Learn Base R effectively
LJJ
2019/10/31
实践1(最初几步)
library(tidyverse)## -- Attaching packages -------------------------------------------------- tidyverse 1.2.1 --## √ ggplot2 3.2.1 √ purrr 0.3.3
## √ tibble 2.1.3 √ dplyr 0.8.3
## √ tidyr 1.0.0 √ stringr 1.4.0
## √ readr 1.3.1 √ forcats 0.4.0## -- Conflicts ----------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()(x <- 1:100)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## [18] 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
## [35] 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
## [52] 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68
## [69] 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85
## [86] 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100sample(x,20)## [1] 13 11 36 83 44 27 31 86 25 78 74 56 98 81 2 21 97 45 8 89set.seed(100)
sample(x,20)## [1] 74 89 78 23 86 70 4 55 95 7 91 93 43 82 61 12 51 72 18 25z <- sample(1:200000,1000) # 不放回抽样
z[1:10]## [1] 126387 123828 8539 187769 123485 20126 78557 149087 16607 15068z[c(1:10)]## [1] 126387 123828 8539 187769 123485 20126 78557 149087 16607 15068z1 <- sample(x,200,replace = TRUE) # 放回抽样
unique(z1)## [1] 96 33 89 80 78 79 13 61 44 53 56 68 91 97 73 25 95 98 84 21 14 16 58
## [24] 75 94 65 11 27 23 9 62 41 31 60 30 83 90 47 72 51 87 64 4 45 38 18
## [47] 99 1 71 49 40 92 6 76 34 15 43 93 24 46 59 69 54 77 63 20 39 7 86
## [70] 74 88 32 37 35 52 82 55 22 50 85 81 57 17 19 48 70 8table(z1)## z1
## 1 4 6 7 8 9 11 13 14 15 16 17 18 19 20 21 22 23 24 25 27 30 31 32 33
## 1 3 3 3 1 2 3 3 3 3 1 2 3 2 2 2 2 3 3 4 2 3 2 1 3
## 34 35 37 38 39 40 41 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
## 4 1 1 4 5 3 2 1 4 3 1 3 1 1 3 2 1 4 1 1 1 1 5 1 5
## 61 62 63 64 65 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87
## 1 1 3 2 3 1 1 1 4 1 1 1 3 1 2 2 4 1 2 2 1 4 1 1 3
## 88 89 90 91 92 93 94 95 96 97 98 99
## 2 3 2 5 2 2 1 5 1 4 4 3data <- tibble(x = 1:10,
y = 2:11,
z = rep(c(TRUE,FALSE),time = 5)
)
setdiff(1:10,2:11) # 集合差## [1] 1setdiff(data$y,data$x) ## [1] 11union(data$x,data$y) # 集合并## [1] 1 2 3 4 5 6 7 8 9 10 11union(data$x,data$y) %>% sort(decreasing = TRUE)## [1] 11 10 9 8 7 6 5 4 3 2 1intersect(data$x,data$y) # 集合∩## [1] 2 3 4 5 6 7 8 9 10sample(1:100,20,prob = 1:100) # 不等概率随机抽样，抽到概率与1:100成比例## [1] 35 78 32 73 88 66 90 33 21 60 50 56 12 7 64 48 76 94 62 43实践2(一些简单运算)
pi*10/2## [1] 15.70796x <- 15*3-5+pi*exp(5.5)
x## [1] 808.7224print(x)## [1] 808.7224(x <- 59/5*6) # 赋值带打印## [1] 70.8pi^(1:5)## [1] 3.141593 9.869604 31.006277 97.409091 306.019685print(x,digits = 5)## [1] 70.8实践3(R数据对象)
x <- pi*10^5
x %>% class() ## [1] "numeric"x %>% typeof()## [1] "double"cars %>% typeof()## [1] "list"cars %>% class()## [1] "data.frame"cars %>% summary()## 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. :120.00cars %>% head()## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10cars %>% tail()## speed dist
## 45 23 54
## 46 24 70
## 47 24 92
## 48 24 93
## 49 24 120
## 50 25 85cars %>% str()## '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 ...cars %>% row.names() # 行名## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14"
## [15] "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28"
## [29] "29" "30" "31" "32" "33" "34" "35" "36" "37" "38" "39" "40" "41" "42"
## [43] "43" "44" "45" "46" "47" "48" "49" "50"cars %>% attributes() # 包括名字，类型和行名## $names
## [1] "speed" "dist"
##
## $class
## [1] "data.frame"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
## [47] 47 48 49 50class(dist~speed) # formula## [1] "formula"plot(cars$dist,cars$speed,xlab = "dist",ylab = "speed") # 散点图
实践4(线性回归)
先介绍cut函数
# Examples
Z <- stats::rnorm(100)
table(cut(Z, breaks = -6:6,include.lowest = TRUE))##
## [-6,-5] (-5,-4] (-4,-3] (-3,-2] (-2,-1] (-1,0] (0,1] (1,2] (2,3]
## 0 0 0 1 13 35 33 14 3
## (3,4] (4,5] (5,6]
## 1 0 0sum(table(cut(Z, breaks = -6:6, labels = FALSE)))## [1] 100sum(graphics::hist(Z, breaks = -6:6, plot = FALSE)$counts)## [1] 100cut(rep(1,5), 4) #-- dummy## [1] (0.9995,1] (0.9995,1] (0.9995,1] (0.9995,1] (0.9995,1]
## Levels: (0.999,0.9995] (0.9995,1] (1,1.0005] (1.0005,1.001]tx0 <- c(9, 4, 6, 5, 3, 10, 5, 3, 5)
x <- rep(0:8, tx0)
stopifnot(table(x) == tx0)
table( cut(x, b = 8))##
## (-0.008,1] (1,2] (2,3] (3,4] (4,5] (5,6]
## 13 6 5 3 10 5
## (6,7] (7,8.01]
## 3 5table( cut(x, breaks = 3*(-2:5)))##
## (-6,-3] (-3,0] (0,3] (3,6] (6,9] (9,12] (12,15]
## 0 9 15 18 8 0 0table( cut(x, breaks = 3*(-2:5), right = FALSE))##
## [-6,-3) [-3,0) [0,3) [3,6) [6,9) [9,12) [12,15)
## 0 0 19 18 13 0 0##--- some values OUTSIDE the breaks :
table(cx <- cut(x, breaks = 2*(0:4)))##
## (0,2] (2,4] (4,6] (6,8]
## 10 8 15 8table(cxl <- cut(x, breaks = 2*(0:4), right = FALSE))##
## [0,2) [2,4) [4,6) [6,8)
## 13 11 13 8which(is.na(cx)); x[is.na(cx)] #-- the first 9 values 0## [1] 1 2 3 4 5 6 7 8 9## [1] 0 0 0 0 0 0 0 0 0which(is.na(cxl)); x[is.na(cxl)] #-- the last 5 values 8## [1] 46 47 48 49 50## [1] 8 8 8 8 8## Label construction:
y <- stats::rnorm(100)
table(cut(y, breaks = pi/3*(-3:3)))##
## (-3.14,-2.09] (-2.09,-1.05] (-1.05,0] (0,1.05] (1.05,2.09]
## 0 15 40 35 9
## (2.09,3.14]
## 1table(cut(y, breaks = pi/3*(-3:3), dig.lab = 4))##
## (-3.142,-2.094] (-2.094,-1.047] (-1.047,0] (0,1.047]
## 0 15 40 35
## (1.047,2.094] (2.094,3.142]
## 9 1table(cut(y, breaks = 1*(-3:3), dig.lab = 4))##
## (-3,-2] (-2,-1] (-1,0] (0,1] (1,2] (2,3]
## 0 18 37 33 11 1table(cut(y, breaks = 1*(-3:3), dig.lab = 4)) %>% sum()## [1] 100# extra digits don't "harm" here
table(cut(y, breaks = 1*(-3:3), right = FALSE))##
## [-3,-2) [-2,-1) [-1,0) [0,1) [1,2) [2,3)
## 0 18 37 33 11 1#- the same, since no exact INT!
## sometimes the default dig.lab is not enough to be avoid confusion:
aaa <- c(1,2,3,4,5,2,3,4,5,6,7)
cut(aaa, 3) %>% class()## [1] "factor"cut(aaa, 3, dig.lab = 4, ordered = TRUE) %>% str()## Ord.factor w/ 3 levels "(0.994,3]"<"(3,5]"<..: 1 1 1 2 2 1 1 2 2 3 ...## one way to extract the breakpoints
labs <- levels(cut(aaa, 3))
cbind(lower = as.numeric( sub("\\((.+),.*", "\\1", labs) ),
upper = as.numeric( sub("[^,]*,([^]]*)\\]", "\\1", labs) ))## lower upper
## [1,] 0.994 3.00
## [2,] 3.000 5.00
## [3,] 5.000 7.01ncol(cars);nrow(cars)## [1] 2## [1] 50dim(cars)## [1] 50 2library(DT)
table(cars$speed)##
## 4 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23 24 25
## 2 2 1 1 3 2 4 4 4 3 2 3 4 3 5 1 1 4 1quantile(cars$speed)## 0% 25% 50% 75% 100%
## 4 12 15 19 25cars %>% datatable()# 简单线性回归
lm_fit <- lm(dist~speed,data = cars)
summary(lm_fit)##
## 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-12cars <- cars %>%
mutate(qspeed = cut(speed,breaks = quantile(speed),include.lowest = TRUE))
cars[3] %>% class() # 数据框## [1] "data.frame"cars[,3] %>% class() # 因子类型## [1] "factor"table(cars$qspeed)##
## [4,12] (12,15] (15,19] (19,25]
## 15 11 12 12cars %>% str()## 'data.frame': 50 obs. of 3 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 ...
## $ qspeed: Factor w/ 4 levels "[4,12]","(12,15]",..: 1 1 1 1 1 1 1 1 1 1 ...plot(cars$dist~cars$qspeed)
lm_fit2 <- lm(dist~.,data = cars) # 多元线性回归
summary(lm_fit2)##
## Call:
## lm(formula = dist ~ ., data = cars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.085 -9.791 -2.837 8.441 42.841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -21.354 11.918 -1.792 0.079889 .
## speed 4.268 1.209 3.531 0.000968 ***
## qspeed(12,15] 2.166 8.406 0.258 0.797837
## qspeed(15,19] -3.888 11.848 -0.328 0.744313
## qspeed(19,25] -3.929 16.745 -0.235 0.815546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.77 on 45 degrees of freedom
## Multiple R-squared: 0.6562, Adjusted R-squared: 0.6256
## F-statistic: 21.47 on 4 and 45 DF, p-value: 5.826e-10lm_fit3 <- lm(dist~qspeed,data = cars) # x为定性变量
summary(lm_fit3)##
## Call:
## lm(formula = dist ~ qspeed, data = cars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37.33 -13.96 -3.75 9.30 50.67
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.200 4.551 3.999 0.000228 ***
## qspeed(12,15] 21.982 6.996 3.142 0.002933 **
## qspeed(15,19] 31.967 6.826 4.683 2.52e-05 ***
## qspeed(19,25] 51.133 6.826 7.491 1.68e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.62 on 46 degrees of freedom
## Multiple R-squared: 0.5609, Adjusted R-squared: 0.5322
## F-statistic: 19.59 on 3 and 46 DF, p-value: 2.517e-08实践5(简单样本描述统计量)
x <- (round(rnorm(200,0,1),digits = 3))
sum(x)## [1] -15.549mean(x)## [1] -0.077745median(x)## [1] -0.1465sd(x)## [1] 1.03166rank(x)## [1] 146.0 185.0 78.0 110.0 81.0 56.0 166.0 85.0 121.0 159.0 30.0
## [12] 128.0 129.0 127.0 13.0 99.5 35.0 196.0 115.0 140.0 194.0 58.0
## [23] 33.0 48.0 89.5 158.0 68.0 91.0 41.0 124.0 63.0 193.0 22.0
## [34] 66.5 74.5 16.0 187.0 43.0 12.0 112.0 188.0 149.0 4.0 133.0
## [45] 123.0 195.0 96.0 126.0 131.0 118.0 97.0 181.0 65.0 119.0 163.0
## [56] 106.0 46.0 102.0 6.0 99.5 164.0 107.0 89.5 40.0 61.0 23.0
## [67] 39.0 145.0 10.0 190.0 171.0 153.0 114.0 176.0 148.0 27.0 104.0
## [78] 15.0 199.0 72.0 150.0 32.0 151.0 57.0 98.0 167.0 156.0 51.0
## [89] 143.0 82.0 42.0 79.0 168.0 111.0 178.0 108.0 74.5 80.0 177.0
## [100] 179.0 142.0 47.0 84.0 116.0 197.0 88.0 113.0 109.0 92.0 173.0
## [111] 189.0 8.0 182.0 36.0 103.0 198.0 70.0 64.0 21.0 38.0 157.0
## [122] 5.0 73.0 155.0 7.0 135.0 120.0 165.0 200.0 44.0 147.0 53.0
## [133] 37.0 62.0 45.0 174.0 144.0 184.0 125.0 69.0 17.0 52.0 11.0
## [144] 20.0 55.0 191.0 122.0 138.0 49.0 76.0 14.0 101.0 95.0 152.0
## [155] 77.0 71.0 87.0 25.0 186.0 130.0 141.0 54.0 175.0 161.0 18.0
## [166] 192.0 134.0 105.0 60.0 66.5 162.0 180.0 28.0 26.0 132.0 139.0
## [177] 9.0 172.0 3.0 31.0 24.0 93.0 154.0 169.0 183.0 29.0 136.0
## [188] 34.0 50.0 170.0 2.0 160.0 1.0 117.0 137.0 94.0 83.0 59.0
## [199] 19.0 86.0range(x)## [1] -3.541 3.021summary(x)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.54100 -0.72500 -0.14650 -0.07774 0.50850 3.02100order(x)## [1] 193 191 179 43 122 59 125 112 177 69 143 39 15 151 78 36 141
## [18] 165 199 144 119 33 66 181 158 174 76 173 186 11 180 82 23 188
## [35] 17 114 133 120 67 64 29 91 38 130 135 57 102 24 149 189 88
## [52] 142 132 162 145 6 84 22 198 169 65 134 31 118 53 34 170 27
## [69] 140 117 156 80 123 35 97 150 155 3 92 98 5 90 197 103 8
## [86] 200 157 106 25 63 28 109 182 196 153 47 51 85 16 60 152 58
## [103] 115 77 168 56 62 96 108 4 94 40 107 73 19 104 194 50 54
## [120] 127 9 147 45 30 139 48 14 12 13 160 49 175 44 167 126 187
## [137] 195 148 176 20 161 101 89 137 68 1 131 75 42 81 83 154 72
## [154] 183 124 87 121 26 10 192 164 171 55 61 128 7 86 93 184 190
## [171] 71 178 110 136 163 74 99 95 100 172 52 113 185 138 2 159 37
## [188] 41 111 70 146 166 32 21 46 18 105 116 79 129x[order(x)] # 从小到大排列## [1] -3.541 -2.977 -2.913 -2.570 -2.274 -2.095 -1.861 -1.784 -1.687 -1.637
## [11] -1.601 -1.591 -1.532 -1.466 -1.456 -1.451 -1.420 -1.416 -1.397 -1.299
## [21] -1.216 -1.214 -1.213 -1.206 -1.201 -1.175 -1.160 -1.151 -1.110 -1.061
## [31] -1.015 -0.999 -0.956 -0.931 -0.922 -0.919 -0.909 -0.862 -0.854 -0.841
## [41] -0.836 -0.835 -0.814 -0.812 -0.792 -0.770 -0.746 -0.743 -0.736 -0.731
## [51] -0.723 -0.716 -0.693 -0.643 -0.632 -0.610 -0.606 -0.605 -0.604 -0.585
## [61] -0.582 -0.573 -0.569 -0.543 -0.516 -0.474 -0.474 -0.435 -0.423 -0.416
## [71] -0.397 -0.358 -0.343 -0.332 -0.332 -0.326 -0.318 -0.304 -0.287 -0.285
## [81] -0.279 -0.276 -0.242 -0.235 -0.220 -0.213 -0.205 -0.199 -0.196 -0.196
## [91] -0.193 -0.188 -0.184 -0.183 -0.172 -0.168 -0.166 -0.160 -0.147 -0.147
## [101] -0.146 -0.132 -0.131 -0.123 -0.114 -0.098 -0.092 -0.060 -0.054 -0.053
## [111] -0.041 -0.009 0.000 0.001 0.005 0.013 0.014 0.021 0.039 0.105
## [121] 0.110 0.116 0.118 0.130 0.143 0.157 0.158 0.183 0.185 0.194
## [131] 0.212 0.220 0.229 0.265 0.267 0.284 0.286 0.291 0.346 0.364
## [141] 0.376 0.389 0.429 0.430 0.439 0.448 0.481 0.489 0.501 0.506
## [151] 0.516 0.522 0.532 0.557 0.560 0.597 0.625 0.642 0.677 0.679
## [161] 0.718 0.721 0.725 0.730 0.759 0.821 0.874 0.918 0.931 0.980
## [171] 0.989 1.027 1.033 1.054 1.118 1.125 1.152 1.235 1.305 1.366
## [181] 1.386 1.434 1.449 1.457 1.462 1.486 1.596 1.633 1.670 1.675
## [191] 1.678 1.733 1.926 1.937 1.938 1.960 2.183 2.406 2.413 3.021sort(x)## [1] -3.541 -2.977 -2.913 -2.570 -2.274 -2.095 -1.861 -1.784 -1.687 -1.637
## [11] -1.601 -1.591 -1.532 -1.466 -1.456 -1.451 -1.420 -1.416 -1.397 -1.299
## [21] -1.216 -1.214 -1.213 -1.206 -1.201 -1.175 -1.160 -1.151 -1.110 -1.061
## [31] -1.015 -0.999 -0.956 -0.931 -0.922 -0.919 -0.909 -0.862 -0.854 -0.841
## [41] -0.836 -0.835 -0.814 -0.812 -0.792 -0.770 -0.746 -0.743 -0.736 -0.731
## [51] -0.723 -0.716 -0.693 -0.643 -0.632 -0.610 -0.606 -0.605 -0.604 -0.585
## [61] -0.582 -0.573 -0.569 -0.543 -0.516 -0.474 -0.474 -0.435 -0.423 -0.416
## [71] -0.397 -0.358 -0.343 -0.332 -0.332 -0.326 -0.318 -0.304 -0.287 -0.285
## [81] -0.279 -0.276 -0.242 -0.235 -0.220 -0.213 -0.205 -0.199 -0.196 -0.196
## [91] -0.193 -0.188 -0.184 -0.183 -0.172 -0.168 -0.166 -0.160 -0.147 -0.147
## [101] -0.146 -0.132 -0.131 -0.123 -0.114 -0.098 -0.092 -0.060 -0.054 -0.053
## [111] -0.041 -0.009 0.000 0.001 0.005 0.013 0.014 0.021 0.039 0.105
## [121] 0.110 0.116 0.118 0.130 0.143 0.157 0.158 0.183 0.185 0.194
## [131] 0.212 0.220 0.229 0.265 0.267 0.284 0.286 0.291 0.346 0.364
## [141] 0.376 0.389 0.429 0.430 0.439 0.448 0.481 0.489 0.501 0.506
## [151] 0.516 0.522 0.532 0.557 0.560 0.597 0.625 0.642 0.677 0.679
## [161] 0.718 0.721 0.725 0.730 0.759 0.821 0.874 0.918 0.931 0.980
## [171] 0.989 1.027 1.033 1.054 1.118 1.125 1.152 1.235 1.305 1.366
## [181] 1.386 1.434 1.449 1.457 1.462 1.486 1.596 1.633 1.670 1.675
## [191] 1.678 1.733 1.926 1.937 1.938 1.960 2.183 2.406 2.413 3.021order(x,decreasing = TRUE)## [1] 129 79 116 105 18 46 21 32 166 146 70 111 41 37 159 2 138
## [18] 185 113 52 172 100 95 99 74 163 136 110 178 71 190 184 93 86
## [35] 7 128 61 55 171 164 192 10 26 121 87 124 183 72 154 83 81
## [52] 42 75 131 1 68 137 89 101 161 20 176 148 195 187 126 167 44
## [69] 175 49 160 13 12 14 48 139 30 45 147 9 127 54 50 194 104
## [86] 19 73 107 40 94 4 108 96 62 56 168 77 115 58 152 16 60
## [103] 85 51 47 153 196 182 109 28 25 63 106 157 200 8 103 197 90
## [120] 5 98 92 3 155 150 35 97 123 80 156 117 140 27 34 170 53
## [137] 118 31 134 65 169 198 22 84 6 145 162 132 142 88 189 149 24
## [154] 102 57 135 130 38 91 29 64 67 120 133 114 17 188 23 82 180
## [171] 11 186 173 76 174 158 181 66 33 119 144 199 165 141 36 78 151
## [188] 15 39 143 69 177 112 125 59 122 43 179 191 193x[order(x,decreasing = TRUE)] # 从大到小排列## [1] 3.021 2.413 2.406 2.183 1.960 1.938 1.937 1.926 1.733 1.678
## [11] 1.675 1.670 1.633 1.596 1.486 1.462 1.457 1.449 1.434 1.386
## [21] 1.366 1.305 1.235 1.152 1.125 1.118 1.054 1.033 1.027 0.989
## [31] 0.980 0.931 0.918 0.874 0.821 0.759 0.730 0.725 0.721 0.718
## [41] 0.679 0.677 0.642 0.625 0.597 0.560 0.557 0.532 0.522 0.516
## [51] 0.506 0.501 0.489 0.481 0.448 0.439 0.430 0.429 0.389 0.376
## [61] 0.364 0.346 0.291 0.286 0.284 0.267 0.265 0.229 0.220 0.212
## [71] 0.194 0.185 0.183 0.158 0.157 0.143 0.130 0.118 0.116 0.110
## [81] 0.105 0.039 0.021 0.014 0.013 0.005 0.001 0.000 -0.009 -0.041
## [91] -0.053 -0.054 -0.060 -0.092 -0.098 -0.114 -0.123 -0.131 -0.132 -0.146
## [101] -0.147 -0.147 -0.160 -0.166 -0.168 -0.172 -0.183 -0.184 -0.188 -0.193
## [111] -0.196 -0.196 -0.199 -0.205 -0.213 -0.220 -0.235 -0.242 -0.276 -0.279
## [121] -0.285 -0.287 -0.304 -0.318 -0.326 -0.332 -0.332 -0.343 -0.358 -0.397
## [131] -0.416 -0.423 -0.435 -0.474 -0.474 -0.516 -0.543 -0.569 -0.573 -0.582
## [141] -0.585 -0.604 -0.605 -0.606 -0.610 -0.632 -0.643 -0.693 -0.716 -0.723
## [151] -0.731 -0.736 -0.743 -0.746 -0.770 -0.792 -0.812 -0.814 -0.835 -0.836
## [161] -0.841 -0.854 -0.862 -0.909 -0.919 -0.922 -0.931 -0.956 -0.999 -1.015
## [171] -1.061 -1.110 -1.151 -1.160 -1.175 -1.201 -1.206 -1.213 -1.214 -1.216
## [181] -1.299 -1.397 -1.416 -1.420 -1.451 -1.456 -1.466 -1.532 -1.591 -1.601
## [191] -1.637 -1.687 -1.784 -1.861 -2.095 -2.274 -2.570 -2.913 -2.977 -3.541sort(x,decreasing = TRUE)## [1] 3.021 2.413 2.406 2.183 1.960 1.938 1.937 1.926 1.733 1.678
## [11] 1.675 1.670 1.633 1.596 1.486 1.462 1.457 1.449 1.434 1.386
## [21] 1.366 1.305 1.235 1.152 1.125 1.118 1.054 1.033 1.027 0.989
## [31] 0.980 0.931 0.918 0.874 0.821 0.759 0.730 0.725 0.721 0.718
## [41] 0.679 0.677 0.642 0.625 0.597 0.560 0.557 0.532 0.522 0.516
## [51] 0.506 0.501 0.489 0.481 0.448 0.439 0.430 0.429 0.389 0.376
## [61] 0.364 0.346 0.291 0.286 0.284 0.267 0.265 0.229 0.220 0.212
## [71] 0.194 0.185 0.183 0.158 0.157 0.143 0.130 0.118 0.116 0.110
## [81] 0.105 0.039 0.021 0.014 0.013 0.005 0.001 0.000 -0.009 -0.041
## [91] -0.053 -0.054 -0.060 -0.092 -0.098 -0.114 -0.123 -0.131 -0.132 -0.146
## [101] -0.147 -0.147 -0.160 -0.166 -0.168 -0.172 -0.183 -0.184 -0.188 -0.193
## [111] -0.196 -0.196 -0.199 -0.205 -0.213 -0.220 -0.235 -0.242 -0.276 -0.279
## [121] -0.285 -0.287 -0.304 -0.318 -0.326 -0.332 -0.332 -0.343 -0.358 -0.397
## [131] -0.416 -0.423 -0.435 -0.474 -0.474 -0.516 -0.543 -0.569 -0.573 -0.582
## [141] -0.585 -0.604 -0.605 -0.606 -0.610 -0.632 -0.643 -0.693 -0.716 -0.723
## [151] -0.731 -0.736 -0.743 -0.746 -0.770 -0.792 -0.812 -0.814 -0.835 -0.836
## [161] -0.841 -0.854 -0.862 -0.909 -0.919 -0.922 -0.931 -0.956 -0.999 -1.015
## [171] -1.061 -1.110 -1.151 -1.160 -1.175 -1.201 -1.206 -1.213 -1.214 -1.216
## [181] -1.299 -1.397 -1.416 -1.420 -1.451 -1.456 -1.466 -1.532 -1.591 -1.601
## [191] -1.637 -1.687 -1.784 -1.861 -2.095 -2.274 -2.570 -2.913 -2.977 -3.541length(x)## [1] 200round(x,2) # 四舍五入## [1] 0.45 1.46 -0.30 -0.05 -0.28 -0.61 0.82 -0.22 0.11 0.68 -1.06
## [12] 0.18 0.18 0.16 -1.53 -0.15 -0.92 1.96 0.00 0.36 1.94 -0.60
## [23] -0.96 -0.74 -0.20 0.64 -0.44 -0.19 -0.84 0.13 -0.57 1.93 -1.21
## [34] -0.47 -0.33 -1.45 1.60 -0.81 -1.59 -0.01 1.63 0.50 -2.57 0.23
## [45] 0.12 1.94 -0.17 0.16 0.21 0.02 -0.17 1.39 -0.52 0.04 0.72
## [56] -0.10 -0.77 -0.13 -2.10 -0.15 0.73 -0.09 -0.20 -0.84 -0.58 -1.21
## [67] -0.85 0.44 -1.64 1.68 0.99 0.53 0.00 1.12 0.49 -1.16 -0.12
## [78] -1.46 2.41 -0.36 0.51 -1.00 0.52 -0.61 -0.16 0.87 0.60 -0.72
## [89] 0.43 -0.28 -0.84 -0.29 0.92 -0.04 1.24 -0.06 -0.33 -0.28 1.15
## [100] 1.30 0.39 -0.75 -0.24 0.01 2.18 -0.20 0.00 -0.05 -0.19 1.03
## [111] 1.67 -1.78 1.43 -0.92 -0.13 2.41 -0.42 -0.54 -1.22 -0.86 0.62
## [122] -2.27 -0.34 0.56 -1.86 0.27 0.10 0.76 3.02 -0.81 0.48 -0.69
## [133] -0.91 -0.57 -0.79 1.05 0.43 1.46 0.14 -0.42 -1.42 -0.72 -1.60
## [144] -1.30 -0.63 1.68 0.12 0.29 -0.74 -0.33 -1.47 -0.15 -0.17 0.52
## [155] -0.32 -0.40 -0.20 -1.20 1.49 0.19 0.38 -0.64 1.12 0.72 -1.42
## [166] 1.73 0.26 -0.11 -0.58 -0.47 0.72 1.37 -1.15 -1.18 0.22 0.35
## [177] -1.69 1.03 -2.91 -1.01 -1.21 -0.18 0.56 0.93 1.45 -1.11 0.28
## [188] -0.93 -0.73 0.98 -2.98 0.68 -3.54 0.01 0.29 -0.18 -0.24 -0.60
## [199] -1.40 -0.21fivenum(x)## [1] -3.5410 -0.7270 -0.1465 0.5110 3.0210quantile(x) # 分位数## 0% 25% 50% 75% 100%
## -3.5410 -0.7250 -0.1465 0.5085 3.0210mad(1:2)## [1] 0.7413# 累计函数
cummax(1:10)## [1] 1 2 3 4 5 6 7 8 9 10cummin(1:10)## [1] 1 1 1 1 1 1 1 1 1 1cumall(1:10<5)## [1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSEcumany(1:10>5)## [1] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUEcummean(1:10)## [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5cumprod(1:10)## [1] 1 2 6 24 120 720 5040 40320
## [9] 362880 3628800cor(x,sin(x/20)) # 线性相关系数## [1] 0.999999cbind(x,sin(x/20)) %>% head()## x
## [1,] 0.448 0.022398127
## [2,] 1.462 0.073034914
## [3,] -0.304 -0.015199415
## [4,] -0.053 -0.002649997
## [5,] -0.279 -0.013949548
## [6,] -0.610 -0.030495271实践6(简单图形)
x <- rnorm(100)
hist(x,col = "light blue")
rug(x) # 加上实际点的大小
stem(x)##
## The decimal point is at the |
##
## -2 | 753
## -1 | 88543222100
## -0 | 9877777655554444443333222222110
## 0 | 001111122223333334444444556778889
## 1 | 2222233344566679
## 2 | 134557# 构造一个线性关系
x <- rnorm(200)
y <- rnorm(200) + x
plot(x,y)
a <- lm(y~x)
abline(a,col = "blue") # 散点图加拟合线
paste("x的最小值 = ",min(x)) # 打印## [1] "x的最小值 = -3.16613468813753"Here is some code which illustrates some of the differences between R and S graphics capabilities. Note that colors are generally specified by a character string name (taken from the X11 rgb.txt file) and that line textures are given similarly. The parameter “bg” sets the background parameter for the plot and there is also an “fg” parameter which sets the foreground color.
# 仔细学习和实践demo
# demo(graphics)
require(datasets)
require(grDevices); require(graphics)
x <- stats::rnorm(50) # 数据
opar <- par(bg = "white")
plot(x, ann = FALSE, type = "n")
abline(h = 0, col = gray(.90))
lines(x, col = "green4", lty = "dotted")
points(x, bg = "limegreen", pch = 21)
title(main = "Simple Use of Color In a Plot",
xlab = "Just a Whisper of a Label",
col.main = "blue", col.lab = gray(.8),
cex.main = 1.2, cex.lab = 1.0, font.main = 4, font.lab = 3)
A little color wheel. This code just plots equally spaced hues in a pie chart. If you have a cheap SVGA monitor (like me) you will probably find that numerically equispaced does not mean visually equispaced. On my display at home, these colors tend to cluster at the RGB primaries. On the other hand on the SGI Indy at work the
## effect is near perfect.
par(bg = "gray")
pie(rep(1,24), col = rainbow(24), radius = 0.95)
title(main = "A Sample Color Wheel", cex.main = 1.4, font.main = 3)
title(xlab = "(Use this as a test of monitor linearity)",
cex.lab = 0.8,
font.lab = 3)
## We have already confessed to having these. This is just showing off X11 color names (and the example (from the postscript manual) is pretty "cute".
pie.sales <- c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12)
names(pie.sales) <- c("Blueberry", "Cherry",
"Apple", "Boston Cream", "Other", "Vanilla Cream")
pie(pie.sales,
col = c("purple","violetred1","green3","cornsilk","cyan","white"))
title(main = "January Pie Sales", cex.main = 1.8, font.main = 1)
title(xlab = "(Don't try this at home kids)", cex.lab = 0.8, font.lab = 3)
Boxplots: I couldn’t resist the capability for filling the “box”.The use of color seems like a useful addition, it focuses attention on the central bulk of the data.
par(bg="cornsilk")
n <- 10
g <- gl(n, 100, n*100)
x <- rnorm(n*100) + sqrt(as.numeric(g))
boxplot(split(x,g), col="lavender", notch=TRUE)
title(main="Notched Boxplots", xlab="Group", font.main=4, font.lab=1)
## An example showing how to fill between curves.
par(bg="white")
n <- 100
x <- c(0,cumsum(rnorm(n)))
y <- c(0,cumsum(rnorm(n)))
xx <- c(0:n, n:0)
yy <- c(x, rev(y))
plot(xx, yy, type="n", xlab="Time", ylab="Distance")
polygon(xx, yy, col="gray")
title("Distance Between Brownian Motions")
Colored plot margins, axis labels and titles. You do need to be careful with these kinds of effects. It’s easy to go completely over the top and you can end up with your lunch all over the keyboard.On the other hand, my market research clients love it.
x <- c(0.00, 0.40, 0.86, 0.85, 0.69, 0.48, 0.54, 1.09, 1.11, 1.73, 2.05, 2.02)
par(bg="lightgray")
plot(x, type="n", axes=FALSE, ann=FALSE)
usr <- par("usr")
rect(usr[1], usr[3], usr[2], usr[4], col="cornsilk", border="black")
lines(x, col="blue")
points(x, pch=21, bg="lightcyan", cex=1.25)
axis(2, col.axis="blue", las=1)
axis(1, at=1:12, lab=month.abb, col.axis="blue")
box()
title(main= "The Level of Interest in R", font.main=4, col.main="red")
title(xlab= "1996", col.lab="red")
A filled histogram, showing how to change the font used for the main title without changing the other annotation.
par(bg="cornsilk")
x <- rnorm(1000)
hist(x, xlim=range(-4, 4, x), col="lavender", main="")
title(main="1000 Normal Random Variates", font.main=3)
A scatterplot matrix
## The good old Iris data (yet again)
pairs(iris[1:4], main="Edgar Anderson's Iris Data", font.main=4, pch=19)
pairs(iris[1:4], main="Edgar Anderson's Iris Data", pch=21,
bg = c("red", "green3", "blue")[unclass(iris$Species)])
## Contour plotting This produces a topographic map of one of Auckland's many volcanic "peaks".
x <- 10*1:nrow(volcano)
y <- 10*1:ncol(volcano)
lev <- pretty(range(volcano), 10)
par(bg = "lightcyan")
pin <- par("pin")
xdelta <- diff(range(x))
ydelta <- diff(range(y))
xscale <- pin[1]/xdelta
yscale <- pin[2]/ydelta
scale <- min(xscale, yscale)
xadd <- 0.5*(pin[1]/scale - xdelta)
yadd <- 0.5*(pin[2]/scale - ydelta)
plot(numeric(0), numeric(0),
xlim = range(x)+c(-1,1)*xadd, ylim = range(y)+c(-1,1)*yadd,
type = "n", ann = FALSE)
usr <- par("usr")
rect(usr[1], usr[3], usr[2], usr[4], col="green3")
contour(x, y, volcano, levels = lev, col="yellow", lty="solid", add=TRUE)
box()
title("A Topographic Map of Maunga Whau", font= 4)
title(xlab = "Meters North", ylab = "Meters West", font= 3)
mtext("10 Meter Contour Spacing", side=3, line=0.35, outer=FALSE,
at = mean(par("usr")[1:2]), cex=0.7, font=3)
## Conditioning plots
par(bg="cornsilk")
coplot(lat ~ long | depth, data = quakes, pch = 21, bg = "green3")
par(opar)实践7(复数运算和求函数极值)
2+4i## [1] 2+4i2+4i-6i## [1] 2-2i(2+4i)/4i## [1] 1-0.5i# 构造一个10维复向量
(z <- complex(real = rnorm(10),imaginary = rnorm(10)))## [1] -1.9345231-1.1796093i -0.3199740+0.7578919i -0.3301140-0.7206507i
## [4] 0.7011787-1.0638143i -2.1542085-1.5763669i -0.2570409-0.8030955i
## [7] -0.1321132+0.9697036i -1.1736460-0.5693483i 1.3367468-0.1874468i
## [10] 1.2776321-0.8104987iRe(z)## [1] -1.9345231 -0.3199740 -0.3301140 0.7011787 -2.1542085 -0.2570409
## [7] -0.1321132 -1.1736460 1.3367468 1.2776321Im(z)## [1] -1.1796093 0.7578919 -0.7206507 -1.0638143 -1.5763669 -0.8030955
## [7] 0.9697036 -0.5693483 -0.1874468 -0.8104987Mod(z)## [1] 2.2658017 0.8226686 0.7926618 1.2741085 2.6693720 0.8432273 0.9786618
## [8] 1.3044548 1.3498253 1.5130274Arg(z) # 辅角## [1] -2.5940221 1.9702840 -2.0003473 -0.9880382 -2.5098667 -1.8805562
## [7] 1.7062035 -2.6899272 -0.1393177 -0.5653129choose(3,2) # 组合## [1] 3factorial(10)## [1] 3628800# 解方程
f <- function(x){
x^3 + 2*x + 1
}
uniroot(f,c(-2,2))## $root
## [1] -0.4533812
##
## $f.root
## [1] 4.302946e-05
##
## $iter
## [1] 8
##
## $init.it
## [1] NA
##
## $estim.prec
## [1] 6.103516e-05实践8(字符型向量)
a <- factor(letters[1:10])
# a[3] <- "w"
a %>% as.numeric()## [1] 1 2 3 4 5 6 7 8 9 10实践9(数据输入与输出)
# x <- scan()
y <- iris
y %>% str()## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...write.table(y,file = "iris.txt")
read.table("iris.txt")## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicawrite_csv(y,"iris.csv")
read_csv("iris.csv")## Parsed with column specification:
## cols(
## Sepal.Length = col_double(),
## Sepal.Width = col_double(),
## Petal.Length = col_double(),
## Petal.Width = col_double(),
## Species = col_character()
## )## # A tibble: 150 x 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <chr>
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## # ... with 140 more rows# read.table("clipboard") 读入剪贴板数据！！！实践10(序列)
seq(1,10,length = 100)## [1] 1.000000 1.090909 1.181818 1.272727 1.363636 1.454545 1.545455
## [8] 1.636364 1.727273 1.818182 1.909091 2.000000 2.090909 2.181818
## [15] 2.272727 2.363636 2.454545 2.545455 2.636364 2.727273 2.818182
## [22] 2.909091 3.000000 3.090909 3.181818 3.272727 3.363636 3.454545
## [29] 3.545455 3.636364 3.727273 3.818182 3.909091 4.000000 4.090909
## [36] 4.181818 4.272727 4.363636 4.454545 4.545455 4.636364 4.727273
## [43] 4.818182 4.909091 5.000000 5.090909 5.181818 5.272727 5.363636
## [50] 5.454545 5.545455 5.636364 5.727273 5.818182 5.909091 6.000000
## [57] 6.090909 6.181818 6.272727 6.363636 6.454545 6.545455 6.636364
## [64] 6.727273 6.818182 6.909091 7.000000 7.090909 7.181818 7.272727
## [71] 7.363636 7.454545 7.545455 7.636364 7.727273 7.818182 7.909091
## [78] 8.000000 8.090909 8.181818 8.272727 8.363636 8.454545 8.545455
## [85] 8.636364 8.727273 8.818182 8.909091 9.000000 9.090909 9.181818
## [92] 9.272727 9.363636 9.454545 9.545455 9.636364 9.727273 9.818182
## [99] 9.909091 10.000000seq(1,10,length.out = 100)## [1] 1.000000 1.090909 1.181818 1.272727 1.363636 1.454545 1.545455
## [8] 1.636364 1.727273 1.818182 1.909091 2.000000 2.090909 2.181818
## [15] 2.272727 2.363636 2.454545 2.545455 2.636364 2.727273 2.818182
## [22] 2.909091 3.000000 3.090909 3.181818 3.272727 3.363636 3.454545
## [29] 3.545455 3.636364 3.727273 3.818182 3.909091 4.000000 4.090909
## [36] 4.181818 4.272727 4.363636 4.454545 4.545455 4.636364 4.727273
## [43] 4.818182 4.909091 5.000000 5.090909 5.181818 5.272727 5.363636
## [50] 5.454545 5.545455 5.636364 5.727273 5.818182 5.909091 6.000000
## [57] 6.090909 6.181818 6.272727 6.363636 6.454545 6.545455 6.636364
## [64] 6.727273 6.818182 6.909091 7.000000 7.090909 7.181818 7.272727
## [71] 7.363636 7.454545 7.545455 7.636364 7.727273 7.818182 7.909091
## [78] 8.000000 8.090909 8.181818 8.272727 8.363636 8.454545 8.545455
## [85] 8.636364 8.727273 8.818182 8.909091 9.000000 9.090909 9.181818
## [92] 9.272727 9.363636 9.454545 9.545455 9.636364 9.727273 9.818182
## [99] 9.909091 10.000000seq(10,-1,-1)## [1] 10 9 8 7 6 5 4 3 2 1 0 -1rep(1:3,times = 2,each = 2)## [1] 1 1 2 2 3 3 1 1 2 2 3 3rep(1:3,1:3)## [1] 1 2 2 3 3 3rep(c(4,5),c(4,5)) -> x
c(1:10,x) # 组合成一个向量## [1] 1 2 3 4 5 6 7 8 9 10 4 4 4 4 5 5 5 5 5rev(1:10)## [1] 10 9 8 7 6 5 4 3 2 1z <- letters[1:10]
z == "a"## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEz <- NULL
z[c(1,3,5)] <- c(1,3,5)
z## [1] 1 NA 3 NA 5z[-c(1:3)]## [1] NA 5rnorm(10)[2]## [1] -0.1170526which(1:10 == 10)## [1] 10实践11(矩阵)
# 逻辑符号的使用
(x <- sample(1:100,12))## [1] 92 3 93 25 16 35 45 14 96 26 27 64all(x > 0)## [1] TRUEall(x!=0)## [1] TRUEany(x>0)## [1] TRUE(1:10)[x>0]## [1] 1 2 3 4 5 6 7 8 9 10 NA NAdiff(x);diff(x,lag = 2) # 差分## [1] -89 90 -68 -9 19 10 -31 82 -70 1 37## [1] 1 22 -77 10 29 -21 51 12 -69 38# 矩阵的构造
(x <- matrix(x,nrow = 3,byrow = TRUE,dimnames = list(letters[1:3],LETTERS[1:4])))## A B C D
## a 92 3 93 25
## b 16 35 45 14
## c 96 26 27 64t(x)## a b c
## A 92 16 96
## B 3 35 26
## C 93 45 27
## D 25 14 64z <- x%*%t(x)
z1 <- z %>% solve()
z1 %*% z # 单位阵## a b c
## a 1.000000e+00 1.110223e-16 0.000000e+00
## b -4.163336e-17 1.000000e+00 1.387779e-16
## c 0.000000e+00 0.000000e+00 1.000000e+00solve(z,1:3) # 解方程## a b c
## -0.0006055576 0.0010173572 0.0004246021实践12(矩阵运算)
x;ncol(x);nrow(x);dim(x)## A B C D
## a 92 3 93 25
## b 16 35 45 14
## c 96 26 27 64## [1] 4## [1] 3## [1] 3 4x[c(2,1),] %>% class()## [1] "matrix"x[,1] %>% class()## [1] "integer"x[1,2]## [1] 3# 第一列大于0的元素,两种方法
x <- matrix(rnorm(100),10)
x[x[,1]>0,1]## [1] 0.82572866 0.06080122 0.03864979 0.51536886x %>% as.data.frame() %>% filter(V1>0)## V1 V2 V3 V4 V5 V6
## 1 0.82572866 0.9226690 0.7971687 -1.2414369 0.1332955 -0.1383640
## 2 0.06080122 -2.3698740 -1.8478519 -0.6646502 -1.7985030 0.5123149
## 3 0.03864979 0.8334227 -1.5739552 0.5993034 1.3864398 -1.4113279
## 4 0.51536886 -0.7585988 1.2700295 0.1814678 0.7936110 -0.3098148
## V7 V8 V9 V10
## 1 1.5077527 -0.6177932 -1.0978133 -0.9754438
## 2 -0.3398264 -1.7691710 -0.6621349 0.2874160
## 3 -0.3276125 -0.1484901 0.5699226 -0.2269017
## 4 -0.7273109 0.3501963 -0.6112226 -0.4958288diag(x)## [1] 0.82572866 0.65706111 -0.44061484 -1.07213885 0.06931859
## [6] 0.10825307 -0.33982642 -0.14849009 0.50518737 -0.49582879diag(diag(x))## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.8257287 0.0000000 0.0000000 0.000000 0.00000000 0.0000000
## [2,] 0.0000000 0.6570611 0.0000000 0.000000 0.00000000 0.0000000
## [3,] 0.0000000 0.0000000 -0.4406148 0.000000 0.00000000 0.0000000
## [4,] 0.0000000 0.0000000 0.0000000 -1.072139 0.00000000 0.0000000
## [5,] 0.0000000 0.0000000 0.0000000 0.000000 0.06931859 0.0000000
## [6,] 0.0000000 0.0000000 0.0000000 0.000000 0.00000000 0.1082531
## [7,] 0.0000000 0.0000000 0.0000000 0.000000 0.00000000 0.0000000
## [8,] 0.0000000 0.0000000 0.0000000 0.000000 0.00000000 0.0000000
## [9,] 0.0000000 0.0000000 0.0000000 0.000000 0.00000000 0.0000000
## [10,] 0.0000000 0.0000000 0.0000000 0.000000 0.00000000 0.0000000
## [,7] [,8] [,9] [,10]
## [1,] 0.0000000 0.0000000 0.0000000 0.0000000
## [2,] 0.0000000 0.0000000 0.0000000 0.0000000
## [3,] 0.0000000 0.0000000 0.0000000 0.0000000
## [4,] 0.0000000 0.0000000 0.0000000 0.0000000
## [5,] 0.0000000 0.0000000 0.0000000 0.0000000
## [6,] 0.0000000 0.0000000 0.0000000 0.0000000
## [7,] -0.3398264 0.0000000 0.0000000 0.0000000
## [8,] 0.0000000 -0.1484901 0.0000000 0.0000000
## [9,] 0.0000000 0.0000000 0.5051874 0.0000000
## [10,] 0.0000000 0.0000000 0.0000000 -0.4958288diag(5)## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 0 0 0 0
## [2,] 0 1 0 0 0
## [3,] 0 0 1 0 0
## [4,] 0 0 0 1 0
## [5,] 0 0 0 0 1# 上三角阵(下三角阵)
x[lower.tri(x)] <- 0
x## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.8257287 0.9226690 0.7971687 -1.2414369 0.13329551 -0.1383640
## [2,] 0.0000000 0.6570611 -0.1817057 1.6865020 -0.37107637 0.4356777
## [3,] 0.0000000 0.0000000 -0.4406148 0.7257203 0.18806940 -1.3619060
## [4,] 0.0000000 0.0000000 0.0000000 -1.0721388 -0.83110466 2.0100132
## [5,] 0.0000000 0.0000000 0.0000000 0.0000000 0.06931859 1.4872069
## [6,] 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 0.1082531
## [7,] 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 0.0000000
## [8,] 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 0.0000000
## [9,] 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 0.0000000
## [10,] 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 0.0000000
## [,7] [,8] [,9] [,10]
## [1,] 1.5077527 -0.6177932 -1.0978133 -0.9754438
## [2,] 0.2705779 -0.6128006 -1.1162094 1.2370579
## [3,] 2.0774103 -1.4312077 1.1407166 1.3957363
## [4,] -2.2624718 -0.7397351 -0.4495942 -0.4946008
## [5,] -0.2175064 0.6601760 -0.6627306 2.1207408
## [6,] 0.3569650 1.3147344 1.2117823 0.3072168
## [7,] -0.3398264 -1.7691710 -0.6621349 0.2874160
## [8,] 0.0000000 -0.1484901 0.5699226 -0.2269017
## [9,] 0.0000000 0.0000000 0.5051874 -0.8652143
## [10,] 0.0000000 0.0000000 0.0000000 -0.4958288实践13(数组)
x <- array(runif(24),c(4,3,2))
x## , , 1
##
## [,1] [,2] [,3]
## [1,] 0.5564733 0.1460640 0.2484502
## [2,] 0.6026462 0.5911484 0.2610301
## [3,] 0.5763511 0.8521872 0.3316857
## [4,] 0.4852481 0.1431292 0.2831816
##
## , , 2
##
## [,1] [,2] [,3]
## [1,] 0.2434482 0.2892165 0.01170668
## [2,] 0.8920505 0.2316299 0.81766258
## [3,] 0.5485288 0.3982102 0.51182080
## [4,] 0.4713584 0.7291094 0.02427472apply(x, 1, max)## [1] 0.5564733 0.8920505 0.8521872 0.7291094apply(x, 2, min)## [1] 0.24344823 0.14312923 0.01170668apply(x,3,mean)## [1] 0.4231329 0.4307514dim(x)## [1] 4 3 2x[,,1]## [,1] [,2] [,3]
## [1,] 0.5564733 0.1460640 0.2484502
## [2,] 0.6026462 0.5911484 0.2610301
## [3,] 0.5763511 0.8521872 0.3316857
## [4,] 0.4852481 0.1431292 0.2831816x[,2,2]## [1] 0.2892165 0.2316299 0.3982102 0.7291094x[c(1,3),,]## , , 1
##
## [,1] [,2] [,3]
## [1,] 0.5564733 0.1460640 0.2484502
## [2,] 0.5763511 0.8521872 0.3316857
##
## , , 2
##
## [,1] [,2] [,3]
## [1,] 0.2434482 0.2892165 0.01170668
## [2,] 0.5485288 0.3982102 0.51182080实践14(矩阵与向量之间的运算)
x <- matrix(1:20,5,4)
x## [,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 20sweep(x,1,1:5,"*") # 把向量1:5的每个元素乘以每一行## [,1] [,2] [,3] [,4]
## [1,] 1 6 11 16
## [2,] 4 14 24 34
## [3,] 9 24 39 54
## [4,] 16 36 56 76
## [5,] 25 50 75 100sweep(x,1,1:5,"+")## [,1] [,2] [,3] [,4]
## [1,] 2 7 12 17
## [2,] 4 9 14 19
## [3,] 6 11 16 21
## [4,] 8 13 18 23
## [5,] 10 15 20 25x*1:5## [,1] [,2] [,3] [,4]
## [1,] 1 6 11 16
## [2,] 4 14 24 34
## [3,] 9 24 39 54
## [4,] 16 36 56 76
## [5,] 25 50 75 100x1 <- scale(x) # 标准化
apply(x1, 2, mean)## [1] 0 0 0 0apply(x1,2,sd)## [1] 1 1 1 1x2 <- scale(x,scale = FALSE) # 中心化
apply(x2,2,mean)## [1] 0 0 0 0apply(x2,2,sd)## [1] 1.581139 1.581139 1.581139 1.581139x3 <- scale(x,scale = TRUE,center = FALSE) # 观察结果
apply(x3,2,mean)## [1] 0.8090398 0.8807710 0.8891812 0.8916793apply(x3,2,sd)## [1] 0.42640143 0.17407766 0.10814761 0.07832604实践15(缺失值,数据的合并)
airquality %>% datatable()airquality %>% str()## 'data.frame': 153 obs. of 6 variables:
## $ Ozone : int 41 36 12 18 NA 28 23 19 8 NA ...
## $ Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ...
## $ Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
## $ Temp : int 67 72 74 62 56 66 65 59 61 69 ...
## $ Month : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Day : int 1 2 3 4 5 6 7 8 9 10 ...complete.cases(airquality) # 判断缺失值的行## [1] TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE
## [12] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [23] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE
## [45] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
## [56] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE
## [67] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE
## [78] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
## [89] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
## [100] TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
## [111] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE
## [122] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [133] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [144] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUEwhich(complete.cases(airquality) == FALSE) # 找到有缺失值的行## [1] 5 6 10 11 25 26 27 32 33 34 35 36 37 39 42 43 45
## [18] 46 52 53 54 55 56 57 58 59 60 61 65 72 75 83 84 96
## [35] 97 98 102 103 107 115 119 150sum(complete.cases(airquality)) # 完整观测值得个数## [1] 111na.omit(airquality) %>% datatable()附加和合并数据
x <- 1:10;x[12] = 3
x## [1] 1 2 3 4 5 6 7 8 9 10 NA 3append(x,77,after = 5) # 在5之后添加77## [1] 1 2 3 4 5 77 6 7 8 9 10 NA 3cbind(1:5,2:6)## [,1] [,2]
## [1,] 1 2
## [2,] 2 3
## [3,] 3 4
## [4,] 4 5
## [5,] 5 6rbind(1:5,2:6)## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 2 3 4 5
## [2,] 2 3 4 5 6(x <- rbind(1:5,rnorm(5),runif(5),1:5,7:11))## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.0000000 2.0000000 3.000000 4.0000000 5.0000000
## [2,] 0.9727596 -0.6282083 -2.359541 -0.8124894 -0.4501734
## [3,] 0.8668731 0.7874362 0.910792 0.1363442 0.4790972
## [4,] 1.0000000 2.0000000 3.000000 4.0000000 5.0000000
## [5,] 7.0000000 8.0000000 9.000000 10.0000000 11.0000000x %>% class()## [1] "matrix"unique(x)## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.0000000 2.0000000 3.000000 4.0000000 5.0000000
## [2,] 0.9727596 -0.6282083 -2.359541 -0.8124894 -0.4501734
## [3,] 0.8668731 0.7874362 0.910792 0.1363442 0.4790972
## [4,] 7.0000000 8.0000000 9.000000 10.0000000 11.0000000x %>% as.data.frame() %>% distinct()## V1 V2 V3 V4 V5
## 1 1.0000000 2.0000000 3.000000 4.0000000 5.0000000
## 2 0.9727596 -0.6282083 -2.359541 -0.8124894 -0.4501734
## 3 0.8668731 0.7874362 0.910792 0.1363442 0.4790972
## 4 7.0000000 8.0000000 9.000000 10.0000000 11.0000000实践16(list)
z <- list(x = 1:3,Tom = c(1:2),a = list("R",letters[1:5],w = "Hi!") )
z$x## [1] 1 2 3z[[1]] # 等价## [1] 1 2 3z$Tom[1]## [1] 1z[[2]][1] # 等价## [1] 1z$a$w## [1] "Hi!"z[[3]] %>% class()## [1] "list"z[[3]][1] %>% class()## [1] "list"z[[3]][[1]]## [1] "R"z[[3]][[2]][1]## [1] "a"实践17(条形图和表)
x <- c(3,3,3,4,1,4,2,1,3,2,5,3,1,2,5,2,3,4,2,2,5,3,1,4,2,2,4,3,5,2)
length(x)## [1] 30barplot(x)
barplot(table(x)) # 正确的图形
barplot(table(x)/length(x))
table(x)/length(x)## x
## 1 2 3 4 5
## 0.1333333 0.3000000 0.2666667 0.1666667 0.1333333实践18(形成表格)
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectquine %>% datatable()table(quine$Age)##
## F0 F1 F2 F3
## 27 46 40 33table(quine$Age,quine$Sex)##
## F M
## F0 10 17
## F1 32 14
## F2 19 21
## F3 19 14tab <- xtabs(~Sex+Age,data = quine)
tab## Age
## Sex F0 F1 F2 F3
## F 10 32 19 19
## M 17 14 21 14tab %>% unclass()## Age
## Sex F0 F1 F2 F3
## F 10 32 19 19
## M 17 14 21 14
## attr(,"call")
## xtabs(formula = ~Sex + Age, data = quine)tapply(quine$Days,quine$Age,mean) # 按照Age求均值## F0 F1 F2 F3
## 14.85185 11.15217 21.05000 19.60606quine %>% group_by(Age) %>% summarise(Days_mean = mean(Days))## # A tibble: 4 x 2
## Age Days_mean
## <fct> <dbl>
## 1 F0 14.9
## 2 F1 11.2
## 3 F2 21.0
## 4 F3 19.6tapply(quine$Days,list(quine$Age,quine$Sex),mean) # 分组求均值## F M
## F0 18.70000 12.58824
## F1 12.96875 7.00000
## F2 18.42105 23.42857
## F3 14.00000 27.21429quine %>% group_by(Age,Sex) %>% summarise(Days_mean = mean(Days))## # A tibble: 8 x 3
## # Groups: Age [4]
## Age Sex Days_mean
## <fct> <fct> <dbl>
## 1 F0 F 18.7
## 2 F0 M 12.6
## 3 F1 F 13.0
## 4 F1 M 7
## 5 F2 F 18.4
## 6 F2 M 23.4
## 7 F3 F 14
## 8 F3 M 27.2实践19(如何写函数)
# 求n以内的素数
ss <- function(x){
z = 2
for (i in 2:x) {
if (any(i%%2:(i-1) == 0) == FALSE){
z = c(z,i)
}
}
return(z)
}
t1 <- Sys.time()
ss(20000) %>% head()## [1] 2 3 5 7 11 13Sys.time()-t1## Time difference of 1.635625 secssystem.time(ss(10000)) # 观察语句执行时间，函数可以不写return，此时返回最后一个return的值## user system elapsed
## 0.40 0.00 0.41实践20(画图)
x <- seq(-3,3,length.out = 20);y <- dnorm(x)
data.frame(x,y) -> w
par(mfrow = c(2,2))
plot(y~x,data = w,main = "正态密度函数")
plot(y~x,data = w,type = "l",main = "正态密度函数")
plot(y~x,data = w,type = "o",main = "正态密度函数")
plot(y~x,data = w,type = "b",main = "正态密度函数")
par(mfrow = c(1,1))实践21(色彩和符号等调节)
plot(1,1,xlim = c(1,7.5),ylim = c(0,5),type = "n")
# 在plot函数后追加点(points)或者线(lines)
points(1:7,rep(4.5,7),cex = seq(1,4,length.out = 7),pch = 0:6)
text(1:7,rep(3.5,7),
labels = paste(0:6,letters[1:7]),
cex = seq(1,4,length.out = 7),
col = 1:7)
points(1:7,rep(2,7),pch = 0:6 + 7)
text(1:7+0.25,rep(2,7),
labels = paste(0:6 + 7),
col = 0:6 + 7
)
points(1:7,rep(1,7),pch = 14:20)
text(1:7 + 0.25,rep(1,7),
labels = paste(14:20),
col = 14:20
)