tidyverse数据处理
tidyverse出自于R大神Hadley Wickham之手，他是Rstudio首席科学家，也是ggplot2的作者。tidyverse就是他将自己所写的包整理成了一整套数据处理的方法，包括ggplot2，dplyr，tidyr，readr，purrr，tibble，stringr, forcats。tidyverse主要的经常用的函数工具：
1.管道符%>%
> iris %>% head(,3)
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 setosa2.filter
> filter(iris,Species == "virginica")
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 6.3 3.3 6.0 2.5 virginica
2 5.8 2.7 5.1 1.9 virginica
3 7.1 3.0 5.9 2.1 virginica
4 6.3 2.9 5.6 1.8 virginica
5 6.5 3.0 5.8 2.2 virginica
6 7.6 3.0 6.6 2.1 virginica
7 4.9 2.5 4.5 1.7 virginica
8 7.3 2.9 6.3 1.8 virginica
... ...3.select
> select(iris, Petal.Length,Petal.Width,Species)
Petal.Length Petal.Width Species
1 1.4 0.2 setosa
2 1.4 0.2 setosa
3 1.3 0.2 setosa
4 1.5 0.2 setosa
5 1.4 0.2 setosa
6 1.7 0.4 setosa
7 1.4 0.3 setosa
8 1.5 0.2 setosa
... ...
> select(iris,-Sepal.Length,-Sepal.Width)
Petal.Length Petal.Width Species
1 1.4 0.2 setosa
2 1.4 0.2 setosa
3 1.3 0.2 setosa
4 1.5 0.2 setosa
5 1.4 0.2 setosa
6 1.7 0.4 setosa
7 1.4 0.3 setosa
8 1.5 0.2 setosa4.arrange
> arrange(iris,Sepal.Length)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.3 3.0 1.1 0.1 setosa
2 4.4 2.9 1.4 0.2 setosa
3 4.4 3.0 1.3 0.2 setosa
4 4.4 3.2 1.3 0.2 setosa
5 4.5 2.3 1.3 0.3 setosa
6 4.6 3.1 1.5 0.2 setosa
7 4.6 3.4 1.4 0.3 setosa
8 4.6 3.6 1.0 0.2 setosa
... ...
> arrange(iris,desc(Sepal.Length))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.9 3.8 6.4 2.0 virginica
2 7.7 3.8 6.7 2.2 virginica
3 7.7 2.6 6.9 2.3 virginica
4 7.7 2.8 6.7 2.0 virginica
5 7.7 3.0 6.1 2.3 virginica
6 7.6 3.0 6.6 2.1 virginica
7 7.4 2.8 6.1 1.9 virginica
8 7.3 2.9 6.3 1.8 virginica5.mutate
> mutate(iris,Spepal.Area = Sepal.Length*Sepal.Width)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species Spepal.Area
1 5.1 3.5 1.4 0.2 setosa 17.85
2 4.9 3.0 1.4 0.2 setosa 14.70
3 4.7 3.2 1.3 0.2 setosa 15.04
4 4.6 3.1 1.5 0.2 setosa 14.26
5 5.0 3.6 1.4 0.2 setosa 18.00
6 5.4 3.9 1.7 0.4 setosa 21.06
7 4.6 3.4 1.4 0.3 setosa 15.64
8 5.0 3.4 1.5 0.2 setosa 17.00
... ...6.summarise
> summarise(iris,count=n(),mean=mean(Sepal.Length))
count mean
1 150 5.8433337.group_by，通常跟summarise一起使用
> group_by(iris,Species) %>% summarise(n(),mean=mean(Sepal.Length),max=max(Sepal.Width))
# A tibble: 3 x 4
Species `n()` mean max
1 setosa 50 5.01 4.4
2 versicolor 50 5.94 3.4
3 virginica 50 6.59 3.8 8.unite
> unite(iris,Sepal.Area,Sepal.Length,Sepal.Width,sep=" x ",remove=FALSE)
Sepal.Area Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 x 3.5 5.1 3.5 1.4 0.2 setosa
2 4.9 x 3 4.9 3.0 1.4 0.2 setosa
3 4.7 x 3.2 4.7 3.2 1.3 0.2 setosa
4 4.6 x 3.1 4.6 3.1 1.5 0.2 setosa
5 5 x 3.6 5.0 3.6 1.4 0.2 setosa
6 5.4 x 3.9 5.4 3.9 1.7 0.4 setosa
... ...
iris_ <- unite(iris,Sepal.Area,Sepal.Length,Sepal.Width,sep=" x ",remove=FALSE)9.seperate
> separate(iris_,Sepal.Area,c("Length_","Width_"),sep=" x ",remove=TRUE)
Length_ Width_ Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 5.1 3.5 1.4 0.2 setosa
2 4.9 3 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 4.6 3.1 1.5 0.2 setosa
5 5 3.6 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 4.6 3.4 1.4 0.3 setosa
... ...10.gather （ggplot2画图的时候经常会用到）
> gather(iris,measurement,value,Sepal.Length:Petal.Width)
Species mesurement value
1 setosa Sepal.Length 5.1
2 setosa Sepal.Length 4.9
3 setosa Sepal.Length 4.7
4 setosa Sepal.Length 4.6
5 setosa Sepal.Length 5.0
6 setosa Sepal.Length 5.4
7 setosa Sepal.Length 4.6
8 setosa Sepal.Length 5.0
9 setosa Sepal.Length 4.4
10 setosa Sepal.Length 4.9
11 setosa Sepal.Length 5.4
12 setosa Sepal.Length 4.8
13 setosa Sepal.Length 4.8
14 setosa Sepal.Length 4.3
15 setosa Sepal.Length 5.8
16 setosa Sepal.Length 5.7
17 setosa Sepal.Length 5.4
... ...
> iris_ <- gather(iris,measurement,value,Sepal.Length:Petal.Width)
11.spread
> spread(iris_,measurement,value)
错误: Each row of output must be identified by a unique combination of keys.
Keys are shared for 600 rows:
* 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350
* 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400
* 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450
* 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 47
> length(iris_$value)
[1] 600
> iris_$id <- 1:600
> spread(iris_,measurement,value)
Species id Petal.Length Petal.Width Sepal.Length Sepal.Width
1 setosa 1 NA NA 5.1 NA
2 setosa 2 NA NA 4.9 NA
3 setosa 3 NA NA 4.7 NA
4 setosa 4 NA NA 4.6 NA
5 setosa 5 NA NA 5.0 NA
6 setosa 6 NA NA 5.4 NA
7 setosa 7 NA NA 4.6 NA
8 setosa 8 NA NA 5.0 NA
9 setosa 9 NA NA 4.4 NA
... ...
参考两篇文章：
http://www.mamicode.com/info-detail-2216961.html
https://mp.weixin.qq.com/s/-NIQ1DhXTHGY2xpanwbf9w