R语言中提供了许多用来整合和重塑数据的强大方法
在整合数据时，往往将多组观测值替换为根据这些观测值计算的描叙性统计量
在重塑数据时，则会通过修改数据的结构（行和列）来决定数据的组织方式
使用SQL语句操作数据（*）
虽然在R语言中有很多优秀的函数，如aggregate和daply可以对数据框统计，但sql功能强大，不仅能实现数据的清洗、统计、运算，还可以实现数据存储、控制、定义和调用
library(sqldf)
示例：
# 安装sqldf包install.packages("sqldf")# 运行结果：# WARNING: Rtools is required to build R packages but is not currently installed. Please # download and install the appropriate version of Rtools before proceeding:## https://cran.rstudio.com/bin/windows/Rtools/# Installing package into ‘C:/Users/Admin/Documents/R/win-library/3.6’# (as ‘lib’ is unspecified)# also installing the dependencies ‘ellipsis’, ‘glue’, ‘bit’, ‘rlang’, ‘vctrs’, ‘digest’, ‘bit64’, ‘blob’, ‘memoise’, ‘pkgconfig’, ‘Rcpp’, ‘BH’, ‘plogr’, ‘gsubfn’, ‘proto’, ‘RSQLite’, ‘DBI’, ‘chron’## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/ellipsis_0.3.0.zip'# Content type 'application/zip' length 44575 bytes (43 KB)# downloaded 43 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/glue_1.4.0.zip'# Content type 'application/zip' length 158233 bytes (154 KB)# downloaded 154 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/bit_1.1-15.2.zip'# Content type 'application/zip' length 252475 bytes (246 KB)# downloaded 246 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/rlang_0.4.5.zip'# Content type 'application/zip' length 1131356 bytes (1.1 MB)# downloaded 1.1 MB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/vctrs_0.2.4.zip'# Content type 'application/zip' length 1027328 bytes (1003 KB)# downloaded 1003 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/digest_0.6.25.zip'# Content type 'application/zip' length 249452 bytes (243 KB)# downloaded 243 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/bit64_0.9-7.zip'# Content type 'application/zip' length 551485 bytes (538 KB)# downloaded 538 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/blob_1.2.1.zip'# Content type 'application/zip' length 47627 bytes (46 KB)# downloaded 46 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/memoise_1.1.0.zip'# Content type 'application/zip' length 36855 bytes (35 KB)# downloaded 35 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/pkgconfig_2.0.3.zip'# Content type 'application/zip' length 22207 bytes (21 KB)# downloaded 21 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/Rcpp_1.0.4.6.zip'# Content type 'application/zip' length 3030802 bytes (2.9 MB)# downloaded 2.9 MB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/BH_1.72.0-3.zip'# Content type 'application/zip' length 18270741 bytes (17.4 MB)# downloaded 17.4 MB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/plogr_0.2.0.zip'# Content type 'application/zip' length 18864 bytes (18 KB)# downloaded 18 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/gsubfn_0.7.zip'# Content type 'application/zip' length 358104 bytes (349 KB)# downloaded 349 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/proto_1.0.0.zip'# Content type 'application/zip' length 472221 bytes (461 KB)# downloaded 461 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/RSQLite_2.2.0.zip'# Content type 'application/zip' length 2275367 bytes (2.2 MB)# downloaded 2.2 MB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/DBI_1.1.0.zip'# Content type 'application/zip' length 607261 bytes (593 KB)# downloaded 593 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/chron_2.3-55.zip'# Content type 'application/zip' length 203176 bytes (198 KB)# downloaded 198 KB## 试开URL’https://cran.rstudio.com/bin/windows/contrib/3.6/sqldf_0.4-11.zip'# Content type 'application/zip' length 78408 bytes (76 KB)# downloaded 76 KB## package ‘ellipsis’ successfully unpacked and MD5 sums checked# package ‘glue’ successfully unpacked and MD5 sums checked# package ‘bit’ successfully unpacked and MD5 sums checked# package ‘rlang’ successfully unpacked and MD5 sums checked# package ‘vctrs’ successfully unpacked and MD5 sums checked# package ‘digest’ successfully unpacked and MD5 sums checked# package ‘bit64’ successfully unpacked and MD5 sums checked# package ‘blob’ successfully unpacked and MD5 sums checked# package ‘memoise’ successfully unpacked and MD5 sums checked# package ‘pkgconfig’ successfully unpacked and MD5 sums checked# package ‘Rcpp’ successfully unpacked and MD5 sums checked# package ‘BH’ successfully unpacked and MD5 sums checked# package ‘plogr’ successfully unpacked and MD5 sums checked# package ‘gsubfn’ successfully unpacked and MD5 sums checked# package ‘proto’ successfully unpacked and MD5 sums checked# package ‘RSQLite’ successfully unpacked and MD5 sums checked# package ‘DBI’ successfully unpacked and MD5 sums checked# package ‘chron’ successfully unpacked and MD5 sums checked# package ‘sqldf’ successfully unpacked and MD5 sums checked## The downloaded binary packages are in# C:\Users\Admin\AppData\Local\Temp\RtmpUHJCna\downloaded_packageslibrary(sqldf)name <- c(rep("张三", 1, 3), rep("李四", 3))subject <- c("数学","语文","英语","数学","语文","英语")score <- c(89, 80, 70, 90, 70, 80)stuid <- c(1, 1, 1, 2, 2, 2)stuscore <- data.frame(name, subject, score, stuid)stuscore# 运行结果：# name subject score stuid# 1 张三 数学 89 1# 2 张三 语文 80 1# 3 张三 英语 70 1# 4 李四 数学 90 2# 5 李四 语文 70 2# 6 李四 英语 80 2sqldf("select name, sum(score) as allscore from stuscore group by name order by allscore")# 运行结果：# name allscore# 1 张三 239# 2 李四 240sqldf("select name, stuid, sum(score) as allscore from stuscore group by name order by allscore")# 运行结果：# name stuid allscore# 1 张三 1 239# 2 李四 2 240sqldf("select stuid, name, subject, max(score) as maxscore from stuscore group by stuid order by maxscore")# 运行结果：# stuid name subject maxscore# 1 1 张三 数学 89# 2 2 李四 数学 90sqldf("select stuid, name, subject, avg(score) as avgscore from stuscore group by stuid order by avgscore")# 运行结果：# stuid name subject avgscore# 1 1 张三 数学 79.66667# 2 2 李四 数学 80.00000
汇总统计数据
数据汇总统计通过aggregate()实现
它首先将数据进行分组（按行），然后对每一组数据进行函数统计，最后把结果组合成一个表格返回
aggregate(x,by,FUN)
其中：
x是待统计的数据对象
by是一个变量名组成的列表，这些变量将被去掉以形成新的观测
FUN是用来计算描述统计量的标量函数，它将被用来计算新的观测值
示例：
score <- data.frame(ID = c(101, 102, 103, 104, 105, 106, 107, 108, 109, 110),score1 = c(92, 86, 85, 74, 82, 88, 96, 91, 84, 72),score2 = c(73, 69, 82, 93, 80, 94, 71, 87, 86, 91),gender = c("male", "male", "female", "female", "female", "female", "female", "male", "male", "male"))score# 运行结果：# ID score1 score2 gender# 1 101 92 73 male# 2 102 86 69 male# 3 103 85 82 female# 4 104 74 93 female# 5 105 82 80 female# 6 106 88 94 female# 7 107 96 71 female# 8 108 91 87 male# 9 109 84 86 male# 10 110 72 91 maleaggregate(score[,c(2,3)],by=list(score[,4]),FUN=mean)# 运行结果：# Group.1 score1 score2# 1 female 85 84.0# 2 male 85 81.2mtcars# 运行结果：# 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 2colnames(mtcars)# 运行结果：# [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" "carb"mtcars$cyl# 运行结果：# [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4attach(mtcars) # 绑定数据集，之后可直接引用变量名# 运行结果：# [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4aggregate(mtcars[,c(1,3)],by=list(cyl,gear),FUN=mean)# 运行结果：# Group.1 Group.2 mpg disp# 1 4 3 21.500 120.1000# 2 6 3 19.750 241.5000# 3 8 3 15.050 357.6167# 4 4 4 26.925 102.6250# 5 6 4 19.750 163.8000# 6 4 5 28.200 107.7000# 7 6 5 19.700 145.0000# 8 8 5 15.400 326.0000
重塑数据
重塑数据可以通过merge函数与melt函数实现。其中，merge函数可以横向合并两个数据框（数据集），melt函数可以实现数据整合的功能
merge函数
粘贴数据结构——R中合并两个数据集可以通过专门的函数merge( )来实现
merge通过相同的列或行名来识别，合并两个数据框或列表，其调用格式如下：
merge(x,y,by = intersect(names(x),names(y)),
by.x = by, by.y = by, all = FALSE, all.x = all, all.y = all,
sort = TRUE, suffixes = c(".x",".y"), no.dups = TRUE,
incomparables = NULL, …)
最后修改时间：2020-09-23 18:23:48
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