R语言进行数据预处理

标签：esc   lin   paste   ctr   msu   class   span   following   4.4   

R语言进行数据预处理wranging

li_volleyball

2016年3月22日

data wrangling with R
packages:tidyr dplyr

Ground rules

library(tidyr)
library(dplyr)
## 
## Attaching package: ‘dplyr‘
## The following objects are masked from ‘package:stats‘:
## 
##     filter, lag
## The following objects are masked from ‘package:base‘:
## 
##     intersect, setdiff, setequal, union
View(iris)
View(mtcars)
select(mtcars,am:1)
##                     am vs  qsec    wt drat  hp  disp cyl  mpg
## Mazda RX4            1  0 16.46 2.620 3.90 110 160.0   6 21.0
## Mazda RX4 Wag        1  0 17.02 2.875 3.90 110 160.0   6 21.0
## Datsun 710           1  1 18.61 2.320 3.85  93 108.0   4 22.8
## Hornet 4 Drive       0  1 19.44 3.215 3.08 110 258.0   6 21.4
## Hornet Sportabout    0  0 17.02 3.440 3.15 175 360.0   8 18.7
## Valiant              0  1 20.22 3.460 2.76 105 225.0   6 18.1
## Duster 360           0  0 15.84 3.570 3.21 245 360.0   8 14.3
## Merc 240D            0  1 20.00 3.190 3.69  62 146.7   4 24.4
## Merc 230             0  1 22.90 3.150 3.92  95 140.8   4 22.8
## Merc 280             0  1 18.30 3.440 3.92 123 167.6   6 19.2
## Merc 280C            0  1 18.90 3.440 3.92 123 167.6   6 17.8
## Merc 450SE           0  0 17.40 4.070 3.07 180 275.8   8 16.4
## Merc 450SL           0  0 17.60 3.730 3.07 180 275.8   8 17.3
## Merc 450SLC          0  0 18.00 3.780 3.07 180 275.8   8 15.2
## Cadillac Fleetwood   0  0 17.98 5.250 2.93 205 472.0   8 10.4
## Lincoln Continental  0  0 17.82 5.424 3.00 215 460.0   8 10.4
## Chrysler Imperial    0  0 17.42 5.345 3.23 230 440.0   8 14.7
## Fiat 128             1  1 19.47 2.200 4.08  66  78.7   4 32.4
## Honda Civic          1  1 18.52 1.615 4.93  52  75.7   4 30.4
## Toyota Corolla       1  1 19.90 1.835 4.22  65  71.1   4 33.9
## Toyota Corona        0  1 20.01 2.465 3.70  97 120.1   4 21.5
## Dodge Challenger     0  0 16.87 3.520 2.76 150 318.0   8 15.5
## AMC Javelin          0  0 17.30 3.435 3.15 150 304.0   8 15.2
## Camaro Z28           0  0 15.41 3.840 3.73 245 350.0   8 13.3
## Pontiac Firebird     0  0 17.05 3.845 3.08 175 400.0   8 19.2
## Fiat X1-9            1  1 18.90 1.935 4.08  66  79.0   4 27.3
## Porsche 914-2        1  0 16.70 2.140 4.43  91 120.3   4 26.0
## Lotus Europa         1  1 16.90 1.513 3.77 113  95.1   4 30.4
## Ford Pantera L       1  0 14.50 3.170 4.22 264 351.0   8 15.8
## Ferrari Dino         1  0 15.50 2.770 3.62 175 145.0   6 19.7
## Maserati Bora        1  0 14.60 3.570 3.54 335 301.0   8 15.0
## Volvo 142E           1  1 18.60 2.780 4.11 109 121.0   4 21.4
mtcars %>% select(am:1)
##                     am vs  qsec    wt drat  hp  disp cyl  mpg
## Mazda RX4            1  0 16.46 2.620 3.90 110 160.0   6 21.0
## Mazda RX4 Wag        1  0 17.02 2.875 3.90 110 160.0   6 21.0
## Datsun 710           1  1 18.61 2.320 3.85  93 108.0   4 22.8
## Hornet 4 Drive       0  1 19.44 3.215 3.08 110 258.0   6 21.4
## Hornet Sportabout    0  0 17.02 3.440 3.15 175 360.0   8 18.7
## Valiant              0  1 20.22 3.460 2.76 105 225.0   6 18.1
## Duster 360           0  0 15.84 3.570 3.21 245 360.0   8 14.3
## Merc 240D            0  1 20.00 3.190 3.69  62 146.7   4 24.4
## Merc 230             0  1 22.90 3.150 3.92  95 140.8   4 22.8
## Merc 280             0  1 18.30 3.440 3.92 123 167.6   6 19.2
## Merc 280C            0  1 18.90 3.440 3.92 123 167.6   6 17.8
## Merc 450SE           0  0 17.40 4.070 3.07 180 275.8   8 16.4
## Merc 450SL           0  0 17.60 3.730 3.07 180 275.8   8 17.3
## Merc 450SLC          0  0 18.00 3.780 3.07 180 275.8   8 15.2
## Cadillac Fleetwood   0  0 17.98 5.250 2.93 205 472.0   8 10.4
## Lincoln Continental  0  0 17.82 5.424 3.00 215 460.0   8 10.4
## Chrysler Imperial    0  0 17.42 5.345 3.23 230 440.0   8 14.7
## Fiat 128             1  1 19.47 2.200 4.08  66  78.7   4 32.4
## Honda Civic          1  1 18.52 1.615 4.93  52  75.7   4 30.4
## Toyota Corolla       1  1 19.90 1.835 4.22  65  71.1   4 33.9
## Toyota Corona        0  1 20.01 2.465 3.70  97 120.1   4 21.5
## Dodge Challenger     0  0 16.87 3.520 2.76 150 318.0   8 15.5
## AMC Javelin          0  0 17.30 3.435 3.15 150 304.0   8 15.2
## Camaro Z28           0  0 15.41 3.840 3.73 245 350.0   8 13.3
## Pontiac Firebird     0  0 17.05 3.845 3.08 175 400.0   8 19.2
## Fiat X1-9            1  1 18.90 1.935 4.08  66  79.0   4 27.3
## Porsche 914-2        1  0 16.70 2.140 4.43  91 120.3   4 26.0
## Lotus Europa         1  1 16.90 1.513 3.77 113  95.1   4 30.4
## Ford Pantera L       1  0 14.50 3.170 4.22 264 351.0   8 15.8
## Ferrari Dino         1  0 15.50 2.770 3.62 175 145.0   6 19.7
## Maserati Bora        1  0 14.60 3.570 3.54 335 301.0   8 15.0
## Volvo 142E           1  1 18.60 2.780 4.11 109 121.0   4 21.4
example1<-data.frame(A=c(paste("x",1:6,sep = "")),
                     B=seq(1,11,2),
                     c=1:6,
                     date=c("2000-08-15","1998-07-15","1995-06-04","1997-07-01","1999-06-01","1996-06-25"))
example1
##    A  B c       date
## 1 x1  1 1 2000-08-15
## 2 x2  3 2 1998-07-15
## 3 x3  5 3 1995-06-04
## 4 x4  7 4 1997-07-01
## 5 x5  9 5 1999-06-01
## 6 x6 11 6 1996-06-25
# 一个变量一列
# 一个观测值一行
#每一种观测在一个表里
#separate()
separate(example1,date,c("Y","m","d"),sep="-")
##    A  B c    Y  m  d
## 1 x1  1 1 2000 08 15
## 2 x2  3 2 1998 07 15
## 3 x3  5 3 1995 06 04
## 4 x4  7 4 1997 07 01
## 5 x5  9 5 1999 06 01
## 6 x6 11 6 1996 06 25
example12<-example1 %>% separate(date,c("Y","m","d"),sep="-")
#unite()
unite(example12,"YM",Y,m,sep="-")
##    A  B c      YM  d
## 1 x1  1 1 2000-08 15
## 2 x2  3 2 1998-07 15
## 3 x3  5 3 1995-06 04
## 4 x4  7 4 1997-07 01
## 5 x5  9 5 1999-06 01
## 6 x6 11 6 1996-06 25
#select()
select(example1,A,B)
##    A  B
## 1 x1  1
## 2 x2  3
## 3 x3  5
## 4 x4  7
## 5 x5  9
## 6 x6 11
select(example1,-A)
##    B c       date
## 1  1 1 2000-08-15
## 2  3 2 1998-07-15
## 3  5 3 1995-06-04
## 4  7 4 1997-07-01
## 5  9 5 1999-06-01
## 6 11 6 1996-06-25
select(example1,B:date)
##    B c       date
## 1  1 1 2000-08-15
## 2  3 2 1998-07-15
## 3  5 3 1995-06-04
## 4  7 4 1997-07-01
## 5  9 5 1999-06-01
## 6 11 6 1996-06-25
select(example1,starts_with("d"))
##         date
## 1 2000-08-15
## 2 1998-07-15
## 3 1995-06-04
## 4 1997-07-01
## 5 1999-06-01
## 6 1996-06-25
select(example1,ends_with("e"))
##         date
## 1 2000-08-15
## 2 1998-07-15
## 3 1995-06-04
## 4 1997-07-01
## 5 1999-06-01
## 6 1996-06-25
select(example1,contains("a"))
##    A       date
## 1 x1 2000-08-15
## 2 x2 1998-07-15
## 3 x3 1995-06-04
## 4 x4 1997-07-01
## 5 x5 1999-06-01
## 6 x6 1996-06-25
#filter()
filter(example1,B>=6)
##    A  B c       date
## 1 x4  7 4 1997-07-01
## 2 x5  9 5 1999-06-01
## 3 x6 11 6 1996-06-25
filter(example1,B>=6,A%in%c("x1","x4","x5"))
##    A B c       date
## 1 x4 7 4 1997-07-01
## 2 x5 9 5 1999-06-01
#mutate()
mutate(example1,ratio=B/c)
##    A  B c       date    ratio
## 1 x1  1 1 2000-08-15 1.000000
## 2 x2  3 2 1998-07-15 1.500000
## 3 x3  5 3 1995-06-04 1.666667
## 4 x4  7 4 1997-07-01 1.750000
## 5 x5  9 5 1999-06-01 1.800000
## 6 x6 11 6 1996-06-25 1.833333
mutate(example1,ratio=B/c,inverse=ratio-1)
##    A  B c       date    ratio   inverse
## 1 x1  1 1 2000-08-15 1.000000 0.0000000
## 2 x2  3 2 1998-07-15 1.500000 0.5000000
## 3 x3  5 3 1995-06-04 1.666667 0.6666667
## 4 x4  7 4 1997-07-01 1.750000 0.7500000
## 5 x5  9 5 1999-06-01 1.800000 0.8000000
## 6 x6 11 6 1996-06-25 1.833333 0.8333333
mutate(example1,cumsum(B))
##    A  B c       date cumsum(B)
## 1 x1  1 1 2000-08-15         1
## 2 x2  3 2 1998-07-15         4
## 3 x3  5 3 1995-06-04         9
## 4 x4  7 4 1997-07-01        16
## 5 x5  9 5 1999-06-01        25
## 6 x6 11 6 1996-06-25        36
mutate(example1,cumsum(B),cummean(B),cumany(B>6),cumall(B>6))
##    A  B c       date cumsum(B) cummean(B) cumany(B > 6) cumall(B > 6)
## 1 x1  1 1 2000-08-15         1          1         FALSE         FALSE
## 2 x2  3 2 1998-07-15         4          2         FALSE         FALSE
## 3 x3  5 3 1995-06-04         9          3         FALSE         FALSE
## 4 x4  7 4 1997-07-01        16          4          TRUE         FALSE
## 5 x5  9 5 1999-06-01        25          5          TRUE         FALSE
## 6 x6 11 6 1996-06-25        36          6          TRUE         FALSE
mutate(example1,cummin(B),cummax(B))
##    A  B c       date cummin(B) cummax(B)
## 1 x1  1 1 2000-08-15         1         1
## 2 x2  3 2 1998-07-15         1         3
## 3 x3  5 3 1995-06-04         1         5
## 4 x4  7 4 1997-07-01         1         7
## 5 x5  9 5 1999-06-01         1         9
## 6 x6 11 6 1996-06-25         1        11
mutate(example1,between(B,4,8))
##    A  B c       date between(B, 4, 8)
## 1 x1  1 1 2000-08-15            FALSE
## 2 x2  3 2 1998-07-15            FALSE
## 3 x3  5 3 1995-06-04             TRUE
## 4 x4  7 4 1997-07-01             TRUE
## 5 x5  9 5 1999-06-01            FALSE
## 6 x6 11 6 1996-06-25            FALSE
mutate(example1,cume_dist(B))
##    A  B c       date cume_dist(B)
## 1 x1  1 1 2000-08-15    0.1666667
## 2 x2  3 2 1998-07-15    0.3333333
## 3 x3  5 3 1995-06-04    0.5000000
## 4 x4  7 4 1997-07-01    0.6666667
## 5 x5  9 5 1999-06-01    0.8333333
## 6 x6 11 6 1996-06-25    1.0000000
example1 %>% mutate(ratio=B/c) %>% select(A,ratio)
##    A    ratio
## 1 x1 1.000000
## 2 x2 1.500000
## 3 x3 1.666667
## 4 x4 1.750000
## 5 x5 1.800000
## 6 x6 1.833333
#cume_dist=相对位置（行序号）/绝对行数

#gather() and spread()
example2<-data.frame(country=c("FR","DE","US"),
                     "2011"=c(7000,5800,15000),
                     "2012"=c(6900,6000,14000),
                     "2013"=c(7000,6200,13000),check.names=F)
example2
##   country  2011  2012  2013
## 1      FR  7000  6900  7000
## 2      DE  5800  6000  6200
## 3      US 15000 14000 13000
#gather()
gather(example2,"year","amount",2:4)
##   country year amount
## 1      FR 2011   7000
## 2      DE 2011   5800
## 3      US 2011  15000
## 4      FR 2012   6900
## 5      DE 2012   6000
## 6      US 2012  14000
## 7      FR 2013   7000
## 8      DE 2013   6200
## 9      US 2013  13000
example2 %>% gather("year","amount",2:4)
##   country year amount
## 1      FR 2011   7000
## 2      DE 2011   5800
## 3      US 2011  15000
## 4      FR 2012   6900
## 5      DE 2012   6000
## 6      US 2012  14000
## 7      FR 2013   7000
## 8      DE 2013   6200
## 9      US 2013  13000
#spread()

example3<-data.frame(city=c("NY","NY","LD","LD","BJ","BJ"),
                     size=rep(c("large","small"),3),
                     n=c(23,147,22,16,121,56))

example3
##   city  size   n
## 1   NY large  23
## 2   NY small 147
## 3   LD large  22
## 4   LD small  16
## 5   BJ large 121
## 6   BJ small  56
spread(example3,size,n)
##   city large small
## 1   BJ   121    56
## 2   LD    22    16
## 3   NY    23   147
example3 %>% spread(size,n)
##   city large small
## 1   BJ   121    56
## 2   LD    22    16
## 3   NY    23   147
#summarise()
example3 %>% summarise(median=median(n),variance=var(n))
##   median variance
## 1   39.5 3190.167
example3 %>% summarise(mean = mean(n), sum = sum(n), n = n())
##       mean sum n
## 1 64.16667 385 6
#arrange()
arrange(example3,n)
##   city  size   n
## 1   LD small  16
## 2   LD large  22
## 3   NY large  23
## 4   BJ small  56
## 5   BJ large 121
## 6   NY small 147
arrange(example3,desc(n))
##   city  size   n
## 1   NY small 147
## 2   BJ large 121
## 3   BJ small  56
## 4   NY large  23
## 5   LD large  22
## 6   LD small  16
#unite of analiysis
example3 %>% group_by(city)
## Source: local data frame [6 x 3]
## Groups: city [3]
## 
##     city   size     n
##   (fctr) (fctr) (dbl)
## 1     NY  large    23
## 2     NY  small   147
## 3     LD  large    22
## 4     LD  small    16
## 5     BJ  large   121
## 6     BJ  small    56
example3 %>% group_by(city) %>% summarise(mean=mean(n),sum=sum(n))
## Source: local data frame [3 x 3]
## 
##     city  mean   sum
##   (fctr) (dbl) (dbl)
## 1     BJ  88.5   177
## 2     LD  19.0    38
## 3     NY  85.0   170
example3 %>% group_by(size) %>% summarise(mean=mean(n))
## Source: local data frame [2 x 2]
## 
##     size     mean
##   (fctr)    (dbl)
## 1  large 55.33333
## 2  small 73.00000
#joining data
X<-data.frame(x1=c("A","B","C","D"),x2=1:4,stringsAsFactors = F)
X
##   x1 x2
## 1  A  1
## 2  B  2
## 3  C  3
## 4  D  4
Y<-data.frame(y1=c("B","C","D","E"),y2=seq(2,8,2),stringsAsFactors = F)
Y
##   y1 y2
## 1  B  2
## 2  C  4
## 3  D  6
## 4  E  8
X1<-data.frame(x1=c("B","C","D","E"),x2=2:5,stringsAsFactors = F)
bind_cols(X,Y)
## Source: local data frame [4 x 4]
## 
##      x1    x2    y1    y2
##   (chr) (int) (chr) (dbl)
## 1     A     1     B     2
## 2     B     2     C     4
## 3     C     3     D     6
## 4     D     4     E     8
bind_rows(X,Y)
## Source: local data frame [8 x 4]
## 
##      x1    x2    y1    y2
##   (chr) (int) (chr) (dbl)
## 1     A     1    NA    NA
## 2     B     2    NA    NA
## 3     C     3    NA    NA
## 4     D     4    NA    NA
## 5    NA    NA     B     2
## 6    NA    NA     C     4
## 7    NA    NA     D     6
## 8    NA    NA     E     8
bind_rows(X,X1)
## Source: local data frame [8 x 2]
## 
##      x1    x2
##   (chr) (int)
## 1     A     1
## 2     B     2
## 3     C     3
## 4     D     4
## 5     B     2
## 6     C     3
## 7     D     4
## 8     E     5
left_join(X,X1,by="x1")
##   x1 x2.x x2.y
## 1  A    1   NA
## 2  B    2    2
## 3  C    3    3
## 4  D    4    4
inner_join(X,X1,by="x1")
##   x1 x2.x x2.y
## 1  B    2    2
## 2  C    3    3
## 3  D    4    4
semi_join(X,X1,by="x1")
##   x1 x2
## 1  B  2
## 2  C  3
## 3  D  4
anti_join(X,X1,bu="x1")
## Joining by: c("x1", "x2")
##   x1 x2
## 1  A  1
anti_join(X1,X,by="x1")
##   x1 x2
## 1  E  5
setdiff(X,X1)
##   x1 x2
## 1  A  1
setdiff(X1,X)
##   x1 x2
## 1  E  5
first <- mtcars[1:20,1:5 ]
second <- mtcars[10:32,1:5 ]

intersect(first, second)
##     mpg cyl  disp  hp drat
## 1  19.2   6 167.6 123 3.92
## 2  17.8   6 167.6 123 3.92
## 3  16.4   8 275.8 180 3.07
## 4  17.3   8 275.8 180 3.07
## 5  15.2   8 275.8 180 3.07
## 6  10.4   8 472.0 205 2.93
## 7  10.4   8 460.0 215 3.00
## 8  14.7   8 440.0 230 3.23
## 9  32.4   4  78.7  66 4.08
## 10 30.4   4  75.7  52 4.93
## 11 33.9   4  71.1  65 4.22
union(first, second)
##     mpg cyl  disp  hp drat
## 1  33.9   4  71.1  65 4.22
## 2  19.2   6 167.6 123 3.92
## 3  30.4   4  75.7  52 4.93
## 4  18.1   6 225.0 105 2.76
## 5  19.7   6 145.0 175 3.62
## 6  15.5   8 318.0 150 2.76
## 7  26.0   4 120.3  91 4.43
## 8  10.4   8 460.0 215 3.00
## 9  14.7   8 440.0 230 3.23
## 10 13.3   8 350.0 245 3.73
## 11 21.5   4 120.1  97 3.70
## 12 19.2   8 400.0 175 3.08
## 13 15.2   8 304.0 150 3.15
## 14 14.3   8 360.0 245 3.21
## 15 32.4   4  78.7  66 4.08
## 16 27.3   4  79.0  66 4.08
## 17 17.8   6 167.6 123 3.92
## 18 30.4   4  95.1 113 3.77
## 19 24.4   4 146.7  62 3.69
## 20 18.7   8 360.0 175 3.15
## 21 21.4   6 258.0 110 3.08
## 22 15.2   8 275.8 180 3.07
## 23 17.3   8 275.8 180 3.07
## 24 22.8   4 140.8  95 3.92
## 25 21.0   6 160.0 110 3.90
## 26 15.8   8 351.0 264 4.22
## 27 16.4   8 275.8 180 3.07
## 28 21.4   4 121.0 109 4.11
## 29 15.0   8 301.0 335 3.54
## 30 22.8   4 108.0  93 3.85
## 31 10.4   8 472.0 205 2.93
setdiff(first, second)
##    mpg cyl  disp  hp drat
## 1 21.0   6 160.0 110 3.90
## 2 22.8   4 108.0  93 3.85
## 3 21.4   6 258.0 110 3.08
## 4 18.7   8 360.0 175 3.15
## 5 18.1   6 225.0 105 2.76
## 6 14.3   8 360.0 245 3.21
## 7 24.4   4 146.7  62 3.69
## 8 22.8   4 140.8  95 3.92
setdiff(second, first)
##     mpg cyl  disp  hp drat
## 1  21.5   4 120.1  97 3.70
## 2  15.5   8 318.0 150 2.76
## 3  15.2   8 304.0 150 3.15
## 4  13.3   8 350.0 245 3.73
## 5  19.2   8 400.0 175 3.08
## 6  27.3   4  79.0  66 4.08
## 7  26.0   4 120.3  91 4.43
## 8  30.4   4  95.1 113 3.77
## 9  15.8   8 351.0 264 4.22
## 10 19.7   6 145.0 175 3.62
## 11 15.0   8 301.0 335 3.54
## 12 21.4   4 121.0 109 4.11
setequal(mtcars, mtcars[32:1, ])
## TRUE

R语言进行数据预处理

标签：esc   lin   paste   ctr   msu   class   span   following   4.4   

原文地址：http://www.cnblogs.com/awishfullyway/p/6633694.html