All Projects → ACCLAB → Dabestr

ACCLAB / Dabestr

Licence: other
Data Analysis with Bootstrap Estimation in R

Programming Languages

r
7636 projects

Projects that are alternatives of or similar to Dabestr

Datascience
Curated list of Python resources for data science.
Stars: ✭ 3,051 (+1705.33%)
Mutual labels:  statistics, data-analysis, data-visualization
Sweetviz
Visualize and compare datasets, target values and associations, with one line of code.
Stars: ✭ 1,851 (+995.27%)
Mutual labels:  statistics, data-analysis, data-visualization
Tablesaw
Java dataframe and visualization library
Stars: ✭ 2,785 (+1547.93%)
Mutual labels:  statistics, data-analysis, data-visualization
Awesome Python Data Science
Probably the best curated list of data science software in Python.
Stars: ✭ 812 (+380.47%)
Mutual labels:  statistics, data-analysis, data-visualization
Dabest Python
Data Analysis with Bootstrapped ESTimation
Stars: ✭ 231 (+36.69%)
Mutual labels:  statistics, data-analysis, data-visualization
Socrat
A Dynamic Web Toolbox for Interactive Data Processing, Analysis, and Visualization
Stars: ✭ 26 (-84.62%)
Mutual labels:  statistics, data-analysis, data-visualization
Teachingmaterial
Various teaching material
Stars: ✭ 159 (-5.92%)
Mutual labels:  statistics, data-analysis
Pbpython
Code, Notebooks and Examples from Practical Business Python
Stars: ✭ 1,724 (+920.12%)
Mutual labels:  data-analysis, data-visualization
Githubist
Github developer, location, language and repo stats for Turkey.
Stars: ✭ 124 (-26.63%)
Mutual labels:  statistics, data-visualization
Awesome Scientific Python
A curated list of awesome scientific Python resources
Stars: ✭ 127 (-24.85%)
Mutual labels:  data-analysis, data-visualization
G2
📊 A highly interactive data-driven visualization grammar for statistical charts.
Stars: ✭ 11,020 (+6420.71%)
Mutual labels:  statistics, data-visualization
Dtale Desktop
Build a data visualization dashboard with simple snippets of python code
Stars: ✭ 128 (-24.26%)
Mutual labels:  data-analysis, data-visualization
Dat8
General Assembly's 2015 Data Science course in Washington, DC
Stars: ✭ 1,516 (+797.04%)
Mutual labels:  data-analysis, data-visualization
Seaborn Tutorial
This repository is my attempt to help Data Science aspirants gain necessary Data Visualization skills required to progress in their career. It includes all the types of plot offered by Seaborn, applied on random datasets.
Stars: ✭ 114 (-32.54%)
Mutual labels:  data-analysis, data-visualization
Datasist
A Python library for easy data analysis, visualization, exploration and modeling
Stars: ✭ 123 (-27.22%)
Mutual labels:  data-analysis, data-visualization
Pythondata
repo for code published on pythondata.com
Stars: ✭ 113 (-33.14%)
Mutual labels:  data-analysis, data-visualization
Deeplearning Notes
Notes for Deep Learning Specialization Courses led by Andrew Ng.
Stars: ✭ 126 (-25.44%)
Mutual labels:  statistics, data-analysis
Ml Workspace
🛠 All-in-one web-based IDE specialized for machine learning and data science.
Stars: ✭ 2,337 (+1282.84%)
Mutual labels:  data-analysis, data-visualization
Data Science Portfolio
A Portfolio of my Data Science Projects
Stars: ✭ 149 (-11.83%)
Mutual labels:  data-analysis, data-visualization
Sourced Ce
source{d} Community Edition (CE)
Stars: ✭ 153 (-9.47%)
Mutual labels:  data-analysis, data-visualization

dabestr

Travis CI build status minimal R version CRAN Status Badge CRAN Download Count Free-to-view citation License

Contents

About

dabestr is a package for Data Analysis using Bootstrap-Coupled ESTimation.

Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values.

An estimation plot has two key features.

  1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution.

  2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes.

Requirements

Your version of R must be 3.5.0 or higher.

Installation

install.packages("dabestr")

# To install the latest development version on Github,
# use the line below.
devtools::install_github("ACCLAB/dabestr")

Usage

library(dabestr)

# Performing unpaired (two independent groups) analysis.
unpaired_mean_diff <- dabest(iris, Species, Petal.Width,
                             idx = c("setosa", "versicolor", "virginica"),
                             paired = FALSE) %>% 
                      mean_diff()

# Display the results in a user-friendly format.
unpaired_mean_diff
#> DABEST (Data Analysis with Bootstrap Estimation in R) v0.3.0
#> ============================================================
#> 
#> Good morning!
#> The current time is 11:10 AM on Monday July 13, 2020.
#> 
#> Dataset    :  iris
#> X Variable :  Species
#> Y Variable :  Petal.Width
#> 
#> Unpaired mean difference of versicolor (n=50) minus setosa (n=50)
#>  1.08 [95CI  1.01; 1.14]
#> 
#> Unpaired mean difference of virginica (n=50) minus setosa (n=50)
#>  1.78 [95CI  1.69; 1.85]
#> 
#> 
#> 5000 bootstrap resamples.
#> All confidence intervals are bias-corrected and accelerated.

# Produce a Cumming estimation plot.
plot(unpaired_mean_diff)

You will find more useful code snippets in this vignette.

How to cite

Moving beyond P values: Everyday data analysis with estimation plots

Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang

Nature Methods 2019, 1548-7105. 10.1038/s41592-019-0470-3

Paywalled publisher site; Free-to-view PDF

Bugs

Please open a new issue. Include a reproducible example (aka reprex) so anyone can copy-paste your code and move quickly towards helping you out!

Contributing

All contributions are welcome; please read the Guidelines for contributing first.

We also have a Code of Conduct to foster an inclusive and productive space.

dabestr in other languages

dabestr is also available in Python and Matlab.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].