After a long post Exploring data files with R, this is the time to get into looping. Instead of looping statements like while, for etc, we shall apply command in R.
Let’s take the mtcars data set available in R.
[code language=”r”]
> mtcars
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 2
[/code]
My aim is to find the mean of the above data. I have already written about summary() in my previous post. It gives the min, max, mean, median for each variables of mtcars.
[code language=”r”]
> summary(mtcars)
mpg cyl disp hp drat
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0 Min. :2.760
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5 1st Qu.:3.080
Median :19.20 Median :6.000 Median :196.3 Median :123.0 Median :3.695
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7 Mean :3.597
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0 3rd Qu.:3.920
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0 Max. :4.930
wt qsec vs am gear
Min. :1.513 Min. :14.50 Min. :0.0000 Min. :0.0000 Min. :3.000
1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:3.000
Median :3.325 Median :17.71 Median :0.0000 Median :0.0000 Median :4.000
Mean :3.217 Mean :17.85 Mean :0.4375 Mean :0.4062 Mean :3.688
3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:4.000
Max. :5.424 Max. :22.90 Max. :1.0000 Max. :1.0000 Max. :5.000
carb
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :2.812
3rd Qu.:4.000
Max. :8.000
[/code]
Apply
To find the mean of all variables, we need to do a looping across all rows of mtcars, which is performed using apply() command.
[code language=”r”]
> apply(mtcars, 2, mean)
mpg cyl disp hp drat wt qsec
20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750
vs am gear carb
0.437500 0.406250 3.687500 2.812500
[/code]
Arguments:
- mtcars – dataset
- 2 – row or column wise calculation. 1 means row, 2 means column
- mean – function
Similar task shall be accomplished using colMeans(), rowMeans().
[code language=”r”]
> rowMeans(mtcars)
Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
29.90727 29.98136 23.59818 38.73955
Hornet Sportabout Valiant Duster 360 Merc 240D
53.66455 35.04909 59.72000 24.63455
Merc 230 Merc 280 Merc 280C Merc 450SE
27.23364 31.86000 31.78727 46.43091
Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
46.50000 46.35000 66.23273 66.05855
Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
65.97227 19.44091 17.74227 18.81409
Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
24.88864 47.24091 46.00773 58.75273
Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
57.37955 18.92864 24.77909 24.88027
Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
60.97182 34.50818 63.15545 26.26273
> colMeans(mtcars)
mpg cyl disp hp drat wt qsec
20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750
vs am gear carb
0.437500 0.406250 3.687500 2.812500
[/code]
But these row or column commands do not have all functions like sd(),scale() etc which is possible with apply command. Lets take a small dataset.
[code language=”r”]
> mtcars5by5 <- mtcars[1:5, 1:5]
> mtcars5by5
mpg cyl disp hp drat
Mazda RX4 21.0 6 160 110 3.90
Mazda RX4 Wag 21.0 6 160 110 3.90
Datsun 710 22.8 4 108 93 3.85
Hornet 4 Drive 21.4 6 258 110 3.08
Hornet Sportabout 18.7 8 360 175 3.15
[/code]
For the above data set, below given is the row wise and column wise sum.
[code language=”r”]
> mtcars5by5$total <- apply(mtcars5by5, 1, sum)
> mtcars5by5$total
[1] 300.90 300.90 231.65 398.48 564.85
> #total is added a new variable in our data set
> mtcars5by5
mpg cyl disp hp drat total
Mazda RX4 21.0 6 160 110 3.90 300.90
Mazda RX4 Wag 21.0 6 160 110 3.90 300.90
Datsun 710 22.8 4 108 93 3.85 231.65
Hornet 4 Drive 21.4 6 258 110 3.08 398.48
Hornet Sportabout 18.7 8 360 175 3.15 564.85
> #column sum
> apply(mtcars5by5, 2, sum)
mpg cyl disp hp drat total
104.90 30.00 1046.00 598.00 17.88 1796.78
[/code]
Transform
Transform() helps us to prepare data. Using this, we shall create n number of new variables.
[code language=”r”]
> transform(mtcars5by5,tot=sum(mtcars5by5[,1:5]),mtper=mpg/drat,ntprod=mpg/hp)
mpg cyl disp hp drat total tot mtper ntprod
Mazda RX4 21.0 6 160 110 3.90 300.90 1796.78 5.384615 0.1909091
Mazda RX4 Wag 21.0 6 160 110 3.90 300.90 1796.78 5.384615 0.1909091
Datsun 710 22.8 4 108 93 3.85 231.65 1796.78 5.922078 0.2451613
Hornet 4 Drive 21.4 6 258 110 3.08 398.48 1796.78 6.948052 0.1945455
Hornet Sportabout 18.7 8 360 175 3.15 564.85 1796.78 5.936508 0.1068571
[/code]
I have added new variables tot, mtper, ntprod above.
lapply
This help us to issue a function over a list. It loops over a list and evaluate a function on each element
[code language=”r”]
> lapply(mtcars5by5, mean)
$mpg
[1] 20.98
$cyl
[1] 6
$disp
[1] 209.2
$hp
[1] 119.6
$drat
[1] 3.576
$total
[1] 359.356
[/code]
It went through the complete list to provide the mean of each variable.
tapply
tapply() helps us to apply the function in a ragged array or a subset of vector.
[code language=”r”]
> tapply(mtcars5by5$mpg, mtcars5by5$cyl, mean)
4 6 8
22.80000 21.13333 18.70000
[/code]
Consider our mtcars data set. I need to find out mean and maximum horse power hp grouped by different gears
[code language=”r”]
> mtcars
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 2
> ctmean
v s
automatic 15.05 20.74286
manual 19.75 28.37143
> tapply(mtcars$hp, mtcars$gear, mean)
3 4 5
176.1333 89.5000 195.6000
> tapply(mtcars$hp, mtcars$gear, max)
3 4 5
245 123 335
[/code]
We got mean horse power for 3, 4 and 5 gears.
We may even provide a list to group the mean operation. In the below given example, we shall calculate the mean for different transmission model am (0 – automatic; 1 – manual) and v/s.
[code language=”r”]
> list(mtcars$am,mtcars$vs)
[[1]]
[1] 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1
[[2]]
[1] 0 0 1 1 0 1 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 0 0 1
> ctmean <- tapply(mtcars$hp, list(mtcars$am,mtcars$vs), mean)
> rownames(ctmean) <- c("automatic", "manual")
> colnames(ctmean) <- c("v", "s")
> ctmean
v s
automatic 194.1667 102.14286
manual 180.8333 80.57143
[/code]
I think I shall stop here. See you in another interesting post.
Have a leisurely weekend.