Typos and comments

01-intro-to-R.Rmd

@@ -15,7 +15,7 @@ knitr::opts_chunk$set(results='hide', fig.path='img/r-lesson-')
 >
 > * Assign names to objects in R with <- and =.
 > * Solve mathematical operations in R.
-> * Describe what a function is in R.
+> * Describe what a function in R is.
 > * Describe what vectors are and how they can be manipulated in R.
 > * Inspect the content of vectors in R and describe their content with class and str.
 
@@ -66,10 +66,8 @@ doesn't work. Now we're stuck over in the console. The
 `+` sign means that it's still waiting for input, so we
 can't type in a new command. To get out of this press the `Esc` key. This will work whenever you're stuck with that `+` sign.
 
-It's great that R is a glorified caluculator, but obviously
-we want to do more interesting things.
-
-To do useful and interesting things, we need to assign _values_ to
+It's great that R is a glorified calculator, but obviously
+we want to do more interesting things, e.g. we can assign _values_ to
 _objects_. To create objects, we need to give it a name followed by the
 assignment operator `<-` and the value we want to give it.
 
@@ -97,10 +95,8 @@ be read as 3 **goes into** `x`.  You can also use `=` or `->`for assignments but
 all contexts so it is good practice to use `<-` for assignments. `=` should only
 be used to specify the values of arguments in functions, see below.
 
-In RStudio, typing <kbd>Alt</kbd> + <kbd>-</kbd> (push <kbd>Alt</kbd> at the
- -same time as the <kbd>-</kbd> key) will write ` <- ` in a single keystroke 
- in a PC, while typing <kbd>Option</kbd> + <kbd>-</kbd> (push <kbd>Option</kbd> at the
- +same time as the <kbd>-</kbd> key) does the same in a Mac.
+In RStudio, typing <kbd>Alt</kbd> + <kbd>-</kbd> (push <kbd>Alt</kbd> at the same time as the <kbd>-</kbd> key) will write ` <- ` in a single keystroke
+ in a PC, while typing <kbd>Option</kbd> + <kbd>-</kbd> (push <kbd>Option</kbd> at the same time as the <kbd>-</kbd> key) does the same in a Mac.
 
 ### Exercise
 
@@ -510,4 +506,3 @@ sessionInfo()
   mailing lists.
 * [How to ask for R help](http://blog.revolutionanalytics.com/2014/01/how-to-ask-for-r-help.html)
   useful guidelines
-

02-starting-with-data.Rmd

@@ -73,7 +73,8 @@ Wow... that was a lot of output. At least it means the data loaded properly. Let
 head(metadata)
 ```
 
-We've just done two very useful things.
+We've just done two very useful things:
+
 1. We've read our data in to R, so now we can work with it in R
 2. We've created a data frame (with the read.csv command) the
 standard way R works with data.
@@ -172,7 +173,7 @@ When we read in a file, any column that contains text is automatically
 assumed to be a factor. Once created, factors can only contain a pre-defined set values, known as
 *levels*. By default, R always sorts *levels* in alphabetical order.
 
-For instance, we see that `cit` is a Factor w/ 3 levels, `minus`, `plus` and `unknown`.
+For instance, we see that `cit` is a Factor w/ 3 levels, `minus`, `plus` and `unknown`. The vector `3 3 3 3 3 3 ... ` indicates that the first samples have `unknown` (third level) citrate status.
 
 <!--
 

04-dplyr.Rmd

@@ -22,10 +22,7 @@ metadata <- read.csv("data/Ecoli_metadata.csv")
 
 Bracket subsetting is handy, but it can be cumbersome and difficult to read, especially for complicated operations. 
 
-Enter `dplyr`. 
-
-`dplyr` is a package for
-making data manipulation easier.
+`dplyr` is a package for making data manipulation easier.
 
 Packages in R are basically sets of additional functions that let you do more
 stuff in R. The functions we've been using, like `str()`, come built into R;

05-data-visualization.Rmd

@@ -20,7 +20,7 @@ metadata <- read.csv('./data/Ecoli_metadata.csv')
 
 The mathematician Richard Hamming once said, "The purpose of computing is insight, not numbers", and the best way to develop insight is often to visualize data. Visualization deserves an entire lecture (or course) of its own, but we can explore a few features of R's plotting packages.
 
-When we are working with large sets of numbers it can be useful to display that information graphically. R has a number of built-in tools for basic graph types such as hisotgrams, scatter plots, bar charts, boxplots and much [more](http://www.statmethods.net/graphs/). We'll test a few of these out here on the `genome_size` vector from our metadata.
+When we are working with large sets of numbers it can be useful to display that information graphically. R has a number of built-in tools for basic graph types such as histograms, scatter plots, bar charts, boxplots and much [more](https://www.statmethods.net/graphs/index.html). We'll test a few of these out here on the `genome_size` vector from our metadata.
 
 
 ```{r simplestats}
@@ -35,8 +35,7 @@ Let's start with a **scatterplot**. A scatter plot provides a graphical view of
 plot(genome_size)
 ```
 
-Each point represents a clone and the value on the x-axis is the clone index in the file, where the values on the y-axis correspond to the genome size for the clone. For any plot you can customize many features of your graphs (fonts, colors, axes, titles) through [graphic options](http://www.statmethods.net/advgraphs/parameters.html)
-For example, we can change the shape of the data point using `pch`.
+Each point represents a clone and the value on the x-axis is the clone index in the file, where the values on the y-axis correspond to the genome size for the clone. For any plot you can customize many features of your graphs (fonts, colors, axes, titles) through [graphic options](http://www.statmethods.net/advgraphs/parameters.html). For example, we can change the shape of the data point using `pch`.
 
 ```{r, fig.align='center'}
 plot(genome_size, pch=8)
@@ -68,7 +67,7 @@ Similar to the scatterplots above, we can pass in arguments to add in extras lik
 
 ```{r, fig.align='center'}
 boxplot(genome_size ~ cit, metadata,  col=c("pink","purple", "darkgrey"),
-        main="Average expression differences between celltypes", ylab="Expression")
+        main="Average expression differences between cell types", ylab="Expression")
 ```
 
 
@@ -93,8 +92,8 @@ ggplot(metadata)
 Geometric objects are the actual marks we put on a plot. Examples include:
 
 * points (`geom_point`, for scatter plots, dot plots, etc)
-* lines (`geom_line`, for time series, trend lines, etc)
 * boxplot (`geom_boxplot`, for, well, boxplots!)
+* lines (`geom_line`, for time series, trend lines, etc)
 
 A plot **must have at least one geom**; there is no upper limit. You can add a geom to a plot using the + operator
 
@@ -199,4 +198,4 @@ dev.off()
 
 Resources:
 ---------
-We have only scratched the surface here. To learn more, see the [ggplot2 reference site](http://docs.ggplot2.org/), and Winston Chang's excellent [Cookbook for R](http://wiki.stdout.org/rcookbook/Graphs/) site. Though slightly out of date, [ggplot2: Elegant Graphics for Data Anaysis](http://www.amazon.com/ggplot2-Elegant-Graphics-Data-Analysis/dp/0387981403) is still the definative book on this subject. Much of the material here was adpapted from [Introduction to R graphics with ggplot2 Tutorial at IQSS](http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html).
+We have only scratched the surface here. To learn more, see the [ggplot2 reference site](http://ggplot2.tidyverse.org/), and Winston Chang's excellent [Cookbook for R](http://www.cookbook-r.com/Graphs/) site. Though slightly out of date, [ggplot2: Elegant Graphics for Data Anaysis](http://www.amazon.com/ggplot2-Elegant-Graphics-Data-Analysis/dp/0387981403) is still the definitive book on this subject. Much of the material here was adpapted from [Introduction to R graphics with ggplot2 Tutorial at IQSS](http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html).

index.Rmd

@@ -22,9 +22,7 @@ experience. These lessons can be taught in a day (~ 6 hours). They start with
 some basic information about R syntax, the RStudio interface, and move through
 how to import CSV files, the structure of data frames, how to deal with factors,
 how to add/remove rows and columns, how to calculate summary statistics from a
-data frame, and a brief introduction to plotting. The last lesson demonstrates
-how to work with databases directly from R.
-
+data frame, and a brief introduction to plotting.
 
 ## Chapters
 
@@ -47,7 +45,7 @@ the data and install everything *before* working through this lesson.
 
 ### Data
 
-Data for the lesson is available [here](https://raw.githubusercontent.com/datacarpentry/R-genomics/gh-pages/data/Ecoli_metadata.csv).
+Data for the lesson are available [here](https://raw.githubusercontent.com/datacarpentry/R-genomics/gh-pages/data/Ecoli_metadata.csv).
 
 We will download this file directly from R during the lessons when we need
 it.