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1: R Programming
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1: Basic Building Blocks 2: Workspace and Files
3: Sequences of Numbers 4: Vectors
5: Missing Values 6: Subsetting Vectors
7: Matrices and Data Frames 8: Logic
9: Functions 10: lapply and sapply
11: vapply and tapply 12: Looking at Data
13: Simulation 14: Dates and Times
15: Base Graphics
Selection: 4
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| The simplest and most common data structure in R is the vector.
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|== | 3%
| Vectors come in two different flavors: atomic vectors and
| lists. An atomic vector contains exactly one data type, whereas
| a list may contain multiple data types. We'll explore atomic
| vectors further before we get to lists.
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|=== | 5%
| In previous lessons, we dealt entirely with numeric vectors,
| which are one type of atomic vector. Other types of atomic
| vectors include logical, character, integer, and complex. In
| this lesson, we'll take a closer look at logical and character
| vectors.
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|===== | 8%
| Logical vectors can contain the values TRUE, FALSE, and NA (for
| 'not available'). These values are generated as the result of
| logical 'conditions'. Let's experiment with some simple
| conditions.
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|====== | 11%
| First, create a numeric vector num_vect that contains the
| values 0.5, 55, -10, and 6.
> num_vect<-c(0.5,55,-10,6)
| Excellent work!
|======== | 14%
| Now, create a variable called tf that gets the result of
| num_vect < 1, which is read as 'num_vect is less than 1'.
> tf<-num_vect<1
| You're the best!
|========= | 16%
| What do you think tf will look like?
1: a vector of 4 logical values
2: a single logical value
Selection: 1
| You are amazing!
|=========== | 19%
| Print the contents of tf now.
> tf
[1] TRUE FALSE TRUE FALSE
| That's a job well done!
|============ | 22%
| The statement num_vect < 1 is a condition and tf tells us
| whether each corresponding element of our numeric vector
| num_vect satisfies this condition.
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|============== | 24%
| The first element of num_vect is 0.5, which is less than 1 and
| therefore the statement 0.5 < 1 is TRUE. The second element of
| num_vect is 55, which is greater than 1, so the statement 55 <
| 1 is FALSE. The same logic applies for the third and fourth
| elements.
...
|=============== | 27%
| Let's try another. Type num_vect >= 6 without assigning the
| result to a new variable.
> num_vect>=6
[1] FALSE TRUE FALSE TRUE
| Nice work!
|================= | 30%
| This time, we are asking whether each individual element of
| num_vect is greater than OR equal to 6. Since only 55 and 6 are
| greater than or equal to 6, the second and fourth elements of
| the result are TRUE and the first and third elements are FALSE.
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|================== | 32%
| The `<` and `>=` symbols in these examples are called 'logical
| operators'. Other logical operators include `>`, `<=`, `==` for
| exact equality, and `!=` for inequality.
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|==================== | 35%
| If we have two logical expressions, A and B, we can ask whether
| at least one is TRUE with A | B (logical 'or' a.k.a. 'union')
| or whether they are both TRUE with A & B (logical 'and' a.k.a.
| 'intersection'). Lastly, !A is the negation of A and is TRUE
| when A is FALSE and vice versa.
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|===================== | 38%
| It's a good idea to spend some time playing around with various
| combinations of these logical operators until you get
| comfortable with their use. We'll do a few examples here to get
| you started.
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|======================= | 41%
| Try your best to predict the result of each of the following
| statements. You can use pencil and paper to work them out if
| it's helpful. If you get stuck, just guess and you've got a 50%
| chance of getting the right answer!
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|======================== | 43%
| (3 > 5) & (4 == 4)
1: FALSE
2: TRUE
Selection: 1
| Keep working like that and you'll get there!
|========================== | 46%
| (TRUE == TRUE) | (TRUE == FALSE)
1: FALSE
2: TRUE
Selection: 2
| Perseverance, that's the answer.
|=========================== | 49%
| ((111 >= 111) | !(TRUE)) & ((4 + 1) == 5)
1: FALSE
2: TRUE
Selection: 2
| You got it right!
|============================= | 51%
| Don't worry if you found these to be tricky. They're supposed
| to be. Working with logical statements in R takes practice, but
| your efforts will be rewarded in future lessons (e.g.
| subsetting and control structures).
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|============================== | 54%
| Character vectors are also very common in R. Double quotes are
| used to distinguish character objects, as in the following
| example.
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|================================ | 57%
| Create a character vector that contains the following words:
| "My", "name", "is". Remember to enclose each word in its own
| set of double quotes, so that R knows they are character
| strings. Store the vector in a variable called my_char.
> my_char<-c("My","name","is")
| Keep up the great work!
|================================= | 59%
| Print the contents of my_char to see what it looks like.
> my_char
[1] "My" "name" "is"
| You got it!
|=================================== | 62%
| Right now, my_char is a character vector of length 3. Let's say
| we want to join the elements of my_char together into one
| continuous character string (i.e. a character vector of length
| 1). We can do this using the paste() function.
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|==================================== | 65%
| Type paste(my_char, collapse = " ") now. Make sure there's a
| space between the double quotes in the `collapse` argument.
| You'll see why in a second.
> paste(my_char,collapse = " ")
[1] "My name is"
| Excellent work!
|====================================== | 68%
| The `collapse` argument to the paste() function tells R that
| when we join together the elements of the my_char character
| vector, we'd like to separate them with single spaces.
...
|======================================= | 70%
| It seems that we're missing something.... Ah, yes! Your name!
...
|========================================= | 73%
| To add (or 'concatenate') your name to the end of my_char, use
| the c() function like this: c(my_char, "your_name_here"). Place
| your name in double quotes where I've put "your_name_here". Try
| it now, storing the result in a new variable called my_name.
> my_name<-c(my_char,"Peter")
| You are doing so well!
|========================================== | 76%
| Take a look at the contents of my_name.
> my_name
[1] "My" "name" "is" "Peter"
| Your dedication is inspiring!
|============================================ | 78%
| Now, use the paste() function once more to join the words in
| my_name together into a single character string. Don't forget
| to say collapse = " "!
> paste(my_name,collapse = " ")
[1] "My name is Peter"
| Great job!
|============================================= | 81%
| In this example, we used the paste() function to collapse the
| elements of a single character vector. paste() can also be used
| to join the elements of multiple character vectors.
...
|=============================================== | 84%
| In the simplest case, we can join two character vectors that
| are each of length 1 (i.e. join two words). Try paste("Hello",
| "world!", sep = " "), where the `sep` argument tells R that we
| want to separate the joined elements with a single space.
> paste("Hello","world!",sep=" ")
[1] "Hello world!"
| Keep up the great work!
|================================================ | 86%
| For a slightly more complicated example, we can join two
| vectors, each of length 3. Use paste() to join the integer
| vector 1:3 with the character vector c("X", "Y", "Z"). This
| time, use sep = "" to leave no space between the joined
| elements.
> paste(1:3,c("X","Y","Z"),sep="")
[1] "1X" "2Y" "3Z"
| You're the best!
|================================================== | 89%
| What do you think will happen if our vectors are of different
| length? (Hint: we talked about this in a previous lesson.)
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|=================================================== | 92%
| Vector recycling! Try paste(LETTERS, 1:4, sep = "-"), where
| LETTERS is a predefined variable in R containing a character
| vector of all 26 letters in the English alphabet.
> paste(LETTERS,1:4,sep="-")
[1] "A-1" "B-2" "C-3" "D-4" "E-1" "F-2" "G-3" "H-4" "I-1" "J-2"
[11] "K-3" "L-4" "M-1" "N-2" "O-3" "P-4" "Q-1" "R-2" "S-3" "T-4"
[21] "U-1" "V-2" "W-3" "X-4" "Y-1" "Z-2"
| Keep up the great work!
|===================================================== | 95%
| Since the character vector LETTERS is longer than the numeric
| vector 1:4, R simply recycles, or repeats, 1:4 until it matches
| the length of LETTERS.
...
|====================================================== | 97%
| Also worth noting is that the numeric vector 1:4 gets 'coerced'
| into a character vector by the paste() function.
...
|========================================================| 100%
| We'll discuss coercion in another lesson, but all it really
| means that the numbers 1, 2, 3, and 4 in the output above are
| no longer numbers to R, but rather characters "1", "2", "3",
| and "4".
...
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