Selection of data frame elements

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!

Exercise 4.5:

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.

# Print out diameter of Mercury (row 1, column 3)
planets_df[1,3]

## [1] 0.382

From planets_df, select all data on Mars (the fourth row). Simply print out the result.

planets_df[4,]

##   name               type diameter rotation rings
## 4 Mars Terrestrial planet    0.532     1.03 FALSE

4.6 Selection of data frame elements

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”]

Exercise 4.6:

Select and print out the first 5 values in the “diameter” column of planets_df.

4.7 Only planets with rings

You 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$diameter

Exercise 4.7:

Use the $ sign to select the rings variable from planets_df. Store the vector that results as rings_vector.

Print out rings_vector to see if you got it right.

# 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  TRUE

4.8 Only planets with rings (2)

You 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 TRUE

This 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.

Exercise 4.8:

Select all columns for planets that have rings.

#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  TRUE

4.9 Only planets with rings but shorter

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.

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 data set 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)

##      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  TRUE

Exercise 4.9:

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.

# 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 FALSE

4.9 Sorting

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 data set. 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 3

10, 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 1000

4.10 Sorting 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.

Exercise 4.10

Sorting 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.

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.

# Use order() to create positions
positions <-  order(planets_df$diameter)

# Use positions to sort planets_df
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

Week 5

Lists:

Lists, why would you need them?

At this point in the course you are already familiar with:

Vectors (one dimensional array): can hold numeric, character or logical values. The elements in a vector all have the same data type.

Matrices (two dimensional array): can hold numeric, character or logical values. The elements in a matrix all have the same data type.

Data frames (two-dimensional objects): can hold numeric, character or logical values. Within a column all elements have the same data type, but different columns can be of different data type.

A list in R is similar to your to-do list at work or school: the different items on that list most likely differ in length, characteristic, and type of activity that has to be done.

A list in R allows you to gather a variety of objects under one name (that is, the name of the list) in an ordered way. These objects can be matrices, vectors, data frames, even other lists, etc. It is not even required that these objects are related to each other in any way.

You could say that a list is some kind super data type: you can store practically any piece of information in it!

Let us create our first list! To construct a list you use the function list():

my_list <- list(comp1, comp2 …)

The arguments to the list function are the list components. Remember, these components can be matrices, vectors, other lists, …

Exercise 5.1:

Construct a list, named my_list, that contains the variables my_vector, my_matrix and my_df as list components.

# Vector with numerics from 1 up to 10
my_vector <- 1:10 

# Matrix with numerics from 1 up to 9
my_matrix <- matrix(1:9, ncol = 3)

# First 10 elements of the built-in data frame mtcars
my_df <- mtcars[1:10,]

# Construct list with these different elements:
my_list <- list(my_vector, my_matrix, my_df)

5.2 Creating a named list

Just like on your to-do list, you want to avoid not knowing or remembering what the components of your list stand for. That is why you should give names to them:

my_list <- list(name1 = your_comp1, name2 = your_comp2) This creates a list with components that are named name1, name2, and so on. If you want to name your lists after you’ve created them, you can use the names() function as you did with vectors. The following commands are fully equivalent to the assignment above:

my_list <- list(your_comp1, your_comp2) names(my_list) <- c(“name1”, “name2”)

Exercise 5.2:

Change the code of the previous exercise by adding names to the components. Use for my_vector the name vec, for my_matrix the name mat and for my_df the name df. Print out my_list so you can inspect the output.

# Vector with numerics from 1 up to 10
my_vector <- 1:10 

# Matrix with numerics from 1 up to 9
my_matrix <- matrix(1:9, ncol = 3)

# First 10 elements of the built-in data frame mtcars
my_df <- mtcars[1:10,]

# Adapt list() call to give the components names
my_list <- list(vec=my_vector, mat=my_matrix, df=my_df)

# Print out my_list
my_list

## $vec
##  [1]  1  2  3  4  5  6  7  8  9 10
## 
## $mat
##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9
## 
## $df
##                    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

5.3 Creating a named list (2)

Being a huge movie fan (remember your job at LucasFilms), you decide to start storing information on good movies with the help of lists.

Start by creating a list for the movie “The Shining”.

mov = ("The Shining")
mov

## [1] "The Shining"

Create a vector with the actors:

act = c("Jack Nicholson", "Shelley Duvall", "Danny Lloyd", "Scatman Crothers", "Barry Nelson")
act

## [1] "Jack Nicholson"   "Shelley Duvall"   "Danny Lloyd"     
## [4] "Scatman Crothers" "Barry Nelson"

Create a dataframe from:

scores=c(4.5, 4.0, 5.0)
sources=c("IMDb1", "IMDb2", "IMDB3")
comments=c("Best Horror Film I have ever seen", "A truly brilliant and scary film from Stanley Kubrick", "A masterpiece of psychological horror")

Create the dataframe:

rev = data.frame(scores, sources, comments)
rev

##   scores sources                                              comments
## 1    4.5   IMDb1                     Best Horror Film I have ever seen
## 2    4.0   IMDb2 A truly brilliant and scary film from Stanley Kubrick
## 3    5.0   IMDB3                 A masterpiece of psychological horror

Create the list:

shining_list <- list(moviename = mov, actors=act, reviews=rev)
shining_list

## $moviename
## [1] "The Shining"
## 
## $actors
## [1] "Jack Nicholson"   "Shelley Duvall"   "Danny Lloyd"     
## [4] "Scatman Crothers" "Barry Nelson"    
## 
## $reviews
##   scores sources                                              comments
## 1    4.5   IMDb1                     Best Horror Film I have ever seen
## 2    4.0   IMDb2 A truly brilliant and scary film from Stanley Kubrick
## 3    5.0   IMDB3                 A masterpiece of psychological horror

5.4 Selecting elements from a list

Your list will often be built out of numerous elements and components. Therefore, getting a single element, multiple elements, or a component out of it is not always straightforward.

One way to select a component is using the numbered position of that component. For example, to “grab” the first component of shining_list you type

shining_list[[1]] A quick way to check this out is typing it in the console. Important to remember: to select elements from vectors, you use single square brackets: [ ]. Don’t mix them up!

You can also refer to the names of the components, with [[ ]] or with the $ sign. Both will select the data frame representing the reviews:

shining_list[[“reviews”]]

shining_list$reviews

Besides selecting components, you often need to select specific elements out of these components. For example, with shining_list[[2]][1] you select from the second component, actors (shining_list[[2]]), the first element ([1]). When you type this in the console, you will see the answer is Jack Nicholson.

Exercise 5.4.1:

Select from shining_list the vector representing the actors. Simply print out this vector.

# Print out the vector representing the actors
shining_list$actors

## [1] "Jack Nicholson"   "Shelley Duvall"   "Danny Lloyd"     
## [4] "Scatman Crothers" "Barry Nelson"

Exercise 5.4.2:

Print the second element of the vector representing the actors

shining_list$actors[2]

## [1] "Shelley Duvall"

5.5 Adding more movie information to the list

Being proud of your first list, you shared it with the members of your movie hobby club. However, one of the senior members, a guy named M. McDowell, noted that you forgot to add the release year. Given your ambitions to become next year’s president of the club, you decide to add this information to the list.

To conveniently add elements to lists you can use the c() function, that you also used to build vectors:

ext_list <- c(my_list , my_val)

This will simply extend the original list, my_list, with the component my_val. This component gets appended to the end of the list. If you want to give the new list item a name, you just add the name as you did before:

ext_list <- c(my_list, my_name = my_val)