clarity_api: Call HUD Export Items & Extras from the Clarity Looker API

In COHHIO/hud.export: A package for retrieve HMIS data and HUD Export items from the BitFocus Clarity Looker API

Call HUD Export Items & Extras from the Clarity Looker API

Description

Calls the Clarity Looker API HUD CSV Export (BETA) & LookML models to return to the HUD Export Items & other datasets on various time ranges via pre-constructed Looks. See ?call_data for details on using all data methods.

Arguments

...

Arguments passed on to readr::read_csv file Either a path to a file, a connection, or literal data (either a single string or a raw vector). Files ending in .gz, .bz2, .xz, or .zip will be automatically uncompressed. Files starting with ⁠http://⁠, ⁠https://⁠, ⁠ftp://⁠, or ⁠ftps://⁠ will be automatically downloaded. Remote gz files can also be automatically downloaded and decompressed. Literal data is most useful for examples and tests. To be recognised as literal data, the input must be either wrapped with I(), be a string containing at least one new line, or be a vector containing at least one string with a new line. Using a value of clipboard() will read from the system clipboard. quote Single character used to quote strings. col_names Either TRUE, FALSE or a character vector of column names. If TRUE, the first row of the input will be used as the column names, and will not be included in the data frame. If FALSE, column names will be generated automatically: X1, X2, X3 etc. If col_names is a character vector, the values will be used as the names of the columns, and the first row of the input will be read into the first row of the output data frame. Missing (NA) column names will generate a warning, and be filled in with dummy names ...1, ...2 etc. Duplicate column names will generate a warning and be made unique, see name_repair to control how this is done. col_types One of NULL, a cols() specification, or a string. See vignette("readr") for more details. If NULL, all column types will be inferred from guess_max rows of the input, interspersed throughout the file. This is convenient (and fast), but not robust. If the guessed types are wrong, you'll need to increase guess_max or supply the correct types yourself. Column specifications created by list() or cols() must contain one column specification for each column. If you only want to read a subset of the columns, use cols_only(). Alternatively, you can use a compact string representation where each character represents one column: c = character i = integer n = number d = double l = logical f = factor D = date T = date time t = time ? = guess _ or - = skip By default, reading a file without a column specification will print a message showing what readr guessed they were. To remove this message, set show_col_types = FALSE or set options(readr.show_col_types = FALSE). col_select Columns to include in the results. You can use the same mini-language as dplyr::select() to refer to the columns by name. Use c() to use more than one selection expression. Although this usage is less common, col_select also accepts a numeric column index. See ?tidyselect::language for full details on the selection language. id The name of a column in which to store the file path. This is useful when reading multiple input files and there is data in the file paths, such as the data collection date. If NULL (the default) no extra column is created. locale The locale controls defaults that vary from place to place. The default locale is US-centric (like R), but you can use locale() to create your own locale that controls things like the default time zone, encoding, decimal mark, big mark, and day/month names. na Character vector of strings to interpret as missing values. Set this option to character() to indicate no missing values. quoted_na Should missing values inside quotes be treated as missing values (the default) or strings. This parameter is soft deprecated as of readr 2.0.0. comment A string used to identify comments. Any text after the comment characters will be silently ignored. trim_ws Should leading and trailing whitespace (ASCII spaces and tabs) be trimmed from each field before parsing it? skip Number of lines to skip before reading data. If comment is supplied any commented lines are ignored after skipping. n_max Maximum number of lines to read. guess_max Maximum number of lines to use for guessing column types. Will never use more than the number of lines read. See vignette("column-types", package = "readr") for more details. name_repair Handling of column names. The default behaviour is to ensure column names are "unique". Various repair strategies are supported: "minimal": No name repair or checks, beyond basic existence of names. "unique" (default value): Make sure names are unique and not empty. "check_unique": No name repair, but check they are unique. "unique_quiet": Repair with the unique strategy, quietly. "universal": Make the names unique and syntactic. "universal_quiet": Repair with the universal strategy, quietly. A function: Apply custom name repair (e.g., name_repair = make.names for names in the style of base R). A purrr-style anonymous function, see rlang::as_function(). This argument is passed on as repair to vctrs::vec_as_names(). See there for more details on these terms and the strategies used to enforce them. num_threads The number of processing threads to use for initial parsing and lazy reading of data. If your data contains newlines within fields the parser should automatically detect this and fall back to using one thread only. However if you know your file has newlines within quoted fields it is safest to set num_threads = 1 explicitly. progress Display a progress bar? By default it will only display in an interactive session and not while knitting a document. The automatic progress bar can be disabled by setting option readr.show_progress to FALSE. show_col_types If FALSE, do not show the guessed column types. If TRUE always show the column types, even if they are supplied. If NULL (the default) only show the column types if they are not explicitly supplied by the col_types argument. skip_empty_rows Should blank rows be ignored altogether? i.e. If this option is TRUE then blank rows will not be represented at all. If it is FALSE then they will be represented by NA values in all the columns. lazy Read values lazily? By default, this is FALSE, because there are special considerations when reading a file lazily that have tripped up some users. Specifically, things get tricky when reading and then writing back into the same file. But, in general, lazy reading (lazy = TRUE) has many benefits, especially for interactive use and when your downstream work only involves a subset of the rows or columns. Learn more in should_read_lazy() and in the documentation for the altrep argument of vroom::vroom().

Details

Methods for calling Export items and all looks contained within specified folders are dynamically added on initialization.

Export_Items

Available HUD Export Items are

• Affiliation

• Assessment

• AssessmentQuestions

• AssessmentResults

• Client

• CurrentLivingSituation

• Disabilities

• EmploymentEducation

• Enrollment

• EnrollmentCoC

• Event

• Exit

• Export

• Funder

• HealthAndDV

• IncomeBenefits

• Inventory

• Organization

• Project

• ProjectCoC

• Services

• User

• YouthEducationStatus

Public fields

folders

{lookr} folder data stored here

Methods

Public methods

• clarity_api$new()

• clarity_api$get_export()

• clarity_api$get_folder_looks()

---

Method new()

initialize the Looker API connection given the path to the ini configuration file.

Usage

clarity_api$new(
  configFile,
  export_folder = 9862,
  daily_folder = 9711,
  look_folders = 9874,
  dirs = list(export = "inst/extdata/export", public = "inst/extdata/public", spm =
    "inst/extdata/spm", extras = "inst/extdata/extras")
)

Arguments

configFile

(character) Path to the Looker .ini configuration file. Only the directory path is needed if the file is entitled Looker.ini

export_folder

(numeric) ID of the folder containing Export looks.

daily_folder

(numeric) ID of the folder containing Export look data added or modified in the past 24 hours (12a-12p)

look_folders

(character/numeric) list of names or numeric IDs of the additional folders containing relevant looks. Numeric IDs are recommended.

dirs

(named list) of default directory paths for where to store the feather files for the following data types:

• export The HUD Export items Default: data/export

• public The public items Default: data/public

• spm The SPM items Default: data/spm

• extras The HUD Extras (custom items) Default: data/extras

This is optional and the path can be provided to individual methods as needed.

---

Method get_export()

Pull all Export items with associate Looks

Usage

clarity_api$get_export(
  path = self$dirs$export,
  .write = TRUE,
  skip = c("Assessment", "AssessmentQuestions", "AssessmentResults",
    "YouthEducationStatus")
)

Arguments

path

(character) The directory path from which to load stored data

.write

(logical) Whether to write data as feather file to path

skip

(character) of Look titles to skip

---

Method get_folder_looks()

Pull all Looks associated with a folder

Usage

clarity_api$get_folder_looks(
  folder,
  details = FALSE,
  .write = FALSE,
  path,
  skip = c("Client_COVID_extras", "Client_Doses_extras")
)

Arguments

folder

(folder) Folder object from folders field

details

(logical) Whether to return Look info for the looks in the specified folder

.write

(logical) Whether to write the folder looks to disk

path

(character) If .write = TRUE, where to write the files too.

skip

(character) vector of the names of looks in the folder to skip

Returns

(list) of specified looks in folder

COHHIO/hud.export documentation built on Sept. 6, 2024, 1:48 a.m.