Title: | Datasets with Strong and Spurious Correlations |
Version: | 0.1 |
Description: | Provides datasets from Vigen (2015) https://web.archive.org/web/20230607181247/https%3A/tylervigen.com/spurious-correlations rescued from the Internet Wayback Machine. These should be preserved for statistics introductory courses as these make it very clear that correlation is not causation. |
License: | CC0 |
Encoding: | UTF-8 |
RoxygenNote: | 7.3.2 |
Depends: | R (≥ 2.10) |
LazyData: | true |
NeedsCompilation: | no |
Packaged: | 2025-09-21 13:40:21 UTC; pacha |
Author: | Tyler Vigen [cph],
Mauricio Vargas Sepulveda
|
Maintainer: | Mauricio Vargas Sepulveda <m.vargas.sepulveda@gmail.com> |
Repository: | CRAN |
Date/Publication: | 2025-09-27 08:30:02 UTC |
Spurious Correlations datasets
Description
A dataset to preserve tylervigen.com correlations.
Usage
spurious_correlations
Format
A tibbles with 155 observations and 9 variables:
- year
Year
- var1
Variable 1
- var2
Variable 2
- var1_short
Variable 1 short name
- var2_short
Variable 2 short name
- var1_unit
Variable 1 unit
- var2_unit
Variable 2 unit
- var1_value
Variable 1 value
- var2_value
Variable 2 value
- source
Source
Source
https://web.archive.org/web/20230607181247/https://tylervigen.com/spurious-correlations
Examples
# Drownings by Falling into a Pool
# correlates with
# Films Nicolas Cage Appeared In
d <- spurious_correlations[spurious_correlations$var2_short == "Nicholas Cage", ]
cor(d$var1_value, d$var2_value)