![3 sweep object modeler 3 sweep object modeler](https://app-help.vectorworks.net/2018/eng/VW2018_Guide/Objects_edit2/Objects_edit200028.png)
The fitted values are toggled off by default to reduce the complexity of the plot, but these can be added if desired.
![3 sweep object modeler 3 sweep object modeler](https://cg.cs.tsinghua.edu.cn/3sweep/repimg.jpg)
We’ll use a combination of geom_line() and geom_ribbon(). Let’s visualize the forecast with ggplot2. These columns are setup in a wide format to enable using the geom_ribbon(). The remaining columns are the forecast confidence intervals (typically 80 and 95, but this can be changed with forecast(level = c(80, 95))). The sw_sweep() function contains an argument fitted = FALSE by default meaning that the model “fitted” values are not returned.
![3 sweep object modeler 3 sweep object modeler](https://www.science.org/cms/10.1126/sciadv.abb7189/asset/02bb1cbb-8a77-426e-a179-5d87b05705e9/assets/graphic/abb7189-f1.jpeg)
The “key” column is then mutated using mutate() to a factor which preserves the order of the keys so “observed” comes first when plotting. The gather() function from the tidyr package is used to reshape the data into a long format data frame with column names “key” and “value” indicating all columns except for index are to be reshaped. The data will need to be manipulated slightly for the facet visualization. We can review the decomposition using ggplot2 as well. We can verify this using has_timetk_idx() from the timetk package. Alcohol_sales_ts <- tk_ts( alcohol_sales_tbl, start = 2007, freq = 12, silent = TRUE)Īlcohol_sales_ts # Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec