mirror of
https://github.com/adambard/learnxinyminutes-docs.git
synced 2024-12-23 23:33:00 +03:00
351 lines
8.6 KiB
Markdown
351 lines
8.6 KiB
Markdown
---
|
|
language: R
|
|
contributors:
|
|
- ["e99n09", "http://github.com/e99n09"]
|
|
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.
|
|
|
|
```python
|
|
|
|
# Comments start with hashtags.
|
|
|
|
# You can't make a multi-line comment per se,
|
|
# but you can stack multiple comments like so.
|
|
|
|
# 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 ?
|
|
|
|
# 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
|
|
|
|
# Finally, we've got not-a-numbers! They're numerics too
|
|
class(NaN) # => [1] "numeric"
|
|
|
|
# CHARACTERS
|
|
|
|
# We've (sort of) got strings! Behold the "character" class
|
|
"plugh" # => [1] "plugh"
|
|
class("plugh") # "character"
|
|
# There's no difference between strings and characters in R
|
|
|
|
# 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
|
|
# Missing data (NA) is logical, too
|
|
class(NA) # => [1] "logical"
|
|
|
|
# FACTORS
|
|
|
|
# The factor class is for categorical data
|
|
# It has an attribute called levels that describes all the possible categories
|
|
factor("dog")
|
|
# =>
|
|
# [1] dog
|
|
# Levels: dog
|
|
# (This will make more sense once we start talking about vectors)
|
|
|
|
# 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!")
|
|
} 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) # => [1] 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 # => [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
|
|
# 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
|
|
# If an index "goes over" you'll get NA:
|
|
vec[6] # => [1] NA
|
|
# You can find the length of your vector with length()
|
|
length(vec) # => [1] 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
|
|
|
|
# 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] 1 4
|
|
# Perform operation on the first column
|
|
3 * mat[,1] # => [1] 3 6 9
|
|
# Ask for a specific cell
|
|
mat[3,2] # => [1] 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) # => [1] 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) # => [1] "data.frame"
|
|
dat
|
|
# =>
|
|
# number species
|
|
# 1 5 dog
|
|
# 2 2 cat
|
|
# 3 1 bird
|
|
# 4 4 dog
|
|
class(dat$number) # => [1] "numeric"
|
|
class(dat[,2]) # => [1] "factor"
|
|
# The data.frame() function converts character vectors to factor vectors
|
|
|
|
# There are many twisty ways to subset data frames, all subtly unalike
|
|
dat$number # => [1] 5 2 1 4
|
|
dat[,1] # => [1] 5 2 1 4
|
|
dat[,"number"] # => [1] 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/](http://www.r-project.org/)
|
|
* [RStudio](http://www.rstudio.com/ide/) is another GUI
|