apply function & some geo map
fx
2020骞12鏈<88>12鏃
R向量化的操作提高了效率
# vector process
sum(1,2,3)## [1] 6# some fx dont make parameter to vetor
mean(1,2,4) ## [1] 1mean(c(1,2,4))## [1] 2.333333apply()
按行或者按列进行计算 for matrix，array ，vector
a <- matrix(seq(1,16),4,4)
a## [,1] [,2] [,3] [,4]
## [1,] 1 5 9 13
## [2,] 2 6 10 14
## [3,] 3 7 11 15
## [4,] 4 8 12 16apply(a, 1, min) # by row## [1] 1 2 3 4apply(a, 2, max) # by col## [1] 4 8 12 16b <- apply(a,c(1:2),function(x) x^2) # by a matrix(dim1 and dim2)
b## [,1] [,2] [,3] [,4]
## [1,] 1 25 81 169
## [2,] 4 36 100 196
## [3,] 9 49 121 225
## [4,] 16 64 144 256print(quantile)## function (x, ...)
## UseMethod("quantile")
## <bytecode: 0x0000000013e7a1f8>
## <environment: namespace:stats>apply(a, 1, quantile,probs=c(0.4,0.8)) # par3,functionname## [,1] [,2] [,3] [,4]
## 40% 5.8 6.8 7.8 8.8
## 80% 10.6 11.6 12.6 13.6rowSums(a)## [1] 28 32 36 40colSums(a)## [1] 10 26 42 58rowMeans(a)## [1] 7 8 9 10# data frame ,we should first transform it into a matrix
# in a three dims array use apply() function
array_3d <- array(seq(1:32),dim = c(4,4,2))
apply(array_3d , 3, sum)## [1] 136 392sum(1:16)## [1] 136apply(array_3d, c(1), min)## [1] 1 2 3 4apply(array_3d, c(2), min)## [1] 1 5 9 13apply(array_3d, c(1,2), sum)## [,1] [,2] [,3] [,4]
## [1,] 18 26 34 42
## [2,] 20 28 36 44
## [3,] 22 30 38 46
## [4,] 24 32 40 48apply(array_3d, c(3), min)## [1] 1 17lapply() sapply()
对整组数据进行计算for list， data frame
sapply 根据输入数据类型的不同产生不同的结果
auto <- read.csv("auto-mpg.csv",header = T)
# vector
lapply(c(1,2,3), sqrt) # list## [[1]]
## [1] 1
##
## [[2]]
## [1] 1.414214
##
## [[3]]
## [1] 1.732051x <- list(a=c(1:10),b=c(1,10,100,1000),c=c(seq(5,50,5)))
p1 <- lapply(x, mean)
p <- sapply(x, mean) # vector
is(p1)## [1] "list" "vector"is(p)## [1] "numeric" "vector"# their results different, lapply return a list,sapply return a data.frame
# used for cols of dataframe !!!!!!! be careful
sapply(auto[,2:6], min)## cylinders displacement horsepower weight acceleration
## 3 68 NA 1613 8# if the result of each col is a equal length vector,the result will be a matrix,(simplify=T,you can set F to return a list )
sapply(auto[,1:3], summary)## mpg cylinders displacement
## Min. 9.00000 3.000000 68.0000
## 1st Qu. 17.50000 4.000000 104.2500
## Median 23.00000 4.000000 148.5000
## Mean 23.51457 5.454774 193.4259
## 3rd Qu. 29.00000 8.000000 262.0000
## Max. 46.60000 8.000000 455.0000sapply(auto[,2],min) ## [1] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 6 6 6 4 4 4 4 4 4 6 8 8 8 8 4 4 4 4 6 6 6 6
## [38] 6 8 8 8 8 8 8 8 6 4 6 6 4 4 4 4 4 4 4 4 4 4 4 4 4 8 8 8 8 8 8 8 8 8 3 8 8
## [75] 8 8 4 4 4 4 4 4 4 4 4 8 8 8 8 8 8 8 8 8 8 8 8 6 6 6 6 6 4 8 8 8 8 6 4 4 4
## [112] 3 4 6 4 8 8 4 4 4 4 8 4 6 8 6 6 6 6 4 4 4 4 6 6 6 8 8 8 8 8 4 4 4 4 4 4 4
## [149] 4 4 4 4 6 6 6 6 8 8 8 8 6 6 6 6 6 8 8 4 4 6 4 4 4 4 6 4 6 4 4 4 4 4 4 4 4
## [186] 4 4 8 8 8 8 6 6 6 6 4 4 4 4 6 6 6 6 4 4 4 4 4 8 4 6 6 8 8 8 8 4 4 4 4 4 8
## [223] 8 8 8 6 6 6 6 8 8 8 8 4 4 4 4 4 4 4 4 6 4 3 4 4 4 4 4 8 8 8 6 6 6 4 6 6 6
## [260] 6 6 6 8 6 8 8 4 4 4 4 4 4 4 4 5 6 4 6 4 4 6 6 4 6 6 8 8 8 8 8 8 8 8 4 4 4
## [297] 4 5 8 4 8 4 4 4 4 4 6 6 4 4 4 4 4 4 4 4 6 4 4 4 4 4 4 4 4 4 4 5 4 4 4 4 4
## [334] 6 3 4 4 4 4 4 4 6 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 8 6 6 4 4 4
## [371] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 4 6 4 4 4 4 4 4 4 4# sapply is sensitive to the input type, it is useful to each item.
sapply(data.frame(auto[,2]),min) ## auto...2.
## 3sapply(c(1,2,3), mean)## [1] 1 2 3tapply() by()
function in a subset of vector
按因子分块计算
auto <- read.csv("auto-mpg.csv",stringsAsFactors = F)
auto$cylinders <- factor(auto$cylinders,levels = c(3,4,5,6,8),labels = c("c3","c4","c5","c6","c8"))
tapply(auto$mpg, auto$cylinders , mean)## c3 c4 c5 c6 c8
## 20.55000 29.28676 27.36667 19.98571 14.96311tapply(auto$mpg,list(auto$model.year,auto$cylinders),mean)## c3 c4 c5 c6 c8
## 70 NA 25.28571 NA 20.50000 14.11111
## 71 NA 27.46154 NA 18.00000 13.42857
## 72 19.0 23.42857 NA NA 13.61538
## 73 18.0 22.72727 NA 19.00000 13.20000
## 74 NA 27.80000 NA 17.85714 14.20000
## 75 NA 25.25000 NA 17.58333 15.66667
## 76 NA 26.76667 NA 20.00000 14.66667
## 77 21.5 29.10714 NA 19.50000 16.00000
## 78 NA 29.57647 20.3 19.06667 19.05000
## 79 NA 31.52500 25.4 22.95000 18.63000
## 80 23.7 34.61200 36.4 25.90000 NA
## 81 NA 32.81429 NA 23.42857 26.60000
## 82 NA 32.07143 NA 28.33333 NAby(auto,auto$cylinders,function(x) cor(x$mpg,x$weight))## auto$cylinders: c3
## [1] 0.6191685
## ------------------------------------------------------------
## auto$cylinders: c4
## [1] -0.5430774
## ------------------------------------------------------------
## auto$cylinders: c5
## [1] -0.04750808
## ------------------------------------------------------------
## auto$cylinders: c6
## [1] -0.4634435
## ------------------------------------------------------------
## auto$cylinders: c8
## [1] -0.5569099# similar to tapply,but by() transforms all dataframe ,tapply only listplyr 分割-应用-合并
library(plyr)## Warning: package 'plyr' was built under R version 3.6.3ddply(auto,"cylinders",function(x) mean(x$mpg))## cylinders V1
## 1 c3 20.55000
## 2 c4 29.28676
## 3 c5 27.36667
## 4 c6 19.98571
## 5 c8 14.96311ddply(auto,~cylinders,function(x) mean(x$mpg))## cylinders V1
## 1 c3 20.55000
## 2 c4 29.28676
## 3 c5 27.36667
## 4 c6 19.98571
## 5 c8 14.96311ddply(auto,c("cylinders","model.year"),function(x)c(mean(x$mpg),min(x$mpg),max(x$mpg)))## cylinders model.year V1 V2 V3
## 1 c3 72 19.00000 19.0 19.0
## 2 c3 73 18.00000 18.0 18.0
## 3 c3 77 21.50000 21.5 21.5
## 4 c3 80 23.70000 23.7 23.7
## 5 c4 70 25.28571 24.0 27.0
## 6 c4 71 27.46154 22.0 35.0
## 7 c4 72 23.42857 18.0 28.0
## 8 c4 73 22.72727 19.0 29.0
## 9 c4 74 27.80000 24.0 32.0
## 10 c4 75 25.25000 22.0 33.0
## 11 c4 76 26.76667 19.0 33.0
## 12 c4 77 29.10714 21.5 36.0
## 13 c4 78 29.57647 21.1 43.1
## 14 c4 79 31.52500 22.3 37.3
## 15 c4 80 34.61200 23.6 46.6
## 16 c4 81 32.81429 25.8 39.1
## 17 c4 82 32.07143 23.0 44.0
## 18 c5 78 20.30000 20.3 20.3
## 19 c5 79 25.40000 25.4 25.4
## 20 c5 80 36.40000 36.4 36.4
## 21 c6 70 20.50000 18.0 22.0
## 22 c6 71 18.00000 16.0 19.0
## 23 c6 73 19.00000 16.0 23.0
## 24 c6 74 17.85714 15.0 21.0
## 25 c6 75 17.58333 15.0 21.0
## 26 c6 76 20.00000 16.5 24.0
## 27 c6 77 19.50000 17.5 22.0
## 28 c6 78 19.06667 16.2 20.8
## 29 c6 79 22.95000 19.8 28.8
## 30 c6 80 25.90000 19.1 32.7
## 31 c6 81 23.42857 17.6 30.7
## 32 c6 82 28.33333 22.0 38.0
## 33 c8 70 14.11111 9.0 18.0
## 34 c8 71 13.42857 12.0 14.0
## 35 c8 72 13.61538 11.0 17.0
## 36 c8 73 13.20000 11.0 16.0
## 37 c8 74 14.20000 13.0 16.0
## 38 c8 75 15.66667 13.0 20.0
## 39 c8 76 14.66667 13.0 17.5
## 40 c8 77 16.00000 15.0 17.5
## 41 c8 78 19.05000 17.5 20.2
## 42 c8 79 18.63000 15.5 23.9
## 43 c8 81 26.60000 26.6 26.6# 基于向量的格式来分割数据
ddply(auto,~cylinders+model.year,function(x)c(mean(x$mpg),min(x$mpg),max(x$mpg)))## cylinders model.year V1 V2 V3
## 1 c3 72 19.00000 19.0 19.0
## 2 c3 73 18.00000 18.0 18.0
## 3 c3 77 21.50000 21.5 21.5
## 4 c3 80 23.70000 23.7 23.7
## 5 c4 70 25.28571 24.0 27.0
## 6 c4 71 27.46154 22.0 35.0
## 7 c4 72 23.42857 18.0 28.0
## 8 c4 73 22.72727 19.0 29.0
## 9 c4 74 27.80000 24.0 32.0
## 10 c4 75 25.25000 22.0 33.0
## 11 c4 76 26.76667 19.0 33.0
## 12 c4 77 29.10714 21.5 36.0
## 13 c4 78 29.57647 21.1 43.1
## 14 c4 79 31.52500 22.3 37.3
## 15 c4 80 34.61200 23.6 46.6
## 16 c4 81 32.81429 25.8 39.1
## 17 c4 82 32.07143 23.0 44.0
## 18 c5 78 20.30000 20.3 20.3
## 19 c5 79 25.40000 25.4 25.4
## 20 c5 80 36.40000 36.4 36.4
## 21 c6 70 20.50000 18.0 22.0
## 22 c6 71 18.00000 16.0 19.0
## 23 c6 73 19.00000 16.0 23.0
## 24 c6 74 17.85714 15.0 21.0
## 25 c6 75 17.58333 15.0 21.0
## 26 c6 76 20.00000 16.5 24.0
## 27 c6 77 19.50000 17.5 22.0
## 28 c6 78 19.06667 16.2 20.8
## 29 c6 79 22.95000 19.8 28.8
## 30 c6 80 25.90000 19.1 32.7
## 31 c6 81 23.42857 17.6 30.7
## 32 c6 82 28.33333 22.0 38.0
## 33 c8 70 14.11111 9.0 18.0
## 34 c8 71 13.42857 12.0 14.0
## 35 c8 72 13.61538 11.0 17.0
## 36 c8 73 13.20000 11.0 16.0
## 37 c8 74 14.20000 13.0 16.0
## 38 c8 75 15.66667 13.0 20.0
## 39 c8 76 14.66667 13.0 17.5
## 40 c8 77 16.00000 15.0 17.5
## 41 c8 78 19.05000 17.5 20.2
## 42 c8 79 18.63000 15.5 23.9
## 43 c8 81 26.60000 26.6 26.6#采用公式的形式来分割数据
# transform and summarize
ddply(auto,~cylinders,transform,mpg.dev=round(mpg-mean(mpg),2)) ## mpg cylinders displacement horsepower weight acceleration model.year
## 1 19.0 c3 70.0 97 2330 13.5 72
## 2 18.0 c3 70.0 90 2124 13.5 73
## 3 21.5 c3 80.0 110 2720 13.5 77
## 4 23.7 c3 70.0 100 2420 12.5 80
## 5 24.0 c4 113.0 95 2372 15.0 70
## 6 27.0 c4 97.0 88 2130 14.5 70
## 7 26.0 c4 97.0 46 1835 20.5 70
## 8 25.0 c4 110.0 87 2672 17.5 70
## 9 24.0 c4 107.0 90 2430 14.5 70
## 10 25.0 c4 104.0 95 2375 17.5 70
## 11 26.0 c4 121.0 113 2234 12.5 70
## 12 27.0 c4 97.0 88 2130 14.5 71
## 13 28.0 c4 140.0 90 2264 15.5 71
## 14 25.0 c4 113.0 95 2228 14.0 71
## 15 25.0 c4 98.0 NA 2046 19.0 71
## 16 22.0 c4 140.0 72 2408 19.0 71
## 17 23.0 c4 122.0 86 2220 14.0 71
## 18 28.0 c4 116.0 90 2123 14.0 71
## 19 30.0 c4 79.0 70 2074 19.5 71
## 20 30.0 c4 88.0 76 2065 14.5 71
## 21 31.0 c4 71.0 65 1773 19.0 71
## 22 35.0 c4 72.0 69 1613 18.0 71
## 23 27.0 c4 97.0 60 1834 19.0 71
## 24 26.0 c4 91.0 70 1955 20.5 71
## 25 24.0 c4 113.0 95 2278 15.5 72
## 26 25.0 c4 97.5 80 2126 17.0 72
## 27 23.0 c4 97.0 54 2254 23.5 72
## 28 20.0 c4 140.0 90 2408 19.5 72
## 29 21.0 c4 122.0 86 2226 16.5 72
## 30 18.0 c4 121.0 112 2933 14.5 72
## 31 22.0 c4 121.0 76 2511 18.0 72
## 32 21.0 c4 120.0 87 2979 19.5 72
## 33 26.0 c4 96.0 69 2189 18.0 72
## 34 22.0 c4 122.0 86 2395 16.0 72
## 35 28.0 c4 97.0 92 2288 17.0 72
## 36 23.0 c4 120.0 97 2506 14.5 72
## 37 28.0 c4 98.0 80 2164 15.0 72
## 38 27.0 c4 97.0 88 2100 16.5 72
## 39 26.0 c4 97.0 46 1950 21.0 73
## 40 20.0 c4 97.0 88 2279 19.0 73
## 41 21.0 c4 140.0 72 2401 19.5 73
## 42 22.0 c4 108.0 94 2379 16.5 73
## 43 19.0 c4 122.0 85 2310 18.5 73
## 44 26.0 c4 98.0 90 2265 15.5 73
## 45 29.0 c4 68.0 49 1867 19.5 73
## 46 24.0 c4 116.0 75 2158 15.5 73
## 47 20.0 c4 114.0 91 2582 14.0 73
## 48 19.0 c4 121.0 112 2868 15.5 73
## 49 24.0 c4 121.0 110 2660 14.0 73
## 50 31.0 c4 79.0 67 1950 19.0 74
## 51 26.0 c4 122.0 80 2451 16.5 74
## 52 32.0 c4 71.0 65 1836 21.0 74
## 53 25.0 c4 140.0 75 2542 17.0 74
## 54 29.0 c4 98.0 83 2219 16.5 74
## 55 26.0 c4 79.0 67 1963 15.5 74
## 56 26.0 c4 97.0 78 2300 14.5 74
## 57 31.0 c4 76.0 52 1649 16.5 74
## 58 32.0 c4 83.0 61 2003 19.0 74
## 59 28.0 c4 90.0 75 2125 14.5 74
## 60 24.0 c4 90.0 75 2108 15.5 74
## 61 26.0 c4 116.0 75 2246 14.0 74
## 62 24.0 c4 120.0 97 2489 15.0 74
## 63 26.0 c4 108.0 93 2391 15.5 74
## 64 31.0 c4 79.0 67 2000 16.0 74
## 65 29.0 c4 97.0 75 2171 16.0 75
## 66 23.0 c4 140.0 83 2639 17.0 75
## 67 23.0 c4 140.0 78 2592 18.5 75
## 68 24.0 c4 134.0 96 2702 13.5 75
## 69 25.0 c4 90.0 71 2223 16.5 75
## 70 24.0 c4 119.0 97 2545 17.0 75
## 71 29.0 c4 90.0 70 1937 14.0 75
## 72 23.0 c4 115.0 95 2694 15.0 75
## 73 23.0 c4 120.0 88 2957 17.0 75
## 74 22.0 c4 121.0 98 2945 14.5 75
## 75 25.0 c4 121.0 115 2671 13.5 75
## 76 33.0 c4 91.0 53 1795 17.5 75
## 77 28.0 c4 107.0 86 2464 15.5 76
## 78 25.0 c4 116.0 81 2220 16.9 76
## 79 25.0 c4 140.0 92 2572 14.9 76
## 80 26.0 c4 98.0 79 2255 17.7 76
## 81 27.0 c4 101.0 83 2202 15.3 76
## 82 29.0 c4 85.0 52 2035 22.2 76
## 83 24.5 c4 98.0 60 2164 22.1 76
## 84 29.0 c4 90.0 70 1937 14.2 76
## 85 33.0 c4 91.0 53 1795 17.4 76
## 86 29.5 c4 97.0 71 1825 12.2 76
## 87 32.0 c4 85.0 70 1990 17.0 76
## 88 28.0 c4 97.0 75 2155 16.4 76
## 89 26.5 c4 140.0 72 2565 13.6 76
## 90 20.0 c4 130.0 102 3150 15.7 76
## 91 19.0 c4 120.0 88 3270 21.9 76
## 92 31.5 c4 98.0 68 2045 18.5 77
## 93 30.0 c4 111.0 80 2155 14.8 77
## 94 36.0 c4 79.0 58 1825 18.6 77
## 95 25.5 c4 122.0 96 2300 15.5 77
## 96 33.5 c4 85.0 70 1945 16.8 77
## 97 29.0 c4 97.0 78 1940 14.5 77
## 98 24.5 c4 151.0 88 2740 16.0 77
## 99 26.0 c4 97.0 75 2265 18.2 77
## 100 25.5 c4 140.0 89 2755 15.8 77
## 101 30.5 c4 98.0 63 2051 17.0 77
## 102 33.5 c4 98.0 83 2075 15.9 77
## 103 30.0 c4 97.0 67 1985 16.4 77
## 104 30.5 c4 97.0 78 2190 14.1 77
## 105 21.5 c4 121.0 110 2600 12.8 77
## 106 43.1 c4 90.0 48 1985 21.5 78
## 107 36.1 c4 98.0 66 1800 14.4 78
## 108 32.8 c4 78.0 52 1985 19.4 78
## 109 39.4 c4 85.0 70 2070 18.6 78
## 110 36.1 c4 91.0 60 1800 16.4 78
## 111 25.1 c4 140.0 88 2720 15.4 78
## 112 30.0 c4 98.0 68 2155 16.5 78
## 113 27.5 c4 134.0 95 2560 14.2 78
## 114 27.2 c4 119.0 97 2300 14.7 78
## 115 30.9 c4 105.0 75 2230 14.5 78
## 116 21.1 c4 134.0 95 2515 14.8 78
## 117 23.2 c4 156.0 105 2745 16.7 78
## 118 23.8 c4 151.0 85 2855 17.6 78
## 119 23.9 c4 119.0 97 2405 14.9 78
## 120 21.6 c4 121.0 115 2795 15.7 78
## 121 31.5 c4 89.0 71 1990 14.9 78
## 122 29.5 c4 98.0 68 2135 16.6 78
## 123 22.3 c4 140.0 88 2890 17.3 79
## 124 31.9 c4 89.0 71 1925 14.0 79
## 125 34.1 c4 86.0 65 1975 15.2 79
## 126 35.7 c4 98.0 80 1915 14.4 79
## 127 27.4 c4 121.0 80 2670 15.0 79
## 128 27.2 c4 141.0 71 3190 24.8 79
## 129 34.2 c4 105.0 70 2200 13.2 79
## 130 34.5 c4 105.0 70 2150 14.9 79
## 131 31.8 c4 85.0 65 2020 19.2 79
## 132 37.3 c4 91.0 69 2130 14.7 79
## 133 28.4 c4 151.0 90 2670 16.0 79
## 134 33.5 c4 151.0 90 2556 13.2 79
## 135 41.5 c4 98.0 76 2144 14.7 80
## 136 38.1 c4 89.0 60 1968 18.8 80
## 137 32.1 c4 98.0 70 2120 15.5 80
## 138 37.2 c4 86.0 65 2019 16.4 80
## 139 28.0 c4 151.0 90 2678 16.5 80
## 140 26.4 c4 140.0 88 2870 18.1 80
## 141 24.3 c4 151.0 90 3003 20.1 80
## 142 34.3 c4 97.0 78 2188 15.8 80
## 143 29.8 c4 134.0 90 2711 15.5 80
## 144 31.3 c4 120.0 75 2542 17.5 80
## 145 37.0 c4 119.0 92 2434 15.0 80
## 146 32.2 c4 108.0 75 2265 15.2 80
## 147 46.6 c4 86.0 65 2110 17.9 80
## 148 27.9 c4 156.0 105 2800 14.4 80
## 149 40.8 c4 85.0 65 2110 19.2 80
## 150 44.3 c4 90.0 48 2085 21.7 80
## 151 43.4 c4 90.0 48 2335 23.7 80
## 152 30.0 c4 146.0 67 3250 21.8 80
## 153 44.6 c4 91.0 67 1850 13.8 80
## 154 40.9 c4 85.0 67 1835 17.3 80
## 155 33.8 c4 97.0 67 2145 18.0 80
## 156 29.8 c4 89.0 62 1845 15.3 80
## 157 35.0 c4 122.0 88 2500 15.1 80
## 158 23.6 c4 140.0 80 2905 14.3 80
## 159 32.4 c4 107.0 72 2290 17.0 80
## 160 27.2 c4 135.0 84 2490 15.7 81
## 161 26.6 c4 151.0 84 2635 16.4 81
## 162 25.8 c4 156.0 92 2620 14.4 81
## 163 30.0 c4 135.0 84 2385 12.9 81
## 164 39.1 c4 79.0 58 1755 16.9 81
## 165 39.0 c4 86.0 64 1875 16.4 81
## 166 35.1 c4 81.0 60 1760 16.1 81
## 167 32.3 c4 97.0 67 2065 17.8 81
## 168 37.0 c4 85.0 65 1975 19.4 81
## 169 37.7 c4 89.0 62 2050 17.3 81
## 170 34.1 c4 91.0 68 1985 16.0 81
## 171 34.7 c4 105.0 63 2215 14.9 81
## 172 34.4 c4 98.0 65 2045 16.2 81
## 173 29.9 c4 98.0 65 2380 20.7 81
## 174 33.0 c4 105.0 74 2190 14.2 81
## 175 34.5 c4 100.0 75 2320 15.8 81
## 176 33.7 c4 107.0 75 2210 14.4 81
## 177 32.4 c4 108.0 75 2350 16.8 81
## 178 32.9 c4 119.0 100 2615 14.8 81
## 179 31.6 c4 120.0 74 2635 18.3 81
## 180 28.1 c4 141.0 80 3230 20.4 81
## 181 28.0 c4 112.0 88 2605 19.6 82
## 182 27.0 c4 112.0 88 2640 18.6 82
## 183 34.0 c4 112.0 88 2395 18.0 82
## 184 31.0 c4 112.0 85 2575 16.2 82
## 185 29.0 c4 135.0 84 2525 16.0 82
## 186 27.0 c4 151.0 90 2735 18.0 82
## 187 24.0 c4 140.0 92 2865 16.4 82
## 188 23.0 c4 151.0 85 3035 20.5 82
## 189 36.0 c4 105.0 74 1980 15.3 82
## 190 37.0 c4 91.0 68 2025 18.2 82
## 191 31.0 c4 91.0 68 1970 17.6 82
## 192 38.0 c4 105.0 63 2125 14.7 82
## 193 36.0 c4 98.0 70 2125 17.3 82
## 194 36.0 c4 120.0 88 2160 14.5 82
## 195 36.0 c4 107.0 75 2205 14.5 82
## 196 34.0 c4 108.0 70 2245 16.9 82
## 197 38.0 c4 91.0 67 1965 15.0 82
## 198 32.0 c4 91.0 67 1965 15.7 82
## 199 38.0 c4 91.0 67 1995 16.2 82
## 200 26.0 c4 156.0 92 2585 14.5 82
## 201 32.0 c4 144.0 96 2665 13.9 82
## 202 36.0 c4 135.0 84 2370 13.0 82
## 203 27.0 c4 151.0 90 2950 17.3 82
## 204 27.0 c4 140.0 86 2790 15.6 82
## 205 44.0 c4 97.0 52 2130 24.6 82
## 206 32.0 c4 135.0 84 2295 11.6 82
## 207 28.0 c4 120.0 79 2625 18.6 82
## 208 31.0 c4 119.0 82 2720 19.4 82
## 209 20.3 c5 131.0 103 2830 15.9 78
## 210 25.4 c5 183.0 77 3530 20.1 79
## 211 36.4 c5 121.0 67 2950 19.9 80
## 212 22.0 c6 198.0 95 2833 15.5 70
## 213 18.0 c6 199.0 97 2774 15.5 70
## 214 21.0 c6 200.0 85 2587 16.0 70
## 215 21.0 c6 199.0 90 2648 15.0 70
## 216 19.0 c6 232.0 100 2634 13.0 71
## 217 16.0 c6 225.0 105 3439 15.5 71
## 218 17.0 c6 250.0 100 3329 15.5 71
## 219 19.0 c6 250.0 88 3302 15.5 71
## 220 18.0 c6 232.0 100 3288 15.5 71
## 221 18.0 c6 258.0 110 2962 13.5 71
## 222 19.0 c6 250.0 100 3282 15.0 71
## 223 18.0 c6 250.0 88 3139 14.5 71
## 224 18.0 c6 225.0 105 3121 16.5 73
## 225 16.0 c6 250.0 100 3278 18.0 73
## 226 18.0 c6 232.0 100 2945 16.0 73
## 227 18.0 c6 250.0 88 3021 16.5 73
## 228 23.0 c6 198.0 95 2904 16.0 73
## 229 18.0 c6 232.0 100 2789 15.0 73
## 230 21.0 c6 155.0 107 2472 14.0 73
## 231 20.0 c6 156.0 122 2807 13.5 73
## 232 20.0 c6 198.0 95 3102 16.5 74
## 233 21.0 c6 200.0 NA 2875 17.0 74
## 234 19.0 c6 232.0 100 2901 16.0 74
## 235 15.0 c6 250.0 100 3336 17.0 74
## 236 16.0 c6 250.0 100 3781 17.0 74
## 237 16.0 c6 258.0 110 3632 18.0 74
## 238 18.0 c6 225.0 105 3613 16.5 74
## 239 19.0 c6 225.0 95 3264 16.0 75
## 240 18.0 c6 250.0 105 3459 16.0 75
## 241 15.0 c6 250.0 72 3432 21.0 75
## 242 15.0 c6 250.0 72 3158 19.5 75
## 243 17.0 c6 231.0 110 3907 21.0 75
## 244 16.0 c6 250.0 105 3897 18.5 75
## 245 15.0 c6 258.0 110 3730 19.0 75
## 246 18.0 c6 225.0 95 3785 19.0 75
## 247 21.0 c6 231.0 110 3039 15.0 75
## 248 20.0 c6 232.0 100 2914 16.0 75
## 249 18.0 c6 171.0 97 2984 14.5 75
## 250 19.0 c6 232.0 90 3211 17.0 75
## 251 22.0 c6 225.0 100 3233 15.4 76
## 252 22.0 c6 250.0 105 3353 14.5 76
## 253 24.0 c6 200.0 81 3012 17.6 76
## 254 22.5 c6 232.0 90 3085 17.6 76
## 255 20.0 c6 225.0 100 3651 17.7 76
## 256 18.0 c6 250.0 78 3574 21.0 76
## 257 18.5 c6 250.0 110 3645 16.2 76
## 258 17.5 c6 258.0 95 3193 17.8 76
## 259 19.0 c6 156.0 108 2930 15.5 76
## 260 16.5 c6 168.0 120 3820 16.7 76
## 261 17.5 c6 250.0 110 3520 16.4 77
## 262 20.5 c6 231.0 105 3425 16.9 77
## 263 19.0 c6 225.0 100 3630 17.7 77
## 264 18.5 c6 250.0 98 3525 19.0 77
## 265 22.0 c6 146.0 97 2815 14.5 77
## 266 19.2 c6 231.0 105 3535 19.2 78
## 267 20.5 c6 200.0 95 3155 18.2 78
## 268 20.2 c6 200.0 85 2965 15.8 78
## 269 20.5 c6 225.0 100 3430 17.2 78
## 270 19.4 c6 232.0 90 3210 17.2 78
## 271 20.6 c6 231.0 105 3380 15.8 78
## 272 20.8 c6 200.0 85 3070 16.7 78
## 273 18.6 c6 225.0 110 3620 18.7 78
## 274 18.1 c6 258.0 120 3410 15.1 78
## 275 17.7 c6 231.0 165 3445 13.4 78
## 276 17.0 c6 163.0 125 3140 13.6 78
## 277 16.2 c6 163.0 133 3410 15.8 78
## 278 21.5 c6 231.0 115 3245 15.4 79
## 279 19.8 c6 200.0 85 2990 18.2 79
## 280 20.2 c6 232.0 90 3265 18.2 79
## 281 20.6 c6 225.0 110 3360 16.6 79
## 282 28.8 c6 173.0 115 2595 11.3 79
## 283 26.8 c6 173.0 115 2700 12.9 79
## 284 19.1 c6 225.0 90 3381 18.7 80
## 285 32.7 c6 168.0 132 2910 11.4 80
## 286 23.5 c6 173.0 110 2725 12.6 81
## 287 30.7 c6 145.0 76 3160 19.6 81
## 288 25.4 c6 168.0 116 2900 12.6 81
## 289 24.2 c6 146.0 120 2930 13.8 81
## 290 22.4 c6 231.0 110 3415 15.8 81
## 291 20.2 c6 200.0 88 3060 17.1 81
## 292 17.6 c6 225.0 85 3465 16.6 81
## 293 25.0 c6 181.0 110 2945 16.4 82
## 294 38.0 c6 262.0 85 3015 17.0 82
## 295 22.0 c6 232.0 112 2835 14.7 82
## 296 18.0 c8 307.0 130 3504 12.0 70
## 297 15.0 c8 350.0 165 3693 11.5 70
## 298 18.0 c8 318.0 150 3436 11.0 70
## 299 16.0 c8 304.0 150 3433 12.0 70
## 300 17.0 c8 302.0 140 3449 10.5 70
## 301 15.0 c8 429.0 198 4341 10.0 70
## 302 14.0 c8 454.0 220 4354 9.0 70
## 303 14.0 c8 440.0 215 4312 8.5 70
## 304 14.0 c8 455.0 225 4425 10.0 70
## 305 15.0 c8 390.0 190 3850 8.5 70
## 306 15.0 c8 383.0 170 3563 10.0 70
## 307 14.0 c8 340.0 160 3609 8.0 70
## 308 15.0 c8 400.0 150 3761 9.5 70
## 309 14.0 c8 455.0 225 3086 10.0 70
## 310 10.0 c8 360.0 215 4615 14.0 70
## 311 10.0 c8 307.0 200 4376 15.0 70
## 312 11.0 c8 318.0 210 4382 13.5 70
## 313 9.0 c8 304.0 193 4732 18.5 70
## 314 14.0 c8 350.0 165 4209 12.0 71
## 315 14.0 c8 400.0 175 4464 11.5 71
## 316 14.0 c8 351.0 153 4154 13.5 71
## 317 14.0 c8 318.0 150 4096 13.0 71
## 318 12.0 c8 383.0 180 4955 11.5 71
## 319 13.0 c8 400.0 170 4746 12.0 71
## 320 13.0 c8 400.0 175 5140 12.0 71
## 321 13.0 c8 350.0 165 4274 12.0 72
## 322 14.0 c8 400.0 175 4385 12.0 72
## 323 15.0 c8 318.0 150 4135 13.5 72
## 324 14.0 c8 351.0 153 4129 13.0 72
## 325 17.0 c8 304.0 150 3672 11.5 72
## 326 11.0 c8 429.0 208 4633 11.0 72
## 327 13.0 c8 350.0 155 4502 13.5 72
## 328 12.0 c8 350.0 160 4456 13.5 72
## 329 13.0 c8 400.0 190 4422 12.5 72
## 330 15.0 c8 304.0 150 3892 12.5 72
## 331 13.0 c8 307.0 130 4098 14.0 72
## 332 13.0 c8 302.0 140 4294 16.0 72
## 333 14.0 c8 318.0 150 4077 14.0 72
## 334 13.0 c8 350.0 175 4100 13.0 73
## 335 14.0 c8 304.0 150 3672 11.5 73
## 336 13.0 c8 350.0 145 3988 13.0 73
## 337 14.0 c8 302.0 137 4042 14.5 73
## 338 15.0 c8 318.0 150 3777 12.5 73
## 339 12.0 c8 429.0 198 4952 11.5 73
## 340 13.0 c8 400.0 150 4464 12.0 73
## 341 13.0 c8 351.0 158 4363 13.0 73
## 342 14.0 c8 318.0 150 4237 14.5 73
## 343 13.0 c8 440.0 215 4735 11.0 73
## 344 12.0 c8 455.0 225 4951 11.0 73
## 345 13.0 c8 360.0 175 3821 11.0 73
## 346 11.0 c8 400.0 150 4997 14.0 73
## 347 12.0 c8 400.0 167 4906 12.5 73
## 348 13.0 c8 360.0 170 4654 13.0 73
## 349 12.0 c8 350.0 180 4499 12.5 73
## 350 15.0 c8 350.0 145 4082 13.0 73
## 351 16.0 c8 400.0 230 4278 9.5 73
## 352 15.0 c8 318.0 150 3399 11.0 73
## 353 11.0 c8 350.0 180 3664 11.0 73
## 354 16.0 c8 302.0 140 4141 14.0 74
## 355 13.0 c8 350.0 150 4699 14.5 74
## 356 14.0 c8 318.0 150 4457 13.5 74
## 357 14.0 c8 302.0 140 4638 16.0 74
## 358 14.0 c8 304.0 150 4257 15.5 74
## 359 16.0 c8 400.0 170 4668 11.5 75
## 360 15.0 c8 350.0 145 4440 14.0 75
## 361 16.0 c8 318.0 150 4498 14.5 75
## 362 14.0 c8 351.0 148 4657 13.5 75
## 363 20.0 c8 262.0 110 3221 13.5 75
## 364 13.0 c8 302.0 129 3169 12.0 75
## 365 17.5 c8 305.0 140 4215 13.0 76
## 366 16.0 c8 318.0 150 4190 13.0 76
## 367 15.5 c8 304.0 120 3962 13.9 76
## 368 14.5 c8 351.0 152 4215 12.8 76
## 369 13.0 c8 318.0 150 3940 13.2 76
## 370 16.5 c8 350.0 180 4380 12.1 76
## 371 13.0 c8 350.0 145 4055 12.0 76
## 372 13.0 c8 302.0 130 3870 15.0 76
## 373 13.0 c8 318.0 150 3755 14.0 76
## 374 17.5 c8 305.0 145 3880 12.5 77
## 375 17.0 c8 260.0 110 4060 19.0 77
## 376 15.5 c8 318.0 145 4140 13.7 77
## 377 15.0 c8 302.0 130 4295 14.9 77
## 378 16.0 c8 400.0 180 4220 11.1 77
## 379 15.5 c8 350.0 170 4165 11.4 77
## 380 15.5 c8 400.0 190 4325 12.2 77
## 381 16.0 c8 351.0 149 4335 14.5 77
## 382 19.9 c8 260.0 110 3365 15.5 78
## 383 19.4 c8 318.0 140 3735 13.2 78
## 384 20.2 c8 302.0 139 3570 12.8 78
## 385 19.2 c8 305.0 145 3425 13.2 78
## 386 18.1 c8 302.0 139 3205 11.2 78
## 387 17.5 c8 318.0 140 4080 13.7 78
## 388 17.0 c8 305.0 130 3840 15.4 79
## 389 17.6 c8 302.0 129 3725 13.4 79
## 390 16.5 c8 351.0 138 3955 13.2 79
## 391 18.2 c8 318.0 135 3830 15.2 79
## 392 16.9 c8 350.0 155 4360 14.9 79
## 393 15.5 c8 351.0 142 4054 14.3 79
## 394 19.2 c8 267.0 125 3605 15.0 79
## 395 18.5 c8 360.0 150 3940 13.0 79
## 396 23.0 c8 350.0 125 3900 17.4 79
## 397 23.9 c8 260.0 90 3420 22.2 79
## 398 26.6 c8 350.0 105 3725 19.0 81
## mpg.dev
## 1 -1.55
## 2 -2.55
## 3 0.95
## 4 3.15
## 5 -5.29
## 6 -2.29
## 7 -3.29
## 8 -4.29
## 9 -5.29
## 10 -4.29
## 11 -3.29
## 12 -2.29
## 13 -1.29
## 14 -4.29
## 15 -4.29
## 16 -7.29
## 17 -6.29
## 18 -1.29
## 19 0.71
## 20 0.71
## 21 1.71
## 22 5.71
## 23 -2.29
## 24 -3.29
## 25 -5.29
## 26 -4.29
## 27 -6.29
## 28 -9.29
## 29 -8.29
## 30 -11.29
## 31 -7.29
## 32 -8.29
## 33 -3.29
## 34 -7.29
## 35 -1.29
## 36 -6.29
## 37 -1.29
## 38 -2.29
## 39 -3.29
## 40 -9.29
## 41 -8.29
## 42 -7.29
## 43 -10.29
## 44 -3.29
## 45 -0.29
## 46 -5.29
## 47 -9.29
## 48 -10.29
## 49 -5.29
## 50 1.71
## 51 -3.29
## 52 2.71
## 53 -4.29
## 54 -0.29
## 55 -3.29
## 56 -3.29
## 57 1.71
## 58 2.71
## 59 -1.29
## 60 -5.29
## 61 -3.29
## 62 -5.29
## 63 -3.29
## 64 1.71
## 65 -0.29
## 66 -6.29
## 67 -6.29
## 68 -5.29
## 69 -4.29
## 70 -5.29
## 71 -0.29
## 72 -6.29
## 73 -6.29
## 74 -7.29
## 75 -4.29
## 76 3.71
## 77 -1.29
## 78 -4.29
## 79 -4.29
## 80 -3.29
## 81 -2.29
## 82 -0.29
## 83 -4.79
## 84 -0.29
## 85 3.71
## 86 0.21
## 87 2.71
## 88 -1.29
## 89 -2.79
## 90 -9.29
## 91 -10.29
## 92 2.21
## 93 0.71
## 94 6.71
## 95 -3.79
## 96 4.21
## 97 -0.29
## 98 -4.79
## 99 -3.29
## 100 -3.79
## 101 1.21
## 102 4.21
## 103 0.71
## 104 1.21
## 105 -7.79
## 106 13.81
## 107 6.81
## 108 3.51
## 109 10.11
## 110 6.81
## 111 -4.19
## 112 0.71
## 113 -1.79
## 114 -2.09
## 115 1.61
## 116 -8.19
## 117 -6.09
## 118 -5.49
## 119 -5.39
## 120 -7.69
## 121 2.21
## 122 0.21
## 123 -6.99
## 124 2.61
## 125 4.81
## 126 6.41
## 127 -1.89
## 128 -2.09
## 129 4.91
## 130 5.21
## 131 2.51
## 132 8.01
## 133 -0.89
## 134 4.21
## 135 12.21
## 136 8.81
## 137 2.81
## 138 7.91
## 139 -1.29
## 140 -2.89
## 141 -4.99
## 142 5.01
## 143 0.51
## 144 2.01
## 145 7.71
## 146 2.91
## 147 17.31
## 148 -1.39
## 149 11.51
## 150 15.01
## 151 14.11
## 152 0.71
## 153 15.31
## 154 11.61
## 155 4.51
## 156 0.51
## 157 5.71
## 158 -5.69
## 159 3.11
## 160 -2.09
## 161 -2.69
## 162 -3.49
## 163 0.71
## 164 9.81
## 165 9.71
## 166 5.81
## 167 3.01
## 168 7.71
## 169 8.41
## 170 4.81
## 171 5.41
## 172 5.11
## 173 0.61
## 174 3.71
## 175 5.21
## 176 4.41
## 177 3.11
## 178 3.61
## 179 2.31
## 180 -1.19
## 181 -1.29
## 182 -2.29
## 183 4.71
## 184 1.71
## 185 -0.29
## 186 -2.29
## 187 -5.29
## 188 -6.29
## 189 6.71
## 190 7.71
## 191 1.71
## 192 8.71
## 193 6.71
## 194 6.71
## 195 6.71
## 196 4.71
## 197 8.71
## 198 2.71
## 199 8.71
## 200 -3.29
## 201 2.71
## 202 6.71
## 203 -2.29
## 204 -2.29
## 205 14.71
## 206 2.71
## 207 -1.29
## 208 1.71
## 209 -7.07
## 210 -1.97
## 211 9.03
## 212 2.01
## 213 -1.99
## 214 1.01
## 215 1.01
## 216 -0.99
## 217 -3.99
## 218 -2.99
## 219 -0.99
## 220 -1.99
## 221 -1.99
## 222 -0.99
## 223 -1.99
## 224 -1.99
## 225 -3.99
## 226 -1.99
## 227 -1.99
## 228 3.01
## 229 -1.99
## 230 1.01
## 231 0.01
## 232 0.01
## 233 1.01
## 234 -0.99
## 235 -4.99
## 236 -3.99
## 237 -3.99
## 238 -1.99
## 239 -0.99
## 240 -1.99
## 241 -4.99
## 242 -4.99
## 243 -2.99
## 244 -3.99
## 245 -4.99
## 246 -1.99
## 247 1.01
## 248 0.01
## 249 -1.99
## 250 -0.99
## 251 2.01
## 252 2.01
## 253 4.01
## 254 2.51
## 255 0.01
## 256 -1.99
## 257 -1.49
## 258 -2.49
## 259 -0.99
## 260 -3.49
## 261 -2.49
## 262 0.51
## 263 -0.99
## 264 -1.49
## 265 2.01
## 266 -0.79
## 267 0.51
## 268 0.21
## 269 0.51
## 270 -0.59
## 271 0.61
## 272 0.81
## 273 -1.39
## 274 -1.89
## 275 -2.29
## 276 -2.99
## 277 -3.79
## 278 1.51
## 279 -0.19
## 280 0.21
## 281 0.61
## 282 8.81
## 283 6.81
## 284 -0.89
## 285 12.71
## 286 3.51
## 287 10.71
## 288 5.41
## 289 4.21
## 290 2.41
## 291 0.21
## 292 -2.39
## 293 5.01
## 294 18.01
## 295 2.01
## 296 3.04
## 297 0.04
## 298 3.04
## 299 1.04
## 300 2.04
## 301 0.04
## 302 -0.96
## 303 -0.96
## 304 -0.96
## 305 0.04
## 306 0.04
## 307 -0.96
## 308 0.04
## 309 -0.96
## 310 -4.96
## 311 -4.96
## 312 -3.96
## 313 -5.96
## 314 -0.96
## 315 -0.96
## 316 -0.96
## 317 -0.96
## 318 -2.96
## 319 -1.96
## 320 -1.96
## 321 -1.96
## 322 -0.96
## 323 0.04
## 324 -0.96
## 325 2.04
## 326 -3.96
## 327 -1.96
## 328 -2.96
## 329 -1.96
## 330 0.04
## 331 -1.96
## 332 -1.96
## 333 -0.96
## 334 -1.96
## 335 -0.96
## 336 -1.96
## 337 -0.96
## 338 0.04
## 339 -2.96
## 340 -1.96
## 341 -1.96
## 342 -0.96
## 343 -1.96
## 344 -2.96
## 345 -1.96
## 346 -3.96
## 347 -2.96
## 348 -1.96
## 349 -2.96
## 350 0.04
## 351 1.04
## 352 0.04
## 353 -3.96
## 354 1.04
## 355 -1.96
## 356 -0.96
## 357 -0.96
## 358 -0.96
## 359 1.04
## 360 0.04
## 361 1.04
## 362 -0.96
## 363 5.04
## 364 -1.96
## 365 2.54
## 366 1.04
## 367 0.54
## 368 -0.46
## 369 -1.96
## 370 1.54
## 371 -1.96
## 372 -1.96
## 373 -1.96
## 374 2.54
## 375 2.04
## 376 0.54
## 377 0.04
## 378 1.04
## 379 0.54
## 380 0.54
## 381 1.04
## 382 4.94
## 383 4.44
## 384 5.24
## 385 4.24
## 386 3.14
## 387 2.54
## 388 2.04
## 389 2.64
## 390 1.54
## 391 3.24
## 392 1.94
## 393 0.54
## 394 4.24
## 395 3.54
## 396 8.04
## 397 8.94
## 398 11.64#所在因子组的均值
ddply(auto,.(cylinders),transform,mpg.dev=round(mpg-mean(mpg),2))## mpg cylinders displacement horsepower weight acceleration model.year
## 1 19.0 c3 70.0 97 2330 13.5 72
## 2 18.0 c3 70.0 90 2124 13.5 73
## 3 21.5 c3 80.0 110 2720 13.5 77
## 4 23.7 c3 70.0 100 2420 12.5 80
## 5 24.0 c4 113.0 95 2372 15.0 70
## 6 27.0 c4 97.0 88 2130 14.5 70
## 7 26.0 c4 97.0 46 1835 20.5 70
## 8 25.0 c4 110.0 87 2672 17.5 70
## 9 24.0 c4 107.0 90 2430 14.5 70
## 10 25.0 c4 104.0 95 2375 17.5 70
## 11 26.0 c4 121.0 113 2234 12.5 70
## 12 27.0 c4 97.0 88 2130 14.5 71
## 13 28.0 c4 140.0 90 2264 15.5 71
## 14 25.0 c4 113.0 95 2228 14.0 71
## 15 25.0 c4 98.0 NA 2046 19.0 71
## 16 22.0 c4 140.0 72 2408 19.0 71
## 17 23.0 c4 122.0 86 2220 14.0 71
## 18 28.0 c4 116.0 90 2123 14.0 71
## 19 30.0 c4 79.0 70 2074 19.5 71
## 20 30.0 c4 88.0 76 2065 14.5 71
## 21 31.0 c4 71.0 65 1773 19.0 71
## 22 35.0 c4 72.0 69 1613 18.0 71
## 23 27.0 c4 97.0 60 1834 19.0 71
## 24 26.0 c4 91.0 70 1955 20.5 71
## 25 24.0 c4 113.0 95 2278 15.5 72
## 26 25.0 c4 97.5 80 2126 17.0 72
## 27 23.0 c4 97.0 54 2254 23.5 72
## 28 20.0 c4 140.0 90 2408 19.5 72
## 29 21.0 c4 122.0 86 2226 16.5 72
## 30 18.0 c4 121.0 112 2933 14.5 72
## 31 22.0 c4 121.0 76 2511 18.0 72
## 32 21.0 c4 120.0 87 2979 19.5 72
## 33 26.0 c4 96.0 69 2189 18.0 72
## 34 22.0 c4 122.0 86 2395 16.0 72
## 35 28.0 c4 97.0 92 2288 17.0 72
## 36 23.0 c4 120.0 97 2506 14.5 72
## 37 28.0 c4 98.0 80 2164 15.0 72
## 38 27.0 c4 97.0 88 2100 16.5 72
## 39 26.0 c4 97.0 46 1950 21.0 73
## 40 20.0 c4 97.0 88 2279 19.0 73
## 41 21.0 c4 140.0 72 2401 19.5 73
## 42 22.0 c4 108.0 94 2379 16.5 73
## 43 19.0 c4 122.0 85 2310 18.5 73
## 44 26.0 c4 98.0 90 2265 15.5 73
## 45 29.0 c4 68.0 49 1867 19.5 73
## 46 24.0 c4 116.0 75 2158 15.5 73
## 47 20.0 c4 114.0 91 2582 14.0 73
## 48 19.0 c4 121.0 112 2868 15.5 73
## 49 24.0 c4 121.0 110 2660 14.0 73
## 50 31.0 c4 79.0 67 1950 19.0 74
## 51 26.0 c4 122.0 80 2451 16.5 74
## 52 32.0 c4 71.0 65 1836 21.0 74
## 53 25.0 c4 140.0 75 2542 17.0 74
## 54 29.0 c4 98.0 83 2219 16.5 74
## 55 26.0 c4 79.0 67 1963 15.5 74
## 56 26.0 c4 97.0 78 2300 14.5 74
## 57 31.0 c4 76.0 52 1649 16.5 74
## 58 32.0 c4 83.0 61 2003 19.0 74
## 59 28.0 c4 90.0 75 2125 14.5 74
## 60 24.0 c4 90.0 75 2108 15.5 74
## 61 26.0 c4 116.0 75 2246 14.0 74
## 62 24.0 c4 120.0 97 2489 15.0 74
## 63 26.0 c4 108.0 93 2391 15.5 74
## 64 31.0 c4 79.0 67 2000 16.0 74
## 65 29.0 c4 97.0 75 2171 16.0 75
## 66 23.0 c4 140.0 83 2639 17.0 75
## 67 23.0 c4 140.0 78 2592 18.5 75
## 68 24.0 c4 134.0 96 2702 13.5 75
## 69 25.0 c4 90.0 71 2223 16.5 75
## 70 24.0 c4 119.0 97 2545 17.0 75
## 71 29.0 c4 90.0 70 1937 14.0 75
## 72 23.0 c4 115.0 95 2694 15.0 75
## 73 23.0 c4 120.0 88 2957 17.0 75
## 74 22.0 c4 121.0 98 2945 14.5 75
## 75 25.0 c4 121.0 115 2671 13.5 75
## 76 33.0 c4 91.0 53 1795 17.5 75
## 77 28.0 c4 107.0 86 2464 15.5 76
## 78 25.0 c4 116.0 81 2220 16.9 76
## 79 25.0 c4 140.0 92 2572 14.9 76
## 80 26.0 c4 98.0 79 2255 17.7 76
## 81 27.0 c4 101.0 83 2202 15.3 76
## 82 29.0 c4 85.0 52 2035 22.2 76
## 83 24.5 c4 98.0 60 2164 22.1 76
## 84 29.0 c4 90.0 70 1937 14.2 76
## 85 33.0 c4 91.0 53 1795 17.4 76
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## 293 5.01
## 294 18.01
## 295 2.01
## 296 3.04
## 297 0.04
## 298 3.04
## 299 1.04
## 300 2.04
## 301 0.04
## 302 -0.96
## 303 -0.96
## 304 -0.96
## 305 0.04
## 306 0.04
## 307 -0.96
## 308 0.04
## 309 -0.96
## 310 -4.96
## 311 -4.96
## 312 -3.96
## 313 -5.96
## 314 -0.96
## 315 -0.96
## 316 -0.96
## 317 -0.96
## 318 -2.96
## 319 -1.96
## 320 -1.96
## 321 -1.96
## 322 -0.96
## 323 0.04
## 324 -0.96
## 325 2.04
## 326 -3.96
## 327 -1.96
## 328 -2.96
## 329 -1.96
## 330 0.04
## 331 -1.96
## 332 -1.96
## 333 -0.96
## 334 -1.96
## 335 -0.96
## 336 -1.96
## 337 -0.96
## 338 0.04
## 339 -2.96
## 340 -1.96
## 341 -1.96
## 342 -0.96
## 343 -1.96
## 344 -2.96
## 345 -1.96
## 346 -3.96
## 347 -2.96
## 348 -1.96
## 349 -2.96
## 350 0.04
## 351 1.04
## 352 0.04
## 353 -3.96
## 354 1.04
## 355 -1.96
## 356 -0.96
## 357 -0.96
## 358 -0.96
## 359 1.04
## 360 0.04
## 361 1.04
## 362 -0.96
## 363 5.04
## 364 -1.96
## 365 2.54
## 366 1.04
## 367 0.54
## 368 -0.46
## 369 -1.96
## 370 1.54
## 371 -1.96
## 372 -1.96
## 373 -1.96
## 374 2.54
## 375 2.04
## 376 0.54
## 377 0.04
## 378 1.04
## 379 0.54
## 380 0.54
## 381 1.04
## 382 4.94
## 383 4.44
## 384 5.24
## 385 4.24
## 386 3.14
## 387 2.54
## 388 2.04
## 389 2.64
## 390 1.54
## 391 3.24
## 392 1.94
## 393 0.54
## 394 4.24
## 395 3.54
## 396 8.04
## 397 8.94
## 398 11.64ddply(auto,.(cylinders),summarise,fre=mean(mpg),p=length(mpg))## cylinders fre p
## 1 c3 20.55000 4
## 2 c4 29.28676 204
## 3 c5 27.36667 3
## 4 c6 19.98571 84
## 5 c8 14.96311 103# 把数据框变大
autos<- list(auto,auto)
autos1<-ldply(autos,I) google map
googlemap API
need a account about money （credit card）,so I can‘t get maps from google now.
so this is incomplete.
# data preparation
wages <- read.csv("nj-wages.csv")
wages$wgclass <- cut(wages$Avgwg,quantile(wages$Avgwg,probs = seq(0,1,0.2)),labes=FALSE,include.lowest = TRUE)
if(!requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("dkahle/ggmap", ref = "tidyup")
register_google(key ="AIzaSyDElWorkdS3FtqXyIznPhpGRQr1h7f-Whw") # API-key
library(ggmap)
shu.map <- get_stamenmap(center=c(40.115,-74.715), zoom =8)
ggmap(shu.map)
# 17英里的正方形区域=1609.344米，可以保存
pal <- palette(rainbow(5)) #创建调试板
attach(wages) # 绑定数据
zoom <- calc_zoom(Long,Lat,wages)
center<-c(mean(wages$Lat),mean(wages$Long))
my.map <- get_stamenmap(center=c(30,90), zoom =8)
ggmap(my.map)+geom_point(aes(x = Long, y = Lat, colour = wgclass),data = wages)
legend("bottomright",legend = paste("<=",round(tapply(Avgwg,wgclass,max))),
pch = 21,
pt.bg = pal,
pt.cex = 1.0,
bg="gray",
title = "Avg wgs")
# 图例的位置，legend，提供图例的文字向量，
# pch图例点的形状，图例点的大小普通数据转为地理数据
sp 数据类型
library(rgdal) library(maps) library(sp) library(maptools)
library(rgdal)## Warning: package 'rgdal' was built under R version 3.6.3## Loading required package: sp## Warning: package 'sp' was built under R version 3.6.3## rgdal: version: 1.5-16, (SVN revision 1050)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.0.4, released 2020/01/28
## Path to GDAL shared files: D:/Application/R-3.6.2/library/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 6.3.1, February 10th, 2020, [PJ_VERSION: 631]
## Path to PROJ shared files: D:/Application/R-3.6.2/library/rgdal/proj
## Linking to sp version:1.4-2
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.library(maps)##
## Attaching package: 'maps'## The following object is masked from 'package:plyr':
##
## ozonelibrary(sp)
library(maptools)## Warning: package 'maptools' was built under R version 3.6.3## Checking rgeos availability: FALSE
## Note: when rgeos is not available, polygon geometry computations in maptools depend on gpclib,
## which has a restricted licence. It is disabled by default;
## to enable gpclib, type gpclibPermit()nj <- read.csv("nj-wages.csv")
# transform into spatial class:"SpatialPointsDataFrame",by adding coor(lon,lat)
coordinates(nj) <- c("Long","Lat")
class(nj) # spatial class:"SpatialPointsDataFrame"## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"plot(nj)
# get map
nj.map <- map("county","new jersey",fill=TRUE,plot=FALSE)
str(nj.map)## List of 4
## $ x : num [1:774] -75 -74.9 -74.9 -74.7 -74.7 ...
## $ y : num [1:774] 39.5 39.6 39.6 39.7 39.7 ...
## $ range: num [1:4] -75.6 -73.9 38.9 41.4
## $ names: chr [1:21] "new jersey,atlantic" "new jersey,bergen" "new jersey,burlington" "new jersey,camden" ...
## - attr(*, "class")= chr "map"# get name
couty_name <- sapply(strsplit (nj.map$names,","),function(x) x[2])
strsplit (nj.map$names,",")## [[1]]
## [1] "new jersey" "atlantic"
##
## [[2]]
## [1] "new jersey" "bergen"
##
## [[3]]
## [1] "new jersey" "burlington"
##
## [[4]]
## [1] "new jersey" "camden"
##
## [[5]]
## [1] "new jersey" "cape may"
##
## [[6]]
## [1] "new jersey" "cumberland"
##
## [[7]]
## [1] "new jersey" "essex"
##
## [[8]]
## [1] "new jersey" "gloucester"
##
## [[9]]
## [1] "new jersey" "hudson"
##
## [[10]]
## [1] "new jersey" "hunterdon"
##
## [[11]]
## [1] "new jersey" "mercer"
##
## [[12]]
## [1] "new jersey" "middlesex"
##
## [[13]]
## [1] "new jersey" "monmouth"
##
## [[14]]
## [1] "new jersey" "morris"
##
## [[15]]
## [1] "new jersey" "ocean"
##
## [[16]]
## [1] "new jersey" "passaic"
##
## [[17]]
## [1] "new jersey" "salem"
##
## [[18]]
## [1] "new jersey" "somerset"
##
## [[19]]
## [1] "new jersey" "sussex"
##
## [[20]]
## [1] "new jersey" "union"
##
## [[21]]
## [1] "new jersey" "warren"#sapply : item return vector\ matrix\list(if simplify=F)
# transform into SpatialPolygon
# ?map2SpatialPolygons
nj.sp <- map2SpatialPolygons(nj.map,IDs = couty_name,proj4string = CRS("+proj=longlat +datum=WGS84"))
class(nj.sp)## [1] "SpatialPolygons"
## attr(,"package")
## [1] "sp"# creat a general dataframe
nj.dat <- read.csv("nj-county-data.csv")
# creat col names to match map
rownames(nj.dat) <- nj.dat$name
# creat class
nj.spdf <- SpatialPolygonsDataFrame(nj.sp,nj.dat) #map ,attr
class(nj.spdf)## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"# plot
plot(nj.spdf)
spplot(nj.spdf,"population",main="population")
# based on wages, per income compare aginst family income
spplot(nj.spdf,c("per_capita_income","median_family_income"))
# add variable in to a spdf(spatial data frame)
# calculate population density to each county
pop_density <- nj.spdf@data$population/nj.spdf@data$area_sq_mi
nj.spdf <- spCbind(nj.spdf,pop_density)
names(nj.spdf@data)## [1] "name" "per_capita_income"
## [3] "median_household_income" "median_family_income"
## [5] "population" "no_households"
## [7] "area_sq_mi" "pop_density"spplot(nj.spdf,"pop_density",main="population_density")
shape 文件读取和绘图
download some shp files in https://www.naturalearthdata.com/
# packges
library(rgdal)
contries_sp <- readOGR("." ,"ne_50m_admin_0_countries")## OGR data source with driver: ESRI Shapefile
## Source: "E:\Study\R-GIS\improve_eff", layer: "ne_50m_admin_0_countries"
## with 241 features
## It has 94 fields
## Integer64 fields read as strings: POP_EST NE_IDclass(contries_sp)## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"#polygon
airport_sp <- readOGR(".","ne_50m_airports")## OGR data source with driver: ESRI Shapefile
## Source: "E:\Study\R-GIS\improve_eff", layer: "ne_50m_airports"
## with 281 features
## It has 10 fieldsclass(airport_sp)## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"# point
plot(contries_sp)
plot(contries_sp,col=contries_sp@data$MAPCOLOR9)
plot(airport_sp,add=TRUE)
map包 绘图
library(maps)
map("world")
map("world",interior = FALSE)
map("world",fill = TRUE,col=palette(rainbow(7)))
map("world","tanzania")
map("france")
map.text("italy")
map("state",interior = FALSE)
map.cities(world.cities)
map("world", "China")
map.cities(country = "China", capitals = 2)
map("state", "New Jersey")
data(us.cities)
map.cities(us.cities, country="NJ")
小结
提高R代码的效率
R的向量化操作优势，允许我们对一组数据同时进行操作，避免了一个元素一个元素的循环
apply 可对某一个维（第一维（一行），第二维（一列），第三维（矩阵））的数据进行操作。
lapply，sapply 对整组数据（列表、数据框）进行操作，list返回列表，sapply具有元素名称的向量或者列名称的数据框
注意：sapply如果是向量，对向量的每一个元素进行操作，如果是输入是数据框的单列，R会将其当成是向量（所以要注意）；如果是数据框，则对数据框每一个变量整体进行操作。返回值依赖于输入数据的类型。
tapply 适合于根据因子，分块进行的运算
plyr 包的使用 分组——应用——合并 ddply（输入对象df，输出对象df） ldply（输入对象list，输出对象df）
地图数据及绘制
- google map API （未完）
- 普通数据转为sp数据，并绘制
- shape数据的读取和绘制，map包自带的数据绘制，
- sp，maps，rgdal，ggmap packages的应用
两天时间里都在练习，有那么点感觉哈！代码会有很多，函数会有很多，想要灵活使用这个工具，清楚其背后的基本知识和逻辑是重要的~~ 勤思考，善总结，总有一天，你会掌握的鸭！ ヾ(◍°∇°◍)ﾉﾞ