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sdamr

An R package to support Statistics: Data analysis and modelling.

This package provides the datasets analysed in Statistics: Data analysis and modelling, as well as functions to compute sample statistics (mode, variance and standard deviations) and create "raincloud" plots.

Installation

Stable release

The stable release is available from CRAN. In the R console, type

install.packages("sdamr")

Development version

The development version can be installed from GitHub, using the remotes package. If you don't have this install, first type

install.packages("remotes")

in the R console. You can then install the sdamr package by typing

remotes::install_github("mspeekenbrink/sdam-r")

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Version

Install

install.packages('sdamr')

Monthly Downloads

300

Version

0.2.0

License

GPL-3

Maintainer

Maarten Speekenbrink

Last Published

November 16th, 2022

Functions in sdamr (0.2.0)

Compute the sample standard deviation

Compute a sample mode

Data based on a post-election survey by YouGov (see https://yougov.co.uk/topics/politics/articles-reports/2017/06/13/how-britain-voted-2017-general-election). Note that the data was recreated by combining frequency and percentage results reported in https://d25d2506sfb94s.cloudfront.net/cumulus_uploads/document/smo1w49ph1/InternalResults_170613_2017Election_Demographics_W.pdf. Due to rounding and other potential inconsistencies, this data set will likely differ from the actual results.

Create a raincloud plot

position_jitternudge

Simultaneously nudge and jitter

plot_qq_marginals

Q-Q plots with distributions in the margins

Redistribution of wealth

Data from Rausch, M. & Zehetleitner, M. (2016) Visibility is not equivalent to confidence in a low contrast orientation discrimination task. Frontiers in Psychology, 7, p. 591 tools:::Rd_expr_doi("10.3389/fpsyg.2016.00591")

Legacy motives and pro-environmental behaviour

Tetris and intrusive memories

Data from Experiment 1 in Guennouni, I., Speekenbrink, M. (2022). Transfer of learned opponent models in repeated games. Computational Brain and Behaviour, 5, 326–342 tools:::Rd_expr_doi("10.1007/s42113-022-00133-6"). Participants (n=52) each play 50 rounds of Rock-Paper-Scissors against an AI player who either adopts a "level-1" or "level-2" strategy. A level-1 strategy assumes the opponent will repeat their last action, and chooses the action that beats this. A level-2 strategy assumes the opponent adopts a level-1 strategy, and chooses the action that beats this. On 10% of rounds, the AI players pick a random action. On the remainder, they act according to their strategy.

Compute the sample variance

Predictions by Paul the Octopus in the 2008 UEFA Cup.

Trump votes in 2016 for 50 US states and the District of Columbia

FIFA 2010 team statistics

Expand all contrast terms in car::Anova

Mean-centered values

geom_flat_violin

Half violin plot

Data from Winter, B., & Burkner, P. (2021) Poisson regression for linguists: A tutorial introduction to modelling count data with brms. Language and Linguistics Compass, 15, e12439 tools:::Rd_expr_doi("10.1111/lnc3.12439")

Predictions by Paul the Octopus in the 2010 FIFA World Cup.

Flat violin geometry

Data from Experiment 5 of Gilder, T. S. E., & Heerey, E. A. (2018). The Role of Experimenter Belief in Social Priming. Psychological Science, 29(3), 403–417.

Data from Experiment 1 of Carragher, D.J., Thomas, N.A., Gwinn, O.S. et al. (2019) Limited evidence of hierarchical encoding in the cheerleader effect. Scientific Reports, 9, 9329. https://doi.org/10.1038/s41598-019-45789-6