tidyr总结篇-续 / 四六文摘

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tidyr总结篇

gather(data,key="“,value=”") ## key是变量,value是值 gather的意义是重新塑造数据的变量，原有数据的变量并不是真正的变量，这时候变量不是变量，变量还是变量。

举例说明: 神奇的gather
参数1：data 参数2：key变量名，参数3：value变量名参数4：gather的变量指定其中-表示除外某向量，全部gather

Sys.setlocale('LC_ALL','C') ## [1] "C" library(tidyverse) ## Registered S3 methods overwritten by 'ggplot2': ## method from ## [.quosures rlang ## c.quosures rlang ## print.quosures rlang ## Registered S3 method overwritten by 'rvest': ## method from ## read_xml.response xml2 ## -- Attaching packages -------------------------------------------- tidyverse 1.2.1 -- ## <U+221A> ggplot2 3.1.0 <U+221A> purrr 0.3.0 ## <U+221A> tibble 2.0.1 <U+221A> dplyr 0.8.0.1 ## <U+221A> tidyr 0.8.2 <U+221A> stringr 1.4.0 ## <U+221A> readr 1.3.1 <U+221A> forcats 0.4.0 ## -- Conflicts ----------------------------------------------- tidyverse_conflicts() -- ## x dplyr::filter() masks stats::filter() ## x dplyr::lag() masks stats::lag() stocks <- tibble( time = as.Date('2009-01-01') + 0:9, X = rnorm(10, 0, 1), Y = rnorm(10, 0, 2), Z = rnorm(10, 0, 4) ) stocks ## # A tibble: 10 x 4 ## time X Y Z ## <date> <dbl> <dbl> <dbl> ## 1 2009-01-01 -0.497 -1.20 5.93 ## 2 2009-01-02 1.22 1.58 -4.43 ## 3 2009-01-03 1.68 -2.50 8.03 ## 4 2009-01-04 1.58 0.744 -2.00 ## 5 2009-01-05 0.775 1.87 -3.14 ## 6 2009-01-06 0.0405 0.629 4.31 ## 7 2009-01-07 -1.42 -1.36 9.63 ## 8 2009-01-08 1.18 5.21 -0.231 ## 9 2009-01-09 -0.581 -1.02 -0.680 ## 10 2009-01-10 0.768 0.900 6.43

gather起stocks中的,X,Y,Z. 新命名一个key,命名一个value, 除去time不变化

gather(stocks, stock, price, -time) ## # A tibble: 30 x 3 ## time stock price ## <date> <chr> <dbl> ## 1 2009-01-01 X -0.497 ## 2 2009-01-02 X 1.22 ## 3 2009-01-03 X 1.68 ## 4 2009-01-04 X 1.58 ## 5 2009-01-05 X 0.775 ## 6 2009-01-06 X 0.0405 ## 7 2009-01-07 X -1.42 ## 8 2009-01-08 X 1.18 ## 9 2009-01-09 X -0.581 ## 10 2009-01-10 X 0.768 ## # ... with 20 more rows stocks %>% gather(stock, price, -time)##保留time不变化 ## # A tibble: 30 x 3 ## time stock price ## <date> <chr> <dbl> ## 1 2009-01-01 X -0.497 ## 2 2009-01-02 X 1.22 ## 3 2009-01-03 X 1.68 ## 4 2009-01-04 X 1.58 ## 5 2009-01-05 X 0.775 ## 6 2009-01-06 X 0.0405 ## 7 2009-01-07 X -1.42 ## 8 2009-01-08 X 1.18 ## 9 2009-01-09 X -0.581 ## 10 2009-01-10 X 0.768 ## # ... with 20 more rows ## mini_iris <- iris[c(1, 51, 101), ] mini_iris ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 51 7.0 3.2 4.7 1.4 versicolor ## 101 6.3 3.3 6.0 2.5 virginica gather(mini_iris,key = "flower_att",value = "value",Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) ## Species flower_att value ## 1 setosa Sepal.Length 5.1 ## 2 versicolor Sepal.Length 7.0 ## 3 virginica Sepal.Length 6.3 ## 4 setosa Sepal.Width 3.5 ## 5 versicolor Sepal.Width 3.2 ## 6 virginica Sepal.Width 3.3 ## 7 setosa Petal.Length 1.4 ## 8 versicolor Petal.Length 4.7 ## 9 virginica Petal.Length 6.0 ## 10 setosa Petal.Width 0.2 ## 11 versicolor Petal.Width 1.4 ## 12 virginica Petal.Width 2.5 gather(mini_iris,key = "flower_att",value = "value",Sepal.Length:Petal.Width) ## Species flower_att value ## 1 setosa Sepal.Length 5.1 ## 2 versicolor Sepal.Length 7.0 ## 3 virginica Sepal.Length 6.3 ## 4 setosa Sepal.Width 3.5 ## 5 versicolor Sepal.Width 3.2 ## 6 virginica Sepal.Width 3.3 ## 7 setosa Petal.Length 1.4 ## 8 versicolor Petal.Length 4.7 ## 9 virginica Petal.Length 6.0 ## 10 setosa Petal.Width 0.2 ## 11 versicolor Petal.Width 1.4 ## 12 virginica Petal.Width 2.5

-表示不gather的变量

gather(mini_iris,key = "flow_att",value = "value",-Species) ## Species flow_att value ## 1 setosa Sepal.Length 5.1 ## 2 versicolor Sepal.Length 7.0 ## 3 virginica Sepal.Length 6.3 ## 4 setosa Sepal.Width 3.5 ## 5 versicolor Sepal.Width 3.2 ## 6 virginica Sepal.Width 3.3 ## 7 setosa Petal.Length 1.4 ## 8 versicolor Petal.Length 4.7 ## 9 virginica Petal.Length 6.0 ## 10 setosa Petal.Width 0.2 ## 11 versicolor Petal.Width 1.4 ## 12 virginica Petal.Width 2.5

省略掉key, value

gather(mini_iris,flow_att,value,-Species)##得到的结果相同 ## Species flow_att value ## 1 setosa Sepal.Length 5.1 ## 2 versicolor Sepal.Length 7.0 ## 3 virginica Sepal.Length 6.3 ## 4 setosa Sepal.Width 3.5 ## 5 versicolor Sepal.Width 3.2 ## 6 virginica Sepal.Width 3.3 ## 7 setosa Petal.Length 1.4 ## 8 versicolor Petal.Length 4.7 ## 9 virginica Petal.Length 6.0 ## 10 setosa Petal.Width 0.2 ## 11 versicolor Petal.Width 1.4 ## 12 virginica Petal.Width 2.5

在管道中演示一套

注意group_by与slice联用时，slice切割的是总分组的数目如果group分了3组，那slice切割1的话就是显示1*3，如果切割1:2的话，那就是2*3，显示6个观测

下面举例说明

展示分组中的序列1，包含3个species

注意slice与group_by的联用

library(dplyr) mini_iris <- iris %>% group_by(Species) %>% slice(1) mini_iris %>% gather(key = flower_att, value = measurement, -Species) ## # A tibble: 12 x 3 ## # Groups: Species [3] ## Species flower_att measurement ## <fct> <chr> <dbl> ## 1 setosa Sepal.Length 5.1 ## 2 versicolor Sepal.Length 7 ## 3 virginica Sepal.Length 6.3 ## 4 setosa Sepal.Width 3.5 ## 5 versicolor Sepal.Width 3.2 ## 6 virginica Sepal.Width 3.3 ## 7 setosa Petal.Length 1.4 ## 8 versicolor Petal.Length 4.7 ## 9 virginica Petal.Length 6 ## 10 setosa Petal.Width 0.2 ## 11 versicolor Petal.Width 1.4 ## 12 virginica Petal.Width 2.5

再来举个例子 - mtcars数据集中的cyl分组为4-6-8 - 切割slice 1:2，即显示2组,4-6-8

by_cyl <- group_by(mtcars, cyl) ## slice(by_cyl, 1:2) ## # A tibble: 6 x 11 ## # Groups: cyl [3] ## mpg cyl disp hp drat wt qsec vs am gear carb ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 ## 2 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 ## 3 21 6 160 110 3.9 2.62 16.5 0 1 4 4 ## 4 21 6 160 110 3.9 2.88 17.0 0 1 4 4 ## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 ## 6 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4

spread函数

这是一个与gather互为逆向操作的函数函数参数有：参数1:data, 参数2:key,参数3:value，达到按key与value展开的效果

library(dplyr) stocks <- data.frame( time = as.Date('2009-01-01') + 0:9, X = rnorm(10, 0, 1), Y = rnorm(10, 0, 2), Z = rnorm(10, 0, 4) ) stocks ## time X Y Z ## 1 2009-01-01 -0.9981963 0.6149012 -1.880305 ## 2 2009-01-02 0.9763906 1.2292060 -2.244749 ## 3 2009-01-03 1.3475060 -1.6466510 4.497477 ## 4 2009-01-04 0.6845907 -2.8694272 -10.145486 ## 5 2009-01-05 -0.3132428 0.2366398 -1.401196 ## 6 2009-01-06 1.0542915 -0.5094071 1.311380 ## 7 2009-01-07 -2.5360015 1.5011045 1.188158 ## 8 2009-01-08 -0.2878114 1.6744369 -3.015077 ## 9 2009-01-09 -0.3004896 -2.8344579 4.376036 ## 10 2009-01-10 -0.1714464 0.8319891 -2.288022

先key value， gather一下

stocksm <- stocks %>% gather(stock, price, -time) stocksm ## time stock price ## 1 2009-01-01 X -0.9981963 ## 2 2009-01-02 X 0.9763906 ## 3 2009-01-03 X 1.3475060 ## 4 2009-01-04 X 0.6845907 ## 5 2009-01-05 X -0.3132428 ## 6 2009-01-06 X 1.0542915 ## 7 2009-01-07 X -2.5360015 ## 8 2009-01-08 X -0.2878114 ## 9 2009-01-09 X -0.3004896 ## 10 2009-01-10 X -0.1714464 ## 11 2009-01-01 Y 0.6149012 ## 12 2009-01-02 Y 1.2292060 ## 13 2009-01-03 Y -1.6466510 ## 14 2009-01-04 Y -2.8694272 ## 15 2009-01-05 Y 0.2366398 ## 16 2009-01-06 Y -0.5094071 ## 17 2009-01-07 Y 1.5011045 ## 18 2009-01-08 Y 1.6744369 ## 19 2009-01-09 Y -2.8344579 ## 20 2009-01-10 Y 0.8319891 ## 21 2009-01-01 Z -1.8803050 ## 22 2009-01-02 Z -2.2447486 ## 23 2009-01-03 Z 4.4974771 ## 24 2009-01-04 Z -10.1454861 ## 25 2009-01-05 Z -1.4011960 ## 26 2009-01-06 Z 1.3113796 ## 27 2009-01-07 Z 1.1881581 ## 28 2009-01-08 Z -3.0150769 ## 29 2009-01-09 Z 4.3760358 ## 30 2009-01-10 Z -2.2880217

spread展开数据

按stock, price展开

stocksm %>% spread(stock, price) ## time X Y Z ## 1 2009-01-01 -0.9981963 0.6149012 -1.880305 ## 2 2009-01-02 0.9763906 1.2292060 -2.244749 ## 3 2009-01-03 1.3475060 -1.6466510 4.497477 ## 4 2009-01-04 0.6845907 -2.8694272 -10.145486 ## 5 2009-01-05 -0.3132428 0.2366398 -1.401196 ## 6 2009-01-06 1.0542915 -0.5094071 1.311380 ## 7 2009-01-07 -2.5360015 1.5011045 1.188158 ## 8 2009-01-08 -0.2878114 1.6744369 -3.015077 ## 9 2009-01-09 -0.3004896 -2.8344579 4.376036 ## 10 2009-01-10 -0.1714464 0.8319891 -2.288022

按time, price展开

stocksm %>% spread(time, price) ## stock 2009-01-01 2009-01-02 2009-01-03 2009-01-04 2009-01-05 2009-01-06 ## 1 X -0.9981963 0.9763906 1.347506 0.6845907 -0.3132428 1.0542915 ## 2 Y 0.6149012 1.2292060 -1.646651 -2.8694272 0.2366398 -0.5094071 ## 3 Z -1.8803050 -2.2447486 4.497477 -10.1454861 -1.4011960 1.3113796 ## 2009-01-07 2009-01-08 2009-01-09 2009-01-10 ## 1 -2.536001 -0.2878114 -0.3004896 -0.1714464 ## 2 1.501104 1.6744369 -2.8344579 0.8319891 ## 3 1.188158 -3.0150769 4.3760358 -2.2880217

说明一下gather-spread的互补性质

stocks ## time X Y Z ## 1 2009-01-01 -0.9981963 0.6149012 -1.880305 ## 2 2009-01-02 0.9763906 1.2292060 -2.244749 ## 3 2009-01-03 1.3475060 -1.6466510 4.497477 ## 4 2009-01-04 0.6845907 -2.8694272 -10.145486 ## 5 2009-01-05 -0.3132428 0.2366398 -1.401196 ## 6 2009-01-06 1.0542915 -0.5094071 1.311380 ## 7 2009-01-07 -2.5360015 1.5011045 1.188158 ## 8 2009-01-08 -0.2878114 1.6744369 -3.015077 ## 9 2009-01-09 -0.3004896 -2.8344579 4.376036 ## 10 2009-01-10 -0.1714464 0.8319891 -2.288022 stocks %>% gather(key=stock,value = price,-time) %>% ##先聚合 spread(key = stock,value = price) %>% ## 又展开还原 identical(stocks) ## 判断与原来的stocks是否完全一样 ## [1] TRUE

总结一下 gather与spread，可以自如的将数据变换为宽数据或窄数据 gather的数据格式非常适用用于ggplot2的导入，用于可视化说到这里了我们就绘制一下吧，当然关于可视化的内容暂时不展开讲。

牛刀小试

library(ggplot2) p<-stocks %>% gather(key = stock,value = price,-time) %>% as_tibble() %>% ##直接导入到ggplot2进行可视化 ggplot2::ggplot(aes(x=stock,y=price,fill=stock))+ geom_boxplot() p

改改颜色

p+scale_fill_brewer(palette="Dark2")

放上自己喜欢的颜色

p+scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))

tidyr::unite函数

能够方便的实现将多列粘贴到一起的功能参数1：data数据框，参数2：新列名，参数3：sep分隔符，参数4:remove=T移除原列下面举例说明，这个功能好用，但用起来比较简单

粘贴vs与am列

library(dplyr) unite_(mtcars, "vs_am", c("vs","am")) ## mpg cyl disp hp drat wt qsec vs_am gear carb ## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0_1 4 4 ## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0_1 4 4 ## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1_1 4 1 ## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1_0 3 1 ## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0_0 3 2 ## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1_0 3 1 ## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0_0 3 4 ## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1_0 4 2 ## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1_0 4 2 ## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1_0 4 4 ## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1_0 4 4 ## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0_0 3 3 ## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0_0 3 3 ## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0_0 3 3 ## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0_0 3 4 ## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0_0 3 4 ## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0_0 3 4 ## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1_1 4 1 ## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1_1 4 2 ## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1_1 4 1 ## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1_0 3 1 ## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0_0 3 2 ## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0_0 3 2 ## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0_0 3 4 ## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0_0 3 2 ## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1_1 4 1 ## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0_1 5 2 ## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1_1 5 2 ## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0_1 5 4 ## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0_1 5 6 ## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0_1 5 8 ## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1_1 4 2

粘贴再分割是可逆的操作

mtcars %>% unite(vs_am, vs, am) %>% separate(vs_am, c("vs", "am")) ## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 ## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 ## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 ## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 ## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 ## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 ## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 ## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 ## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 ## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 ## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 ## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 ## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 ## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 ## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 ## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 ## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 ## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 ## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 ## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 ## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 ## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 ## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 ## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 ## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 ## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 ## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 ## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 ## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 ## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 ## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 ## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2

separate函数-逆向的unite操作

参数1：data,参数2: 要拆分的列，参数3：拆分成的新变量，参数4：Sep分割模式这个函数能做到将1列拆解为多列,用法与unite非常相似这里不重复过多，举几个简单示例说明即可

library(dplyr) df <- data.frame(x = c(NA, "a.b", "a.d", "b.c")) df ## x ## 1 <NA> ## 2 a.b ## 3 a.d ## 4 b.c

分割x为A-B

df %>% separate(x, c("A", "B")) ## A B ## 1 <NA> <NA> ## 2 a b ## 3 a d ## 4 b c

如果你想只保留第二个变量

df %>% separate(x, c(NA, "B")) ## B ## 1 <NA> ## 2 b ## 3 d ## 4 c

一个比较难办的问题是，如果需要裂解的列，裂解出来并不是相同的长度怎么办？separate函数提供了几个参数，extra与fill参数来控制裂解的方式 extra用于控制裂解碎片过多，warn：警告信息但扔掉多余，drop：扔掉但并不警告，merge：不扔掉,多余的merge起来 fill，warn警告但从左侧开始填充，right，右侧填充NA,left左侧填充NA

df <- data.frame(x = c("a", "a b", "a b c", NA)) df ## x ## 1 a ## 2 a b ## 3 a b c ## 4 <NA>

这样的方式会有warning

df %>% separate(x, c("a", "b")) ## Warning: Expected 2 pieces. Additional pieces discarded in 1 rows [3]. ## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [1]. ## a b ## 1 a <NA> ## 2 a b ## 3 a b ## 4 <NA> <NA>

扔掉多余信息，右侧填充NA

df %>% separate(x, c("a", "b"), extra = "drop", fill = "right") ## a b ## 1 a <NA> ## 2 a b ## 3 a b ## 4 <NA> <NA>

merge多余的，并从左侧开始填充

df ## x ## 1 a ## 2 a b ## 3 a b c ## 4 <NA> df %>% separate(x, c("a", "b"), extra = "merge", fill = "left") ## a b ## 1 <NA> a ## 2 a b ## 3 a b c ## 4 <NA> <NA>

同上

df <- data.frame(x = c("x: 123", "y: error: 7")) df ## x ## 1 x: 123 ## 2 y: error: 7 df %>% separate(x, c("key", "value"), ": ", extra = "merge") ## key value ## 1 x 123 ## 2 y error: 7

tidyr总结篇-续