Rmd workflow proofing

rmarkdown-workflow.Rmd

@@ -1,24 +1,24 @@
 # R Markdown workflow
 
-Earlier, we discussed a basic workflow for capturing your R code where you work  interactively in the _console_, then capture what works in the _script editor_. R Markdown effectively puts the console and the script editor in same place, blurring the lines between interactive exploration and long-term code capture. You can rapidly iterate within a chunk, editing and re-executing with Cmd/Ctrl + Shift + Enter. When you're happy, you move on and start a new chunk.
+Earlier, we discussed a basic workflow for capturing your R code where you work  interactively in the _console_, then capture what works in the _script editor_. R Markdown brings together the console and the script editor, blurring the lines between interactive exploration and long-term code capture. You can rapidly iterate within a chunk, editing and re-executing with Cmd/Ctrl + Shift + Enter. When you're happy, you move on and start a new chunk.
 
-R Markdown is also important because it so tightly integrates prose and code. This makes it a great __analysis notebook__ because it lets you develop code and record your thoughts. An analysis notebook shares many of the same goals as a classic lab notebook in the physical sciences:
+R Markdown is also important because it so tightly integrates prose and code. This makes it a great __analysis notebook__ because it lets you develop code and record your thoughts. An analysis notebook shares many of the same goals as a classic lab notebook in the physical sciences. It:
 
-*   Record what you did and why you did it. Regardless of how great your
+*   Records what you did and why you did it. Regardless of how great your
     memory is, if you don't record what you do, there will come a time when
     you have forgotten important details. Write them down so you don't forget!
 
-*   To support rigorous thinking. You are more likely to come up with a strong
+*   Supports rigorous thinking. You are more likely to come up with a strong
     analysis if you record your thoughts as you go, and continue to reflect
     on them. This also saves you time when you eventually write up your
     analysis to share with others.
 
-*   To help others understand your work. It is rare to do data analysis by
+*   Helps others understand your work. It is rare to do data analysis by
     yourself, and you'll often be working as part of a team. A lab notebook
     helps you share not only what you've done, but why you did it with your
     colleagues or lab mates.
 
-Much of the good advice about using lab notebooks effectively can also be translated to analysis notebooks. I've drawn on my own experiences and Colin Purrington's advice on lab notebooks  <http://colinpurrington.com/tips/lab-notebooks> to come up with the following list of tips:
+Much of the good advice about using lab notebooks effectively can also be translated to analysis notebooks. I've drawn on my own experiences and Colin Purrington's advice on lab notebooks  (<http://colinpurrington.com/tips/lab-notebooks>) to come up with the following tips:
 
 *   Ensure each notebook has a descriptive title, an evocative filename, and a
     first paragraph that briefly describes the aims of the analysis.
@@ -38,23 +38,26 @@ Much of the good advice about using lab notebooks effectively can also be transl
     leave it in the notebook. That will help you avoid going down the same
     dead end when you come back to the analysis in the future.
 
-*   Generally, you're better off doing data entry outside of R. If you need
-    a small snippet of data, clearly lay it out using `tibble::tribble()`.
+*   Generally, you're better off doing data entry outside of R. But if you 
+    do need to record a small snippet of data, clearly lay it out using
+    `tibble::tribble()`.
 
 *   If you discover an error in a data file, never modify it directly, but
     instead write code to correct the value. Explain why you made the fix.
 
-*   Before you finish for the day, make sure you can compile the notebook
-    using knitr (aftering clearing caches if you're using them). That will
+*   Before you finish for the day, make sure you can knit the notebook
+    (if you're using caching, make sure to clear the caches). That will
     let you fix any problems while the code is still fresh in your mind.
 
 *   If you want your code to be reproducible in the long-run (i.e. so you can
     come back to run it next month or next year), you'll need to track the
     versions of the packages that your code uses. A rigorous approach is to use
-    __packrat__, <http://rstudio.github.io/packrat/>, which stores packages in
-    your project directory. A quick and dirty hack is to include a chunk that
-    runs `sessionInfo()` --- that won't let easily recreate your packages as
-    they are today, but at least you know what they were.
+    __packrat__, <http://rstudio.github.io/packrat/>, which store packages 
+    in your project directory, or __checkpoint__,
+    <https://github.com/RevolutionAnalytics/checkpoint>, which will reinstall
+    packages avaialble on a specified date. A quick and dirty hack is to include
+    a chunk that runs `sessionInfo()` --- that won't you let easily recreate 
+    your packages as they are today, but at least you'll know what they were.
 
 *   You are going to create many, many, many analysis notebooks over the course
     of your career. How are you going to organise them so you can find them