finalized samples plot

graphics_bioinf.Rmd

@@ -531,16 +531,39 @@ count_boxplot <- ggplot(counts_crc_tidy,
                      fill = sample_id) ) + 
                  geom_boxplot() +
                  ylim(c(0, 10)) + 
+                 scale_fill_brewer(palette = "Paired") +
                  theme(axis.text.x = element_text(angle = 90, hjust = 1))
-                  
+
+count_boxplot                  
 ```
 
 We can see that the count distributions are not very different between the 
 samples. Here, limited the y--axis in order to ignore the outliers and tilted
-the x--Axis labels, so that we can actually read them.
+the x--Axis labels, so that we can actually read them. `r CRANpkg("ggplot2") `
+allows you to directly use palettes from the [Colorbrewer project](http://colorbrewer2.org).
+Here, we use a qualitative paletter called "Paired" instead of the default one.
+
+## Exercise: Adapt the boxplot
+
+ Experiment with the color variable and try to create faceted plots using 
+`r facet_wrap()` or `r facet_grid()` : Do you see a pattern if you color / wrap the 
+boxplots by tissue and or patient ?
+
+
+```{r exp_boxplot}
+count_boxplot_tissue <- ggplot(counts_crc_tidy,
+                 aes(x = sample_id_by_median, 
+                     y = log2(count), 
+                     fill = tissue)) + 
+                 geom_boxplot() +
+                 ylim(c(0, 10)) + 
+                 theme(axis.text.x = element_text(angle = 90, hjust = 1))
+
+count_boxplot_tissue
 
-## Exercise:  
+count_boxplot_tissue + facet_grid( patient ~ .)
 
+```
 
 
 <!-- the median gene expression per sample relative to a reference -->