# Datasets

    library(REDCapSync)

Start by loading an existing or test project. There are two named datasets by default: REDCapSync and REDCapSync_raw.

    save_dir <- tempdir() # replace with your intended folder

    project <- load_project("TEST_CLASSIC")

    project$sync() #optional sync

    dataset <- project$load_dataset("REDCapSync")

    # add quick custom variable
    # in future version you can use project$add_field for this
    dataset$data$merged$letter_b <- dataset$data$merged$var_text_letters == "b"

    # send data in global environment
    dataset$to_envir(globalenv())

    # save data in custom location
    dataset$save(dir_other = save_dir)

    # open folder where file was saved
    browseURL(save_dir)

You can also chain together certain functions… So this is the same as above.

    save_dir <- tempdir() # replace with your intended folder

    dataset <- projects$load("TEST_CLASSIC")$sync()$load_dataset("REDCapSync")
    # add quick custom variable
    # in future version you can use project$add_field for this
    dataset$data$merged$letter_b <- dataset$data$merged$var_text_letters == "b"

    # send data in global environment
    dataset$to_envir(globalenv())

    # save data in custom location
    dataset$save(dir_other = save_dir) # dir_other not needed for non-test-projects

    # open folder where file was saved
    browseURL(save_dir)

This single line will load, sync, generate dataset, and send to your global environment!

    projects$load("TEST_CLASSIC")$
      sync()$
      load_dataset("REDCapSync", envir = globalenv())

You can also generate ad-hoc custom datasets. But if you plan to keep it refreshing to the directory you can add it to your project. The next time you run sync it will be passed to you directory as defined.

    save_dir <- tempdir() # replace with your intended folder

    #load and sync (load_project(...) is the same as projects$load(...))
    project <- load_project("TEST_LONGITUDINAL")$sync()

    # see field_names
    project$metadata$fields$field_name

    males_dataset <- project$generate_dataset(dataset_name = "males",
                                              envir = globalenv(), # global env
                                              filter_field = "birth_sex",
                                              filter_choices = "Male")
    # if you check merged and survey it will only contain males
    unique(males_dataset$data$merged$birth_sex) # Male
    unique(males_dataset$data$survey$birth_sex) # Male

    females_dataset <- project$generate_dataset(dataset_name = "females",
                                                envir = globalenv(), # global env
                                                filter_field = "birth_sex",
                                                filter_choices = "Female")
    # if you check merged and survey it will only contain males
    unique(females_dataset$data$merged$birth_sex) # Female
    unique(females_dataset$data$survey$birth_sex) # Female

    # if you want this saved to excel one time you can do
    females_dataset$save(dir_other = save_dir) # dir_other not needed for default

    # To keep this analysis refreshing with each sync use project$add_dataset(..,)
    project$add_dataset(dataset_name = "males",
                        filter_field = "birth_sex",
                        filter_choices = "Male")
    # not it can be loaded by name!
    males_dataset <- project$load_dataset("males") # will not send to global

    project$load_dataset("males", envir = globalenv()) # will send to global

    project$save_dataset("males") # will save AND keep track in project object

    project$sync() # once defined will save datasets with sync

Putting it all together, you should think of meaningful datasets, add them to project with `project$add_dataset`, load them for use in R with `project$load_dataset`, and/or save as excel sheets with `project$save_dataset` or `project$sync`.

    #load and sync add datasets and save
    project <- load_project("TEST_LONGITUDINAL")$
      sync()$
      add_dataset(dataset_name = "males",
                  filter_field = "birth_sex",
                  filter_choices = "Male")$
      add_dataset(dataset_name = "females",
                  filter_field = "birth_sex",
                  filter_choices = "Female")
    # load males
    project$load_dataset("males", envir = globalenv()) # will send to global

    # load females
    project$load_dataset("females", envir = globalenv()) # will send to global

    # load all default
    project$load_dataset("REDCapSync", envir = globalenv()) # will send to global

    # can trigger saves with `project$save_datasets()` or default `project$sync()`
    project$save_datasets()
    project$sync()