Subsetting Data in R – Lab
Subsetting Data in R – Lab
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In this lab you can use the interactive console to explore but please record your commands here. Remember anything you type here can be “sent” to the console with Cmd-Enter (OS-X) or Cntr-Enter (Windows/Linux) (But only in side the {r} areas).
library(dplyr)
library(tidyverse)
Check to see if you have the mtcars dataset
## 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
## 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
## 28 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
What class is mtcars?
class(mtcars)
## [1] “data.frame”
How many observations (rows) and variables (columns) are in the mtcars dataset?
dim(mtcars)
## [1] 32 11
nrow(mtcars)
ncol(mtcars)
glimpse(mtcars)
## Rows: 32
## Columns: 11
## $ mpg
## $ cyl
## $ disp
## $ hp
## $ drat
## $ wt
## $ qsec
## $ vs
## $ am
## $ gear
## $ carb
Copy mtcars into an object called cars and rename mpg in cars to MPG. Use rename
cars = mtcars
cars = rename(cars, MPG = mpg)
head(cars)
## MPG cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
Convert the column names of cars to all upper case. Use rename_all, and the toupper command (or colnames).
cars = rename_all(cars, toupper)
head(cars)
## MPG CYL DISP HP DRAT WT QSEC VS AM GEAR CARB
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
cars = mtcars
cn = colnames(cars) # extract column names
cn = toupper(cn) # make them uppercase
colnames(cars) = cn # reassign
head(cars)
## MPG CYL DISP HP DRAT WT QSEC VS AM GEAR CARB
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
You can create a column called car using the rownames_to_column function.
cars = rownames_to_column(mtcars, var = “car”)
Subset the columns from cars that end in “p” and call it pvars, use ends_with().
pvars = select(cars, car, ends_with(“p”))
Create a subset of the data that only contains the columns: wt, qsec, and hp and assign this object to carsSub – what are the dimensions of this dataset? Use select() (and dim):
carsSub = select(cars, car, wt, qsec, hp)
dim(carsSub)
## [1] 32 4
Convert the column names of carsSub to all upper case. Use rename_all(), and the toupper command (or colnames)
carsSub = rename_all(carsSub, toupper)
Subset the rows of cars that get more than 20 miles per gallon (mpg) of fuel efficiency – how many are there? Use filter()
cars_mpg = filter(cars, mpg > 20)
dim(cars_mpg)
## [1] 14 12
nrow(cars_mpg)
glimpse(cars_mpg)
## Rows: 14
## Columns: 12
## $ car
## $ mpg
## $ cyl
## $ disp
## $ hp
## $ drat
## $ wt
## $ qsec
## $ vs
## $ am
## $ gear
## $ carb
# filter(cars, mpg > 20)
There are 14 cars. There are nrow(cars_mpg) cars.
cars %>% filter(mpg > 20) %>% nrow()
filter(cars, mpg > 20) %>% nrow()
Subset the rows that get less than 16 miles per gallon (mpg) of fuel efficiency and have more than 100 horsepower (hp) – how many are there?
filter(cars, mpg < 16 & hp > 100)
## car mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 2 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 3 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 4 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 5 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 6 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 7 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 8 28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 9 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 10 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
nrow(filter(cars, mpg < 16 & hp > 100))
nrow(filter(cars, mpg < 16, hp > 100))
cars %>% filter(mpg < 16, hp > 100) %>% nrow()
Create a subset from the cars data that only contains the columns: wt, qsec, and hp for only the cars with 8 cylinders and reassign this object to carsSub – what are the dimensions of this dataset?
carsSub = filter(cars, cyl == 8)
carsSub = select(carsSub, wt, qsec, hp, car)
dim(carsSub)
## [1] 14 4
carsSub = cars %>%
filter(cyl == 8) %>%
select(wt, qsec, hp, car)
dim(carsSub)
## [1] 14 4
Re-order the rows of carsSub by weight in increasing order. Use arrange()
carsSub = arrange(carsSub, wt)
Create a new variable in carsSub called wt2, which is equal to wt^2, using mutate(). Use piping %>%:
carsSub %>% mutate(wt2 = wt^2)
## wt qsec hp car wt2
## 1 3.170 14.50 264 Ford Pantera L 10.04890
## 2 3.435 17.30 150 AMC Javelin 11.79922
## 3 3.440 17.02 175 Hornet Sportabout 11.83360
## 4 3.520 16.87 150 Dodge Challenger 12.39040
## 5 3.570 15.84 245 Duster 360 12.74490
## 6 3.570 14.60 335 Maserati Bora 12.74490
## 7 3.730 17.60 180 Merc 450SL 13.91290
## 8 3.780 18.00 180 Merc 450SLC 14.28840
## 9 3.840 15.41 245 28 14.74560
## 10 3.845 17.05 175 Pontiac Firebird 14.78403
## 11 4.070 17.40 180 Merc 450SE 16.56490
## 12 5.250 17.98 205 Cadillac Fleetwood 27.56250
## 13 5.345 17.42 230 Chrysler Imperial 28.56902
## 14 5.424 17.82 215 29.41978
carsSub = carsSub %>% mutate(wt2 = wt^2)
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