editing text

Author: jrkirk61

episodes/30-dplyr.Rmd

@@ -34,6 +34,12 @@ download.file(url = "https://github.com/carpentries-incubator/bioc-intro/raw/mai
 > This episode is based on the Data Carpentries's *Data Analysis and
 > Visualisation in R for Ecologists* lesson.
 
+## HPCBio Notes
+
+If you’re signed up for an HPCBio workshop, **this episode is required** reading. 
+You may watch a recording of this episode by viewing the video named "Episode 5: Manipulating and analyzing data with dplyr" on our video playlist. 
+Please see your email for a URL to the playlist.
+
 ## Data manipulation using **`dplyr`** and **`tidyr`**
 
 Bracket subsetting is handy, but it can be cumbersome and difficult to

episodes/40-visualization.Rmd

@@ -34,6 +34,12 @@ rna <- read.csv("data/rnaseq.csv")
 > This episode is based on the Data Carpentries's *Data Analysis and
 > Visualisation in R for Ecologists* lesson.
 
+## HPCBio Notes
+
+If you’re signed up for an HPCBio workshop, **this episode is required** reading. 
+You may watch a recording of this episode by viewing the video named "Episode 6: Data visualization" on our video playlist. 
+Please see your email for a URL to the playlist.
+
 ## Data Visualization
 
 We start by loading the required packages. **`ggplot2`** is included in
@@ -687,30 +693,6 @@ ggplot(data = mean_exp_by_chromosome, mapping = aes(x = time,
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-The `facet_wrap` geometry extracts plots into an arbitrary number of
-dimensions to allow them to cleanly fit on one page. On the other hand,
-the `facet_grid` geometry allows you to explicitly specify how you want
-your plots to be arranged via formula notation (`rows ~ columns`; a `.`
-can be used as a placeholder that indicates only one row or column).
-
-Let's modify the previous plot to compare how the mean gene expression
-of males and females has changed through time:
-
-```{r mean-exp-time-facet-sex-rows, purl=TRUE}
-# One column, facet by rows
-ggplot(data = mean_exp_by_time_sex,
-       mapping = aes(x = time, y = mean_exp, color = gene)) +
-  geom_line() +
-  facet_grid(sex ~ .)
-```
-
-```{r mean-exp-time-facet-sex-columns, purl=TRUE}
-# One row, facet by column
-ggplot(data = mean_exp_by_time_sex,
-       mapping = aes(x = time, y = mean_exp, color = gene)) +
-  geom_line() +
-  facet_grid(. ~ sex)
-```
 
 ## `ggplot2` themes
 
@@ -942,7 +924,7 @@ install.packages("patchwork")
 ```
 
 ```{r patchworkplot1, purl=TRUE}
-library("patchwork")
+library(patchwork)
 count_gene_chromosome + exp_boxplot_sex
 ## or count_gene_chromosome | exp_boxplot_sex
 ```
@@ -985,7 +967,7 @@ install.packages("gridExtra")
 ```
 
 ```{r gridarrange-example, message=FALSE, fig.width=10, purl=TRUE}
-library("gridExtra")
+library(gridExtra)
 grid.arrange(count_gene_chromosome, exp_boxplot_sex, ncol = 2)
 ```
 

episodes/60-next-steps.Rmd

@@ -26,7 +26,7 @@ exercises: 45
 
 ## HPCBio Notes
 
-If you're signed up for an HPCBio workshop, **this episode 7 is optional** but recommended reading if you're signed up for Traditional Bulk RNA-Seq Analysis.
+If you're signed up for an HPCBio workshop, **this episode 7 is optional** but recommended reading if you're signed up for any RNA-Seq Analysis workshop.
 There is no recording available for this episode, so please follow along with the text-based lesson below.
 
 ## Next steps