Upcoming Assignments
| Assignments | Open Time | Due Time |
|---|---|---|
| Module 4 Data Quiz | Friday, Sept 14 (1:00 am EST) | Sunday, Sept 16 (11:55 pm EST) |
| Module 4 Conceptual Quiz | Friday, Sept 14 (1:00 am EST) | Sunday, Sept 16 (11:55 pm EST) |
Notes from Discussion Board/Office Hours
Difference between = and <-
In R both <- and = can be used to assign values to variables/objects, however there are some slight differences in how R uses these operators behind-the-scenes. This blog post demonstrates the some these differences examples.
Typically I recommend always using <- for assigning variables, and I mostly reserve = for arguments within functions. A nice shortcut for <- is Alt + -.
# My preferred uses of <- and =
q <- 0.12345
rounded_q <- round(q, digits = 0.2)Subsetting dataframes
Using the subset() function:
The subset() function can be used to select specific parts of a dataframe. It includes a subset argument for defining which rows will be kept, and a select argument for choosing which columns to include. Here’s some examples using the mtcarrs data set:
# Cars with more than 6 cylinders
subset(mtcars, subset = cyl > 6)## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 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
## 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
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8# Cars with more than 6 cylinders and high miles per gallon
subset(mtcars, subset = cyl > 6 & hp > 150)## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 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
## 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
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8# Cars with more than 6 cylinders for high horsepower
subset(mtcars, subset = cyl > 6 | hp > 150)## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 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
## 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
## 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# Just keep variables of interest
subset(mtcars, subset = cyl > 6 | hp > 100, select = c("cyl", "hp"))## cyl hp
## Mazda RX4 6 110
## Mazda RX4 Wag 6 110
## Hornet 4 Drive 6 110
## Hornet Sportabout 8 175
## Valiant 6 105
## Duster 360 8 245
## Merc 280 6 123
## Merc 280C 6 123
## Merc 450SE 8 180
## Merc 450SL 8 180
## Merc 450SLC 8 180
## Cadillac Fleetwood 8 205
## Lincoln Continental 8 215
## Chrysler Imperial 8 230
## Dodge Challenger 8 150
## AMC Javelin 8 150
## Camaro Z28 8 245
## Pontiac Firebird 8 175
## Lotus Europa 4 113
## Ford Pantera L 8 264
## Ferrari Dino 6 175
## Maserati Bora 8 335
## Volvo 142E 4 109Using operators:
We can also select part of a dataframe using brackets []. Remember that this notation uses [rows,columns]to define a subset. It may be useful to also use the $ operator, which most commonly used is to select a single column in a dataframe using the name of the column (as opposed to the index). This will return a vector, which may be useful for subsetting in an index (recall the Battleship example for the lecture). Here are the same examples as above just using the operators:
# Cars with more than 6 cylinders
mtcars[mtcars$cyl > 6,]## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8# Cars with more than 6 cylinders and high miles per gallon
mtcars[mtcars$cyl > 6 & mtcars$hp > 150,]## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8# Cars with more than 6 cylinders or high horsepower
mtcars[mtcars$cyl > 6 | mtcars$hp > 150,]## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8# Just keep variables of interest
mtcars[mtcars$cyl > 6 | mtcars$hp > 150, c("cyl", "hp")]## cyl hp
## Hornet Sportabout 8 175
## Duster 360 8 245
## Merc 450SE 8 180
## Merc 450SL 8 180
## Merc 450SLC 8 180
## Cadillac Fleetwood 8 205
## Lincoln Continental 8 215
## Chrysler Imperial 8 230
## Dodge Challenger 8 150
## AMC Javelin 8 150
## Camaro Z28 8 245
## Pontiac Firebird 8 175
## Ford Pantera L 8 264
## Ferrari Dino 6 175
## Maserati Bora 8 335Both the these approaches will work the same, so use whichever you prefer.
Useful keyboard shortcuts
| Windows Shortcut: | Mac Shortcut: | Action: |
|---|---|---|
Ctrl + Enter |
Cmd + Enter |
Run current line/selection |
Alt + Enter |
Alt + Enter |
Run current line/selection (retain cursor position) |
Ctrl + Shift + Enter |
Cmd + Shift + Enter |
Run Entire |
Alt + - |
Alt + - |
Add <- |
Alt + Shift + K |
Alt + Shift + K |
Show Shortcuts |
Full list of shortcuts available here
Extracting files from compressed (zipped) folders
In this course (and perhaps in your research) we will sometimes work with large files that must be compressed. Typically we will use .zip compression, which can be extracted by most newer PC and Mac without additional software. Other compression may require specific software to unzip (check the Properties of the folder, and try Google if you are unsure how to open). It is important that you either Extract the contents of the compressed folder, or manually move the files to a new location once to open the folder. This is because R will not allow you to specify a path to a file in a compressed folder.
Other notes
- Cheat sheet for base R
- List of other cheat sheets.
- Dataframes can be exported as CSV files using the
write.csv()function. - RStudio is available on UFApps.
- You can run RStudio on the cloud at https://rstudio.cloud/.
- This video demonstrates how to find and remove outliers using the
identify()function. - Here’s a site with practice problems.