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----
-language: R
-contributors:
- - ["e99n09", "http://github.com/e99n09"]
-<<<<<<< HEAD
-=======
- - ["isomorphismes", "http://twitter.com/isomorphisms"]
- - ["kalinn", "http://github.com/kalinn"]
->>>>>>> 6e38442b857a9d8178b6ce6713b96c52bf4426eb
-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
-
-# Comments start with number symbols.
-
-# You can't make multi-line comments,
-# but you can stack multiple comments like so.
-
-# in Windows you can use CTRL-ENTER to execute a line.
-# on Mac it is COMMAND-ENTER
-
-
-
-#############################################################################
-# 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) # 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) # 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
-
-# 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
-# 2 | 011223334555566667778888899900001111223333344455555666688888999
-# 4 | 111222333445566779001233344567
-# 6 | 000112233578012234468
-# 8 | 045790018
-# 10 | 04507
-# 12 | 1471
-# 14 | 56
-# 16 | 7
-# 18 | 9
-# 20 |
-# 22 | 25
-# 24 | 3
-# 26 |
-# 28 |
-# 30 |
-# 32 |
-# 34 |
-# 36 | 1
-
-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 |
-#
-# 48 | 1
-# 50 |
-# 52 | 15578
-# 54 | 44571222466689
-# 56 | 023334677000124455789
-# 58 | 00122366666999933445777
-# 60 | 122445567800133459
-# 62 | 112666799035
-# 64 | 00011334581257889
-# 66 | 003683579
-# 68 | 0019156
-# 70 | 079357
-# 72 | 89
-# 74 | 84
-# 76 | 56
-# 78 | 4
-# 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
-
-# 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")
-
-# 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
-# [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)
-#
-# The decimal point is at the |
-#
-# 0 | 000000000
-# 1 | 000000000000
-# 2 | 00000000000000000000000000
-# 3 | 00000000000000000000
-# 4 | 000000000000
-# 5 | 0000000
-# 6 | 000000
-# 7 | 0000
-# 8 | 0
-# 9 | 0
-# 10 | 0
-# 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
-
-# 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)
-
-# 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
-
-
-
-##################################################
-# 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"
-# 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
-# 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
-# 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
-"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"
-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
-# In R, a "logical" is a boolean
-class(TRUE) # "logical"
-class(FALSE) # "logical"
-# 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"
-# 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
-
-# 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 female
-# Levels: female male
-# 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"
-# 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 = c("beak", "feathers", "wings", "eyes")
-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
-
-# Also note: those were just the basic data types
-# There are many more data types, such as for dates, time series, etc.
-
-
-
-##################################################
-# Variables, loops, if/else
-##################################################
-
-# 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
-
-# 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("4 is greater than 3")
-} else {
- print("4 is not greater than 3")
-}
-# =>
-# [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
-
-
-
-###########################################################################
-# Data structures: Vectors, matrices, data frames, and arrays
-###########################################################################
-
-# ONE-DIMENSIONAL
-
-# 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
-# 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
-# grab just the first or last few entries in the vector,
-head(vec, 1) # 8
-tail(vec, 2) # 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
-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
-# 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)
-
-# 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
-# =>
-# [,1] [,2]
-# [1,] 1 4
-# [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"
-# Ask for the first row
-mat[1,] # 1 4
-# Perform operation on the first column
-3 * mat[,1] # 3 6 9
-# Ask for a specific cell
-mat[3,2] # 6
-
-# Transpose the whole matrix
-t(mat)
-# =>
-# [,1] [,2] [,3]
-# [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
-# =>
-# [,1] [,2]
-# [1,] "1" "dog"
-# [2,] "2" "cat"
-# [3,] "3" "bird"
-# [4,] "4" "dog"
-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]))
-
-# rbind() sticks vectors together row-wise to make a matrix
-mat3 <- rbind(c(1,2,4,5), c(6,7,0,4))
-mat3
-# =>
-# [,1] [,2] [,3] [,4]
-# [1,] 1 2 4 5
-# [2,] 6 7 0 4
-# Ah, everything of the same class. No coercions. Much better.
-
-# TWO-DIMENSIONAL (DIFFERENT CLASSES)
-
-# 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
-# =>
-# 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
-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$house != "G",]
-# =>
-# house houseFounderName studentName year
-# 4 H Helga Cedric 3
-# 5 R Rowena Cho 1
-# 6 S Salazar Draco 0
-
-# MULTI-DIMENSIONAL (ALL ELEMENTS OF ONE TYPE)
-
-# Arrays creates n-dimensional tables
-# All elements must be of the same type
-# You can make a two-dimensional table (sort of like a matrix)
-array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4))
-# =>
-# [,1] [,2] [,3] [,4]
-# [1,] 1 4 8 3
-# [2,] 2 5 9 6
-# You can use array to make three-dimensional matrices too
-array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
-# =>
-# , , 1
-#
-# [,1] [,2]
-# [1,] 2 8
-# [2,] 300 9
-# [3,] 4 0
-#
-# , , 2
-#
-# [,1] [,2]
-# [1,] 5 66
-# [2,] 60 7
-# [3,] 0 847
-
-# LISTS (MULTI-DIMENSIONAL, POSSIBLY RAGGED, OF DIFFERENT TYPES)
-
-# Finally, R has lists (of vectors)
-list1 <- list(time = 1:40)
-list1$price = c(rnorm(40,.5*list1$time,4)) # random
-list1
-# You can get items in the list like so
-list1$time # 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
-# =>
-# [,1] [,2]
-# [1,] 1 4
-# [2,] 2 5
-# [3,] 3 6
-# Use apply(X, MARGIN, FUN) to apply function FUN to a matrix X
-# 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, jiggle)
-# =>
-# [,1] [,2]
-# [1,] 3 15
-# [2,] 7 19
-# [3,] 11 23
-# Other functions: ?lapply, ?sapply
-
-# 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
-tail(pets, 1) # last row
-
-# To save a data frame or matrix as a .csv file
-write.csv(pets, "pets2.csv") # to make a new .csv file
-# set working directory with setwd(), look it up with getwd()
-
-# Try ?read.csv and ?write.csv for more information
-
-
-
-#########################
-# 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")
-# Plot regression line on existing plot
-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/)
-
-
-
-```
-
-## 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