##————————————————————–## ## A Basic Introduction to R ## ##————————————————————–##
# arithmetic, interacting with the interpreter
# basic arithmetic operations2 + 3 # addition 2 - 3 # subtraction 2*3 # multiplication 2/3 # division 2^3 # exponentiation
# precedence of operators
4^2 - 3*2 1 - 6 + 4 2^-3
(4^2) - (3*2) # use parentheses to group. It also adds some clarity. 4 + 3^2 (4 + 3)^2
-2–3 -2 - -3 # use spaces for clarity
# functions, arguments to functions, obtaining help and informationlog(100) # natural log log(100, base = 10) # log base-10 log10(100) # equivalent log(100, b = 10) # argument abbreviation
args(log) # arguments of the log() function
help(“log”) # documentation ? log # equivalent example(“log”) # execute examples in help page
log(100, 10) # specifying arguments by position
apropos(“log”) # What’s this? # answer: apropos helps to find the name for the function. it is useful when we know the name of specific function but we want to make sure about it. help.search(“log”)
RSiteSearch(“loglinear”, “functions”) #Try to find something else!
# creating vectorsc(1, 2, 3, 4) # combine
1:4 # integer-sequence operator 4:1 -1:2 # note precedence -(1:2) seq(1, 4) seq(2, 8, by = 2) # specify interval seq(0, 1, by = 0.1) # non-integer sequence seq(0, 1, length = 11) # specify number of elements
# vectorized arithmeticc(1, 2, 3, 4)/2 c(1, 2, 3, 4)/c(4, 3, 2, 1) log(c(0.1, 1, 10, 100), 10)
c(1, 2, 3, 4) + c(4, 3) # recycling: no warning c(1, 2, 3, 4) + c(4, 3, 2) # produces warning. Why? # answer: because the number of 2 arrays are not match. the numbers in the seconf vector should be 2, or 4. if 3 numbers in the #second vector sum up with first
#elements in the first vector, one element from first vector remains and there is no other number to be able to sum up with. # creating variables (named objects) by assignment
x <- c(1, 2, 3, 4) # assignment x # print
x = c(1, 2, 3, 4) # can use = for assignment (best avoided) x
# Briefly explain the difference between <- and = (if any)
x/2 # equivalent to c(1, 2, 3, 4)/2 (y <- sqrt(x)) # parentheses to assign and print trick
(x <- rnorm(100)) head(x) # first few summary(x) # a “generic” function
# character and logical data(words <- c(“To”, “be”, “or”, “not”, “to”, “be”)) paste(words, collapse =” “)
(logical.values <- c(TRUE, TRUE, FALSE, TRUE)) !logical.values # negation (not operator)
# coercionsum(logical.values) # number of TRUE (coercion to numeric) sum(!logical.values) # number of FALSE (TRUE-> 1, FALSE -> 0) c(“A”, FALSE, 3.0) # coerced to character c(10, FALSE, -6.5, TRUE) # coerced to numeric
# basic indexingx[12] # 12th element words[2] # second element logical.values[3] # third element x[6:15] # elements 6 through 15 x[c(1, 3, 5)] # 1st, 3rd, 5th elements
x[-(11:100)] # omit elements 11 through 100 x[1:10] # same!
# What happened with the 0th element? Do you know of any # answer: R start from index 1. there is no zero index in R # other language that uses 0-based numbering? #answer: Python, java
v <- 1:4 v[c(TRUE, FALSE, FALSE, TRUE)] # logical indexing
#comparison and logical operators1 == 2 # equal to 1 != 2 # not equal to 1 <= 2 # less than or equal to 1 < 1:3 # less than (vectorized) 3:1 > 1:3 # greater than 3:1 >= 1:3 # greater than or equal to
TRUE & c(TRUE, FALSE) # logical and c(TRUE, FALSE, FALSE) | c(TRUE, TRUE, FALSE) # logical or
(z <- x[1:10]) # first 10 elements of x z < -0.5 # is each element less than -0.5? z > 0.5 # is each element greater than 0.5 z < -0.5 | z > 0.5 # < and > are of higher precedence than | abs(z) > 0.5 # absolute value, equivalent to last expression z[abs(z) > 0.5] # values of z for which |z| > 0.5
# user-defined functions.
# This will come in handy later! mean(x) # of 100 random-normal numbers
sum(x)/length(x) # equivalent
# Pack that into a function
myMean <- function(x) { sum(x)/length(x) } myMean # can be printed like any object
myMean(x) y # from sqrt(c(1, 2, 3, 4)) myMean(y) myMean(1:100) head(x) # global x undisturbed
mySD <- function(x) { sqrt(sum((x - myMean(x))^2)/(length(x) - 1)) } mySD(1:100) sd(1:100) # check
# cleaning upobjects() remove(v, x, y, z, logical.values, words) objects()
# using traceback()letters mySD(letters)
traceback()
# Let's play a bit with a sample dataset.
# In general, you'll first load packages and then use particular
# functions. For now, we will just briefly describe the data and
# generate a simple plot.library(“car”) # load car package (for functions and data)
data(mtcars) head(mtcars, n = 10) # first 10 cases brief(mtcars) # abbreviated output. What is this for? #answer: give us the summary of output dim(mtcars) # rows and columns View(mtcars) # in the RStudio data viewer. Not always ideal… summary(mtcars) # Using the summary() generic function help(“mtcars”) # Some extra information…
# Examining the Datawith(mtcars, hist(mpg)) #What is with for? # answer: with is wrapper that let us use hist function analyze the mtcars data based on mpg scatterplotMatrix( ~ prestige + education + income, id = list(n = 3), data = Duncan)