0引言
對高維數據的可視化是一個難點問題，臉譜圖是根據人的臉、嘴、面部表情、眼睛、頭髮、鼻子和耳朵的長短寬等特徵來刻畫不同維度之間的關係。將各個維度的數據轉化爲人們熟知的面部表情來展示，這樣的可視化圖能夠使人快速去獲取各個維度的信息，使得數據更加形象化。R語言畫臉譜圖的包不只一個，本節主要介紹aplpack包裏的faces函數的使用方法。所用的代碼來自faces函數的例子。完整代碼可以運行?faces查看。
1、臉譜圖的各個指標
臉譜圖的指標如下表所示：
| 臉譜圖的15個指標 |
|---|
| 1 臉的高度 2臉的寬度3 臉型4嘴巴厚度 5, 嘴巴寬度6 微笑7 眼睛的高度8 眼睛寬度 9 頭髮長度 10 頭髮寬度11頭髮風格12 鼻子高度13 鼻子寬度14 耳朵寬度15耳朵高度 |
2、參數介紹
下面是函數的主要參數：
> faces
function (xy, which.row, fill = FALSE, face.type = 1, nrow.plot,
ncol.plot, scale = TRUE, byrow = FALSE, main, labels, print.info = TRUE,
na.rm = FALSE, ncolors = 20, col.nose = rainbow(ncolors),
col.eyes = rainbow(ncolors, start = 0.6, end = 0.85), col.hair = terrain.colors(ncolors),
col.face = heat.colors(ncolors), col.lips = rainbow(ncolors,
start = 0, end = 0.2), col.ears = rainbow(ncolors, start = 0,
end = 0.2), plot.faces = TRUE, cex = 2)
2.1 xy
這個參數輸入的是數據，類型可以是矩陣可以是數據框。一行觀測值一個臉譜圖。
2.2 face.type
這個參數是可以設置爲0、1、2、其他。
| 參數設置 | 含義 |
|---|---|
| 0 | 白色 |
| 1 | 彩色 |
| 2 | 聖誕老人+彩色 |
| 其他 | 同1的效果：彩色 |
3、數據介紹
下面是數據，也是採用的R語言中內置的例子使用的數據。維度是七16組觀測。
> data(longley)
> longley
GNP.deflator GNP Unemployed Armed.Forces Population Year Employed
1947 83.0 234.289 235.6 159.0 107.608 1947 60.323
1948 88.5 259.426 232.5 145.6 108.632 1948 61.122
1949 88.2 258.054 368.2 161.6 109.773 1949 60.171
1950 89.5 284.599 335.1 165.0 110.929 1950 61.187
1951 96.2 328.975 209.9 309.9 112.075 1951 63.221
1952 98.1 346.999 193.2 359.4 113.270 1952 63.639
1953 99.0 365.385 187.0 354.7 115.094 1953 64.989
1954 100.0 363.112 357.8 335.0 116.219 1954 63.761
1955 101.2 397.469 290.4 304.8 117.388 1955 66.019
1956 104.6 419.180 282.2 285.7 118.734 1956 67.857
1957 108.4 442.769 293.6 279.8 120.445 1957 68.169
1958 110.8 444.546 468.1 263.7 121.950 1958 66.513
1959 112.6 482.704 381.3 255.2 123.366 1959 68.655
1960 114.2 502.601 393.1 251.4 125.368 1960 69.564
1961 115.7 518.173 480.6 257.2 127.852 1961 69.331
1962 116.9 554.894 400.7 282.7 130.081 1962 70.5514、案例展示
> faces(longley[1:9,],face.type=0)
effect of variables:
modified item Var
"height of face " "GNP.deflator"
"width of face " "GNP"
"structure of face" "Unemployed"
"height of mouth " "Armed.Forces"
"width of mouth " "Population"
"smiling " "Year"
"height of eyes " "Employed"
"width of eyes " "GNP.deflator"
"height of hair " "GNP"
"width of hair " "Unemployed"
"style of hair " "Armed.Forces"
"height of nose " "Population"
"width of nose " "Year"
"width of ear " "Employed"
"height of ear " "GNP.deflator"
> faces(longley[1:9,],face.type=1)
effect of variables:
modified item Var
"height of face " "GNP.deflator"
"width of face " "GNP"
"structure of face" "Unemployed"
"height of mouth " "Armed.Forces"
"width of mouth " "Population"
"smiling " "Year"
"height of eyes " "Employed"
"width of eyes " "GNP.deflator"
"height of hair " "GNP"
"width of hair " "Unemployed"
"style of hair " "Armed.Forces"
"height of nose " "Population"
"width of nose " "Year"
"width of ear " "Employed"
"height of ear " "GNP.deflator"
注：代碼裏輸出的是十五個特徵對應的數據維度。
5、總結
最終希望大家能夠把自己的數據完美的可視化出來。