h-elena

decent 괜찮은

ex) It's not a great job, but the pay is decent.

He's very decent to me.

They are in decent clothes.

I just want to live a decent life.

library(caret)

createDataPartition()

: 층화 균등 추출에 사용되는 함수이다. 계층 별로 비율을 동일하게, 랜덤하게 뽑아준다.

   1) createDataPartition(y, times = 1, p = 0.5, list = TRUE, groups = min(5, length(y)))

      - y : 대상 vector

      - p : 선택할 데이터 확률

   2) 사용예

      - df <- data.frame(replicate(10,sample(1:3, 20,rep=TRUE)))

      - createDataPartition(y = df$X1, p = 0.7, list = FALSE, groups = min(2, length(df$X1)))

[출처] R 표본 샘플링 - 홀드아웃 holdout 방법|작성자 나리

https://blog.naver.com/nyaminyam/221246413590

[출처] sample, createDataPartition 사용법|작성자 이해할때까지

https://blog.naver.com/wujuchoi/221058021095

> library(reshape2)

> 

> fm<-melt(id=1:4, french_fries)

> head(fm)

  time treatment subject rep variable value

1    1         1       3   1   potato   2.9

2    1         1       3   2   potato  14.0

3    1         1      10   1   potato  11.0

4    1         1      10   2   potato   9.9

5    1         1      15   1   potato   1.2

6    1         1      15   2   potato   8.8

> 

> # dcast()는 melt 전 상태로 돌려주는 듯!

> # ...  => 나열 되지 않은 나머지 모든 변수

> x<-dcast(fm, time+treatment+subject+rep~variable)

> head(x)

  time treatment subject rep potato buttery grassy rancid painty

1    1         1       3   1    2.9     0.0    0.0    0.0    5.5

2    1         1       3   2   14.0     0.0    0.0    1.1    0.0

3    1         1      10   1   11.0     6.4    0.0    0.0    0.0

4    1         1      10   2    9.9     5.9    2.9    2.2    0.0

5    1         1      15   1    1.2     0.1    0.0    1.1    5.1

6    1         1      15   2    8.8     3.0    3.6    1.5    2.3

> 

> x<-dcast(fm, time+treatment+subject+rep~...)

> head(x)

  time treatment subject rep potato buttery grassy rancid painty

1    1         1       3   1    2.9     0.0    0.0    0.0    5.5

2    1         1       3   2   14.0     0.0    0.0    1.1    0.0

3    1         1      10   1   11.0     6.4    0.0    0.0    0.0

4    1         1      10   2    9.9     5.9    2.9    2.2    0.0

5    1         1      15   1    1.2     0.1    0.0    1.1    5.1

6    1         1      15   2    8.8     3.0    3.6    1.5    2.3

> 

> 

> head(fm)

  time treatment subject rep variable value

1    1         1       3   1   potato   2.9

2    1         1       3   2   potato  14.0

3    1         1      10   1   potato  11.0

4    1         1      10   2   potato   9.9

5    1         1      15   1   potato   1.2

6    1         1      15   2   potato   8.8

> dcast(fm, time~variable) # 1행만 id 지정; 72는 행의 수

Aggregation function missing: defaulting to length

   time potato buttery grassy rancid painty

1     1     72      72     72     72     72

2     2     72      72     72     72     72

3     3     72      72     72     72     72

4     4     72      72     72     72     72

5     5     72      72     72     72     72

6     6     72      72     72     72     72

7     7     72      72     72     72     72

8     8     72      72     72     72     72

9     9     60      60     60     60     60

10   10     60      60     60     60     60

> 

> 

> nrow(filter(fm, time==1 & variable == "potato"))

[1] 72

beast 짐승; 싫은 사람/것; (특정한) 것

ex) The maths exam was a real beast.

His new guitar is a very expensive beast.

This beast of a gadget will be worth my wait.

niche market 틈새시장

It could've been better.

그저 그랬어요

It could've been realistic.

더 현실적일 수 있었는데(그렇지 않았음)

It could've been worse.

더 나쁠 수도 있었는데(그렇지 않았음)

meh 시시한, 지루한

footage 장면, (자료)화면

slick 매끄러운, 잔소리, 번지르르하다, 능수능란한, 미끄러운

show off 자랑하다

insistence 주장

hamper 방해하다; 빨래 바구니

oil slick 기름띠

Pare de se queixar comigo.  

저한테 투정 좀 그만 부리세요.

턱 

queixo

데이터의 범위를 일치시키거나 분포를 유사하게 만들어 주는 등의 작업

> summary(cars)

     speed           dist      

 Min.   : 4.0   Min.   :  2.00 

 1st Qu.:12.0   1st Qu.: 26.00 

 Median :15.0   Median : 36.00 

 Mean   :15.4   Mean   : 42.98 

 3rd Qu.:19.0   3rd Qu.: 56.00 

 Max.   :25.0   Max.   :120.00 

> str(cars)

'data.frame':      50 obs. of  2 variables:

 $ speed: num  4 4 7 7 8 9 10 10 10 11 ...

 $ dist : num  2 10 4 22 16 10 18 26 34 17 ...

> head(cars)

  speed dist

1     4    2

2     4   10

3     7    4

4     7   22

5     8   16

6     9   10

> lm(speed~dist, cars)

Call:

lm(formula = speed ~ dist, data = cars)

Coefficients:

(Intercept)         dist 

     8.2839       0.1656 

>

> m<-lm(dist~speed, cars)

> summary(m)

Call:

lm(formula = dist ~ speed, data = cars)

Residuals:

    Min      1Q  Median      3Q     Max

-29.069  -9.525  -2.272   9.215  43.201

Coefficients:

            Estimate Std. Error t value Pr(>|t|)   

(Intercept) -17.5791     6.7584  -2.601   0.0123 * 

speed         3.9324     0.4155   9.464 1.49e-12 ***

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.38 on 48 degrees of freedom

Multiple R-squared:  0.6511,    Adjusted R-squared:  0.6438

F-statistic: 89.57 on 1 and 48 DF,  p-value: 1.49e-12

>

> # Intercept = 절편

> # coefficients(회귀식의 계수) 종속변수 = 독립변수*계수 + 절편

> # dist=speed*3.932-17.579

> # 계수가 양수이므로 양의 상관관계를 지님

>

>

>

> # 추정해보기(신뢰구간)

>

> predict(m, data.frame(speed=3), interval="confidence")

        fit       lwr      upr

1 -5.781869 -17.02659 5.462853

>

> # fit = 예측값

> # lwr, upr = 신뢰구간

> # speed가 3일 때, dist의 평균값은 -17.03~5.46 사이에 있음

>

>

> plot(cars)

> abline(coef(m)) #coef는 회선의 계수

> coef(m)

(Intercept)       speed

 -17.579095    3.932409

>

>

> [출처] [R프로그래밍] RStudio 선형 회귀 (1) 단순 선형 회귀|작성자 아트 

http://chloe-ynlee.me/221302458526

denunciar 신고하다, 고발하다, 기소하다

impedier 방해하다, 탄핵하다

Se fizesse corpo mole, teria aceitado a oferta do Vasco.

If I were a soft-spoken, I would have accepted Vasco's offer.

fizesse : 접속법 반과거

teria : 과거미래

ex) O médico dele recomendou-lhe que fizesse repouso absoluto.

repouso 휴식, 안심

absoluto 절대의, 완전한, 무조건적

par(mar=c(1,1,1,1))

워드클라우드 전에 적어주면 됨