# fditraglia/econ722

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# Notes for my In-class Demo on February 27th

• Learn to touch-type well
• Learn how to use a real text editor (vim, emacs, atom, etc.)
• Learn how to work effectively with LaTeX
• Learn version control (git/Github) and use it for everything
• Spend time and effort improving your writing and presentation skills
• Use open-source software (R, Julia, Python, etc.)
• Don't use Matlab! Don't use STATA! Don't use SAS!
• Practice reproducible research
• Use R/C++ and R packages!

## My Demo

### Part I - Creating a Simple R Package

1. Make sure we've installed devtools and roxygen2. On Ubuntu, you may need to add a few missing packages using apt-get to get devtools to install. I can help you with this.

2. In RStudio do: File > New Project > New Directory > R Package

3. Give the package a name, say where to put it, create a git repository, then click "Create Project."

4. A whole bunch of files have appeared! Let's take a quick look at the key ones: Description, and the R directory. The Description contains information about your package and what other packages it depends on, while the R directory is where you put any R source files for your package.

5. There's already a function in this package, called hello. Let's build the package and test out the function: Build > Clean and Rebuild

6. Look at your packages list: you can see testpkg there! Try the command hello() at the R console.

7. Let's get rid of hello and make a more interesting function: say a simple simulation dgp. We'll put it in a new file called dgp.R

 dgp <- function(sample_size, intercept, slope, error_sd){
x <- runif(sample_size)
epsilon <- rnorm(sample_size, sd = error_sd)
y <- intercept + slope * x + epsilon
return(y)
}

8. Now we'll re-build the pacakge and see that dgp is available.

9. You're going to forget what dgp does, so let's document it. Do: Code > Insert Roxygen Skeleton. Fill out the fields. Then we're going to delete NAMESPACE since we'll have roxygen handle this for us. Finally, do devtools::document(). If everything worked, when you type ?dgp at the console, you'll see your helpfile!

10. Talk about the difference between functions that exported and those that aren't. For your own private use, it's reasonable just to export everything. But if you're sharing a package with users, some functions are purely "internal" and you don't need to export them.

11. Let's create another function that calls dgp. The nice thing about a package is that after loading it, all functions in the package are available: you don't have to manually source them. We can either put this new functions in their own file or in the same as dgp. I'll put them in a new file: estimator.R You should always document everything, but in the interest of time, I'll leave the fields blank.

linear_regression <- function(regression_data){
x <- regression_data$x y <- regression_data$y
slope_est <- cov(x, y) / var(x)
intercept_est <- mean(y) - slope_est * mean(x)
out <- c(slope = slope_est, intercept = intercept_est)
}

simulation_replication <- function(sample_size, intercept, slope, error_sd){
regression_data <- dgp(sample_size, intercept, slope, error_sd)
return(linear_regression(regression_data))
}

simulation_study <- function(n_reps, sample_size, intercept, slope, error_sd){
out <- replicate(n_reps, simulation_replication(sample_size, intercept, slope, error_sd))
return(t(out))
}

12. What about adding data to our package? Every paper needs an empirical example, right? You can easily store a dataset inside your package so it's easily accessible to you as you work. If your data needs to be cleaned or pre-processed, you can document all of these steps and include them in the package. Do the following: devtools::use_data_raw(). Now there's a new directory for your raw data and any files used to process it. These will not be run every time that you build your package, which is a good thing!

13. Let's get some data and put it in our package. Create a new R script in the data-raw directory called download_and_clean_data.R or something like that. Then type the following:

data_url <- "http://ditraglia.com/econ103/survey_clean.csv"
stringsAsFactors = FALSE)
height_handspan <- survey[,c('height', 'handspan')]
devtools::use_data(height_handspan)

14. Now save, source the script, and re-build the package. Now the height-handspan regression data is available in your package. Notice that there's now a data directory and if you type height_handspan at the console after loading your package, you'll see the data. We should document our data. Here's how: create a file called data.R in the R directory containing something like this:

#' Height and hanspan for Econ 103 Students
#'
#' A dataset containing the height and handspan of Econ 103 Students
#'
#' @format A data frame with 69 rows and 2 variables:
#' \describe{
#'   \item{height}{height, in inches}
#'   \item{handspan}{handspan, in centimeters}
#' }
#' @source \url{http://ditraglia.com/econ103/survey_clean.csv/}
"height_handspan"

15. Now do devtools::document() and then clean and re-build the package. You can also set your build options so RStudio uses roxygen automatically. Click More in the Build menu.

16. Now let's share our package on github. Click on the git tab and then commit the changes. Ordinarily we'd make many commits, each of which is a small change but this is just a simple example. Now we need to get this on github. You'll need a github account. Sign up: it's free. Click Create New Repository and then give it the same name as our local package: test_pkg. Don't initialize with a README. Then follow the instructions under push an existing repository from the command line. You can access the git command line in R under Git > More > Shell.

17. Now let's check to see that the package is on github. Since this is a public repository, you can easily install it on your own computer by using the following command devtools::install_github("fditraglia/testpkg")