Remove tidyverse from vignetes (use dplyr and ggplot2)

vignettes/anomalize_methods.Rmd

@@ -20,7 +20,9 @@ knitr::opts_chunk$set(
 )
 
 library(anomalize)
-library(tidyverse)
+# load necessary tidyverse packages for analysis
+library(dplyr)
+library(ggplot2)
 ```
 
 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. 

vignettes/anomalize_quick_start_guide.Rmd

@@ -20,6 +20,10 @@ knitr::opts_chunk$set(
   warning = F,
   fig.align = "center"
 )
+
+library(tibbletime)
+library(dplyr)
+library(anomalize)
 ```
 
 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`. 
@@ -284,7 +288,3 @@ Business Science offers two 1-hour courses on Anomaly Detection:
 - [Learning Lab 18](https://university.business-science.io/p/learning-labs-pro) - Time Series Anomaly Detection with `anomalize`
 
 - [Learning Lab 17](https://university.business-science.io/p/learning-labs-pro) - Anomaly Detection with `H2O` Machine Learning
-
-
-
-

vignettes/forecasting_with_cleaned_anomalies.Rmd

@@ -17,8 +17,11 @@ knitr::opts_chunk$set(
   fig.align = "center"
 )
 
+library(dplyr)
+library(ggplot2)
+library(tidyquant)
 library(anomalize)
-library(tidyverse)
+library(timetk)
 ```
 
 
@@ -29,12 +32,14 @@ library(tidyverse)
 
 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}
+```{r setup, message=FALSE, eval=FALSE}
 library(tidyverse)
 library(tidyquant)
 library(anomalize)
 library(timetk)
+```
 
+```{r}
 # NOTE: timetk now has anomaly detection built in, which 
 #  will get the new functionality going forward.
 #  Use this script to prevent overwriting legacy anomalize: