R语言实战
高猛
2024-02-29
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## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
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## Mean :15.4 Mean : 42.98
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a <- c(1,2,3,4,5)
b <- c("one", "two", "three")
c <- c(TRUE, TRUE, FALSE,FALSE)
print(a)## [1] 1 2 3 4 5print(b)## [1] "one" "two" "three"print(c)## [1] TRUE TRUE FALSE FALSEa <- c(1, 2, 5, 3, 6, -2, 4)
a[3]## [1] 5a[c(1,3,5)]## [1] 1 5 6a[2:6]## [1] 2 5 3 6 -2y1 <- matrix(1:20,nrow = 5, ncol = 4)
print(y1)## [,1] [,2] [,3] [,4]
## [1,] 1 6 11 16
## [2,] 2 7 12 17
## [3,] 3 8 13 18
## [4,] 4 9 14 19
## [5,] 5 10 15 20y2 <- matrix(1:20, nrow = 5, ncol = 4, byrow = TRUE)
print(y2)## [,1] [,2] [,3] [,4]
## [1,] 1 2 3 4
## [2,] 5 6 7 8
## [3,] 9 10 11 12
## [4,] 13 14 15 16
## [5,] 17 18 19 20##
cells <- c(1, 26, 24, 68)
rnames <- c("R1", "R2")
cnames <- c("C1", "C2")
mymatrix <- matrix(cells, nrow = 2, ncol = 2, byrow = TRUE,
dimnames = list(rnames, cnames))
print(mymatrix)## C1 C2
## R1 1 26
## R2 24 68mymatrix1 <- matrix(cells, nrow = 2, ncol = 2, byrow = FALSE,
dimnames = list(rnames, cnames))
print(mymatrix1)## C1 C2
## R1 1 24
## R2 26 68x <- matrix(1:10, nrow = 2)
print(x)## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 3 5 7 9
## [2,] 2 4 6 8 10x[2,]## [1] 2 4 6 8 10x[,1]## [1] 1 2x[1,4]## [1] 7x[1,2:5]## [1] 3 5 7 9x[1, c(2,5)]## [1] 3 9创建三维数组(2×3×4)：
dim1 <- c("A1", "A2")
dim2 <- c("B1", "B2","B3")
dim3 <- c("C1", "C2", "C3", "C4")
z <- array(1:24, c(2,3,4), dimnames = list(dim1, dim2, dim3))
print(z)## , , C1
##
## B1 B2 B3
## A1 1 3 5
## A2 2 4 6
##
## , , C2
##
## B1 B2 B3
## A1 7 9 11
## A2 8 10 12
##
## , , C3
##
## B1 B2 B3
## A1 13 15 17
## A2 14 16 18
##
## , , C4
##
## B1 B2 B3
## A1 19 21 23
## A2 20 22 24z[1,2,3]## [1] 15z[,,3]## B1 B2 B3
## A1 13 15 17
## A2 14 16 18数据框结构：可以将数值型和字符型放到一起。矩阵不行 数据框的每一列数据的模式必须唯一
patientID <- c(1, 2, 3, 4)
age <- c(25, 34, 28, 52)
diabetes <- c("Type1", "Type2", "Type3","Type4")
status <- c("poor", "improved", "poor", "improved")
patientdata <- data.frame(patientID, age, diabetes, status)
print(patientdata)## patientID age diabetes status
## 1 1 25 Type1 poor
## 2 2 34 Type2 improved
## 3 3 28 Type3 poor
## 4 4 52 Type4 improvedpatientdata[1:2]## patientID age
## 1 1 25
## 2 2 34
## 3 3 28
## 4 4 52patientdata[1:2,]## patientID age diabetes status
## 1 1 25 Type1 poor
## 2 2 34 Type2 improvedpatientdata[c("diabetes","status")]# 指定列名## diabetes status
## 1 Type1 poor
## 2 Type2 improved
## 3 Type3 poor
## 4 Type4 improvedpatientdata$age # 指定age这一列## [1] 25 34 28 52table(patientdata$diabetes, patientdata$status)##
## improved poor
## Type1 0 1
## Type2 1 0
## Type3 0 1
## Type4 1 0summary(mtcars$mpg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90plot(mtcars$mpg, mtcars$disp)
plot(mtcars$mpg, mtcars$wt)
# 也可以写成如下代码
attach(mtcars) #函数attach()可将数据框添加到R的搜索路径中。
summary(mpg) # R在遇到一个变量名以后，将检查搜索路 ## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90plot(mpg,disp) #径中的数据框，以定位到这个变量。
plot(mpg, wt)
detach(mtcars) # 函数detach()将数据框从搜索路径中移除。
# 当名称相同的对象不止一个时，attach()方法就不好了。# 也可以使用with来实现上述代码
with(mtcars, {
summary(mpg, disp, wt)
plot(mpg, disp)
plot(mpg, wt)
})
with(mtcars,summary(mpg))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90# 函数with()的局限性在于，赋值仅在此函数的括号内生效。考虑以下代码：
with(mtcars, {
stats <- summary(mpg)
print(stats)
})## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90#stats # 找不到该变量# 如果你需要创建在with()结构以外存在的对象，使用特殊赋值符<<-替代标准赋值符（<-）即可，
with(mtcars,{
nokeepstates <- summary(mpg)
keepstates <<- summary(mpg)
})
# print(nokeepstates) #找不到该位置
print(keepstates)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90#实例标识符,通过数据框操作函数中的row.names选项指定。
patientdata <- data.frame(patientID, age, diabetes, status,
row.names = patientID)
print(patientdata)## patientID age diabetes status
## 1 1 25 Type1 poor
## 2 2 34 Type2 improved
## 3 3 28 Type3 poor
## 4 4 52 Type4 improved# 名义型变量：
diabetes <- c("Type1", "Type2", "Type3","Type4")
diabetes1 <- factor(diabetes)
#print(diabetes)
print(diabetes1)## [1] Type1 Type2 Type3 Type4
## Levels: Type1 Type2 Type3 Type4# 有序性变量
status <- c("poor", "improved", "Excellent", "poor")
status1 <- factor(status, ordered = TRUE)
print(status1)## [1] poor improved Excellent poor
## Levels: Excellent < improved < poor# 通过指定levels选项来覆盖默认排序。
status2 <- factor(status, ordered = TRUE, levels = c("poor", "improved", "Excellent"))
print(status2)## [1] poor improved Excellent poor
## Levels: poor < improved < Excellent# 普通因子和有序因子的不同是如何影响数据分析的:
patientID <- c(1, 2, 3, 4)
age <- c(25, 34, 28, 52)
diabetes <- c("Type1", "Type2", "Type1","Type1")
status <- c("poor", "improved", "Excellent", "poor")
diabetes <- factor(diabetes)
status <- factor(status, order = TRUE)
patientdata <- data.frame(patientID, age, diabetes, status)
str(patientdata) # 显示对象的结构## 'data.frame': 4 obs. of 4 variables:
## $ patientID: num 1 2 3 4
## $ age : num 25 34 28 52
## $ diabetes : Factor w/ 2 levels "Type1","Type2": 1 2 1 1
## $ status : Ord.factor w/ 3 levels "Excellent"<"improved"<..: 3 2 1 3print(patientdata)## patientID age diabetes status
## 1 1 25 Type1 poor
## 2 2 34 Type2 improved
## 3 3 28 Type1 Excellent
## 4 4 52 Type1 poorsummary(patientdata)## patientID age diabetes status
## Min. :1.00 Min. :25.00 Type1:3 Excellent:1
## 1st Qu.:1.75 1st Qu.:27.25 Type2:1 improved :1
## Median :2.50 Median :31.00 poor :2
## Mean :2.50 Mean :34.75
## 3rd Qu.:3.25 3rd Qu.:38.50
## Max. :4.00 Max. :52.00# 列表（list）是R的数据类型中最为复杂的一种。
#列表允许你整合若干（可能无关的）对象到单个对象名下。例如，某个列表中可能是若干向量、矩阵、数据框，甚至其他列表的组合。
g <- "My First List"
h <- c(25, 26, 18, 39)
j <- matrix(1:10, nrow = 5)
k <- c("one", "two", "three")
mylist <- list(title=g, ages = h, j, k)
print(mylist)## $title
## [1] "My First List"
##
## $ages
## [1] 25 26 18 39
##
## [[3]]
## [,1] [,2]
## [1,] 1 6
## [2,] 2 7
## [3,] 3 8
## [4,] 4 9
## [5,] 5 10
##
## [[4]]
## [1] "one" "two" "three"mylist[4]## [[1]]
## [1] "one" "two" "three"mylist[[2]]## [1] 25 26 18 39mylist[["ages"]]## [1] 25 26 18 39mylist[4]## [[1]]
## [1] "one" "two" "three"x <- c(1,2,3)
x[7] <- 10
print(x)## [1] 1 2 3 NA NA NA 10mydata <- data.frame(age=numeric(0), gender=character(0), weight=numeric(0))
mydata <- edit(mydata)
print(mydata)## age gender weight
## 1 1 男 67# 2.3.2从带分隔符的文本文件导入数据
# grades <- read.table("studentgrades.csv", header = TRUE, sep = ",", row.names = "STUDENTID")
# # 逗号分隔文件（csv）
# print(grades)# 2.3.3 导入Excel数据
# 导入 readxl 包
library(readxl)## Warning: 程辑包'readxl'是用R版本4.3.2 来建造的# 指定 Excel 文件路径
excel_file <- "C:/Users/Administrator/Documents/myfile.xls.xlsx"
# 读取 Excel 文件中的数据
data <- read_excel(excel_file, sheet = "Sheet1") # 根据实际情况修改 sheet 名称
# 查看读取的数据
print(data)## # A tibble: 2 × 3
## 姓名 年龄 成绩
## <chr> <dbl> <dbl>
## 1 高猛 28 90
## 2 周瑜 26 88# 第三章图形初阶
pdf("mygraph.pdf") # 将图形保存成pdf
# 可以在交互窗口一句句运行
attach(mtcars)
plot(wt, mpg)
abline(lm(mpg~wt)) # 向图形添加了一条最优拟合曲线
title("Regression of MPG on Weight")
detach(mtcars)
dev.off() # 将图形保存成pdf## png
## 2# 通过执行如plot()、hist()（绘制直方图）或boxplot()这样的高级绘图命令来创建一幅新图形时，通常会覆盖掉先前的图形。如何才能创建多个图形并随时查看每一个呢？方法有若干。
#第一种方法，你可以在创建一幅新图形之前打开一个新的图形窗口：
# dev.new()
# statements to create graph 1
# dev.new()
# statements to create graph 2
# etc.# Ctrl + Shift + c 是注释所选中的内容dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)
plot(dose, drugA, type = "b")
# type = "b"表示同时绘制点和线opar <- par(no.readonly = TRUE) # 复制了一份当前的图形参数设置
par(lty=2, pch=17) # lty=2:虚线；pch=17：实心三角
# 通过函数par()来指定这些选项。以这种方式设定的参数值除非被再次修改，否则将在会话结束前一直有效。
plot(dose, drugA, type="b")
par(opar)plot(dose, drugA, type = "b", lty = 2, pch = 17)
plot(dose, drugA, type = "b", lty = 2, pch = 0)
# type="b", lty=2, pch=17 这就是键值对啊！
pch图样
lty图样
plot(dose, drugA, type = 'b', lty = 3, lwd = 3, pch = 15, cex = 2)
plot(dose, drugA, type = 'b', lty = 3, lwd = 3, pch = 15, cex = 4)
# cex 指定符号的大小。3.3.2 颜色
#
在R中，可以通过颜色下标、颜色名称、十六进制的颜色值、RGB值或HSV值来指定颜色。举例来说，col=1、col=“white”、col=“#FFFFFF”、col=rgb(1,1,1)和col=hsv(0,0,1)都是表示白色的等价方式。
# 函数colors()可以返回所有可用颜色的名称。
colors()## [1] "white" "aliceblue" "antiquewhite"
## [4] "antiquewhite1" "antiquewhite2" "antiquewhite3"
## [7] "antiquewhite4" "aquamarine" "aquamarine1"
## [10] "aquamarine2" "aquamarine3" "aquamarine4"
## [13] "azure" "azure1" "azure2"
## [16] "azure3" "azure4" "beige"
## [19] "bisque" "bisque1" "bisque2"
## [22] "bisque3" "bisque4" "black"
## [25] "blanchedalmond" "blue" "blue1"
## [28] "blue2" "blue3" "blue4"
## [31] "blueviolet" "brown" "brown1"
## [34] "brown2" "brown3" "brown4"
## [37] "burlywood" "burlywood1" "burlywood2"
## [40] "burlywood3" "burlywood4" "cadetblue"
## [43] "cadetblue1" "cadetblue2" "cadetblue3"
## [46] "cadetblue4" "chartreuse" "chartreuse1"
## [49] "chartreuse2" "chartreuse3" "chartreuse4"
## [52] "chocolate" "chocolate1" "chocolate2"
## [55] "chocolate3" "chocolate4" "coral"
## [58] "coral1" "coral2" "coral3"
## [61] "coral4" "cornflowerblue" "cornsilk"
## [64] "cornsilk1" "cornsilk2" "cornsilk3"
## [67] "cornsilk4" "cyan" "cyan1"
## [70] "cyan2" "cyan3" "cyan4"
## [73] "darkblue" "darkcyan" "darkgoldenrod"
## [76] "darkgoldenrod1" "darkgoldenrod2" "darkgoldenrod3"
## [79] "darkgoldenrod4" "darkgray" "darkgreen"
## [82] "darkgrey" "darkkhaki" "darkmagenta"
## [85] "darkolivegreen" "darkolivegreen1" "darkolivegreen2"
## [88] "darkolivegreen3" "darkolivegreen4" "darkorange"
## [91] "darkorange1" "darkorange2" "darkorange3"
## [94] "darkorange4" "darkorchid" "darkorchid1"
## [97] "darkorchid2" "darkorchid3" "darkorchid4"
## [100] "darkred" "darksalmon" "darkseagreen"
## [103] "darkseagreen1" "darkseagreen2" "darkseagreen3"
## [106] "darkseagreen4" "darkslateblue" "darkslategray"
## [109] "darkslategray1" "darkslategray2" "darkslategray3"
## [112] "darkslategray4" "darkslategrey" "darkturquoise"
## [115] "darkviolet" "deeppink" "deeppink1"
## [118] "deeppink2" "deeppink3" "deeppink4"
## [121] "deepskyblue" "deepskyblue1" "deepskyblue2"
## [124] "deepskyblue3" "deepskyblue4" "dimgray"
## [127] "dimgrey" "dodgerblue" "dodgerblue1"
## [130] "dodgerblue2" "dodgerblue3" "dodgerblue4"
## [133] "firebrick" "firebrick1" "firebrick2"
## [136] "firebrick3" "firebrick4" "floralwhite"
## [139] "forestgreen" "gainsboro" "ghostwhite"
## [142] "gold" "gold1" "gold2"
## [145] "gold3" "gold4" "goldenrod"
## [148] "goldenrod1" "goldenrod2" "goldenrod3"
## [151] "goldenrod4" "gray" "gray0"
## [154] "gray1" "gray2" "gray3"
## [157] "gray4" "gray5" "gray6"
## [160] "gray7" "gray8" "gray9"
## [163] "gray10" "gray11" "gray12"
## [166] "gray13" "gray14" "gray15"
## [169] "gray16" "gray17" "gray18"
## [172] "gray19" "gray20" "gray21"
## [175] "gray22" "gray23" "gray24"
## [178] "gray25" "gray26" "gray27"
## [181] "gray28" "gray29" "gray30"
## [184] "gray31" "gray32" "gray33"
## [187] "gray34" "gray35" "gray36"
## [190] "gray37" "gray38" "gray39"
## [193] "gray40" "gray41" "gray42"
## [196] "gray43" "gray44" "gray45"
## [199] "gray46" "gray47" "gray48"
## [202] "gray49" "gray50" "gray51"
## [205] "gray52" "gray53" "gray54"
## [208] "gray55" "gray56" "gray57"
## [211] "gray58" "gray59" "gray60"
## [214] "gray61" "gray62" "gray63"
## [217] "gray64" "gray65" "gray66"
## [220] "gray67" "gray68" "gray69"
## [223] "gray70" "gray71" "gray72"
## [226] "gray73" "gray74" "gray75"
## [229] "gray76" "gray77" "gray78"
## [232] "gray79" "gray80" "gray81"
## [235] "gray82" "gray83" "gray84"
## [238] "gray85" "gray86" "gray87"
## [241] "gray88" "gray89" "gray90"
## [244] "gray91" "gray92" "gray93"
## [247] "gray94" "gray95" "gray96"
## [250] "gray97" "gray98" "gray99"
## [253] "gray100" "green" "green1"
## [256] "green2" "green3" "green4"
## [259] "greenyellow" "grey" "grey0"
## [262] "grey1" "grey2" "grey3"
## [265] "grey4" "grey5" "grey6"
## [268] "grey7" "grey8" "grey9"
## [271] "grey10" "grey11" "grey12"
## [274] "grey13" "grey14" "grey15"
## [277] "grey16" "grey17" "grey18"
## [280] "grey19" "grey20" "grey21"
## [283] "grey22" "grey23" "grey24"
## [286] "grey25" "grey26" "grey27"
## [289] "grey28" "grey29" "grey30"
## [292] "grey31" "grey32" "grey33"
## [295] "grey34" "grey35" "grey36"
## [298] "grey37" "grey38" "grey39"
## [301] "grey40" "grey41" "grey42"
## [304] "grey43" "grey44" "grey45"
## [307] "grey46" "grey47" "grey48"
## [310] "grey49" "grey50" "grey51"
## [313] "grey52" "grey53" "grey54"
## [316] "grey55" "grey56" "grey57"
## [319] "grey58" "grey59" "grey60"
## [322] "grey61" "grey62" "grey63"
## [325] "grey64" "grey65" "grey66"
## [328] "grey67" "grey68" "grey69"
## [331] "grey70" "grey71" "grey72"
## [334] "grey73" "grey74" "grey75"
## [337] "grey76" "grey77" "grey78"
## [340] "grey79" "grey80" "grey81"
## [343] "grey82" "grey83" "grey84"
## [346] "grey85" "grey86" "grey87"
## [349] "grey88" "grey89" "grey90"
## [352] "grey91" "grey92" "grey93"
## [355] "grey94" "grey95" "grey96"
## [358] "grey97" "grey98" "grey99"
## [361] "grey100" "honeydew" "honeydew1"
## [364] "honeydew2" "honeydew3" "honeydew4"
## [367] "hotpink" "hotpink1" "hotpink2"
## [370] "hotpink3" "hotpink4" "indianred"
## [373] "indianred1" "indianred2" "indianred3"
## [376] "indianred4" "ivory" "ivory1"
## [379] "ivory2" "ivory3" "ivory4"
## [382] "khaki" "khaki1" "khaki2"
## [385] "khaki3" "khaki4" "lavender"
## [388] "lavenderblush" "lavenderblush1" "lavenderblush2"
## [391] "lavenderblush3" "lavenderblush4" "lawngreen"
## [394] "lemonchiffon" "lemonchiffon1" "lemonchiffon2"
## [397] "lemonchiffon3" "lemonchiffon4" "lightblue"
## [400] "lightblue1" "lightblue2" "lightblue3"
## [403] "lightblue4" "lightcoral" "lightcyan"
## [406] "lightcyan1" "lightcyan2" "lightcyan3"
## [409] "lightcyan4" "lightgoldenrod" "lightgoldenrod1"
## [412] "lightgoldenrod2" "lightgoldenrod3" "lightgoldenrod4"
## [415] "lightgoldenrodyellow" "lightgray" "lightgreen"
## [418] "lightgrey" "lightpink" "lightpink1"
## [421] "lightpink2" "lightpink3" "lightpink4"
## [424] "lightsalmon" "lightsalmon1" "lightsalmon2"
## [427] "lightsalmon3" "lightsalmon4" "lightseagreen"
## [430] "lightskyblue" "lightskyblue1" "lightskyblue2"
## [433] "lightskyblue3" "lightskyblue4" "lightslateblue"
## [436] "lightslategray" "lightslategrey" "lightsteelblue"
## [439] "lightsteelblue1" "lightsteelblue2" "lightsteelblue3"
## [442] "lightsteelblue4" "lightyellow" "lightyellow1"
## [445] "lightyellow2" "lightyellow3" "lightyellow4"
## [448] "limegreen" "linen" "magenta"
## [451] "magenta1" "magenta2" "magenta3"
## [454] "magenta4" "maroon" "maroon1"
## [457] "maroon2" "maroon3" "maroon4"
## [460] "mediumaquamarine" "mediumblue" "mediumorchid"
## [463] "mediumorchid1" "mediumorchid2" "mediumorchid3"
## [466] "mediumorchid4" "mediumpurple" "mediumpurple1"
## [469] "mediumpurple2" "mediumpurple3" "mediumpurple4"
## [472] "mediumseagreen" "mediumslateblue" "mediumspringgreen"
## [475] "mediumturquoise" "mediumvioletred" "midnightblue"
## [478] "mintcream" "mistyrose" "mistyrose1"
## [481] "mistyrose2" "mistyrose3" "mistyrose4"
## [484] "moccasin" "navajowhite" "navajowhite1"
## [487] "navajowhite2" "navajowhite3" "navajowhite4"
## [490] "navy" "navyblue" "oldlace"
## [493] "olivedrab" "olivedrab1" "olivedrab2"
## [496] "olivedrab3" "olivedrab4" "orange"
## [499] "orange1" "orange2" "orange3"
## [502] "orange4" "orangered" "orangered1"
## [505] "orangered2" "orangered3" "orangered4"
## [508] "orchid" "orchid1" "orchid2"
## [511] "orchid3" "orchid4" "palegoldenrod"
## [514] "palegreen" "palegreen1" "palegreen2"
## [517] "palegreen3" "palegreen4" "paleturquoise"
## [520] "paleturquoise1" "paleturquoise2" "paleturquoise3"
## [523] "paleturquoise4" "palevioletred" "palevioletred1"
## [526] "palevioletred2" "palevioletred3" "palevioletred4"
## [529] "papayawhip" "peachpuff" "peachpuff1"
## [532] "peachpuff2" "peachpuff3" "peachpuff4"
## [535] "peru" "pink" "pink1"
## [538] "pink2" "pink3" "pink4"
## [541] "plum" "plum1" "plum2"
## [544] "plum3" "plum4" "powderblue"
## [547] "purple" "purple1" "purple2"
## [550] "purple3" "purple4" "red"
## [553] "red1" "red2" "red3"
## [556] "red4" "rosybrown" "rosybrown1"
## [559] "rosybrown2" "rosybrown3" "rosybrown4"
## [562] "royalblue" "royalblue1" "royalblue2"
## [565] "royalblue3" "royalblue4" "saddlebrown"
## [568] "salmon" "salmon1" "salmon2"
## [571] "salmon3" "salmon4" "sandybrown"
## [574] "seagreen" "seagreen1" "seagreen2"
## [577] "seagreen3" "seagreen4" "seashell"
## [580] "seashell1" "seashell2" "seashell3"
## [583] "seashell4" "sienna" "sienna1"
## [586] "sienna2" "sienna3" "sienna4"
## [589] "skyblue" "skyblue1" "skyblue2"
## [592] "skyblue3" "skyblue4" "slateblue"
## [595] "slateblue1" "slateblue2" "slateblue3"
## [598] "slateblue4" "slategray" "slategray1"
## [601] "slategray2" "slategray3" "slategray4"
## [604] "slategrey" "snow" "snow1"
## [607] "snow2" "snow3" "snow4"
## [610] "springgreen" "springgreen1" "springgreen2"
## [613] "springgreen3" "springgreen4" "steelblue"
## [616] "steelblue1" "steelblue2" "steelblue3"
## [619] "steelblue4" "tan" "tan1"
## [622] "tan2" "tan3" "tan4"
## [625] "thistle" "thistle1" "thistle2"
## [628] "thistle3" "thistle4" "tomato"
## [631] "tomato1" "tomato2" "tomato3"
## [634] "tomato4" "turquoise" "turquoise1"
## [637] "turquoise2" "turquoise3" "turquoise4"
## [640] "violet" "violetred" "violetred1"
## [643] "violetred2" "violetred3" "violetred4"
## [646] "wheat" "wheat1" "wheat2"
## [649] "wheat3" "wheat4" "whitesmoke"
## [652] "yellow" "yellow1" "yellow2"
## [655] "yellow3" "yellow4" "yellowgreen"length(colors())## [1] 657# R中也有多种用于创建连续型颜色向量的函数，包括rainbow()、heat.colors()、terrain.colors()、topo.colors()以及cm.colors()。
rainbow(10) #可以生成10种连续的“彩虹型”颜色。## [1] "#FF0000" "#FF9900" "#CCFF00" "#33FF00" "#00FF66" "#00FFFF" "#0066FF"
## [8] "#3300FF" "#CC00FF" "#FF0099"#颜色配对，RColorBrewer特别受到欢迎。
library(RColorBrewer)
n <- 7
mycolors <- brewer.pal(n, "Set1")
# 从Set1调色板中抽取了7种用十六进制表示的颜色并返回一个向量
barplot(rep(1,n), col = mycolors)
brewer.pal.info # 所有可选调色板的列表## maxcolors category colorblind
## BrBG 11 div TRUE
## PiYG 11 div TRUE
## PRGn 11 div TRUE
## PuOr 11 div TRUE
## RdBu 11 div TRUE
## RdGy 11 div FALSE
## RdYlBu 11 div TRUE
## RdYlGn 11 div FALSE
## Spectral 11 div FALSE
## Accent 8 qual FALSE
## Dark2 8 qual TRUE
## Paired 12 qual TRUE
## Pastel1 9 qual FALSE
## Pastel2 8 qual FALSE
## Set1 9 qual FALSE
## Set2 8 qual TRUE
## Set3 12 qual FALSE
## Blues 9 seq TRUE
## BuGn 9 seq TRUE
## BuPu 9 seq TRUE
## GnBu 9 seq TRUE
## Greens 9 seq TRUE
## Greys 9 seq TRUE
## Oranges 9 seq TRUE
## OrRd 9 seq TRUE
## PuBu 9 seq TRUE
## PuBuGn 9 seq TRUE
## PuRd 9 seq TRUE
## Purples 9 seq TRUE
## RdPu 9 seq TRUE
## Reds 9 seq TRUE
## YlGn 9 seq TRUE
## YlGnBu 9 seq TRUE
## YlOrBr 9 seq TRUE
## YlOrRd 9 seq TRUEn <- 10
mycolors = rainbow(n)
pie(rep(1,n), labels = mycolors, col = mycolors)
mygrays <- gray(0:n/n) # 将生成n阶灰度色。
pie(rep(1,n), labels = mygrays, col = mygrays)