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--- a/r.html.markdown
+++ b/r.html.markdown
@@ -2,165 +2,384 @@
language: R
contributors:
- ["e99n09", "http://github.com/e99n09"]
+ - ["isomorphismes", "http://twitter.com/isomorphisms"]
filename: learnr.r
---
-R is a statistical computing language. It has lots of good built-in functions for uploading and cleaning data sets, running common statistical tests, and making graphs. You can also easily compile it 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
+```r
-# Comments start with hashtags.
+# 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.
-# Hit COMMAND-ENTER to execute a line
+# in Windows or Mac, hit COMMAND-ENTER to execute a line
-#########################
-# The absolute basics
-#########################
-# NUMBERS
-# We've got doubles! Behold the "numeric" class
-5 # => [1] 5
-class(5) # => [1] "numeric"
-# We've also got integers! They look suspiciously similar,
-# but indeed are different
-5L # => [1] 5
-class(5L) # => [1] "integer"
-# Try ?class for more information on the class() function
-# In fact, you can look up the documentation on just about anything with ?
+#############################################################################
+# Stuff you can do without understanding anything about programming
+#############################################################################
-# All the normal operations!
-10 + 66 # => [1] 76
-53.2 - 4 # => [1] 49.2
-2 * 2.0 # => [1] 4
-3L / 4 # => [1] 0.75
-3 %% 2 # => [1] 1
+# 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!
-# Finally, we've got not-a-numbers! They're numerics too
-class(NaN) # => [1] "numeric"
+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
-# CHARACTERS
+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
-# We've (sort of) got strings! Behold the "character" class
-"plugh" # => [1] "plugh"
-class("plugh") # "character"
+# 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
-
-# We've got booleans! Behold the "logical" class
-class(TRUE) # => [1] "logical"
-class(FALSE) # => [1] "logical"
-# Behavior is normal
-TRUE == TRUE # => [1] TRUE
-TRUE == FALSE # => [1] FALSE
-FALSE != FALSE # => [1] FALSE
-FALSE != TRUE # => [1] TRUE
+# 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) # => [1] "logical"
+class(NA) # "logical"
+# Use | and & for logic operations.
+# OR
+TRUE | FALSE # TRUE
+# AND
+TRUE & FALSE # FALSE
+# 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
-# It has an attribute called levels that describes all the possible categories
-factor("dog")
+# 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
# =>
-# [1] dog
-# Levels: dog
-# (This will make more sense once we start talking about vectors)
+# 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
-# VARIABLES
+# Also note: those were just the basic data types
+# There are many more data types, such as for dates, time series, etc.
-# Lots of way to assign stuff
+
+
+##################################################
+# 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
-# We can use coerce variables to different classes
-as.numeric(y) # => [1] 1
-as.character(x) # => [1] "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:
-myFunc <- function(x) {
- x <- x * 4
- x <- x - 1
+jiggle <- function(x) {
+ x = x + rnorm(1, sd=.1) #add in a bit of (controlled) noise
return(x)
}
-
# Called like any other R function:
-myFunc(5) # => [1] 19
+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.
vec <- c(8, 9, 10, 11)
-vec # => [1] 8 9 10 11
-# The class of a vector is the class of its components
-class(vec) # => [1] "numeric"
-# If you vectorize items of different classes, weird coercions happen
-c(TRUE, 4) # => [1] 1 4
-c("dog", TRUE, 4) # => [1] "dog" "TRUE" "4"
-
-# We ask for specific components like so (R starts counting from 1)
-vec[1] # => [1] 8
+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] 1 3
-# or grab just the first or last entry in the vector
-head(vec, 1) # => [1] 8
-tail(vec, 1) # => [1] 11
+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] # => [1] NA
+vec[6] # NA
# You can find the length of your vector with length()
-length(vec) # => [1] 4
-
+length(vec) # 4
# You can perform operations on entire vectors or subsets of vectors
-vec * 4 # => [1] 16 20 24 28
-vec[2:3] * 5 # => [1] 25 30
-# and there are many built-in functions to summarize vectors
-mean(vec) # => [1] 9.5
-var(vec) # => [1] 1.666667
-sd(vec) # => [1] 1.290994
-max(vec) # => [1] 11
-min(vec) # => [1] 8
-sum(vec) # => [1] 38
+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)
@@ -175,11 +394,12 @@ mat
# 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] 1 4
+mat[1,] # 1 4
# Perform operation on the first column
-3 * mat[,1] # => [1] 3 6 9
+3 * mat[,1] # 3 6 9
# Ask for a specific cell
-mat[3,2] # => [1] 6
+mat[3,2] # 6
+
# Transpose the whole matrix
t(mat)
# =>
@@ -187,16 +407,24 @@ 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
# =>
-# [,1] [,2]
-# [1,] "1" "dog"
-# [2,] "2" "cat"
-# [3,] "3" "bird"
+# [,1] [,2]
+# [1,] "1" "dog"
+# [2,] "2" "cat"
+# [3,] "3" "bird"
# [4,] "4" "dog"
-class(mat2) # => [1] matrix
+class(mat2) # matrix
# Again, note what happened!
# Because matrices must contain entries all of the same class,
# everything got converted to the character class
@@ -209,33 +437,138 @@ mat3
# [,1] [,2] [,3] [,4]
# [1,] 1 2 4 5
# [2,] 6 7 0 4
-# Aah, everything of the same class. No coercions. Much better.
+# Ah, everything of the same class. No coercions. Much better.
# 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) # => [1] "data.frame"
-dat
+# 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
# =>
-# number species
-# 1 5 dog
-# 2 2 cat
-# 3 1 bird
-# 4 4 dog
-class(dat$number) # => [1] "numeric"
-class(dat[,2]) # => [1] "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 # => [1] 5 2 1 4
-dat[,1] # => [1] 5 2 1 4
-dat[,"number"] # => [1] 5 2 1 4
+students$year # 3 2 2 1 0 -1
+students[,2] # 3 2 2 1 0 -1
+students[,"year"] # 3 2 2 1 0 -1
+
+# An augmented version of the data.frame structure is the data.table
+# If you're working with huge or panel data, or need to merge a few data
+# sets, data.table can be a good choice. Here's a whirlwind tour:
+install.packages("data.table") # download the package from CRAN
+require(data.table) # load it
+students <- as.data.table(students)
+students # note the slightly different print-out
+# =>
+# name year house
+# 1: Cedric 3 H
+# 2: Fred 2 G
+# 3: George 2 G
+# 4: Cho 1 R
+# 5: Draco 0 S
+# 6: Ginny -1 G
+students[name=="Ginny"] # get rows with name == "Ginny"
+# =>
+# name year house
+# 1: Ginny -1 G
+students[year==2] # get rows with year == 2
+# =>
+# name year house
+# 1: Fred 2 G
+# 2: George 2 G
+# data.table makes merging two data sets easy
+# let's make another data.table to merge with students
+founders <- data.table(house=c("G","H","R","S"),
+ founder=c("Godric","Helga","Rowena","Salazar"))
+founders
+# =>
+# house founder
+# 1: G Godric
+# 2: H Helga
+# 3: R Rowena
+# 4: S Salazar
+setkey(students, house)
+setkey(founders, house)
+students <- founders[students] # merge the two data sets by matching "house"
+setnames(students, c("house","houseFounderName","studentName","year"))
+students[,order(c("name","year","house","houseFounderName")), with=F]
+# =>
+# studentName year house houseFounderName
+# 1: Fred 2 G Godric
+# 2: George 2 G Godric
+# 3: Ginny -1 G Godric
+# 4: Cedric 3 H Helga
+# 5: Cho 1 R Rowena
+# 6: Draco 0 S Salazar
+
+# data.table makes summary tables easy
+students[,sum(year),by=house]
+# =>
+# house V1
+# 1: G 3
+# 2: H 3
+# 3: R 1
+# 4: S 0
+
+# To drop a column from a data.frame or data.table,
+# assign it the NULL value
+students$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$house != "G",]
+# =>
+# house houseFounderName studentName year
+# 4 H Helga Cedric 3
+# 5 R Rowena Cho 1
+# 6 S Salazar Draco 0
-# MULTI-DIMENSIONAL (ALL OF ONE CLASS)
+# MULTI-DIMENSIONAL (ALL ELEMENTS OF ONE TYPE)
# Arrays creates n-dimensional tables
+# All elements must be of the same type
# You can make a two-dimensional table (sort of like a matrix)
array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4))
# =>
@@ -247,15 +580,17 @@ array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
# =>
# , , 1
#
-# [,1] [,2]
-# [1,] 1 4
-# [2,] 2 5
+# [,1] [,2]
+# [1,] 2 8
+# [2,] 300 9
+# [3,] 4 0
#
# , , 2
#
# [,1] [,2]
-# [1,] 8 1
-# [2,] 9 2
+# [1,] 5 66
+# [2,] 60 7
+# [3,] 0 847
# LISTS (MULTI-DIMENSIONAL, POSSIBLY RAGGED, OF DIFFERENT TYPES)
@@ -263,15 +598,23 @@ array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
list1 <- list(time = 1:40)
list1$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
@@ -284,7 +627,7 @@ 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
@@ -295,16 +638,18 @@ 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
@@ -316,10 +661,13 @@ 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!
@@ -329,18 +677,25 @@ 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/)
+
+
```