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[#]: subject: "The Functions in the R Stats Package"
[#]: via: "https://www.opensourceforu.com/2022/08/the-functions-in-the-r-stats-package/"
[#]: author: "Shakthi Kannan https://www.opensourceforu.com/author/shakthi-kannan/"
[#]: collector: "lkxed"
[#]: translator: "tanloong"
[#]: reviewer: " "
[#]: publisher: " "
[#]: url: " "
The Functions in the R Stats Package
======
We have learnt the basics of the R programming language, its syntax and semantics, and are now ready to delve into the statistics domain using R. In this eleventh article in the R series, we will learn to use the statistics functions available in the R stats package.
As we have done in the earlier parts of this series, we are going to use the R version 4.1.2 installed on Parabola GNU/Linux-libre (x86-64) for the code snippets.
```
$ R --version
R version 4.1.2 (2021-11-01) -- “Bird Hippie”
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under the terms of the
GNU General Public License versions 2 or 3.
For more information about these matters see https://www.gnu.org/licenses/
```
### Mean
The R function for calculating the arithmetic mean is mean. It accepts an R object ‘x’ as an argument, and a ‘trim’ option to trim any fractions before computing the mean value. The ‘na.rm’ logical argument can be used to ignore any NA values. Its syntax is as follows:
```
mean(x, trim = 0, na.rm = FALSE, ...)
```
The function supports numerical and logical values, as well as date and time intervals. A few examples for the mean function are given below:
```
> mean(c(1, 2, 3))
2
> mean(c(1:5, 10, 20))
6.428571
> mean(c(FALSE, TRUE, FALSE))
0.3333333
> mean(c(TRUE, TRUE, TRUE))
1
```
Consider the ‘Bank Marketing Data Set’ for a Portuguese banking institution that is available from the UCI Machine Learning Repository available at *https://archive.ics.uci.edu/ml/datasets/Bank+Marketing*. The data can be used for public research. There are four data sets available, and we will use the read.*csv()* function to import the data from a ‘bank*.csv*’ file.
```
> bank <- read.csv(file=”bank.csv”, sep=”;”)
> bank[1:3,]
age job marital education default balance housing loan contact day
1 30 unemployed married primary no 1787 no no cellular 19
2 33 services married secondary no 4789 yes yes cellular 11
3 35 management single tertiary no 1350 yes no cellular 16
month duration campaign pdays previous poutcome y
1 oct 79 1 -1 0 unknown no
2 may 220 1 339 4 failure no
3 apr 185 1 330 1 failure no
```
We can find the mean age from the bank data as follows:
```
> mean(bank$age)
41.1701
```
### Median
The *median* function in the R stats package computes the sample median. It accepts a numeric vector ‘x’, and a logical value ‘na.rm’ to indicate if it should discard ‘NA’ values before computing the median. Its syntax is as follows:
```
median(x, na.rm = FALSE, ...)
```
A couple of examples are given below:
```
> median(3:5)
4
> median(c(3:5, 50, 150))
[1] 5
```
We can now find the median age from the bank data set as follows:
```
> median(bank$age)
39
```
### Pair
The*pair* function can be used to combine two vectors. It accepts two arguments, and vectors ‘x’ and ‘y’. Both the vectors should have the same length. The syntax is as follows:
```
Pair(x, y)
```
The function returns a two column matrix of class ‘Pair’. An example is given below:
```
> Pair(c(1,2,3), c(4,5,6))
x y
[1,] 1 4
[2,] 2 5
[3,] 3 6
attr(,”class”)
[1] “Pair”
```
The function is used for paired tests such as *t.test* and *wilcox.test*.
### dist
The *dist* function is used to calculate the distance matrix for the rows in a data matrix, and accepts the following arguments:
| Argument | Description |
| :- | :- |
| x | A numeric matrix |
| method | The distance measure to be used |
| diag | Prints diagonal of distance matrix if TRUE |
| upper | Prints upper triangle of distance matrix if TRUE |
| p | Power of the Minkowski distance |
The distance measurement methods supported are: ‘euclidean’, ‘maximum’, ‘manhattan’, ‘canberra’, ‘binary’ and ‘minkowski’. The distance measurement for the age data set using the Euclidean distance is given below:
```
> dist(bank$age, method=”euclidean”, diag=FALSE, upper=FALSE, p=2)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
2 3
3 5 2
4 0 3 5
5 29 26 24 29
6 5 2 0 5 24
7 6 3 1 6 23 1
8 9 6 4 9 20 4 3
9 11 8 6 11 18 6 5 2
10 13 10 8 13 16 8 7 4 2
11 9 6 4 9 20 4 3 0 2 4
12 13 10 8 13 16 8 7 4 2 0 4
13 6 3 1 6 23 1 0 3 5 7 3 7
14 10 13 15 10 39 15 16 19 21 23 19 23 16
15 1 2 4 1 28 4 5 8 10 12 8 12 5 11
16 10 7 5 10 19 5 4 1 1 3 1 3 4 20 9
17 26 23 21 26 3 21 20 17 15 13 17 13 20 36 25 16
18 7 4 2 7 22 2 1 2 4 6 2 6 1 17 6 3 19
19 5 8 10 5 34 10 11 14 16 18 14 18 11 5 6 15 31 12
20 1 2 4 1 28 4 5 8 10 12 8 12 5 11 0 9 25 6 6
21 8 5 3 8 21 3 2 1 3 5 1 5 2 18 7 2 18 1 13 7
22 12 9 7 12 17 7 6 3 1 1 3 1 6 22 11 2 14 5 17 11 4
23 14 11 9 14 15 9 8 5 3 1 5 1 8 24 13 4 12 7 19 13 6 2
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
...
```
The binary distance measurement output for the same bank age data is as follows:
```
> dist(bank$age, method=”binary”, diag=FALSE, upper=FALSE, p=2)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
2 0
3 0 0
4 0 0 0
5 0 0 0 0
6 0 0 0 0 0
7 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
```
### Quantile
The quantiles can be generated for a numeric vector ‘x’ along with their given probabilities using the quantile function. The ‘NA’ and ‘NaN’ values are not allowed in the vector, unless ‘na.rm’ is set to TRUE. The probability of 0 is for the lowest observation and 1 for the highest. Its syntax is as follows:
```
quantile(x, ...)
```
The quantile function accepts the following arguments:
| Argument | Description |
| :- | :- |
| x | A numeric vector |
| probs | A vector of probabilities between 0 and 1 |
| na.rm | NA and NaN values are ignored if TRUE |
| names | If TRUE, the result has the names attribute |
| type | An integer to select any of the nine quantile algorithms |
| digits | The decimal precision to use |
| … | Additional arguments passed to or from other methods |
The *rnorm* function can be used to generate random values for the normal distribution. It can take ‘n’ number of observations, a vector of ‘means’, and a vector ‘sd’ of standard deviations as its arguments. We can obtain the quantiles for the values produced by the ‘rnorm’ function. For example:
```
> quantile(x <- rnorm(100))
0% 25% 50% 75% 100%
-1.978171612 -0.746829079 -0.009440368 0.698271134 1.897942805
```
The quantiles for bank age data with the following probabilities can be generated as follows:
```
> quantile(bank$age, probs = c(0.1, 0.5, 1, 2, 5, 10, 50)/100)
0.1% 0.5% 1% 2% 5% 10% 50%
20.0 22.6 24.0 25.0 27.0 29.0 39.0
```
### Interquartile range
The *IQR* function computes the interquartile range for the numeric values of a vector. Its syntax is as follows:
```
IQR(x, na.rm = FALSE, type = 7)
```
The ‘type’ argument specifies an integer to select the quantile algorithm, which is discussed in Hyndman and Fan (1996). The interquartile range for the bank age can be computed as shown below:
```
> IQR(bank$age, na.rm = FALSE, type=7)
16
```
### Standard deviation
The *sd* function is used to calculate the standard deviation for a set of values. It accepts a numeric vector ‘x’ and a logical value for ‘na.rm’ to remove missing values. Its syntax is as follows:
```
sd(x, na.rm = FALSE)
```
The standard deviation for a vector with length zero or 1 is ‘NA’. A couple of examples are given below:
```
> sd(1:10)
3.02765
> sd(1)
NA
```
We can calculate the standard deviation for the bank age column as follows:
```
> sd(bank$age)
10.57621
```
You are encouraged to explore more functions available in the stats packages available in R.
--------------------------------------------------------------------------------
via: https://www.opensourceforu.com/2022/08/the-functions-in-the-r-stats-package/
作者：[Shakthi Kannan][a]
选题：[lkxed][b]
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://www.opensourceforu.com/author/shakthi-kannan/
[b]: https://github.com/lkxed

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[#]: subject: "The Functions in the R Stats Package"
[#]: via: "https://www.opensourceforu.com/2022/08/the-functions-in-the-r-stats-package/"
[#]: author: "Shakthi Kannan https://www.opensourceforu.com/author/shakthi-kannan/"
[#]: collector: "lkxed"
[#]: translator: "tanloong"
[#]: reviewer: " "
[#]: publisher: " "
[#]: url: " "
R 语言 stats 包中的函数
======
我们已经学习了 R 语言的基础知识，包括其语法以及语法所对应的语义，现在准备使用 R 向统计学领域进发。本文是 R 系列的第十一篇文章，我们将学习如何使用 R 语言 stats 包中提供的统计函数。
与此系列之前的文章一样，我们将使用安装在 Parabola GNU/Linux-libre (x86-64) 上的 R 4.1.2 版本来运行文中的代码。
```
$ R --version
R version 4.1.2 (2021-11-01) -- "Bird Hippie"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under the terms of the
GNU General Public License versions 2 or 3.
For more information about these matters see https://www.gnu.org/licenses/
```
### mean 函数
在 R 中 *mean* 函数用来计算算术平均值。该函数接受一个 R 对象 `x` 作为参数，以及一个 `trim` 选项来在计算均值之前剔除任意比例的数据 (LCTT 译注：比如对于一个含有 7 个元素的向量 `x`，设置 `trim` 为 0.2 表示分别去掉 `x` 中最大和最小的前 20% (即 1.4 个) 的元素，所去掉的元素的个数会向下取整，所以最终会去掉 1 个最大值和 1 个最小值；`trim` 取值范围为 $[0, 0.5]$，默认为 0)。<ruby>逻辑参数<rt>logical argument</rt></ruby> (`TRUE` 或 `FALSE`) `na.rm` 可以设置是否忽略空值 (`NA`)。该函数的语法如下：
```
mean(x, trim = 0, na.rm = FALSE, ...)
```
该函数支持数值、逻辑值、日期和 <ruby>时间区间<rt>time intervals</rt></ruby>。下面是使用 `mean` 函数的一些例子：
```
> mean(c(1, 2, 3))
2
> mean(c(1:5, 10, 20))
6.428571
> mean(c(FALSE, TRUE, FALSE))
0.3333333
> mean(c(TRUE, TRUE, TRUE))
1
```
我们使用 UCI 机器学习库提供的一个采集自葡萄牙银行机构的“银行营销数据集”作为样本数据。该数据可用于公共研究，包含 4 个 csv 文件，我们使用 `read.csv()` 函数导入其中的 *bank.csv* 文件。
```
> bank <- read.csv(file="bank.csv", sep=";")
> bank[1:3,]
age job marital education default balance housing loan contact day
1 30 unemployed married primary no 1787 no no cellular 19
2 33 services married secondary no 4789 yes yes cellular 11
3 35 management single tertiary no 1350 yes no cellular 16
month duration campaign pdays previous poutcome y
1 oct 79 1 -1 0 unknown no
2 may 220 1 339 4 failure no
3 apr 185 1 330 1 failure no
```
下面是计算 age 列均值的示例：
```
> mean(bank$age)
41.1701
```
### median 函数
R 语言 stats 包中的 *median* 函数用来计算样本的中位数。该函数接受一个数值向量 `x`，以及一个逻辑值 `na.rm` 用来设置在计算中位数之前是否去除 `NA` 值。该函数的语法如下：
```
median(x, na.rm = FALSE, ...)
```
下面是使用该函数的两个例子：
```
> median(3:5)
4
> median(c(3:5, 50, 150))
[1] 5
```
现在我们可以计算银行数据中 age 列的中位数：
```
> median(bank$age)
39
```
### pair 函数
*pair* 函数用来合并两个向量，接受向量 `x` 和向量 `y` 两个参数。`x` 和 `y` 的长度必须相等。
```
Pair(x, y)
```
该函数返回一个 `Pair` 类的列数为 2 的矩阵，示例如下：
```
> Pair(c(1,2,3), c(4,5,6))
x y
[1,] 1 4
[2,] 2 5
[3,] 3 6
attr(,"class")
[1] "Pair"
```
该函数常用于像 T 检验和 Wilcox 检验等的 <ruby>配对检验<rt>paired test</rt></ruby>。
### dist 函数
*dist* 函数用来计算数据矩阵中各行之间的距离矩阵，接受以下参数：
| 参数 | 描述 |
| :- | :- |
| x | 数值矩阵 |
| method | 距离测量方法 |
| diag | 若为 TRUE，则打印距离矩阵的对角线 |
| upper | 若为 TRUE，则打印距离矩阵的上三角 |
| p | 闵可夫斯基距离的幂次 (见下文 LCTT 译注) |
该函数提供的距离测量方法包括：<ruby>欧式距离<rt>euclidean</rt></ruby>、<ruby>最大距离<rt>maximum</rt></ruby>、<ruby>曼哈顿距离<rt>manhattan</rt></ruby>、<ruby>堪培拉距离<rt>canberra</rt></ruby>、<ruby>二进制距离<rt>binary</rt></ruby> 和 <ruby>闵可夫斯基距离<rt>minkowski</rt></ruby>，默认为欧式距离 (LCTT 译注：
**欧式距离**指两点之间线段的长度，比如二维空间中 A 点 $(x_1, y_1)$ 和 B 点 $(x_2, y_2)$ 的欧式距离是 $\sqrt{(x_1 - x_2)^2 + (y_1 - y_2)^2}$；**最大距离**指 n 维向量空间中两点在各维度上的距离的最大值，比如 A 点 $(3, 6, 8, 9)$ 和 B 点 $(1, 8, 9, 10)$ 之间的最大距离是 $max(|3 - 1|, |6 - 8|, |8 - 9|, |9 - 10|)$，等于 2；**曼哈顿距离**指 n 维向量空间中两点在各维度上的距离之和，比如二维空间中 A 点 $(x_1, y_1)$ 和 B 点 $(x_2, y_2)$ 之间的曼哈顿距离是 $|x_1 - x_2| + |y_1 - y_2|$；**堪培拉距离**的公式是 $\sum_{i=1}^{n}\frac{|V1_i - V2_i|}{|V1_i| + |V2_i|}$；**二进制距离**首先将两个向量中的各元素看作其二进制形式，然后剔除在两个向量中对应值均为 0 的维度，最后计算在剩下的维度上两个向量间的对应值不相同的比例，比如 $V1 = (1, 3, 0, 5, 0)$ 和 $V2 = (11, 13, 0, 15, 10)$ 的二进制形式分别是 $(1, 1, 0, 1, 0)$ 和 $(1, 1, 0, 1, 1)$，其中第 3 个维度的对应值均为 0，剔除该维度之后为 $(1, 1, 1, 0)$ 和 $(1, 1, 1, 1)$，在剩余的 4 个维度中只有最后一个维度在两个向量之间的值不同，最终结果为 0.25；**闵可夫斯基距离**是欧式距离和曼哈顿距离的推广，公式是 $\sqrt[p]{\sum_{i=1}^{n}{|V1_i - V2_i|^p}}$，当 $p=1$ 时相当于曼哈顿距离，当 $p=2$ 时相当于欧式距离。)。下面是使用欧式距离计算 age 列距离矩阵的示例：
```
> dist(bank$age, method="euclidean", diag=FALSE, upper=FALSE, p=2)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
2 3
3 5 2
4 0 3 5
5 29 26 24 29
6 5 2 0 5 24
7 6 3 1 6 23 1
8 9 6 4 9 20 4 3
9 11 8 6 11 18 6 5 2
10 13 10 8 13 16 8 7 4 2
11 9 6 4 9 20 4 3 0 2 4
12 13 10 8 13 16 8 7 4 2 0 4
13 6 3 1 6 23 1 0 3 5 7 3 7
14 10 13 15 10 39 15 16 19 21 23 19 23 16
15 1 2 4 1 28 4 5 8 10 12 8 12 5 11
16 10 7 5 10 19 5 4 1 1 3 1 3 4 20 9
17 26 23 21 26 3 21 20 17 15 13 17 13 20 36 25 16
18 7 4 2 7 22 2 1 2 4 6 2 6 1 17 6 3 19
19 5 8 10 5 34 10 11 14 16 18 14 18 11 5 6 15 31 12
20 1 2 4 1 28 4 5 8 10 12 8 12 5 11 0 9 25 6 6
21 8 5 3 8 21 3 2 1 3 5 1 5 2 18 7 2 18 1 13 7
22 12 9 7 12 17 7 6 3 1 1 3 1 6 22 11 2 14 5 17 11 4
23 14 11 9 14 15 9 8 5 3 1 5 1 8 24 13 4 12 7 19 13 6 2
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
...
```
改用二进制距离的计算结果如下：
```
> dist(bank$age, method="binary", diag=FALSE, upper=FALSE, p=2)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
2 0
3 0 0
4 0 0 0
5 0 0 0 0
6 0 0 0 0 0
7 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
```
### quantile 函数
*quantile* 函数用于计算数值向量 `x` 的分位数及其对应的概率。当设置 `na.rm` 为 `TRUE` 时，该函数将忽略向量中的 `NA` 和 `NaN` 值。概率 0 对应最小观测值，概率 1 对应最大观测值。该函数的语法如下:
```
quantile(x, ...)
```
*quantile* 函数接受以下参数：
| 参数 | 描述 |
| :- | :- |
| x | 数值向量 |
| probs | 概率向量，取值为 $[0, 1]$ (LCTT 译注：默认为 `(0, 0.25, 0.5, 0.75, 1)`)|
| na.rm | 若为 TRUE，忽略向量中的 NA 和 NaN 值 |
| names | 若为 TRUE，在结果中包含命名属性 |
| type | 整数类型，用于选择任意一个九种分位数算法 (LCTT 译注：默认为 7)|
| digits | 小数精度 |
| … | 传递给其他方法的额外参数 |
*rnorm* 函数可用于生成正态分布的随机数。它可以接受要生成的观测值的数量 n，一个均值向量以及一个标准差向量。下面是一个计算 `rnorm` 函数生成的随机数的四分位数的示例：
```
> quantile(x <- rnorm(100))
0% 25% 50% 75% 100%
-1.978171612 -0.746829079 -0.009440368 0.698271134 1.897942805
```
下面是生成银行年龄数据对应概率下的分位数的示例：
```
> quantile(bank$age, probs = c(0.1, 0.5, 1, 2, 5, 10, 50)/100)
0.1% 0.5% 1% 2% 5% 10% 50%
20.0 22.6 24.0 25.0 27.0 29.0 39.0
```
### IQR 函数
*IQR* 函数用于计算向量中数值的 <ruby>四分位距<rt>interquartile range</rt></ruby>。其语法如下：
```
IQR(x, na.rm = FALSE, type = 7)
```
参数 `type` 指定了一个整数以选择分位数算法，该算法在 [Hyndman and Fan (1996)][c] 中进行了讨论。下面是计算银行年龄四分位距的示例：
```
> IQR(bank$age, na.rm = FALSE, type=7)
16
```
### sd 函数
*sd* 函数用来计算一组数值中的标准差。该函数接受一个 <ruby>数值向量<rt>numeric vector</rt></ruby> `x` 和一个逻辑值 `na.rm`。`na.rm` 用来设置在计算时是否忽略缺失值。该函数的语法如下：
```
sd(x, na.rm = FALSE)
```
对于长度为 0 或 1 的向量，该函数返回 `NA`。下面是两个例子：
```
> sd(1:10)
3.02765
> sd(1)
NA
```
下面是计算 age 列标准差的示例：
```
> sd(bank$age)
10.57621
```
R 语言 stats 包中还有很多其他函数，鼓励你自行探索。
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via: https://www.opensourceforu.com/2022/08/the-functions-in-the-r-stats-package/
作者：[Shakthi Kannan][a]
选题：[lkxed][b]
译者：[tanloong](https://github.com/tanloong)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://www.opensourceforu.com/author/shakthi-kannan/
[b]: https://github.com/lkxed
[c]: https://www.amherst.edu/media/view/129116/.../Sample+Quantiles.pdf