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| language | contributors | filename | |||
|---|---|---|---|---|---|
| R |
|
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.
# Comments start with hashtags.
# You can't make a multi-line comment per se,
# but you can stack multiple comments like so.
# in Windows, 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
# 735 320 325 392 524 450
length(rivers) # how many rivers were measured?
# 141
summary(rivers)
# 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)
#
# 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
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()
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
#Basic statistical operations don't require any programming knowledge either
#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
#########################
# 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"
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
# CHARACTERS
# There's no difference between strings and characters in R
"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."
# LOGICALS
# booleans
class(TRUE) # "logical"
class(FALSE) # "logical"
# Behavior is normal
TRUE == TRUE # TRUE
TRUE == FALSE # FALSE
FALSE != FALSE # FALSE
FALSE != TRUE # TRUE
# Missing data (NA) is logical, too
class(NA) # "logical"
# 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"
factor(c("female", "female", "male", "NA", "female"))
# female female male NA female
# Levels: female male NA
data(infert) #Infertility after Spontaneous and Induced Abortion
levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs"
# 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
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!")
} else {
print("Noooo! This is blatantly illogical!")
}
# =>
# [1] "Huzzah! It worked!"
# FUNCTIONS
# Defined like so:
myFunc <- function(x) {
x <- x * 4
x <- x - 1
return(x)
}
# Called like any other R function:
myFunc(5) # 19
#########################
# Fun with data: vectors, matrices, data frames, and arrays
#########################
# ONE-DIMENSIONAL
# You can vectorize anything, so long as all components 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 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
head(vec, 1) # 8
tail(vec, 1) # 11
# 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
# and there are 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
# 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
# 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
# Aah, 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) # "data.frame"
dat
# =>
# number species
# 1 5 dog
# 2 2 cat
# 3 1 bird
# 4 4 dog
class(dat$number) # "numeric"
class(dat[,2]) # "factor"
# The data.frame() function converts character vectors to factor vectors
# There are many twisty ways to subset data frames, all subtly unalike
dat$number # 5 2 1 4
dat[,1] # 5 2 1 4
dat[,"number"] # 5 2 1 4
# MULTI-DIMENSIONAL (ALL OF ONE CLASS)
# Arrays creates n-dimensional tables
# You can make a two-dimensional table (sort of like a matrix)
array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4))
# =>
# [,1] [,2] [,3] [,4]
# [1,] 1 4 8 3
# [2,] 2 5 9 6
# You can use array to make three-dimensional matrices too
array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
# =>
# , , 1
#
# [,1] [,2]
# [1,] 1 4
# [2,] 2 5
#
# , , 2
#
# [,1] [,2]
# [1,] 8 1
# [2,] 9 2
# LISTS (MULTI-DIMENSIONAL, POSSIBLY RAGGED, OF DIFFERENT TYPES)
# Finally, R has lists (of vectors)
list1 <- list(time = 1:40)
list1$price = c(rnorm(40,.5*list1$time,4)) # random
list1
# You can get items in the list like so
list1$time
# You can subset list items like vectors
list1$price[4]
#########################
# The apply() family of functions
#########################
# Remember mat?
mat
# =>
# [,1] [,2]
# [1,] 1 4
# [2,] 2 5
# [3,] 3 6
# Use apply(X, MARGIN, FUN) to apply function FUN to a matrix X
# over rows (MAR = 1) or columns (MAR = 2)
# That is, R does FUN to each row (or column) of X, much faster than a
# for or while loop would do
apply(mat, MAR = 2, myFunc)
# =>
# [,1] [,2]
# [1,] 3 15
# [2,] 7 19
# [3,] 11 23
# Other functions: ?lapply, ?sapply
# Don't feel too intimidated; everyone agrees they are rather confusing
# The plyr package aims to replace (and improve upon!) the *apply() family.
install.packages("plyr")
require(plyr)
?plyr
#########################
# Loading data
#########################
# "pets.csv" is a file on the internet
pets <- read.csv("http://learnxinyminutes.com/docs/pets.csv")
pets
head(pets, 2) # first two rows
tail(pets, 1) # last row
# To save a data frame or matrix as a .csv file
write.csv(pets, "pets2.csv") # to make a new .csv file
# set working directory with setwd(), look it up with getwd()
# Try ?read.csv and ?write.csv for more information
#########################
# Plots
#########################
# Scatterplots!
plot(list1$time, list1$price, main = "fake data")
# Regressions!
linearModel <- lm(price ~ time, data = list1)
linearModel # outputs result of regression
# 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"))
# Try the ggplot2 package for more and better graphics
install.packages("ggplot2")
require(ggplot2)
?ggplot2
How do I get R?
- Get R and the R GUI from http://www.r-project.org/
- RStudio is another GUI