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I moved some things around. I preserved isomorphismes' "no programming knowledge first" approach; I really like this. In the "programming-heavy" part of the tutorial, I emphasized from the very beginning that every "single" thing in R, like 4 or "foo", is really just a vector of length 1. I added in some whirlwind tours of data.table and ggplot2.
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@ -6,34 +6,42 @@ contributors:
filename: learnr.r 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 ```python
# Comments start with number symbols. # 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. # 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 # Stuff you can do without understanding anything about programming
################################################################### #############################################################################
data() # Browse pre-loaded data sets # In this section, we show off some of the cool stuff you can do in
data(rivers) # Lengths of Major North American Rivers # R without understanding anything about programming. Do not worry
ls() # Notice that "rivers" appears in the workspace # about understanding everything the code does. Just enjoy!
head(rivers) # peek at the dataset
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 # 735 320 325 392 524 450
length(rivers) # how many rivers were measured? length(rivers) # how many rivers were measured?
# 141 # 141
summary(rivers) summary(rivers) # what are some summary statistics?
# Min. 1st Qu. Median Mean 3rd Qu. Max. # Min. 1st Qu. Median Mean 3rd Qu. Max.
# 135.0 310.0 425.0 591.2 680.0 3710.0 # 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 | # The decimal point is 2 digit(s) to the right of the |
# #
# 0 | 4 # 0 | 4
@ -56,8 +64,8 @@ stem(rivers) #stem-and-leaf plot (like a histogram)
# 34 | # 34 |
# 36 | 1 # 36 | 1
stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
stem(log(rivers)) #Notice that the data are neither normal nor log-normal! Take that, Bell Curve fundamentalists. # Take that, Bell curve fundamentalists.
# The decimal point is 1 digit(s) to the left of the | # 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 | # 80 |
# 82 | 2 # 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 # Here's another neat data set that comes pre-loaded. R has tons of these.
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()
data(discoveries) data(discoveries)
plot(discoveries, col="#333333", lwd=3, xlab="Year", main="Number of important discoveries per year") plot(discoveries, col="#333333", lwd=3, xlab="Year",
plot(discoveries, col="#333333", lwd=3, type = "h", xlab="Year", main="Number of important discoveries per 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),
#rather than leaving the default ordering (by year) we could also sort to see what's typical # we could also sort to see what's typical:
sort(discoveries) 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 # [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 # [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) max(discoveries)
# 12 # 12
summary(discoveries) summary(discoveries)
# Min. 1st Qu. Median Mean 3rd Qu. Max. # Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.0 2.0 3.0 3.1 4.0 12.0 # 0.0 2.0 3.0 3.1 4.0 12.0
# Roll a die a few times
#Basic statistical operations don't require any programming knowledge either
#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 # 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) rnorm(9)
# [1] 0.07528471 1.03499859 1.34809556 -0.82356087 0.61638975 -1.88757271 # [1] 0.07528471 1.03499859 1.34809556 -0.82356087 0.61638975 -1.88757271
# [7] -0.59975593 0.57629164 1.08455362 # [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"
# Basic programming stuff # 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
# NUMBERS # You can use scientific notation too
5e4 # 50000
# "numeric" means double-precision floating-point numbers 6.02e23 # Avogadro's number
5 # 5 1.6e-35 # Planck length
class(5) # "numeric" # You can also have infinitely large or small numbers
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"
class(Inf) # "numeric" class(Inf) # "numeric"
class(-Inf) # "numeric" #used in for example integrate( dnorm(x), 3, Inf ) -- which obviates Z-score tables class(-Inf) # "numeric"
# You might use "Inf", for example, in integrate( dnorm(x), 3, Inf);
# but beware, NaN isn't the only weird type... # this obviates Z-score tables.
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
# 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 # CHARACTERS
# There's no difference between strings and characters in R # There's no difference between strings and characters in R
"Horatio" # "Horatio"
"Horatio" # "Horatio"
class("Horatio") # "character" class("Horatio") # "character"
substr("Fortuna multis dat nimis, nulli satis.", 9, 15) # "multis " class('H') # "character"
gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.") # "Fortøna møltis dat nimis, nølli satis." # 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 # LOGICALS
# In R, a "logical" is a boolean
# booleans
class(TRUE) # "logical" class(TRUE) # "logical"
class(FALSE) # "logical" class(FALSE) # "logical"
# Behavior is normal # Their behavior is normal
TRUE == TRUE # TRUE TRUE == TRUE # TRUE
TRUE == FALSE # FALSE TRUE == FALSE # FALSE
FALSE != FALSE # FALSE FALSE != FALSE # FALSE
FALSE != TRUE # TRUE FALSE != TRUE # TRUE
# Missing data (NA) is logical, too # Missing data (NA) is logical, too
class(NA) # "logical" 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 # FACTORS
# The factor class is for categorical data # The factor class is for categorical data
# which can be ordered (like childrens' grade levels) # Factors can be ordered (like childrens' grade levels) or unordered (like gender)
# or unordered (like gender)
levels(factor(c("female", "male", "male", "female", "NA", "female"))) # "female" "male" "NA"
factor(c("female", "female", "male", "NA", "female")) factor(c("female", "female", "male", "NA", "female"))
# female female male NA female # female female male NA female
# Levels: female male NA # 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$education) # "0-5yrs" "6-11yrs" "12+ yrs"
data(infert) #Infertility after Spontaneous and Induced Abortion # WEIRD TYPES
levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs" # 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 # VARIABLES
# Lots of way to assign stuff:
# Lots of way to assign stuff
x = 5 # this is possible x = 5 # this is possible
y <- "1" # this is preferred y <- "1" # this is preferred
TRUE -> z # this works but is weird TRUE -> z # this works but is weird
# We can use coerce variables to different classes
as.numeric(y) # 1
as.character(x) # "5"
# LOOPS # LOOPS
# We've got for loops # We've got for loops
for (i in 1:4) { for (i in 1:4) {
print(i) print(i)
} }
# We've got while loops # We've got while loops
a <- 10 a <- 10
while (a > 4) { while (a > 4) {
cat(a, "...", sep = "") cat(a, "...", sep = "")
a <- a - 1 a <- a - 1
} }
# Keep in mind that for and while loops run slowly in R # Keep in mind that for and while loops run slowly in R
# Operations on entire vectors (i.e. a whole row, a whole column) # Operations on entire vectors (i.e. a whole row, a whole column)
# or apply()-type functions (we'll discuss later) are preferred # or apply()-type functions (we'll discuss later) are preferred
# IF/ELSE # IF/ELSE
# Again, pretty standard # Again, pretty standard
if (4 > 3) { if (4 > 3) {
print("Huzzah! It worked!") print("4 is greater than 3")
} else { } 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 # FUNCTIONS
# Defined like so: # Defined like so:
jiggle <- function(x) { 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) return(x)
} }
# Called like any other R function: # 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
#########################
# Fun with data: vectors, matrices, data frames, and arrays
######################### ###########################################################################
# Data structures: Vectors, matrices, data frames, and arrays
###########################################################################
# ONE-DIMENSIONAL # 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 <- c(8, 9, 10, 11)
vec # 8 9 10 11 vec # 8 9 10 11
# The class of a vector is the class of its components # We ask for specific elements by subsetting with square brackets
class(vec) # "numeric" # (Note that R starts counting from 1)
# If you vectorize items of different classes, weird coercions happen vec[1] # 8
c(TRUE, 4) # 1 4 letters[18] # "r"
c("dog", TRUE, 4) # "dog" "TRUE" "4" LETTERS[13] # "M"
month.name[9] # "September"
# We ask for specific components like so (R starts counting from 1) c(6, 8, 7, 5, 3, 0, 9)[3] # 7
vec[1] # 8
# We can also search for the indices of specific components, # We can also search for the indices of specific components,
which(vec %% 2 == 0) # 1 3 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 head(vec, 1) # 8
tail(vec, 1) # 11 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: # 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() # 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 # You can perform operations on entire vectors or subsets of vectors
vec * 4 # 16 20 24 28 vec * 4 # 16 20 24 28
vec[2:3] * 5 # 25 30 vec[2:3] * 5 # 25 30
any(vec[2:3] == 8) # FALSE
# and there are many built-in functions to summarize vectors # and there are many built-in functions to summarize vectors
mean(vec) # 9.5 mean(vec) # 9.5
var(vec) # 1.666667 var(vec) # 1.666667
sd(vec) # 1.290994 sd(vec) # 1.290994
max(vec) # 11 max(vec) # 11
min(vec) # 8 min(vec) # 8
sum(vec) # 38 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) # TWO-DIMENSIONAL (ALL ONE CLASS)
@ -361,6 +389,7 @@ mat[1,] # 1 4
3 * mat[,1] # 3 6 9 3 * mat[,1] # 3 6 9
# Ask for a specific cell # Ask for a specific cell
mat[3,2] # 6 mat[3,2] # 6
# Transpose the whole matrix # Transpose the whole matrix
t(mat) t(mat)
# => # =>
@ -368,6 +397,14 @@ t(mat)
# [1,] 1 2 3 # [1,] 1 2 3
# [2,] 4 5 6 # [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 # cbind() sticks vectors together column-wise to make a matrix
mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog")) mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog"))
mat2 mat2
@ -395,24 +432,85 @@ mat3
# TWO-DIMENSIONAL (DIFFERENT CLASSES) # TWO-DIMENSIONAL (DIFFERENT CLASSES)
# For columns of different classes, use the data frame # For columns of different classes, use the data frame
dat <- data.frame(c(5,2,1,4), c("dog", "cat", "bird", "dog")) # This data structure is so useful for statistical programming,
names(dat) <- c("number", "species") # name the columns # a version of it was added to Python in the package "pandas".
class(dat) # "data.frame"
dat 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 # name year house
# 1 5 dog # 1 Cedric 3 H
# 2 2 cat # 2 Fred 2 G
# 3 1 bird # 3 George 2 G
# 4 4 dog # 4 Cho 1 R
class(dat$number) # "numeric" # 5 Draco 0 S
class(dat[,2]) # "factor" # 6 Ginny -1 G
class(students$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 # 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 # There are many twisty ways to subset data frames, all subtly unalike
dat$number # 5 2 1 4 students$year # 3 2 2 1 0 -1
dat[,1] # 5 2 1 4 students[,2] # 3 2 2 1 0 -1
dat[,"number"] # 5 2 1 4 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) # 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 <- 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 list1
# You can get items in the list like so # You can get items in the list like so
list1$time list1$time # one way
# You can subset list items like vectors 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$price[4] list1$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 # The apply() family of functions
######################### ##################################################
# Remember mat? # Remember mat?
mat mat
@ -467,7 +573,7 @@ mat
# over rows (MAR = 1) or columns (MAR = 2) # over rows (MAR = 1) or columns (MAR = 2)
# That is, R does FUN to each row (or column) of X, much faster than a # That is, R does FUN to each row (or column) of X, much faster than a
# for or while loop would do # for or while loop would do
apply(mat, MAR = 2, myFunc) apply(mat, MAR = 2, jiggle)
# => # =>
# [,1] [,2] # [,1] [,2]
# [1,] 3 15 # [1,] 3 15
@ -478,16 +584,18 @@ apply(mat, MAR = 2, myFunc)
# Don't feel too intimidated; everyone agrees they are rather confusing # Don't feel too intimidated; everyone agrees they are rather confusing
# The plyr package aims to replace (and improve upon!) the *apply() family. # The plyr package aims to replace (and improve upon!) the *apply() family.
install.packages("plyr") install.packages("plyr")
require(plyr) require(plyr)
?plyr ?plyr
######################### #########################
# Loading data # Loading data
######################### #########################
# "pets.csv" is a file on the internet # "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 <- read.csv("http://learnxinyminutes.com/docs/pets.csv")
pets pets
head(pets, 2) # first two rows 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 # Try ?read.csv and ?write.csv for more information
######################### #########################
# Plots # Plots
######################### #########################
# BUILT-IN PLOTTING FUNCTIONS
# Scatterplots! # Scatterplots!
plot(list1$time, list1$price, main = "fake data") plot(list1$time, list1$price, main = "fake data")
# Regressions! # Regressions!
@ -512,18 +623,25 @@ linearModel # outputs result of regression
abline(linearModel, col = "red") abline(linearModel, col = "red")
# Get a variety of nice diagnostics # Get a variety of nice diagnostics
plot(linearModel) plot(linearModel)
# Histograms! # Histograms!
hist(rpois(n = 10000, lambda = 5), col = "thistle") hist(rpois(n = 10000, lambda = 5), col = "thistle")
# Barplots! # 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
# Try the ggplot2 package for more and better graphics # Try the ggplot2 package for more and better graphics
install.packages("ggplot2") install.packages("ggplot2")
require(ggplot2) require(ggplot2)
?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/)
``` ```