R, Python 분석과 프로그래밍의 친구 (by R Friend)

# Importing common libraries

import numpy as np

import pandas as pd

# sample DataFrame

df = pd.DataFrame({'group': ['a', 'a', 'a', 'b', 'b', 'b'], 

                  'value_1': np.arange(6), 

                 'value_2': np.random.randn(6)})

df

# Making GroupBy object

grouped = df.groupby('group')

grouped

<pandas.core.groupby.DataFrameGroupBy object at 0x11136f550>

  (1) GroupBy 메소드를 이용한 집계 (GroupBy aggregation using methods)

count(): 그룹 내 non-NA 개수 

sum(): 그룹 내 non-NA 합 

grouped.count()

grouped.sum() # DataFrame

grouped.sum()['value_2'] # Series

group

a   -0.012473
b    0.072545 

Name: value_2, dtype: float64

pd.DataFrame(grouped.sum()['value_2']) # DataFrame 

min(): 그룹 내 non-NA 값 중 최소값 

max(): 그룹 내 non-NA 값 중 최대값 

grouped.min()

grouped.max()

mean(): 그룹 내 non-NA 값들의 평균값 

median(): 그룹 내 non-NA 값들의 중앙값 

grouped.mean()

grouped.median()

std(): 그룹 내 표준편차

var(): 그룹 내 분산

quantile(): 그룹 내 분위수 

grouped.std() 

grouped.var()

 # interpolation

grouped.quantile(0.1) 

first(): 그룹 내 non-NA 값 중 첫번째 값 

last(): 그룹 내 non-NA 값 중 마지막 값 

grouped.first()

grouped.last()

describe(): 그룹 별 기술통계량 

- 옆으로 길게

 describe().T: 그룹 별 기술통계량 

- 세로로 길게

grouped.describe()['value_1']

grouped.describe()['value_1'].T

  (2) 함수를 이용한 GroupBy 집계: grouped.agg(function)

def iqr_func(x):

    q3, q1 = np.percentile(x, [75, 25])

    iqr = q3 - q1

    return iqr

grouped.aggregate(function) 

grouped.agg(function) 

grouped.aggregate(iqr_func)

grouped.agg(iqr_func)

grouped.quantile([0.75, 0.25])

 1-1. len(tuple) : 튜플의 전체 길이 (length)

# en() : Gives the total length of the tuple

>>> len((1, 2, 3))

3

 1-2. max(tuple) : 튜플 안에 있는 요소값 중 최대값 반환 (문자는 알파벳 기준)

# max(): Returns item from the tuple with max value

>>> len((1, 2, 3))

3

>>> max((1, 2, 3, 4, 5))

5

>>> max(('a', 'b', 'c', 'd', 'e')) # As for character, in order of alphabet

'e'

# TypeError for max() method when 'str' and 'int' are mixed in a tuple

>>> max((1, 2, 3, 'a', 'b', 'c'))

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

TypeError: '>' not supported between instances of 'str' and 'int'

 1-3. min(tuple) : 튜플 안에 있는 요소값 중 최소값 반환 (문자는 알파벳 기준)

# min() : Returns item from the tuple with min value

>>> min((1, 2, 3, 4, 5))

1

>>> min(('a', 'b', 'c', 'd', 'e'))

'a'

 1-4. tuple(seq) : 리스트를 튜플로 변환 (converting a list into tuple)

# tuple(seq) : Converts a list to tuple

>>> my_list = [1, 2, 'a', 'b']

>>> type(my_list)

<class 'list'>

>>> 

>>> my_tup = tuple(my_list)

my_tup

>>> (1, 2, 'a', 'b')

>>> type(my_tup)

<class 'tuple'>

 2-1. tuple.count() : 튜플 내 요소의 개수 세기

# tuple.count(obj.) : Returns the total number of obj. in tuple

>>> tup = (1, 2, 3, 4, 5, 2, 2)

>>> tup.count(2)

3

 2-2. tuple.index(obj.) : 튜플 내 요소가 있는 위치 index 반환

# tuple.index(obj.) : Returns the index of a obj. in tuple

>>> tup = (1, 2, 3, 4, 5, 2, 2)

>>> tup.index(2)

1

[참고]  메소드 (Method)

내장 함수(Built-in Function)와는 달리 문자열 자료형과 같이 특정 자료형이 가지고 있는 함수를 메소드(Method) 라고 합니다. 메소드는 객체 지향 프로그래밍의 기능에 대응하는 파이썬 용어입니다. 함수와 거의 동일한 의미이지만 메소드는 클래스의 멤버라는 점이 다릅니다. 

len() : 문자열 길이

# len() : Returns the length of the string

>>> a = 'I Love Python'

>>> len(a)

13

 min(), max() : 문자열 내 문자, 혹은 숫자의 최소값, 최대값 (알파벳 순서, 숫자 순서 기반)

# max(str), min(str) : Returns the max, min alphabetical character from the string str

>>> d = 'abc'

>>> f = '123'

>>> 

>>> min(d)

'a'

>>> max(d)

'c'

>>> 

>>> min(f)

'1'

>>> max(f)

'3'

 count() :  문자열 안에서 매개변수로 입력한 문자열이 몇 개 들어있는지 개수를 셈 

                (begin, end 위치 설정 가능)

# count() : Counts how many times str occurs in string

>>> a = 'I Love Python'

>>> a.count('o')

2

>>> 

>>> a = 'I Love Python'

>>> a.count('o', 7, len(a)) # count(string, begin, end)

1

>>> 

>>> a.count('k') # there is no 'k' character in 'a' string

0

  startswith() : 문자열이 매개변수로 입력한 문자열로 시작하면 True, 그렇지 않으면 False 반환

# startswith(): Determines if string or a substring of string

>>> a = 'I Love Python'

>>> a.startswith('I')

True

>>> a.startswith('I Lo')

True

>>> a.startswith('U')

False

  endswith() : 문자열이 매개변수로 입력한 문자열로 끝나면 True, 그렇지 않으면 False 반환

# endswith(): Determines if string or a substring of string

>>> a = 'I Love Python'

>>> a.endswith('Python')

True

>>> a.endswith('Pycham')

False

 find() : 문자열에 매개변수로 입력한 문자열이 있는지를 앞에서 부터 찾아 index 반환, 없으면 '-1' 반환

# find() : Search forwards, Determine if str occurs in string and return the index

>>> a = 'I Love Python'

>>> a.find('o')

3

>>> a.find('k') # if there is no string, then '-1'

-1

  rfind() : 문자열에 매개변수로 입력한 문자열이 있는지를 뒤에서 부터 찾아 index 반환, 없으면 '-1' 반환

# rfind() : Same as find(), but search backwards in string

>>> a = 'I Love Python'

>>> a.rfind('o')

11

 index() :  find()와 기능 동일하나, 매개변수로 입력한 문자열이 없으면 ValueError 발생

# index(): Same as find(), but raises an exception if str not found

>>> a = 'I Love Python'

>>> a.index('o')

3

>>> a.index('k') # ValueError: substring not found

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

ValueError: substring not found

 rindex() : index()와 기능 동일하나, 뒤에서 부터 매개변수의 문자열이 있는지를 찾음

# rindex(): Same as index(), but search backwards in string

>>> a = 'I Love Python'

>>> a.rindex('o')

11

>>> a.rindex('k') # ValueError: substring not found

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

ValueError: substring not found

  isalnum() : 문자열이 알파벳과 숫자로만 이루어졌으면 True, 그렇지 않으면 False

>>> a = 'I Love Python'

>>> d = 'abc'

>>> e = '123abc'

>>> f = '123'

>>> 

# isalnum() : Returns true if string has at least 1 character and 

#                  all characters are alphanumeric and false otherwise

>>> a.isalnum() # False

False

>>> d.isalnum() # True

True

>>> e.isalnum() # True

True

>>> f.isalnum() # True

True

  isalpha() : 문자열이 알파벳(영어, 한글 등)으로만 이루어졌으면 True, 그렇지 않으면 False

>>> a = 'I Love Python'

>>> d = 'abc'

>>> e = '123abc'

>>> f = '123'

>>> 

# isalpha() : Returns true if string has at least 1 character 

#                 and all characters are alphabetic and false otherwise

>>> a.isalpha() # False (there is space between characters)

False

>>> d.isalpha() # True

True

>>> e.isalpha() # False

False

>>> f.isalpha() # False

False

  isdigit() : 문자열이 숫자만 포함하고 있으면 True, 그렇지 않으면 False, isnumeric()과 동일

>>> a = 'I Love Python'

>>> d = 'abc'

>>> e = '123abc'

>>> f = '123'

>>> 

# isdigit() : Returns true if string contains only digits and false otherwise

>>> a.isdigit() # False 

False

>>> d.isdigit() # False

False

>>> e.isdigit() # False

False

>>> f.isdigit() # True

True

  isnumeric() : 문자열이 숫자로만 이루어져 있으면 True, 그렇지 않으면 False, isdigit()과 동일

>>> a = 'I Love Python'

>>> d = 'abc'

>>> e = '123abc'

>>> f = '123'

>>> 

# isnumeric(): Returns true if a unicode string contains only numeric characters

>>> a.isnumeric() # False

False

>>> d.isnumeric() # False

False

>>> e.isnumeric() # False

False

>>> f.isnumeric() # True

True

  isdecimal() : 문자열이 10진수 문자이면 True, 그렇지 않으면 False

>> a = 'I Love Python'

>>> d = 'abc'

>>> e = '123abc'

>>> f = '123'

>>> 

# isdecimal(): Returns true if a unicode string contains only decimal characters

>>> a.isdecimal() # False

False

>>> d.isdecimal() # False

False

>>> e.isdecimal() # False

False

>>> f.isdecimal() # True 

True

  islower() : 문자열이 모두 소문자로만 되어있으면 True, 그렇지 않으면 False

>>> a = 'I Love Python'

>>> g = 'i love python'

>>> h = 'I LOVE PYTHON'

>>> 

# islower(): Returns true if string has at least 1 cased character 

#            and all cased characters are in lowercase and false otherwise

>>> a.islower() # False

False

>>> g.islower() # True

True

>>> h.islower() # False

False

  isupper() : 문자열이 모두 대문자로만 되어있으면 True, 그렇지 않으면 False

>>> a = 'I Love Python'

>>> g = 'i love python'

>>> h = 'I LOVE PYTHON'

>>> 

# isupper(): Returns true if string has at least one cased character 

#           and all cased characters are in uppercase and false otherwise

>>> a.isupper() # False

False

>>> g.isupper() # False

False

>>> h.isupper() # True

True

  lower() : 문자열 내 모든 대문자를 모두 소문자(a lowercase letter)로 변환

>>> a = 'I Love Python'

>>> 

# lower(): Converts all uppercase letters in string to lowercase

>>> a.lower()

'i love python'

 upper() :  문자열 내 모든 소문자를 모두 대문자(a uppercase letter)로 변환

>>> a = 'I Love Python'

>>> 

# upper(): Converts lowercase letters in string to uppercase

>>> a.upper()

'I LOVE PYTHON'

 swapcase() : 문자열 내 소문자는 대문자로 변환, 대문자는 소문자로 변환

# swapcase(): Inverts case for all letters in string

>>> a = 'I Love Python'

>>> a.swapcase() # 'i lOVE pYTHON'

'i lOVE pYTHON'

>>> 

>>> g = 'i love python'

>>> g.swapcase() # 'I LOVE PYTHON' (same as upper())

'I LOVE PYTHON'

>>> 

>>> h = 'I LOVE PYTHON'

>>> h.swapcase() # 'i love python' (same as lower())

'i love python'

  istitle() : 문자열이 제목 형식에 맞게 대문자로 시작하고 이후는 소문자이면 True, 그렇지 않으면 False

>>> a = 'I Love Python'

>>> g = 'i love python'

>>> h = 'I LOVE PYTHON'

>>> 

# istitle(): Returns true if string is properly "titlecased" and false otherwise

>>> a.istitle() # True

True

>>> g.istitle() # False

False

>>> h.istitle() # False

False

 title() : 문자열을 제목 형식(titlecased)에 맞게 시작은 대문자로, 나머지는 소문자로 변환 

>>> g = 'i love python'

>>> h = 'I LOVE PYTHON'

>>> 

# title(): Returns "titlecased" version of string, that is, 

#          all words begin with uppercase and the rest are lowercase

>>> g.title() # 'I Love Python'

'I Love Python'

>>> h.title() # 'I Love Python'

'I Love Python'

  capitalize)=() : 문자열 내 첫번째 문자를 대문자로 변환하고, 나머지는 모두 소문자로 변환

>>> a = 'I Love Python'

>>> g = 'i love python'

>>> h = 'I LOVE PYTHON'

>>> 

# capitalize(): Capitalizes first letter of string

>>> a.capitalize() # 'I love python'

'I love python'

>>> g.capitalize() # 'I love python'

'I love python'

>>> h.capitalize() # 'I love python'

'I love python'

 lstrip() : 문자열의 왼쪽에 있는 공백을 제거

# lstrip() : Removes all leading whitespace in string

>>> b = '      I Love Python'

>>> b.lstrip()

'I Love Python'

  rstrip() : 문자열의 오른쪽에 있는 공백을 제거

# rstrip() : Removes all trailing whitespace of string

>>> c = 'I Love Python      '

>>> c.rstrip()

'I Love Python'

  strip() : 문자열의 양쪽에 있는 공백을 제거

# strip() : Performs both lstrip() and rstrip() on string

>>> '     I Love Python     '.strip()

'I Love Python'

  isspace() : 문자열이 단지 공백(whitespace)으로만 되어있을 경우 True, 그렇지 않으면 False

# isspace(): Returns true if string contains only whitespace characters and false otherwise

>>> i = '     '

>>> j = '      I Love Python'

>>> 

>>> i.isspace() # True

True

>>> j.isspace() # False

False

  center(width) : 총 길이가 매개변수로 받는 문자열폭(width)만큼 되도록 공백을 추가하여 중앙 정렬

# center(): Returns a space-padded string with the original string 

#                centered to a total of width columns

>>> a = 'I Love Python'

>>> a.center(21)

'    I Love Python    '

  split() : 문자열을 구분자(delimiter, separator) 기준에 따라 나누기

# split(): Splits string according to delimiter str (space if not provided) 

#    and returns list of substrings; split into at most num substrings if given

>>> x = 'haha, hoho, hihi'

>>> x.split(sep=',') # as a list ['haha', ' hoho', ' hihi']

['haha', ' hoho', ' hihi']

>>> ha, ho, hi = x.split(sep=',')

>>> ha

'haha'

>>> ho

' hoho'

>>> hi

' hihi'

>>> a = 'I Love Python'

>>> a.split(' ') # without arg 'sep='

['I', 'Love', 'Python']

>>> a.split() # default delimiter is space if not provided

['I', 'Love', 'Python'] 

  splitlines() : 여러개의 줄로 이루어진 문자열을 줄 별로 구분하여 리스트 생성

# splitlines(): returns a list with all the lines in string, 

#     optionally including the line breaks (if num is supplied and is true)

>>> y = 'haha, \nhoho, \nhihi'

>>> y

'haha, \nhoho, \nhihi'

>>> y.splitlines() # ['haha, ', 'hoho, ', 'hihi']

['haha, ', 'hoho, ', 'hihi']

 replace(old, new, max) : old 문자열을 new 문자열로 교체.  단, max 매개변수 있으면, max 개수 만큼만 교체하고 이후는 무시

# replace(old, new): Replaces all occurrences of old in string with new 

#        or at most max occurrences if max given

>>> a = 'I Love Python'

>>> a.replace('Python', 'R')

'I Love R'

>>> 

>>> 

>>> a_2 = 'I Love Python, Python, Python, Python, Python~!!!'

>>> a_2.replace('Python', 'R', 3) # str.replace(old, new, max)

'I Love R, R, R, Python, Python~!!!'

 join() :  여러개의 문자열을 구분자(separator) 문자열을 사이에 추가하여 붙이기

# join(): Merges (concatenates) the string representations of elements 

#         in sequence seq into a string, with separator string

>>> mylist = ['I', 'Love', 'Python']

>>> print(mylist)

['I', 'Love', 'Python']

>>> 

>>> mystring = '_'.join(mylist)

>>> print(mystring) # 'I_Love_Python'

I_Love_Python

# To concatenate item in list to strings with join() method

>>> mylist_num = [1, 2, 3, 4, 5]

>>> print(mylist_num) # [1, 2, 3, 4, 5]

[1, 2, 3, 4, 5]

>>> 

>>> mylist_str = ''.join(map(str, mylist_num))

>>> print(mylist_str) # 12345

12345

>>> 

>>> '_'.join(map(str, mylist_num)) # '1_2_3_4_5'

'1_2_3_4_5'

 zfill(width) : 문자열을 매개변수 width만큼 길이로 만들되, 추가로 필요한 자리수만큼 '0'을 채움

# zfill(width): Returns original string leftpadded with zeros to a total of width characters; 

#      intended for numbers, zfill() retains any sign given (less one zero)

>>> f = '123'

>>> f.zfill(10)

'0000000123' 

 ljust(width[, fillchar]) : 문자열을 매개변수 width만큼 길이로 만들되, 왼쪽은 원본 문자열로 채우고, 

오른쪽에 추가로 필요한 자리수만큼 매개변수 fillchar 문자열로 채움

# ljust(): Returns a space-padded string with the original string left-justified 

#          to a total of width columns

# str.ljust(width[, fillchar])

>>> a = 'I Love Python'

>>> a.ljust(20, 'R')

'I Love PythonRRRRRRR'

 rjust(width[, fillchar]) : 문자열을 매개변수 width만큼 길이로 만들되, 오른쪽은 원본 문자열로 채우고, 

 왼쪽에 추가로 필요한 자리수만큼 매개변수 fillchar 문자열로 채움

# rjust(): Returns a space-padded string with the original string right-justified 

#          to a total of width columns

>>> a.rjust(20, 'R')

'RRRRRRRI Love Python'

>>> a.rjust(20, ' ')

'       I Love Python'

학급 (class)

수학 점수 (math score)

평균 (mean)

표준편차(sd)

 3학년 1반

25, 55, 60, 70, 100

62

27.06 

 3학년 2반

55, 60, 60, 65, 70

62

5.70 

 산술통계

 통계량 (statistics)

R function 

 중심화 경향

(central

tendency)

 산술평균 (arithmetic mean)

 mean()

 which.max(table())

 기하평균 (geometric mean)

 prod(x)^(1/n)1/mean(1/x)

where, n = length(x)

 연평균성장률 (CAGR

 : Componded Average Growth Rate)

 (FV/IV)^(1/n)-1

where, IV : initial value of an investment
          FV : final value  of an investment
          n : investment periods

 조화평균 (harmonic mean)

 1/mean(1/x)

 가중평균 (weighted average)

 weighted.mean()

 퍼짐 정도

(dispersion)

 분산 (variance)

 var()

 변이계수 (coefficient of variation)

 100*sd(x)/mean(x)

 diff(range())

 IQR (Inter Quartile Range)

 최소값 (min)

 최대값 (max)

 quantile(x, probs=c(,,,,))

 분포형태와

대칭정도

(distribution)

 왜도 (skewness)

 skewness(), fBasics package

 첨도 (kurtosis)

 kurtosis(), fBasics package

 qqnorm(), qqline(), qqplot()

> library(MASS)

> str(Cars93)
'data.frame':	93 obs. of  27 variables:
 $ Manufacturer      : Factor w/ 32 levels "Acura","Audi",..: 1 1 2 2 3 4 4 4 4 5 ...
 $ Model             : Factor w/ 93 levels "100","190E","240",..: 49 56 9 1 6 24 54 74 73 35 ...
 $ Type              : Factor w/ 6 levels "Compact","Large",..: 4 3 1 3 3 3 2 2 3 2 ...
 $ Min.Price         : num  12.9 29.2 25.9 30.8 23.7 14.2 19.9 22.6 26.3 33 ...
 $ Price             : num  15.9 33.9 29.1 37.7 30 15.7 20.8 23.7 26.3 34.7 ...
 $ Max.Price         : num  18.8 38.7 32.3 44.6 36.2 17.3 21.7 24.9 26.3 36.3 ...
 $ MPG.city          : int  25 18 20 19 22 22 19 16 19 16 ...
 $ MPG.highway       : int  31 25 26 26 30 31 28 25 27 25 ...
 $ AirBags           : Factor w/ 3 levels "Driver & Passenger",..: 3 1 2 1 2 2 2 2 2 2 ...
 $ DriveTrain        : Factor w/ 3 levels "4WD","Front",..: 2 2 2 2 3 2 2 3 2 2 ...
 $ Cylinders         : Factor w/ 6 levels "3","4","5","6",..: 2 4 4 4 2 2 4 4 4 5 ...
 $ EngineSize        : num  1.8 3.2 2.8 2.8 3.5 2.2 3.8 5.7 3.8 4.9 ...
 $ Horsepower        : int  140 200 172 172 208 110 170 180 170 200 ...
 $ RPM               : int  6300 5500 5500 5500 5700 5200 4800 4000 4800 4100 ...
 $ Rev.per.mile      : int  2890 2335 2280 2535 2545 2565 1570 1320 1690 1510 ...
 $ Man.trans.avail   : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 1 1 1 1 ...
 $ Fuel.tank.capacity: num  13.2 18 16.9 21.1 21.1 16.4 18 23 18.8 18 ...
 $ Passengers        : int  5 5 5 6 4 6 6 6 5 6 ...
 $ Length            : int  177 195 180 193 186 189 200 216 198 206 ...
 $ Wheelbase         : int  102 115 102 106 109 105 111 116 108 114 ...
 $ Width             : int  68 71 67 70 69 69 74 78 73 73 ...
 $ Turn.circle       : int  37 38 37 37 39 41 42 45 41 43 ...
 $ Rear.seat.room    : num  26.5 30 28 31 27 28 30.5 30.5 26.5 35 ...
 $ Luggage.room      : int  11 15 14 17 13 16 17 21 14 18 ...
 $ Weight            : int  2705 3560 3375 3405 3640 2880 3470 4105 3495 3620 ...
 $ Origin            : Factor w/ 2 levels "USA","non-USA": 2 2 2 2 2 1 1 1 1 1 ...
 $ Make              : Factor w/ 93 levels "Acura Integra",..: 1 2 4 3 5 6 7 9 8 10 . 

> # Histogram, Price by Type
> library(MASS) # Cars93 dataset
> library(ggplot2)
> ggplot(Cars93, aes(x=Price)) + 
+   geom_histogram(binwidth=5, fill = "blue", colour = "black") + 
+   ggtitle("Histogram, Price by Type") + 
+   facet_grid(Type ~ .)

> # variance : var()
> 
> var(Cars93$Price)
[1] 93.30458
> 
> with(Cars93, tapply(Price, Type, var))
   Compact      Large    Midsize      Small     Sporty        Van 
 44.714500  40.164000 150.426320   3.815333  63.596099   3.527500

> # standard deviation : sd()
> 
> sd(Cars93$Price)
[1] 9.65943
> 
> with(Cars93, tapply(Price, Type, sd))
  Compact     Large   Midsize     Small    Sporty       Van 
 6.686890  6.337507 12.264841  1.953288  7.974716  1.878164

> # coefficient of variation : sd()/mean()
> 
> with(Cars93, 100*sd(Price)/mean(Price))
[1] 49.51096
> 
> attach(Cars93)
> with(Cars93[Type == c("Compact"),], 100*sd(Price)/mean(Price))
[1] 36.71594
> with(Cars93[Type == c("Large"),], 100*sd(Price)/mean(Price))
[1] 26.08028
> with(Cars93[Type == c("Midsize"),], 100*sd(Price)/mean(Price))
[1] 45.06121
> with(Cars93[Type == c("Small"),], 100*sd(Price)/mean(Price))
[1] 19.21267
> with(Cars93[Type == c("Sporty"),], 100*sd(Price)/mean(Price))
[1] 41.12193
> with(Cars93[Type == c("Van"),], 100*sd(Price)/mean(Price))
[1] 9.833319
> detach(Cars93)

> # example : stock price's mean, sd of company A and company B
> 
> company_A_mean <- c(10000)
> company_A_sd <- c(1000)
> 
> company_B_mean <- c(50000)
> company_B_sd <- c(2000)
> 
> 
> coe_var_A <- 100*company_A_sd/company_A_mean
> coe_var_A
[1] 10
> 
> coe_var_B <- 100*company_B_sd/company_B_mean
> coe_var_B
[1] 4

> ##----------
> # min, max, range, IQR, percentile
> attach(Cars93)
> 
> # min : min()
> min(Price)
[1] 7.4
> tapply(Price, Type, min)
Compact   Large Midsize   Small  Sporty     Van 
   11.1    18.4    13.9     7.4    10.0    16.3 
> 
> # max : max()
> max(Price)
[1] 61.9
> tapply(Price, Type, max)
Compact   Large Midsize   Small  Sporty     Van 
   31.9    36.1    61.9    15.9    38.0    22.7 
> 
> # range : diff(range())
> diff(range(Price))
[1] 54.5
> 
> diff(range(Cars93[Type==c("Compact"),]$Price))
[1] 20.8
> diff(range(Cars93[Type==c("Large"),]$Price))
[1] 17.7
> diff(range(Cars93[Type==c("Midsize"),]$Price))
[1] 48
> diff(range(Cars93[Type==c("Small"),]$Price))
[1] 8.5
> diff(range(Cars93[Type==c("Sporty"),]$Price))
[1] 28
> diff(range(Cars93[Type==c("Van"),]$Price))
[1] 6.4
> 
> # Percentile : quantile(var, probs=c(,,))
> quantile(Price, c(0.25, 0.75))
 25%  75% 
12.2 23.3 
> 
> quantile(Cars93[Type==c("Compact"),]$Price, c(0.25, 0.75))
   25%    75% 
13.375 20.675 
> quantile(Cars93[Type==c("Large"),]$Price, c(0.25, 0.75))
  25%   75% 
20.00 26.95 
> quantile(Cars93[Type==c("Midsize"),]$Price, c(0.25, 0.75))
   25%    75% 
16.775 34.200 
> quantile(Cars93[Type==c("Small"),]$Price, c(0.25, 0.75))
 25%  75% 
 8.6 11.3 
> quantile(Cars93[Type==c("Sporty"),]$Price, c(0.25, 0.75))
   25%    75% 
14.175 22.425 
> quantile(Cars93[Type==c("Van"),]$Price, c(0.25, 0.75))
 25%  75% 
19.0 19.7 
> 
> 
> # IQR : IQR()
> IQR(Price)
[1] 11.1
> 
> IQR(Cars93[Type==c("Compact"),]$Price)
[1] 7.3
> IQR(Cars93[Type==c("Large"),]$Price)
[1] 6.95
> IQR(Cars93[Type==c("Midsize"),]$Price)
[1] 17.425
> IQR(Cars93[Type==c("Small"),]$Price)
[1] 2.7
> IQR(Cars93[Type==c("Sporty"),]$Price)
[1] 8.25
> IQR(Cars93[Type==c("Van"),]$Price)
[1] 0.7
> detach(Cars93)

> # box plot with mean
> ggplot(Cars93, aes(x = Type, y = Price)) +
+   geom_boxplot(width=0.8, outlier.size=3, outlier.shape=16, outlier.colour="red") +
+   stat_summary(fun.y="mean", geom="point", shape=21, size=3, fill="blue") +
+   ggtitle("Box Plot by Car Type, adding mean") 

R 기술통계 함수

> x <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
> mean(x)
[1] 5.5 

> x <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
> median(x)
[1] 5.5
> y <- c(1, 2, 3, 4, 5, 6, 7, 8, 9)
> median(y)
[1] 5

> min(x)
[1] 1
> min(y)
[1] 1 

> my_vec <- c(-5, 3, 10, 3, -12, NA)
> my_vec
[1]  -5   3  10   3 -12  NA
> 
> min(my_vec)
[1] NA
> 
> which.min(my_vec) # index of min value in 'my_vec' vector
[1] 5
> 
> my_vec[which.min(my_vec)]
[1] -12

> max(x)
[1] 10
> max(y) 

> my_vec <- c(-5, 3, 10, 3, -12, NA)
> my_vec
[1]  -5   3  10   3 -12  NA
> 
> max(my_vec)
[1] NA
> 
> which.max(my_vec) # index of max value in 'my_vec' vector
[1] 3
> 
> my_vec[which.max(my_vec)]
[1] 10 

> range(x)
[1]  1 10
> range(y)
[1] 1 9 

> IQR(x)
[1] 4.5
> IQR(y)
[1] 4 

> summary(x)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    3.25    5.50    5.50    7.75   10.00 

> var(x)
[1] 9.166667
> var(y)
[1] 7.5 

> sd(x); sd(y)
[1] 3.02765
[1] 2.738613 

> install.packages("fBasics") # 왜도, 첨도 분석 가능한 package 설치
> library(fBasics) # package 호출
> hist(mtcars$mpg)
 
 
 
 

 
 
> skewness(mtcars$mpg)
[1] 0.610655
attr(,"method")
[1] "moment"

> kurtosis(mtcars$mpg)
[1] -0.372766
attr(,"method")
[1] "excess"

> sum(x)
[1] 55
> sum(y)
[1] 45

> diff(x, lag=1)
[1] 1 1 1 1 1 1 1 1 1
> diff(x, lag=2)
[1] 2 2 2 2 2 2 2 2
> diff(x, lag=3)
[1] 3 3 3 3 3 3 3 

> # 벡터에 length() 사용 시

> length(x)
[1] 10
> length(y)
[1] 9 

> 
> # 데이터 프레임에 length()사용 시
> length(mtcars)
[1] 11
> 
> # 데이터 프레임의 특정 변수에 length($) 사용 시
> length(mtcars$mpg)
[1] 32

> rank(x)
 [1]  1  2  3  4  5  6  7  8  9 10
> 
> rank(-x)
 [1] 10  9  8  7  6  5  4  3  2  1
>

> mtcars$mpg
 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5
[23] 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 15.0 21.4
> 
> rank(mtcars$mpg, 
+      na.last = TRUE, 
+      ties.method = c("max"))
 [1] 20 20 25 22 15 14  4 26 25 17 13 11 12  8  2  2  5 31 30 32 23  9  8  3 17 28 27 30 10 18  6 22
 
> ##-----------------------
> ## rank() {base package}
> ##-----------------------
> 
> # if there are no ties(i.e., equal values), no problem at all
> x <- c(1, 5, 9, 7)
> rank(x) 
[1] 1 2 4 3
> 
> 
> # if there are ties, ties can be handled in several ways
> y <- c(1, 1, 1, 5, 9, 7) 
> 
> # returns average, default setting
> rank(y)
[1] 2 2 2 4 6 5
> rank(y, ties.method = c("average")) 
[1] 2 2 2 4 6 5
> 
> # first occurrence wins
> rank(y, ties.method = c("first")) 
[1] 1 2 3 4 6 5
> 
> # ties broken at random
> rank(y, ties.method = c("random")) 
[1] 3 2 1 4 6 5
> 
> rank(y, ties.method = c("random")) # ...random one more time
[1] 1 3 2 4 6 5
> 
> rank(y, ties.method = c("random")) # ...random...again
[1] 1 2 3 4 6 5
> 
> # rank by max value as used classically
> rank(y, ties.method = c("max")) 
[1] 3 3 3 4 6 5
> 
> # rank by min value as in Sports
> rank(y, ties.method = c("min"))
[1] 1 1 1 4 6 5