Diving in to vector manipulation
Dataframes/dataset are nothing but variables (i.e., columns) bound together. In R, variables are also called vectors, i.e. a series of values stored within the same variable. In the past two weeks we have been playing with column subsetting in various ways… which basically has meant for you to create separate vectors from a dataframe. Now we will learn how to create a vector by hand, i.e. without passing by a dataframe. This may sound useless a first, but it will come handy if you want to use R to make calculations for your next homework assignments.
We can make up a vector by using the c() function (where “c” is for “combine”).
# Add 5 numeric values to the vector "my_vec" below by adding them in the parenthesis and separating them with a comma.
my_vec <- c(____, _____, _____, _____, _____)
Notice that a vector cannot contain different datatypes. All values contained in a vector must be of the same datatype.
my_vec2 <- c("jon", "roberto", "sandra", "panini", "bowie")
my_vec3 <- c(my_vec, my_vec2) # This won't give you an error, but it will convert everything to character by default.
We are now going to work on the vectors above, and explore some of the properties and functions. Similarly to dataframes, vectors can be subset by using the square brackets [] and adding the number corresponding to the position of the number you want to refer to.
first <- my_vec3[1] # among the numbers stored in 'my_vec3', what number will this be?
second <- my_vec3[_] # gimme the second number stored in 'my_vec3'
third <- ____ # gimme the third number stored in 'my_vec3'
eighth <- ____ # gimme the eighth number stored in 'my_vec3'
last <- ____ # gimme the last number stored in 'my_vec3'
QUESTION: Why can’t we use the ‘$’ operator for vectors?
Once you have a vector you can do all of the calculations you want – exactly in the same way you did it for a dataframe. You can check the length of the vector my_vec3, i.e. the number of values stored in the vector. Use the function length().
my_vec3.length <- _____ # can you already guess that the length is before running this?
You can also sum the values stored in the vector. Use the sum() function.
my_vec3.sum <- _____
Now, calculate the mean by hand. I know the mean() function is very convenient… but it’ll be just 5 values, and it’s always good to brush things up, right? Use the variables we just created above (i.e., sum and length).
my_vec3.meanHand <- _____
Does the mean() function give you the same result?
my_vec3.mean <- _____
Calculate the standard deviation of my_vec3 above by hand. It’s gonna be a pain, I know – bear with me please!
my_vec3.SS <- # You could use the square-bracket method to refer to each value of the vector. You also have already calculated and stored the mean above; make sure you use it! Finally, don't forget to square each difference!
variance <- _____ # what do we do now?
my_vec3.sdHand <- _____ # what should we finally do to get the standard deviation from the variance? Here, the function sqrt() may be kinda helpful here...
Cool – now let’s check if you get the same result with the sd() function
my_vec3.sd <- ____ # does this give you the same result?
As I was saying before, dataframes (or datasets) are just vectors bound together. This means you can creat your own dataframe by combining vectors! The data.frame() function will help with that.
_____ <- data.frame(___, ___) # make a dataframe of the vectors 'my_vec' and 'my_vec2'
____ # call the new dataframe and see what it looks like!