# scicomptools Vignette

## Overview

The `scicomptools` package is built to house self-contained tools built by the NCEAS Scientific Computing Team. These functions are written based on the needs of various synthesis working groups so each function operates more or less independently of the others. We hope these tools will be valuable to users in and outside of the context for which they were designed!

This vignette describes some of the main functions of `scicomptools` using the examples.

    library(scicomptools)

### Extract Summary Statistics Tables

Our most requested task–by a wide margin–is to help streamline an analytical workflow by stripping the summary statistics (e.g., test statistic, P value, degrees of freedom, etc.) from whichever statistical test the group has decided to use. As we’ve developed extraction workflows for various tests we have centralized them in the `stat_extract` function.

Currently, `stat_extract` accepts model fit objects from `lmerTest::lmer`, `stats::lm`, `stats::nls`, `stats::t.test`, `nlme::lme`, `ecodist::MRM`, and `RRPP::trajectory.analysis`. All model fits should be passed to the `mod_fit` argument. If the model is a trajectory analysis, the `traj_angle` argument must be supplied as well (either “deg” or “rad” for whether the multivariate angle should be expressed in degrees or radians). Once the models are fit, they can be passed to `stat_extract` for extraction of typically-desired summary values.

Note: `stat_extract` determines the model type automatically so this need not be specified.

    # Fit models of several accepted types
    ## Student's t-Test
    mod_t <- t.test(x = 1:10, y = 7:20)

    ## Nonlinear Least Squares
    mod_nls <- fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), data = DNase)

    # Extract the relevant information
    ## t-Test
    scicomptools::stat_extract(mod_fit = mod_t)
    #>   Estimate       DF  T_Value      P_Value
    #> 1      5.5 21.98221 -5.43493 1.855282e-05
    #> 2     13.5 21.98221 -5.43493 1.855282e-05

    ## NLS
    scicomptools::stat_extract(mod_fit = mod_nls)
    #>   Term Estimate  Std_Error  T_Value      P_Value
    #> 1 Asym 2.485319 0.06287043 39.53081 1.519040e-88
    #> 2 xmid 1.518117 0.06396583 23.73325 2.704648e-56
    #> 3 scal 1.098307 0.02442013 44.97549 2.203937e-97

As more tests are used by working groups we plan on adding them to the set of model objects that `stat_extract` supports. Feel free to post a GitHub issue if you have a model type in mind that you’d like to request we expand `stat_extract` to support.

### Create Google Drive Table of Contents

Google Drive is by far the most common platform that the working groups we support leverage to store data. The `googledrive` package makes integrating script inputs and outputs with specified Drive folders easy but the internal folder structure of these Google Drives often becomes convoluted as projects evolve and priorities shift.

We wrote the `drive_toc` function to identify all folders in a given Drive folder (either the top-level link or any sub-folder link will work) and create a diagram of this hierarchy to make it simpler for group’s to visualize their Drive’s “table of contents”.

This function has a `url` argument which requires a Drive ID (i.e., a link passed through `googledrive::as_id`). There is also an `ignore_names` argument that allows users to specify folder names that they would like excluded from the table of contents; this is useful if many sub-folders contain a “deprecated” or “archive” folder as this would greatly clutter the full table of contents to include repeated folders like these. Finally, this process can take a few seconds for complicated Drives so a message is returned by default for which folder the function is identifying sub-folders. The `quiet` argument defaults to `FALSE` so that progress is reported but it can be set to `TRUE` if you desire no message be returned.

    scicomptools::drive_toc(url = googledrive::as_id("https://drive.google.com/drive/u/0/folders/folder-specific-characters"),
                            ignore_names = c("Archive", "Deprecated", "Backups"),
                            quiet = FALSE)

### Read in Multiple Excel Sheets

Working groups often store their data in Microsoft Excel files with multiple sheets (often one sheet per data type or per collection location). We wrote `read_xl_sheets` to import every sheet in a user-specified MS Excel file and store each sheet in a list. If every sheet has the same columns, you can then easily unlist them into a flat dataframe (rather than the somewhat laborious process of reading in each sheet separately).

`read_xl_sheets` only has a single argument (`file_name`) which accepts the name of (and path to) the MS Excel file to read.

    # Read in sheets
    sheet_list <- scicomptools::read_xl_sheets(file_name = system.file("extdata", "faux_data.xlsx",
                                                         package = "scicomptools"))

    # Show structure
    dplyr::glimpse(sheet_list)
    #> List of 2
    #>  $ my_data: tibble [5 × 5] (S3: tbl_df/tbl/data.frame)
    #>   ..$ col_1: chr [1:5] "a" "b" "c" "d" ...
    #>   ..$ col_2: chr [1:5] "A" "A" "A" "B" ...
    #>   ..$ col_3: num [1:5] 2 43 2 2 2
    #>   ..$ col_4: num [1:5] 234 435 32 45 4
    #>   ..$ col_5: num [1:5] 236 478 34 47 6
    #>  $ data_2 : tibble [2 × 2] (S3: tbl_df/tbl/data.frame)
    #>   ..$ col_2      : chr [1:2] "A" "B"
    #>   ..$ temperature: num [1:2] 10 20

We also wrote a companion function named `read_xl_format` where the specific formatting of all cells in the sheets of an Excel file is extracted. This is useful if fill color or text formatting is used to denote information that is not redundant with columns (i.e., information that is lost when reading an Excel sheet into an API). `read_xl_format` uses the same syntax as `read_xl_sheets` to maximize interoperability.

    # Read in *format of* sheets
    form_list <- scicomptools::read_xl_format(file_name = system.file("extdata", "faux_data.xlsx",
                                                         package = "scicomptools"))

    # Show structure of that
    dplyr::glimpse(form_list)
    #> Rows: 36
    #> Columns: 13
    #> $ sheet         <chr> "my_data", "my_data", "my_data", "my_data", "my_data", "…
    #> $ address       <chr> "A1", "B1", "C1", "D1", "E1", "A2", "B2", "C2", "D2", "E…
    #> $ row           <int> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4,…
    #> $ col           <int> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4,…
    #> $ cell_contents <chr> "col_1", "col_2", "col_3", "col_4", "col_5", "a", "A", "…
    #> $ comment       <chr> NA, NA, NA, NA, "Nick Lyon:Hello world", NA, NA, NA, NA,…
    #> $ formula       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "SUM(C2:D2)", NA, NA…
    #> $ bold          <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR…
    #> $ italic        <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
    #> $ underline     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    #> $ font_size     <dbl> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, …
    #> $ font_color    <chr> "FF000000", "FF000000", "FF000000", "FF000000", "FF00000…
    #> $ cell_color    <chr> NA, NA, NA, NA, NA, NA, NA, "FFFFFFFF", NA, NA, NA, NA, …

### Handling Two Working Directories

Many of the working groups that we support work both on local computers as well as in remote servers. We leverage Git and GitHub to pass code updates back and forth between the two environments but updating the working directory every time such a pivot is made is cumbersome.

We have thus written `wd_loc` for this purpose! `wd_loc` allows users to specify two working directory paths and toggle between them using a logical argument.

The `local` argument accepts a logical value. If `local = TRUE`, then the file path specified in the `local_path` argument is returned as a character string. If `local = FALSE` then the file path specified in the `remote_path` argument is used. `local_path` defaults to `base::getwd()` so it need not be specified if you are using RStudio Projects.

You can run `wd_loc` inside of `base::setwd` if desired though we recommend assigning the file path to a “path” object and invoking that whenever import or export must be done.

    scicomptools::wd_loc(local = TRUE,
                         local_path = getwd(),
                         remote_path = file.path("path on server"))

    #> /Users/.../scicomptools/vignettes

### Checking Tokens

Some operations require passing a user’s personal access token (a.k.a. “PAT”) to RStudio. These workflows can fail unexpectedly if the token is improperly set or expires so it is valuable to double check whether the token is still embedded.

The `token_check` function checks whether a token is attached and messages whether or not one is found. Currently this function supports checks for Qualtrics and GitHub tokens but this can be expanded if need be (post a GitHub issue if you have another token in mind!). Additionally, if `secret = TRUE` (the default) the success message simply identifies whether a token is found. If `secret = FALSE` the success message prints the token string in the console.

    scicomptools::token_check(api = "github", secret = TRUE)

### Exporting GitHub Issues as PDF Files

Sometimes it’s helpful to save GitHub issues as a reference for the future, but manually exporting each issue as a PDF file can be time-consuming. This is why `issue_extract` was created to streamline this process. When given the URL of a GitHub repository and a numeric vector of issue numbers, this function will start a remote Google Chrome browser session via Chromote and automatically navigate to the specified GitHub issues to export them as PDF files.

`issue_extract` works for both public and non-public GitHub repositories. Since non-public GitHub repositories require authentication, first you will need to save your credentials and then pass them into `issue_extract`. Below is an example of using this function to export GitHub issues from a private GitHub Enterprise repository.

The first step is to open a new Chrome session.

    # Start new Chrome session
    b <- ChromoteSession$new()
    # Open interactive window
    b$view()

Then navigate to any page on the GitHub Enterprise/private repository and enter your credentials or login information on the browser session. Once you’ve logged in, save the cookies associated with this browser session as an R object using `b$Network$getCookies()`. Export this R object for later use and close the browser once you’re done.

    # Navigate to a random Github Enterprise issue
    # Make sure to log in to GitHub Enterprise
    b$Page$navigate("https://github.nceas.ucsb.edu/LTER/lter-wg-scicomp/issues/278")

    # Save credentials as cookies
    cookies <- b$Network$getCookies()
    # Export cookies for later use
    saveRDS(cookies, "cookies.rds")

    # Close the browser tab/window
    b$close()

Finally, restart your R session. Now you should be able to pass the file containing your cookies into `issue_extract` and access GitHub Enterprise/private repositories!

    # After saving cookies, you should restart R
    # Then read in the cookies and export the necessary issues

    # Export GitHub issues #295 through #300 for a GitHub Enterprise repository
    issue_extract(repo_url = "https://github.nceas.ucsb.edu/LTER/lter-wg-scicomp",
                  issue_nums = 295:300,
                  export_folder = "scicomp_issues",
                  cookies = "cookies.rds")