diff --git a/r.html.markdown b/r.html.markdown
index ea94ae42..dfc945c1 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
-###################################################################
+#############################################################################
-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
+# 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)
+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 +127,249 @@ 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
+##################################################
+# 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"
-
-
-
-#########################
-# 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"
+# 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 integer 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 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
+# in this tutorial:
+data(infert) # "Infertility after Spontaneous and Induced Abortion"
+levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs"
-data(infert) #Infertility after Spontaneous and Induced Abortion
-levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs"
+# WEIRD TYPES
+# A quick summary of some of the weirder types in R
+class(Inf) # "numeric"
+class(-Inf) # "numeric"
+class(NaN) # "numeric"
+class(NA) # "logical"
+class(NULL) # 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 classes 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
+# 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.
+# As explained above, every single element in R is already a vector
+# Make sure the elements of long vectors all have the same type
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 entry in the vector,
head(vec, 1) # 8
tail(vec, 1) # 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 there are 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 +389,7 @@ 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 +397,14 @@ 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
@@ -395,24 +432,85 @@ mat3
# 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
+# 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
+
+# A popular replacement for 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")
+require(data.table)
+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"]
+# =>
+# name year house
+# 1: Ginny -1 G
+students[year==2]
+# =>
+# name year house
+# 1: Fred 2 G
+# 2: George 2 G
+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
+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
# MULTI-DIMENSIONAL (ALL OF ONE CLASS)
@@ -446,15 +544,23 @@ 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$price[4]
-#########################
+# Lists are not the most efficient data structure to work with in R;
+# unless you have a very good reason, you should stick to data.frames
+# Lists are often returned by functions that perform linear regressions
+
+##################################################
# The apply() family of functions
-#########################
+##################################################
# Remember mat?
mat
@@ -467,7 +573,7 @@ mat
# over rows (MAR = 1) or columns (MAR = 2)
# That is, R does FUN to each row (or column) of X, much faster than a
# for or while loop would do
-apply(mat, MAR = 2, myFunc)
+apply(mat, MAR = 2, jiggle)
# =>
# [,1] [,2]
# [1,] 3 15
@@ -478,16 +584,18 @@ apply(mat, MAR = 2, myFunc)
# Don't feel too intimidated; everyone agrees they are rather confusing
# The plyr package aims to replace (and improve upon!) the *apply() family.
-
install.packages("plyr")
require(plyr)
?plyr
+
+
#########################
# Loading data
#########################
# "pets.csv" is a file on the internet
+# (but it could just as easily be be a file on your own computer)
pets <- read.csv("http://learnxinyminutes.com/docs/pets.csv")
pets
head(pets, 2) # first two rows
@@ -499,10 +607,13 @@ write.csv(pets, "pets2.csv") # to make a new .csv file
# Try ?read.csv and ?write.csv for more information
+
+
#########################
# Plots
#########################
+# BUILT-IN PLOTTING FUNCTIONS
# Scatterplots!
plot(list1$time, list1$price, main = "fake data")
# Regressions!
@@ -512,18 +623,25 @@ 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/)
+
+
```