Chapter 5 Graph (W4)

시각화는 중요하다.

  • Plot
  • Histogram
  • Dot Plots
  • Bar Plots
  • Line Charts
  • Pie Charts

5.1 Plot

  • plot(x,y)
  • abline()
  • title(“차트제목 입력”)
head(mtcars)  #mtcars 데이터에 무엇이 있는지 보자, head()는 맨위 6개 데이터만 보여줌
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
plot(mtcars$wt, mtcars$mpg)
title("Plot test")

5.2 Histogram

  • 히스토그램(histogram)
  • hist()
hist(mtcars$mpg)

5.3 Dot Chart

  • dotchart()
    • label: y좌표 라벨링
    • cex: 글자크기
    • main: 차트제목
    • xlab: x좌표 라벨링

기본

dotchart(mtcars$mpg)

기본 + 옵션

dotchart(mtcars$mpg, labels = row.names(mtcars), cex = 0.7, 
    main = "Gas Milage for Car Models", xlab = "Miles Per Gallon")

5.4 Bar Plot

  • barplot()
    • main: 차트제목
    • xlab: x좌표 라벨링

기본

counts <- table(mtcars$gear)
barplot(counts)

기본 + 옵션적용

counts <- table(mtcars$gear)
barplot(counts, main = "Car Distribution", xlab = "Number of Gears")

5.5 Line Chart

  • lines()
    • type
x <- c(1:5)
y <- x  # create some data 
par(pch = 22, col = "red")  # plotting symbol and color 
par(mfrow = c(2, 4))  # all plots on one page 
opts = c("p", "l", "o", "b", "c", "s", "S", "h")
for (i in 1:length(opts)) {
    heading = paste("type=", opts[i])
    plot(x, y, type = "n", main = heading)
    lines(x, y, type = opts[i])
}

5.6 Pie Chart

  • pie()
    • labels
    • main

기본형

myData <- c(10, 12, 4, 16, 8)
myLabels <- c("US", "UK", "Australia", "Germany", "France")
pie(myData, labels = myLabels, main = "Pie Chart of Countries")

기본 + 옵션

# Pie Chart with Percentages
myData <- c(10, 12, 4, 16, 8)
myLabels <- c("US", "UK", "Australia", "Germany", "France")
pct <- round(myData/sum(myData) * 100)
myLabels <- paste(myLabels, pct)  # add percents to labels 
myLabels <- paste(myLabels, "%", sep = "")  # ad % to labels 
pie(myData, labels = myLabels, col = rainbow(length(myLabels)), 
    main = "Pie Chart of Countries")

기본형 + 옵션 (3D)

# 3D Exploded Pie Chart
library(plotrix)
myData <- c(10, 12, 4, 16, 8)
myLabels <- c("US", "UK", "Australia", "Germany", "France")
pie3D(myData, labels = myLabels, explode = 0.1, main = "Pie Chart of Countries ")

# Pie Chart from data frame with Appended Sample Sizes
mytable <- table(iris$Species)
lbls <- paste(names(mytable), "\n", mytable, sep = "")
pie(mytable, labels = lbls, main = "Pie Chart of Species\n (with sample sizes)")

5.7 Boxplots

# Boxplot of MPG by Car Cylinders
boxplot(mpg ~ cyl, data = mtcars, main = "Car Milage Data", 
    xlab = "Number of Cylinders", ylab = "Miles Per Gallon")

5.8 Google Chart

구글챠트 [Link]

5.8.1 Line Chart

  • library(googleVis)
  • data.frame(라벨링, 데이터1, 데이터2)

코드를 실행하면, 자동으로 브라우저(IE, chrome)가 실행되며 그래프를 보여줌

library(googleVis)
df = data.frame(country = c("US", "GB", "BR"), val1 = c(10, 
    13, 14), val2 = c(23, 12, 32))
Line <- gvisLineChart(df)
plot(Line)

5.8.2 Bar Chart

  • library(googleVis)
  • data.frame(라벨링, 데이터1, 데이터2)

코드를 실행하면, 자동으로 브라우저(IE, chrome)가 실행되며 그래프를 보여줌

library(googleVis)
df = data.frame(country = c("US", "GB", "BR"), val1 = c(10, 
    13, 14), val2 = c(23, 12, 32))
Bar <- gvisBarChart(df)
plot(Bar)