There are so many cool and wonderful things that you can do in the DATA step in SAS, that it’s really hard to showcase a few of them. So, let’s start by reviewing information the SAS DATA Step from the SASsyFridays blog. Then if we have time, let’s start investigating some of the functions we can use in the DATA Step, DIF() and LAG().
If you have any specific questions, please bring them and we can work through them together.
We will continue our journey with SAS and data, with a workshop that concentrates on data visualization and descriptive statistics. Steps that we would undertake as we being working with our research data.
The notes and accompanying SAS code are available here.
The podcast for this workshop is available here. PLease note that you must have a UGuelph account and you must either be on campus or using the UG VPN to view the podcast.
With low interest in this week’s tutorial, I have decided to cancel the session.
Please visit https://oacstats.blog/2018/05/01/s18-spss-workshop/ to review the different topics of the SPSS workshop.
If you have any questions, please feel free to book an appointment to go over them – http://oacstats.youcanbook.me
Thank-you and sorry for the inconvenience,
When you think about conducting any statistical analysis, your starting point is data. R has a slightly different way of working with your data. Being aware of the differnt types of data in R, can help save a little time when you use a new package and it is asking you about your data. So let’s review a few definitions of the different data types observed in R.
A quick overview of the different types of data you can work with in R.
testform <- c(12, 13, 15)
class(testform)
> class(testform)
[1] “numeric”
Numbers are handled in a couple of ways in R. These are referred to as the Numeric Classes of R, and two that we will are known as integer and double. Having a basic understanding of these different numeric classes will come in handy.
Let’s review the different data types available to you in R.
# a numeric vector
a = c(2, 4.5, 6, 12)
# a character vector
b = c(“green”, “blue”, “yellow”)
# a logical vector
c = (TRUE, TRUE, FALSE, TRUE)
Coding Explanation:
a = ; b = ; c = ; creating vectors called a, b, c respectively. Please note that a <- is the same as a =
c(x, x, x ) tells R that we are creating a vector or a column with the contents found in the parentheses. The , tells R to drop to the next row in the vector/column being created.
character values must be contained in ” “, but logical values do not.
# creates a 5 x 4 numeric matrix – 5 rows by 4 columns
y <- matrix(1:20, nrow=5,ncol=4)
Coding Explanation:
y = or y <- create a matrix called y
matrix( ) – call the function matrix to create the matrix y
1:20 – the values of the matrix
nrows = let’s R know how many rows are in the matrix that you are creating
ncol= let’s R know how many columns are in the matrix that you are creating.
Resulting matrix y will look like:
> y
[,1] [,2] [,3] [,4]
[1,] 1 6 11 16
[2,] 2 7 12 17
[3,] 3 8 13 18
[4,] 4 9 14 19
[5,] 5 10 15 20
d <- c(10, 12, 31, 4)
e <- c(“blue”, “green”, “red”, NA)
f <- c(TRUE, TRUE, TRUE, FALSE)
sampledata <- data.frame(d, e, f)
names(sampledata) <- c(“ID”, “Colour”, “Passed”) # variable names
Coding Explanation:
sampledata <- or sampledata = name of the data frame that we are creating
data.frame( ) calling on the function that creates a data frame
d, e, f tells R that we are creating the data frame with the 3 vectors in the order of d, followed by e, followed by f
names(sac(“ID”, “Colour”, “Passed”) mpledata) – providing variable names within the data frame
c(“ID”, “Colour”, “Passed”) – creating or identifying the 3 variable names within the data frame: ID, Colour, Passed are the variable names
# a string, a numeric vector, a matrix, and a scaler
wlist <- list(name=”Fred”, mynumbers=a, mymatrix=y, age=5.3)
Coding Explanation:
wlist <- or wlist = creating a list called wlist
list( ) – calling the function to create a list
name=”Fred”, mynumbers=a, mymatrix=y, age=5.3 values that are to be contained in the list called wlist
Factors are categorical variables in your data. You can have a nominal factor or you can have an ordinal factor. Yup, those words again – remember nominal and ordinal data are categorical pieces of data, so you can fall into one group or another. Nominal, there is no relationship or order to the categories, whereas ordinal data there is an order to the different levels.
