R Tutorial: Reading multivariate data

hi I'm Sarah d'etre and I teach at the University of Glasgow in the UK I'll be your instructor for this course on multivariate probability distributions in our multivariate distributions are designed to describe the probability distributions of more than one random variable at the same time since the variables are often correlated exploring them individually would only provide limited insight in this course you will learn how to read and analyze multivariate data you will explore several plotting techniques and learn how to use common statistical distributions including the Gaussian distribution and T distribution lastly you will learn about techniques for dealing with high dimensional data such as principal component analysis multivariate data is mostly rectangular in shape meaning it is organized by rows and columns where the rows represent the individual observations and the columns represent the individual variables data sets may or may not include row names or numbers or column headers we should also be aware that some datasets might come with missing entries first let us look at the iris dataset from the cambridge university website the iris dataset contains three Ivy species with 50 samples from each species the first four columns list the length and width of the sepals and petals and the last column contains the species name this dataset does not include the column names and the separator between columns is a white space in the second dataset the birth weight is stored locally the first row of this data set contains the column names and the first column contains the row numbers the entries are separated by commas we will learn how to read in these two data sets in the next slides first assign the URL to an object iris underscore a world then use rate doc table with iris underscore URL as the first argument specify that the separator is a white space and set header equals to false since the data set does not include column names the data set is called iris underscore row if the dataset is stored locally replace the URL name with a phylum using head with n equals four as the argument to view the first four rows of the data set we see that art has provided generic column names v1 through v4 and row names one through four we can assign column names to each of the variables using the call names function now the head function displays the new column names the names function can be used to check the current names of the columns specific columns can be accessed by their column number or by the column names the last column species represents the three different species setosa virginica and versicolor however the different species are currently coded as numeric variables with values from 1 through 3 we modify the last column to be a categorical variable which are calls a factor using AZ dot factor function thus STR function now shows that species is a factor with 3 levels although the variable was changed to a factor the different species are still coded as integers the recode function from the car library allows us to rename the integers 1 2 & 3 to the actual species names in contrast to the iris set the birth weight dataset has clearly defined names for the columns and rows and the entries are separated by commas we use read dot CSV to read the data and specify that the first column contains the row numbers using the argument Rho dot names equals 1 now let's read a data set from an extra