In MatthiasUckert/RsClassification: Classification App

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Installation

# devtools::install_github(
#   repo = "https://github.com/MatthiasUckert/RsClassification",
#   build_vignettes = TRUE
#   )

Library

library(RsClassification); library(fs); library(openxlsx); library(kableExtra)

Overview

Really short description on how to use the RsClassification library

Example Project

The library includes a test project as reference. The project can be downloaded with the download_example_project() function (see below).

# RsClassification::download_example_project(
#   .dir = "FULL_PATH_TO_LOCAL_FOLDER",
#   .name = "TEST PROJECT"
# )

A project must always contain one excel workbook (Excel Name: input.xlsx) and a folder with documents (Folder Name: docs). The name of the parent folder is used as the general project name.

The test project underlying data can also be accessed:

.dir <- system.file("extdata/test_project", package = "RsClassification")
fs::dir_tree(.dir)

The input xlsx must contain two sheets:

• Sheet 1: IDs

  • The IDssheet contains a table that lists all entries to classify (id 1:n) and the attributes you want to assign to each entry (column names).

  • Additionally in row (id = 0) you can specify the column types (numeric, date, checkbox, select, dropdown, autocomplete, password, and handsontable (not implemented yet), see rhandsontable::hot_col(type).

  • IDs must be unique

• Sheet 2: Documents

  • In the Documents sheet to you can map any document in the docs folder to an IDs in the IDssheet

The example file looks like this

.path    <- file.path(.dir, "input.xlsx")
tab_ids  <- read.xlsx(.path, "IDs")
tab_docs <- read.xlsx(.path, "Documents")

IDs Sheet

tab_ids %>% kbl() %>% kable_paper(full_width = FALSE)

Documents Sheet

tab_docs %>% kbl() %>% kable_paper(full_width = FALSE)

You can start classifying by using the run_app() function

# RsClassification::run_app()

The rest is hopefully self-explanatory :-D

Library will be heavily updated over the next weeks/months. Hopefully not so many breaking changes

MatthiasUckert/RsClassification documentation built on Dec. 17, 2021, 3:18 a.m.