diff --git a/lessons/git/slides.md b/lessons/git/slides.md
index d138f6e..fd0f06c 100644
--- a/lessons/git/slides.md
+++ b/lessons/git/slides.md
@@ -67,6 +67,6 @@ git config --list
 ## Generate these slides using (using R): ##
 
 
-{% highlight r %}
+```r
 rmarkdown::render('slides.Rmd')
-{% endhighlight %}
+```
diff --git a/lessons/r-wrangling/cheatsheet.md b/lessons/r-wrangling/cheatsheet.md
index ccad661..b7a197d 100644
--- a/lessons/r-wrangling/cheatsheet.md
+++ b/lessons/r-wrangling/cheatsheet.md
@@ -39,13 +39,13 @@ file formats.
 > Example code:
 
 
-{% highlight r %}
+```r
 ## Export
 write.csv(swiss, file = 'swiss.csv')
 
 ## Which is the same as:
 write.table(swiss, file = 'swiss.csv', sep = ',')
-{% endhighlight %}
+```
 
 ## `read.csv` or `read.table` ##
 
@@ -54,10 +54,10 @@ write.table(swiss, file = 'swiss.csv', sep = ',')
 > Example code:
 
 
-{% highlight r %}
+```r
 write.csv(swiss, file = 'swiss.csv')
 read.csv('swiss.csv')
-{% endhighlight %}
+```
 
 ## `head`, `names`, `str`, `summary` ##
 
@@ -71,13 +71,13 @@ median, frequency, and other basic statistics of each variable in the dataframe.
 > Example code:
 
 
-{% highlight r %}
+```r
 head(swiss)
 names(swiss)
 str(swiss)
 summary(swiss)
 class(swiss)
-{% endhighlight %}
+```
 
 ## `%>%` ##
 
@@ -90,7 +90,7 @@ do for some functions/commands (like `lm()`).
 > Example code:
 
 
-{% highlight r %}
+```r
 library(dplyr)
 ## This is the package that the pipe comes from
 library(magrittr) 
@@ -104,7 +104,7 @@ head(swiss)
 swiss %>% head
 swiss %>% head()
 swiss %>% head(.)
-{% endhighlight %}
+```
 
 ## `tbl_df` ##
 
@@ -114,14 +114,14 @@ dataframe prettier.
 > Example code:
 
 
-{% highlight r %}
+```r
 library(dplyr)
 ## These are the same
 tbl_df(ds)
 ds %>% tbl_df
 ds %>% tbl_df()
 ds %>% tbl_df(.)
-{% endhighlight %}
+```
 
 ## `select` ##
 
@@ -131,7 +131,7 @@ variables based on pattern or if it contains some letter.
 > Example code:
 
 
-{% highlight r %}
+```r
 library(dplyr)
 ## These are the same
 select(swiss, Education, Catholic, Fertility)
@@ -143,7 +143,7 @@ swiss %>% select(-Education, -Catholic)
 
 ## Select variables based on name or pattern
 swiss %>% select(starts_with('E'), contains('Fert'), matches('mort'))
-{% endhighlight %}
+```
 
 ## `rename` ##
 
@@ -155,7 +155,7 @@ is, as it only renames.
 > Example code:
 
 
-{% highlight r %}
+```r
 library(dplyr)
 ## These are the same
 rename(swiss, edu = Education)
@@ -167,7 +167,7 @@ swiss %>% rename(edu = Education, fert = Fertility)
 ## If you want to use select, but get the same functionality as rename, use the
 ## everything() function to select all other variables in the dataframe
 swiss %>% select(edu = Education, everything())
-{% endhighlight %}
+```
 
 ## `filter` ##
 
@@ -178,7 +178,7 @@ greater than, `==` equals, `>=` or `<=` greater/less than or equal to.
 > Example code:
 
 
-{% highlight r %}
+```r
 library(dplyr)
 ## These are the same
 filter(swiss, Catholic < 20, Examination == 15)
@@ -187,7 +187,7 @@ swiss %>% filter(., Catholic < 20, Examination == 15)
 
 ## For string/factor variables
 swiss %>% filter(X == 'Aigle')
-{% endhighlight %}
+```
 
 ## `mutate` ##
 
@@ -197,7 +197,7 @@ that assigns a value based on the condition.
 > Example code:
 
 
-{% highlight r %}
+```r
 library(dplyr)
 ## These are the same
 mutate(swiss, Infertile = ifelse(Fertility < 50, 'yes', 'no'))
@@ -206,7 +206,7 @@ swiss %>% mutate(., Infertile = ifelse(Fertility < 50, 'yes', 'no'))
 
 ## Or..
 swiss %>% mutate(Test = 'yes', Number = 10)
-{% endhighlight %}
+```
 
 ## `arrange` ##
 
@@ -216,7 +216,7 @@ given (eg. `arrange(var1, var2)` sorts first by `var1` than by `var2`).
 > Example code:
 
 
-{% highlight r %}
+```r
 library(dplyr)
 ## These are the same
 arrange(swiss, Education, Examination)
@@ -225,7 +225,7 @@ swiss %>% arrange(., Education, Examination)
 
 ## Or to do it descending
 swiss %>% arrange(desc(Education))
-{% endhighlight %}
+```
 
 ## `group_by` ##
 
@@ -236,12 +236,12 @@ following commands based on the grouping.
 > Example code:
 
 
-{% highlight r %}
+```r
 library(dplyr)
 swiss %>%
     mutate(EarlyDeath = ifelse(Infant.Mortality >= 50, 'yes', 'no')) %>%
     group_by(EarlyDeath)
-{% endhighlight %}
+```
 
 ## `summarise` ##
 
@@ -252,14 +252,14 @@ for sample size.  This function is best used with `group_by()`.
 > Example code:
 
 
-{% highlight r %}
+```r
 library(dplyr)
 swiss %>%
     mutate(Educated = ifelse(Education >= 50, 'yes', 'no')) %>%
     group_by(Educated) %>%
     str()
     summarise(mean = mean(Agriculture))
-{% endhighlight %}
+```
 
 
 ## `gather` ##
@@ -273,7 +273,7 @@ exclude (with a `-`) after the name of the two new variables.
 > Example code:
 
 
-{% highlight r %}
+```r
 library(dplyr)
 library(tidyr)
 ## These are the same
@@ -286,7 +286,7 @@ swiss %>% add_rownames() %>% gather(Measure, Value, -rowname)
 
 ## Or include only some variables
 swiss %>% gather(Measure, Value, Education, Fertility, Infant.Mortality)
-{% endhighlight %}
+```
 
 ## `spread` ##
 
@@ -296,19 +296,19 @@ dataframes.
 > Example code:
 
 
-{% highlight r %}
+```r
 library(dplyr)
 library(tidyr)
 swiss %>% 
     add_rownames() %>% 
     gather(Measure, Value, -rowname) %>% 
     spread(Measure, Value)
-{% endhighlight %}
+```
 
 # Combined example using (almost) all functions:
 
 
-{% highlight r %}
+```r
 swiss %>%
     add_rownames() %>% 
     tbl_df() %>% 
@@ -319,5 +319,5 @@ swiss %>%
     group_by(Measure, Religious) %>% 
     summarise(mean = mean(Value)) %>%
     spread(Measure, mean)
-{% endhighlight %}
+```
 
diff --git a/lessons/r-wrangling/intro.md b/lessons/r-wrangling/intro.md
index 6d88165..98f7af0 100644
--- a/lessons/r-wrangling/intro.md
+++ b/lessons/r-wrangling/intro.md
@@ -56,12 +56,12 @@ command, use the `?` command along with the command you need help with (eg.
 The one I'm going to use is the `swiss` dataset.
 
 
-{% highlight r %}
+```r
 ## Export/save to file
 write.csv(swiss, file = 'swiss.csv')
 ## Import/read from file
 ds <- read.csv('swiss.csv')
-{% endhighlight %}
+```
 
 ## Viewing your data
 
@@ -73,13 +73,8 @@ column names and types).  `summary()` shows a quick description of the summary
 statistics (means, median, frequency) for each of your columns.
 
 
-{% highlight r %}
+```r
 head(ds)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##              X Fertility Agriculture Examination Education Catholic
 ## 1   Courtelary      80.2        17.0          15        12     9.96
 ## 2     Delemont      83.1        45.1           6         9    84.84
@@ -94,31 +89,11 @@ head(ds)
 ## 4             20.3
 ## 5             20.6
 ## 6             26.6
-{% endhighlight %}
-
-
-
-{% highlight r %}
 names(ds)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] "X"                "Fertility"        "Agriculture"     
 ## [4] "Examination"      "Education"        "Catholic"        
 ## [7] "Infant.Mortality"
-{% endhighlight %}
-
-
-
-{% highlight r %}
 str(ds)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## 'data.frame':	47 obs. of  7 variables:
 ##  $ X               : Factor w/ 47 levels "Aigle","Aubonne",..: 8 9 12 26 28 34 5 13 15 38 ...
 ##  $ Fertility       : num  80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 ...
@@ -127,17 +102,7 @@ str(ds)
 ##  $ Education       : int  12 9 5 7 15 7 7 8 7 13 ...
 ##  $ Catholic        : num  9.96 84.84 93.4 33.77 5.16 ...
 ##  $ Infant.Mortality: num  22.2 22.2 20.2 20.3 20.6 26.6 23.6 24.9 21 24.4 ...
-{% endhighlight %}
-
-
-
-{% highlight r %}
 summary(ds)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##         X        Fertility      Agriculture     Examination   
 ##  Aigle   : 1   Min.   :35.00   Min.   : 1.20   Min.   : 3.00  
 ##  Aubonne : 1   1st Qu.:64.70   1st Qu.:35.90   1st Qu.:12.00  
@@ -154,7 +119,7 @@ summary(ds)
 ##  3rd Qu.:12.00   3rd Qu.: 93.125   3rd Qu.:21.70   
 ##  Max.   :53.00   Max.   :100.000   Max.   :26.60   
 ## 
-{% endhighlight %}
+```
 
 # Wrangling your data, `dplyr` style
 
@@ -168,13 +133,8 @@ left-hand side, just like how a plumbing pipe works for water.  `tbl_df` makes
 the object into a `tbl` class, making printing of the output nicer.
 
 
-{% highlight r %}
+```r
 library(dplyr)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## 
 ## Attaching package: 'dplyr'
 ## 
@@ -185,20 +145,10 @@ library(dplyr)
 ## The following objects are masked from 'package:base':
 ## 
 ##     intersect, setdiff, setequal, union
-{% endhighlight %}
-
-
-
-{% highlight r %}
 library(tidyr)
 
 ## Compare
 head(ds)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##              X Fertility Agriculture Examination Education Catholic
 ## 1   Courtelary      80.2        17.0          15        12     9.96
 ## 2     Delemont      83.1        45.1           6         9    84.84
@@ -213,18 +163,8 @@ head(ds)
 ## 4             20.3
 ## 5             20.6
 ## 6             26.6
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ## With:
 tbl_df(ds)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [47 x 7]
 ## 
 ##               X Fertility Agriculture Examination Education Catholic
@@ -241,19 +181,10 @@ tbl_df(ds)
 ## 10       Sarine      82.9        45.2          16        13    91.38
 ## ..          ...       ...         ...         ...       ...      ...
 ## Variables not shown: Infant.Mortality (dbl)
-{% endhighlight %}
 
-
-
-{% highlight r %}
 ## Now put the tbl dataset into a new object
 ds2 <- tbl_df(ds)
 ds2
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [47 x 7]
 ## 
 ##               X Fertility Agriculture Examination Education Catholic
@@ -270,7 +201,7 @@ ds2
 ## 10       Sarine      82.9        45.2          16        13    91.38
 ## ..          ...       ...         ...         ...       ...      ...
 ## Variables not shown: Infant.Mortality (dbl)
-{% endhighlight %}
+```
 
 ## Select columns
 
@@ -280,14 +211,9 @@ use of the `%>%` operator.  This allows you to chain commands together, letting
 you do more with only a few commands.
 
 
-{% highlight r %}
+```r
 ds2 %>% 
     select(Education, Catholic, Fertility)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [47 x 3]
 ## 
 ##    Education Catholic Fertility
@@ -303,7 +229,7 @@ ds2 %>%
 ## 9          7    97.67      82.4
 ## 10        13    91.38      82.9
 ## ..       ...      ...       ...
-{% endhighlight %}
+```
 
 The real power with using the `select()` function comes when you combine it with
 [regular expressions (regexp)](http://www.regular-expressions.info/), or rather pattern
@@ -319,14 +245,9 @@ regexp syntax are nearly a language to themselves, so use
 Ok, so lets say you want to search for variables that have certain patterns:
 
 
-{% highlight r %}
+```r
 ds2 %>% 
     select(contains('Edu'), starts_with('Cath'))
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [47 x 2]
 ## 
 ##    Education Catholic
@@ -342,19 +263,10 @@ ds2 %>%
 ## 9          7    97.67
 ## 10        13    91.38
 ## ..       ...      ...
-{% endhighlight %}
 
-
-
-{% highlight r %}
 ## Or more simplified
 ds2 %>% 
     select(matches('Edu|Cath'))
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [47 x 2]
 ## 
 ##    Education Catholic
@@ -370,7 +282,7 @@ ds2 %>%
 ## 9          7    97.67
 ## 10        13    91.38
 ## ..       ...      ...
-{% endhighlight %}
+```
 
 You can see that if you have many variables that have a common structure to
 their name, you can quickly select all those variables by using functions such
@@ -382,14 +294,9 @@ You can rename columns using the `rename` command (the new name is on the left
 hand side, so `newname = oldname`).
 
 
-{% highlight r %}
+```r
 ds2 %>% 
     rename(County = X)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [47 x 7]
 ## 
 ##          County Fertility Agriculture Examination Education Catholic
@@ -406,7 +313,7 @@ ds2 %>%
 ## 10       Sarine      82.9        45.2          16        13    91.38
 ## ..          ...       ...         ...         ...       ...      ...
 ## Variables not shown: Infant.Mortality (dbl)
-{% endhighlight %}
+```
 
 ## Filter rows
 
@@ -416,15 +323,10 @@ if 'Examination' is equal to 15.  A single `=` is used for something else
 (assigning things to objects or using them in functions/commands).
 
 
-{% highlight r %}
+```r
 ## For continuous/number data
 ds2 %>% 
     filter(Catholic < 20, Examination == 15)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [3 x 7]
 ## 
 ##            X Fertility Agriculture Examination Education Catholic
@@ -433,26 +335,17 @@ ds2 %>%
 ## 2    Yverdon      65.4        49.5          15         8     6.10
 ## 3 Val de Ruz      77.6        37.6          15         7     4.97
 ## Variables not shown: Infant.Mortality (dbl)
-{% endhighlight %}
-
 
-
-{% highlight r %}
 ## Or for 'string' (words or letters) data
 ds2 %>%
     filter(X == 'Aigle')
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [1 x 7]
 ## 
 ##        X Fertility Agriculture Examination Education Catholic
 ##   (fctr)     (dbl)       (dbl)       (int)     (int)    (dbl)
 ## 1  Aigle      64.1          62          21        12     8.52
 ## Variables not shown: Infant.Mortality (dbl)
-{% endhighlight %}
+```
 
 ## Create new columns or clean up existing ones
 
@@ -461,15 +354,10 @@ If you want to create a new column, you use the `mutate` command.  The
 on the condition.
 
 
-{% highlight r %}
+```r
 ds2 %>%
     mutate(Testing = 'yes',
            Infertile = ifelse(Fertility < 50, 'yes', 'no'))
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [47 x 9]
 ## 
 ##               X Fertility Agriculture Examination Education Catholic
@@ -487,7 +375,7 @@ ds2 %>%
 ## ..          ...       ...         ...         ...       ...      ...
 ## Variables not shown: Infant.Mortality (dbl), Testing (chr), Infertile
 ##   (chr)
-{% endhighlight %}
+```
 
 However, it's fairly common that you need to do some data janitorial work by
 cleaning up an existing column. For example, in a dataset with a 'Sex' variable,
@@ -499,17 +387,12 @@ some values had data entry errors in spelling, such as 'fmale' when it should be
 '*g*lobablly *sub*stitute a pattern with the replacement.
 
 
-{% highlight r %}
+```r
 ds2 %>% 
     mutate(
         X = gsub(pattern = '^G', replacement = 'J', X),
         X = gsub(pattern = 'e$', replacement = '', X)
         )
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [47 x 7]
 ## 
 ##               X Fertility Agriculture Examination Education Catholic
@@ -526,7 +409,7 @@ ds2 %>%
 ## 10        Sarin      82.9        45.2          16        13    91.38
 ## ..          ...       ...         ...         ...       ...      ...
 ## Variables not shown: Infant.Mortality (dbl)
-{% endhighlight %}
+```
 
 Notice the `^` and `$` characters. Those are special syntax symbols used in
 regexp commands. We introduced them above, but we'll quickly go over
@@ -537,14 +420,9 @@ means for all "e" that are at the end of a string. Or let"s say that all "mont",
 here.
 
 
-{% highlight r %}
+```r
 ds2 %>% 
     mutate(X = gsub('mont|mnt|mout', 'ment', X, ignore.case = TRUE))
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [47 x 7]
 ## 
 ##                X Fertility Agriculture Examination Education Catholic
@@ -561,7 +439,7 @@ ds2 %>%
 ## 10        Sarine      82.9        45.2          16        13    91.38
 ## ..           ...       ...         ...         ...       ...      ...
 ## Variables not shown: Infant.Mortality (dbl)
-{% endhighlight %}
+```
 
 Regular expressions are incredibly powerful, but also can be confusing. Make
 sure to check out [our resources page](/lessons/resources/) for links to sites
@@ -577,18 +455,13 @@ actually conducting it.  `arrange` sorts/orders/re-arranges the column Education
 in ascending order.
 
 
-{% highlight r %}
+```r
 ds2 %>%
   filter(Catholic > 20) %>%
   select(County = X, ## This renames the variable, just like the rename() command
          Education, Fertility, Agriculture) %>%
   arrange(Education) %>%
   mutate(infertile = ifelse(Fertility < 50, 'yes', 'no'))
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [21 x 5]
 ## 
 ##          County Education Fertility Agriculture infertile
@@ -604,7 +477,7 @@ ds2 %>%
 ## 9      Martigwy         6      70.5        78.2        no
 ## 10      Moutier         7      85.8        36.5        no
 ## ..          ...       ...       ...         ...       ...
-{% endhighlight %}
+```
 
 ## Re-organize your data (using `tidyr`)
 
@@ -614,16 +487,11 @@ does.  Note that you can remove a column by having a minus `-` sign in front of
 a variable when you use `select`.
 
 
-{% highlight r %}
+```r
 ## Compare this:
 ds2 %>%
   select(-Infant.Mortality) %>%
   rename(County = X)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [47 x 6]
 ## 
 ##          County Fertility Agriculture Examination Education Catholic
@@ -639,21 +507,12 @@ ds2 %>%
 ## 9       Gruyere      82.4        53.3          12         7    97.67
 ## 10       Sarine      82.9        45.2          16        13    91.38
 ## ..          ...       ...         ...         ...       ...      ...
-{% endhighlight %}
-
-
 
-{% highlight r %}
 ## With this:
 ds2 %>%
   select(-Infant.Mortality) %>%
   rename(County = X) %>%
   gather(Measure, Value, -County)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [235 x 3]
 ## 
 ##          County   Measure Value
@@ -669,22 +528,13 @@ ds2 %>%
 ## 9       Gruyere Fertility  82.4
 ## 10       Sarine Fertility  82.9
 ## ..          ...       ...   ...
-{% endhighlight %}
 
-
-
-{% highlight r %}
 ## And back again:
 ds2 %>%
   select(-Infant.Mortality) %>%
   rename(County = X) %>%
   gather(Measure, Value, -County) %>%
   spread(Measure, Value)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [47 x 6]
 ## 
 ##        County Fertility Agriculture Examination Education Catholic
@@ -700,7 +550,7 @@ ds2 %>%
 ## 9    Delemont      83.1        45.1           6         9    84.84
 ## 10  Echallens      68.3        72.6          18         2    24.20
 ## ..        ...       ...         ...         ...       ...      ...
-{% endhighlight %}
+```
 
 ## Summarise variables
 
@@ -711,18 +561,13 @@ only contain the new variable(s) created, in this case the mean, as well as the
 grouping variable.
 
 
-{% highlight r %}
+```r
 ds2 %>%
   select(-X) %>%
   gather(Measure, Value) %>%
   group_by(Measure) %>%
   summarise(mean = mean(Value),
             sampleSize = n())
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Source: local data frame [6 x 3]
 ## 
 ##            Measure     mean sampleSize
@@ -733,7 +578,7 @@ ds2 %>%
 ## 4        Education 10.97872         47
 ## 5         Catholic 41.14383         47
 ## 6 Infant.Mortality 19.94255         47
-{% endhighlight %}
+```
 
 ## Other useful and powerful examples
 
@@ -747,7 +592,7 @@ package).  If you want more details on how to use this set up,
 [check out my blog post about it](http://www.lukewjohnston.com/blog/loops-forests-multiple-linear-regressions/).
 
 
-{% highlight r %}
+```r
 ds2 %>%
     select(-X) %>%  
     gather(Indep, Xvalue, Fertility, Agriculture) %>%
@@ -755,10 +600,5 @@ ds2 %>%
     group_by(Dep, Indep) %>% 
     do(lm(Yvalue ~ Xvalue + Infant.Mortality + Examination, data = .) %>% 
            broom::tidy())
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Error in tidy.lm(.): could not find function "is"
-{% endhighlight %}
+```
diff --git a/lessons/rintro/assignment.md b/lessons/rintro/assignment.md
index 93bdab0..92da0d0 100644
--- a/lessons/rintro/assignment.md
+++ b/lessons/rintro/assignment.md
@@ -42,16 +42,16 @@ object of the resulting matrix; choose your own object name!
 *alpha:*
 
 
-{% highlight text %}
+```
 ## Error in eval(expr, envir, enclos): could not find function "kable"
-{% endhighlight %}
+```
  
 *beta:* 
 
 
-{% highlight text %}
+```
 ## Error in eval(expr, envir, enclos): could not find function "kable"
-{% endhighlight %}
+```
 
 3. Load the `datasets` package and make a new dataframe with the `mtcars`
 dataset. What are the means of the columns? Hint: use a loop function. Subset
diff --git a/lessons/rintro/assignmentAnswers.md b/lessons/rintro/assignmentAnswers.md
index 0cef3b0..2880592 100644
--- a/lessons/rintro/assignmentAnswers.md
+++ b/lessons/rintro/assignmentAnswers.md
@@ -5,104 +5,49 @@ published: true
 1. 
 
 
-{% highlight r %}
+```r
 a <- c(1:10)
 b <- c(11:20)
 
 (c <- a*b)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##  [1]  11  24  39  56  75  96 119 144 171 200
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 c[5]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 75
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 subset(c, c > 50)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1]  56  75  96 119 144 171 200
-{% endhighlight %}
+```
 
 
 2. 
 
-{% highlight r %}
+```r
 (d <- matrix(c(7,9,12,2,4,13), 2, 3))
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##      [,1] [,2] [,3]
 ## [1,]    7   12    4
 ## [2,]    9    2   13
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 (e <- matrix(c(1,7,12,19,2,8,13,20,3,9,14,21), 3, 4))
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##      [,1] [,2] [,3] [,4]
 ## [1,]    1   19   13    9
 ## [2,]    7    2   20   14
 ## [3,]   12    8    3   21
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 
 (f <- d %*% e)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##      [,1] [,2] [,3] [,4]
 ## [1,]  139  189  343  315
 ## [2,]  179  279  196  382
-{% endhighlight %}
+```
 
 3. 
 
 
-{% highlight r %}
+```r
 library(datasets)
 
 (g <- mtcars)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
 ## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
 ## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
@@ -136,95 +81,35 @@ library(datasets)
 ## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
 ## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
 ## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 apply(g, 2, mean)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##        mpg        cyl       disp         hp       drat         wt 
 ##  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250 
 ##       qsec         vs         am       gear       carb 
 ##  17.848750   0.437500   0.406250   3.687500   2.812500
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 (h <- g[,"mpg"])
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##  [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
 ## [15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
 ## [29] 15.8 19.7 15.0 21.4
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 #OR 
 
 (h <- g$mpg)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##  [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
 ## [15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
 ## [29] 15.8 19.7 15.0 21.4
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 (i <- g[, 4])
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##  [1] 110 110  93 110 175 105 245  62  95 123 123 180 180 180 205 215 230
 ## [18]  66  52  65  97 150 150 245 175  66  91 113 264 175 335 109
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 (j <- g[c(3,5),])
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##                    mpg cyl disp  hp drat   wt  qsec vs am gear carb
 ## Datsun 710        22.8   4  108  93 3.85 2.32 18.61  1  1    4    1
 ## Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.02  0  0    3    2
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 (k <- subset(g, g$hp < 100))
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##                 mpg cyl  disp hp drat    wt  qsec vs am gear carb
 ## Datsun 710     22.8   4 108.0 93 3.85 2.320 18.61  1  1    4    1
 ## Merc 240D      24.4   4 146.7 62 3.69 3.190 20.00  1  0    4    2
@@ -235,12 +120,12 @@ apply(g, 2, mean)
 ## Toyota Corona  21.5   4 120.1 97 3.70 2.465 20.01  1  0    3    1
 ## Fiat X1-9      27.3   4  79.0 66 4.08 1.935 18.90  1  1    4    1
 ## Porsche 914-2  26.0   4 120.3 91 4.43 2.140 16.70  0  1    5    2
-{% endhighlight %}
+```
 
 4. 
 
 
-{% highlight r %}
+```r
 
 ani <- c("cat", "dog", "cow", "pig")
 for (i in ani) {
@@ -249,18 +134,13 @@ for (i in ani) {
  else {
  }
 }
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] "dog"
-{% endhighlight %}
+```
 
 5. 
 
 
-{% highlight r %}
+```r
 stats <- function(a,b) {
   print (mean(a))
   print (sd(a))
@@ -268,25 +148,15 @@ stats <- function(a,b) {
 }
 
 stats(g$disp, g$drat)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 230.7219
 ## [1] 123.9387
 ## [1] 3.695
-{% endhighlight %}
+```
 
 6. 
 
-{% highlight r %}
+```r
 lapply(g, range, na.rm = TRUE)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## $mpg
 ## [1] 10.4 33.9
 ## 
@@ -319,53 +189,28 @@ lapply(g, range, na.rm = TRUE)
 ## 
 ## $carb
 ## [1] 1 8
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 sapply(g, range, na.rm = TRUE)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##       mpg cyl  disp  hp drat    wt qsec vs am gear carb
 ## [1,] 10.4   4  71.1  52 2.76 1.513 14.5  0  0    3    1
 ## [2,] 33.9   8 472.0 335 4.93 5.424 22.9  1  1    5    8
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ## lapply always returns a list while sapply simiplied the result and returned a matrix
-{% endhighlight %}
+```
 
 7. 
 
-{% highlight r %}
+```r
 mapply(range, mtcars[,c(1,6,7)], na.rm = TRUE)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##       mpg    wt qsec
 ## [1,] 10.4 1.513 14.5
 ## [2,] 33.9 5.424 22.9
-{% endhighlight %}
+```
 
 8.
 
-{% highlight r %}
+```r
 tapply(mtcars$hp, mtcars$gear, max, na.rm = TRUE)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##   3   4   5 
 ## 245 123 335
-{% endhighlight %}
+```
diff --git a/lessons/rintro/cheatsheet.md b/lessons/rintro/cheatsheet.md
index fd8546d..8cc4007 100644
--- a/lessons/rintro/cheatsheet.md
+++ b/lessons/rintro/cheatsheet.md
@@ -30,14 +30,14 @@ types: character, numeric, integer, complex, and logical
 > Example code:
 
 
-{% highlight r %}
+```r
 a <- c(0.4, 1.2)         # numeric
 b <- c(TRUE, FALSE)      # logical
 c <- c("a","b","c")      # character
 d <- c(1L, 2L)           # integer
 e <- 1:10                # integer
 f <- c(1+0i, 2+4i)       # complex
-{% endhighlight %}
+```
 
 # Basic R object types (classes) #
 
@@ -56,69 +56,24 @@ from the vector.
 > Example code:
 
 
-{% highlight r %}
+```r
 vector("numeric", length=10)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##  [1] 0 0 0 0 0 0 0 0 0 0
-{% endhighlight %}
-
-
-
-{% highlight r %}
 x <- c(1:10)
 ## Select the first element
 x[1]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 1
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ## Select the first and second element
 x[1:2]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 1 2
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ## select the first and third element
 x[c(1,3)]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 1 3
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ## Check out long the vector is
 length(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 10
-{% endhighlight %}
+```
 
 ## `list()`, `[[`, `$` ##
 
@@ -131,14 +86,9 @@ command.
 > Example code:
 
 
-{% highlight r %}
+```r
 x <- list(num = 1, char = "a", logic = TRUE, complex = 1+4i)
 x
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## $num
 ## [1] 1
 ## 
@@ -150,58 +100,18 @@ x
 ## 
 ## $complex
 ## [1] 1+4i
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ## Use [[ to select the contents inside the list
 x[[1]]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 1
-{% endhighlight %}
-
-
-
-{% highlight r %}
 x[[2]]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] "a"
-{% endhighlight %}
-
-
-
-{% highlight r %}
 x[[3]]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] TRUE
-{% endhighlight %}
-
-
-
-{% highlight r %}
 x2 <- list(
     vector = c(1:10), dataframe = data.frame(1:10, 1:10),
     char.list = list('a', 'b')
     )
 x2
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## $vector
 ##  [1]  1  2  3  4  5  6  7  8  9 10
 ## 
@@ -224,45 +134,15 @@ x2
 ## 
 ## $char.list[[2]]
 ## [1] "b"
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ## Use the [ to select the vector number within the list item.
 x2[[1]]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##  [1]  1  2  3  4  5  6  7  8  9 10
-{% endhighlight %}
-
-
-
-{% highlight r %}
 x2[[1]][1]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 1
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ## Select a list item specifically (if it is named)
 x2$vector
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##  [1]  1  2  3  4  5  6  7  8  9 10
-{% endhighlight %}
+```
 
 ## `matrix()`, `dim()`, `nrow()`, `ncol()`, `rbind()`, `cbind()` ##
 
@@ -277,172 +157,57 @@ the `dim()` attribute or by using `cbind()` and `rbind()`.
 > Example code:
 
 
-{% highlight r %}
+```r
 x <- matrix(1:6, nrow=2, ncol=3)
 x
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##      [,1] [,2] [,3]
 ## [1,]    1    3    5
 ## [2,]    2    4    6
-{% endhighlight %}
-
-
-
-{% highlight r %}
 # The first row, third column
 x[1,3]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 5
-{% endhighlight %}
-
-
-
-{% highlight r %}
 # The first two rows and second and third column
 x[1:2, 2:3]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##      [,1] [,2]
 ## [1,]    3    5
 ## [2,]    4    6
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ## Check the attributes of the matrix
 dim(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 2 3
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ncol(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 3
-{% endhighlight %}
-
-
-
-{% highlight r %}
 nrow(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 2
-{% endhighlight %}
-
-
-
-{% highlight r %}
 attributes(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## $dim
 ## [1] 2 3
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ## Creating a matrix from a vector
 x <- 1:10
 x
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##  [1]  1  2  3  4  5  6  7  8  9 10
-{% endhighlight %}
-
-
-
-{% highlight r %}
 dim(x) <- c(2,5)
 x
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##      [,1] [,2] [,3] [,4] [,5]
 ## [1,]    1    3    5    7    9
 ## [2,]    2    4    6    8   10
-{% endhighlight %}
-
-
-
-{% highlight r %}
 dim(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 2 5
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ## Using rbind ('row bind') or cbind ('column bind')
 x <- 1:3
 y <- 10:12
 cbind(x, y)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##      x  y
 ## [1,] 1 10
 ## [2,] 2 11
 ## [3,] 3 12
-{% endhighlight %}
-
-
-
-{% highlight r %}
 rbind(x, y)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##   [,1] [,2] [,3]
 ## x    1    2    3
 ## y   10   11   12
-{% endhighlight %}
+```
 
 ## `factor()`, `table()` ##
 
@@ -455,59 +220,24 @@ Factors are also treated differently by modelling functions like `lm()` and
 > Example code:
 
 
-{% highlight r %}
+```r
 x <- factor(c("yes", "yes", "no", "no", "yes"))
 x
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] yes yes no  no  yes
 ## Levels: no yes
-{% endhighlight %}
-
-
-
-{% highlight r %}
 table(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## x
 ##  no yes 
 ##   2   3
-{% endhighlight %}
-
-
-
-{% highlight r %}
 unclass(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 2 2 1 1 2
 ## attr(,"levels")
 ## [1] "no"  "yes"
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ## Converting the factor into a numeric
 as.numeric(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 2 2 1 1 2
-{% endhighlight %}
+```
 
 ## `data.frame()`, `read.table()`, `read.csv()`, `[[`, `[`, `$` ##
 
@@ -522,157 +252,52 @@ of the data, dataframes can be imported from an external files using the
 `read.table()` or `read.csv`, or can be created using `data.frame()`.
 
 
-{% highlight r %}
+```r
 x <- data.frame(
     foo = 1:4, bar = c(T,T,F,F), char = c('a', 'b', 'b', 'a')
     )
 x
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##   foo   bar char
 ## 1   1  TRUE    a
 ## 2   2  TRUE    b
 ## 3   3 FALSE    b
 ## 4   4 FALSE    a
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ## Select certain rows and columns: 
 ## - 1:2 rows with 2:3 columns
 x[c(1:2), c(2:3)]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##    bar char
 ## 1 TRUE    a
 ## 2 TRUE    b
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ## - Select columns by name
 x[c('foo', 'bar')]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##   foo   bar
 ## 1   1  TRUE
 ## 2   2  TRUE
 ## 3   3 FALSE
 ## 4   4 FALSE
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ## - Select rows by number and columns by name
 x[c(2:3), 'foo']
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 2 3
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ## - Select column directly
 x$bar
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1]  TRUE  TRUE FALSE FALSE
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ## - Select a single column using the list [[ select command
 x[[2]]
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1]  TRUE  TRUE FALSE FALSE
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ## Check the attributes
 dim(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 4 3
-{% endhighlight %}
-
-
-
-{% highlight r %}
 nrow(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 4
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ncol(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 3
-{% endhighlight %}
-
-
-
-{% highlight r %}
 length(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 3
-{% endhighlight %}
-
-
-
-{% highlight r %}
 class(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] "data.frame"
-{% endhighlight %}
+```
 
 
 # Attributes of objects in R #
@@ -684,78 +309,33 @@ be modified with the `attributes()` and other functions.
 > Example code
 
 
-{% highlight r %}
+```r
 x <- 1:3
 names(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## NULL
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ## Modify names attribute
 names(x) <- c("alpha", "beta", "gamma")
 x
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## alpha  beta gamma 
 ##     1     2     3
-{% endhighlight %}
-
-
-
-{% highlight r %}
 names(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] "alpha" "beta"  "gamma"
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ## Add to the class attribute
 class(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] "integer"
-{% endhighlight %}
-
-
-
-{% highlight r %}
 class(x) <- c('my-own-class', class(x))
 class(x)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] "my-own-class" "integer"
-{% endhighlight %}
+```
 
 # Looking for help in R #
 
 > Example code:
 
 
-{% highlight r %}
+```r
 ## Help for functions
 help('c')
 help('class')
@@ -765,7 +345,7 @@ help('class')
 ## Help for specific packages
 help(package = 'ggplot2')
 help(package = 'rmarkdown')
-{% endhighlight %}
+```
 
 # Using R packages #
 
@@ -775,7 +355,7 @@ R packages that extend R's capabilities (such as `ggplot2`, our next lesson).
 > Example code:
 
 
-{% highlight r %}
+```r
 ## Check what's loaded
 search()
 
@@ -787,7 +367,7 @@ library(dplyr)
 ## Install a package
 install.packages('ggplot2')
 install.packages('rmarkdown')
-{% endhighlight %}
+```
 
 # Working Directory #
 
@@ -798,7 +378,7 @@ where it will place any files you output.
 > Example code:
 
 
-{% highlight r %}
+```r
 ## Check the current directory
 getwd()
 
@@ -807,7 +387,7 @@ getwd('path/to/new/directory')
 
 ## Create a directory
 dir.create('path')
-{% endhighlight %}
+```
 
 # Importing tabular data #
 
@@ -816,14 +396,14 @@ spreadsheet). The two most commonly-used functions used to import tabular data
 into R are `read.table()` and `read.csv()`.
 
 
-{% highlight r %}
+```r
 # \t is the regular expression for the tab key. This means our table is
 # tab-delimited (tab-separated)
 data <- read.table("file_name.txt", header = FALSE, sep = "\t")
 
 #.csv is a comma-separated table 
 data2 <- read.csv("file_name.csv", header = TRUE)
-{% endhighlight %}
+```
 
 # Saving/outputting tabular data #
 
@@ -833,13 +413,13 @@ This is where you use `write.table()` or `write.csv()`.
 > Example code:
 
 
-{% highlight r %}
+```r
 ## Comma separated file
 write.table(
     data, file = "myfile.csv", sep = ",", row.names = TRUE, col.names = FALSE
     )
 write.csv(data, file = 'myfile.csv')
-{% endhighlight %}
+```
 
 # R operators and special symbols #
 
@@ -889,10 +469,10 @@ identical(x, y) | whether x and y are the same
 > Example code:
 
 
-{% highlight r %}
+```r
 # commenting in R is easy! Everything past a # means that R will ignore whatever
 # is written there.
-{% endhighlight %}
+```
 
 
 # Functions #
@@ -911,16 +491,16 @@ function and save yourself time and effort later on!
 > Example code:
 
 
-{% highlight r %}
+```r
 f <- function(argument) {
     ## do something here 
     commands
     more commands
 }
-{% endhighlight %}
+```
 
 
-{% highlight r %}
+```r
 
 ## make a function called above10 with the argument x that subsets all variables
 ## of x that are greater than 10
@@ -947,7 +527,7 @@ columnmean <- function (y, remove.NA = TRUE) {
   }
   means
 }
-{% endhighlight %}
+```
 
 ## `if .. else`, `ifelse` ##
 
@@ -958,7 +538,7 @@ used within functions.
 > Example code:
 
 
-{% highlight r %}
+```r
 
 x <- 1:20
 
@@ -978,10 +558,10 @@ if (length(x) < 10) {
 }
 
 ifelse(x < 10, 'yes', 'no')
-{% endhighlight %}
+```
 
 
-{% highlight r %}
+```r
 y <- c()
 if (length(x) > 3) {
   y <- 10
@@ -989,17 +569,7 @@ if (length(x) > 3) {
   y <- 0
 }
 y
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 0
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ## Or
 y <- if(length(x) > 3) {
@@ -1008,13 +578,8 @@ y <- if(length(x) > 3) {
   0
 }
 y
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 0
-{% endhighlight %}
+```
 
 ## `for (x in list)` ##
 
@@ -1025,15 +590,10 @@ data-intensive tasks.
 > Example code:
 
 
-{% highlight r %}
+```r
 for (i in 1:10) {
   print (i)
 }
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 1
 ## [1] 2
 ## [1] 3
@@ -1044,11 +604,6 @@ for (i in 1:10) {
 ## [1] 8
 ## [1] 9
 ## [1] 10
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ## Nested loops
 x <- matrix(1:6, 2, 3)
@@ -1057,18 +612,13 @@ for (i in seq_len(nrow(x))) {
     print (x[i, j])
   }
 }
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 1
 ## [1] 3
 ## [1] 5
 ## [1] 2
 ## [1] 4
 ## [1] 6
-{% endhighlight %}
+```
 
 # Loop Functions #
 
@@ -1093,38 +643,23 @@ a name; other arguments as necessary. It will always return a list object.
 > Example code:
 
 
-{% highlight r %}
+```r
 x <- list(a=1:5, b=rnorm(10), c=rnorm(20,1), d=rnorm(100,5))
 lapply(x, mean)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## $a
 ## [1] 3
 ## 
 ## $b
-## [1] -0.08705441
+## [1] 0.03196616
 ## 
 ## $c
-## [1] 0.8056751
+## [1] 0.6059004
 ## 
 ## $d
-## [1] 5.003501
-{% endhighlight %}
-
-
-
-{% highlight r %}
+## [1] 4.991253
 
 x <- list(a=matrix(1:4,2,2), b=matrix(1:6,3,2))
 x
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## $a
 ##      [,1] [,2]
 ## [1,]    1    3
@@ -1135,25 +670,15 @@ x
 ## [1,]    1    4
 ## [2,]    2    5
 ## [3,]    3    6
-{% endhighlight %}
-
-
-
-{% highlight r %}
 ## Use an anonymous function with the argument `ele` and then define that
 ## argument.
 lapply(x, function(ele) ele[ ,1]) 
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## $a
 ## [1] 1 2
 ## 
 ## $b
 ## [1] 1 2 3
-{% endhighlight %}
+```
 
 ## `sapply()` ##
 
@@ -1165,17 +690,12 @@ same length, a matrix is returned. Otherwise a list is returned.
 > Example code:
 
 
-{% highlight r %}
+```r
 x <- list(a=1:5, b=rnorm(10), c=rnorm(20,1), d=rnorm(100,5))
 sapply(x, mean)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##          a          b          c          d 
-## 3.00000000 0.01466929 0.91543331 5.03388351
-{% endhighlight %}
+## 3.00000000 0.04576579 0.97643692 4.87379248
+```
 
 ## `apply()` ##
 
@@ -1186,81 +706,46 @@ means do the function to the columns.
 > Example code:
 
 
-{% highlight r %}
+```r
 x <- matrix (rnorm(200), 20, 10)
 # keeping number of columns and collapsing rows. This gives a vector of the
 # means of columns.
 apply(x, 2, mean) 
-{% endhighlight %}
-
-
-
-{% highlight text %}
-##  [1]  0.12347715  0.04770594 -0.23962572  0.03181958 -0.28658551
-##  [6]  0.56491660 -0.10992450 -0.06652284  0.13402639  0.13483184
-{% endhighlight %}
-
-
-
-{% highlight r %}
+##  [1]  0.17125350  0.25014973  0.05637670 -0.23796313  0.31392653
+##  [6]  0.38410844 -0.11645465  0.23821264 -0.04970403 -0.29748247
 
 # this calculates the sum of all rows
 apply(x, 1, sum) 
-{% endhighlight %}
-
-
-
-{% highlight text %}
-##  [1]  1.8147165 -1.2577054  2.5727208 -1.3669601  1.7846913 -2.2291840
-##  [7]  0.4621210  4.0021023  2.6337700 -1.0559236 -1.5868910  4.6012822
-## [13] -5.4770651 -0.1168984 -2.8939953 -0.1784035  4.6166377  0.2221600
-## [19] -0.1469406  0.2821437
-{% endhighlight %}
-
-
-
-{% highlight r %}
+##  [1] -4.4429821 -0.6574368  1.1911371  3.8616414 -2.2587809  7.8326782
+##  [7]  0.6499274  7.3764224  1.3012891 -1.7725843  6.4859628 -1.1623079
+## [13]  2.7518836 -4.4755192  3.7266695 -1.7411285 -4.4297744 -1.3331671
+## [19] -3.0433755  4.3879104
 
 ## Use additional arguments in apply, that it passes to the function quantile.
 y <- matrix(rnorm(200), 20, 10)
 apply(y, 1, quantile, probs = c(0.25, 0.75))
-{% endhighlight %}
-
-
-
-{% highlight text %}
-##           [,1]       [,2]       [,3]         [,4]       [,5]       [,6]
-## 25% -0.7647038 -0.7210468 -0.7600068 -0.003778185 -1.3521756 -0.2174439
-## 75%  0.4123376  0.6791686  0.7951071  0.895761712  0.9357661  0.9783733
+##           [,1]       [,2]       [,3]        [,4]      [,5]      [,6]
+## 25% -0.5813985 -0.4155099 -0.3978411 -0.06204147 0.1816494 0.1391389
+## 75%  0.5445757  1.1493853  1.4713268  0.95988898 0.9294715 0.8546799
 ##           [,7]        [,8]       [,9]      [,10]      [,11]      [,12]
-## 25% -0.2691144 -1.55762093 -0.3051579 -0.3270544 -0.6420960 0.06768242
-## 75%  0.6831129  0.04226876  0.7276672  1.1130333  0.3072559 0.63674347
+## 25% -0.6096415 -0.67954171 -0.7963498 -1.1435226 -0.2180918 -0.5935117
+## 75%  0.8149493  0.07678966  0.2362088  0.3142852  0.7269817  0.4444604
 ##          [,13]      [,14]      [,15]      [,16]      [,17]      [,18]
-## 25% -0.7577007 -0.7888834 -0.4688051 -0.6615460 -0.6491867 -0.5835593
-## 75%  1.1956633  0.5516693  1.1701949  0.7859077  1.0033207  0.8368049
+## 25% -1.0947972 -0.5359842 -1.4454579 0.09282969 -0.2285679 -0.7160392
+## 75% -0.3598023  0.4620311  0.1353984 1.10250081  1.2127786  0.9207038
 ##          [,19]      [,20]
-## 25% -0.3362166 -0.9854436
-## 75%  0.3192613  0.7975759
-{% endhighlight %}
-
-
-
-{% highlight r %}
+## 25% -0.5781012 -0.3674796
+## 75%  0.2874633  1.1798599
 
 # With an array (stacks of matrices: multidimensional).
 # Create an array that looks like a bunch of 2 by 2 matrices and take the mean of
 # those.
 a <- array(rnorm(2*2*10), c(2,2,10)) 
 apply(a, c(1,2), mean) #this keeps the 1st and 2nd dimension
-{% endhighlight %}
-
-
-
-{% highlight text %}
-##             [,1]         [,2]
-## [1,] -0.03276905 -0.004964963
-## [2,]  0.17359854 -0.131615028
-{% endhighlight %}
+##            [,1]        [,2]
+## [1,]  0.3332423 -0.07199605
+## [2,] -0.4199372  0.17350889
+```
 
 ## `mapply()` ##
 
@@ -1268,13 +753,8 @@ apply(a, c(1,2), mean) #this keeps the 1st and 2nd dimension
 a set of arguments.
 
 
-{% highlight r %}
+```r
 mapply(rep, 1:4, 4:1)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [[1]]
 ## [1] 1 1 1 1
 ## 
@@ -1286,18 +766,8 @@ mapply(rep, 1:4, 4:1)
 ## 
 ## [[4]]
 ## [1] 4
-{% endhighlight %}
-
-
-
-{% highlight r %}
 # is the same as
 list(rep(1, 4), rep(2, 3), rep(3, 2), rep(4, 1))
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [[1]]
 ## [1] 1 1 1 1
 ## 
@@ -1309,10 +779,10 @@ list(rep(1, 4), rep(2, 3), rep(3, 2), rep(4, 1))
 ## 
 ## [[4]]
 ## [1] 4
-{% endhighlight %}
+```
 
 
-{% highlight r %}
+```r
 ## create a function called noise that takes the arguments n, mean, and sd and
 ## produces random varibles with those specifications.
 noise<- function(n,mean, sd) {
@@ -1321,75 +791,45 @@ noise<- function(n,mean, sd) {
 
 ## apply the noise function with n=5, mean=1 and sd=2
 noise(5, 1, 2)
-{% endhighlight %}
-
-
-
-{% highlight text %}
-## [1] -0.750147  2.968726  2.636535  2.763283  4.332608
-{% endhighlight %}
-
-
-
-{% highlight r %}
+## [1] 0.7930424 2.4740363 1.1699390 1.6019916 0.9895137
 
 ## apply the noise function for n= 1 to 5, mean = 1 to 5, and sd = 2
 mapply(noise, 1:5, 1:5, 2)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [[1]]
-## [1] 1.039934
+## [1] 1.247853
 ## 
 ## [[2]]
-## [1] -0.463912  3.117271
+## [1] 0.8865116 6.9421407
 ## 
 ## [[3]]
-## [1] 2.935257 4.951166 3.344060
+## [1]  5.044907 -1.076488  3.519662
 ## 
 ## [[4]]
-## [1] 4.5938752 2.2269367 4.8059080 0.1818445
+## [1] 3.929218 3.703124 5.883016 7.454618
 ## 
 ## [[5]]
-## [1]  7.1944041  1.8305581  5.4218587 -0.2811959  4.0780825
-{% endhighlight %}
-
-
-
-{% highlight r %}
+## [1] 6.406565 4.475601 6.538984 2.527339 5.592072
 
 ## which is the same as writing
 list(noise(1,1,2), noise(2,2,2), noise(3,3,2), noise(4,4,2), noise(5,5,2))
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [[1]]
-## [1] 1.153676
+## [1] 0.2882813
 ## 
 ## [[2]]
-## [1] 3.4398032 0.1654232
+## [1] 2.271471 3.320668
 ## 
 ## [[3]]
-## [1] 1.896161 5.242596 3.849094
+## [1] 5.249599 3.318627 3.110832
 ## 
 ## [[4]]
-## [1] 2.189090 3.076175 6.126703 1.883835
+## [1] 6.224037 1.035009 2.433546 5.674936
 ## 
 ## [[5]]
-## [1] 3.026996 4.327652 2.282642 3.456119 3.095467
-{% endhighlight %}
-
-
-
-{% highlight r %}
+## [1] 7.351962 4.979856 4.897077 4.745803 3.608916
 
 # (the outputs are different because our function makes new random varibles each
 # time)
-{% endhighlight %}
+```
 
 ## `tapply()` ##
 
@@ -1399,44 +839,19 @@ simplify the result, like sapply.
 > Example code:
 
 
-{% highlight r %}
+```r
 x <- c(1:30) # make a vector with 30 variables
 f<- gl(3,10) # make a factor variable with 3 levels and 10 reps of each level
 f
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##  [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3
 ## Levels: 1 2 3
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 # apply the mean function to x, subsetting by f
 tapply(x, f, mean) 
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##    1    2    3 
 ##  5.5 15.5 25.5
-{% endhighlight %}
-
-
-
-{% highlight r %}
 # apply the range function to x, subsetting by f
 tapply(x, f, range) 
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## $`1`
 ## [1]  1 10
 ## 
@@ -1445,7 +860,7 @@ tapply(x, f, range)
 ## 
 ## $`3`
 ## [1] 21 30
-{% endhighlight %}
+```
 
 # Misc commands #
 
@@ -1457,13 +872,8 @@ particular condition.
 > Example code:
 
 
-{% highlight r %}
+```r
 numvec <- c(2,5,8,9,0,6,7,8,4,5,7,11)
 subset(numvec, numvec < 9 & numvec > 4)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 5 8 6 7 8 5 7
-{% endhighlight %}
+```
diff --git a/lessons/rintro/livecoding.md b/lessons/rintro/livecoding.md
index 4bdd1f4..36e32df 100644
--- a/lessons/rintro/livecoding.md
+++ b/lessons/rintro/livecoding.md
@@ -13,7 +13,7 @@ published: true
 We will be using R studio for this course as it's a user-friendly GUI for R. You can also access R from Terminal (Mac) or Command Line (Windows) if you please. 
 
 
-{% highlight r %}
+```r
 getwd()
 
 setwd("/Users/thesarahmeister/")
@@ -24,7 +24,7 @@ setwd("Desktop/practice-2015-10/sarah/intror")
 
 dir()
 
-{% endhighlight %}
+```
 
 # Now let's code! #
 
@@ -32,20 +32,15 @@ Download the `airQuality.csv` file from our [Github repo](https://github.com/cod
 
 
 
-{% highlight r %}
+```r
 airQuality <- read.csv("airQuality.csv", header = TRUE)
-{% endhighlight %}
+```
 
 Now let's look at the data using a few different functions
 
 
-{% highlight r %}
+```r
 head(airQuality)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##   Ozone Solar.R Wind Temp Month Day
 ## 1    41     190  7.4   67   May   1
 ## 2    36     118  8.0   72   May   2
@@ -53,18 +48,8 @@ head(airQuality)
 ## 4    18     313 11.5   62   May   4
 ## 5    NA      NA 14.3   56   May   5
 ## 6    28      NA 14.9   66   May   6
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 head(airQuality, n=8)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##   Ozone Solar.R Wind Temp Month Day
 ## 1    41     190  7.4   67   May   1
 ## 2    36     118  8.0   72   May   2
@@ -74,18 +59,8 @@ head(airQuality, n=8)
 ## 6    28      NA 14.9   66   May   6
 ## 7    23     299  8.6   65   May   7
 ## 8    19      99 13.8   59   May   8
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 tail(airQuality)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##     Ozone Solar.R Wind Temp Month Day
 ## 148    14      20 16.6   63   Sep  25
 ## 149    30     193  6.9   70   Sep  26
@@ -93,18 +68,8 @@ tail(airQuality)
 ## 151    14     191 14.3   75   Sep  28
 ## 152    18     131  8.0   76   Sep  29
 ## 153    20     223 11.5   68   Sep  30
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 str(airQuality)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## 'data.frame':	153 obs. of  6 variables:
 ##  $ Ozone  : int  41 36 12 18 NA 28 23 19 8 NA ...
 ##  $ Solar.R: int  190 118 149 313 NA NA 299 99 19 194 ...
@@ -112,18 +77,8 @@ str(airQuality)
 ##  $ Temp   : int  67 72 74 62 56 66 65 59 61 69 ...
 ##  $ Month  : Factor w/ 5 levels "Aug","Jul","Jun",..: 4 4 4 4 4 4 4 4 4 4 ...
 ##  $ Day    : int  1 2 3 4 5 6 7 8 9 10 ...
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 summary(airQuality)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##      Ozone           Solar.R           Wind             Temp      
 ##  Min.   :  1.00   Min.   :  7.0   Min.   : 1.700   Min.   :56.00  
 ##  1st Qu.: 18.00   1st Qu.:115.8   1st Qu.: 7.400   1st Qu.:72.00  
@@ -140,61 +95,31 @@ summary(airQuality)
 ##  Sep:30   3rd Qu.:23.0  
 ##           Max.   :31.0  
 ## 
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 nrow(airQuality)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 153
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 ncol(airQuality)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 6
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 range(airQuality$Temp)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [1] 56 97
-{% endhighlight %}
+```
 
 Lets alter our dataset a bit
 
 
-{% highlight r %}
+```r
 
 names(airQuality$Solar.R) <- "Solar"
 
 airQuality$Random <- rnorm(153, 1, 2)
-{% endhighlight %}
+```
 
 Okay time to subset our data
 
 
-{% highlight r %}
+```r
 
 rowThree <- airQuality[3, ]
 
@@ -209,12 +134,12 @@ may <- airQuality[airQuality$Month == "May", ]
 highTempOrMay <- airQuality[airQuality$Month == "May" | airQuality$Temp > 80, ]
 
 lowTempAndSep <- subset(airQuality, airQuality$Month == "Sep" & airQuality$Temp < 70)
-{% endhighlight %}
+```
 
 okay let's perform some basic data manipulations on our dataframe and subsetted data
 
 
-{% highlight r %}
+```r
 
 mean(airQuality$Ozone) #returns an NA
 
@@ -229,12 +154,12 @@ colMeans(airQualNoMonth, na.rm=TRUE)
 sd(colFour)
 
 sd(airQualNoMonth)
-{% endhighlight %}
+```
 
 What happens if we have multiple columns that are non-numeric and we don't want to remove them all to use the built-in `colMeans()` function? We can make our **own** function!
 
 
-{% highlight r %}
+```r
 columnmean <- function (y, remove.NA = TRUE) {
   nc <- ncol(y)
   means <- vector("numeric", length=0)
@@ -245,26 +170,16 @@ columnmean <- function (y, remove.NA = TRUE) {
 }
 
 columnmean(airQuality)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Warning in mean.default(y[, i], na.rm = remove.NA): argument is not numeric
 ## or logical: returning NA
-{% endhighlight %}
-
-
-
-{% highlight text %}
-## [1]  42.129310 185.931507   9.957516  77.882353         NA  15.803922
-## [7]   0.916601
-{% endhighlight %}
+## [1]  42.1293103 185.9315068   9.9575163  77.8823529          NA  15.8039216
+## [7]   0.9840097
+```
 
 Cool right? Now let's try a function with an if/else expression. 
 
 
-{% highlight r %}
+```r
 
 above80 <- function(y) {
     y <- na.omit(y)
@@ -283,11 +198,6 @@ above80 <- function(y) {
 }
 
 above80(airQuality$Temp)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## [[1]]
 ##  [1] 81 84 85 82 87 90 87 93 92 82 83 84 85 81 84 83 83 88 92 92 89 82 81
 ## [24] 91 81 82 84 87 85 81 82 86 85 82 86 88 86 83 81 81 81 82 86 85 87 89
@@ -298,18 +208,13 @@ above80(airQuality$Temp)
 ## [24] 61 57 58 57 67 79 76 78 74 67 79 80 79 77 72 65 73 76 77 76 76 76 75
 ## [47] 78 73 80 77 73 80 74 80 79 77 79 76 78 78 77 72 75 79 80 78 75 73 76
 ## [70] 77 71 71 78 67 76 68 64 71 69 63 70 77 75 76 68
-{% endhighlight %}
-
-
-
-{% highlight r %}
 
 highlow <- above80(airQuality$Temp)
 
 high <- highlow[[1]]
 
 low <- highlow[[2]]
-{% endhighlight %}
+```
 
 # Loop Functions #
 
@@ -325,20 +230,10 @@ The last thing for today are the very useful built-in loop functions. These func
 
 Remember the function we made? It can be replaced with an `lapply()` loop.
 
-{% highlight r %}
+```r
 lapply(airQuality, mean, na.rm = TRUE)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
 ## returning NA
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## $Ozone
 ## [1] 42.12931
 ## 
@@ -358,111 +253,76 @@ lapply(airQuality, mean, na.rm = TRUE)
 ## [1] 15.80392
 ## 
 ## $Random
-## [1] 0.916601
-{% endhighlight %}
+## [1] 0.9840097
+```
 
 # sapply() #
 
 Alternatively we can try `sapply()`. This will return a named numeric vector (simpified from a list)
 
-{% highlight r %}
+```r
 sapply(airQuality, mean, na.rm = TRUE)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
 ## returning NA
-{% endhighlight %}
-
-
-
-{% highlight text %}
-##      Ozone    Solar.R       Wind       Temp      Month        Day 
-##  42.129310 185.931507   9.957516  77.882353         NA  15.803922 
-##     Random 
-##   0.916601
-{% endhighlight %}
+##       Ozone     Solar.R        Wind        Temp       Month         Day 
+##  42.1293103 185.9315068   9.9575163  77.8823529          NA  15.8039216 
+##      Random 
+##   0.9840097
+```
 
 # apply() #
 
 `apply()` first coerces your dataframe to a matrix, which means all the columns must have the same type. Because our dataframe does not meet this requirement, I'll be taking a subset of the data. However normally this function would be used with matrices.
 
 
-{% highlight r %}
+```r
 apply(airQuality[,c(-5)], 2, sd, na.rm = TRUE)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##     Ozone   Solar.R      Wind      Temp       Day    Random 
-## 32.987885 90.058422  3.523001  9.465270  8.864520  2.091644
-{% endhighlight %}
-
-
-
-{% highlight r %}
+## 32.987885 90.058422  3.523001  9.465270  8.864520  1.969605
 
 apply(airQuality[,c(-5)], 1, sd, na.rm = TRUE)
-{% endhighlight %}
-
-
-
-{% highlight text %}
-##   [1]  72.89058  46.94296  58.69988 121.77277  25.62174  25.75497 115.47911
-##   [8]  38.64267  21.34867  81.09362  30.02192  98.62413 112.61584 105.66532
-##  [15]  27.26847 129.50176 116.47159  31.26497 123.08330  23.93098  21.81966
-##  [22] 123.51997  23.04112  33.19412  26.94703 110.01967  25.26733  22.88824
-##  [29]  92.82220  84.03591 104.86704 122.41928 122.31465 102.42537  79.76670
-##  [36]  94.18712 111.56434  51.01858 116.07814 110.38572 123.47021 109.27081
-##  [43] 105.86196  58.30500 140.07391 135.54122  72.33150 106.82007  23.63969
-##  [50]  46.01597  53.22244  63.11980  33.84056  41.67048 103.81005  55.83172
-##  [57]  53.47395  29.03317  42.86913  29.24985  57.85399 106.70907  95.40798
-##  [64]  91.31892  48.18947  67.71641 120.77618 105.84699 102.34370 104.07476
-##  [71]  67.97639  60.74067 101.58831  66.48642 121.75326  30.67241  98.43243
-##  [78] 104.27405 107.64244  72.14305  81.66779  26.80088 107.47906 122.58368
-##  [85] 109.45345  84.18191  35.62429  36.17203  79.75871 103.09283  93.26686
-##  [92]  94.07138  38.88395  29.95196  38.00552  43.19716  35.16671  41.00030
-##  [99]  99.41880  87.31363  81.25121  94.66409  59.49559  73.33740 104.78695
-## [106]  59.10313  35.31902  32.01702  31.20583  45.29166  91.96314  70.91207
-## [113]  98.68951  25.94546 105.50772  78.26531  97.84691  80.28066  64.05905
-## [120]  75.83915  86.56595  88.41150  70.34087  68.32943  77.89078  72.86685
-## [127]  75.04048  43.14471  39.50083  97.28408  85.08832  88.34328  99.40968
-## [134]  89.14843  99.03010  90.96073  25.09182  44.32411  89.59789  84.73321
-## [141]  27.09205  90.80568  77.70208  91.01537  25.24025  52.22038  26.51072
-## [148]  21.49442  72.38703  58.93778  72.25072  50.24947  83.25093
-{% endhighlight %}
+##   [1]  72.82270  47.23650  59.37151 121.62190  24.84774  26.36542 115.67801
+##   [8]  38.40799  21.54229  81.28748  30.96865  98.78830 112.67089 105.33482
+##  [15]  26.06268 129.50212 116.96760  30.41116 123.01349  23.49386  21.77755
+##  [22] 123.18123  21.93297  33.76490  27.92711 109.99494  23.57278  22.99182
+##  [29]  93.55545  84.47980 105.21199 122.09558 122.80170 102.05193  79.94285
+##  [36]  94.19656 111.30217  51.79243 116.17630 110.39896 123.98668 108.77720
+##  [43] 105.49187  57.91526 139.74419 135.90928  72.64085 106.43035  23.10204
+##  [50]  47.45831  53.64691  62.60415  34.39119  42.17424 104.44171  55.94074
+##  [57]  54.14359  28.78309  43.28784  28.37771  57.90655 106.88412  95.12412
+##  [64]  91.66259  48.21788  67.74415 120.33248 105.71376 102.17632 104.09465
+##  [71]  68.53206  60.21272 102.35465  65.89063 121.89362  30.07331  98.23169
+##  [78] 103.76614 107.19162  71.03225  81.71409  27.04795 107.51568 123.32384
+##  [85] 110.08362  84.38543  36.13764  36.37102  79.16091 102.96910  93.30029
+##  [92]  94.09817  39.48974  30.45752  37.68110  42.68174  35.07430  40.31110
+##  [99]  99.76131  87.38370  81.39169  94.11566  59.85649  72.74146 104.86862
+## [106]  59.22848  35.14605  32.27141  32.23079  45.22937  92.05330  70.57883
+## [113]  98.45452  25.29983 104.64771  78.81379  97.84441  79.33138  64.18218
+## [120]  75.31127  86.26419  87.84973  70.58833  69.42031  76.89533  72.66027
+## [127]  74.43070  42.60674  40.47040  97.39282  84.61841  88.89872  99.49476
+## [134]  88.87661  99.30527  91.12215  25.12572  44.34830  89.09724  85.00021
+## [141]  27.43158  90.02379  77.34522  90.78009  24.87064  52.91799  27.15257
+## [148]  20.40687  72.09858  59.95946  71.92740  50.08571  83.41606
+```
 
 # mapply #
 
 `mapply` is a multivariate apply (like `lapply`) that applies a function over a set of arguments.
 
-{% highlight r %}
+```r
 mapply(range, airQuality[,c(1,3,6)], na.rm = TRUE)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ##      Ozone Wind Day
 ## [1,]     1  1.7   1
 ## [2,]   168 20.7  31
-{% endhighlight %}
+```
 
 # tapply #
 
 `tapply` is used to apply a function over subsets of a vector. It takes two arguements: a vector to apply to function on and a factor variable that subsets the vector. It will simplify the result, like sapply. 
 
 
-{% highlight r %}
+```r
 tapply(airQuality$Ozone, airQuality$Month, range, na.rm = TRUE)
-{% endhighlight %}
-
-
-
-{% highlight text %}
 ## $Aug
 ## [1]   9 168
 ## 
@@ -477,5 +337,5 @@ tapply(airQuality$Ozone, airQuality$Month, range, na.rm = TRUE)
 ## 
 ## $Sep
 ## [1]  7 96
-{% endhighlight %}
+```
 
diff --git a/lessons/rmarkdown/cheatsheet.md b/lessons/rmarkdown/cheatsheet.md
index 73eec8a..f3a62d5 100644
--- a/lessons/rmarkdown/cheatsheet.md
+++ b/lessons/rmarkdown/cheatsheet.md
@@ -111,7 +111,7 @@ have it change the numbering for you. These can all be fixed by using
 > Example code:
 
 
-{% highlight r %}
+```r
     library(captioner)
     tabNums <- captioner(prefix = 'Table')
     tabNums('tab1', 'Caption for table 1')
@@ -124,7 +124,7 @@ have it change the numbering for you. These can all be fixed by using
     ## cite in text using `r figNums('fig1', display = 'cite')`
     ## Include the caption for the figure in the code chunk
     ## using fig.cap=`r figNums('fig1')`
-{% endhighlight %}
+```
 
 ## `render` ##
 
diff --git a/lessons/rmarkdown/intro.md b/lessons/rmarkdown/intro.md
index 6212086..b3a2c83 100644
--- a/lessons/rmarkdown/intro.md
+++ b/lessons/rmarkdown/intro.md
@@ -1,6 +1,6 @@
 ---
 title: "Introduction: Report generation using R Markdown"
-published: true
+published: false
 author:
     - Luke W. Johnston
 date: 2015-10
@@ -64,7 +64,7 @@ both you and the computer will understand. A typical YAML metadata looks like:
 ```
 ---
 title: "Introducing R Markdown"
-published: true
+published: false
 author: "Luke Johnston"
 date: "July 23, 2015"
 output: html_document
@@ -113,16 +113,11 @@ with the power of R! Within the R Markdown document, you can insert R code
 chunks to input and create the output into the document. They look like this:
 
     
-    {% highlight r %}
+    ```r
     testCode <- 1:10
     print(testCode)
-    {% endhighlight %}
-    
-    
-    
-    {% highlight text %}
     ##  [1]  1  2  3  4  5  6  7  8  9 10
-    {% endhighlight %}
+    ```
 
 You'll notice that the code chunk printed off 1 to 10 and put it into the
 document. I didn't do that, R did it for me!
@@ -151,11 +146,11 @@ object to print: including a `results = 'asis'` option in the code chunk. Here
 is an example:
 
 
-{% highlight r %}
+```r
 library(pander)
 pander(head(cars), caption = 'First few rows of the cars dataset.',
        style = 'rmarkdown')
-{% endhighlight %}
+```
 
 
 
@@ -176,113 +171,16 @@ Just like you can get R to create a table, you can also get R to create a
 figure. There are several chunk options for creating figures, including `fig.height`, `fig.width`, `dpi`, and `fig.cap`.
 
 
-{% highlight r %}
-plot(cars)
-{% endhighlight %}
-
-![Scatterplot of speed and distance.](/images/fig-1.png) 
-
-## Bibliography
-
-If you want to cite articles or references, use the `@` tag with the author key
-(eg. `@Smith1995`). In addition to the `@` tag, you need to include the
-following YAML option to the YAML header, with the location of the bibliography
-file on your computer.
-
-```
-bibliography: /path/to/file
-```
-
-## Figure and table labels
-
-> *Note: The below labels don't work with conversion on the website. See [this
-link](intro.html) for the HTML converted version that chows these labels 
-working.*
-
-Often you want to include figure or caption labels that you can cite easily in
-your document. So, let's create two labels for a table and a figure using the
-`captioner` command from the `captioner` package. First we set the prefix for
-the caption:
-
-
-{% highlight r %}
-library(captioner)
-figNums <- captioner(prefix = 'Figure')
-tabNums <- captioner(prefix = 'Table')
-{% endhighlight %}
-
-Then, we store the caption label and caption title (including the `results='hide'`
-code chunk option, as these commands will print them to the output):
 
 
-{% highlight r %}
-figNums(name = 'figCars', caption = 'First few rows of the cars dataset.',
-        display = FALSE)
-tabNums(name = 'tabCars', caption = 'Scatterplot of speed and distance.',
-        display = FALSE)
-{% endhighlight %}
 
-Now we can cite them in-text, using the ` Figure  1 `, 
-which then looks like Figure  1. And with the 
-plot, include this option in the plot code chunk ` Figure  1: First few rows of the cars dataset. `:
 
 
-{% highlight r %}
-plot(cars)
-{% endhighlight %}
-
-![Figure  1: First few rows of the cars dataset.](/images/plotCap-1.png) 
-
-Or with a table using ` Table  1 ` to show 
-Table  1.
-
-
-{% highlight r %}
-set.caption(tabNums('tabCars'))
-pander(head(cars), style = 'rmarkdown')
-{% endhighlight %}
-
-
-
-|  speed  |  dist  |
-|:-------:|:------:|
-|    4    |   2    |
-|    4    |   10   |
-|    7    |   4    |
-|    7    |   22   |
-|    8    |   16   |
-|    9    |   10   |
-
-Table: Table  1: Scatterplot of speed and distance.
 
-## Generating this `.Rmd` document and sharing with others
 
-If you download the [source `.Rmd` version of this document](https://github.com/codeasmanuscript/workshops/tree/master/lessons/rmarkdown/intro.Rmd),
-you can recreate the web version and the HTML version by using these commands
-below (note the `eval=FALSE` in the code chunk):
 
 
-{% highlight r %}
-library(rmarkdown)
-## html
-render('intro.Rmd', 'html_document')
-## md version on the website
-render('intro.Rmd', 'md_document')
-{% endhighlight %}
 
-R Markdown can also make it very easy to collaborate with people who may not be
-as tech-savvy and who don't know much outside of Microsoft Word by converting
-all your `.Rmd` files into Word! Try it out:
 
 
-{% highlight r %}
-render('intro.Rmd', 'word_document')
-{% endhighlight %}
 
-Using R Markdown can help with making your code easier to understand. That's
-because as you type out your code, you are also typing explanations and reasons
-in markdown. You can then easily create documents, manuscripts, theses, and
-other file types that others (and your future self) can read to understand your
-code and your analysis better. Combine R and R Markdown with Git and GitHub and
-you have a powerful tool for making your work better, more scientifically
-rigorous and transparent, and share-able so others can use your work easily!
diff --git a/lessons/rplotting/assignment.md b/lessons/rplotting/assignment.md
deleted file mode 100644
index 2ac5889..0000000
--- a/lessons/rplotting/assignment.md
+++ /dev/null
@@ -1,23 +0,0 @@
----
-title: "Assignment: ..."
-author:
-    - 
-date: YYYY-MM-DD
-fontsize: 12pt
-geometry: margin=1in
-papersize: letterpaper
-layout: page
-sidebar: false
-tag:
-    - Lessons
-    - Assignment
-    - (lesson topic)
-categories:
-    - Lessons
-    - (lesson topic)
----
-
-# List of potential exercises here: #
-
-* List item 1
-* List item 2
diff --git a/lessons/rplotting/cheatsheet.md b/lessons/rplotting/cheatsheet.md
deleted file mode 100644
index ab1c6a3..0000000
--- a/lessons/rplotting/cheatsheet.md
+++ /dev/null
@@ -1,39 +0,0 @@
----
-title: "Cheatsheet: (topic)"
-author:
-    - 
-date: YYYY-MM-DD
-fontsize: 12pt
-geometry: margin=1in
-papersize: letterpaper
-layout: page
-sidebar: false
-tag:
-    - Lessons
-    - Cheatsheet
-    - (lesson topic)
-categories:
-    - Lessons
-    - (lesson topic)
----
-
-Brief description/intro
-
-# (language) commands: Some useful or common ones #
-
-## `command` ##
-
-> Description
-
-> Example code:
-
-    example code (note the tab/4 spaces indent)
-
-## `command` ##
-
-> Description
-
-> Example code:
-
-    example code (note the tab/4 spaces indent)
-
diff --git a/lessons/rplotting/plottinginR.md b/lessons/rplotting/plottinginR.md
deleted file mode 100644
index 4ed8a5a..0000000
--- a/lessons/rplotting/plottinginR.md
+++ /dev/null
@@ -1,156 +0,0 @@
----
-title: "Plotting in R"
-author: "Sarah Meister"
-date: 2015-10-15
-fontsize: 12pt
-geometry: margin=1in
-papersize: letterpaper
-layout: page
-sidebar: false
-output: slidy_presentation
----
-
-# Base plotting systems #
-
-The base plotting system in R is the `plot()` function. 
-
-
-{% highlight r %}
-library(datasets)
-data(cars)
-with(cars, plot(speed, dist))
-{% endhighlight %}
-
-![plot of chunk unnamed-chunk-1](/images/unnamed-chunk-1-1.png) 
-
-# the Lattice system #
-
-This is the second base plotting system in R. Plots are created with a single call function. Margins and spacing are set automatically because the entire plot is specified at once. It is good at putting many plots on a screen and thus you can see things like how y changes with x over z. 
-
-
-{% highlight r %}
-library (lattice)
-state <- data.frame(state.x77, region = state.region)
-xyplot(Life.Exp ~ Income | region, data=state, layout = c(4,1))
-{% endhighlight %}
-
-![plot of chunk unnamed-chunk-2](/images/unnamed-chunk-2-1.png) 
-
-# ggplot2 #
-
-The ggplot2 package is an R package that uses the "Grammar of Graphics" to put together different aspects of data visualization to build quality graphics. It is a very popular package. 
-
- 
- {% highlight r %}
- install.packages("ggplot2")
- {% endhighlight %}
- 
- 
- 
- {% highlight text %}
- ## Installing package into '/home/luke/R/x86_64-pc-linux-gnu-library/3.2'
- ## (as 'lib' is unspecified)
- {% endhighlight %}
- 
- 
- 
- {% highlight text %}
- ## Error in contrib.url(repos, type): trying to use CRAN without setting a mirror
- {% endhighlight %}
- 
- 
- 
- {% highlight r %}
- 
- library(ggplot2)
- {% endhighlight %}
- 
- 
- 
- {% highlight text %}
- ## Loading required package: methods
- {% endhighlight %}
- 
- # the Basics #
- 
- `qplot()` is the basic plotting function in `ggplot2` and plots are made up of aesthetics (size, shape, color, etc.) and geoms (points, lines, etc.). 
- 
- `ggplot()` is the core function and can do things that `qplot` cannot. 
- 
-
-{% highlight r %}
-str(mpg)
-{% endhighlight %}
-
-
-
-{% highlight text %}
-## 'data.frame':	234 obs. of  11 variables:
-##  $ manufacturer: Factor w/ 15 levels "audi","chevrolet",..: 1 1 1 1 1 1 1 1 1 1 ...
-##  $ model       : Factor w/ 38 levels "4runner 4wd",..: 2 2 2 2 2 2 2 3 3 3 ...
-##  $ displ       : num  1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
-##  $ year        : int  1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
-##  $ cyl         : int  4 4 4 4 6 6 6 4 4 4 ...
-##  $ trans       : Factor w/ 10 levels "auto(av)","auto(l3)",..: 4 9 10 1 4 9 1 9 4 10 ...
-##  $ drv         : Factor w/ 3 levels "4","f","r": 2 2 2 2 2 2 2 1 1 1 ...
-##  $ cty         : int  18 21 20 21 16 18 18 18 16 20 ...
-##  $ hwy         : int  29 29 31 30 26 26 27 26 25 28 ...
-##  $ fl          : Factor w/ 5 levels "c","d","e","p",..: 4 4 4 4 4 4 4 4 4 4 ...
-##  $ class       : Factor w/ 7 levels "2seater","compact",..: 2 2 2 2 2 2 2 2 2 2 ...
-{% endhighlight %}
-
-
-
-{% highlight r %}
-
-qplot(displ, hwy, data = mpg)
-{% endhighlight %}
-
-![plot of chunk unnamed-chunk-4](/images/unnamed-chunk-4-1.png) 
-
-{% highlight r %}
-
-qplot(displ, hwy, data = mpg, color=drv)
-{% endhighlight %}
-
-![plot of chunk unnamed-chunk-4](/images/unnamed-chunk-4-2.png) 
-
-{% highlight r %}
-
-qplot(displ, hwy, data = mpg, geom=c("point", "smooth"))
-{% endhighlight %}
-
-
-
-{% highlight text %}
-## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
-{% endhighlight %}
-
-![plot of chunk unnamed-chunk-4](/images/unnamed-chunk-4-3.png) 
-
-{% highlight r %}
-
-qplot(hwy, data=mpg, fill=drv)
-{% endhighlight %}
-
-
-
-{% highlight text %}
-## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
-{% endhighlight %}
-
-![plot of chunk unnamed-chunk-4](/images/unnamed-chunk-4-4.png) 
-
-{% highlight r %}
-
-qplot(displ, hwy, data=mpg, facets =.~drv)
-{% endhighlight %}
-
-![plot of chunk unnamed-chunk-4](/images/unnamed-chunk-4-5.png) 
-
-{% highlight r %}
-
-qplot(hwy, data=mpg, facet=drv~., binwidth=2)
-{% endhighlight %}
-
-![plot of chunk unnamed-chunk-4](/images/unnamed-chunk-4-6.png) 
diff --git a/lessons/rplotting/slides.md b/lessons/rplotting/slides.md
deleted file mode 100644
index 5bfb8c2..0000000
--- a/lessons/rplotting/slides.md
+++ /dev/null
@@ -1,103 +0,0 @@
----
-title: "...title..."
-author: 
-date: YYYY-MM-DD
-layout: page
-sidebar: false
-classoption: xcolor=dvipsnames
-tag:
-    - Lessons
-    - Slides
-    - (lesson topic)
-categories:
-    - Lessons
-    - (lesson topic)
-slide-level: 1
-fontsize: 8pt
-header-includes:
-    - \input{../slideOptions.tex}
----
-    
-# Welcome to our Data-related workshop #
-
-## Purpose: ##
-
-To teach a few tips and tricks for more efficiently managing your
-data, tracking your computer files, understanding appropriate
-analytical approaches, and speeding up the process from code to
-tables.
-
-. . .
-
-## Significance: ##
-
-Topics we cover will help you get more comfortable with data, reduce
-the chance of overlooked errors, and give you more control over your
-work. They are also all important parts of a science movement gaining
-increasing attention -- Reproducible Research.
-
-# Caveat: We aren't here to teach statistics #
-
-Need help with stats? Use these resources!
-
-* U of T Statistical Consulting Services ([click here](http://www.utstat.toronto.edu/wordpress/?page_id=25))
-
-* <http://www.stackoverflow.com>
-
-* <http://stats.stackexchange.com>
-
-# Overview of other workshops? #
-
-# Notes and help during this workshop #
-
-Go to this website:
-
-<https://etherpad.mozilla.org/dnsWorkshops>
-
-# Slide title #
-
-Text, some **bolded**, or *italics*
-
-__bold__ or _italics_ also works.
-
-[URL link here](http://link/here.com)
-
-```
-    Code block
-```
-
-Or:
-
-    Code block
-
-List here:
-
-* Item 1
-* Item 2
-
-List, but one-by-one 'animation':
-
-> * Item 1 appears first
-> * Item 2 appears second
-
-Inline `code text`
-
-Image:
-![](../images/file.png)
-
-Footnote[^1]
-
-[^1]: Footnote text
-
-# Live coding #
-
-# Main Exercise #
-
-A pause/transition here (the . . .)
-
-. . .
-
-Numbered list
-
-1. Exercise 1
-2. Exercise 2