Merge pull request #618 from e99n09/patch-1

[R] Significant new content

1 files changed, 343 insertions, 180 deletions

diff --git a/r.html.markdown b/r.html.markdown
index ea94ae42..cd09e8da 100644
--- a/r.html.markdown
+++ b/r.html.markdown
@@ -6,34 +6,42 @@ contributors:
 filename: learnr.r
 ---
 
-R is a statistical computing language. It has lots of libraries for uploading and cleaning data sets, running statistical procedures, and making graphs. You can also run `R`commands within a LaTeX document.
+R is a statistical computing language. It has lots of libraries for uploading and cleaning data sets, running statistical procedures, and making graphs. You can also run `R` commands within a LaTeX document.
 
 ```python
 
 # Comments start with number symbols.
 
-# You can't make a multi-line comment per se,
+# You can't make multi-line comments,
 # but you can stack multiple comments like so.
 
-# in Windows, hit COMMAND-ENTER to execute a line
+# in Windows or Mac, hit COMMAND-ENTER to execute a line
 
 
-###################################################################
+
+#############################################################################
 # Stuff you can do without understanding anything about programming
-###################################################################
+#############################################################################
+
+# In this section, we show off some of the cool stuff you can do in
+# R without understanding anything about programming. Do not worry
+# about understanding everything the code does. Just enjoy!
 
-data()	# Browse pre-loaded data sets
-data(rivers)	# Lengths of Major North American Rivers
-ls()	# Notice that "rivers" appears in the workspace
-head(rivers)	# peek at the dataset
+data()	        # browse pre-loaded data sets
+data(rivers)	# get this one: "Lengths of Major North American Rivers"
+ls()	        # notice that "rivers" now appears in the workspace
+head(rivers)	# peek at the data set
 # 735 320 325 392 524 450
+
 length(rivers)	# how many rivers were measured?
 # 141
-summary(rivers)
+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 
-stem(rivers)	#stem-and-leaf plot (like a histogram)
-#
+
+# make a stem-and-leaf plot (a histogram-like data visualization)
+stem(rivers)
+
 #  The decimal point is 2 digit(s) to the right of the |
 #
 #   0 | 4
@@ -56,8 +64,8 @@ stem(rivers)	#stem-and-leaf plot (like a histogram)
 #  34 | 
 #  36 | 1
 
-
-stem(log(rivers))	#Notice that the data are neither normal nor log-normal! Take that, Bell Curve fundamentalists.
+stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
+# Take that, Bell curve fundamentalists.
 
 #  The decimal point is 1 digit(s) to the left of the |
 #
@@ -80,17 +88,19 @@ stem(log(rivers))	#Notice that the data are neither normal nor log-normal! Take
 #  80 | 
 #  82 | 2
 
+# make a histogram:
+hist(rivers, col="#333333", border="white", breaks=25) # play around with these parameters
+hist(log(rivers), col="#333333", border="white", breaks=25) # you'll do more plotting later
 
-hist(rivers, col="#333333", border="white", breaks=25)	#play around with these parameters
-hist(log(rivers), col="#333333", border="white", breaks=25)	#you'll do more plotting later
-
-#Here's another neat data set that comes pre-loaded. R has tons of these. data()
+# Here's another neat data set that comes pre-loaded. R has tons of these.
 data(discoveries)
-plot(discoveries, col="#333333", lwd=3, xlab="Year", main="Number of important discoveries per year")
-plot(discoveries, col="#333333", lwd=3, type = "h", xlab="Year", main="Number of important discoveries per year")
+plot(discoveries, col="#333333", lwd=3, xlab="Year",
+     main="Number of important discoveries per year")
+plot(discoveries, col="#333333", lwd=3, type = "h", xlab="Year",
+     main="Number of important discoveries per year")
 
-
-#rather than leaving the default ordering (by year) we could also sort to see what's typical
+# Rather than leaving the default ordering (by year),
+# we could also sort to see what's typical:
 sort(discoveries)
 #  [1]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  2  2  2  2
 # [26]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  3  3  3
@@ -117,231 +127,250 @@ stem(discoveries, scale=2)
 
 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 
 
-
-
-
-#Basic statistical operations don't require any programming knowledge either
-
-#roll a die a few times
+# Roll a die a few times
 round(runif(7, min=.5, max=6.5))
 # 1 4 6 1 4 6 4
+# Your numbers will differ from mine unless we set the same random.seed(31337)
 
-#your numbers will differ from mine unless we set the same random.seed(31337)
-
-
-#draw from a standard Gaussian 9 times
+# Draw from a standard Gaussian 9 times
 rnorm(9)
 # [1]  0.07528471  1.03499859  1.34809556 -0.82356087  0.61638975 -1.88757271
 # [7] -0.59975593  0.57629164  1.08455362
 
 
 
-
-
-
-
-
-
-#########################
-# Basic programming stuff
-#########################
-
-# NUMBERS
-
-# "numeric" means double-precision floating-point numbers
-5	# 5
-class(5)	# "numeric"
-5e4	# 50000				#handy when dealing with large,small,or variable orders of magnitude
-6.02e23	# Avogadro's number
-1.6e-35	# Planck length
-
-# long-storage integers are written with L
-5L	# 5
-class(5L)	# "integer"
-
-# Try ?class for more information on the class() function
-# In fact, you can look up the documentation on `xyz` with ?xyz
-# or see the source for `xyz` by evaluating xyz
-
-# Arithmetic
-10 + 66	# 76
-53.2 - 4	# 49.2
-2 * 2.0	# 4
-3L / 4	# 0.75
-3 %% 2	# 1
-
-# Weird number types
-class(NaN)	# "numeric"
+##################################################
+# Data types and basic arithmetic
+##################################################
+
+# Now for the programming-oriented part of the tutorial.
+# In this section you will meet the important data types of R:
+# integers, numerics, characters, logicals, and factors.
+# There are others, but these are the bare minimum you need to
+# get started.
+
+# INTEGERS
+# Long-storage integers are written with L
+5L # 5
+class(5L) # "integer"
+# (Try ?class for more information on the class() function.)
+# In R, every single value, like 5L, is considered a vector of length 1
+length(5L) # 1
+# You can have an integer vector with length > 1 too:
+c(4L, 5L, 8L, 3L) # 4 5 8 3
+length(c(4L, 5L, 8L, 3L)) # 4
+class(c(4L, 5L, 8L, 3L)) # "integer"
+
+# NUMERICS
+# A "numeric" is a double-precision floating-point number
+5 # 5
+class(5) # "numeric"
+# Again, everything in R is a vector;
+# you can make a numeric vector with more than one element
+c(3,3,3,2,2,1) # 3 3 3 2 2 1
+# You can use scientific notation too
+5e4 # 50000
+6.02e23 # Avogadro's number
+1.6e-35 # Planck length
+# You can also have infinitely large or small numbers
 class(Inf)	# "numeric"
-class(-Inf)	# "numeric"		#used in for example integrate( dnorm(x), 3, Inf ) -- which obviates Z-score tables
-
-# but beware, NaN isn't the only weird type...
-class(NA)	# see below
-class(NULL)	# NULL
-
-
-# SIMPLE LISTS
-c(6, 8, 7, 5, 3, 0, 9)	# 6 8 7 5 3 0 9
-c('alef', 'bet', 'gimmel', 'dalet', 'he')	# "alef"   "bet"    "gimmel" "dalet"  "he"
-c('Z', 'o', 'r', 'o') == "Zoro"	# FALSE FALSE FALSE FALSE
-
-#some more nice built-ins
-5:15	# 5  6  7  8  9 10 11 12 13 14 15
-
-seq(from=0, to=31337, by=1337)
-#  [1]     0  1337  2674  4011  5348  6685  8022  9359 10696 12033 13370 14707
-# [13] 16044 17381 18718 20055 21392 22729 24066 25403 26740 28077 29414 30751
-
-letters
-#  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
-# [20] "t" "u" "v" "w" "x" "y" "z"
-
-month.abb	# "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"
-
-
-# Access the n'th element of a list with list.name[n] or sometimes list.name[[n]]
-letters[18]	# "r"
-LETTERS[13]	# "M"
-month.name[9]	# "September"
-c(6, 8, 7, 5, 3, 0, 9)[3]	# 7
-
-
+class(-Inf)	# "numeric"
+# You might use "Inf", for example, in integrate( dnorm(x), 3, Inf);
+# this obviates Z-score tables.
+
+# BASIC ARITHMETIC
+# You can do arithmetic with numbers
+# Doing arithmetic on a mix of integers and numerics gives you another numeric
+10L + 66L # 76      # integer plus integer gives integer
+53.2 - 4  # 49.2    # numeric minus numeric gives numeric
+2.0 * 2L  # 4       # numeric times integer gives numeric
+3L / 4    # 0.75    # integer over numeric gives numeric
+3 %% 2	  # 1       # the remainder of two numerics is another numeric
+# Illegal arithmetic yeilds you a "not-a-number":
+0 / 0 # NaN
+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
 
 # CHARACTERS
-
 # There's no difference between strings and characters in R
-
-"Horatio"	# "Horatio"
+"Horatio" # "Horatio"
 class("Horatio") # "character"
-substr("Fortuna multis dat nimis, nulli satis.", 9, 15)	# "multis "
-gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.")	# "Fortøna møltis dat nimis, nølli satis."
-
-
+class('H') # "character"
+# Those were both character vectors of length 1
+# Here is a longer one:
+c('alef', 'bet', 'gimmel', 'dalet', 'he')
+# =>
+# "alef"   "bet"    "gimmel" "dalet"  "he"
+length(c("Call","me","Ishmael")) # 3
+# You can do regex operations on character vectors:
+substr("Fortuna multis dat nimis, nulli satis.", 9, 15) # "multis "
+gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.") # "Fortøna møltis dat nimis, nølli satis."
+# R has several built-in character vectors:
+letters
+# =>
+#  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
+# [20] "t" "u" "v" "w" "x" "y" "z"
+month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"
 
 # LOGICALS
-
-# booleans
+# In R, a "logical" is a boolean
 class(TRUE)	# "logical"
 class(FALSE)	# "logical"
-# Behavior is normal
+# Their behavior is normal
 TRUE == TRUE	# TRUE
 TRUE == FALSE	# FALSE
 FALSE != FALSE	# FALSE
 FALSE != TRUE	# TRUE
 # Missing data (NA) is logical, too
 class(NA)	# "logical"
-
-
+# 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
 
 # FACTORS
-
 # The factor class is for categorical data
-# which can be ordered (like childrens' grade levels)
-# or unordered (like gender)
-levels(factor(c("female", "male", "male", "female", "NA", "female")))	# "female" "male"   "NA" 
-
+# Factors can be ordered (like childrens' grade levels) or unordered (like gender)
 factor(c("female", "female", "male", "NA", "female"))
 #  female female male   NA     female
 # Levels: female male NA
+# The "levels" are the values the categorical data can take
+levels(factor(c("male", "male", "female", "NA", "female"))) # "female" "male"   "NA" 
+# If a factor vector has length 1, its levels will have length 1, too
+length(factor("male")) # 1
+length(levels(factor("male"))) # 1
+# Factors are commonly seen in data frames, a data structure we will cover later
+data(infert) # "Infertility after Spontaneous and Induced Abortion"
+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
+# =>
+# [1] "beak"     "feathers" "wings"    "eyes"    
+parakeet <- NULL
+parakeet
+# =>
+# NULL
+
+# TYPE COERCION
+# Type-coercion is when you force a value to take on a different type
+as.character(c(6, 8)) # "6" "8"
+as.logical(c(1,0,1,1)) # TRUE FALSE  TRUE  TRUE
+# If you put elements of different types into a vector, weird coercions happen:
+c(TRUE, 4) # 1 4
+c("dog", TRUE, 4) # "dog"  "TRUE" "4"
+as.numeric("Bilbo")
+# =>
+# [1] NA
+# Warning message:
+# NAs introduced by coercion 
 
-data(infert)	#Infertility after Spontaneous and Induced Abortion
-levels(infert$education)	# "0-5yrs"  "6-11yrs" "12+ yrs"
+# Also note: those were just the basic data types
+# There are many more data types, such as for dates, time series, etc.
 
 
 
-# VARIABLES
+##################################################
+# Variables, loops, if/else
+##################################################
 
-# Lots of way to assign stuff
+# A variable is like a box you store a value in for later use.
+# We call this "assigning" the value to the variable.
+# Having variables lets us write loops, functions, and if/else statements
+
+# VARIABLES
+# Lots of way to assign stuff:
 x = 5 # this is possible
 y <- "1" # this is preferred
 TRUE -> z # this works but is weird
 
-# We can use coerce variables to different classes
-as.numeric(y)	# 1
-as.character(x)	# "5"
-
 # LOOPS
-
 # We've got for loops
 for (i in 1:4) {
   print(i)
 }
-
 # We've got while loops
 a <- 10
 while (a > 4) {
 	cat(a, "...", sep = "")
 	a <- a - 1
 }
-
 # Keep in mind that for and while loops run slowly in R
 # 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!")
+	print("4 is greater than 3")
 } else {
-	print("Noooo! This is blatantly illogical!")
+	print("4 is not greater than 3")
 }
 # =>
-# [1] "Huzzah! It worked!"
+# [1] "4 is greater than 3"
 
 # FUNCTIONS
-
 # Defined like so:
 jiggle <- function(x) {
 	x = x + rnorm(1, sd=.1)	#add in a bit of (controlled) noise
 	return(x)
 }
-
 # Called like any other R function:
 jiggle(5)	# 5±ε. After set.seed(2716057), jiggle(5)==5.005043
 
-#########################
-# Fun with data: vectors, matrices, data frames, and arrays
-#########################
+
+
+###########################################################################
+# Data structures: Vectors, matrices, data frames, and arrays
+###########################################################################
 
 # ONE-DIMENSIONAL
 
-# You can vectorize anything, so long as all components have the same type
+# Let's start from the very beginning, and with something you already know: vectors.
 vec <- c(8, 9, 10, 11)
 vec	#  8  9 10 11
-# The class of a vector is the class of its components
-class(vec)	# "numeric"
-# If you vectorize items of different classes, weird coercions happen
-c(TRUE, 4)	# 1 4
-c("dog", TRUE, 4)	# "dog"  "TRUE" "4"
-
-# We ask for specific components like so (R starts counting from 1)
-vec[1]	# 8
+# We ask for specific elements by subsetting with square brackets
+# (Note that R starts counting from 1)
+vec[1]		# 8
+letters[18]	# "r"
+LETTERS[13]	# "M"
+month.name[9]	# "September"
+c(6, 8, 7, 5, 3, 0, 9)[3]	# 7
 # We can also search for the indices of specific components,
 which(vec %% 2 == 0)	# 1 3
-# or grab just the first or last entry in the vector
+# grab just the first or last few entries in the vector,
 head(vec, 1)	# 8
-tail(vec, 1)	# 11
+tail(vec, w)	# 10 11
+# or figure out if a certain value is in the vector
+any(vec == 10) # TRUE
 # If an index "goes over" you'll get NA:
 vec[6]	# NA
 # You can find the length of your vector with length()
 length(vec)	# 4
-
 # You can perform operations on entire vectors or subsets of vectors
 vec * 4	# 16 20 24 28
 vec[2:3] * 5	# 25 30
-# and there are many built-in functions to summarize vectors
+any(vec[2:3] == 8) # FALSE
+# and R has many built-in functions to summarize vectors
 mean(vec)	# 9.5
 var(vec)	# 1.666667
-sd(vec)	# 1.290994
+sd(vec)		# 1.290994
 max(vec)	# 11
 min(vec)	# 8
 sum(vec)	# 38
+# Some more nice built-ins:
+5:15	# 5  6  7  8  9 10 11 12 13 14 15
+seq(from=0, to=31337, by=1337)
+# =>
+#  [1]     0  1337  2674  4011  5348  6685  8022  9359 10696 12033 13370 14707
+# [13] 16044 17381 18718 20055 21392 22729 24066 25403 26740 28077 29414 30751
 
 # TWO-DIMENSIONAL (ALL ONE CLASS)
 
@@ -361,6 +390,7 @@ mat[1,]	# 1 4
 3 * mat[,1]	# 3 6 9
 # Ask for a specific cell
 mat[3,2]	# 6
+
 # Transpose the whole matrix
 t(mat)
 # =>
@@ -368,6 +398,14 @@ t(mat)
 # [1,]    1    2    3
 # [2,]    4    5    6
 
+# Matrix multiplication
+mat %*% t(mat)
+# =>
+#      [,1] [,2] [,3]
+# [1,]   17   22   27
+# [2,]   22   29   36
+# [3,]   27   36   45
+
 # cbind() sticks vectors together column-wise to make a matrix
 mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog"))
 mat2
@@ -390,33 +428,138 @@ mat3
 #      [,1] [,2] [,3] [,4]
 # [1,]    1    2    4    5
 # [2,]    6    7    0    4
-# Aah, everything of the same class. No coercions. Much better.
+# Ah, everything of the same class. No coercions. Much better.
 
 # TWO-DIMENSIONAL (DIFFERENT CLASSES)
 
-# For columns of different classes, use the data frame
-dat <- data.frame(c(5,2,1,4), c("dog", "cat", "bird", "dog"))
-names(dat) <- c("number", "species") # name the columns
-class(dat)	# "data.frame"
-dat
+# For columns of different types, use a data frame
+# This data structure is so useful for statistical programming,
+# a version of it was added to Python in the package "pandas".
+
+students <- data.frame(c("Cedric","Fred","George","Cho","Draco","Ginny"),
+                       c(3,2,2,1,0,-1),
+                       c("H", "G", "G", "R", "S", "G"))
+names(students) <- c("name", "year", "house") # name the columns
+class(students)	# "data.frame"
+students
 # =>
-#   number species
-# 1      5     dog
-# 2      2     cat
-# 3      1    bird
-# 4      4     dog
-class(dat$number)	# "numeric"
-class(dat[,2])	# "factor"
+#     name year house
+# 1 Cedric    3     H
+# 2   Fred    2     G
+# 3 George    2     G
+# 4    Cho    1     R
+# 5  Draco    0     S
+# 6  Ginny   -1     G
+class(students$year)	# "numeric"
+class(students[,3])	# "factor"
+# find the dimensions
+nrow(students)	# 6
+ncol(students)	# 3
+dim(students)	# 6 3
 # The data.frame() function converts character vectors to factor vectors
+# by default; turn this off by setting stringsAsFactors = FALSE when
+# you create the data.frame
+?data.frame
 
 # There are many twisty ways to subset data frames, all subtly unalike
-dat$number	# 5 2 1 4
-dat[,1]	# 5 2 1 4
-dat[,"number"]	# 5 2 1 4
+students$year	# 3  2  2  1  0 -1
+students[,2]	# 3  2  2  1  0 -1
+students[,"year"]	# 3  2  2  1  0 -1
+
+# An augmented version of the data.frame structure is the data.table
+# If you're working with huge or panel data, or need to merge a few data
+# sets, data.table can be a good choice. Here's a whirlwind tour:
+install.packages("data.table") # download the package from CRAN
+require(data.table) # load it
+students <- as.data.table(students)
+students # note the slightly different print-out
+# =>
+#      name year house
+# 1: Cedric    3     H
+# 2:   Fred    2     G
+# 3: George    2     G
+# 4:    Cho    1     R
+# 5:  Draco    0     S
+# 6:  Ginny   -1     G
+students[name=="Ginny"] # get rows with name == "Ginny"
+# =>
+#     name year house
+# 1: Ginny   -1     G
+students[year==2] # get rows with year == 2
+# =>
+#      name year house
+# 1:   Fred    2     G
+# 2: George    2     G
+# data.table makes merging two data sets easy
+# let's make another data.table to merge with students
+founders <- data.table(house=c("G","H","R","S"),
+                       founder=c("Godric","Helga","Rowena","Salazar"))
+founders
+# =>
+#    house founder
+# 1:     G  Godric
+# 2:     H   Helga
+# 3:     R  Rowena
+# 4:     S Salazar
+setkey(students, house)
+setkey(founders, house)
+students <- founders[students] # merge the two data sets by matching "house"
+setnames(students, c("house","houseFounderName","studentName","year"))
+students[,order(c("name","year","house","houseFounderName")), with=F]
+# =>
+#    studentName year house houseFounderName
+# 1:        Fred    2     G           Godric
+# 2:      George    2     G           Godric
+# 3:       Ginny   -1     G           Godric
+# 4:      Cedric    3     H            Helga
+# 5:         Cho    1     R           Rowena
+# 6:       Draco    0     S          Salazar
+
+# data.table makes summary tables easy
+# =>
+# students[,sum(year),by=house]
+#    house V1
+# 1:     G  3
+# 2:     H  3
+# 3:     R  1
+# 4:     S  0
+
+# To drop a column from a data.frame or data.table,
+# assign it the NULL value
+students$houseFounderName <- NULL
+students
+# =>
+#    studentName year house
+# 1:        Fred    2     G
+# 2:      George    2     G
+# 3:       Ginny   -1     G
+# 4:      Cedric    3     H
+# 5:         Cho    1     R
+# 6:       Draco    0     S
+
+# Drop a row by subsetting
+# Using data.table:
+students[studentName != "Draco"]
+# =>
+#    house studentName year
+# 1:     G        Fred    2
+# 2:     G      George    2
+# 3:     G       Ginny   -1
+# 4:     H      Cedric    3
+# 5:     R         Cho    1
+# Using data.frame:
+students <- as.data.frame(students)
+students[students$house != "G",]
+# =>
+#   house houseFounderName studentName year
+# 4     H            Helga      Cedric    3
+# 5     R           Rowena         Cho    1
+# 6     S          Salazar       Draco    0
 
-# MULTI-DIMENSIONAL (ALL OF ONE CLASS)
+# MULTI-DIMENSIONAL (ALL ELEMENTS OF ONE TYPE)
 
 # Arrays creates n-dimensional tables
+# All elements must be of the same type
 # You can make a two-dimensional table (sort of like a matrix)
 array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4))
 # =>
@@ -446,15 +589,23 @@ array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
 list1 <- list(time = 1:40)
 list1$price = c(rnorm(40,.5*list1$time,4)) # random
 list1
-
 # You can get items in the list like so
-list1$time
-# You can subset list items like vectors
+list1$time # one way
+list1[["time"]] # another way
+list1[[1]] # yet another way
+# =>
+#  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
+# [34] 34 35 36 37 38 39 40
+# You can subset list items like any other vector
 list1$price[4]
 
-#########################
+# Lists are not the most efficient data structure to work with in R;
+# unless you have a very good reason, you should stick to data.frames
+# Lists are often returned by functions that perform linear regressions
+
+##################################################
 # The apply() family of functions
-#########################
+##################################################
 
 # Remember mat?
 mat
@@ -467,7 +618,7 @@ mat
 # over rows (MAR = 1) or columns (MAR = 2)
 # That is, R does FUN to each row (or column) of X, much faster than a
 # for or while loop would do
-apply(mat, MAR = 2, myFunc)
+apply(mat, MAR = 2, jiggle)
 # =>
 #      [,1] [,2]
 # [1,]    3   15
@@ -478,16 +629,18 @@ apply(mat, MAR = 2, myFunc)
 # Don't feel too intimidated; everyone agrees they are rather confusing
 
 # The plyr package aims to replace (and improve upon!) the *apply() family.
-
 install.packages("plyr")
 require(plyr)
 ?plyr
 
+
+
 #########################
 # Loading data
 #########################
 
 # "pets.csv" is a file on the internet
+# (but it could just as easily be be a file on your own computer)
 pets <- read.csv("http://learnxinyminutes.com/docs/pets.csv")
 pets
 head(pets, 2) # first two rows
@@ -499,10 +652,13 @@ write.csv(pets, "pets2.csv") # to make a new .csv file
 
 # Try ?read.csv and ?write.csv for more information
 
+
+
 #########################
 # Plots
 #########################
 
+# BUILT-IN PLOTTING FUNCTIONS
 # Scatterplots!
 plot(list1$time, list1$price, main = "fake data")
 # Regressions!
@@ -512,18 +668,25 @@ linearModel # outputs result of regression
 abline(linearModel, col = "red")
 # Get a variety of nice diagnostics
 plot(linearModel)
-
 # Histograms!
 hist(rpois(n = 10000, lambda = 5), col = "thistle")
-
 # Barplots!
 barplot(c(1,4,5,1,2), names.arg = c("red","blue","purple","green","yellow"))
 
+# GGPLOT2
+# But these are not even the prettiest of R's plots
 # Try the ggplot2 package for more and better graphics
-
 install.packages("ggplot2")
 require(ggplot2)
 ?ggplot2
+pp <- ggplot(students, aes(x=house))
+pp + geom_histogram()
+ll <- as.data.table(list1)
+pp <- ggplot(ll, aes(x=time,price))
+pp + geom_point()
+# ggplot2 has excellent documentation (available http://docs.ggplot2.org/current/)
+
+
 
 ```