[custom] fix lesson contents

.Rbuildignore

@@ -1,2 +1,4 @@
+^renv$
+^renv\.lock$
 ^tic\.R$
 ^\.travis\.yml$

episodes/03-basics-factors-dataframes.Rmd

@@ -734,7 +734,7 @@ We can save data to a file. We will save our `SRR2584863_variants` object
 to a .csv file using the `write.csv()` function:
 
 ```{r, purl=FALSE}
-write.csv(SRR2584863_variants, file = "../data/SRR2584863_variants.csv")
+write.csv(SRR2584863_variants, file = "data/SRR2584863_variants.csv")
 ```
 
 The `write.csv()` function has some additional arguments listed in the help, but

episodes/05-dplyr.Rmd

@@ -107,7 +107,7 @@ The Tidyverse's `readr` package provides its own unique way of loading .csv file
 Now let's load our vcf .csv file using `read_csv()`:
 
 ```{r, include=FALSE, echo=FALSE, eval=TRUE, purl=FALSE}
-variants <- read_csv("../data/combined_tidy_vcf.csv") 
+variants <- read_csv("data/combined_tidy_vcf.csv") 
 ```
 
 ### Taking a quick look at data frames

episodes/07-knitr-markdown.Rmd

@@ -343,7 +343,7 @@ code.
 
 You can make *every* number in your report reproducible. Use
 <code>\`r</code> and <code>\`</code> for an in-line code chunk,
-like so: <code>\`r round(some\_value, 2)\`</code>. The code will be
+like so: ``` ``r "r round(some_value, 2)"`` ```. The code will be
 executed and replaced with the *value* of the result.
 
 Don't let these in-line chunks get split across lines.

episodes/08-r-help.Rmd

@@ -19,9 +19,6 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r, include=FALSE, info=output}
-```
-
 ## Getting help with R
 
 <img src="fig/oreilly_book_covers.png" alt="rstudio default session" style="width: 600px;"/>
@@ -72,27 +69,27 @@ on any analysis you are doing. When you run the same script that has worked fine
 a dozen times before, looking back at these notes will remind you that you
 upgraded R and forget to check your script.
 
-> ```
-> sessionInfo()
-> ```
-
-> ```
-> R version 3.2.3 (2015-12-10)
-> Platform: x86_64-pc-linux-gnu (64-bit)
-> Running under: Ubuntu 14.04.3 LTS
-> 
-> locale:
-> [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8
-> [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
-> [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C
-> [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
-> 
-> attached base packages:
-> [1] stats     graphics  grDevices utils     datasets  methods   base
-> 
-> loaded via a namespace (and not attached):
-> [1] tools_3.2.3     packrat_0.4.9-1
-> ```
+```
+sessionInfo()
+```
+
+```
+R version 3.2.3 (2015-12-10)
+Platform: x86_64-pc-linux-gnu (64-bit)
+Running under: Ubuntu 14.04.3 LTS
+
+locale:
+[1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8
+[4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
+[7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C
+[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
+
+attached base packages:
+[1] stats     graphics  grDevices utils     datasets  methods   base
+
+loaded via a namespace (and not attached):
+[1] tools_3.2.3     packrat_0.4.9-1
+```
 
 Many times, there may be some issues with your data and the way it is formatted.
 In that case, you may want to share that data with someone else. However, you
@@ -103,24 +100,24 @@ yet, we will get to it!) and save it to a file. In our example we will use the
 `dput()` function on the `iris` dataframe which is an example dataset that is
 installed in R:
 
-> ```
-> dput(head(iris)) # iris is an example data.frame that comes with R
->                  # the `head()` function just takes the first 6 lines of the iris dataset
-> ```
+```
+dput(head(iris)) # iris is an example data.frame that comes with R
+                 # the `head()` function just takes the first 6 lines of the iris dataset
+```
 
 This generates some output (below) which you will be better able to interpret
 after covering the other R lessons. This info would be helpful in understanding
 how the data is formatted and possibly revealing problematic issues.
 
-> ```
-> structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4),
->     Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.9), Petal.Length = c(1.4,
->     1.4, 1.3, 1.5, 1.4, 1.7), Petal.Width = c(0.2, 0.2, 0.2,
->     0.2, 0.2, 0.4), Species = structure(c(1L, 1L, 1L, 1L, 1L,
->     1L), .Label = c("setosa", "versicolor", "virginica"), class = "factor")), .Names = c("Sepal.Length",
-> "Sepal.Width", "Petal.Length", "Petal.Width", "Species"), row.names = c(NA,
-> 6L), class = "data.frame")
-> ```
+```
+structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4),
+    Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.9), Petal.Length = c(1.4,
+    1.4, 1.3, 1.5, 1.4, 1.7), Petal.Width = c(0.2, 0.2, 0.2,
+    0.2, 0.2, 0.4), Species = structure(c(1L, 1L, 1L, 1L, 1L,
+    1L), .Label = c("setosa", "versicolor", "virginica"), class = "factor")), .Names = c("Sepal.Length",
+"Sepal.Width", "Petal.Length", "Petal.Width", "Species"), row.names = c(NA,
+6L), class = "data.frame")
+```
 
 Alternatively, you can also save objects in R memory to a file by specifying
 the name of the object, in this case the `iris` data frame, and passing a