Vectors

Vectors Basics

  • A vector is a sequence of values that all have the same type
  • You can create a vector using the c() function, which stands for “combine”

    

  

  

  





    

  • Using the str function we learned last time shows that this is a vector of 4 character strings
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You can select pieces of a vector by “slicing” the vector (like slicing a pizza). This is done using square brackets []. In general [] in R means, “give me a piece of something”.


If we put one number in the brackets it will us the value that position:



  
  

    

    

      

    

  

  

  





Try changing this to get the values at different positions.


If we use two numbers separated by a colon this will give us all the values in the range of those numbers. For 1:3 will use us the first through third values.



  
  

    

    

      

    

  

  

  





    

  • 1:3 works by makeing a vector of the whole numbers 1 through 3.
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  • So, this is the same as states[1:3] is the same as states[c(1, 2, 3)]
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  • You can use a vector to get any subset or order you want states[c(4, 1, 3)]
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  • Many functions in R take a vector as input and return a value
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  • This includes the function length which determines how many items are in a vector
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  • We can also calculate common summary statistics
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  • For example, if we have a vector of population counts
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Do Basic Vectors.








Null values


    

  • So far we’ve worked with vectors that contain no missing values
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  • But most real world data has values that are missing for a variety of reasons
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  • For example, kangaroo rats don’t like being caught by humans and are pretty good at escaping before you’ve finished measuring them
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  • Missing values, known as “null” values, are written in R as NA with no quotes, which is short for “not available”
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  • So a vector of 4 population counts with the third value missing would look like
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  • If we try to take the mean of this vector we get NA?
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  • Hard to say what a calculation including NA should be
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  • So most calculations return NA when NA is in the data
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  • Can tell many functions to remove the NA before calculating
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  • Do this using an optional argument, which is an argument that we don’t have to include unless we want to modify the default behavior of the function
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  • Add optional arguments by providing their name (na.rm), =, and the value that we want those arguments to take (TRUE)
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Do Nulls in Vectors.








Working with multiple vectors


    

  • Build on example where we have information on states and population counts by adding areas
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Vector math


    

  • We can divide the count vector by the area vector to get a vector of the density of individuals in that area
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  • This works because when we divide vectors, R divides the first value in the first vector by the first value in the second vector, then divides the second values in each vector, and so on
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  • Element-wise: operating on one element at a time
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Filtering


    

  • Subsetting or “filtering” is done using []
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  • Like with slicing, the [] say “give me a piece of something”
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  • Selects parts of vectors based on “conditions” not position
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  • Get the density values in site a
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  • == is how we indicate “equal to” in most programming languages.
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  • Not =. = is used for assignment.
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  • Can also do “not equal to”
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  • Numerical comparisons like greater or less than
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  • Select states that meet with some restrictions on density
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  • Can subset a vector based on itself
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  • If we want to look at the densities greater than 3
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  • density is both the vector being subset and part of the condition
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  • Multiple vectors can be used together to perform element-wise math, where we do the same calculation for each position in the vectors
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  • We can also filter the values in vector based on the values in another vector or itself
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Do Shrub Volume Vectors 1-3.