Removing references to dropbox and some minor updates to the final ex…

…ercise

Week 2/Exercise 2 - Reading in your data.R

@@ -14,7 +14,7 @@
 # for, and once you've selected it, it prints the full file path to that file. You can then use that as an input 
 # to read.csv. 
 
-# Try running file.choose() and navigate to wherever you've copied the shared dropbox folder. Select the 'week
+# Try running file.choose() and navigate to wherever you've copied the Week 2 folder. Select the 'week
 # 2 data.csv' file.
 file_path <- file.choose()
 
@@ -32,7 +32,7 @@ week2 <- read.csv(file.choose(), stringsAsFactors = FALSE)
 # is set to where your data are, you only need to input the name of the data file to read.csv() in order to
 # load it, without specifying a file path. 
 
-# Try setting the working directory below to wherever you copied the shared dropbox folder for this week.
+# Try setting the working directory below to wherever you copied the folder for this week.
 # Remember that you can use file.choose() to get the path to a file in that folder, but here you'll need to 
 # make sure you've specified the path to the folder, not any particular file. If you get an error, it 
 # likely means you've specified the file path wrong. Remember that this is case sensitive, and that you'll

Week 3/Exercise 1 - Week 2 revision.R

@@ -6,7 +6,7 @@
 
 ## Reading in your data ##
 
-# 2. Set you working directory to wherever you've copied the Week 3 dropbox folder. 
+# 2. Set you working directory to wherever you've copied the Week 3 folder. 
 
 # Remember that you can run getwd() to find out where your current working directory is, and then modify that
 # path and use setwd() to change your working directory.

Week 3/Exercise 3 - Analysing Continuous Data.R

@@ -1,7 +1,7 @@
 ### Analysing Continuous Data in R ###
 
 # For this exercise, we'll read in some more real data. If you haven't already, set you working directory
-# to wherever you've copied the Week 3 dropbox folder. Remember that if you can't get setwd() to work, 
+# to wherever you've copied the Week 3 folder. Remember that if you can't get setwd() to work, 
 # you can use the dropdown menu in RStudio (Session -> Set working directory -> choose directory). 
 
 # Once you've done that, run the following:

Week 6/Exercise 5 - Tidying complex data.R

@@ -1,4 +1,9 @@
-status <- read.csv('/Users/Sean/Dropbox/Post-Doc/R course/Raw materials/Week 6/Data/status data messy.csv', 
+# In this example I'll be demonstrating some of the principles of tidying data using a complicated, messy dataset
+# collected via Qualtrics (courtesy of Dr Katie Greenaway)
+
+# As always, you'll want to point this reference to wherever you have saved the data from dropbox. 
+
+status <- read.csv('/Users/Sean/Dropbox/Data/status data messy.csv', 
                    stringsAsFactors = FALSE)
 
 # This dataset is quite an untidy one (though I have cleaned it slightly from its original state). Take a look:
@@ -127,4 +132,7 @@ head(statuscast)
 summary(lmer(charisma ~ 1 + exp_cond*auth_cond + (1|pid) + (1|subject), data = statuscast))
 
 
+# I haven't included any exercises for this final demonstration, but I do encourage you to try out the principles
+# I've shown here on your own data. Best of luck!
+