--- title: "Authorized Users" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Authorized Users} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` ```{r setup} library(databraryr) ``` ## Requesting authorization Access to restricted data requires registration and formal approval by an institution. The registration process involves the creation of an (email-account-based) user account and secure password. Once institutional authorization has been granted, a user may gain access to shared video, audio, and other data. See for more information about gaining access to restricted data. Many commands in the `databraryr` package return meaningful results *without* or *prior to* formal authorization. ## Once you are authorized Congratulations! Your institution has approved your access to Databrary's identifiable data. Now, it's time to set-up `databraryr` so you can access these materials. Once you are authorized, you will gain access to a much wider range of materials on Databrary. When that happens, you'll load the package with `library(databraryr)` and then run `login_db(email = "")`, substituting your actual Databrary account for ``, of course. I prefer to give the package name when I do this, so the following is how I do the same thing. As of 0.6.0, the package supports http request using the `httr2` package. This produces far more transparent responses, but requires a small bit of preparation on the user's part. First, we generate and store in a variable a default http request. ```{r make_default_request, eval=FALSE} drq <- make_default_request() ``` Then we give that request to other functions, as needed. For example, to call `login_db()`, we do so as follows: ```{r login, eval=FALSE} databraryr::login_db(email = "", rq = drq) ``` If this is the first time you've logged in, you will be asked to enter your Databrary password in a separate window. If everything works out, you should see the function return `TRUE`. Congratulations, you are ready to access Databrary's restricted shared information along with any private, but unshared information you have access to. **NOTE**: You can save yourself some time if you store your Databrary login (email) as an environment variable: 1. Install the `usethis` package via `install.packages('usethis')`. 2. Run `usethis::edit_r_environ()`. This will open your `.Renviron` file in a new window. 3. Edit the `.Renviron` file by adding a line with `DATABRARY_LOGIN="youremail@yourinstitution.edu"`, substituting your actual Databrary login email. 4. Save the file, and restart R. Now, you can run `Sys.getenv("DATABRARY_LOGIN")`, and it will return your Databrary login. And going forward, you can use `Sys.getenv("DATABRARY_LOGIN")` wherever you would enter your Databrary login: ```{r login-w-env-var, eval=FALSE} databraryr::login_db(email = Sys.getenv("DATABRARY_LOGIN"), rq = drq) ``` **NOTE**: You can also save yourself even more time by storing your Databrary user account (email) and password in your computer's secure credentials database using the `keyring` package. The `keyring` package uses the encrypted file that your computer's operating system uses for storing other passwords. There are alternative ways of storing user credentials, but this is the recommended one. To do this for the first time, use the `store` and `overwrite` parameters in `login_db()`: ```{r, eval=FALSE} databraryr::login_db(email = "", store = TRUE, overwrite = TRUE, rq = drq) ``` This overwrites and securely stores your credentials, so that the next time you log in, you need only use this command: ```{r, eval=FALSE} databraryr::login_db(email = "", store = TRUE, rq = drq) ``` or if you've stored your email as an environment variable: ```{r, eval=FALSE} databraryr::login_db(email = Sys.getenv("DATABRARY_LOGIN"), store = TRUE, rq = drq) ``` ### Logging out The package also has a log out command. ```{r eval=FALSE} databraryr::logout_db(rq = drq) ``` **NOTE**: Most `databraryr` functions have a verbose (`vb`) parameter. If you set this to `TRUE`, you will get more information about what's going on behind the scenes. I use this to debug workflows.