / push to sync
3. run renv::activate()/renv::restore() to install all the relavant libraries in the renv
4. install latest ManyEcoEvo:: package with: renv::install("egouldo/ManyEcoEvo@v2.3.0")

you should be able to get it to render.

Alternatively, you can download and load the latest ManYEcoEvo, mess around with the chunk code interactively, and post a solution here for me to upgrade, if that's easier!

And if you can't get it going with either of those, I'll come back to this later.

[hannahsfraser]

I'm sorry, Elliot. I cant seem to get the damn thing to work. renv::restore keeps failing so I cant seem to get all the packages I need. I'm going to have to put this back on your plate.

[egouldo]

No sweat Hannah, I’m sorry renv has been such a massive pain to work with!! I will consider switching to a different platform in the future I think.

…

Sent from my iPhone On 27 Aug 2024, at 10:30 AM, Hannah Fraser ***@***.***> wrote:  I'm sorry, Elliot. I cant seem to get the damn thing to work. renv::restore keeps failing so I cant seem to get all the packages I need. I'm going to have to put this back on your plate. — Reply to this email directly, view it on GitHub<#46 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/ACAC6KXTG7A7KDDHLV74PWLZTPCDJAVCNFSM6AAAAABLURWMW2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDGMJRGM2DEMJYGE>. You are receiving this because you were assigned.Message ID: ***@***.***>