Update r.html.markdown

Fixed the mistake about integers that yuhui brought up. Deleted the confusing suggestion about command-enter (doesn't work for all GUIs or on Windows). Added some more information about vectors. Added some information about conditional clauses. Included some information at the very end on where to get R and R GUIs

r.html.markdown

@@ -5,7 +5,7 @@ author_url: http://github.com/e99n09
 
 ---
 
-R is a statistical computing language.
+R is a statistical computing language. It has lots of good built-in functions for uploading and cleaning data sets, running common statistical tests, and making graphs. You can also easily compile it within a LaTeX document.
 
 ```r
 
@@ -14,36 +14,28 @@ R is a statistical computing language.
 # You can't make a multi-line comment per se,
 # but you can stack multiple comments like so.
 
-# Protip: hit COMMAND-ENTER to execute a line
-
 ###################################################################################
 # The absolute basics
 ###################################################################################
 
-# NUMERICS
+# NUMBERS
 
-# We've got numbers! Behold the "numeric" class
+# We've got doubles! Behold the "numeric" class
 5 # => [1] 5
 class(5) # => [1] "numeric"
+# We've also got integers! They look suspiciously similar,
+# but indeed are different
+5L # => [1] 5
+class(5L) # => [1] "integer"
 # Try ?class for more information on the class() function
 # In fact, you can look up the documentation on just about anything with ?
 
-# Numerics are like doubles. There's no such thing as integers
-5 == 5.0 # => [1] TRUE
-# Because R doesn't distinguish between integers and doubles,
-# R shows the "integer" form instead of the equivalent "double" form
-# whenever it's convenient:
-5.0 # => [1] 5
-
 # All the normal operations!
 10 + 66 # => [1] 76
 53.2 - 4 # => [1] 49.2
-3.37 * 5.4 # => [1] 18.198
 2 * 2.0 # => [1] 4
-3 / 4 # => [1] 0.75
-2.0 / 2 # => [1] 1
+3L / 4 # => [1] 0.75
 3 %% 2 # => [1] 1
-4 %% 2 # => [1] 0
 
 # Finally, we've got not-a-numbers! They're numerics too
 class(NaN) # => [1] "numeric"
@@ -107,6 +99,17 @@ while (a > 4) {
 # Operations on entire vectors (i.e. a whole row, a whole column)
 # or apply()-type functions (we'll discuss later) are preferred
 
+# IF/ELSE
+
+# Again, pretty standard
+if (4 > 3) {
+	print("Huzzah! It worked!")
+} else {
+	print("Noooo! This is blatantly illogical!")
+}
+# =>
+# [1] "Huzzah! It worked!"
+
 # FUNCTIONS
 
 # Defined like so:
@@ -126,8 +129,8 @@ myFunc(5) # => [1] 19
 # ONE-DIMENSIONAL
 
 # You can vectorize anything, so long as all components have the same type
-vec <- c(4, 5, 6, 7)
-vec # => [1] 4 5 6 7
+vec <- c(8, 9, 10, 11)
+vec # => [1]  8  9 10 11
 # The class of a vector is the class of its components
 class(vec) # => [1] "numeric"
 # If you vectorize items of different classes, weird coersions happen
@@ -135,15 +138,27 @@ c(TRUE, 4) # => [1] 1 4
 c("dog", TRUE, 4) # => [1] "dog"  "TRUE" "4"
 
 # We ask for specific components like so (R starts counting from 1)
-vec[1] # => [1] 4
-# We can also search for the indices of specific components
-which(vec %% 2 == 0) 
+vec[1] # => [1] 8
+# We can also search for the indices of specific components,
+which(vec %% 2 == 0) # => [1] 1 3
+# or grab just the first or last entry in the vector
+head(vec, 1) # => [1] 8
+tail(vec, 1) # => [1] 11
 # If an index "goes over" you'll get NA:
 vec[6] # => [1] NA
+# You can find the length of your vector with length()
+length(vec) # => [1] 4
 
 # You can perform operations on entire vectors or subsets of vectors
 vec * 4 # => [1] 16 20 24 28
 vec[2:3] * 5 # => [1] 25 30
+# and there are many built-in functions to summarize vectors
+mean(vec) # => [1] 9.5
+var(vec) # => [1] 1.666667
+sd(vec) # => [1] 1.290994
+max(vec) # => [1] 11
+min(vec) # => [1] 8
+sum(vec) # => [1] 38
 
 # TWO-DIMENSIONAL (ALL ONE CLASS)
 
@@ -192,7 +207,7 @@ mat3
 #      [,1] [,2] [,3] [,4]
 # [1,]    1    2    4    5
 # [2,]    6    7    0    4
-# Aah, everything of the same class. No coersions. Much better.
+# Aah, everything of the same class. No coercions. Much better.
 
 # TWO-DIMENSIONAL (DIFFERENT CLASSES)
 
@@ -273,7 +288,6 @@ apply(mat, MAR = 2, myFunc)
 # [2,]    7   19
 # [3,]   11   23
 # Other functions: ?lapply, ?sapply
-# Don't feel too intimiated; everyone agrees they are rather confusing
 
 # The plyr package aims to replace (and improve upon!) the *apply() family.
 
@@ -298,18 +312,18 @@ write.csv(pets, "pets2.csv") # to make a new .csv file in the working directory
 # Try ?read.csv and ?write.csv for more information
 
 ###################################################################################
-# Plots
+# Plots and tests
 ###################################################################################
 
 # Scatterplots!
 plot(list1$time, list1$price, main = "fake data")
-# Fit a linear model
-myLm <- lm(price  ~ time, data = list1)
-myLm # outputs result of regression
+# Regressions!
+linearModel <- lm(price  ~ time, data = list1)
+linearModel # outputs result of regression
 # Plot regression line on existing plot
-abline(myLm, col = "red")
+abline(linearModel, col = "red")
 # Get a variety of nice diagnostics
-plot(myLm)
+plot(linearModel)
 
 # Histograms!
 hist(rpois(n = 10000, lambda = 5), col = "thistle")
@@ -325,4 +339,7 @@ require(ggplot2)
 
 ```
 
+## How do I get R?
 
+* Get R and the R GUI from [http://www.r-project.org/](http://www.r-project.org/)
+* [RStudio](http://www.rstudio.com/ide/) is another GUI