[1062BPY12001] Data analysis with R / April 19

R 語⾔言與資料分析
function, packages, and control flow

求助範例
(conti.)
說明
語法
參數
回傳值
範例

FunctionArguments
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
> ?sd
> ?rnorm
> ?matrix
> ?sample
x a numeric vector or an R object which is coercible to one by
as.double(x).
na.rm logical. Should missing values be removed?
Description
This function computes the standard deviation of the values in x. If na.rm is TRUE then
missing values are removed before computation proceeds.
Usage
sd(x, na.rm = FALSE)
Arguments

Argument Matching
R functions arguments can be matched positionally or by name.
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)

Argument Matching
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.
The following two calls are equivalent.
> 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, ...)
> x = seq(1, 200, 1)
> e = rnorm(200)*50
> y = x * 5 + e
> mydata = data.frame(x, y)
> lm(data = mydata, y ~ x, model = FALSE,1:100)
> lm(y ~ x, mydata, 1:100, model = FALSE)

Practice:subseting&descriptivestatistics
> rm(list = ls())
> iris <- iris # data frame
> iris.1 <- iris$Species == “setosa" # `iris.1`: logical vector
> iris.setosa <- iris[iris.1,] # data frame of `setosa`
> iris.3 <- iris$Species == “virginica" # `iris.3`: logical vector
> iris.virginica <- iris[iris.3,] # data frame of `virginica`
> mean(iris.setosa[,1])
[1] 5.006
[1] 3.428
[1] 1.462
[1] 0.246
> sd(iris.setosa[,1])
[1] 0.3524897
[1] 0.3790644
[1] 0.173664
[1] 0.1053856

Practice:subseting&descriptivestatistics
[1] 5.006
[1] 3.428
[1] 1.462
[1] 0.246
[1] 0.3524897
[1] 0.3790644
[1] 0.173664
[1] 0.1053856
> colMeans(iris.setosa[,-5])
Sepal.Length Sepal.Width Petal.Length Petal.Width
5.006 3.428 1.462 0.246
> apply(iris.setosa[,-5], 2, FUN = mean)
5.006 3.428 1.462 0.246
> apply(iris.setosa[,-5], 2, FUN = sd) # try `min`, `max`, and `length`
0.3524897 0.3790644 0.1736640 0.1053856

R Packages
•･ Select repositories... => 選擇套件所在資源庫 => OK
•･ Packages => Install package(s) => CRAN mirror => installr, rgl,
scatterplot3d (按Ctrl可多重選取) => OK
> install.packages(c(“rgl","scatterplot3d","Rcmdr"))
> install.packages("foreign", repos = "http://cran.csie.ntu.edu.tw")
> library(rgl) # 載⼊入套件，same as > library("rgl")
> detach(package:rgl) # 卸載套件
> remove.packages("rgl") # 移除套件
> .libPaths() # 列出library安裝⽬目錄

R Packages/helps
•･ library(help=“[package name]”) # 檢視套件內容
> library(help=“stats")
•･ ?[package name] # 簡略說明
> ?stats
•･ ?[package name]::[function name] # 函數使⽤用說明
> ?stats::lm

Practice:`psych`package
> library(psych)
> describeBy(iris, group=iris[,5])
Descriptive statistics by group
group: setosa
vars n mean sd median trimmed mad min max range skew kurtosis se
Sepal.Length 1 50 5.01 0.35 5.0 5.00 0.30 4.3 5.8 1.5 0.11 -0.45 0.05
Sepal.Width 2 50 3.43 0.38 3.4 3.42 0.37 2.3 4.4 2.1 0.04 0.60 0.05
Petal.Length 3 50 1.46 0.17 1.5 1.46 0.15 1.0 1.9 0.9 0.10 0.65 0.02
Petal.Width 4 50 0.25 0.11 0.2 0.24 0.00 0.1 0.6 0.5 1.18 1.26 0.01
Species* 5 50 1.00 0.00 1.0 1.00 0.00 1.0 1.0 0.0 NaN NaN 0.00
---------------------------------------------------------------------------
group: versicolor
Sepal.Length 1 50 5.94 0.52 5.90 5.94 0.52 4.9 7.0 2.1 0.10 -0.69 0.07
Sepal.Width 2 50 2.77 0.31 2.80 2.78 0.30 2.0 3.4 1.4 -0.34 -0.55 0.04
Petal.Length 3 50 4.26 0.47 4.35 4.29 0.52 3.0 5.1 2.1 -0.57 -0.19 0.07
Petal.Width 4 50 1.33 0.20 1.30 1.32 0.22 1.0 1.8 0.8 -0.03 -0.59 0.03
Species* 5 50 2.00 0.00 2.00 2.00 0.00 2.0 2.0 0.0 NaN NaN 0.00
---------------------------------------------------------------------------
group: virginica
Sepal.Length 1 50 6.59 0.64 6.50 6.57 0.59 4.9 7.9 3.0 0.11 -0.20 0.09
Sepal.Width 2 50 2.97 0.32 3.00 2.96 0.30 2.2 3.8 1.6 0.34 0.38 0.05
Petal.Length 3 50 5.55 0.55 5.55 5.51 0.67 4.5 6.9 2.4 0.52 -0.37 0.08
Petal.Width 4 50 2.03 0.27 2.00 2.03 0.30 1.4 2.5 1.1 -0.12 -0.75 0.04
Species* 5 50 3.00 0.00 3.00 3.00 0.00 3.0 3.0 0.0 NaN NaN 0.00

> library(psych)
> describeBy(iris[,-5], group=iris[,5])
Descriptive statistics by group
group: setosa
Sepal.Length 1 50 5.01 0.35 5.0 5.00 0.30 4.3 5.8 1.5 0.11 -0.45 0.05
Sepal.Width 2 50 3.43 0.38 3.4 3.42 0.37 2.3 4.4 2.1 0.04 0.60 0.05
Petal.Length 3 50 1.46 0.17 1.5 1.46 0.15 1.0 1.9 0.9 0.10 0.65 0.02
Petal.Width 4 50 0.25 0.11 0.2 0.24 0.00 0.1 0.6 0.5 1.18 1.26 0.01
---------------------------------------------------------------------------
group: versicolor
Sepal.Length 1 50 5.94 0.52 5.90 5.94 0.52 4.9 7.0 2.1 0.10 -0.69 0.07
Sepal.Width 2 50 2.77 0.31 2.80 2.78 0.30 2.0 3.4 1.4 -0.34 -0.55 0.04
Petal.Length 3 50 4.26 0.47 4.35 4.29 0.52 3.0 5.1 2.1 -0.57 -0.19 0.07
Petal.Width 4 50 1.33 0.20 1.30 1.32 0.22 1.0 1.8 0.8 -0.03 -0.59 0.03
---------------------------------------------------------------------------
group: virginica
Sepal.Length 1 50 6.59 0.64 6.50 6.57 0.59 4.9 7.9 3.0 0.11 -0.20 0.09
Sepal.Width 2 50 2.97 0.32 3.00 2.96 0.30 2.2 3.8 1.6 0.34 0.38 0.05
Petal.Length 3 50 5.55 0.55 5.55 5.51 0.67 4.5 6.9 2.4 0.52 -0.37 0.08
Petal.Width 4 50 2.03 0.27 2.00 2.03 0.30 1.4 2.5 1.1 -0.12 -0.75 0.04

> pairs.panels(iris)

> ppairs.panels(iris,bg=c("red","yellow","blue")[iris$Species],pch=21,main="Fisher
Iris data by Species”)

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:testingacondition
· for:executealoopafixednumberoftimes
· while:executealoopwhileaconditionistrue
· repeat:executeaninfiniteloop
· break:breaktheexecutionofaloop
· next:skipaninterationofaloop
· return:exitafunction
Most control structures are not used in interactive sessions, but rather when writing functions or
longer expresisons.

for
for loops take an interator variable and assign it successive values from a sequence or vector. For loops
are most commonly used for iterating over the elements of an object (list, vector, etc.)
This loop takes the i variable and in each iteration of the loop gives it values 1, 2, 3, ..., 10, and then
exits.
for(i in 1:10) {
print(i)
}

for
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)) {
print(x[i])
}
for(letter in x) {
print(letter)
}
for(i in 1:4) print(x[i])

Nested for loops
for loops can be nested.
Be careful with nesting though. Nesting beyond 2–3 levels is often very difficult to read/understand.
x <- matrix(1:6, 2, 3)
for(i in seq_len(nrow(x))) {
for(j in seq_len(ncol(x))) {
print(x[i, j])
}
}

for
> print(paste("The year is", 2011))
[1] "The year is 2011"
[1] "The year is 2015”
...
...
for (i in 2010:2015){
print(paste("The year is", i))
}
for (year in c(2010,2011,2012,2013,2014,2015)){
print(paste("The year is", year))
}

if(<condition>) {
## do something
}
Example: if statement
x <- 5
if(x > 0){
print("Positive number")
}
Output
[1] "Positive number"
Control Structures: if

if...else statement
The syntax of if...else statement is:
if (test_expression) {
## do something
} else {
## do something else
}
x <- -5
if(x > 0){
print("Non-negative number")
} else {
print("Negative number")
}
Output
[1] "Negative number" 如果執⾏行的指令只有⼀一⾏行，⽐比較優雅的寫法如下：
if(x > 0) print("Non-negative number") else print("Negative number”)
x <- -5
y <- if(x > 0) 5 else 6
y
[1] 6

x <- 0
if (x < 0) {
print("Negative number")
} else if (x > 0) {
print("Positive number")
} else
print("Zero")
Output
[1] "Zero"
if(test_expression1) {
## do something
} else if(test_expression2){
## do something different
} else {
## do something different
}

[1062BPY12001] Data analysis with R / April 19

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[1062BPY12001] Data analysis with R / April 19