我们用一个R内置的测试数据airquality举例什么是：
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
str(airquality)
'data.frame': 153 obs. of 6 variables:
$ ozone : int 41 36 12 18 NA 28 23 19 8 NA ...
$ solar.r: int 190 118 149 313 NA NA 299 99 19 194 ...
$ wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
$ temp : int 67 72 74 62 56 66 65 59 61 69 ...
$ month : int 5 5 5 5 5 5 5 5 5 5 ...
$ day : int 1 2 3 4 5 6 7 8 9 10 ...
长数据：
"ozone" "solar.r" "wind" "temp" "month" "day"都是airquality的变量variable 名称，value值就是对应每个检测的值，这样的数据非常适合数据可视化。
head(melt(airquality), n = 10)
No id variables; using all as measure variables
variable value
1 ozone 41
2 ozone 36
3 ozone 12
4 ozone 18
5 ozone NA
6 ozone 28
7 ozone 23
8 ozone 19
9 ozone 8
10 ozone NA
宽数据：
宽数据通常是变量为列，检测为行所组成的数据框Data frame
head(airquality, n =10)
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
7 23 299 8.6 65 5 7
8 19 99 13.8 59 5 8
9 8 19 20.1 61 5 9
10 NA 194 8.6 69 5 10
# 1.工作目录
setwd("reshape2")
# 2.安装和导入
# install.packages("reshape2")
library(reshape2)
# 3.功能测试
help(package="reshape2")
### 3.1 acast()，Cast functions Cast a molten data frame into an array or data frame.
str(acast)
# function (data, formula, fun.aggregate = NULL, ..., margins = NULL, subset = NULL,
# fill = NULL, drop = TRUE, value.var = guess_value(data))
# Cast functions Cast a molten data frame into an array or data frame.
names(airquality) <- tolower(names(airquality))
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
head(acast(aqm, day ~ month ~ variable))
, , ozone
5 6 7 8 9
1 41 NA 135 39 96
2 36 NA 49 9 78
3 12 NA 32 16 73
4 18 NA NA 78 91
5 NA NA 64 35 47
6 28 NA 40 66 32
, , solar.r
5 6 7 8 9
1 190 286 269 83 167
2 118 287 248 24 197
3 149 242 236 77 183
4 313 186 101 NA 189
5 NA 220 175 NA 95
6 NA 264 314 NA 92
, , wind
5 6 7 8 9
1 7.4 8.6 4.1 6.9 6.9
2 8.0 9.7 9.2 13.8 5.1
3 12.6 16.1 9.2 7.4 2.8
4 11.5 9.2 10.9 6.9 4.6
5 14.3 8.6 4.6 7.4 7.4
6 14.9 14.3 10.9 4.6 15.5
, , temp
5 6 7 8 9
1 67 78 84 81 91
2 72 74 85 81 92
3 74 67 81 82 93
4 62 84 84 86 93
5 56 85 83 85 87
6 66 79 83 87 84
acast(aqm, month ~ variable, mean)
# ozone solar.r wind temp
# 5 23.61538 181.2963 11.622581 65.54839
# 6 29.44444 190.1667 10.266667 79.10000
# 7 59.11538 216.4839 8.941935 83.90323
# 8 59.96154 171.8571 8.793548 83.96774
# 9 31.44828 167.4333 10.180000 76.90000
acast(aqm, month ~ variable, mean, margins = TRUE)
# ozone solar.r wind temp (all)
# 5 23.61538 181.2963 11.622581 65.54839 68.70696
# 6 29.44444 190.1667 10.266667 79.10000 87.38384
# 7 59.11538 216.4839 8.941935 83.90323 93.49748
# 8 59.96154 171.8571 8.793548 83.96774 79.71207
# 9 31.44828 167.4333 10.180000 76.90000 71.82689
# (all) 42.12931 185.9315 9.957516 77.88235 80.05722
dcast(aqm, month ~ variable, mean, margins = c("month", "variable"))
# month ozone solar.r wind temp (all)
# 1 5 23.61538 181.2963 11.622581 65.54839 68.70696
# 2 6 29.44444 190.1667 10.266667 79.10000 87.38384
# 3 7 59.11538 216.4839 8.941935 83.90323 93.49748
# 4 8 59.96154 171.8571 8.793548 83.96774 79.71207
# 5 9 31.44828 167.4333 10.180000 76.90000 71.82689
# 6 (all) 42.12931 185.9315 9.957516 77.88235 80.05722
### 3.2 melt( )，宽数据转化为长数据，Convert an object into a molten data frame.
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
# 6 5 6 ozone 28
# 7 5 7 ozone 23
### 3.3 colsplit()
?colsplit
# Split a vector into multiple columns
x <- c("a_1_T", "a_2_F", "b_2_T", "c_3_F")
vars <- colsplit(x, "_", c("trt", "time", "Boolean_value"))
vars
# trt time Boolean_value
# 1 a 1 TRUE
# 2 a 2 FALSE
# 3 b 2 TRUE
# 4 c 3 FALSE
str(vars)
# 'data.frame': 4 obs. of 3 variables:
# $ trt : chr "a" "a" "b" "c"
# $ time : int 1 2 2 3
# $ Boolean_value: logi TRUE FALSE TRUE FALSE
### 3.4 recast()，Recast: melt and cast in a single step
### Recast: melt and cast in a single step
?recast
recast(french_fries, time ~ variable, id.var = 1:4)
# Aggregation function missing: defaulting to length
# time potato buttery grassy rancid painty
# 1 1 72 72 72 72 72
# 2 2 72 72 72 72 72
# 3 3 72 72 72 72 72
# 4 4 72 72 72 72 72
# 5 5 72 72 72 72 72
# 6 6 72 72 72 72 72
# 7 7 72 72 72 72 72
# 8 8 72 72 72 72 72
# 9 9 60 60 60 60 60
# 10 10 60 60 60 60 60
### 3.5 reshape2: built-in data
str(tips)
# 'data.frame': 244 obs. of 7 variables:
# $ total_bill: num 17 10.3 21 23.7 24.6 ...
# $ tip : num 1.01 1.66 3.5 3.31 3.61 4.71 2 3.12 1.96 3.23 ...
# $ sex : Factor w/ 2 levels "Female","Male": 1 2 2 2 1 2 2 2 2 2 ...
# $ smoker : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
# $ day : Factor w/ 4 levels "Fri","Sat","Sun",..: 3 3 3 3 3 3 3 3 3 3 ...
# $ time : Factor w/ 2 levels "Dinner","Lunch": 1 1 1 1 1 1 1 1 1 1 ...
# $ size : int 2 3 3 2 4 4 2 4 2 2 ...
# In all he recorded 244 tips. The data was reported in a collection of case studies for business statistics (Bryant & Smith 1995).
str(smiths)
# 'data.frame': 2 obs. of 5 variables:
# $ subject: Factor w/ 2 levels "John Smith","Mary Smith": 1 2
# $ time : int 1 1
# $ age : num 33 NA
# $ weight : num 90 NA
# $ height : num 1.87 1.54
# A small demo dataset describing John and Mary Smith. Used in the introductory vignette.
str(french_fries)
# 'data.frame': 696 obs. of 9 variables:
# $ time : Factor w/ 10 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
# $ treatment: Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
# $ subject : Factor w/ 12 levels "3","10","15",..: 1 1 2 2 3 3 4 4 5 5 ...
# $ rep : num 1 2 1 2 1 2 1 2 1 2 ...
# $ potato : num 2.9 14 11 9.9 1.2 8.8 9 8.2 7 13 ...
# $ buttery : num 0 0 6.4 5.9 0.1 3 2.6 4.4 3.2 0 ...
# $ grassy : num 0 0 0 2.9 0 3.6 0.4 0.3 0 3.1 ...
# $ rancid : num 0 1.1 0 2.2 1.1 1.5 0.1 1.4 4.9 4.3 ...
# $ painty : num 5.5 0 0 0 5.1 2.3 0.2 4 3.2 10.3 ...
# This data was collected from a sensory experiment conducted at Iowa State University in 2004. The investigators were interested in the effect of using three different fryer oils had on the taste of the fries.
### 3.6 查看reshape2的描述信息
help(package="reshape2")
Package: reshape2
Title: Flexibly Reshape Data: A Reboot of the Reshape Package
Version: 1.4.4
Author: Hadley Wickham <h.wickham@gmail.com>
Maintainer: Hadley Wickham <h.wickham@gmail.com>
Description: Flexibly restructure and aggregate data using just two
functions: melt and 'dcast' (or 'acast').
License: MIT + file LICENSE
URL: https://github.com/hadley/reshape
BugReports: https://github.com/hadley/reshape/issues
Depends: R (>= 3.1)
Imports: plyr (>= 1.8.1), Rcpp, stringr
Suggests: covr, lattice, testthat (>= 0.8.0)
LinkingTo: Rcpp
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
NeedsCompilation: yes
Packaged: 2020-04-09 12:27:19 UTC; hadley
Repository: CRAN
Date/Publication: 2020-04-09 13:50:02 UTC
Built: R 4.0.0; x86_64-w64-mingw32; 2020-05-02 21:38:15 UTC; windows
Archs: i386, x64
# 4.收尾
sessionInfo()
# R version 4.0.3 (2020-10-10)
# Platform: x86_64-w64-mingw32/x64 (64-bit)
# Running under: Windows 10 x64 (build 18363)
#
# Matrix products: default
#
# locale:
# [1] LC_COLLATE=Chinese (Simplified)_China.936
# [2] LC_CTYPE=Chinese (Simplified)_China.936
# [3] LC_MONETARY=Chinese (Simplified)_China.936
# [4] LC_NUMERIC=C
# [5] LC_TIME=Chinese (Simplified)_China.936
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] reshape2_1.4.4
#
# loaded via a namespace (and not attached):
# [1] compiler_4.0.3 magrittr_2.0.1 plyr_1.8.6 tools_4.0.3 yaml_2.2.1
# [6] Rcpp_1.0.6 tinytex_0.29 stringi_1.5.3 stringr_1.4.0 xfun_0.21
