R语言笔记二(控制结构)
Control Structures
Control structures in R allow you to control the flow of execution of the program, depending on runtime conditions. Common structures are
- if, else: testing a condition
- for: execute a loop a fixed number of times
- while: execute a loop while a condition is true
- repeat: execute an infinite loop
- break: break the execution of a loop
- next: skip an interation of a loop
return: exit a function
Most control structures are not used in interactive sessions, but rather when writing functions or longer expresisons.
if
if(<condition>) {
## do something
} else {
## do something else
}
if() {
## do something
} else if() {
## do something different
} else {
## do something different
} This is a valid if/else structure.
if(x > 3) {
y <- 10
} else {
y <- 0
}So is this one.
y <- if(x > 3) {
10
} else {
0
}Of course, the else clause is not necessary.
if() {
}
if() {
} for
for loops take an integrator variable（积分变量） and assign it successive values（连续值） from a sequence or vector. For loops are most commonly used for iterating over（迭代） the elements of an object (list, vector, etc.)
for(i in 1:10) {
print(i)
}This loop takes the i variable and in each iteration of the loop gives it values 1, 2, 3, …, 10, and then exits.
These three loops have the same behavior.
x <- c("a", "b", "c", "d")
for(i in 1:4) {
print(x[i])
}
for(i in seq_along(x)) { ## seq_along()
print(x[i])
}
for(letter in x) {
print(letter)
}
for(i in 1:4) print(x[i])Nested for loops 嵌套循环
for loops can be nested.
x <- matrix(1:6, 2, 3)
for(i in seq_len(nrow(x))) {
for(j in seq_len(ncol(x))) {
print(x[i, j])
}
}Be careful with nesting though. Nesting beyond 2–3 levels is often very difficult to read/understand.
while
While loops begin by testing a condition. If it is true, then they execute the loop body. Once the loop body is executed, the condition is tested again, and so forth.
count <- 0
while(count < 10) {
print(count)
count <- count + 1
}While loops can potentially result in infinite loops if not written properly. Use with care!
Sometimes there will be more than one condition in the test.
z <- 5
while(z >= 3 && z <= 10) {
print(z)
coin <- rbinom(1, 1, 0.5)
if(coin == 1) { ## random walk
z <- z + 1
} else {
z <- z - 1
}
}Conditions are always evaluated from left to right.
repeat
Repeat initiates an infinite loop; these are not commonly used in statistical applications but they do have their uses. The only way to exit a repeat loop is to call break.
x0 <- 1
tol <- 1e-8
repeat {
x1 <- computeEstimate()
if(abs(x1 - x0) < tol) {
break
} else {
x0 <- x1
}
}The loop in the previous slide is a bit dangerous because there’s no guarantee it will stop. Better to set a hard limit on the number of iterations (e.g. using a for loop) and then report whether convergence was achieved or not.
next, return
next is used to skip an iteration of a loop
for(i in 1:100) {
if(i <= 20) {
## Skip the first 20 iterations
next
}
## Do something here
}return signals that a function should exit and return a given value
Summary
- Control structures like if, while, and for allow you to control the flow of an R program
- Infinite loops should generally be avoided, even if they are theoretically correct.
- Control structures mentioned here are primarily useful for writing programs; for command-line interactive work, the *apply functions are more useful.
Functions
add2<- function(x,y){
x + y
}x<-1:20
above <- function(x,n){
use<- x>n
x[use]
}above11 <- function(x,n=11){
use<- x>n
x[use]
}column.mean <- function(x,removeNA = TRUE) {
nc <- ncol(x)
means <- numeric(nc)
for(i in 1:nc){
means[i] <- mean(x[,i],na.rm = removeNA)
}
means
}Functions are created using the function() directive and are stored as R objects just like anything else. In particular, they are R objects of class “function”.
>f <- function() {
## Do something interesting
}
Functions in R are “first class objects”, which means that they can be treated much like any other R object. Importantly
Functions can be passed as arguments to other functionsFunctions can be nested, so that you can define a function inside of another functionThe return value of a function is the last expression in the function body to be evaluated.
Functions have named arguments which potentially have default values.
- The formal arguments are the arguments included in the function definition
- The formals function returns a list of all the formal arguments of a function
- Not every function call in R makes use of all the formal arguments
- Function arguments can be missing or might have default values
Argument Matching
R functions arguments can be matched positionally or by name. So the following calls to sd are all equivalent
> mydata <- rnorm(100)
> sd(mydata)
> sd(x = mydata)
> sd(x = mydata, na.rm = FALSE)
> sd(na.rm = FALSE, x = mydata)
> sd(na.rm = FALSE, mydata)
Even though it’s legal, I don’t recommend messing around with the order of the arguments too much, since it can lead to some confusion.
You can mix positional matching with matching by name. When an argument is matched by name, it is “taken out” of the argument list and the remaining unnamed arguments are matched in the order that they are listed in the function definition.
> args(lm)
function (formula, data, subset, weights, na.action,
method = "qr", model = TRUE, x = FALSE,
y = FALSE, qr = TRUE, singular.ok = TRUE,
contrasts = NULL, offset, ...)
The following two calls are equivalent.
>lm(data = mydata, y ~ x, model = FALSE, 1:100) ##lm() 线性回归 **
>lm(y ~ x, mydata, 1:100, model = FALSE)
- Most of the time, named arguments are useful on the command line when you have a long argument list and you want to use the defaults for everything except for an argument near the end of the list
- Named arguments also help if you can remember the name of the argument and not its position on the argument list (plotting is a good example).
Function arguments can also be partially matched, which is useful for interactive work. The order of operations when given an argument is
- Check for exact match for a named argument
- Check for a partial match
- Check for a positional match
Defining a Function
>f <- function(a, b = 1, c = 2, d = NULL) {
}
In addition to not specifying a default value, you can also set an argument value to NULL.
Lazy Evaluation
Arguments to functions are evaluated lazily, so they are evaluated only as needed.
>f <- function(a, b) {
a^2
}
f(2)
[1] 4
This function never actually uses the argument b, so calling f(2) will not produce an error because the 2 gets positionally matched to a.
>f <- function(a, b) {
print(a)
print(b)
}
f(45)
[1] 45
Error: argument "b" is missing, with no default
Notice that “45” got printed first before the error was triggered. This is because b did not have to be evaluated until after print(a). Once the function tried to evaluate print(b) it had to throw an error.
The “…” Argument
The … argument indicate a variable number of arguments that are usually passed on to other functions.
… is often used when extending another function and you don’t want to copy the entire argument list of the original function
>myplot <- function(x, y, type = "l", ...) {
plot(x, y, type = type, ...)
}
Generic functions use … so that extra arguments can be passed to methods (more on this later)
> mean
function (x, ...)
UseMethod("mean")
The … argument is also necessary when the number of arguments passed to the function cannot be known in advance.
> args(paste)
function (..., sep = " ", collapse = NULL)
> args(cat)
function (..., file = "", sep = " ", fill = FALSE,
labels = NULL, append = FALSE)
Arguments Coming After the “…” Argument
One catch with … is that any arguments that appear after … on the argument list must be named explicitly and cannot be partially matched.
> args(paste)
function (..., sep = " ", collapse = NULL)
> paste("a", "b", sep = ":")
[1] "a:b"
> paste("a", "b", se = ":")
[1] "a b :"
A Diversion on Binding Values to Symbol
How does R know which value to assign to which symbol? When I type
> lm <- function(x) { x * x }
> lm
function(x) { x * x }
how does R know what value to assign to the symbol lm? Why doesn’t it give it the value of lm that is in the stats package?
When R tries to bind a value to a symbol, it searches through a series of environments to find the ppropriate value. When you are working on the command line and need to retrieve the value of an R object, the order is roughly
- Search the global environment for a symbol name matching the one requested
- Search the namespaces of each of the packages on the search list
The search list can be found by using the search function.
> search()
[1] ".GlobalEnv" "tools:rstudio" "package:stats"
[4] "package:graphics" "package:grDevices" "package:utils"
[7] "package:datasets" "package:methods" "Autoloads"
[10] "package:base"
Binding Values to Symbol ？
- The global environment or the user’s workspace is always the first element of the search list and the base package is always the last.
- The order of the packages on the search list matters!
- User’s can configure which packages get loaded on startup so you cannot assume that there will be a set list of packages available.
- When a user loads a package with library the namespace of that package gets put in position 2 of the search list (by default) and everything else gets shifted down the list.
- Note that R has separate namespaces for functions and non-functions so it’s possible to have an object named c and a function named c.
Scoping Rules
Symbol Binding
The scoping rules for R are the main feature that make it different from the original S language.
- The scoping rules determine how a value is associated with a free variable in a function.
- R uses lexical scoping or static scoping. A common alternative is dynamic scoping.
词法作用域（lexical scoping）等同于静态作用域（static scoping）。
动态作用域：就是整个程序运行的时候只有一个env。
什么是env呢？env就是一组binding。
binding是什么呢？binding就是从identifer到value的映射。
dynamic scoping: 每次函数求值的时候都会在这唯一的一个env里查询或更新。
static scoping: 是每次函数求值的时候都创建一个新的env，包含了函数定义时候的所能访问到的各种binding。这个新的env连同那个函数一起，俗称闭包Closure。
现在大多数程序设计语言都是采用静态作用域规则。
- Related to the scoping rules is how R uses the search list to bind a value to a symbol.
- Lexical scoping turns out to be particularly useful for simplifying statistical computations.
Lexical Scoping
Consider the following function.
>f <- function(x, y) {
x^2 + y / z
}
This function has 2 formal arguments x and y. In the body of the function there is another symbol z. In this case z is called a free variable. The scoping rules of a language determine how values are assigned to free variables. Free variables are not formal arguments(形式参数) and are not local variables (assigned insided the function body).
Lexical scoping in R means that
the values of free variables are searched for in the environment in which the function was defined
What is an environment?
- An environment is a collection of (symbol, value) pairs, i.e. x is a symbol and 3.14 might be its value.
- Every environment has a parent environment; it is possible for an environment to have multiple “children”
- the only environment without a parent is the empty environment
- A function + an environment = a closure or function closure.
Searching for the value for a free variable: ？
- If the value of a symbol is not found in the environment in which a function was defined, then the search is continued in the parent environment.
- The search continues down the sequence of parent environments until we hit the top-level environment; this usually the global environment (workspace) or the namespace of a package.
- After the top-level environment, the search continues down the search list until we hit the empty environment. If a value for a given symbol cannot be found once the empty environment is arrived at, then an error is thrown.
R Scoping Rules
Why does all this matter?
- Typically, a function is defined in the global environment, so that the values of free variables are just found in the user’s workspace
- This behavior is logical for most people and is usually the “right thing” to do
- However, in R you can have functions defined inside other functions
- Languages like C don’t let you do this Now things get interesting — In this case the environment in which a function is defined is the body of another function!
>make.power <- function(n) { pow <- function(x) { x^n } pow }
This function returns another function as its value.
> cube <- make.power(3)
> square <- make.power(2)
> cube(3)
[1] 27
> square(3)
[1] 9
Exploring a Function Closure **
What’s in a function’s environment?
> ls(environment(cube))
[1] "n" "pow"
> get("n", environment(cube))
[1] 3
> ls(environment(square))
[1] "n" "pow"
> get("n", environment(square))
[1] 2
Lexical vs. Dynamic Scoping
>y <- 10
f <- function(x) {
y <- 2
y^2 + g(x)
}
g <- function(x) {
x*y
}
What is the value of
f(3)
34 ##两个函数的y取值不一样
- With lexical scoping the value of y in the function g is looked up in the environment in which the function was defined, in this case the global environment, so the value of y is 10.
- With dynamic scoping, the value of y is looked up in the environment from which the function was called (sometimes referred to as the calling environment).
- In R the calling environment is known as the parent frame - So the value of y would be 2.
When a function is defined in the global environment and is subsequently called from the global environment, then the defining environment and the calling environment are the same. This can sometimes give the appearance of dynamic scoping.
> g <- function(x) {
+ a <- 3
+ x+a+y
+ }
> g(2)
Error in g(2) : object "y" not found
> y <- 3
> g(2)
[1] 8
Other Languages
Other languages that support lexical scoping
- Scheme
- Perl
- Python
- Common Lisp (all languages converge to Lisp)
Consequences of Lexical Scoping
- In R, all objects must be stored in memory
- All functions must carry a pointer to their respective defining environments, which could be anywhere
- In S-PLUS, free variables are always looked up in the global workspace, so everything can be stored on the disk because the “defining environment” of all functions is the same.
Scoping Rules - Optimization Example(OPTIONAL)
Application: Optimization ** ？
Why is any of this information useful?
- Optimization routines in R like optim, nlm, and optimize require you to pass a function whose argument is a vector of parameters (e.g. a log-likelihood)
- However, an object function might depend on a host of other things besides its parameters (like data)
- When writing software which does optimization, it may be desirable to allow the user to hold certain parameters fixed
Maximizing a Normal Likelihood
Write a “constructor” function
>make.NegLogLik <- function(data, fixed=c(FALSE,FALSE)) {
params <- fixed
function(p) {
params[!fixed] <- p
mu <- params[1]
sigma <- params[2]
a <- -0.5*length(data)*log(2*pi*sigma^2)
b <- -0.5*sum((data-mu)^2) / (sigma^2)
-(a + b)
}
}
Note: Optimization functions in R minimize functions, so you need to use the negative log-likelihood.
> set.seed(1); normals <- rnorm(100, 1, 2)
> nLL <- make.NegLogLik(normals)
> nLL
function(p) {
params[!fixed] <- p
mu <- params[1]
sigma <- params[2]
a <- -0.5*length(data)*log(2*pi*sigma^2)
b <- -0.5*sum((data-mu)^2) / (sigma^2)
-(a + b)
}
> ls(environment(nLL))
[1] "data" "fixed" "params"
Estimating Parameters **
> optim(c(mu = 0, sigma = 1), nLL)$par
mu sigma
1.218239 1.787343
Fixing σ = 2
> nLL <- make.NegLogLik(normals, c(FALSE, 2))
> optimize(nLL, c(-1, 3))$minimum
[1] 1.217775
Fixing μ = 1
> nLL <- make.NegLogLik(normals, c(1, FALSE))
> optimize(nLL, c(1e-6, 10))$minimum
[1] 1.800596
Plotting the Likelihood **
nLL <- make.NegLogLik(normals, c(1, FALSE))
x <- seq(1.7, 1.9, len = 100)
y <- sapply(x, nLL)
plot(x, exp(-(y - min(y))), type = "l")
nLL <- make.NegLogLik(normals, c(FALSE, 2))
x <- seq(0.5, 1.5, len = 100)
y <- sapply(x, nLL)
plot(x, exp(-(y - min(y))), type = "l")
Lexical Scoping Summary
- Objective functions can be “built” which contain all of the necessary data for evaluating the function
- No need to carry around long argument lists — useful for interactive and exploratory work.
- Code can be simplified and cleand up
- Reference: Robert Gentleman and Ross Ihaka (2000). “Lexical Scope and Statistical Computing,” JCGS, 9, 491–508
Dates and Times in R
R has developed a special representation of date**s and **times
- Dates are represented by the Date class
- Times are represented by the POSIX**ct or the **POSIXlt class
- Dates are stored internally as the number of days since 1970-01-01
- Tmes are stored internally as the number of seconds since 1970-01-01
Dates in R
Dates are represented by the Date class and can be coerced from a character string using the as.Date() function.
>x <- as.Date("1970-01-01")
x
## [1] "1970-01-01"
unclass(x)
## [1] 0
unclass(as.Date("1970-01-02"))
## [1] 1
Times in R
Times are represented using the POSIXct or the POSIXlt class
- POSIXct is just a very large integer under the hood; it use a useful class when you want to store times in something like a data frame
- POSIXlt is a list underneath and it stores a bunch of other useful information like the day of the week, day of the year, month, day of the month
There are a number of generic functions (泛型函数) that work on dates and times
- weekdays: give the day of the week
- months: give the month name
- quarters: give the quarter number (“Q1”, “Q2”, “Q3”, or “Q4”)
Times can be coerced from a character string using the as.POSIXlt or as.POSIXct function.
>x <- Sys.time()
x
## [1] "2013-01-24 22:04:14 EST"
p <- as.POSIXlt(x)
names(unclass(p))
## [1] "sec" "min" "hour" "mday" "mon"
## [6] "year" "wday" "yday" "isdst"
p$sec
## [1] 14.34
You can also use the POSIXct format.
>x <- Sys.time()
x ## Already in ‘POSIXct’ format
## [1] "2013-01-24 22:04:14 EST"
unclass(x)
## [1] 1359083054
x$sec
## Error: $ operator is invalid for atomic vectors
p <- as.POSIXlt(x)
p$sec
## [1] 14.37
Finally, there is the strptime function in case your dates are written in a different format
>datestring <- c("January 10, 2012 10:40", "December 9, 2011 9:10")
x <- strptime(datestring, "%B %d, %Y %H:%M")
x
## [1] "2012-01-10 10:40:00 EST" "2011-12-09 09:10:00 EST"
class(x)
## [1] "POSIXlt" "POSIXt"
I can never remember the formatting strings. Check ?strptime for details.
Operations on Dates and Times
You can use mathematical operations on dates and times. Well, really just + and -. You can do comparisons too (i.e. ==, <=)
>x <- as.Date("2012-01-01")
y <- strptime("9 Jan 2011 11:34:21", "%d %b %Y %H:%M:%S")
x-y
## Warning: Incompatible methods ("-.Date",
## "-.POSIXt") for "-"
## Error: non-numeric argument to binary operator
x <- as.POSIXlt(x)
x-y
## Time difference of 356.3 days
Even keeps track of leap years, leap seconds, daylight savings, and time zones.
>x <- as.Date("2012-03-01") y <- as.Date("2012-02-28")
x-y
## Time difference of 2 days
x <- as.POSIXct("2012-10-25 01:00:00")
y <- as.POSIXct("2012-10-25 06:00:00", tz = "GMT")
y-x
## Time difference of 1 hours
Summary
- Dates and times have special classes in R that allow for numerical and statistical calculations
- Dates use the Date class
- Times use the POSIXct and POSIXlt class
- Character strings can be coerced to Date/Time classes using the strptime function or the as.Date, as.POSIXlt, or as.POSIXct