NEW BOT Телеграм, страница - 308122981

|R| Experts

1.05K subscribers

376 photos

35 videos

58 files

205 links

@R_Experts
🔴آمار علم جان بخشیدن به داده‌هاست.
🔷ارتباط با ما
@iamrezaei
لینک یوتیوب و اینستاگرام و ویرگول:
https://zil.ink/expertstv

Download Telegram

About

Blog

Apps

Platform

1.05K subscribers

#Example
#data.frame

@R_Experts

945 views00:25

ابتدا پکیج MASS
را نصب و فراخوانی کنید

> library(MASS)      # load the MASS package

> painters

              Composition Drawing Colour Expression School

Da Udine               10       8     16          3      A

Da Vinci               15      16      4         14      A

Del Piombo              8      13     16          7      A

Del Sarto              12      16      9          8      A

Fr. Penni               0      15      8          0      A

Guilio Romano          15      16      4         14      A

این داده ها مربوط به نقاشان کلاسیک قرن 18 میباشند که در رسته های ترکیب بندی ،رنگ بندی،... دسته بندی شده اند

> painters$School

 [1] A A A A A A A A A A B B B B B B C C C C C C D D D D

[27] D D D D D D E E E E E E E F F F F G G G G G G G H H

[53] H H

Levels: A B C D E F G H

با این دستور میتوانیم مشاهده کنیم که سطح جایی که اموزش دیدن چگونه است
که در 8 سطح میباشد

@R_Experts

682 viewsedited 17:17

> school = painters$School      # the painter schools 
> school.freq = table(school)   # apply the table function 
> school.freq 
school 
 A  B  C  D  E  F  G  H 
10  6  6 10  7  4  7  4 
> cbind(school.freq) 
  school.freq 
A          10 
B           6 
C           6 
D          10 
E           7 
F           4 
G           7 
H           4

همان طور که مشاهده میکنید جدول فراوانی بین تعداد نقاشان و سطح جایی که اموزش دیدن رو اوردیم و به یک جدول تبدیل کردیم
و این جدول رو سپس ستونی نمودیم
مثلا
A 10
نشان دهنده ی این است که در سطح اموزش A
10 نفر اموزش دیدند

@R_Experts

633 views17:26

> school.relfreq = school.freq / nrow(painters)

> school.relfreq

school

       A        B        C        D        E        F

0.185185 0.111111 0.111111 0.185185 0.129630 0.074074

       G        H

0.129630 0.074074

> barplot(school.freq)

> colors = c("red", "yellow", "green", "violet",

+   "orange", "blue", "pink", "cyan")

> barplot(school.freq,         # apply the barplot function

+   col=colors)                # set the color palette

ابتدا فراوانی نسبی این داده ها را محاسبه و سپس نمودار هیستوگرام انها را رسم کرد هایم که نمودار ها را در پایین مشاهده میکنید 👇👇👇

@R_Experts

633 viewsedited 17:33

596 views17:34

603 views17:35

> pie(school.freq)      
> colors = c("red", "yellow", "green", "violet", 
+   "orange", "blue", "pink", "cyan") 
> pie(school.freq,             # apply the pie function 
+   col=colors)                # set the color palette

نمودار دایره ای مربوطه👇👇👇👇

@R_Experts

613 views17:37

634 views17:38

#Solution_Practice_3

> cc<-c(1,2,3,4,5,2,1,2,3,4,3,2,1,2,3,4,3,2,1,2,5,4,3,2,1)
> A<-matrix(cc,5,5)
> A
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    2    3    4    5
[2,]    2    1    2    3    4
[3,]    3    2    1    2    3
[4,]    4    3    2    1    2
[5,]    5    4    3    2    1
> b<-matrix(c(7,-1,-3,5,17),5,1)
> b
     [,1]
[1,]    7
[2,]   -1
[3,]   -3
[4,]    5
[5,]   17
> x<-solve(A)%*%b
> x
     [,1]
[1,]   -2
[2,]    3
[3,]    5
[4,]    2
[5,]   -4

691 views17:53

#توابع_ریاضی

@R_Experts

704 views21:03

#factor_function
فرم کلی این تابع به صورت :
factor(x = character(), levels, labels = levels,
exclude = NA, ordered = is.ordered(x))

که از یک رشته شی محتويات انها را خارج و در
Levels
قرار میدهد و میتوان به انها برچسب نیز نسبت داد ، و ordered ترتیب را برای ما مشخص میکند

> mons = c("March","April","January","November","January",
+ "September","October","September","November","August",
+ "January","November","November","February","May","August",
+ "July","December","August","August","September","November",
+ "February","April")
> mons = factor(mons)
> table(mons)
mons
    April    August  December  February   January      July
        2         4         1         2         3         1
    March       May  November   October September
        1         1         5         1         3

ordered=TRUE

ترتیب اعضای فاکتور را مشخص میکند

> fert = c(10,20,20,50,10,20,10,50,20)
> fert = factor(fert,levels=c(10,20,50),ordered=TRUE)
> fert
[1] 10 20 20 50 10 20 10 50 20
Levels: 10 < 20 < 50

1.04K viewsedited 16:19

abs(x)

x: Numeric value, array or vector

> abs(-1)

[1] 1


> abs(20)

[1] 20


> abs(0)

[1] 0


> x <- c(-2,4,0,45,9,-4)
> abs(x)

[1]  2  4  0 45  9  4


> x <- matrix(c(-3,5,-7,1,-9,4),nrow=3,ncol=2,byrow=TRUE)
> abs(x[1,])

[1] 3 5


> abs(x[,1])

[1] 3 7 9

520 views16:22

#atan

atan() function returns the radian arctangent of number data.

atan(x)

x: Numeric value, array or vector

> atan(1)

[1] 0.7853982


> atan(0)

[1] 0


> atan(0.5)

[1] 0.4636476


> x <- c(1, 0, 0.5)
> atan(x)

[1] 0.7853982 0.0000000 0.4636476

509 views16:23

#beta

beta() function return the beta function and the natural logarithm of the beta function.

B(a,b) = Γ(a)Γ(b)/Γ(a+b)

beta(a, b)

a,b: non-negative numeric vectors

> beta(4,9)

[1] 0.0005050505



> x <- c(3,6, 4)
> y <- c(7,4, 12)
> beta(x,y)

[1] 0.0039682540 0.0019841270 0.0001831502

516 views16:28

#choose()

choose(n,r)

n: n elements
r: r subset elements
...

nCr = n!/(r! * (n-r)!)

> choose(5,2)

[1] 10


> choose(2,1)

[1] 2

522 views16:30

#log( )

function computes natural logarithms (Ln) for a number or vector. log10 computes common logarithms (Lg).log2 computes binary logarithms (Log2). log(x,b) computes logarithms with base b.

>log(5)     #ln5

[1] 1.609438


>log10(5)    #lg5

[1] 0.69897


>log2(5)    #log25

[1] 2.321928


>log(9,base=3)  #log39 = 2

[1] 2

>x <- rep(1:12)
>x

 [1]  1  2  3  4  5  6  7  8  9 10 11 12


>log(x)

 [1] 0.0000000 0.6931472 1.0986123 1.3862944 1.6094379 1.7917595 1.9459101
 [8] 2.0794415 2.1972246 2.3025851 2.3978953 2.4849066


>log(x,6)

 [1] 0.0000000 0.3868528 0.6131472 0.7737056 0.8982444 1.0000000 1.0860331
 [8] 1.1605584 1.2262944 1.2850972 1.3382908 1.3868528

526 views16:33

log10() function computes base 10 logarithm.

log10(x)

x: numeric vector

> log10(100)

[1] 2


> x <- c(100,1000, 10000)
> log10(x)

[1] 2 3 4

505 views16:34

#exp

exp(x) function compute the exponential value of a number or number vector, ex.

> x <- 5

> exp(x)

[1] 148.4132

> y <- rep(1:20)

> exp(y)

         [,1]     [,2]     [,3]     [,4]      [,5]

[1,] 2.718282 20.08554 148.4132 1096.633  8103.084

[2,] 7.389056 54.59815 403.4288 2980.958 22026.466

expm1() function computes exp() minus 1:»>حاصل منهای 1

> expm1(5)

[1] 147.4132

> expm1(rep(1:20))

         [,1]     [,2]     [,3]     [,4]      [,5]

[1,] 1.718282 19.08554 147.4132 1095.633  8102.084

[2,] 6.389056 53.59815 402.4288 2979.958 22025.466

518 viewsedited 16:41

#factorial

factorial() function computes the factorial of a number.

factorial(x)

x: numeric vector

> factorial(2)  #2 × 1

[1] 2


> factorial(1)   #1 × 1

[1] 1


> factorial(3)  #3 × 2 × 1

[1] 6


> factorial(4)  #4 × 3 × 2 × 1

[1] 24


> factorial(c(4,3,2))

[1] 24  6  2

511 views16:42

#max_min

max() function computes the maximun value of a vector. min() function computes the minimum value of a vector.

max(x,na.rm=FALSE)
min(x,na.rm=FALSE)

• x: number vector
• na.rm: whether NA should be removed, if not, NA will be returned
...

> x <- c(1,2.3,2,3,4,8,12,43,-4,-1)
> max(x)

[1] 43


> min(x)

[1] -4

Missing value affect the results:

> y<- c(x,NA)
> y

 [1]  1.0  2.3  2.0  3.0  4.0  8.0 12.0 43.0 -4.0 -1.0   NA

> max(y)

[1] NA

> min(y)

[1] NA

After define na.rm=TRUE, result is meaningful:

> max(y,na.rm=TRUE)

[1] 43

Compare more than 1 vectors:

> x2 <- c(-100,-43,0,3,1,-3)
> min(x,x2)

[1] -100

525 views16:45

#atan2

atan2(y, x) function returns the radian arctangent between the x-axis and the vector from the origin to (x, y).

در مختصات دکارتی این نقطه نسبت به مبدا زاویه ای تولید میکند را تانژانت معکوس رادیانش را بر میگرداند
atans(y, x)

x, y: Numeric value, array or vector

> atan2(2,1)

[1] 1.107149


> y <- c(2,3)
> x <- c(5,6)
> atan2(y,x)

[1] 0.3805064 0.4636476

510 views16:52