Quetelet's Body Mass Index

작업환경 정리

• 현재 작업디렉토리 찾아보기

getwd()

## [1] "/Users/coop2711/Dropbox/works/class/Stat_Methods/R.WD"

• 검색가능한 package 와 data 열거

search()

##  [1] ".GlobalEnv"        "package:knitr"     "package:stats"    
##  [4] "package:graphics"  "package:grDevices" "package:utils"    
##  [7] "package:datasets"  "KoreaEnv"          "package:methods"  
## [10] "Autoloads"         "package:base"

rn96.txt 자료 읽어들이고, 기초통계 요약하기

• rn96.txt 자료 읽어들이기, 원 자료를 보고 header 매개변수 추가

rn96<-read.table("rn96.txt",header=TRUE)

• rn96 의 자료구조 살피기.

str(rn96)

## 'data.frame':    41 obs. of  2 variables:
##  $ height: int  161 155 158 170 160 156 162 158 158 167 ...
##  $ weight: int  50 49 42 65 60 52 58 46 45 51 ...

• height 와 weight 의 기초통계 살피기

summary(rn96)

##      height          weight     
##  Min.   :150.0   Min.   :42.00  
##  1st Qu.:156.0   1st Qu.:48.00  
##  Median :159.0   Median :52.00  
##  Mean   :159.3   Mean   :52.02  
##  3rd Qu.:162.0   3rd Qu.:55.00  
##  Max.   :170.0   Max.   :65.00

• 평균과 표준편차만 살펴보려면

apply(rn96,2,mean)

##    height    weight 
## 159.26829  52.02439

apply(rn96,2,sd)

##   height   weight 
## 4.266289 5.667838

• height 와 weight 의 산점도 그리기

plot(weight~height, data=rn96)

• 선형회귀선 추가하기

plot(weight~height, data=rn96)
abline(lm(weight~height, data=rn96)$coefficient)

• 선형모형으로 분석하기

lm(weight~height, data=rn96)

## 
## Call:
## lm(formula = weight ~ height, data = rn96)
## 
## Coefficients:
## (Intercept)       height  
##      0.2369       0.3252

• 회귀계수와 관련 통계량 살피기

summary(lm(weight~height, data=rn96))

## 
## Call:
## lm(formula = weight ~ height, data = rn96)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.6120 -3.2868 -0.5875  2.7622 13.9893 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.2369    32.8626   0.007    0.994
## height        0.3252     0.2063   1.576    0.123
## 
## Residual standard error: 5.565 on 39 degrees of freedom
## Multiple R-squared:  0.0599, Adjusted R-squared:  0.0358 
## F-statistic: 2.485 on 1 and 39 DF,  p-value: 0.123

• lowess 회귀선 추가하기

plot(weight~height, data=rn96)
abline(lm(weight~height, data=rn96)$coefficient)
lines(lowess(rn96$height,rn96$weight),col="red")

• png 포맷으로 내보내기

png("rn-plot.png",height=640,width=640)
plot(weight~height, data=rn96)
abline(lm(weight~height, data=rn96)$coefficient)
lines(lowess(rn96$height,rn96$weight),col="red")
dev.off()

## quartz_off_screen 
##                 2

BMI 계산하고 줄기-잎 그리기

• 조금 편하게 작업하기 위해서

attach(rn96)
search()

##  [1] ".GlobalEnv"        "rn96"              "package:knitr"    
##  [4] "package:stats"     "package:graphics"  "package:grDevices"
##  [7] "package:utils"     "package:datasets"  "KoreaEnv"         
## [10] "package:methods"   "Autoloads"         "package:base"

• BMI 공식은 \( \frac{몸무게(kg)}{키^2(m)} \)

rn96$BMI<-round(weight/(height/100)^2,digits=1)
head(rn96)

##   height weight  BMI
## 1    161     50 19.3
## 2    155     49 20.4
## 3    158     42 16.8
## 4    170     65 22.5
## 5    160     60 23.4
## 6    156     52 21.4

• 다음 작업이 왜 필요한지 생각해 볼 것.

detach(rn96)
attach(rn96)

• BMI 값들의 줄기-잎 그림 그리기

stem(BMI)

## 
##   The decimal point is at the |
## 
##   16 | 855
##   18 | 00223480135555569
##   20 | 11478145667
##   22 | 1355146
##   24 | 17
##   26 | 
##   28 | 0

• 조금더 알아보기 쉽게 매개변수를 바꾸기

stem(BMI,scale=2)

## 
##   The decimal point is at the |
## 
##   16 | 8
##   17 | 55
##   18 | 0022348
##   19 | 0135555569
##   20 | 11478
##   21 | 145667
##   22 | 1355
##   23 | 146
##   24 | 17
##   25 | 
##   26 | 
##   27 | 
##   28 | 0

• 각 변수는정규분포에 가까울까?

qqnorm(weight)

qqnorm(height)

qqnorm(BMI)

• weight와 height의 줄기-잎 그림

stem(weight)

## 
##   The decimal point is at the |
## 
##   42 | 00
##   44 | 000
##   46 | 000
##   48 | 000000
##   50 | 00000
##   52 | 000000000
##   54 | 000
##   56 | 000
##   58 | 0
##   60 | 000
##   62 | 00
##   64 | 0

stem(height)

## 
##   The decimal point is at the |
## 
##   150 | 0
##   152 | 0
##   154 | 0000000
##   156 | 00000
##   158 | 0000000
##   160 | 000000000
##   162 | 00000
##   164 | 000
##   166 | 0
##   168 | 0
##   170 | 0