tidycells <img src="vignettes/ext/logo.png" align="right" width="200"/>

Read Tabular Data from Diverse Sources and Easily Make Them Tidy

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Author

Indranil Gayen

TL;DR

Given a file_name which is a path of a file that contains table(s). Run this read_cells() in the R-console to see whether support is present for the file type. If support is present, just run

read_cells(file_name)

Note

• Just start with a small file, as heuristic-algorithm are not well-optimized (yet).
• If the target table has numerical values as data and text as their attribute (identifier of the data elements), straight forward method is sufficient in the majority of situations. Otherwise, you may need to utilize other functions.

A Word of Warning :

Many functions in this package are heuristic-algorithm based. Thus, outcomes may be unexpected. I recommend you to try read_cells on the target file. If the outcome is what you are expecting, it is fine. If not try again with read_cells(file_name, at_level = "compose"). If after that also the output is not as expected then other functions are required to be used. At that time start again with read_cells(file_name, at_level = "make_cells") and proceed to further functions.

Introduction

The package provides utilities to read, cells from complex tabular data and heuristic detection based ‘structural assignment’ of those cells to a columnar or tidy format.

Read functionality has the ability to read (in a unified manner) structured, partially structured or unstructured tabular data (usually spreadsheets for public data dissemination and aimed for common human understanding) from various types of documents. The tabular information is read as cells. The ‘structure assignment’ functionality has both supervised and unsupervised way of assigning cells data to columnar/tidy format. Multiple disconnected blocks of tables in a single sheet are also handled appropriately.

These tools are suitable for unattended conversation of (maybe a pile of) messy tables (like government data) into a consumable format(usable for further analysis and data wrangling).

Installation

Install the CRAN version:

install.packages("tidycells")

To install the development version from GitHub you’ll need remotes package in R (comes with devtools). Assuming you have remotes you can install this package in R with the following command:

# devtools::install_github is actually remotes::install_github
remotes::install_github("r-rudra/tidycells")

To start with tidycells, I invite you to see vignette("tidycells-intro") or check out tidycells-website (to see vignette you need to install the package with vignette. That can be done in above command (remotes::install_github) by specifying build_vignettes = TRUE. Note that, it might be time consuming. CRAN version comes with prebuilt-vignette).

Quick Overview

Let’s take a quick look at an example data as given in

system.file("extdata", "marks.xlsx", package = "tidycells", mustWork = TRUE)

The data looks like (in excel)

<img src="vignettes/ext/marks.png" width="451px" />

Let’s try tidycells functions in this data

Read at once

# you should have tidyxl installed
system.file("extdata", "marks.xlsx", package = "tidycells", mustWork = TRUE) %>% 
  read_cells()

| collated_1 | collated_2 | collated_3 | collated_4 | collated_5 | table_tag | value | | :---------- | :---------- | :---------- | :----------- | :----------------- | :--------- | :---- | | Score | Male | School A | Student Name | Utsyo Roy | Sheet1 | 95 | | Score | Male | School A | Student Name | Nakshatra Gayen | Sheet1 | 99 | | Score | Female | School A | Student Name | Titas Gupta | Sheet1 | 89 | | Score | Female | School A | Student Name | Ujjaini Gayen | Sheet1 | 100 | | Score | Male | School B | Student | Indranil Gayen | Sheet1 | 70 | | Score | Male | School B | Student | S Gayen | Sheet1 | 75 | | Score | Female | School B | Student | Sarmistha Senapati | Sheet1 | 81 | | Score | Female | School B | Student | Shtuti Roy | Sheet1 | 90 | | Score | Male | School C | Name | I Roy | Sheet1 | 50 | | Score | Male | School C | Name | S Ghosh | Sheet1 | 59 | | Score | Female | School C | Name | S Senapati | Sheet1 | 61 | | Score | Female | School C | Name | U Gupta | Sheet1 | 38 |

The function read_cells is a set of ordered operations connected together. The flowchart of read_cells:

<img src="vignettes/ext/read_cells.svg" width="356px" style="display: block; margin: auto;" />

Let’s understand step by step procedures followed by read_cells.

# if you have tidyxl installed
d <- system.file("extdata", "marks.xlsx", package = "tidycells", mustWork = TRUE) %>% 
  read_cells(at_level = "make_cells") %>% 
  .[[1]]

Or

# or you may do
d <- system.file("extdata", "marks_cells.rds", package = "tidycells", mustWork = TRUE) %>% 
  readRDS()

Then

d <- numeric_values_classifier(d)
da <- analyze_cells(d)

After this you need to run compose_cells (with argument print_attribute_overview = TRUE)

dc <- compose_cells(da, print_attribute_overview = TRUE)

<img src="vignettes/ext/compose_cells_cli1.png" width="451px" />

If you want a well-aligned columns then you may like to do

# bit tricky and tedious unless you do print_attribute_overview = TRUE in above line
dcfine <- dc %>% 
  dplyr::mutate(name = dplyr::case_when(
    data_block == 1 ~ major_row_left_2_1,
    data_block == 2 ~ major_col_bottom_1_1,
    data_block == 3 ~ major_row_left_1_1
  ),
  sex = dplyr::case_when(
    data_block == 1 ~ major_row_left_1_1,
    data_block == 2 ~ major_col_bottom_2_1,
    data_block == 3 ~ minor_row_right_1_1
  ),
  school = dplyr::case_when(
    data_block == 1 ~ minor_col_top_1_1,
    data_block == 2 ~ minor_corner_topLeft_1_1,
    data_block == 3 ~ minor_col_top_1_1
  )) %>% 
  dplyr::select(school,sex, name, value)

head(dcfine) looks like

| school | sex | name | value | | :------- | :----- | :-------------- | :---: | | School A | Male | Utsyo Roy | 95 | | School A | Male | Nakshatra Gayen | 99 | | School A | Female | Titas Gupta | 89 | | School A | Female | Ujjaini Gayen | 100 | | School B | Male | Indranil Gayen | 70 | | School B | Male | S Gayen | 75 |

This is still not good right! You had to manually pick some weird column-names and spent some time and energy (when it was evident from data which columns should be aligned with whom).

The collate_columns functions does exactly this for you. So instead of manually picking column-names after compose cells you can simply run

# collate_columns(dc) should be same with 
# direct read_cells() result except table_tag column
collate_columns(dc) %>% 
  head()

| collated_1 | collated_2 | collated_3 | collated_4 | collated_5 | value | | :---------- | :---------- | :---------- | :----------- | :-------------- | :---: | | Score | Male | School A | Student Name | Utsyo Roy | 95 | | Score | Male | School A | Student Name | Nakshatra Gayen | 99