Merge pull request #527 from AlexsLemonade/auto_render_live

GHA: Automated live (rendered) versions of the notebooks

intro-to-R-tidyverse/02-intro_to_ggplot2-live.Rmd

@@ -228,10 +228,41 @@ For now, we will choose the value of 5.5 (that is close to a Bonferroni correcti
 
 ```
 
-We can change the x and y labels by using `ylab` and `xlab` functions and add a
-title using `ggtitle`.
+We can change the x and y labels using a few different strategies. 
+One approach is to use functions `xlab()` and `ylab()` individually to set, respectively, the x-axis label and the the y-axis label.
 
-```{r ggplot-label}
+
+```{r ggplot-label-1}
+ggplot(
+  tumor_normal_df,
+  aes(
+    x = log_fold_change,
+    y = neg_log10_p,
+    color = avg_expression
+  )
+) +
+  geom_point(alpha = 0.2) +
+  geom_hline(yintercept = 5.5, color = "darkgreen") +
+  theme_bw() +
+  # Add labels with separate functions:
+  xlab("log2 Fold Change Tumor/Normal") + 
+  ylab("-log10 p value") 
+```
+
+
+Alternatively, we can use the `ggplot2` function `labs()`, which takes individual arguments for each label we want want to set. 
+We can also include the argument `title` to add an overall plot title.
+
+```{r ggplot-label-2, live = TRUE}
+
+  # Add x and y labels and overall plot title with arguments to labs():
+
+```
+
+Something great about the `labs()` function is you can also use it to specify labels for your *legends* derived from certain aesthetics. 
+In this plot, our legend is derived from a *color aesthetic*, so we can specify the keyword "color" to update the legend title.
+
+```{r ggplot-label-aes}
 ggplot(
   tumor_normal_df,
   aes(
@@ -243,18 +274,26 @@ ggplot(
   geom_point(alpha = 0.2) +
   geom_hline(yintercept = 5.5, color = "darkgreen") +
   theme_bw() +
-  xlab("log2 Fold Change Tumor/Normal") + # Add an x label
-  ylab("-log10 p value") + # Add a y label
-  ggtitle("Astrocytoma Tumor vs Normal Cerebellum") # Add main title
+  # Add x and y labels and overall plot title with arguments to labs():
+  labs(
+    x = "log2 Fold Change Tumor/Normal",
+    y = "-log10 p value",
+    title = "Astrocytoma Tumor vs Normal Cerebellum",
+    # Use the color keyword to label the color legend
+    color = "Average expression"
+  )
+  
 ```
 
+
 Use this chunk to make the same kind of plot as the previous chunk but instead plot the male female contrast data, that is stored in `male_female_df`. 
 
 ```{r mf-volcano, live = TRUE}
 # Use this chunk to make the same kind of volcano plot, but with the male-female contrast data.
 
 ```
 
+
 Turns out, we don't have to plot each contrast separately, instead, we can use the original data frame that contains all three contrasts' data, `stats_df`, and add a `facet_wrap` to make each contrast its own plot. 
 
 ```{r ggplot-facets}
@@ -270,9 +309,12 @@ ggplot(
   geom_hline(yintercept = 5.5, color = "darkgreen") +
   theme_bw() +
   facet_wrap(~contrast) +
-  xlab("log2 Fold Change") + # Now that this includes the other contrasts,
+  labs(
+    x  = "log2 Fold Change", # Now that this includes the other contrasts,
                              # we'll make this label more general
-  ylab("-log10 p value") +
+    y = "-log10 p value",
+    color = "Average expression"
+  ) +
   coord_cartesian(xlim = c(-25, 25)) # zoom in on the x-axis
 ```
 
@@ -292,8 +334,11 @@ volcano_plot <- ggplot(
   geom_hline(yintercept = 5.5, color = "darkgreen") +
   theme_bw() +
   facet_wrap(~contrast) +
-  xlab("log2 Fold Change") +
-  ylab("-log10 p value") +
+  labs(
+    x  = "log2 Fold Change",
+    y = "-log10 p value",
+    color = "Average expression"
+  ) +
   coord_cartesian(xlim = c(-25, 25))
 ```
 

scRNA-seq/02-filtering_scRNA-live.Rmd

@@ -177,7 +177,7 @@ The remainder of the `ggplot` code should look familiar.
 # Plot the density of the means using ggplot2
 ggplot(mapping = aes(x = gene_means)) +
   geom_density() +
-  xlab("Mean gene count") 
+  labs(x = "Mean gene count") 
 ```
 
 That plot is not quite as informative as we might like, as a few genes with high expression are making the scale just a *bit* wide.
@@ -269,7 +269,7 @@ Let's plot this using the same style and type of graph as above.
 ```{r genes_expressed_plot}
 ggplot(mapping = aes(x = num_genes_exp)) +
   geom_density(fill = "lightblue") +  
-  xlab("Number of genes expressed") +
+  labs(x = "Number of genes expressed") +
   theme_classic() 
 ```
 
@@ -445,7 +445,7 @@ gene_info <- data.frame(rowData(bladder_sce_filtered))
 # Plot the detected percentage
 ggplot(gene_info, aes(x = detected) )+
   geom_density(fill = "lightblue") +
-  xlab("Percent of Cells Expressing Each Gene") + 
+  labs(x = "Percent of Cells Expressing Each Gene") + 
   theme_classic()
 ```
 

scRNA-seq/04-dimension_reduction_scRNA-live.Rmd

@@ -306,8 +306,9 @@ We can add a function like that curve to a `ggplot` with a `stat_function` layer
 ggplot(as.data.frame(gene_variance), aes(x = mean, y = total)) +
   geom_point(alpha = 0.1) +
   stat_function(fun = metadata(gene_variance)$trend, color = "blue") + 
-  xlab("Mean log-expression") + 
-  ylab("Variance") +
+  labs(
+    x = "Mean log-expression",
+    y = "Variance") +
   theme_bw()
 ```
 

scRNA-seq/06-overrepresentation_analysis-live.Rmd

@@ -376,7 +376,7 @@ Let's look at a bar plot of the significant results.
 ```{r cm_barplot}
 barplot(cellmarker_ora_results) +
   # This is a ggplot, so we can use the following to label the x-axis!
-  ggplot2::xlab("count")
+  ggplot2::labs(x = "count")
 ```
 
 One thing that the [web version of CellMarker](http://bio-bigdata.hrbmu.edu.cn/CellMarker/) allows us to do is to get a better idea of _marker prevalence_.