You may remember from the chapter about matrices that all the elements that you put in a matrix should be of the same type. Back then, your dataset on Star Wars only contained numeric elements.
When doing a market research survey, however, you often have questions such as:
The output, namely the respondents’ answers to the questions formulated above, is a dataset of different data types. You will often find yourself working with datasets that contain different data types instead of only one.
A data frame has the variables of a dataset as columns and the observations as rows. This will be a familiar concept for those coming from different statistical software packages such as SAS or SPSS.
Instructions
Submit the answer. The data from the built-in example data frame mtcars will be printed to the console.
# Print out built-in R data frame
knitr::kable(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 |
Wow, that is a lot of cars!
Working with large datasets is not uncommon in data analysis. When you work with (extremely) large datasets and data frames, your first task as a data analyst is to develop a clear understanding of its structure and main elements. Therefore, it is often useful to show only a small part of the entire dataset.
So how to do this in R? Well, the function head() enables you to show the first observations of a data frame. Similarly, the function tail() prints out the last observations in your dataset.
Both head() and tail() print a top line called the ‘header’, which contains the names of the different variables in your dataset.
Instructions
Call head() on the mtcars dataset to have a look at the header and the first observations.
### Call head() on mtcars
knitr::kable(head(mtcars)
)| 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 |
Wonderful! So, what do we have in this dataset? For example, hp represents the car’s horsepower; the Datsun has the lowest horse power of the 6 cars that are displayed. For a full overview of the variables’ meaning, type ?mtcars in the console and read the help page.
Another method that is often used to get a rapid overview of your data is the function str(). The function str() shows you the structure of your dataset. For a data frame it tells you:
Applying the str() function will often be the first thing that you do when receiving a new dataset or data frame. It is a great way to get more insight in your dataset before diving into the real analysis.
Instructions
Investigate the structure of mtcars. Make sure that you see the same numbers, variables and data types as mentioned above.
# Investigate the structure of mtcars
str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...You can find lots of information by viewing the str() of a dataset.
Since using built-in datasets is not even half the fun of creating your own datasets, the rest of this chapter is based on your personally developed dataset. Put your jet pack on because it is time for some space exploration!
As a first goal, you want to construct a data frame that describes the main characteristics of eight planets in our solar system. According to your good friend Buzz, the main features of a planet are:
After doing some high-quality research on Wikipedia, you feel confident enough to create the necessary vectors: name, type, diameter, rotation and rings; these vectors have already been coded up in the editor. The first element in each of these vectors correspond to the first observation.
You construct a data frame with the data.frame() function. As arguments, you pass the vectors from before: they will become the different columns of your data frame. Because every column has the same length, the vectors you pass should also have the same length. But don’t forget that it is possible (and likely) that they contain different types of data.
Instructions
Use the function data.frame() to construct a data frame. Pass the vectors name, type, diameter, rotation and rings as arguments to data.frame(), in this order. Call the resulting data frame planets_df.
# Definition of vectors
name <- c("Mercury", "Venus", "Earth",
"Mars", "Jupiter", "Saturn",
"Uranus", "Neptune")
type <- c("Terrestrial planet",
"Terrestrial planet",
"Terrestrial planet",
"Terrestrial planet", "Gas giant",
"Gas giant", "Gas giant", "Gas giant")
diameter <- c(0.382, 0.949, 1, 0.532,
11.209, 9.449, 4.007, 3.883)
rotation <- c(58.64, -243.02, 1, 1.03,
0.41, 0.43, -0.72, 0.67)
rings <- c(FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE)
# Create a data frame from the vectors
planets_df <-data.frame(name, type, diameter, rotation, rings)The logical next step, as you know by now, is inspecting the data frame you just created.
The planets_df data frame should have 8 observations and 5 variables. It has been made available in the workspace, so you can directly use it.
Instructions
Use str() to investigate the structure of the new planets_df variable.
# Check the structure of planets_df
str(planets_df)## 'data.frame': 8 obs. of 5 variables:
## $ name : chr "Mercury" "Venus" "Earth" "Mars" ...
## $ type : chr "Terrestrial planet" "Terrestrial planet" "Terrestrial planet" "Terrestrial planet" ...
## $ diameter: num 0.382 0.949 1 0.532 11.209 ...
## $ rotation: num 58.64 -243.02 1 1.03 0.41 ...
## $ rings : logi FALSE FALSE FALSE FALSE TRUE TRUE ...Awesome! Now that you have a clear understanding of the planets_df dataset, it’s time to see how you can select elements from it.
Similar to vectors and matrices, you select elements from a data frame with the help of square brackets [ ]. By using a comma, you can indicate what to select from the rows and the columns respectively. For example:
my_df[1,2] selects the value at the first row and second column in my_df.
my_df[1:3,2:4] selects rows 1, 2, 3 and columns 2, 3, 4 in my_df.
Sometimes you want to select all elements of a row or column. For example, my_df[1, ] selects all elements of the first row. Let us now apply this technique on planets_df!
Instructions
From planets_df, select the diameter of Mercury: this is the value at the first row and the third column. Simply print out the result.
From planets_df, select all data on Mars (the fourth row). Simply print out the result.
# The planets_df data frame from the previous exercise is pre-loaded
# Print out diameter of Mercury (row 1, column 3)
planets_df[1,3]## [1] 0.382# Print out data for Mars (entire fourth row)
planets_df[4,]## name type diameter rotation rings
## 4 Mars Terrestrial planet 0.532 1.03 FALSEGreat! Apart from selecting elements from your data frame by index, you can also use the column names.
Instead of using numerics to select elements of a data frame, you can also use the variable names to select columns of a data frame.
Suppose you want to select the first three elements of the type column. One way to do this is
planets_df[1:3,2]A possible disadvantage of this approach is that you have to know (or look up) the column number of type, which gets hard if you have a lot of variables. It is often easier to just make use of the variable name:
planets_df[1:3,"type"]Instructions
Select and print out the first 5 values in the “diameter” column of planets_df.
# The planets_df data frame from the previous exercise is pre-loaded
# Select first 5 values of diameter column
planets_df[1:5, "diameter"]## [1] 0.382 0.949 1.000 0.532 11.209You will often want to select an entire column, namely one specific variable from a data frame. If you want to select all elements of the variable diameter, for example, both of these will do the trick:
planets_df[,3]
planets_df[,"diameter"]However, there is a short-cut. If your columns have names, you can use the $ sign:
planets_df$diameterInstructions
# planets_df is pre-loaded in your workspace
# Select the rings variable from planets_df
rings_vector <- planets_df$rings
# Print out rings_vector
rings_vector## [1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUEYou probably remember from high school that some planets in our solar system have rings and others do not. Unfortunately you can not recall their names. Could R help you out?
If you type rings_vector in the console, you get:
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUEThis means that the first four observations (or planets) do not have a ring (FALSE), but the other four do (TRUE). However, you do not get a nice overview of the names of these planets, their diameter, etc.
Let’s try to use rings_vector to select the data for the four planets with rings.
The code in the editor selects the name column of all planets that have rings. Adapt the code so that instead of only the name column, all columns for planets that have rings are selected.
planets_df[rings_vector, "name"]# planets_df and rings_vector are pre-loaded in your workspace
# Adapt the code to select all columns for planets with rings
planets_df[rings_vector, ]## name type diameter rotation rings
## 5 Jupiter Gas giant 11.209 0.41 TRUE
## 6 Saturn Gas giant 9.449 0.43 TRUE
## 7 Uranus Gas giant 4.007 -0.72 TRUE
## 8 Neptune Gas giant 3.883 0.67 TRUEWonderful! This is a rather tedious solution. The next exercise will teach you how to do it in a more concise way.
So what exactly did you learn in the previous exercises? You selected a subset from a data frame (planets_df) based on whether or not a certain condition was true (rings or no rings), and you managed to pull out all relevant data. Pretty awesome! By now, NASA is probably already flirting with your CV ;-).
Now, let us move up one level and use the function subset(). You should see the subset() function as a short-cut to do exactly the same as what you did in the previous exercises.
subset(my_df, subset = some_condition)The first argument of subset() specifies the dataset for which you want a subset. By adding the second argument, you give R the necessary information and conditions to select the correct subset.
The code below will give the exact same result as you got in the previous exercise, but this time, you didn’t need the rings_vector!
subset(planets_df, subset = rings)Instructions
Use subset() on planets_df to select planets that have a diameter smaller than Earth. Because the diameter variable is a relative measure of the planet’s diameter w.r.t that of planet Earth, your condition is diameter < 1.
# planets_df is pre-loaded in your workspace
# Select planets with diameter < 1
subset(planets_df, subset = diameter < 1)## name type diameter rotation rings
## 1 Mercury Terrestrial planet 0.382 58.64 FALSE
## 2 Venus Terrestrial planet 0.949 -243.02 FALSE
## 4 Mars Terrestrial planet 0.532 1.03 FALSEGreat! Not only is the subset() function more concise, it is probably also more understandable for people who read your code.
Making and creating rankings is one of mankind’s favorite affairs. These rankings can be useful (best universities in the world), entertaining (most influential movie stars) or pointless (best 007 look-a-like).
In data analysis you can sort your data according to a certain variable in the dataset. In R, this is done with the help of the function order().
order() is a function that gives you the ranked position of each element when it is applied on a variable, such as a vector for example:
a <- c(100, 10, 1000)
order(a)
[1] 2 1 310, which is the second element in a, is the smallest element, so 2 comes first in the output of order(a). 100, which is the first element in a is the second smallest element, so 1 comes second in the output of order(a).
This means we can use the output of order(a) to reshuffle a:
a[order(a)]
[1] 10 100 1000Instructions
Experiment with the order() function in the console. Submit the answer when you are ready to continue.
# Play around with the order function in the console
b <- c(10,5,80)
order(b)## [1] 2 1 3Great! Now let’s use the order() function to sort your data frame!
Alright, now that you understand the order() function, let us do something useful with it. You would like to rearrange your data frame such that it starts with the smallest planet and ends with the largest one. A sort on the diameter column.
Instructions
Call order() on planets_df$diameter (the diameter column of planets_df). Store the result as positions.
Now reshuffle planets_df with the positions vector as row indexes inside square brackets. Keep all columns. Simply print out the result.
# planets_df is pre-loaded in your workspace
# Use order() to create positions
positions <- order(planets_df$diameter)
# Use positions to sort planets_df
#planets_df[positions,]
knitr::kable(planets_df[positions,])| name | type | diameter | rotation | rings | |
|---|---|---|---|---|---|
| 1 | Mercury | Terrestrial planet | 0.382 | 58.64 | FALSE |
| 4 | Mars | Terrestrial planet | 0.532 | 1.03 | FALSE |
| 2 | Venus | Terrestrial planet | 0.949 | -243.02 | FALSE |
| 3 | Earth | Terrestrial planet | 1.000 | 1.00 | FALSE |
| 8 | Neptune | Gas giant | 3.883 | 0.67 | TRUE |
| 7 | Uranus | Gas giant | 4.007 | -0.72 | TRUE |
| 6 | Saturn | Gas giant | 9.449 | 0.43 | TRUE |
| 5 | Jupiter | Gas giant | 11.209 | 0.41 | TRUE |
Wonderful! This exercise concludes the chapter on data frames. Remember that data frames ` are extremely important in R, you will need them all the time