Merge pull request #342 from UBC-DSCI/patch-pdf

Patch pdf

Author: trevorcampbell

build_html.sh

@@ -1,2 +1,2 @@
 # Script to generate HTML book
-docker run --rm -m 5g -v $(pwd):/home/rstudio/introduction-to-datascience ubcdsci/intro-to-ds:v0.12.0 /bin/bash -c "cd /home/rstudio/introduction-to-datascience; Rscript _build_html.r"
+docker run --rm -m 5g -v $(pwd):/home/rstudio/introduction-to-datascience ubcdsci/intro-to-ds:v0.15.0 /bin/bash -c "cd /home/rstudio/introduction-to-datascience; Rscript _build_html.r"

build_pdf.sh

@@ -22,7 +22,7 @@ cp -r data/ pdf/data
 cp -r img/ pdf/img
 
 # Build the book with bookdown
-docker run --rm -m 5g -v $(pwd):/home/rstudio/introduction-to-datascience ubcdsci/intro-to-ds:v0.12.0 /bin/bash -c "cd /home/rstudio/introduction-to-datascience/pdf; Rscript _build_pdf.r"
+docker run --rm -m 5g -v $(pwd):/home/rstudio/introduction-to-datascience ubcdsci/intro-to-ds:v0.15.0 /bin/bash -c "cd /home/rstudio/introduction-to-datascience/pdf; Rscript _build_pdf.r"
 
 # clean files in pdf dir
 rm -rf pdf/references.bib

classification1.Rmd

@@ -1184,11 +1184,13 @@ fairly nuanced, and a careful treatment would require a lot more detail and math
 For the present purposes, it will suffice to rebalance the data by *oversampling* the rare class. \index{oversampling}
 In other words, we will replicate rare observations multiple times in our data set to give them more
 voting power in the $K$-nearest neighbor algorithm. In order to do this, we will add an oversampling
-step to the earlier `uc_recipe` recipe with the `step_upsample` function. \index{recipe!step\_upsample}
+step to the earlier `uc_recipe` recipe with the `step_upsample` function from the `themis` R package. \index{recipe!step\_upsample}
 We show below how to do this, and also
 use the `group_by` and `summarize` functions to see that our classes are now balanced:
 
-```{r 05-upsample-cancer, warning=FALSE}
+```{r 05-upsample-cancer, warning = FALSE, message = FALSE}
+library(themis)
+
 ups_recipe <- recipe(Class ~ ., data = rare_cancer) |>
   step_upsample(Class, over_ratio = 1, skip = FALSE) |>
   prep()