Data Used
library(plyr)
myCars = cbind(vehicle = row.names(mtcars), mtcars)
row.names(myCars) = NULL
myCars
vehicle mpg cyl disp hp drat wt qsec vs am gear carb
1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
arrange() Order a data frame by its colums
# sort myCars data by cylinder and displacement
myCars1 = arrange(myCars, cyl, disp)
myCars1
vehicle mpg cyl disp hp drat wt qsec vs am gear carb
1 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
2 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
3 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
4 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
5 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
6 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
7 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
8 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
9 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
10 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
11 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
12 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
13 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
14 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
15 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
16 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
17 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
18 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
19 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
20 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
21 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
22 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
24 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
25 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
26 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
27 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
28 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
29 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
30 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
31 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
32 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
# Sort with displacement in descending order
myCars2 = arrange(myCars, cyl, desc(disp))
myCars2
vehicle mpg cyl disp hp drat wt qsec vs am gear carb
1 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
2 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
3 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
4 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
5 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
6 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
7 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
8 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
9 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
10 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
11 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
12 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
13 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
14 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
15 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
16 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
17 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
18 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
19 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
20 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
21 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
22 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
23 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
24 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
25 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
26 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
27 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
28 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
29 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
30 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
31 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
32 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
colwise() Column-wise function
numcolwise(), catcolwise()
# Count number of missing values
nmissing <- function(x) sum(is.na(x))
# Apply to every column in a data frame
colwise(nmissing)(baseball)
id year stint team lg g ab r h X2b X3b hr rbi sb cs bb so ibb hbp
1 0 0 0 0 0 0 0 0 0 0 0 0 12 250 4525 0 1305 7528 377
sh sf gidp
1 960 7390 5272
# To operate only on specified columns
baseball1 = ddply(baseball, .(year), colwise(nmissing, c("sb", "cs", "so")))
head(baseball1)
year sb cs so
1 1871 0 0 0
2 1872 0 0 0
3 1873 0 0 0
4 1874 0 0 0
5 1875 0 0 0
6 1876 15 15 0
# specify a boolean function that determines whether or not a column should
# be included
baseball2 = ddply(baseball, .(year), colwise(nmissing, is.character))
head(baseball2)
year id team lg
1 1871 0 0 0
2 1872 0 0 0
3 1873 0 0 0
4 1874 0 0 0
5 1875 0 0 0
6 1876 0 0 0
baseball3 = ddply(baseball, .(year), colwise(nmissing, is.numeric))
head(baseball3)
year stint g ab r h X2b X3b hr rbi sb cs bb so ibb hbp sh sf gidp
1 1871 0 0 0 0 0 0 0 0 0 0 0 0 0 7 7 7 7 7
2 1872 0 0 0 0 0 0 0 0 0 0 0 0 0 13 13 13 13 13
3 1873 0 0 0 0 0 0 0 0 0 0 0 0 0 13 13 13 13 13
4 1874 0 0 0 0 0 0 0 0 0 0 0 0 0 15 15 15 15 15
5 1875 0 0 0 0 0 0 0 0 0 0 0 0 0 17 17 17 17 17
6 1876 0 0 0 0 0 0 0 0 0 15 15 0 0 15 15 15 15 15
# or numcolwise()
baseball4 = ddply(baseball, .(year), numcolwise(nmissing))
head(baseball4)
year stint g ab r h X2b X3b hr rbi sb cs bb so ibb hbp sh sf gidp
1 1871 0 0 0 0 0 0 0 0 0 0 0 0 0 7 7 7 7 7
2 1872 0 0 0 0 0 0 0 0 0 0 0 0 0 13 13 13 13 13
3 1873 0 0 0 0 0 0 0 0 0 0 0 0 0 13 13 13 13 13
4 1874 0 0 0 0 0 0 0 0 0 0 0 0 0 15 15 15 15 15
5 1875 0 0 0 0 0 0 0 0 0 0 0 0 0 17 17 17 17 17
6 1876 0 0 0 0 0 0 0 0 0 15 15 0 0 15 15 15 15 15
baseball5 = ddply(baseball, .(year), colwise(nmissing, is.discrete))
head(baseball5)
year id team lg
1 1871 0 0 0
2 1872 0 0 0
3 1873 0 0 0
4 1874 0 0 0
5 1875 0 0 0
6 1876 0 0 0
# or catcolwise()
baseball6 = ddply(baseball, .(year), catcolwise(nmissing))
head(baseball6)
year id team lg
1 1871 0 0 0
2 1872 0 0 0
3 1873 0 0 0
4 1874 0 0 0
5 1875 0 0 0
6 1876 0 0 0
count() Count the number of occurences.
# Count of each value of 'id' in the first 100 cases
count(baseball[1:100, ], vars = "id")
id freq
1 ansonca01 8
2 bennech01 1
3 burdoja01 7
4 forceda01 9
5 galvipu01 1
6 gerhajo01 5
7 hinespa01 6
8 jonesch01 6
9 mathebo01 7
10 morrijo01 2
11 nelsoca01 5
12 orourji01 6
13 shaffor01 4
14 snydepo01 5
15 startjo01 7
16 suttoez01 7
17 whitede01 7
18 yorkto01 7
# Count of ids, weighted by their 'g' loading
count(baseball[1:100, ], vars = "id", wt_var = "g")
id freq
1 ansonca01 432
2 bennech01 49
3 burdoja01 414
4 forceda01 380
5 galvipu01 13
6 gerhajo01 209
7 hinespa01 302
8 jonesch01 134
9 mathebo01 327
10 morrijo01 127
11 nelsoca01 193
12 orourji01 356
13 shaffor01 90
14 snydepo01 250
15 startjo01 389
16 suttoez01 344
17 whitede01 386
18 yorkto01 396
# Count of times each player appeared in each of the years they played
count(baseball[1:100, ], c("id", "year"))[17:20, ]
id year freq
17 forceda01 1871 1
18 forceda01 1872 2
19 forceda01 1873 1
20 forceda01 1874 1
desc() Descending order
desc(1:5)
[1] -1 -2 -3 -4 -5
desc(letters[1:5])
[1] -1 -2 -3 -4 -5
Session Information
sessionInfo()
R version 3.0.2 (2013-09-25)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=Chinese (Simplified)_People's Republic of China.936
[2] LC_CTYPE=Chinese (Simplified)_People's Republic of China.936
[3] LC_MONETARY=Chinese (Simplified)_People's Republic of China.936
[4] LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_People's Republic of China.936
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] plyr_1.8 knitr_1.5
loaded via a namespace (and not attached):
[1] evaluate_0.5.1 formatR_0.10 stringr_0.6.2 tools_3.0.2