SOCR ≫ | DSPA ≫ | DSPA2 Topics ≫ |

Go over the motivational cases included in Chapter 1 (Introduction).

Confirm that you have installed R/RStudio. You should be able to download and load in RStudio as shown in Chapter 1.

Then, complete the following examples.

Load in the long-format SOCR Parkinson’s Disease data and export it as *wide format*. You can only select any 5 variables (not all), but note that there are several time observations for each subject. You can try using the `reshape()`

method or tidyverse techniques.

Create a Data Frame storing the SOCR Parkinson’s Disease data and call `summary()`

and `Hmsc::describe()`

to summarize some of the feature characteristics.

Using the same SOCR Parkinson’s Disease data:

- Extract the first 10 subjects.
- Find the cases for which
`L_caudate_ComputeArea<600`

.

- Sort the subjects based on
`L_caudate_Volume`

. - Generate frequency and probability tables for
`Gender`

and`Age`

. - Compute the mean
`Age`

and the correlation between`Age`

and`Weight`

. - Plot Histogram and density of
`R_fusiform_gyrus_Volume`

and scatterplot`L_fusiform_gyrus_Volume`

and`R_fusiform_gyrus_Volume`

.

*Note*: You don’t have to apply these data filters sequentially, but this can also be done for deeper stratification.

Generate \(1,000\) standard normal variables and \(1,200\) Cauchy distributed random variables and generate a *quantile-quantile* (Q-Q) probability plot of the pair of samples.

Generate an `R`

function that given an object (e.g., vector, matrix, array, tensor), it computes the *arithmetic average* and compare it against the `mean()`

function.