tidyverse_cran_downloads

A dataset containing the daily download counts from 2017-01-01 to 2018-03-01
for the following tidyverse packages:<ul>
<li><code>tidyr</code></li>
<li><code>lubridate</code></li>
<li><code>dplyr</code></li>
<li><code>broom</code></li>
<li><code>tidyquant</code></li>
<li><code>tidytext</code></li>
<li><code>ggplot2</code></li>
<li><code>purrr</code></li>
<li><code>stringr</code></li>
<li><code>forcats</code></li>
<li><code>knitr</code></li>
<li><code>readr</code></li>
<li><code>tibble</code></li>
<li><code>tidyverse</code></li>
</ul>

datasets

The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.
The main functions are time_decompose(), anomalize(), and time_recompose().
When combined, it's quite simple to decompose time series, detect anomalies,
and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series).
Time series decomposition is used to remove trend and seasonal components via the time_decompose() function
and methods include seasonal decomposition of time series by Loess ("stl") and
seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements
two methods for anomaly detection of residuals including using an inner quartile range ("iqr")
and generalized extreme studentized deviation ("gesd"). These methods are based on
those used in the 'forecast' package and the Twitter 'AnomalyDetection' package.
Refer to the associated functions for specific references for these methods.

Matt Dancho

anomalize

Tidy Anomaly Detection

Davis Vaughan

tidyverse_cran_downloads function

A <code>grouped_tbl_time</code> object with 6,375 rows and 3 variables:<dl>
<dt>date</dt>
<dd>Date of the daily observation</dd><dt>count</dt>
<dd>Number of downloads that day</dd><dt>package</dt>
<dd>The package corresponding to the daily download number</dd>
</dl>

Format

Downloads of various "tidyverse" packages from CRAN — tidyverse_cran_downloads

A <code>grouped_tbl_time</code> object with 6,375 rows and 3 variables:<dl>
<dt>date</dt>
<dd>Date of the daily observation</dd>

<dt>count</dt>
<dd>Number of downloads that day</dd>

<dt>package</dt>
<dd>The package corresponding to the daily download number</dd>


</dl>

tidyverse_cran_downloads: Downloads of various "tidyverse" packages from CRAN

Description

Usage

Arguments

Format