{"id":27288,"date":"2024-01-31T17:00:19","date_gmt":"2024-01-31T09:00:19","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27288"},"modified":"2024-01-31T17:00:21","modified_gmt":"2024-01-31T09:00:21","slug":"%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86%e7%9a%84%e5%91%bd%e4%bb%a4%e5%a4%aa%e5%a4%8d%e6%9d%82%e4%ba%86%e6%9d%a5%e7%9c%8b%e7%9c%8b%e6%95%b0%e6%8d%ae%e5%b7%ab%e5%b8%88datawizard","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27288","title":{"rendered":"\u6570\u636e\u5904\u7406\u7684\u547d\u4ee4\u592a\u590d\u6742\u4e86?\u6765\u770b\u770b\u6570\u636e\u5deb\u5e08datawizard"},"content":{"rendered":"

\u6570\u636e\u5904\u7406\u662f\u6211\u4eec\u4f5c\u4e3a\u751f\u4fe1\u7814\u7a76\u4eba\u5458\u6700\u91cd\u8981\u4e5f\u662f\u6700\u9ebb\u70e6\u7684\u4e00\u6b65\uff0cR\u5305\u5343\u7f57\u4e07\u8c61\uff0c\u8981\u6c42\u7684\u683c\u5f0f\u4e5f\u662f\u4e94\u82b1\u516b\u95e8\u7684\uff0c\u6709\u7ecf\u9a8c\u7684\u5c0f\u4f19\u4f34\u5e94\u8be5\u53ef\u4ee5\u660e\u767d\uff0c\u5728\u6211\u4eec\u8dd1\u6570\u636e\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u5934\u75bc\u7684\u4e0d\u662f\u540e\u7eed\u4ee3\u7801\u7684\u8fd0\u884c\uff0c\u662f\u7b2c\u4e00\u6b65\uff01\u6570\u636e\u7684\u5904\u7406\uff0c\u800c\u4eca\u5929\u5c0f\u679c\u60f3\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u7684\u8fd9\u4e2aR\u5305\u5c31\u662f\u4e00\u4e2a\u8f7b\u91cf\u5316\u7684\u6570\u636e\u5904\u7406\u5305\uff0c\u51fd\u6570\u7684\u547d\u4ee4\u5177\u6709\u660e\u663e\u7684\u903b\u8f91\u6027\uff0c\u6e05\u6670\u6613\u61c2\u3002\u5b83\u5c31\u662f\u6570\u636e\u5deb\u5e08\u2013datawizardR\u5305\uff01<\/p>\n

datawizardR\u5305\u662f\u4e00\u4e2a\u5341\u5206\u795e\u5947\u7684R\u5305\uff0c\u5b83\u53ef\u4ee5\u5e2e\u52a9\u4f60\u8f7b\u677e\u5730\u5b8c\u6210\u6570\u636e\u5206\u6790\u7684\u5404\u4e2a\u6b65\u9aa4\uff0c\u6bd4\u5982\u6574\u7406\u6570\u636e\u3001\u9884\u5904\u7406\u6570\u636e\u3001\u8f6c\u6362\u6570\u636e\u3001\u8ba1\u7b97\u6570\u636e\u7684\u7edf\u8ba1\u91cf\u7b49\u7b49\u3002\u5b83\u5c31\u50cf\u4e00\u4e2a\u6570\u636e\u9b54\u6cd5\u5e08\uff0c\u53ef\u4ee5\u7528\u7b80\u5355\u7684\u51fd\u6570\u6765\u64cd\u7eb5\u6570\u636e\uff0c\u8ba9\u4f60\u7684\u6570\u636e\u5206\u6790\u53d8\u5f97\u66f4\u52a0\u5feb\u901f\u548c\u9ad8\u6548\u3002<\/p>\n

\u63a5\u4e0b\u6765\uff0c\u5c31\u8ba9\u5c0f\u679c\u5e26\u7740\u5927\u5bb6\u4e00\u8d77\u4e86\u89e3\u8fd9\u4e2aR\u5305\u5427\uff01<\/p>\n

# Step 0 \u5b89\u88c5\u5e76\u8f7d\u5165<\/p>\n

options(timeout = 999)
\noptions(stringsAsFactors = F)<\/p>\n

if(!require(datawizard))devtools::install_github(“easystats\/datawizard”)
\nlibrary(datawizard)
\nlibrary(tidyverse)<\/p>\n

<\/a>\u6570\u636e\u6e05\u6d17<\/h1>\n

colnames(mtcars)<\/p>\n

## [1] “mpg” “cyl” “disp” “hp” “drat” “wt” “qsec” “vs” “am” “gear”
\n## [11] “carb”<\/p>\n

# \u76f4\u63a5\u7b5b\u9009\u67d0\u5217\u6570\u636e
\ndata_match(mtcars, data.frame(vs = 0, am = 1))<\/p>\n

## mpg cyl disp hp drat wt qsec vs am gear carb
\n## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
\n## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
\n## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
\n## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
\n## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
\n## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8<\/p>\n

# \u9650\u5b9a\u6761\u4ef6\u8fdb\u884c\u7b5b\u9009
\ndata_filter(mtcars, mpg < 20 & mpg > 15)<\/p>\n

## mpg cyl disp hp drat wt qsec vs am gear carb
\n## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
\n## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
\n## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
\n## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
\n## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
\n## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3<\/p>\n

# \u53ef\u4ee5\u901a\u8fc7find_columns() \u6216 get_columns() \u6765\u67e5\u627e\u6570\u636e\u6846\u4e2d\u7684\u5217\uff0c\u6216\u68c0\u7d22\u6240\u9009\u5217\u7684\u6570\u636e
\nfind_columns(mtcars, ends_with(“p”))<\/p>\n

## [1] “disp” “hp”<\/p>\n

get_columns(mtcars, ends_with(“p”)) %>% head() # \u6216\u8005\u662fstarts_with()<\/p>\n

## disp hp
\n## Mazda RX4 160 110
\n## Mazda RX4 Wag 160 110
\n## Datsun 710 108 93
\n## Hornet 4 Drive 258 110
\n## Hornet Sportabout 360 175
\n## Valiant 225 105<\/p>\n

# \u63d0\u53d6\u53d8\u91cf
\ndata_extract(mtcars, “mpg”)<\/p>\n

## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
\n## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
\n## [31] 15.0 21.4<\/p>\n

data_extract(mtcars, ends_with(“p”)) %>% head()<\/p>\n

## disp hp
\n## Mazda RX4 160 110
\n## Mazda RX4 Wag 160 110
\n## Datsun 710 108 93
\n## Hornet 4 Drive 258 110
\n## Hornet Sportabout 360 175
\n## Valiant 225 105<\/p>\n

# \u5220\u9664\u53d8\u91cf
\ndata_remove(mtcars, “disp”) %>% head()<\/p>\n

## mpg cyl hp drat wt qsec vs am gear carb
\n## Mazda RX4 21.0 6 110 3.90 2.620 16.46 0 1 4 4
\n## Mazda RX4 Wag 21.0 6 110 3.90 2.875 17.02 0 1 4 4
\n## Datsun 710 22.8 4 93 3.85 2.320 18.61 1 1 4 1
\n## Hornet 4 Drive 21.4 6 110 3.08 3.215 19.44 1 0 3 1
\n## Hornet Sportabout 18.7 8 175 3.15 3.440 17.02 0 0 3 2
\n## Valiant 18.1 6 105 2.76 3.460 20.22 1 0 3 1<\/p>\n

# \u53d8\u91cf\u91cd\u547d\u540d
\ndata_rename(mtcars, select = “rename”, before = “disp”) %>% head()<\/p>\n

## 1 2 3 4 5 6 7 8 9 10 11
\n## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
\n## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
\n## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
\n## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
\n## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
\n## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1<\/p>\n

# \u5408\u5e76\u6570\u636e\u6846
\n## \u521b\u5efa\u4e24\u4e2a\u793a\u4f8b\u6570\u636e\u6846
\nx <- data.frame(a = 1:3, b = c(“a”, “b”, “c”), c = 5:7, id = 1:3)
\ny <- data.frame(c = 6:8, d = c(“f”, “g”, “h”), e = 100:102, id = 2:4)
\nx<\/p>\n

## a b c id
\n## 1 1 a 5 1
\n## 2 2 b 6 2
\n## 3 3 c 7 3<\/p>\n

y<\/p>\n

## c d e id
\n## 1 6 f 100 2
\n## 2 7 g 101 3
\n## 3 8 h 102 4<\/p>\n

## \u8fd9\u91cc\u662f\u4e00\u4e9b\u6570\u636e\u6846\u5408\u5e76\u7684\u8fde\u63a5\u65b9\u5f0f\u54e6\uff0c\u719f\u6089SQL\u7684\u5c0f\u4f19\u4f34\u5e94\u8be5\u5c31\u4e0d\u4f1a\u89c9\u5f97\u964c\u751f\u5566\uff0c\u8fd9\u91cc\u5c0f\u679c\u5c31\u5570\u55e6\u4e00\u70b9\uff0c\u6bcf\u4e2a\u90fd\u89e3\u91ca\u4e00\u4e0b\u54e6~
\n# \u5168\u5916\u8fde\u63a5\uff0c\u4fdd\u7559\u6240\u6709\u884c\uff0c\u65e0\u8bba\u5728\u54ea\u4e2a\u6570\u636e\u96c6\u4e2d\u662f\u5426\u6709\u5339\u914d\u3002
\ndata_merge(x, y, join = “full”)<\/p>\n

## a b c id d e
\n## 3 1 a 5 1 <NA> NA
\n## 1 2 b 6 2 f 100
\n## 2 3 c 7 3 g 101
\n## 4 NA <NA> 8 4 h 102<\/p>\n

# \u5de6\u8fde\u63a5\uff0c\u4fdd\u7559\u5de6\u8fb9\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u884c\uff0c\u5e76\u5c06\u53f3\u8fb9\u6570\u636e\u96c6\u4e2d\u7684\u5339\u914d\u884c\u5408\u5e76\u3002
\ndata_merge(x, y, join = “left”)<\/p>\n

## a b c id d e
\n## 3 1 a 5 1 <NA> NA
\n## 1 2 b 6 2 f 100
\n## 2 3 c 7 3 g 101<\/p>\n

# \u53f3\u8fde\u63a5\uff0c\u4fdd\u7559\u53f3\u8fb9\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u884c\uff0c\u5e76\u5c06\u5de6\u8fb9\u6570\u636e\u96c6\u4e2d\u7684\u5339\u914d\u884c\u5408\u5e76\u3002
\ndata_merge(x, y, join = “right”)<\/p>\n

## a b c id d e
\n## 1 2 b 6 2 f 100
\n## 2 3 c 7 3 g 101
\n## 3 NA <NA> 8 4 h 102<\/p>\n

# \u5185\u8fde\u63a5\uff0c\u53ea\u4fdd\u7559\u5728\u4e24\u4e2a\u6570\u636e\u96c6\u4e2d\u90fd\u6709\u5339\u914d\u7684\u884c\u3002
\ndata_merge(x, y, join = “inner”)<\/p>\n

## a b c id d e
\n## 1 2 b 6 2 f 100
\n## 2 3 c 7 3 g 101<\/p>\n

# \u5c06\u6240\u6709\u6570\u636e\u4fdd\u5b58\uff0c\u4e0a\u4e0b\u5408\u5e76\u3002
\ndata_merge(x, y, join = “bind”)<\/p>\n

## a b c id d e
\n## 1 1 a 5 1 <NA> NA
\n## 2 2 b 6 2 <NA> NA
\n## 3 3 c 7 3 <NA> NA
\n## 4 NA <NA> 6 2 f 100
\n## 5 NA <NA> 7 3 g 101
\n## 6 NA <NA> 8 4 h 102<\/p>\n

# \u7b80\u5355\u6765\u8bf4\uff0c\u5c06x\u6570\u636e\u6846\u4e2d\u4e0ey\u6570\u636e\u6846c\u5217\u6709\u5171\u540c\u5143\u7d20\u7684\u884c\u63d0\u53d6\u51fa\u6765
\ndata_merge(x, y, join = “semi”, by = “c”)<\/p>\n

## a b c id
\n## 2 2 b 6 2
\n## 3 3 c 7 3<\/p>\n

# \u53cd\u4e4b\uff0c\u8fd9\u4e2a\u662f\u5c06x\u6570\u636e\u6846\u4e2d\u4e0ey\u6570\u636e\u6846c\u5217\u6ca1\u6709\u5171\u540c\u5143\u7d20\u7684\u884c\u63d0\u53d6\u51fa\u6765
\ndata_merge(x, y, join = “anti”, by = “c”)<\/p>\n

## a b c id
\n## 1 1 a 5 1<\/p>\n

<\/a>\u6570\u636e\u91cd\u5851<\/h1>\n

\u957f\u5bbd\u6570\u636e\u7684\u8f6c\u6362<\/p>\n

wide_data <- data.frame(replicate(5, rnorm(10)))<\/p>\n

head(data_to_long(wide_data))<\/p>\n

## name value
\n## 1 X1 0.42465528
\n## 2 X2 0.13473229
\n## 3 X3 2.26852458
\n## 4 X4 1.06321951
\n## 5 X5 -0.04557565
\n## 6 X1 -0.01311497<\/p>\n

<\/a>\u6570\u636e\u7c7b\u578b\u7684\u8f6c\u6362<\/h1>\n

\u5728\u751f\u4fe1\u7684\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u4f1a\u4f7f\u7528\u4e0d\u540c\u7684\u7edf\u8ba1\u5b66\u65b9\u6cd5\uff0c\u6709\u65f6\u4e0d\u540c\u7684\u65b9\u6cd5\u5bf9\u6570\u636e\u7684\u5206\u5e03\u6709\u4e0d\u540c\u7684\u8981\u6c42\uff0cdatawizard\u5305\u63d0\u4f9b\u4e86\u6570\u636e\u8f6c\u6362\u7684\u51fd\u6570\u4ee5\u8fbe\u5230\u4e00\u952e\u8f6c\u6362\u7684\u6548\u679c\u3002\u4ee5\u4e0b\u662f\u5c0f\u679c\u5217\u4e3e\u7684\u4e24\u4e2a\u4f8b\u5b50\u3002<\/p>\n

df <- data.frame(a = c(-2,-1,0,1,2),b = c(3,4,5,6,7))
\ndf<\/p>\n

## a b
\n## 1 -2 3
\n## 2 -1 4
\n## 3 0 5
\n## 4 1 6
\n## 5 2 7<\/p>\n

nor_df <- normalize(df)
\nnor_df<\/p>\n

## a b
\n## 1 0.00 0.00
\n## 2 0.25 0.25
\n## 3 0.50 0.50
\n## 4 0.75 0.75
\n## 5 1.00 1.00<\/p>\n

stand_df <- standardize(df)
\nstand_df<\/p>\n

## a b
\n## 1 -1.2649111 -1.2649111
\n## 2 -0.6324555 -0.6324555
\n## 3 0.0000000 0.0000000
\n## 4 0.6324555 0.6324555
\n## 5 1.2649111 1.2649111<\/p>\n

\u9664\u6b64\u4e4b\u5916\uff0cWinsorize\uff0cCenter\uff0cRanktransform\uff0cRotate or transpose\u7b49\u5904\u7406\u65b9\u6cd5\uff0c\u6709\u5174\u8da3\u7684\u5c0f\u4f19\u4f34\u53ef\u4ee5\u81ea\u884c\u7814\u7a76\u4e00\u4e0b\u54e6\uff01<\/p>\n

<\/a>\u6570\u636e\u5c5e\u6027<\/h1>\n

\u9664\u4e86\u6570\u636e\u5904\u7406\u4e4b\u5916\uff0cdatawizard\u5305\u8fd8\u6709\u4e00\u4e2a\u529f\u80fd\uff0c\u90a3\u5c31\u662f\u5bf9\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\uff0c\u4e3a\u6570\u636e\u6846\u4e2d\u7684\u6240\u6709\u53d8\u91cf\u63d0\u4f9b\u5168\u9762\u7684\u63cf\u8ff0\u6027\u6458\u8981\u7684\u65b9\u6cd5\u3002<\/p>\n

## Variable | Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing
\n## ——————————————————————————————–
\n## mpg | 20.09 | 6.03 | 7.53 | [10.40, 33.90] | 0.67 | -0.02 | 32 | 0
\n## cyl | 6.19 | 1.79 | 4.00 | [4.00, 8.00] | -0.19 | -1.76 | 32 | 0
\n## disp | 230.72 | 123.94 | 221.53 | [71.10, 472.00] | 0.42 | -1.07 | 32 | 0
\n## hp | 146.69 | 68.56 | 84.50 | [52.00, 335.00] | 0.80 | 0.28 | 32 | 0
\n## drat | 3.60 | 0.53 | 0.84 | [2.76, 4.93] | 0.29 | -0.45 | 32 | 0
\n## wt | 3.22 | 0.98 | 1.19 | [1.51, 5.42] | 0.47 | 0.42 | 32 | 0
\n## qsec | 17.85 | 1.79 | 2.02 | [14.50, 22.90] | 0.41 | 0.86 | 32 | 0
\n## vs | 0.44 | 0.50 | 1.00 | [0.00, 1.00] | 0.26 | -2.06 | 32 | 0
\n## am | 0.41 | 0.50 | 1.00 | [0.00, 1.00] | 0.40 | -1.97 | 32 | 0
\n## gear | 3.69 | 0.74 | 1.00 | [3.00, 5.00] | 0.58 | -0.90 | 32 | 0
\n## carb | 2.81 | 1.62 | 2.00 | [1.00, 8.00] | 1.16 | 2.02 | 32 | 0<\/p>\n

\u5305\u62ec\u4e86\u5747\u503c\u3001\u79bb\u6563\u5ea6\u3001\u8303\u56f4\u3001\u504f\u5ea6\u3001\u5cf0\u5ea6\u7b49\u5ea6\u91cf\u3002<\/p>\n

\u90a3\u4e48\u4eca\u5929\u5c0f\u679c\u7684\u5206\u4eab\u5c31\u5230\u8fd9\u91cc\u5566\uff01\u5e0c\u671b\u4eca\u5929\u5c0f\u679c\u7684\u5206\u4eab\u5e2e\u52a9\u5230\u5404\u4f4d\u5c0f\u4f19\u4f34\u5728\u6570\u636e\u5904\u7406\u65b9\u5411\u66f4\u8fdb\u4e00\u6b65\uff01 \u5982\u679c\u5c0f\u4f19\u4f34\u6709\u5176\u4ed6\u6570\u636e\u5206\u6790\u9700\u6c42\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4f7f\u7528\u672c\u516c\u53f8\u65b0\u5f00\u53d1\u7684\u751f\u4fe1\u5206\u6790\u5c0f\u5de5\u5177\u4e91\u5e73\u53f0\uff0c\u96f6\u4ee3\u7801\u5b8c\u6210\u5206\u6790\uff0c\u975e\u5e38\u65b9\u4fbf\u5662\uff0c\u4e91\u5e73\u53f0\u7f51\u5740\u4e3a\uff1a(http:\/\/www.biocloudservice.com\/home.html)<\/p>\n","protected":false},"excerpt":{"rendered":"

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