簡易繪圖功能

林嶔 (Lin, Chin)

Lesson 9

第一節:基礎繪圖函數簡介-1(1)

– 請至這裡下載本週的範例資料

dat = read.csv("Example data.csv", header = TRUE)
head(dat)
##       eGFR Disease Survival.time Death Diabetes Cancer      SBP      DBP
## 1 34.65379       1     0.4771037     0        0      1 121.2353 121.3079
## 2 37.21183       1     3.0704424     0        1      1 122.2000 122.6283
## 3 32.60074       1     0.2607117     1        0      0 118.9136 121.7621
## 4 29.68481       1            NA    NA        0      0 118.2212 112.7043
## 5 28.35726       0     0.1681673     1        0      0 116.7469 115.7705
## 6 33.95012       1     1.2238556     0        0      0 119.9936 116.3872
##   Education Income
## 1         2      0
## 2         2      0
## 3         0      0
## 4         1      0
## 5         0      0
## 6         1      0

第一節:基礎繪圖函數簡介-1(2)

  1. 直方圖:需要使用函數「hist()」
hist(dat[,"eGFR"])

  1. 盒鬚圖:需要使用函數「boxplot()」
boxplot(dat[,"eGFR"])

  1. 圓餅圖:需要使用函數「pie()」以及函數「table()」
pie(table(dat[,"Education"]))

  1. 長條圖:需要使用函數「barplot()」以及函數「table()」
barplot(table(dat[,"Education"]))

第一節:基礎繪圖函數簡介-1(3)

– 在R裡面的顏色可以在Colors in R裡查看

– 另外,這裡教一個新函數「par()」,他可以指定繪圖環境。其中最常見的應用為把4張圖放在同一張畫布內:

par(mfrow = c(2, 2))
hist(dat[,"eGFR"], col = "red")
boxplot(dat[,"eGFR"], col = "blue")
pie(table(dat[,"Education"]), col = c("blue", "red", "green"))
barplot(table(dat[,"Education"]), col = c("gray90", "gray50", "gray10"))

pdf("plot1.pdf", height = 8, width = 8, family = "serif")
par(mfrow = c(2, 2))
hist(dat[,"eGFR"], col = "red")
boxplot(dat[,"eGFR"], col = "blue")
pie(table(dat[,"Education"]), col = c("blue", "red", "green"))
barplot(table(dat[,"Education"]), col = c("gray90", "gray50", "gray10"))
dev.off()

練習1:調整繪圖參數

練習1答案

boxplot(count ~ spray, data = InsectSprays, col = "lightgray")

boxplot(dat[,"eGFR"] ~ dat[,"Disease"], col = c("blue", "red"), ylab = "eGFR", xlab = "Disease", main = "eGFR value by Disease status", lwd = 1.5)

第二節:基礎繪圖函數簡介-2(1)

plot(dat[,"SBP"], dat[,"DBP"], ylab = "DBP", xlab = "SBP", main = "Scatter plot of SBP and DBP")

plot(dat[,"SBP"], dat[,"DBP"], ylab = "DBP", xlab = "SBP", main = "Scatter plot of SBP and DBP", pch = 19)

第二節:基礎繪圖函數簡介-2(2)

– 函數「lines()」的效果是按照順序把幾個點連起來,舉例來說…

– 註:函數「plot.new()」及函數「plot.window()」是拿來開一張新畫布用的!

x = c(1, 4, 7)
y = c(2, 9, 6)
plot.new()
plot.window(xlim = c(0, 10), ylim = c(0, 10))
lines(x, y)

z = 0:1000/100
x = sin(z) #三角函數sin
y = cos(z) #三角函數cos
plot.new()
plot.window(xlim = c(-1, 1), ylim = c(-1, 1))
lines(x, y)

第二節:基礎繪圖函數簡介-2(3)

– 預測線的方程式,需要先用第7課所學到的函數「glm()」幫忙建立,你看得懂下面的程式碼嗎?

# 建立MODEL以及預測線的座標
X = dat[,"SBP"]
Y = dat[,"DBP"]
model = glm(Y~X)
COEF = model$coefficients
x = c(0, 200)
y = COEF[1] + COEF[2] * x

plot(dat[,"SBP"], dat[,"DBP"], ylab = "DBP", xlab = "SBP", main = "Scatter plot of SBP and DBP", pch = 19)
lines(x, y, col = "red", lwd = 2)

第二節:基礎繪圖函數簡介-2(4)

  1. 函數「text()」可以為你的圖片上加文字描述
x = c(1, 0, -1, 0)
y = c(0, 1, 0, -1)
t = c("A", "B", "C", "D")
plot.new()
plot.window(xlim = c(-1, 1), ylim = c(-1, 1))
text(x, y, t)

  1. 函數「points()」可以為你的圖片上加點
x = c(1, 0, -1, 0)
y = c(0, 1, 0, -1)
plot.new()
plot.window(xlim = c(-1, 1), ylim = c(-1, 1))
points(x, y, pch = 1:4)

  1. 函數「legend()」可以為你的圖片加上註釋
plot.new()
plot.window(xlim = c(-1, 1), ylim = c(-1, 1))
legend("topleft", c("Female", "Male"), col = c("red", "blue"), pch = c(15, 19), bg = "gray90")
legend(0, 0, c("estimates", "95% CI"), lty = c(1, 2), lwd = 2, col = "black")

  1. 函數「polygon()」可以畫多邊形
x = c(1, 0, -1, 0)
y = c(0, 1, 0, -1)
plot.new()
plot.window(xlim = c(-1, 1), ylim = c(-1, 1))
polygon(x, y, col = "green")

練習2:手刻一張圖

– 畫出下面這張圖!

##   Variable Disease:0   Disease:1   p-value
## 1 "eGFR"   "33.2±6.4" "34.4±7.2" "0.020"

練習2答案

m0 = 33.2
s0 = 6.4/sqrt(sum(dat[,"Disease"] == 0, na.rm = TRUE))
m1 = 34.4
s1 = 7.2/sqrt(sum(dat[,"Disease"] == 1, na.rm = TRUE))

XXX = c(m0, m1)
barplot(XXX, col = c("gray50", "white"), xlab = "Disease", ylab = "eGFR", ylim = c(0, 43))
lines(c(1.9, 1.9), c(m1 - qnorm(0.975) * s1, m1 + qnorm(0.975) * s1), lwd = 3)
lines(c(1.75, 2.05), c(m1 + qnorm(0.975) * s1, m1 + qnorm(0.975) * s1), lwd = 3)
lines(c(1.75, 2.05), c(m1 - qnorm(0.975) * s1, m1 - qnorm(0.975) * s1), lwd = 3)
lines(c(0.7, 0.7), c(m0 - qnorm(0.975) * s0, m0 + qnorm(0.975) * s0), lwd = 3)
lines(c(0.55, 0.85), c(m0 + qnorm(0.975) * s0, m0 + qnorm(0.975) * s0), lwd = 3)
lines(c(0.55, 0.85), c(m0 - qnorm(0.975) * s0, m0 - qnorm(0.975) * s0), lwd = 3)
lines(c(0.7, 0.7, 1.9, 1.9), c(36, 38, 38, 36), lwd = 3)
text(1.3, 40, "p = 0.020")
legend("topright", c("Control", "Case"), fill = c("gray50", "white"))

第三節:色彩透明度與函數(1)

– 資料量多的時候經常會遇到這樣的問題,這時候我們可能需要告訴使用者不同區域點的密度。

plot(dat[,"SBP"], dat[,"DBP"], ylab = "DBP", xlab = "SBP", main = "Scatter plot of SBP and DBP", cex = 2)

plot(dat[,"SBP"], dat[,"DBP"], ylab = "DBP", xlab = "SBP", main = "Scatter plot of SBP and DBP", pch = 19, cex = 2)