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