diff --git a/.Rbuildignore b/.Rbuildignore index 7e77372..eb898e0 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -12,3 +12,4 @@ ^CRAN-RELEASE$ ^CRAN-SUBMISSION$ ^\.github$ +^pkgdown$ diff --git a/README.Rmd b/README.Rmd index bd7f2b0..bfef36a 100644 --- a/README.Rmd +++ b/README.Rmd @@ -28,6 +28,7 @@ knitr::opts_chunk$set( warning = F ) library(anomalize) +library(dplyr) # for pipe ``` @@ -79,12 +80,11 @@ install.packages("anomalize") ## Getting Started -Load the `tidyverse` and `anomalize` packages. +Load the `anomalize` package. Usually, you will also load the tidyverse as well! ```{r, eval = F} -library(tidyverse) library(anomalize) - +library(tidyverse) # NOTE: timetk now has anomaly detection built in, which # will get the new functionality going forward. # Use this script to prevent overwriting legacy anomalize: @@ -99,7 +99,6 @@ Next, let's get some data. `anomalize` ships with a data set called `tidyverse_ Suppose we want to determine which daily download "counts" are anomalous. It's as easy as using the three main functions (`time_decompose()`, `anomalize()`, and `time_recompose()`) along with a visualization function, `plot_anomalies()`. ```{r tidyverse_anoms_1, fig.height=8} -library(magrittr) tidyverse_cran_downloads %>% # Data Manipulation / Anomaly Detection time_decompose(count, method = "stl") %>% diff --git a/README.md b/README.md index a609925..af13c27 100644 --- a/README.md +++ b/README.md @@ -78,12 +78,12 @@ install.packages("anomalize") ## Getting Started -Load the `tidyverse` and `anomalize` packages. +Load the `anomalize` package. Usually, you will also load the tidyverse +as well! ``` r -library(tidyverse) library(anomalize) - +library(tidyverse) # NOTE: timetk now has anomaly detection built in, which # will get the new functionality going forward. # Use this script to prevent overwriting legacy anomalize: @@ -102,7 +102,6 @@ anomalous. It’s as easy as using the three main functions visualization function, `plot_anomalies()`. ``` r -library(magrittr) tidyverse_cran_downloads %>% # Data Manipulation / Anomaly Detection time_decompose(count, method = "stl") %>% diff --git a/man/figures/README-tidyverse_anoms_1-1.png b/man/figures/README-tidyverse_anoms_1-1.png index 766b160..704f8f6 100644 Binary files a/man/figures/README-tidyverse_anoms_1-1.png and b/man/figures/README-tidyverse_anoms_1-1.png differ diff --git a/man/figures/README-unnamed-chunk-3-1.png b/man/figures/README-unnamed-chunk-3-1.png index dd6975c..b1ce2b7 100644 Binary files a/man/figures/README-unnamed-chunk-3-1.png and b/man/figures/README-unnamed-chunk-3-1.png differ diff --git a/man/figures/anomalize-logo.png b/man/figures/logo.png similarity index 100% rename from man/figures/anomalize-logo.png rename to man/figures/logo.png diff --git a/pkgdown/favicon/apple-touch-icon-120x120.png b/pkgdown/favicon/apple-touch-icon-120x120.png new file mode 100644 index 0000000..f86e5d2 Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon-120x120.png differ diff --git a/pkgdown/favicon/apple-touch-icon-152x152.png b/pkgdown/favicon/apple-touch-icon-152x152.png new file mode 100644 index 0000000..908e8c6 Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon-152x152.png differ diff --git a/pkgdown/favicon/apple-touch-icon-180x180.png b/pkgdown/favicon/apple-touch-icon-180x180.png new file mode 100644 index 0000000..fc8cc08 Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon-180x180.png differ diff --git a/pkgdown/favicon/apple-touch-icon-60x60.png b/pkgdown/favicon/apple-touch-icon-60x60.png new file mode 100644 index 0000000..f718d80 Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon-60x60.png differ diff --git a/pkgdown/favicon/apple-touch-icon-76x76.png b/pkgdown/favicon/apple-touch-icon-76x76.png new file mode 100644 index 0000000..0fb396b Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon-76x76.png differ diff --git a/pkgdown/favicon/apple-touch-icon.png b/pkgdown/favicon/apple-touch-icon.png new file mode 100644 index 0000000..1d1c50c Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon.png differ diff --git a/pkgdown/favicon/favicon-16x16.png b/pkgdown/favicon/favicon-16x16.png new file mode 100644 index 0000000..73a3d2e Binary files /dev/null and b/pkgdown/favicon/favicon-16x16.png differ diff --git a/pkgdown/favicon/favicon-32x32.png b/pkgdown/favicon/favicon-32x32.png new file mode 100644 index 0000000..4ad7a61 Binary files /dev/null and b/pkgdown/favicon/favicon-32x32.png differ diff --git a/pkgdown/favicon/favicon.ico b/pkgdown/favicon/favicon.ico new file mode 100644 index 0000000..4495d84 Binary files /dev/null and b/pkgdown/favicon/favicon.ico differ diff --git a/vignettes/anomalize_methods.Rmd b/vignettes/anomalize_methods.Rmd index 5aa8aa2..f1ed517 100644 --- a/vignettes/anomalize_methods.Rmd +++ b/vignettes/anomalize_methods.Rmd @@ -23,6 +23,12 @@ library(anomalize) # load necessary tidyverse packages for analysis library(dplyr) library(ggplot2) + +# NOTE: timetk now has anomaly detection built in, which +# will get the new functionality going forward. + +anomalize <- anomalize::anomalize +plot_anomalies <- anomalize::plot_anomalies ``` Anomaly detection is critical to many disciplines, but possibly none more important than in __time series analysis__. A time series is the sequential set of values tracked over a time duration. The definition we use for an __anomaly__ is simple: an anomaly is something that happens that (1) was unexpected or (2) was caused by an abnormal event. Therefore, the problem we intend to solve with `anomalize` is providing methods to accurately detect these "anomalous" events. @@ -62,7 +68,7 @@ The Twitter method is a similar decomposition method to that used in Twitter's ` Load two libraries to perform the comparison. -```{r, eval = F} +```r library(tidyverse) library(anomalize) diff --git a/vignettes/anomalize_quick_start_guide.Rmd b/vignettes/anomalize_quick_start_guide.Rmd index 858e12b..12aa506 100644 --- a/vignettes/anomalize_quick_start_guide.Rmd +++ b/vignettes/anomalize_quick_start_guide.Rmd @@ -23,7 +23,13 @@ knitr::opts_chunk$set( library(tibbletime) library(dplyr) +library(ggplot2) library(anomalize) +# NOTE: timetk now has anomaly detection built in, which +# will get the new functionality going forward. + +anomalize <- anomalize::anomalize +plot_anomalies <- anomalize::plot_anomalies ``` The `anomalize` package is a feature rich package for performing anomaly detection. It's geared towards time series analysis, which is one of the biggest needs for understanding when anomalies occur. We have a quick start section called "5-Minutes to Anomalize" for those looking to jump right in. We also have a detailed section on parameter adjustment for those looking to understand what nobs they can turn. Finally, for those really looking to get under the hood, we have another vignette called "Anomalize Methods" that gets into a deep discussion on STL, Twitter, IQR and GESD methods that are used to power `anomalize`. @@ -41,7 +47,7 @@ Check out our entire [Software Intro Series](https://www.youtube.com/watch?v=Gk_ Load libraries. -```{r, eval = T, message = F, warning = F} +```r library(tidyverse) library(tibbletime) library(anomalize) diff --git a/vignettes/forecasting_with_cleaned_anomalies.Rmd b/vignettes/forecasting_with_cleaned_anomalies.Rmd index d5b8a11..8515926 100644 --- a/vignettes/forecasting_with_cleaned_anomalies.Rmd +++ b/vignettes/forecasting_with_cleaned_anomalies.Rmd @@ -32,7 +32,7 @@ library(timetk) We can often get better forecast performance by cleaning anomalous data prior to forecasting. This is the perfect use case for integrating the `clean_anomalies()` function into your ___forecast workflow___. -```{r setup, message=FALSE, eval=FALSE} +```r library(tidyverse) library(tidyquant) library(anomalize)