Merge remote-tracking branch 'adambard/master'

Conflicts:
	swift.html.markdown

1 files changed, 131 insertions, 24 deletions

diff --git a/r.html.markdown b/r.html.markdown
index c555d748..8539b10e 100644
--- a/r.html.markdown
+++ b/r.html.markdown
@@ -3,6 +3,7 @@ language: R
 contributors:
     - ["e99n09", "http://github.com/e99n09"]
     - ["isomorphismes", "http://twitter.com/isomorphisms"]
+    - ["kalinn", "http://github.com/kalinn"]
 filename: learnr.r
 ---
 
@@ -15,7 +16,8 @@ R is a statistical computing language. It has lots of libraries for uploading an
 # You can't make multi-line comments,
 # but you can stack multiple comments like so.
 
-# in Windows or Mac, hit COMMAND-ENTER to execute a line
+# in Windows you can use CTRL-ENTER to execute a line.
+# on Mac it is COMMAND-ENTER
 
 
 
@@ -36,8 +38,8 @@ head(rivers)	# peek at the data set
 length(rivers)	# how many rivers were measured?
 # 141
 summary(rivers) # what are some summary statistics?
-#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#  135.0   310.0   425.0   591.2   680.0  3710.0 
+#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
+#  135.0   310.0   425.0   591.2   680.0  3710.0
 
 # make a stem-and-leaf plot (a histogram-like data visualization)
 stem(rivers)
@@ -54,14 +56,14 @@ stem(rivers)
 #  14 | 56
 #  16 | 7
 #  18 | 9
-#  20 | 
+#  20 |
 #  22 | 25
 #  24 | 3
-#  26 | 
-#  28 | 
-#  30 | 
-#  32 | 
-#  34 | 
+#  26 |
+#  28 |
+#  30 |
+#  32 |
+#  34 |
 #  36 | 1
 
 stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
@@ -70,7 +72,7 @@ stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
 #  The decimal point is 1 digit(s) to the left of the |
 #
 #  48 | 1
-#  50 | 
+#  50 |
 #  52 | 15578
 #  54 | 44571222466689
 #  56 | 023334677000124455789
@@ -85,7 +87,7 @@ stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
 #  74 | 84
 #  76 | 56
 #  78 | 4
-#  80 | 
+#  80 |
 #  82 | 2
 
 # make a histogram:
@@ -108,7 +110,7 @@ sort(discoveries)
 # [76]  4  4  4  4  5  5  5  5  5  5  5  6  6  6  6  6  6  7  7  7  7  8  9 10 12
 
 stem(discoveries, scale=2)
-# 
+#
 #  The decimal point is at the |
 #
 #   0 | 000000000
@@ -122,14 +124,14 @@ stem(discoveries, scale=2)
 #   8 | 0
 #   9 | 0
 #  10 | 0
-#  11 | 
+#  11 |
 #  12 | 0
 
 max(discoveries)
 # 12
 summary(discoveries)
-#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#    0.0     2.0     3.0     3.1     4.0    12.0 
+#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
+#    0.0     2.0     3.0     3.1     4.0    12.0
 
 # Roll a die a few times
 round(runif(7, min=.5, max=6.5))
@@ -196,6 +198,14 @@ class(NaN) # "numeric"
 # You can do arithmetic on two vectors with length greater than 1,
 # so long as the larger vector's length is an integer multiple of the smaller
 c(1,2,3) + c(1,2,3) # 2 4 6
+# Since a single number is a vector of length one, scalars are applied 
+# elementwise to vectors
+(4 * c(1,2,3) - 2) / 2 # 1 3 5
+# Except for scalars, use caution when performing arithmetic on vectors with 
+# different lengths. Although it can be done, 
+c(1,2,3,1,2,3) * c(1,2) # 1 4 3 2 2 6
+# Matching lengths is better practice and easier to read
+c(1,2,3,1,2,3) * c(1,2,1,2,1,2) 
 
 # CHARACTERS
 # There's no difference between strings and characters in R
@@ -229,6 +239,16 @@ FALSE != FALSE	# FALSE
 FALSE != TRUE	# TRUE
 # Missing data (NA) is logical, too
 class(NA)	# "logical"
+# Use | and & for logic operations.
+# OR
+TRUE | FALSE	# TRUE
+# AND
+TRUE & FALSE	# FALSE
+# Applying | and & to vectors returns elementwise logic operations
+c(TRUE,FALSE,FALSE) | c(FALSE,TRUE,FALSE) # TRUE TRUE FALSE
+c(TRUE,FALSE,TRUE) & c(FALSE,TRUE,TRUE) # FALSE FALSE TRUE
+# You can test if x is TRUE
+isTRUE(TRUE)	# TRUE
 # Here we get a logical vector with many elements:
 c('Z', 'o', 'r', 'r', 'o') == "Zorro" # FALSE FALSE FALSE FALSE FALSE
 c('Z', 'o', 'r', 'r', 'o') == "Z" # TRUE FALSE FALSE FALSE FALSE
@@ -252,9 +272,10 @@ levels(infert$education) # "0-5yrs"  "6-11yrs" "12+ yrs"
 # NULL
 # "NULL" is a weird one; use it to "blank out" a vector
 class(NULL)	# NULL
+parakeet = c("beak", "feathers", "wings", "eyes")
 parakeet
 # =>
-# [1] "beak"     "feathers" "wings"    "eyes"    
+# [1] "beak"     "feathers" "wings"    "eyes"
 parakeet <- NULL
 parakeet
 # =>
@@ -271,7 +292,7 @@ as.numeric("Bilbo")
 # =>
 # [1] NA
 # Warning message:
-# NAs introduced by coercion 
+# NAs introduced by coercion
 
 # Also note: those were just the basic data types
 # There are many more data types, such as for dates, time series, etc.
@@ -411,10 +432,10 @@ mat %*% t(mat)
 mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog"))
 mat2
 # =>
-#      [,1] [,2]   
-# [1,] "1"  "dog"  
-# [2,] "2"  "cat"  
-# [3,] "3"  "bird" 
+#      [,1] [,2]
+# [1,] "1"  "dog"
+# [2,] "2"  "cat"
+# [3,] "3"  "bird"
 # [4,] "4"  "dog"
 class(mat2)	# matrix
 # Again, note what happened!
@@ -656,15 +677,101 @@ write.csv(pets, "pets2.csv") # to make a new .csv file
 
 
 #########################
+# Statistical Analysis
+#########################
+
+# Linear regression!
+linearModel <- lm(price  ~ time, data = list1)
+linearModel # outputs result of regression
+# =>
+# Call:
+# lm(formula = price ~ time, data = list1)
+# 
+# Coefficients:
+# (Intercept)         time  
+#      0.1453       0.4943  
+summary(linearModel) # more verbose output from the regression
+# =>
+# Call:
+# lm(formula = price ~ time, data = list1)
+#
+# Residuals:
+#     Min      1Q  Median      3Q     Max 
+# -8.3134 -3.0131 -0.3606  2.8016 10.3992 
+#
+# Coefficients:
+#             Estimate Std. Error t value Pr(>|t|)    
+# (Intercept)  0.14527    1.50084   0.097    0.923    
+# time         0.49435    0.06379   7.749 2.44e-09 ***
+# ---
+# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+#
+# Residual standard error: 4.657 on 38 degrees of freedom
+# Multiple R-squared:  0.6124,	Adjusted R-squared:  0.6022 
+# F-statistic: 60.05 on 1 and 38 DF,  p-value: 2.44e-09
+coef(linearModel) # extract estimated parameters
+# =>
+# (Intercept)        time 
+#   0.1452662   0.4943490 
+summary(linearModel)$coefficients # another way to extract results
+# =>
+#              Estimate Std. Error    t value     Pr(>|t|)
+# (Intercept) 0.1452662 1.50084246 0.09678975 9.234021e-01
+# time        0.4943490 0.06379348 7.74920901 2.440008e-09
+summary(linearModel)$coefficients[,4] # the p-values 
+# =>
+#  (Intercept)         time 
+# 9.234021e-01 2.440008e-09 
+
+# GENERAL LINEAR MODELS
+# Logistic regression
+set.seed(1)
+list1$success = rbinom(length(list1$time), 1, .5) # random binary
+glModel <- glm(success  ~ time, data = list1, 
+	family=binomial(link="logit"))
+glModel # outputs result of logistic regression
+# =>
+# Call:  glm(formula = success ~ time, 
+#	family = binomial(link = "logit"), data = list1)
+#
+# Coefficients:
+# (Intercept)         time  
+#     0.17018     -0.01321  
+# 
+# Degrees of Freedom: 39 Total (i.e. Null);  38 Residual
+# Null Deviance:	    55.35 
+# Residual Deviance: 55.12 	 AIC: 59.12
+summary(glModel) # more verbose output from the regression
+# =>
+# Call:
+# glm(formula = success ~ time, 
+#	family = binomial(link = "logit"), data = list1)
+
+# Deviance Residuals: 
+#    Min      1Q  Median      3Q     Max  
+# -1.245  -1.118  -1.035   1.202   1.327  
+# 
+# Coefficients:
+#             Estimate Std. Error z value Pr(>|z|)
+# (Intercept)  0.17018    0.64621   0.263    0.792
+# time        -0.01321    0.02757  -0.479    0.632
+# 
+# (Dispersion parameter for binomial family taken to be 1)
+#
+#     Null deviance: 55.352  on 39  degrees of freedom
+# Residual deviance: 55.121  on 38  degrees of freedom
+# AIC: 59.121
+# 
+# Number of Fisher Scoring iterations: 3
+
+
+#########################
 # Plots
 #########################
 
 # BUILT-IN PLOTTING FUNCTIONS
 # Scatterplots!
 plot(list1$time, list1$price, main = "fake data")
-# Regressions!
-linearModel <- lm(price  ~ time, data = list1)
-linearModel # outputs result of regression
 # Plot regression line on existing plot
 abline(linearModel, col = "red")
 # Get a variety of nice diagnostics