[DataMining] 1. R Basic Programming

R Programming install & basic¶

• R Install
• Basic R Programming
• Basic Data handling for datamining with R

R & R-Studio install¶

• R-Cran (R) : https://cran.r-project.org/mirrors.html

• R-studio (RStudio) : https://www.rstudio.com/products/rstudio/download/

• Start R-studio

• 설치한 R-studio를 통해 수행해도 되고 Jupyter notebook에 R-Kernel을 만들어서 사용해도 무관하다.

1. Basic Calculation¶

In [1]:

3 + 4

7

In [2]:

4 - 2

2

In [4]:

17 %% 3 # 나머지

2

In [5]:

6 * 3

18

In [6]:

7 / 2

3.5

In [7]:

7 %/% 2 # 몫

3

2. Scala(스칼라)¶

• R 에서의 데이터 타입은 문자형, 숫자형, 논리형 등 형태로 존재하는데 이러한 데이터 타입을 하나의 값만 가지는 것을 Scala(스칼라) 라고 한다.

In [11]:

1

1

In [12]:

"String"

'String'

In [15]:

TRUE

TRUE

In [16]:

FALSE

FALSE

3. Vector(벡터)¶

• 위의 Scala 값들을 묶어서 표현하는 것을 벡터라고 한다. 같은 데이터 타입의 값들을 여러개 묶어서 표현한다.
• 숫자 + 문자로 벡터를 구성하게 된다면 해당 숫자형은 문자형으로 변환된다.
• 숫자 + 논리형태로 가지게 된다면 논리형의 경우 TRUE -> 1, FALSE -> 0 으로 표현

In [17]:

c(1,2,3)

1. 1
2. 2
3. 3

In [18]:

c("a",2,3)

1. 'a'
2. '2'
3. '3'

In [20]:

c(TRUE,1,2)

1. 1
2. 1
3. 2

In [21]:

c("a","b")

1. 'a'
2. 'b'

In [22]:

c(1, 2, 3) + c(5, 6, 7) # 각 요소끼리 덧셈

1. 6
2. 8
3. 10

In [23]:

c(1, 2, 3) + 5 # 각 요소에 각각 덧셈(5를)

1. 6
2. 7
3. 8

In [26]:

c(1, 2, 3) + c(3, 4) # 길이가 맞지 않을 경우 3, 4 순으로 해서 순서대로 더한다.

Warning message in c(1, 2, 3) + c(3, 4):
“두 객체의 길이가 서로 배수관계에 있지 않습니다”

1. 4
2. 6
3. 6

In [27]:

c(1, 2, 3,4) + c(3, 4) # 1 + 3, 2 + 4, 3 + 3, 4 + 4

1. 4
2. 6
3. 6
4. 8

4. Variable(변수)¶

• 이름 그대로 변하는 수이다.
• 우리가 원하는 값을 담고 있는 그릇이라고 생각하면 된다.
• 원하는 그릇에 원하는 음식을 담듯이, 원하는 그릇에 원하는 값을 담으면 된다.
• 이와 반대되는 말이 상수가 있는데 상수는 변하지 않는 수 즉, 한번 담게 되면 고정되는 값이라고 생각하면 된다.

4-1 Assign Variable¶

In [28]:

people <- 40 # 엑셀의 A1, B1 등과 같다고 보면 된다. 
people

40

In [29]:

people <- c(1,2,3)
people

1. 1
2. 2
3. 3

4-2 Delete Variable¶

In [30]:

ls() # 메모리에 저장되어있는 변수 명을 출력.

'people'

In [31]:

rm(people)

모든 변수를 삭제 하고 싶다면

In [32]:

rm(list=ls())

4-3 Calculate Variable¶

In [33]:

people <- c(1,2,3)

In [34]:

people * 3

1. 3
2. 6
3. 9

In [36]:

people 
# 변수의 값을 연산하고 끝날뿐이지 결과값을 다시 할당하지 않는다면 일회성이라고 생각하면된다.

1. 1
2. 2
3. 3

In [37]:

people <- people * 2
people # 재할당.

1. 2
2. 4
3. 6

5. Indexing¶

• Python과 달리 1부터 시작한다.
• [시작:끝] 끝은 포함한다. 이 또한 파이썬과 다르다. 파이썬의 경우 시작은 포함 끝은 포함하지 않기 때문에.

In [38]:

people[1] # 1번만

2

In [40]:

people[1:2] # 1번부터 2번까지

1. 2
2. 4

6. Function (함수)¶

• 사용자 지정함수 또한 만들어서 사용할 수 있다.
• 함수의 도움말을 알기 위해서는 ?함수명 을 하게 되면 help가 나오게된다.

In [41]:

floor(3.4)

3

In [42]:

ceiling(2.1)

3

In [45]:

c(round(3.4),round(3.5))

1. 3
2. 4

In [47]:

user_add <- function(a,b){
    c <- a + a + b + b
    return(c)
}

In [48]:

user_add(1,2)

6

7. List (리스트)¶

• 벡터의 경우 같은 종류의 데이터 타입만 여러개 가질 수 있다.
• 하지만 리스트의 경우 다른 종류의 데이터 타입을 여러개를 동시에 가질 수 있는 형태의 자료형이다.
• json형태와 비슷하게 key,value 형태로 저장한 후 접근도 가능하다.

In [49]:

a <- list(1,2,3,'a',TRUE)
a

1. 1
2. 2
3. 3
4. 'a'
5. TRUE

In [52]:

b <- list(a=1,b=3,c=TRUE,d='a')
b

$a

1

$b

3

$c

TRUE

$d

'a'

In [53]:

b$a

1

8. DataFrame(데이터프레임)¶

• 엑셀의 하나의 시트라고 생각하면 될 것 같다.
• 하나의 테이블에 컬럼과 로우가 있듯이 표현되는 데이터 형태이다.

In [54]:

car <- read.csv('automobile.csv')
# 다양한 속성을 통해 header를 가지고 올지, row를 새로 만들지 등을 정할 수 있다.

In [55]:

head(car) # 상위 5줄을 볼 수 있다.

symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
2	164	audi	gas	std	four	sedan	fwd	front	99.8	⋯	109	mpfi	3.19	3.4	10.0	102	5500	24	30	13950
2	164	audi	gas	std	four	sedan	4wd	front	99.4	⋯	136	mpfi	3.19	3.4	8.0	115	5500	18	22	17450
1	158	audi	gas	std	four	sedan	fwd	front	105.8	⋯	136	mpfi	3.19	3.4	8.5	110	5500	19	25	17710
1	158	audi	gas	turbo	four	sedan	fwd	front	105.8	⋯	131	mpfi	3.13	3.4	8.3	140	5500	17	20	23875
2	192	bmw	gas	std	two	sedan	rwd	front	101.2	⋯	108	mpfi	3.50	2.8	8.8	101	5800	23	29	16430
0	192	bmw	gas	std	four	sedan	rwd	front	101.2	⋯	108	mpfi	3.50	2.8	8.8	101	5800	23	29	16925

In [56]:

car2 <- read.csv(file.choose()) # R-Studio를통해서 수행하면 된다.

Error in file.choose(): 파일선택이 취소되었습니다
Traceback:

1. read.csv(file.choose())
2. read.table(file = file, header = header, sep = sep, quote = quote, 
 .     dec = dec, fill = fill, comment.char = comment.char, ...)
3. file.choose()

In [57]:

View(car) 
# 이 또한 Rstudio를 통해서 하면 된다. 새로운 창을 통해 data를 전체적으로 볼 수 있다.

Error in View(car): ‘View()’ not yet supported in the Jupyter R kernel
Traceback:

1. View(car)
2. stop(sQuote("View()"), " not yet supported in the Jupyter R kernel")

Size of data¶

In [58]:

dim(car) # rows x columns

1. 159
2. 26

Row indexing¶

In [59]:

car[1,]

symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
2	164	audi	gas	std	four	sedan	fwd	front	99.8	⋯	109	mpfi	3.19	3.4	10	102	5500	24	30	13950

In [60]:

car[1:5,]

symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
2	164	audi	gas	std	four	sedan	fwd	front	99.8	⋯	109	mpfi	3.19	3.4	10.0	102	5500	24	30	13950
2	164	audi	gas	std	four	sedan	4wd	front	99.4	⋯	136	mpfi	3.19	3.4	8.0	115	5500	18	22	17450
1	158	audi	gas	std	four	sedan	fwd	front	105.8	⋯	136	mpfi	3.19	3.4	8.5	110	5500	19	25	17710
1	158	audi	gas	turbo	four	sedan	fwd	front	105.8	⋯	131	mpfi	3.13	3.4	8.3	140	5500	17	20	23875
2	192	bmw	gas	std	two	sedan	rwd	front	101.2	⋯	108	mpfi	3.50	2.8	8.8	101	5800	23	29	16430

Columns indexing¶

In [63]:

head(car['maker'])

maker
audi
audi
audi
audi
bmw
bmw

In [66]:

head(car[3]) # 컬럼의 위치를 선택하여서 볼 수 있다.

maker
audi
audi
audi
audi
bmw
bmw

In [67]:

head(car[c("maker","body")])

maker	body
audi	sedan
audi	sedan
audi	sedan
audi	sedan
bmw	sedan
bmw	sedan

In [68]:

head(car[c(3,7)])

maker	body
audi	sedan
audi	sedan
audi	sedan
audi	sedan
bmw	sedan
bmw	sedan

In [69]:

head(car[,c(3,7)])

maker	body
audi	sedan
audi	sedan
audi	sedan
audi	sedan
bmw	sedan
bmw	sedan

In [70]:

car[1:2,3:7]

maker	fuel	aspiration	doors	body
audi	gas	std	four	sedan
audi	gas	std	four	sedan

Compare values in data¶

In [71]:

subset(car, horsepower > 145)

	symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
33	0	145	jaguar	gas	std	four	sedan	rwd	front	113.0	⋯	258	mpfi	3.63	4.17	8.1	176	4750	15	19	32250
49	3	142	mercedes-benz	gas	std	two	convertible	rwd	front	96.6	⋯	234	mpfi	3.46	3.10	8.3	155	4750	16	18	35056
72	0	128	nissan	gas	std	four	sedan	fwd	front	100.4	⋯	181	mpfi	3.43	3.27	9.0	152	5200	17	22	13499
73	0	108	nissan	gas	std	four	wagon	fwd	front	100.4	⋯	181	mpfi	3.43	3.27	9.0	152	5200	17	22	14399
74	0	108	nissan	gas	std	four	sedan	fwd	front	100.4	⋯	181	mpfi	3.43	3.27	9.0	152	5200	19	25	13499
75	3	194	nissan	gas	std	two	hatchback	rwd	front	91.3	⋯	181	mpfi	3.43	3.27	9.0	160	5200	19	25	17199
76	3	194	nissan	gas	turbo	two	hatchback	rwd	front	91.3	⋯	181	mpfi	3.43	3.27	7.8	200	5200	17	23	19699
77	1	231	nissan	gas	std	two	hatchback	rwd	front	99.2	⋯	181	mpfi	3.43	3.27	9.0	160	5200	19	25	18399
96	3	150	saab	gas	turbo	two	hatchback	fwd	front	99.1	⋯	121	mpfi	3.54	3.07	9.0	160	5500	19	26	18150
97	2	104	saab	gas	turbo	four	sedan	fwd	front	99.1	⋯	121	mpfi	3.54	3.07	9.0	160	5500	19	26	18620
138	3	197	toyota	gas	std	two	hatchback	rwd	front	102.9	⋯	171	mpfi	3.27	3.35	9.3	161	5200	20	24	16558
139	3	197	toyota	gas	std	two	hatchback	rwd	front	102.9	⋯	171	mpfi	3.27	3.35	9.3	161	5200	19	24	15998
140	-1	90	toyota	gas	std	four	sedan	rwd	front	104.5	⋯	171	mpfi	3.27	3.35	9.2	156	5200	20	24	15690
153	-2	103	volvo	gas	turbo	four	sedan	rwd	front	104.3	⋯	130	mpfi	3.62	3.15	7.5	162	5100	17	22	18420
154	-1	74	volvo	gas	turbo	four	wagon	rwd	front	104.3	⋯	130	mpfi	3.62	3.15	7.5	162	5100	17	22	18950
156	-1	95	volvo	gas	turbo	four	sedan	rwd	front	109.1	⋯	141	mpfi	3.78	3.15	8.7	160	5300	19	25	19045

In [73]:

subset(car, horsepower < 60)

	symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
9	2	121	chevrolet	gas	std	two	hatchback	fwd	front	88.4	⋯	61	2bbl	2.91	3.03	9.5	48	5100	47	53	5151
20	2	137	honda	gas	std	two	hatchback	fwd	front	86.6	⋯	92	1bbl	2.91	3.41	9.6	58	4800	49	54	6479
61	1	128	nissan	diesel	std	two	sedan	fwd	front	94.5	⋯	103	idi	2.99	3.47	21.9	55	4800	45	50	7099
118	0	91	toyota	diesel	std	four	sedan	fwd	front	95.7	⋯	110	idi	3.27	3.35	22.5	56	4500	34	36	7898
119	0	91	toyota	diesel	std	four	hatchback	fwd	front	95.7	⋯	110	idi	3.27	3.35	22.5	56	4500	38	47	7788
141	2	122	volkswagen	diesel	std	two	sedan	fwd	front	97.3	⋯	97	idi	3.01	3.40	23.0	52	4800	37	46	7775
143	2	94	volkswagen	diesel	std	four	sedan	fwd	front	97.3	⋯	97	idi	3.01	3.40	23.0	52	4800	37	46	7995

In [74]:

subset(car, horsepower <= 60)

	symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
9	2	121	chevrolet	gas	std	two	hatchback	fwd	front	88.4	⋯	61	2bbl	2.91	3.03	9.5	48	5100	47	53	5151
20	2	137	honda	gas	std	two	hatchback	fwd	front	86.6	⋯	92	1bbl	2.91	3.41	9.6	58	4800	49	54	6479
22	1	101	honda	gas	std	two	hatchback	fwd	front	93.7	⋯	79	1bbl	2.91	3.07	10.1	60	5500	38	42	5399
61	1	128	nissan	diesel	std	two	sedan	fwd	front	94.5	⋯	103	idi	2.99	3.47	21.9	55	4800	45	50	7099
118	0	91	toyota	diesel	std	four	sedan	fwd	front	95.7	⋯	110	idi	3.27	3.35	22.5	56	4500	34	36	7898
119	0	91	toyota	diesel	std	four	hatchback	fwd	front	95.7	⋯	110	idi	3.27	3.35	22.5	56	4500	38	47	7788
141	2	122	volkswagen	diesel	std	two	sedan	fwd	front	97.3	⋯	97	idi	3.01	3.40	23.0	52	4800	37	46	7775
143	2	94	volkswagen	diesel	std	four	sedan	fwd	front	97.3	⋯	97	idi	3.01	3.40	23.0	52	4800	37	46	7995

In [75]:

subset(car, horsepower == 60)

	symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
22	1	101	honda	gas	std	two	hatchback	fwd	front	93.7	⋯	79	1bbl	2.91	3.07	10.1	60	5500	38	42	5399

In [77]:

head(subset(car, body != 'sedan'))

	symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
9	2	121	chevrolet	gas	std	two	hatchback	fwd	front	88.4	⋯	61	2bbl	2.91	3.03	9.50	48	5100	47	53	5151
10	1	98	chevrolet	gas	std	two	hatchback	fwd	front	94.5	⋯	90	2bbl	3.03	3.11	9.60	70	5400	38	43	6295
12	1	118	dodge	gas	std	two	hatchback	fwd	front	93.7	⋯	90	2bbl	2.97	3.23	9.41	68	5500	37	41	5572
13	1	118	dodge	gas	std	two	hatchback	fwd	front	93.7	⋯	90	2bbl	2.97	3.23	9.40	68	5500	31	38	6377
14	1	118	dodge	gas	turbo	two	hatchback	fwd	front	93.7	⋯	98	mpfi	3.03	3.39	7.60	102	5500	24	30	7957
15	1	148	dodge	gas	std	four	hatchback	fwd	front	93.7	⋯	90	2bbl	2.97	3.23	9.40	68	5500	31	38	6229

논리¶

• and

In [78]:

subset(car, horsepower > 100 & fuel == 'diesel')

	symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
45	-1	93	mercedes-benz	diesel	turbo	four	sedan	rwd	front	110.0	⋯	183	idi	3.58	3.64	21.5	123	4350	22	25	25552
46	-1	93	mercedes-benz	diesel	turbo	four	wagon	rwd	front	110.0	⋯	183	idi	3.58	3.64	21.5	123	4350	22	25	28248
47	0	93	mercedes-benz	diesel	turbo	two	hardtop	rwd	front	106.7	⋯	183	idi	3.58	3.64	21.5	123	4350	22	25	28176
48	-1	93	mercedes-benz	diesel	turbo	four	sedan	rwd	front	115.6	⋯	183	idi	3.58	3.64	21.5	123	4350	22	25	31600
158	-1	95	volvo	diesel	turbo	four	sedan	rwd	front	109.1	⋯	145	idi	3.01	3.40	23.0	106	4800	26	27	22470

• or

In [80]:

head(subset(car, body == 'wagon' | wheels == '4wd'))

	symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
2	2	164	audi	gas	std	four	sedan	4wd	front	99.4	⋯	136	mpfi	3.19	3.40	8.0	115	5500	18	22	17450
18	-1	110	dodge	gas	std	four	wagon	fwd	front	103.3	⋯	122	2bbl	3.34	3.46	8.5	88	5000	24	30	8921
26	0	78	honda	gas	std	four	wagon	fwd	front	96.5	⋯	92	1bbl	2.92	3.41	9.2	76	6000	30	34	7295
46	-1	93	mercedes-benz	diesel	turbo	four	wagon	rwd	front	110.0	⋯	183	idi	3.58	3.64	21.5	123	4350	22	25	28248
64	1	103	nissan	gas	std	four	wagon	fwd	front	94.5	⋯	97	2bbl	3.15	3.29	9.4	69	5200	31	37	7349
68	1	103	nissan	gas	std	four	wagon	fwd	front	94.5	⋯	97	2bbl	3.15	3.29	9.4	69	5200	31	37	7999

• not

In [81]:

head(subset(car, !(body == 'wagon' | wheels == '4wd')))

	symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
1	2	164	audi	gas	std	four	sedan	fwd	front	99.8	⋯	109	mpfi	3.19	3.40	10.0	102	5500	24	30	13950
3	1	158	audi	gas	std	four	sedan	fwd	front	105.8	⋯	136	mpfi	3.19	3.40	8.5	110	5500	19	25	17710
4	1	158	audi	gas	turbo	four	sedan	fwd	front	105.8	⋯	131	mpfi	3.13	3.40	8.3	140	5500	17	20	23875
5	2	192	bmw	gas	std	two	sedan	rwd	front	101.2	⋯	108	mpfi	3.50	2.80	8.8	101	5800	23	29	16430
6	0	192	bmw	gas	std	four	sedan	rwd	front	101.2	⋯	108	mpfi	3.50	2.80	8.8	101	5800	23	29	16925
7	0	188	bmw	gas	std	two	sedan	rwd	front	101.2	⋯	164	mpfi	3.31	3.19	9.0	121	4250	21	28	20970

• 보론

In [82]:

dat = data.frame(num=c(36925,36941,12345), name=c('abc', 'def', 'abd'))
dat

num	name
36925	abc
36941	def
12345	abd

In [83]:

subset(dat, num %/% 100 == 369) # 100으로 나눈 몫이 369인 경우

num	name
36925	abc
36941	def

In [84]:

subset(dat, startsWith(as.character(name), 'ab')) # name의 시작이 ab인 경우

	num	name
1	36925	abc
3	12345	abd

In [85]:

subset(dat, endsWith(as.character(name), 'bc')) # name의 끝이 bc인경우

num	name
36925	abc

데이터 저장¶

In [86]:

wagon <- subset(car, body=='wagon')
head(wagon)

	symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
18	-1	110	dodge	gas	std	four	wagon	fwd	front	103.3	⋯	122	2bbl	3.34	3.46	8.5	88	5000	24	30	8921
26	0	78	honda	gas	std	four	wagon	fwd	front	96.5	⋯	92	1bbl	2.92	3.41	9.2	76	6000	30	34	7295
46	-1	93	mercedes-benz	diesel	turbo	four	wagon	rwd	front	110.0	⋯	183	idi	3.58	3.64	21.5	123	4350	22	25	28248
64	1	103	nissan	gas	std	four	wagon	fwd	front	94.5	⋯	97	2bbl	3.15	3.29	9.4	69	5200	31	37	7349
68	1	103	nissan	gas	std	four	wagon	fwd	front	94.5	⋯	97	2bbl	3.15	3.29	9.4	69	5200	31	37	7999
73	0	108	nissan	gas	std	four	wagon	fwd	front	100.4	⋯	181	mpfi	3.43	3.27	9.0	152	5200	17	22	14399

In [87]:

write.csv(wagon,'wagon.csv')

클립보드에 복사¶

In [88]:

write.table(wagon,'clipboard',sep = '\t') # r_studio 에서 가능.

Error in file(file, ifelse(append, "a", "w")): Unix에서 클립보드에 대한 'mode'는 반드시 'r'이어야 합니다
Traceback:

1. write.table(wagon, "clipboard", sep = "\t")
2. file(file, ifelse(append, "a", "w"))

Statistics¶

In [89]:

max(car$length)

202.6

In [90]:

min(car$length)

141.1

In [91]:

var(car$length)

132.783604808534

In [92]:

sd(car$length)

11.5231768539988

• Summary

In [94]:

summary(car$length) # 전체 변수에 대해서 다 보려면 $length를 없애면 된다.

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  141.1   165.7   172.4   172.4   177.8   202.6 

Handling data¶

NA¶

• Not Available
• 결측치

In [95]:

is.na(NA)

TRUE

자주 쓰이는 데이터 다루기 관련 함수¶

• dim : size of dataframe
• nrow : number of rows
• ncol : number of columns
• head : top 5 data of dataframe
• names : name of columns
• table : count of data, 명목데이터에 대해서

문자열 관련 함수¶

• nchar : number of characters(length)
• substr : extract string
• paste : concate strings
• grep : search string in data
• grepl : match strings in lots data and get logical type
  • FALSE, TRUE

dplyr¶

• library in R, handling data and getting statistics information
• 복잡한 필터링, 정렬, 변환, 통계를 손쉽게 할 수 있도록 도와주는 패키지
• filter()
• slice()
• arrange()
• select()
• rename()
• distinct()
• mutate()
• transmute()
• summarise() / summarize()

Install Package¶

In [ ]:

install.packages('dplyr')

Load Package¶

In [96]:

library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

Load data¶

In [98]:

car <- read.csv('automobile.csv')
head(car)

symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
2	164	audi	gas	std	four	sedan	fwd	front	99.8	⋯	109	mpfi	3.19	3.4	10.0	102	5500	24	30	13950
2	164	audi	gas	std	four	sedan	4wd	front	99.4	⋯	136	mpfi	3.19	3.4	8.0	115	5500	18	22	17450
1	158	audi	gas	std	four	sedan	fwd	front	105.8	⋯	136	mpfi	3.19	3.4	8.5	110	5500	19	25	17710
1	158	audi	gas	turbo	four	sedan	fwd	front	105.8	⋯	131	mpfi	3.13	3.4	8.3	140	5500	17	20	23875
2	192	bmw	gas	std	two	sedan	rwd	front	101.2	⋯	108	mpfi	3.50	2.8	8.8	101	5800	23	29	16430
0	192	bmw	gas	std	four	sedan	rwd	front	101.2	⋯	108	mpfi	3.50	2.8	8.8	101	5800	23	29	16925

Summarise¶

In [99]:

summarise(car, mean(length))

mean(length)
172.4138

In [101]:

summarise(car, age=mean(length))

age
172.4138

In [102]:

summarise(car, mean(length), mean(width))

mean(length)	mean(width)
172.4138	65.60755

In [104]:

summarise(car, mean_car = mean(price))

mean_car
11445.73

Chain ( %>% )¶

• dplyr 패키지에서 정의된 연산자
• 여러가지 작접들을 연결하는데 사용.
• x %>% f() 형식으로 결과는 f(x) 와 동일하다.
• x %>% f() %>% g() 결과: g(f(x))

In [105]:

car %>% summarise(mean(length))

mean(length)
172.4138

Filter¶

In [107]:

car %>% filter(body == 'sedan') %>% head()

symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
2	164	audi	gas	std	four	sedan	fwd	front	99.8	⋯	109	mpfi	3.19	3.4	10.0	102	5500	24	30	13950
2	164	audi	gas	std	four	sedan	4wd	front	99.4	⋯	136	mpfi	3.19	3.4	8.0	115	5500	18	22	17450
1	158	audi	gas	std	four	sedan	fwd	front	105.8	⋯	136	mpfi	3.19	3.4	8.5	110	5500	19	25	17710
1	158	audi	gas	turbo	four	sedan	fwd	front	105.8	⋯	131	mpfi	3.13	3.4	8.3	140	5500	17	20	23875
2	192	bmw	gas	std	two	sedan	rwd	front	101.2	⋯	108	mpfi	3.50	2.8	8.8	101	5800	23	29	16430
0	192	bmw	gas	std	four	sedan	rwd	front	101.2	⋯	108	mpfi	3.50	2.8	8.8	101	5800	23	29	16925

In [108]:

car %>% 
    filter(body=='sedan') %>% 
    summarise(mean(length))

mean(length)
176.5456

In [110]:

car %>% 
    filter(body=='hatchback' & price > 10000) %>% 
    summarise(mean(engine_size))

mean(engine_size)
147.6429

Grouping¶

In [111]:

car %>% 
    group_by(fuel) %>%
    summarise(mean(city_mpg))

fuel	mean(city_mpg)
diesel	30.40000
gas	26.11806

• 출시 5년 미만인 자동차의 메이커 평균 구매 가격

In [113]:

car %>% filter(horsepower < 100)%>% 
    group_by(fuel) %>%
    summarise(mean(city_mpg))

fuel	mean(city_mpg)
diesel	34.20000
gas	29.46512

컬럼더하기(mutate)¶

In [115]:

car %>% mutate(diff_mpg = highway_mpg - city_mpg) %>% head()

symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price	diff_mpg
2	164	audi	gas	std	four	sedan	fwd	front	99.8	⋯	mpfi	3.19	3.4	10.0	102	5500	24	30	13950	6
2	164	audi	gas	std	four	sedan	4wd	front	99.4	⋯	mpfi	3.19	3.4	8.0	115	5500	18	22	17450	4
1	158	audi	gas	std	four	sedan	fwd	front	105.8	⋯	mpfi	3.19	3.4	8.5	110	5500	19	25	17710	6
1	158	audi	gas	turbo	four	sedan	fwd	front	105.8	⋯	mpfi	3.13	3.4	8.3	140	5500	17	20	23875	3
2	192	bmw	gas	std	two	sedan	rwd	front	101.2	⋯	mpfi	3.50	2.8	8.8	101	5800	23	29	16430	6
0	192	bmw	gas	std	four	sedan	rwd	front	101.2	⋯	mpfi	3.50	2.8	8.8	101	5800	23	29	16925	6

컬럼 선택¶

In [116]:

car %>% select(1) %>% head()

symboling
2
2
1
1
2
0

행 선택¶

In [117]:

car %>% slice(1:10)

symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
2	164	audi	gas	std	four	sedan	fwd	front	99.8	⋯	109	mpfi	3.19	3.40	10.0	102	5500	24	30	13950
2	164	audi	gas	std	four	sedan	4wd	front	99.4	⋯	136	mpfi	3.19	3.40	8.0	115	5500	18	22	17450
1	158	audi	gas	std	four	sedan	fwd	front	105.8	⋯	136	mpfi	3.19	3.40	8.5	110	5500	19	25	17710
1	158	audi	gas	turbo	four	sedan	fwd	front	105.8	⋯	131	mpfi	3.13	3.40	8.3	140	5500	17	20	23875
2	192	bmw	gas	std	two	sedan	rwd	front	101.2	⋯	108	mpfi	3.50	2.80	8.8	101	5800	23	29	16430
0	192	bmw	gas	std	four	sedan	rwd	front	101.2	⋯	108	mpfi	3.50	2.80	8.8	101	5800	23	29	16925
0	188	bmw	gas	std	two	sedan	rwd	front	101.2	⋯	164	mpfi	3.31	3.19	9.0	121	4250	21	28	20970
0	188	bmw	gas	std	four	sedan	rwd	front	101.2	⋯	164	mpfi	3.31	3.19	9.0	121	4250	21	28	21105
2	121	chevrolet	gas	std	two	hatchback	fwd	front	88.4	⋯	61	2bbl	2.91	3.03	9.5	48	5100	47	53	5151
1	98	chevrolet	gas	std	two	hatchback	fwd	front	94.5	⋯	90	2bbl	3.03	3.11	9.6	70	5400	38	43	6295

정렬¶

In [118]:

car %>% arrange(symboling) %>% head()

symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
-2	103	volvo	gas	std	four	sedan	rwd	front	104.3	⋯	141	mpfi	3.78	3.15	9.5	114	5400	23	28	12940
-2	103	volvo	gas	std	four	sedan	rwd	front	104.3	⋯	141	mpfi	3.78	3.15	9.5	114	5400	24	28	15985
-2	103	volvo	gas	turbo	four	sedan	rwd	front	104.3	⋯	130	mpfi	3.62	3.15	7.5	162	5100	17	22	18420
-1	110	dodge	gas	std	four	wagon	fwd	front	103.3	⋯	122	2bbl	3.34	3.46	8.5	88	5000	24	30	8921
-1	93	mercedes-benz	diesel	turbo	four	sedan	rwd	front	110.0	⋯	183	idi	3.58	3.64	21.5	123	4350	22	25	25552
-1	93	mercedes-benz	diesel	turbo	four	wagon	rwd	front	110.0	⋯	183	idi	3.58	3.64	21.5	123	4350	22	25	28248

In [122]:

car %>% arrange(desc(symboling)) %>% head()

symboling	normalized_losses	maker	fuel	aspiration	doors	body	wheels	engine_location	wheel_base	⋯	engine_size	fuel_system	bore	stroke	compression_ratio	horsepower	peak_rpm	city_mpg	highway_mpg	price
3	145	dodge	gas	turbo	two	hatchback	fwd	front	95.9	⋯	156	mfi	3.60	3.90	7.0	145	5000	19	24	12964
3	142	mercedes-benz	gas	std	two	convertible	rwd	front	96.6	⋯	234	mpfi	3.46	3.10	8.3	155	4750	16	18	35056
3	153	mitsubishi	gas	turbo	two	hatchback	fwd	front	96.3	⋯	110	spdi	3.17	3.46	7.5	116	5500	23	30	9959
3	153	mitsubishi	gas	std	two	hatchback	fwd	front	96.3	⋯	122	2bbl	3.35	3.46	8.5	88	5000	25	32	8499
3	194	nissan	gas	std	two	hatchback	rwd	front	91.3	⋯	181	mpfi	3.43	3.27	9.0	160	5200	19	25	17199
3	194	nissan	gas	turbo	two	hatchback	rwd	front	91.3	⋯	181	mpfi	3.43	3.27	7.8	200	5200	17	23	19699

Join¶

• 두개의 데이터를 결합

In [123]:

x = data.frame(name = c('a','b','c'), v1 = 1:3)
y = data.frame(name = c('b', 'b', 'c', 'd'), v2 = 1:4)

In [124]:

x

name	v1
a	1
b	2
c	3

In [125]:

y

name	v2
b	1
b	2
c	3
d	4

In [126]:

left_join(x,y) # x의 데이터의 name(같은 이름의 컬럼) 기준으로 결합

Joining, by = "name"
Warning message:
“Column `name` joining factors with different levels, coercing to character vector”

name	v1	v2
a	1	NA
b	2	1
b	2	2
c	3	3

In [127]:

right_join(x,y) # y의 데이터의 기준으로.

Joining, by = "name"
Warning message:
“Column `name` joining factors with different levels, coercing to character vector”

name	v1	v2
b	2	1
b	2	2
c	3	3
d	NA	4

In [128]:

inner_join(x,y) # x,y 의 name에 동일한 값이 있는 것만

Joining, by = "name"
Warning message:
“Column `name` joining factors with different levels, coercing to character vector”

name	v1	v2
b	2	1
b	2	2
c	3	3

In [129]:

full_join(x,y) # 카디션 곱

Joining, by = "name"
Warning message:
“Column `name` joining factors with different levels, coercing to character vector”

name	v1	v2
a	1	NA
b	2	1
b	2	2
c	3	3
d	NA	4

In [130]:

semi_join(x,y)

Joining, by = "name"
Warning message:
“Column `name` joining factors with different levels, coercing to character vector”

name	v1
b	2
c	3

semi_join()¶

return all rows from x where there are matching values in y, keeping just columns from x. A semi join differs from an inner join because an inner join will return one row of x for each matching row of y, where a semi join will never duplicate rows of x.

In [132]:

?semi_join