Rename logo + add favicons + build readme

.Rbuildignore

@@ -12,3 +12,4 @@
 ^CRAN-RELEASE$
 ^CRAN-SUBMISSION$
 ^\.github$
+^pkgdown$

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") %>%

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") %>%

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)
 

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)

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)