This is an R Markdown (Rmd) document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. It was designed to simplify creating HTML documents, but Statistics will use only PDF documents.

The content of an R Markdown document is created in the R Studio script editor. Formatting commands in the text are converted to a PDF output when you click the *Knit* button, located on the toolbar at the top of the script editor.

In R Studio, the menu item File/New File/R Markdown choice produces a dialog box for output type. To create a new document, select document from the left side of the dialog box, enter a name and title, and choose PDF. A template will open in the script editor, with file extension .Rmd. We will provide templates for problem set solutions, and you will start by simply opening the file.

Let's get started.

The first six lines of this file are referred to as the **header**. R Markdown is very fussy about the form of the header---the three dashed lines above and below lines 2-5 must appear exactly as in this document, and there must be a blank space between the colon and the descriptive text. Also, the title, author, and date fields must be kept within double quotes. The output line specifies the output format as PDF; you will never need to change that line for the purposes of this course.

1. First, rename this file to include your first initial and last name -- e.g., pset_00_summer2020_j_vu. (Use File > "Save As")

2. In this document, edit the header to include your name: replace the due date with your name in the "date" field. Click *Knit PDF*. This should produce a PDF file located in the same folder as the Rmd file, with a name like pset_00_summer2020_j_vu.pdf. Note that the file name for a PDF created from an Rmd document will be the same, except with a different file extension. The file name and title of the document, however, can be different.

If you did not get a PDF file, try to diagnose the problem by looking at the error messages in the R Markdown section of the Console. The error messages are not always helpful, so if you cannot solve the issue, ask a TF for help or talk with another student.

Plain text is prepared in paragraphs, as in the first part of this document. *Text* enclosed in asterisks is *italicized* in the PDF output. **Text** enclosed in double asterisks appears in **bold font**. There must be no space between the asterisks and the enclosed text.

3. Write a brief paragraph describing previous coursework in statistics (if any) and share your motivation for taking Statistics 100 this summer. *Knit* the document. Note that each time you *knit* the document, the output overwrites the previous version.

Your paragraph...

Bulleted lists are produced using the formatting commands:

- Item 1
- Item 2
+ Item 2a
+ Item 2b

The list must be preceded by a blank line, and 4 spaces should be used before sub-items.

4. Write a bulleted list giving your year of graduation, your field of study, and the country you are from. Under the entry for your country, prepare subitems with the name of your state and city. *Knit* the document and inspect it to make sure the PDF is correctly produced.

Your bulleted list...



To start a new page in the PDF document, enter the text 'newpage' preceded by a backslash, as in...(new page coming in the PDF!)

\newpage

### Using R with R Markdown

The real power of R Markdown is that it allows for short R programs to be included in the Rmd file, with both the program and its output automatically being produced in the PDF document.

R programs in an Rmd file are located in **code chunks**, which appear as grey-shaded blocks as shown below starting on line 61. You can embed an R code chunk by either typing the three backticks (`) followed by an "r" enclosed in braces, then the additional three backticks to close the chunk, or simply press the *Insert Chunk* button from the *Chunks* menu on the far right of the toolbar on top of the script editor.

Some datasets you will use in this course will be included in the \texttt{oibiostat} package. To view which datasets are included in the package, go to the "Packages" pane and scroll down to \texttt{oibiostat}; clicking the blue hyperlink opens the documentation page that lists the datasets included in the package.

Note that if you are *not* working on R Studio Cloud, you will need to install the \texttt{oibiostat} package by running the following code. The package only needs to be installed once.

```{r, eval = FALSE}
install.packages("devtools")
devtools::install_github("OI-Biostat/oi_biostat_data")
```

The following code chunks in this section use data included in the package. To load the package, use the \texttt{library} command. This command only needs to be used once in a document, in the first R chunk that requires data from the package.

```{r}
library(oibiostat) #loads the package
data("famuss")    #loads the famuss dataset
mean(famuss$weight)
```

It is also possible to view the output of a code chunk without having to *Knit* the entire document.

- To run a single line, place your cursor on the line you want to run and press Ctrl/Cmd + Enter. Try this out with the lines in the following chunk. The output appears directly below the code. To clear the output, press the X in the upper right hand corner of the preview.

- To run an entire chunk, place your cursor within the chunk and press Ctrl/Cmd + Shift + Enter. Try this out in the following chunk. The output is now accessible between the two panes.

- The Run drop-down menu in the upper right hand corner of the script editor provides other options for running chunks, such as running all chunks above or below a certain point. The gear drop-down menu, next to the *Knit* button, provides options for expanding or collapsing all output in a document.

```{r, fig.height = 3.0}
hist(famuss$weight)
median(famuss$weight)
```

### File Organization and Loading Datasets

Over the course of the semester, we will be working with various datasets; datasets not included in the \texttt{oibiostat} package will be posted on the course site and on the R Studio Cloud workspace.\footnote{The \texttt{uis} data used in this section and the next one come from the University of Massachusetts AIDS Research Unit (UMARO) IMPACT Study (abbreviated uis) and is described in detail in Table 1.3 of the text *Applied Survival Analysis*, by Hosmer and Lemeshow. The version of the \texttt{uis} data used with this lab is restricted to a subset of 8 demographic variables and to cases with no missing data on these variables (575 of the original 628 cases).}

If you are working in R Studio Cloud, keep each project and associated files separate and house all data files, R Script files, and R Markdown files in the same project folder.

If you are working with a local installation of R and R Studio, it will be helpful for you to set up a clear organization scheme for storing datasets and other files once you upload them to R Studio. Doing so will make it much easier to locate datasets and load them into R Markdown files. We recommend the following basic structure for your file setup:

- psets
+ pset_00
   + datasets
+ pset_01
   + datasets
+ ...
- sections
+ section_01
   + datasets
+ section_02
   + datasets
+ ...
- lectures
+ lecture_00
   + datasets
+ lecture_01
   + datasets
+ ...

5. If you are working with a local install, set up the nested folder structure (if not, go to Step 6). For example, the pset folder will contain a series of folders for each p-set, starting with one for Problem Set 0. Close this file and relocate it to the pset_00 folder.

6. In R Studio Cloud, confirm that the uis dataset is present in the "datasets" subfolder. With a local install, download the \texttt{uis} dataset (named uis.Rdata) from the course site and save it in the "datasets" subfolder for pset_00.

7. The following code will load the dataset and produce a histogram. To run the code, change eval=FALSE to eval=TRUE. *Knit* the document. The histogram should now be visible in the PDF output. Any new R chunks you create will default to the eval=TRUE option.

```{r, eval=FALSE}
#load the uis data
load("datasets/uis.Rdata")

#produce a histogram
hist(uis$AGE)
```

8. When you run a code chunk in an R Markdown file, the commands get sent to the console to be executed. For example, running the command to load \texttt{uis.Rdata} results in \texttt{uis} appearing in the Environment pane.

There is another way to load datasets; clicking the name of an \texttt{.Rdata} file in the Files pane also loads data into the global environment. However, the important thing to remember is that while this will allow you to work with the data by running code chunks within an R Markdown file, it does *not* load the data during the knitting process.

9. Let's test that out. On the Environment pane, click the broom icon next to the Import Dataset button, then choose Yes from the confirmation pop-up; this clears objects such as variables and datasets from the workspace. Delete the line from the Step 7 code chunk containing the load command using Cut (you will need to Paste the load command back in Step 10).

Load the \texttt{uis} data by clicking the name of the file in the Files pane; confirm that the dataset name appears in the Environment pane and that running the chunk from Step 7 still produces a histogram.

Try to *Knit* the document. This should return an error message reporting that the object 'uis' is not found.

10. Replace the load command in the code chunk from Step 7. *Knit* the document. During the knitting process, the document is being compiled line-by-line. If there is no command to load a dataset within the R Markdown file, any commands that require that dataset will not be able to run.

Additionally, the document is compiled in order; i.e., from top to bottom. If, for example, the command to load the \texttt{uis} data appeared *after* the histogram command, the same error message would appear. Once an error is detected, the compiler does not "read further" into the document.

Thus, when working with data files, be sure to check that they are loaded within the R Markdown file itself. There are different functions for loading data depending on whether the data are in \texttt{.Rdata} versus \texttt{.csv} format, and whether the data are stored in a package like \texttt{oibiostat}.

This is an R Markdown (Rmd) document. Markdown is a simple formatti...