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diff --git a/r.html.markdown~ b/r.html.markdown~ new file mode 100644 index 00000000..ee9e7c90 --- /dev/null +++ b/r.html.markdown~ @@ -0,0 +1,807 @@ +--- +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 <NA> 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 |