Merge branch 'master' into patch-1

1 files changed, 149 insertions, 148 deletions

diff --git a/r.html.markdown b/r.html.markdown
index 2aedf625..2746d1eb 100644
--- a/r.html.markdown
+++ b/r.html.markdown
@@ -4,6 +4,7 @@ contributors:
     - ["e99n09", "http://github.com/e99n09"]
     - ["isomorphismes", "http://twitter.com/isomorphisms"]
     - ["kalinn", "http://github.com/kalinn"]
+    - ["mribeirodantas", "http://github.com/mribeirodantas"]
 filename: learnr.r
 ---
 
@@ -29,13 +30,13 @@ R is a statistical computing language. It has lots of libraries for uploading an
 # 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)	# 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
+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?
+length(rivers)  # how many rivers were measured?
 # 141
 summary(rivers) # what are some summary statistics?
 #   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
@@ -91,14 +92,15 @@ stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
 #  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)
+hist(log(rivers), col = "#333333", border = "white", breaks = 25)
+# play around with these parameters, you'll do more plotting later
 
 # 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",
+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",
+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),
@@ -109,7 +111,7 @@ sort(discoveries)
 # [51]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  4  4  4  4  4  4  4  4
 # [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)
+stem(discoveries, scale = 2)
 #
 #  The decimal point is at the |
 #
@@ -134,7 +136,7 @@ summary(discoveries)
 #    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))
+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)
 
@@ -157,69 +159,68 @@ rnorm(9)
 
 # INTEGERS
 # Long-storage integers are written with L
-5L # 5
-class(5L) # "integer"
+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
+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"
+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"
+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
+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
+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"
+class(Inf)  # "numeric"
+class(-Inf) # "numeric"
 # You might use "Inf", for example, in integrate(dnorm, 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
+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 yields you a "not-a-number":
-0 / 0 # NaN
-class(NaN) # "numeric"
+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
+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
+(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) 
+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 most times
+c(1, 2, 3, 1, 2, 3) * c(1, 2, 1, 2, 1, 2)   # 1 4 3 2 2 6
 
 # CHARACTERS
 # There's no difference between strings and characters in R
-"Horatio" # "Horatio"
-class("Horatio") # "character"
-class("H") # "character"
+"Horatio"           # "Horatio"
+class("Horatio")    # "character"
+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"
+# => "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 "
+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
@@ -230,64 +231,61 @@ month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "D
 
 # LOGICALS
 # In R, a "logical" is a boolean
-class(TRUE)	# "logical"
-class(FALSE)	# "logical"
+                
+class(TRUE)     # "logical"
+class(FALSE)    # "logical"
 # Their behavior is normal
-TRUE == TRUE	# TRUE
-TRUE == FALSE	# FALSE
-FALSE != FALSE	# FALSE
-FALSE != TRUE	# TRUE
+TRUE == TRUE    # TRUE
+TRUE == FALSE   # FALSE
+FALSE != FALSE  # FALSE
+FALSE != TRUE   # TRUE
 # Missing data (NA) is logical, too
-class(NA)	# "logical"
+class(NA)       # "logical"
 # Use | and & for logic operations.
 # OR
-TRUE | FALSE	# TRUE
+TRUE | FALSE    # TRUE
 # AND
-TRUE & FALSE	# FALSE
+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
+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
+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
+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
-# 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
+# Factors can be ordered (like grade levels) or unordered (like colors)
+factor(c("blue", "blue", "green", NA, "blue"))
+#  blue blue green   <NA>   blue
+# Levels: blue green
 # The "levels" are the values the categorical data can take
 # Note that missing data does not enter the levels
-levels(factor(c("male", "male", "female", NA, "female"))) # "female" "male"
+levels(factor(c("green", "green", "blue", NA, "blue"))) # "blue" "green"
 # If a factor vector has length 1, its levels will have length 1, too
-length(factor("male")) # 1
-length(levels(factor("male"))) # 1
+length(factor("green"))         # 1
+length(levels(factor("green"))) # 1
 # Factors are commonly seen in data frames, a data structure we will cover later
-data(infert) # "Infertility after Spontaneous and Induced Abortion"
+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
+class(NULL) # NULL
 parakeet = c("beak", "feathers", "wings", "eyes")
-parakeet
-# =>
-# [1] "beak"     "feathers" "wings"    "eyes"
+parakeet # "beak"     "feathers" "wings"    "eyes"
 parakeet <- NULL
-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
+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"
+c(TRUE, 4)          # 1 4
+c("dog", TRUE, 4)   # "dog"  "TRUE" "4"
 as.numeric("Bilbo")
 # =>
 # [1] NA
@@ -309,14 +307,15 @@ as.numeric("Bilbo")
 
 # VARIABLES
 # Lots of way to assign stuff:
-x = 5 # this is possible
-y <- "1" # this is preferred
-TRUE -> z # this works but is weird
+x = 5       # this is possible
+y <- "1"    # this is preferred traditionally
+TRUE -> z   # this works but is weird
+# Refer to the Internet for the behaviors and preferences about them.
 
 # LOOPS
 # We've got for loops
 for (i in 1:4) {
-  print(i)
+	print(i)
 }
 # We've got while loops
 a <- 10
@@ -341,11 +340,11 @@ if (4 > 3) {
 # FUNCTIONS
 # Defined like so:
 jiggle <- function(x) {
-	x = x + rnorm(1, sd=.1)	#add in a bit of (controlled) noise
+	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
+jiggle(5)   # 5±ε. After set.seed(2716057), jiggle(5)==5.005043
 
 
 
@@ -357,39 +356,39 @@ jiggle(5)	# 5±ε. After set.seed(2716057), jiggle(5)==5.005043
 
 # 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
+vec     #  8  9 10 11
 # 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
+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
+which(vec %% 2 == 0) # 1 3
 # grab just the first or last few entries in the vector,
-head(vec, 1)	# 8
-tail(vec, 2)	# 10 11
+head(vec, 1)    # 8
+tail(vec, 2)    # 10 11
 # or figure out if a certain value is in the vector
-any(vec == 10) # TRUE
+any(vec == 10)  # TRUE
 # If an index "goes over" you'll get NA:
-vec[6]	# NA
+vec[6]      # NA
 # You can find the length of your vector with length()
-length(vec)	# 4
+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
-any(vec[2:3] == 8) # FALSE
+vec * 4             # 32 36 40 44
+vec[2:3] * 5        # 45 50
+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
-max(vec)	# 11
-min(vec)	# 8
-sum(vec)	# 38
+mean(vec)   # 9.5
+var(vec)    # 1.666667
+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)
+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
@@ -397,7 +396,7 @@ seq(from=0, to=31337, by=1337)
 # TWO-DIMENSIONAL (ALL ONE CLASS)
 
 # You can make a matrix out of entries all of the same type like so:
-mat <- matrix(nrow = 3, ncol = 2, c(1,2,3,4,5,6))
+mat <- matrix(nrow = 3, ncol = 2, c(1, 2, 3, 4, 5, 6))
 mat
 # =>
 #      [,1] [,2]
@@ -405,13 +404,13 @@ mat
 # [2,]    2    5
 # [3,]    3    6
 # Unlike a vector, the class of a matrix is "matrix", no matter what's in it
-class(mat) # => "matrix"
+class(mat)      # "matrix" "array"
 # Ask for the first row
-mat[1,]	# 1 4
+mat[1, ]        # 1 4
 # Perform operation on the first column
-3 * mat[,1]	# 3 6 9
+3 * mat[, 1]    # 3 6 9
 # Ask for a specific cell
-mat[3,2]	# 6
+mat[3, 2]       # 6
 
 # Transpose the whole matrix
 t(mat)
@@ -437,14 +436,14 @@ mat2
 # [2,] "2"  "cat"
 # [3,] "3"  "bird"
 # [4,] "4"  "dog"
-class(mat2)	# matrix
+class(mat2) # matrix
 # Again, note what happened!
 # Because matrices must contain entries all of the same class,
 # everything got converted to the character class
-c(class(mat2[,1]), class(mat2[,2]))
+c(class(mat2[, 1]), class(mat2[, 2]))
 
 # rbind() sticks vectors together row-wise to make a matrix
-mat3 <- rbind(c(1,2,4,5), c(6,7,0,4))
+mat3 <- rbind(c(1, 2, 4, 5), c(6, 7, 0, 4))
 mat3
 # =>
 #      [,1] [,2] [,3] [,4]
@@ -458,11 +457,11 @@ mat3
 # 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"))
+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"
+class(students) # "data.frame"
 students
 # =>
 #     name year house
@@ -472,21 +471,22 @@ students
 # 4    Cho    1     R
 # 5  Draco    0     S
 # 6  Ginny   -1     G
-class(students$year)	# "numeric"
-class(students[,3])	# "factor"
+class(students$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
+nrow(students)  # 6
+ncol(students)  # 3
+dim(students)   # 6 3
+# The data.frame() function used to convert character vectors to factor
+# vectors by default; This has changed in R 4.0.0. If your R version is
+# older, 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
-students$year	# 3  2  2  1  0 -1
-students[,2]	# 3  2  2  1  0 -1
-students[,"year"]	# 3  2  2  1  0 -1
+students$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
@@ -503,19 +503,19 @@ students # note the slightly different print-out
 # 4:    Cho    1     R
 # 5:  Draco    0     S
 # 6:  Ginny   -1     G
-students[name=="Ginny"] # get rows with name == "Ginny"
+students[name == "Ginny"] # get rows with name == "Ginny"
 # =>
 #     name year house
 # 1: Ginny   -1     G
-students[year==2] # get rows with year == 2
+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 <- data.table(house   = c("G"     , "H"    , "R"     , "S"),
+                       founder = c("Godric", "Helga", "Rowena", "Salazar"))
 founders
 # =>
 #    house founder
@@ -526,8 +526,8 @@ founders
 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]
+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
@@ -538,7 +538,7 @@ students[,order(c("name","year","house","houseFounderName")), with=F]
 # 6:       Draco    0     S          Salazar
 
 # data.table makes summary tables easy
-students[,sum(year),by=house]
+students[, sum(year), by = house]
 # =>
 #    house V1
 # 1:     G  3
@@ -571,7 +571,7 @@ students[studentName != "Draco"]
 # 5:     R         Cho    1
 # Using data.frame:
 students <- as.data.frame(students)
-students[students$house != "G",]
+students[students$house != "G", ]
 # =>
 #   house houseFounderName studentName year
 # 4     H            Helga      Cedric    3
@@ -583,13 +583,13 @@ students[students$house != "G",]
 # 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))
+array(c(c(1, 2, 4, 5), c(8, 9, 3, 6)), dim = c(2, 4))
 # =>
 #      [,1] [,2] [,3] [,4]
 # [1,]    1    4    8    3
 # [2,]    2    5    9    6
 # You can use array to make three-dimensional matrices too
-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))
+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))
 # =>
 # , , 1
 #
@@ -609,7 +609,7 @@ 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))
 
 # Finally, R has lists (of vectors)
 list1 <- list(time = 1:40)
-list1$price = c(rnorm(40,.5*list1$time,4)) # random
+list1$price = c(rnorm(40, .5*list1$time, 4)) # random
 list1
 # You can get items in the list like so
 list1$time # one way
@@ -682,7 +682,7 @@ write.csv(pets, "pets2.csv") # to make a new .csv file
 #########################
 
 # Linear regression!
-linearModel <- lm(price  ~ time, data = list1)
+linearModel <- lm(price ~ time, data = list1)
 linearModel # outputs result of regression
 # =>
 # Call:
@@ -719,7 +719,7 @@ 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 
+summary(linearModel)$coefficients[, 4] # the p-values 
 # =>
 #  (Intercept)         time 
 # 9.234021e-01 2.440008e-09 
@@ -728,8 +728,7 @@ summary(linearModel)$coefficients[,4] # the p-values
 # 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 <- glm(success  ~ time, data = list1, family=binomial(link="logit"))
 glModel # outputs result of logistic regression
 # =>
 # Call:  glm(formula = success ~ time, 
@@ -745,8 +744,10 @@ glModel # outputs result of logistic regression
 summary(glModel) # more verbose output from the regression
 # =>
 # Call:
-# glm(formula = success ~ time, 
-#	family = binomial(link = "logit"), data = list1)
+# glm(
+#	formula = success ~ time,
+#	family = binomial(link = "logit"),
+#	data = list1)
 
 # Deviance Residuals: 
 #    Min      1Q  Median      3Q     Max  
@@ -780,7 +781,7 @@ 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"))
+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
@@ -788,10 +789,10 @@ barplot(c(1,4,5,1,2), names.arg = c("red","blue","purple","green","yellow"))
 install.packages("ggplot2")
 require(ggplot2)
 ?ggplot2
-pp <- ggplot(students, aes(x=house))
+pp <- ggplot(students, aes(x = house))
 pp + geom_bar()
 ll <- as.data.table(list1)
-pp <- ggplot(ll, aes(x=time,price))
+pp <- ggplot(ll, aes(x = time, price))
 pp + geom_point()
 # ggplot2 has excellent documentation (available http://docs.ggplot2.org/current/)