-
R包学习——reshape包中melt、cast、merge函数用法
目录
本文使用的例子皆为官方example，在Rstudio中使用 ?+函数名 即可查看。
1. melt：Melt an object into a form suitable for easy casting.
> head(tips) total_bill tip sex smoker day time size 1 16.99 1.01 Female No Sun Dinner 2 2 10.34 1.66 Male No Sun Dinner 3 3 21.01 3.50 Male No Sun Dinner 3 4 23.68 3.31 Male No Sun Dinner 2 5 24.59 3.61 Female No Sun Dinner 4 6 25.29 4.71 Male No Sun Dinner 4 > head(melt(tips)) Using sex, smoker, day, time as id variables sex smoker day time variable value 1 Female No Sun Dinner total_bill 16.99 2 Male No Sun Dinner total_bill 10.34 3 Male No Sun Dinner total_bill 21.01 4 Male No Sun Dinner total_bill 23.68 5 Female No Sun Dinner total_bill 24.59 6 Male No Sun Dinner total_bill 25.29 > > head(airquality) ozone solar.r wind temp month day 1 41 190 7.4 67 5 1 2 36 118 8.0 72 5 2 3 12 149 12.6 74 5 3 4 18 313 11.5 62 5 4 5 NA NA 14.3 56 5 5 6 28 NA 14.9 66 5 6 > names(airquality) <- tolower(names(airquality)) > head(melt(airquality, id=c("month", "day"))) month day variable value 1 5 1 ozone 41 2 5 2 ozone 36 3 5 3 ozone 12 4 5 4 ozone 18 5 5 5 ozone NA 6 5 6 ozone 28 > > head(ChickWeight) weight time chick diet 1 42 0 1 1 2 51 2 1 1 3 59 4 1 1 4 64 6 1 1 5 76 8 1 1 6 93 10 1 1 > names(ChickWeight) <- tolower(names(ChickWeight)) > head(melt(ChickWeight, id=2:4)) time chick diet variable value 1 0 1 1 weight 42 2 2 1 1 weight 51 3 4 1 1 weight 59 4 6 1 1 weight 64 5 8 1 1 weight 76 6 10 1 1 weight 93- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
- 22
- 23
- 24
- 25
- 26
- 27
- 28
- 29
- 30
- 31
- 32
- 33
- 34
- 35
- 36
- 37
- 38
- 39
- 40
- 41
- 42
- 43
- 44
- 45
- 46
- 47
- 48
- 49
- 50
- 51
- 52
- 53
2. cast：Cast a molten data frame into the reshaped or aggregated form you want
> names(airquality) <- tolower(names(airquality)) > aqm <- melt(airquality, id=c("month", "day"), na.rm=TRUE) > head(aqm) month day variable value 1 5 1 ozone 41 2 5 2 ozone 36 3 5 3 ozone 12 4 5 4 ozone 18 5 5 6 ozone 28 6 5 7 ozone 23 > str(cast(aqm, day ~ month ~ variable)) num [1:31, 1:5, 1:4] 41 36 12 18 NA 28 23 19 8 NA ... - attr(*, "dimnames")=List of 3 ..$ day : Named chr [1:31] "1" "2" "3" "4" ... .. ..- attr(*, "names")= chr [1:31] "1" "20" "39" "58" ... ..$ month : Named chr [1:5] "5" "6" "7" "8" ... .. ..- attr(*, "names")= chr [1:5] "1" "600" "8" "12" ... ..$ variable: Named chr [1:4] "ozone" "solar.r" "wind" "temp" .. ..- attr(*, "names")= chr [1:4] "1" "2" "3" "4" > cast(aqm, month ~ variable, mean) month ozone solar.r wind temp 1 5 23.61538 181.2963 11.622581 65.54839 2 6 29.44444 190.1667 10.266667 79.10000 3 7 59.11538 216.4839 8.941935 83.90323 4 8 59.96154 171.8571 8.793548 83.96774 5 9 31.44828 167.4333 10.180000 76.90000 > str(cast(aqm, month ~ variable, mean)) List of 5 $ month : int [1:5] 5 6 7 8 9 $ ozone : num [1:5] 23.6 29.4 59.1 60 31.4 $ solar.r: num [1:5] 181 190 216 172 167 $ wind : num [1:5] 11.62 10.27 8.94 8.79 10.18 $ temp : num [1:5] 65.5 79.1 83.9 84 76.9 - attr(*, "row.names")= int [1:5] 1 2 3 4 5 - attr(*, "idvars")= chr "month" - attr(*, "rdimnames")=List of 2 ..$ :'data.frame': 5 obs. of 1 variable: .. ..$ month: int [1:5] 5 6 7 8 9 ..$ :'data.frame': 4 obs. of 1 variable: .. ..$ variable: Factor w/ 4 levels "ozone","solar.r",..: 1 2 3 4- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
- 22
- 23
- 24
- 25
- 26
- 27
- 28
- 29
- 30
- 31
- 32
- 33
- 34
- 35
- 36
- 37
- 38
- 39
- 40
- 41
- 42
- 43
- 44
3. merge：Merge two data frames by common columns or row names, or do other versions of database join operations.
（1）数据准备
> authors <- data.frame( + ## I(*) : use character columns of names to get sensible sort order + surname = I(c("Tukey", "Venables", "Tierney", "Ripley", "McNeil")), + nationality = c("US", "Australia", "US", "UK", "Australia"), + deceased = c("yes", rep("no", 4))) > authors surname nationality deceased 1 Tukey US yes 2 Venables Australia no 3 Tierney US no 4 Ripley UK no 5 McNeil Australia no- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
# 建立副本，新增一列surname改成name，删除surname列 > authorN <- within(authors, { name <- surname; rm(surname) }) > authorN nationality deceased name 1 US yes Tukey 2 Australia no Venables 3 US no Tierney 4 UK no Ripley 5 Australia no McNeil- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
> books <- data.frame( + name = I(c("Tukey", "Venables", "Tierney", + "Ripley", "Ripley", "McNeil", "R Core")), + title = c("Exploratory Data Analysis", + "Modern Applied Statistics ...", + "LISP-STAT", + "Spatial Statistics", "Stochastic Simulation", + "Interactive Data Analysis", + "An Introduction to R"), + other.author = c(NA, "Ripley", NA, NA, NA, NA, + "Venables & Smith")) > books name title other.author 1 Tukey Exploratory Data Analysis <NA> 2 Venables Modern Applied Statistics ... Ripley 3 Tierney LISP-STAT <NA> 4 Ripley Spatial Statistics <NA> 5 Ripley Stochastic Simulation <NA> 6 McNeil Interactive Data Analysis <NA> 7 R Core An Introduction to R Venables & Smith- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
（2）默认以两个dataframe都拥有的列（列名相同）进行merge
> (m0 <- merge(authorN, books)) name nationality deceased title other.author 1 McNeil Australia no Interactive Data Analysis <NA> 2 Ripley UK no Spatial Statistics <NA> 3 Ripley UK no Stochastic Simulation <NA> 4 Tierney US no LISP-STAT <NA> 5 Tukey US yes Exploratory Data Analysis <NA> 6 Venables Australia no Modern Applied Statistics ... Ripley- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
（3）当两个dataframe没有相同列名时，可以指定列进行merge
注意：merge的几种使用方法等同于dplyr包中的四种函数（inner_join、left_join、right_join、full_join）
i. inner_join
> library(dplyr) > (m1 <- merge(authors, books, by.x = "surname", by.y = "name")) surname nationality deceased title other.author 1 McNeil Australia no Interactive Data Analysis <NA> 2 Ripley UK no Spatial Statistics <NA> 3 Ripley UK no Stochastic Simulation <NA> 4 Tierney US no LISP-STAT <NA> 5 Tukey US yes Exploratory Data Analysis <NA> 6 Venables Australia no Modern Applied Statistics ... Ripley > (inner_join(authors, books, by = c("surname" = "name"))) surname nationality deceased title other.author 1 Tukey US yes Exploratory Data Analysis <NA> 2 Venables Australia no Modern Applied Statistics ... Ripley 3 Tierney US no LISP-STAT <NA> 4 Ripley UK no Spatial Statistics <NA> 5 Ripley UK no Stochastic Simulation <NA> 6 McNeil Australia no Interactive Data Analysis <NA>- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
> (m2 <- merge(books, authors, by.x = "name", by.y = "surname")) name title other.author nationality deceased 1 McNeil Interactive Data Analysis <NA> Australia no 2 Ripley Spatial Statistics <NA> UK no 3 Ripley Stochastic Simulation <NA> UK no 4 Tierney LISP-STAT <NA> US no 5 Tukey Exploratory Data Analysis <NA> US yes 6 Venables Modern Applied Statistics ... Ripley Australia no- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
ii. left_join
> merge(authors, books, by.x = "surname", by.y = "name", all.x = TRUE) surname nationality deceased title other.author 1 McNeil Australia no Interactive Data Analysis <NA> 2 Ripley UK no Spatial Statistics <NA> 3 Ripley UK no Stochastic Simulation <NA> 4 Tierney US no LISP-STAT <NA> 5 Tukey US yes Exploratory Data Analysis <NA> 6 Venables Australia no Modern Applied Statistics ... Ripley > left_join(authors, books, by = c("surname" = "name")) surname nationality deceased title other.author 1 Tukey US yes Exploratory Data Analysis <NA> 2 Venables Australia no Modern Applied Statistics ... Ripley 3 Tierney US no LISP-STAT <NA> 4 Ripley UK no Spatial Statistics <NA> 5 Ripley UK no Stochastic Simulation <NA> 6 McNeil Australia no Interactive Data Analysis <NA>- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
iii. right_join
> merge(authors, books, by.x = "surname", by.y = "name", all.y = TRUE) surname nationality deceased title other.author 1 McNeil Australia no Interactive Data Analysis <NA> 2 R Core <NA> <NA> An Introduction to R Venables & Smith 3 Ripley UK no Spatial Statistics <NA> 4 Ripley UK no Stochastic Simulation <NA> 5 Tierney US no LISP-STAT <NA> 6 Tukey US yes Exploratory Data Analysis <NA> 7 Venables Australia no Modern Applied Statistics ... Ripley > right_join(authors, books, by = c("surname" = "name")) surname nationality deceased title other.author 1 Tukey US yes Exploratory Data Analysis <NA> 2 Venables Australia no Modern Applied Statistics ... Ripley 3 Tierney US no LISP-STAT <NA> 4 Ripley UK no Spatial Statistics <NA> 5 Ripley UK no Stochastic Simulation <NA> 6 McNeil Australia no Interactive Data Analysis <NA> 7 R Core <NA> <NA> An Introduction to R Venables & Smith- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
iv. full_join
> merge(authors, books, by.x = "surname", by.y = "name", all = TRUE) surname nationality deceased title other.author 1 McNeil Australia no Interactive Data Analysis <NA> 2 R Core <NA> <NA> An Introduction to R Venables & Smith 3 Ripley UK no Spatial Statistics <NA> 4 Ripley UK no Stochastic Simulation <NA> 5 Tierney US no LISP-STAT <NA> 6 Tukey US yes Exploratory Data Analysis <NA> 7 Venables Australia no Modern Applied Statistics ... Ripley > full_join(authors, books, by = c("surname" = "name")) surname nationality deceased title other.author 1 Tukey US yes Exploratory Data Analysis <NA> 2 Venables Australia no Modern Applied Statistics ... Ripley 3 Tierney US no LISP-STAT <NA> 4 Ripley UK no Spatial Statistics <NA> 5 Ripley UK no Stochastic Simulation <NA> 6 McNeil Australia no Interactive Data Analysis <NA> 7 R Core <NA> <NA> An Introduction to R Venables & Smith- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
注意：这里由于数据集的原因，right_join和full_join的结果看不出来差距，要明白其中原理，右连接是以Y数据集为准，全连接是要顾及两个数据集。
4. example of using ‘incomparables’
> x <- data.frame(k1 = c(NA,NA,3,4,5), k2 = c(1,NA,NA,4,5), data = 1:5) > x k1 k2 data 1 NA 1 1 2 NA NA 2 3 3 NA 3 4 4 4 4 5 5 5 5 > y <- data.frame(k1 = c(NA,2,NA,4,5), k2 = c(NA,NA,3,4,5), data = 1:5) > y k1 k2 data 1 NA NA 1 2 2 NA 2 3 NA 3 3 4 4 4 4 5 5 5 5 # inner_join > merge(x, y, by = c("k1","k2")) # NA's match k1 k2 data.x data.y 1 4 4 4 4 2 5 5 5 5 3 NA NA 2 1 # 按照指定列merge，注意一个NA分别对应了两次NA > merge(x, y, by = "k1") # NA's match, so 6 rows k1 k2.x data.x k2.y data.y 1 4 4 4 4 4 2 5 5 5 5 5 3 NA 1 1 NA 1 4 NA 1 1 3 3 5 NA NA 2 NA 1 6 NA NA 2 3 3 > merge(x, y, by = "k2") # NA's match, so 6 rows k2 k1.x data.x k1.y data.y 1 4 4 4 4 4 2 5 5 5 5 5 3 NA NA 2 NA 1 4 NA NA 2 2 2 5 NA 3 3 NA 1 6 NA 3 3 2 2 # 去掉空值 > merge(x, y, by = "k2", incomparables = NA) # 2 rows k2 k1.x data.x k1.y data.y 1 4 4 4 4 4 2 5 5 5 5 5- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
- 22
- 23
- 24
- 25
- 26
- 27
- 28
- 29
- 30
- 31
- 32
- 33
- 34
- 35
- 36
- 37
- 38
- 39
- 40
- 41
- 42
- 43
- 44
- 45
- 46
- 47
-
相关阅读:
uniapp 小程序canvas插入网络图片
解决：Loading class `com.mysql.jdbc.Driver‘. This is deprecated.
（附源码）ssm失物招领平台 毕业设计 271621
cookie时效无限延长方案
Qt开发技术：Q3D图表开发笔记（三）：Q3DSurface三维曲面图介绍、Demo以及代码详解
Ant Design+react 路由跳转
嵌入式分享合集16
【Java面试】面试遇到宽泛的问题，这么回答就稳了，谈谈你对Redis的理解
学习笔记——《LINUX设备驱动程序（第三版）》Linux设备模型：内核添加、删除设备、驱动程序
【Linux】socket网络编程
- 原文地址：https://blog.csdn.net/narutodzx/article/details/127977849
