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Black and White Screen when opening the Simpsons Website
Do you remember the Simpsons Movie? It was possible to create a simpsons avatar on the website - which needed flash. I found the old webpage on archive.org and with Ruffle" it starts loading... but it is only some white screen getting black :)
https://web.archive.org/web/20130323034746/http://www.simpsonsmovie.com/main.html
Do you know if this side will be supported by Ruffle in the future? I am using the nightly from today (2020-12-31) on firefox 84.0.1.
@spsanderson The stats team will discuss your query and get back to you asap. In regard to Probability Distributions standards, our stats dev-guide states:
Unlike most other categories of standards, packages which fit in this category will also generally be expected to fit into at least one other category of statistical software.
This would be first package that goes against that general principle. That said, it is clearly well-developed, and looks like a significant contribution to probability distributions in general. We just have to decide whether we'll relax that condition for your submission.
Thank you. And if a relaxing the standards is not going to happen then maybe some direction on how to make it fit the standard? As in maybe it also fits EDA?
Good point - our criteria for fit with stats submission is very easy: If you think you could comply with half or more of all EDA standards, then it "fits" in that category too. Could you have a quick scan through those standards and report back? Seems to me it should comply. Thanks!
@mpadge I will take a look now. I think it should fit EDA at face value as there is plotting, parameter estimation, summary table functions and AIC calculation functions.
@mpadge
Here is an example of why I think it may fit with EDA as well:
Plotting:
library(TidyDensity)
library(dplyr)
# Create a data frame with 100 random numbers
set.seed(123)
df_tbl <- tidy_normal(.n = 100, .num_sims = 5)
# Plot different aspects of the data
# The default plot is a density plot
tidy_autoplot(df_tbl)
# A qq plot
tidy_autoplot(df_tbl, .plot_type = "qq")
# A quantile plot
tidy_autoplot(df_tbl, .plot_type = "quantile")
# A probability plot
tidy_autoplot(df_tbl, .plot_type = "probability")
Summary Stats:
# A tibble: 1 × 12
mean_val median_val std_val min_val max_val skewness kurtosis range iqr variance ci_low ci_high
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0.0346 0.0207 0.973 -2.66 3.24 0.0861 2.95 5.90 1.26 0.946 -1.77 2.00
> tidy_distribution_summary_tbl(df_tbl, sim_number)
# A tibble: 5 × 13
sim_number mean_val median_val std_val min_val max_val skewness kurtosis range iqr variance
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 0.0904 0.0618 0.913 -2.31 2.19 0.0605 2.84 4.50 1.19 0.833
2 2 -0.108 -0.226 0.967 -2.05 3.24 0.639 3.66 5.29 1.27 0.935
3 3 0.120 0.0359 0.950 -1.76 2.29 0.326 2.46 4.05 1.29 0.902
4 4 -0.0362 -0.00351 1.04 -2.47 2.57 -0.0312 2.74 5.04 1.42 1.08
5 5 0.106 0.165 0.989 -2.66 2.40 -0.431 3.31 5.06 1.12 0.979
# ℹ 2 more variables: ci_low <dbl>, ci_high <dbl>
Bootstraping the data:
> # Bootstrap plotting a single simulation of df_tbl
> tidy_bootstrap(filter(df_tbl, sim_number == 1)[["y"]])
# A tibble: 2,000 × 2
sim_number bootstrap_samples
<fct> <list>
1 1 <dbl [80]>
2 2 <dbl [80]>
3 3 <dbl [80]>
4 4 <dbl [80]>
5 5 <dbl [80]>
6 6 <dbl [80]>
7 7 <dbl [80]>
8 8 <dbl [80]>
9 9 <dbl [80]>
10 10 <dbl [80]>
# ℹ 1,990 more rows
# ℹ Use `print(n = ...)` to see more rows
Bootstrap Plots
tidy_bootstrap(filter(df_tbl, sim_number == 1)[["y"]]) |>
bootstrap_stat_plot(y)
tidy_bootstrap(filter(df_tbl, sim_number == 1)[["y"]], .num_sims = 100) |>
bootstrap_stat_plot(y, .show_groups = TRUE)
Warning message:
Setting '.num_sims' to less than 2000 means that results can be potentially unstable. Consider
setting to 2000 or more.
> tidy_bootstrap(filter(df_tbl, sim_number == 1)[["y"]], .num_sims = 100) |>
+ bootstrap_stat_plot(y, .show_groups = TRUE, .stat = "cmax")
Warning message:
Setting '.num_sims' to less than 2000 means that results can be potentially unstable. Consider
setting to 2000 or more.
Stats about the distribution
> # Stats about the distribution
> # All distributions
> util_normal_stats_tbl(df_tbl) |>
+ glimpse()
Rows: 1
Columns: 17
$ tidy_function <chr> "tidy_gaussian"
$ function_call <chr> "Gaussian c(0, 1)"
$ distribution <chr> "Gaussian"
$ distribution_type <chr> "continuous"
$ points <dbl> 100
$ simulations <dbl> 5
$ mean <dbl> 0
$ median <dbl> 0.02071715
$ mode <dbl> 0
$ std_dv <dbl> 1
$ coeff_var <dbl> Inf
$ skewness <dbl> 0
$ kurtosis <dbl> 3
$ computed_std_skew <dbl> 0.0861201
$ computed_std_kurt <dbl> 2.953595
$ ci_lo <dbl> -1.774248
$ ci_hi <dbl> 1.99998
> # A single distribution
> util_normal_stats_tbl(df_tbl |> filter(sim_number == 2)) |>
+ glimpse()
Rows: 1
Columns: 17
$ tidy_function <chr> "tidy_gaussian"
$ function_call <chr> "Gaussian c(0, 1)"
$ distribution <chr> "Gaussian"
$ distribution_type <chr> "continuous"
$ points <dbl> 100
$ simulations <dbl> 5
$ mean <dbl> 0
$ median <dbl> -0.22583
$ mode <dbl> 0
$ std_dv <dbl> 1
$ coeff_var <dbl> Inf
$ skewness <dbl> 0
$ kurtosis <dbl> 3
$ computed_std_skew <dbl> 0.6387938
$ computed_std_kurt <dbl> 3.656954
$ ci_lo <dbl> -1.610118
$ ci_hi <dbl> 2.051234
Random walks of data and Viz
# Create and plot a random walk of the data
tidy_random_walk(df_tbl |> filter(sim_number == 1), .initial_value = 100,
.value_type = "cum_prod") |>
tidy_random_walk_autoplot()
tidy_random_walk(df_tbl, .initial_value = 100, .value_type = "cum_sum") |>
tidy_random_walk_autoplot()
Getting AIC
> # Getting AIC of distribution
> util_normal_aic(df_tbl |> filter(sim_number == 1) |> pull(y))
[1] 268.5385
@mpadge I just wanted to check back in and get thoughts of if I should also check off the EDA.
Thank you :)
@spsanderson Yes, I definitely think so. That category is "Exploratory Data Analysis and Summary Statistics", and your package certainly has a strong focus on that latter component. Seems like you'd easily tick off at least half of those standards, so please go ahead and document compliance with those. When you're done you can call @ropensci-review-bot check srr yourself to get a statement of compliance and we'll proceed from there. Thanks!
I'm sorry human, I don't understand that. You can see what commands I support by typing:
@ropensci-review-bot help
Sorry @spsanderson, I'll find time tomorrow to debug why the bot hasn't responded there
Hi @mpadge the bot says this is not an srr package, should I do something else? Thanks
Yes, you should indeed. You'll need to document compliance with the statistical standards. The general procedure is described in https://stats-devguide.ropensci.org, and the specific tools for documenting compliance within your code as part of our srr package. (The bot response above just indicates that you've not yet done that.) Feel free to ask any questions you might have during the process.
Hi @mpadge I'm finally getting down to it! Would you be able to tell me if the below is what your looking for, I'm pretty sure it is.
#' Bootstrap Density Tibble
#'
#' @family Bootstrap
#' @family Augment Function
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @details This function takes as input the output of the `tidy_bootstrap()` or
#' `bootstrap_unnest_tbl()` and returns an augmented tibble that has the following
#' columns added to it: _`x`_, _`y`_, _`dx`_, and _`dy`_.
#'
#' It looks for an attribute that comes from using `tidy_bootstrap()` or
#' `bootstrap_unnest_tbl()` so it will not work unless the data comes from one of
#' those functions.
#'
#' @description Add density information to the output of `tidy_bootstrap()`, and
#' `bootstrap_unnest_tbl()`.
#'
#' @param .data The data that is passed from the `tidy_bootstrap()` or
#' `bootstrap_unnest_tbl()` functions.
#'
#' @examples
#' x <- mtcars$mpg
#'
#' tidy_bootstrap(x) |>
#' bootstrap_density_augment()
#'
#' tidy_bootstrap(x) |>
#' bootstrap_unnest_tbl() |>
#' bootstrap_density_augment()
#'
#' @return
#' A tibble
#'
#' @srrstats {PD1.0}.
#' @export
#'
bootstrap_density_augment <- function(.data) {
atb <- attributes(.data)
# Checks
if (!is.data.frame(.data)) {
rlang::abort(
message = "'.data' is expecting a data.frame/tibble. Please supply.",
use_cli_format = TRUE
)
}
if (!atb$tibble_type %in% c("tidy_bootstrap", "tidy_bootstrap_nested")) {
rlang::abort(
message = "Must pass data to this function from either tidy_bootstrap() or
bootstrap_unnest_tbl().",
use_cli_format = TRUE
)
}
# Add density data
if (atb$tibble_type == "tidy_bootstrap_nested") {
df_tbl <- dplyr::as_tibble(.data) |>
TidyDensity::bootstrap_unnest_tbl()
}
if (atb$tibble_type == "tidy_bootstrap") {
df_tbl <- dplyr::as_tibble(.data)
}
df_tbl <- df_tbl |>
dplyr::nest_by(sim_number) |>
dplyr::mutate(dens_tbl = list(
stats::density(unlist(data),
n = nrow(data)
)[c("x", "y")] |>
purrr::set_names("dx", "dy") |>
dplyr::as_tibble()
)) |>
tidyr::unnest(cols = c(data, dens_tbl)) |>
dplyr::mutate(x = dplyr::row_number()) |>
dplyr::ungroup() |>
dplyr::select(sim_number, x, y, dx, dy, dplyr::everything())
# Return
attr(df_tbl, "tibble_type") <- "bootstrap_density"
attr(df_tbl, "incoming_tibble_type") <- atb$tibble_type
attr(df_tbl, ".num_sims") <- atb$.num_sims
attr(df_tbl, "dist_with_params") <- atb$dist_with_params
attr(df_tbl, "distribution_family_type") <- atb$distribution_family_type
return(df_tbl)
}
@spsanderson That's getting there, but the idea is to write a statement of how you comply with each standard. A single sentence generally suffices. The compliance statements are intended to help reviews instantly see how the code they're about to look at complies with the standard. Have a look at some of our other stats packages for examples.
@mpadge thank you! I'll work on that and then ask you to look again before I proceed with the other ~150+ functions.
Dear @spsanderson as the new rotating EiC I'm reviewing the status of all open issues. I see you've discussed this pre-submission with @mpadge. Is there anything else you'd like to share before we can decide if this package is in scope and ready for a full submission?
It is not yet ready i have to tag everything, im way behind in development over the passed few months
Steven P Sanderson II, MPH Amazon Author Profile https://www.amazon.com/stores/Steven-P.-Sanderson/author/B0CZ157DHK?ref_=pe_2466670_811284380&isDramIntegrated=true&shoppingPortalEnabled=true Book on Lulu http://goo.gl/lmrlFI Personal Site http://www.spsanderson.com
On Sun, Feb 2, 2025 at 12:02 PM Mauro Lepore @.***> wrote:
Dear @spsanderson https://github.com/spsanderson as the new rotating EiC I'm reviewing the status of all open issues. I see you've discussed this pre-submission with @mpadge https://github.com/mpadge. Is there anything else you'd like to share before we can decide if this package is in scope and ready for a full submission?
— Reply to this email directly, view it on GitHub https://github.com/ropensci/software-review/issues/657#issuecomment-2629473299, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAPCNS6Q3L7NMT2EMUGZR6T2NZFTTAVCNFSM6AAAAABOBHYIQCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDMMRZGQ3TGMRZHE . You are receiving this because you were mentioned.Message ID: @.***>
@spsanderson for reporting the status, and for opening a pre-submission issue.
This issue seems to have accomplished it's main goal, i.e. to discuss what it would take for this package to be in scope for a full submission. Seeing that it's not ready yet, I will go ahead and close this issue so you don't feel rushed and we can focus on ongoing work.
Whenever you're ready, feel free to open a new pre-submission or full-submission, pointing to this one and summarizing how it then meets our scope requirements.
Thanks for considering rOpenSci as a home for your work. We look forward to seeing you here again.