This package creates some tables of clinical study. Table 1 is created by table1() to describe baseline characteristics, which is essential in every clinical study. Created by table2(), the function of Table 2 is to explore influence factors. And Table 3 created by table3() is able to make stratified analysis.

Helps the user to build and register schema descriptions of disorganised (messy) tables. Disorganised tables are tables that are not in a topologically coherent form, where packages such as tidyr could be used for reshaping. The schema description documents the arrangement of input tables and is used to reshape them into a standardised (tidy) output format.

Bindings for the Tabula <https://tabula.technology/> Java library, which can extract tables from PDF files. This tool can reduce time and effort in data extraction processes in fields like investigative journalism. It allows for automatic and manual table extraction, the latter facilitated through a Shiny interface, enabling manual areas selection\ with a computer mouse for data retrieval.

Efficient tabulation with Stata-like output. For each unique value of the variable, it shows the number of observations with that value, proportion of observations with that value, and cumulative proportion, in descending order of frequency. Accepts data.table, tibble, or data.frame as input. Efficient with big data: if you give it a data.table, tab() uses data.table syntax.

Translate double and integer valued data into character values formatted for tabulation in manuscripts or other types of academic reports.

Tabu search algorithm for binary configurations. A basic version of the algorithm as described by Fouskakis and Draper (2007) <doi:10.1111/j.1751-5823.2002.tb00174.x>.

This package provides an R interface to the Tabler HTML template. tablerDash is a light Bootstrap 4 dashboard template. There are different layouts available such as a one page dashboard or a multi-page template, where the navigation menu is contained in the navigation bar.

The maximum likelihood classifier (MLC) is one of the most common classifiers used for remote sensing imagery. This package uses RcppArmadillo to provide a fast implementation of the MLC to train and predict over tabular data (data.frame). The algorithms were based on Mather (1985) <doi:10.1080/01431168508948456> method.

The tabularmap is one of the visualization methods for efficiently displaying data consisting of multiple elements by tiling them. When dealing with geospatial, it corrects for differences in visibility between areas.

This package provides two classes extending data.table class. Simple tableList class wraps data.table and any additional structures together. More complex tableMatrix class combines data.table and matrix'. See <http://github.com/InferenceTechnologies/tableMatrix> for more information and examples.

This package provides a user friendly interface to generation of booktab style tables using xtable'.

This package provides a toolbox for comparing two data frames. This package is defunct. I recommend you use the "versus" package instead.

Facilities to work with vector and raster data in efficient repeatable and systematic work flow. Missing functionality in existing packages is included here to allow extraction from raster data with simple features and Spatial types and to make extraction consistent and straightforward. Extract cell numbers from raster data and return the cells as a data frame rather than as lists of matrices or vectors. The functions here allow spatial data to be used without special handling for the format currently in use.

This package provides a specialization of dplyr data manipulation verbs that parse and build expressions which are ultimately evaluated by data.table', letting it handle all optimizations. A set of additional verbs is also provided to facilitate some common operations on a subset of the data.

Table 1 is the classical way to describe the patients in a clinical study. The amount of splits in the data in such a table is limited. Table1Heatmap draws a heatmap of all crosstables that can be generated with the data. Users can choose between showing the actual crosstables or direction of effect of associations, and highlight associations by number of patients or p-values. v1.2 - fixed "missing "no visible global function definition for ..".

Access to processed 10x (droplet) and SmartSeq2 (on FACS-sorted cells) single-cell RNA-seq data from the Tabula Muris consortium (http://tabula-muris.ds.czbiohub.org/).

This package provides a wrapper to a set of algorithms designed to recognise positional cues present in hierarchical for-human Tables (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into machine-readable LongForm Dataframes.

This package provides access to RNA-seq data generated by the Tabula Muris Senis project via the Bioconductor project. The data is made available without restrictions by the Chan Zuckerberg Biohub. It is provided here without further processing, collected in the form of SingleCellExperiment objects.