https://github.com/erikgahner/awesome-statistics
A curated collection of links to statistics material
https://github.com/erikgahner/awesome-statistics
awesome data-analysis data-visualization statistics
Last synced: 7 months ago
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A curated collection of links to statistics material
- Host: GitHub
- URL: https://github.com/erikgahner/awesome-statistics
- Owner: erikgahner
- Created: 2022-04-16T10:07:25.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-04-13T15:26:13.000Z (8 months ago)
- Last Synced: 2025-04-22T07:19:20.595Z (8 months ago)
- Topics: awesome, data-analysis, data-visualization, statistics
- Homepage:
- Size: 303 KB
- Stars: 127
- Watchers: 3
- Forks: 11
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
- awesome-official-statistics-software - Awesome statistics
- awesome-data-analysis - Awesome Statistics - A curated list of statistics resources, software, and learning materials. (🎲 Statistics & Probability / Resources)
README
---
output: md_document
---
# Awesome statistics [](https://github.com/sindresorhus/awesome)
```{r include=FALSE}
knitr::opts_chunk$set(comment = NULL, echo = FALSE, message = FALSE, warning = FALSE, results = "asis")
```
```{r introCode, echo=FALSE, message=FALSE, warning=FALSE}
library("tidyverse")
library("kableExtra")
df_raw <- read_csv("data.csv")
df <- df_raw |>
filter(dead == 0, awesome == 1) |>
transmute(title = paste0("[", title, "](", url, ")"), category)
```
The repository consists of a dataset with curated links to material dealing with statistics and data. There is a total of `r NROW(df_raw[df_raw$dead == 0,])` active links in the dataset. The `r NROW(df_raw[df_raw$awesome == 1 & df_raw$dead == 0,])` awesome/recommended links in the dataset are listed below. Feel free to add additional links to the dataset.
# Most recent links added to the dataset
```{r, echo=FALSE, message=FALSE, warning=FALSE}
df_raw |>
arrange(desc(id)) |>
slice(1:15) |>
transmute(title = paste0("[", title, "](", url, ")"), category) |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
# General
```{r generateTableGeneral, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "general") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Probability and uncertainty
```{r generateTableProbability, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "probability") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Distributions
```{r generateTableDistribution, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "distribution") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Causality
```{r generateTableCausality, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "causality") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Ethics and Fairness
```{r generateTableEthics, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "ethics") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
# Analysis
## Hypothesis testing
```{r generateTableHypothesis, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "hypothesis") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Correlation
```{r generateTableCorrelation, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "correlation") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Spatial
```{r generateTableSpatial, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "spatial") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Regression
```{r generateTableRegression, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "regression") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Bayesian
```{r generateTableBayesian, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "Bayesian") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Meta-analysis
```{r generateTableMetaanalysis, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "metaanalysis") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Hierarchical modeling
```{r generateTableMultilevel, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "multilevel") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Machine learning
```{r generateTableML, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "machinelearning") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Experiments
```{r generateTableExperiments, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "experiments") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
# Data visualisation
```{r generateTableViz, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "visualisation") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
# Data
## General
```{r generateTableData, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "data") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Missing data
```{r generateTableMissingData, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "missingdata") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Sample size
```{r generateTableSamplesize, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "samplesize") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Datasets
```{r generateTableDatasets, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "dataset") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
# Statistical software
## General
```{r generateTableSoftware, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "software") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## R
```{r generateTableR, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "R") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Python
```{r generateTablePython, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "Python") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Julia
```{r generateTableJulia, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "Julia") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Stata
```{r generateTableStata, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "Stata") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
## Excel
```{r generateTableExcel, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "Excel") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
# Replication, open science and reproducibility
```{r generateTableReplication, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "replication") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
# Surveys
```{r generateTableSurveys, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "surveys") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
# Teaching
```{r generateTableTeaching, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "teaching") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
# History
```{r generateTableHistory, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "history") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```
# Funny
```{r generateTableFunny, echo=FALSE, message=FALSE, warning=FALSE}
df |>
filter(category == "funny") |>
pull(title) |>
pander::pandoc.list(add.end.of.list = FALSE)
```