Instructor: Colin Grudzien

## Instructions: We will work through the following series of activities as a group. Follow the instructions in each sub-section when the instructor assigns you to a breakout room. ## Activities: ### Activity 1: vectors and matrices #### Question 1: We can declare a matrix full of zeros: ```{r eval=FALSE} matrix_example <- matrix(0, ncol=6, nrow=3) matrix_example ``` And similar to other data structures, we can ask things about our matrix: ```{r eval=FALSE} class(matrix_example) typeof(matrix_example) str(matrix_example) dim(matrix_example) nrow(matrix_example) ncol(matrix_example) ``` What do you think will be the result of `length(matrix_example)`? Try it. Were you right? Why / why not? #### Question 2: Make matrix containing the numbers `1:50`, with 5 columns and 10 rows. Did the `matrix` function fill your matrix by column, or by row, as its default behavior? See if you can figure out how to change this. (hint: read the documentation for `matrix`!) ### Activity 2: practice with subsetting data #### Question 1: Given the following code: ```{r} x <- c(5.4, 6.2, 7.1, 4.8, 7.5) names(x) <- c('a', 'b', 'c', 'd', 'e') print(x) ``` Come up with at least 2 different commands that will produce the following output: ```{r} x[2:4] ``` #### Question 2: Given the following code: ```{r eval=FALSE} x <- c(5.4, 6.2, 7.1, 4.8, 7.5) names(x) <- c('a', 'b', 'c', 'd', 'e') print(x) ``` Write a subsetting command to return the values in x that are greater than 4 and less than 7. #### Question 3: Selecting elements of a vector that match any of a list of components is a very common data analysis task. For example, the gapminder data set contains `country` and `continent` variables, but no information between these two scales. Suppose we want to pull out information from southeast Asia: how do we set up an operation to produce a logical vector that is `TRUE` for all of the countries in southeast Asia and `FALSE` otherwise? Suppose you have these data: ```{r} seAsia <- c("Myanmar","Thailand","Cambodia","Vietnam","Laos") require(gapminder) ## extract the `country` column from a data frame (we'll see this later); ## convert from a factor to a character; ## and get just the non-repeated elements countries <- unique(as.character(gapminder$country)) print(countries) ``` There's a wrong way (using only `==`), which will give you a warning; a clunky way (using the logical operators `==` and `|`); and an elegant way (using `%in%`). See whether you can come up with all three and explain how they (don't) work.