Managing Data
Arranging and rearranging data is key to succesul work.
# ON SUBSETTING
#
remove(list = ls()) # Clear your workspace
data(mtcars)
attach(mtcars)
names(mtcars)## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
## [11] "carb"# Subsetting variables
# Create a smaller dataframe with only the variables mpg, cyl, and qsec.
mtcars_small_1 = mtcars[, c(1,2, 7)]
mtcars_small_1## mpg cyl qsec
## Mazda RX4 21.0 6 16.46
## Mazda RX4 Wag 21.0 6 17.02
## Datsun 710 22.8 4 18.61
## Hornet 4 Drive 21.4 6 19.44
## Hornet Sportabout 18.7 8 17.02
## Valiant 18.1 6 20.22
## Duster 360 14.3 8 15.84
## Merc 240D 24.4 4 20.00
## Merc 230 22.8 4 22.90
## Merc 280 19.2 6 18.30
## Merc 280C 17.8 6 18.90
## Merc 450SE 16.4 8 17.40
## Merc 450SL 17.3 8 17.60
## Merc 450SLC 15.2 8 18.00
## Cadillac Fleetwood 10.4 8 17.98
## Lincoln Continental 10.4 8 17.82
## Chrysler Imperial 14.7 8 17.42
## Fiat 128 32.4 4 19.47
## Honda Civic 30.4 4 18.52
## Toyota Corolla 33.9 4 19.90
## Toyota Corona 21.5 4 20.01
## Dodge Challenger 15.5 8 16.87
## AMC Javelin 15.2 8 17.30
## Camaro Z28 13.3 8 15.41
## Pontiac Firebird 19.2 8 17.05
## Fiat X1-9 27.3 4 18.90
## Porsche 914-2 26.0 4 16.70
## Lotus Europa 30.4 4 16.90
## Ford Pantera L 15.8 8 14.50
## Ferrari Dino 19.7 6 15.50
## Maserati Bora 15.0 8 14.60
## Volvo 142E 21.4 4 18.60 mtcars_small_2 = mtcars[, 3:6] # now choose variables 3 to 6
mtcars_small_2## disp hp drat wt
## Mazda RX4 160.0 110 3.90 2.620
## Mazda RX4 Wag 160.0 110 3.90 2.875
## Datsun 710 108.0 93 3.85 2.320
## Hornet 4 Drive 258.0 110 3.08 3.215
## Hornet Sportabout 360.0 175 3.15 3.440
## Valiant 225.0 105 2.76 3.460
## Duster 360 360.0 245 3.21 3.570
## Merc 240D 146.7 62 3.69 3.190
## Merc 230 140.8 95 3.92 3.150
## Merc 280 167.6 123 3.92 3.440
## Merc 280C 167.6 123 3.92 3.440
## Merc 450SE 275.8 180 3.07 4.070
## Merc 450SL 275.8 180 3.07 3.730
## Merc 450SLC 275.8 180 3.07 3.780
## Cadillac Fleetwood 472.0 205 2.93 5.250
## Lincoln Continental 460.0 215 3.00 5.424
## Chrysler Imperial 440.0 230 3.23 5.345
## Fiat 128 78.7 66 4.08 2.200
## Honda Civic 75.7 52 4.93 1.615
## Toyota Corolla 71.1 65 4.22 1.835
## Toyota Corona 120.1 97 3.70 2.465
## Dodge Challenger 318.0 150 2.76 3.520
## AMC Javelin 304.0 150 3.15 3.435
## Camaro Z28 350.0 245 3.73 3.840
## Pontiac Firebird 400.0 175 3.08 3.845
## Fiat X1-9 79.0 66 4.08 1.935
## Porsche 914-2 120.3 91 4.43 2.140
## Lotus Europa 95.1 113 3.77 1.513
## Ford Pantera L 351.0 264 4.22 3.170
## Ferrari Dino 145.0 175 3.62 2.770
## Maserati Bora 301.0 335 3.54 3.570
## Volvo 142E 121.0 109 4.11 2.780 # Subsetting obervations
mt_rows_3= mtcars_small_1[1:10,] # this selects the first 10 observations
mt_rows_3## mpg cyl qsec
## Mazda RX4 21.0 6 16.46
## Mazda RX4 Wag 21.0 6 17.02
## Datsun 710 22.8 4 18.61
## Hornet 4 Drive 21.4 6 19.44
## Hornet Sportabout 18.7 8 17.02
## Valiant 18.1 6 20.22
## Duster 360 14.3 8 15.84
## Merc 240D 24.4 4 20.00
## Merc 230 22.8 4 22.90
## Merc 280 19.2 6 18.30 # Subsetting using logical queries
# In this case choose all brands that have 4 cylinders
mtcars_cyl = mtcars[cyl == 4, ] # select the observations that meet the stated condition
mtcars_cyl## mpg cyl disp hp drat wt qsec vs am gear carb
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 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
## 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
## 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
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 mtcars_small_3 = mtcars[cyl %in% c(2,4,6),]
mtcars_small_4 = mtcars[with(mtcars, cyl == 4, hp > 100),]
# Using the function subset:
mtcars_small_5 = subset(mtcars, mpg < 20)
mtcars_small_5 = subset(mtcars, mpg < 20 & cyl == 8)
# using the function: select
mtcars_small_6 = subset(mtcars, mpg < 20 & cyl == 8, hp)
mtcars_small_6 = subset(mtcars, mpg < 20 & cyl == 8, c(4,6))
mtcars_small_7 = subset(mtcars, mpg < 20 & cyl == 8, select = c(4,6))
mtcars_small_8 = subset(mtcars, mpg < 20 & cyl == 8, select = hp:am)
# subsetting BOTH variables and Observations
names(mtcars)## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
## [11] "carb" mtcars_small_9 = subset(mtcars, cyl == 4, c(1:3, 7))
mtcars_small_10 = mtcars[cyl == 4, c(1:3, 7)]
Homework or Classwork
Use the data set state_data_csv.
Create a smaller data set consisting only of Population, Income, and Life Expectancy
Using the smaller data set, show a plot of Income and Life Expectancy
Make sure the plot has a line through the data showing the best fit OLS line.