version 1.0.4

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: infer
 Title: Tidy Statistical Inference
-Version: 1.0.3
+Version: 1.0.4
 Authors@R: 
  c(person(given = "Andrew",
  family = "Bray",
@@ -86,7 +86,7 @@ Encoding: UTF-8
 LazyData: true
 RoxygenNote: 7.2.1
 NeedsCompilation: no
-Packaged: 2022-08-22 17:55:29 UTC; simoncouch
+Packaged: 2022-12-01 21:08:25 UTC; simoncouch
 Author: Andrew Bray [aut],
  Chester Ismay [aut] (<https://orcid.org/0000-0003-2820-2547>),
  Evgeni Chasnovski [aut] (<https://orcid.org/0000-0002-1617-4019>),
@@ -103,4 +103,4 @@ Author: Andrew Bray [aut],
  Brian Fannin [ctb]
 Maintainer: Simon Couch <simonpatrickcouch@gmail.com>
 Repository: CRAN
-Date/Publication: 2022-08-22 18:50:03 UTC
+Date/Publication: 2022-12-02 00:10:10 UTC

NEWS.md

@@ -1,3 +1,9 @@
+# infer v1.0.4
+
+* Fixed bug in p-value shading where shaded regions no longer correctly overlaid
+ histogram bars.
+* Addressed deprecation warning ahead of upcoming dplyr release.
+
 # infer v1.0.3
 
 * Fix R-devel HTML5 NOTEs.

R/shade_p_value.R

@@ -336,7 +336,8 @@ hist_area <- function(data, obs_stat, direction, yval) {
  # "almost vertical" lines with `geom_area()` usage. If don't do this, then
  # area might be shaded under line segments connecting edges of consequtive
  # histogram bars.
- x_extra <- sort(c(x, g_data[["xmin"]], g_data[["xmax"]]))
+ x_extra <- switch(direction, left = g_data[["xmax"]], right = g_data[["xmin"]])
+ x_extra <- sort(c(x, x_extra))
  x_grid <- switch(
  # `direction` can be one of "left" or "right" at this point of execution
  direction,

R/utils.R

@@ -83,7 +83,7 @@ standardize_variable_types <- function(x) {
  dplyr::mutate(
  dplyr::across(
  where(~ is.logical(.x)),
- ~ factor(.x, levels = c("TRUE", "FALSE")),
+ ~ factor(.x, levels = c("TRUE", "FALSE"))
  )
  ) %>%
  # integer to numeric

README.md

@@ -0,0 +1,178 @@
+
+# infer R Package <img src="man/figures/infer.png" align="right" width=280 />
+
+<!--figs/infer.svg-->
+<!--http://www.r-pkg.org/badges/version/infer-->
+<!--figs/main.svg-->
+<!--https://img.shields.io/codecov/c/github/tidymodels/infer/main.svg-->
+
+[![R-CMD-check](https://github.com/tidymodels/infer/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/tidymodels/infer/actions/workflows/check-standard.yaml)
+[![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version/infer)](https://cran.r-project.org/package=infer)
+[![Coverage
+Status](https://img.shields.io/codecov/c/github/tidymodels/infer/main.svg)](https://codecov.io/github/tidymodels/infer/?branch=main)
+
+The objective of this package is to perform statistical inference using
+an expressive statistical grammar that coheres with the `tidyverse`
+design framework. The package is centered around 4 main verbs,
+supplemented with many utilities to visualize and extract value from
+their outputs.
+
+- `specify()` allows you to specify the variable, or relationship
+ between variables, that you’re interested in.
+- `hypothesize()` allows you to declare the null hypothesis.
+- `generate()` allows you to generate data reflecting the null
+ hypothesis.
+- `calculate()` allows you to calculate a distribution of statistics
+ from the generated data to form the null distribution.
+
+To learn more about the principles underlying the package design, see
+`vignette("infer")`.
+
+<div class="figure">
+
+<img src="https://raw.githubusercontent.com/tidymodels/infer/main/figs/ht-diagram.png" alt="A diagram showing four steps to carry out randomization-based inference: specify hypothesis, generate data, calculate statistic, and visualize. From left to right, each step is connected by an arrow, while the diagram indicates that generating data and calculating statistics can happen iteratively." />
+<p class="caption">
+</p>
+
+</div>
+
+If you’re interested in learning more about randomization-based
+statistical inference generally, including applied examples of this
+package, we recommend checking out [Statistical Inference Via Data
+Science: A ModernDive Into R and the Tidyverse](https://moderndive.com/)
+and [Introduction to Modern
+Statistics](https://openintro-ims.netlify.app/).
+
+### Installation
+
+------------------------------------------------------------------------
+
+To install the current stable version of `infer` from CRAN:
+
+``` r
+install.packages("infer")
+```
+
+To install the developmental stable version of `infer`, make sure to
+install `remotes` first. The `pkgdown` website for this version is at
+[infer.tidymodels.org](https://infer.tidymodels.org/).
+
+``` r
+install.packages("remotes")
+remotes::install_github("tidymodels/infer")
+```
+
+### Contributing
+
+------------------------------------------------------------------------
+
+We welcome others helping us make this package as user-friendly and
+efficient as possible. Please review our
+[contributing](https://github.com/tidymodels/infer/blob/main/CONTRIBUTING.md)
+and [conduct](https://github.com/tidymodels/infer/blob/main/CONDUCT.md)
+guidelines. By participating in this project you agree to abide by its
+terms.
+
+For questions and discussions about tidymodels packages, modeling, and
+machine learning, please [post on RStudio
+Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question). If you think you have
+encountered a bug, please [submit an
+issue](https://github.com/tidymodels/infer/issues). Either way, learn
+how to create and share a [reprex](https://reprex.tidyverse.org/articles/learn-reprex.html) (a minimal,
+reproducible example), to clearly communicate about your code. Check out
+further details on [contributing guidelines for tidymodels
+packages](https://www.tidymodels.org/contribute/) and [how to get
+help](https://www.tidymodels.org/help/).
+
+### Examples
+
+------------------------------------------------------------------------
+
+These examples are pulled from the “Full infer Pipeline Examples”
+vignette, accessible by calling `vignette("observed_stat_examples")`.
+They make use of the `gss` dataset supplied by the package, providing a
+sample of data from the [General Social Survey](https://gss.norc.org).
+The data looks like this:
+
+``` r
+# load in the dataset
+data(gss)
+
+# take a glimpse at it
+str(gss)
+```
+
+ ## tibble [500 × 11] (S3: tbl_df/tbl/data.frame)
+ ## $ year : num [1:500] 2014 1994 1998 1996 1994 ...
+ ## $ age : num [1:500] 36 34 24 42 31 32 48 36 30 33 ...
+ ## $ sex : Factor w/ 2 levels "male","female": 1 2 1 1 1 2 2 2 2 2 ...
+ ## $ college: Factor w/ 2 levels "no degree","degree": 2 1 2 1 2 1 1 2 2 1 ...
+ ## $ partyid: Factor w/ 5 levels "dem","ind","rep",..: 2 3 2 2 3 3 1 2 3 1 ...
+ ## $ hompop : num [1:500] 3 4 1 4 2 4 2 1 5 2 ...
+ ## $ hours : num [1:500] 50 31 40 40 40 53 32 20 40 40 ...
+ ## $ income : Ord.factor w/ 12 levels "lt $1000"<"$1000 to 2999"<..: 12 11 12 12 12 12 12 12 12 10 ...
+ ## $ class : Factor w/ 6 levels "lower class",..: 3 2 2 2 3 3 2 3 3 2 ...
+ ## $ finrela: Factor w/ 6 levels "far below average",..: 2 2 2 4 4 3 2 4 3 1 ...
+ ## $ weight : num [1:500] 0.896 1.083 0.55 1.086 1.083 ...
+
+As an example, we’ll run an analysis of variance on `age` and `partyid`,
+testing whether the age of a respondent is independent of their
+political party affiliation.
+
+Calculating the observed statistic,
+
+``` r
+F_hat <- gss %>% 
+ specify(age ~ partyid) %>%
+ calculate(stat = "F")
+```
+
+Then, generating the null distribution,
+
+``` r
+null_dist <- gss %>%
+ specify(age ~ partyid) %>%
+ hypothesize(null = "independence") %>%
+ generate(reps = 1000, type = "permute") %>%
+ calculate(stat = "F")
+```
+
+Visualizing the observed statistic alongside the null distribution,
+
+``` r
+visualize(null_dist) +
+ shade_p_value(obs_stat = F_hat, direction = "greater")
+```
+
+<div class="figure">
+
+<img src="https://raw.githubusercontent.com/tidymodels/infer/main/README_files/figure-gfm/viz-1.png" alt="A histogram showing a distribution of F statistics, right-tailed and centered around one. The x axis ranges from zero to five. The region of the histogram to the right of the observed statistic, just above two, is shaded red to represent the p-value." />
+<p class="caption">
+</p>
+
+</div>
+
+Calculating the p-value from the null distribution and observed
+statistic,
+
+``` r
+null_dist %>%
+ get_p_value(obs_stat = F_hat, direction = "greater")
+```
+
+ ## # A tibble: 1 × 1
+ ## p_value
+ ## <dbl>
+ ## 1 0.055
+
+Note that the formula and non-formula interfaces (i.e. `age ~ partyid`
+vs. `response = age, explanatory = partyid`) work for all implemented
+inference procedures in `infer`. Use whatever is more natural for you.
+If you will be doing modeling using functions like `lm()` and `glm()`,
+though, we recommend you begin to use the formula `y ~ x` notation as
+soon as possible.
+
+Other resources are available in the package vignettes! See
+`vignette("observed_stat_examples")` for more examples like the one
+above, and `vignette("infer")` for discussion of the underlying
+principles of the package design.