Title:	Statistics: Data Analysis and Modelling
Version:	0.2.0
Description:	Data sets and functions to support the books "Statistics: Data analysis and modelling" by Speekenbrink, M. (2021) https://mspeekenbrink.github.io/sdam-book/ and "An R companion to Statistics: data analysis and modelling" by Speekenbrink, M. (2021) https://mspeekenbrink.github.io/sdam-r-companion/. All datasets analysed in these books are provided in this package. In addition, the package provides functions to compute sample statistics (variance, standard deviation, mode), create raincloud and enhanced Q-Q plots, and expand Anova results into omnibus tests and tests of individual contrasts.
License:	GPL-3
Encoding:	UTF-8
Language:	en-GB
LazyData:	true
RoxygenNote:	7.2.1
URL:	https://mspeekenbrink.github.io/sdam-r/
Depends:	R (≥ 3.5.0)
Imports:	methods, stats, dplyr, ggplot2, grid, car
NeedsCompilation:	no
Packaged:	2022-11-13 19:29:53 UTC; maarten
Author:	Maarten Speekenbrink [aut, cre]
Maintainer:	Maarten Speekenbrink <m.speekenbrink@ucl.ac.uk>
Repository:	CRAN
Date/Publication:	2022-11-16 21:50:03 UTC

Flat violin geometry

Description

Flat violin geometry

Anchoring

Description

Numerical judgments of the height of the Mount Everest after a low or high anchor. This dataset comes from the ManyLabs 1 study

Usage

anchoring

Format

A data frame with 4632 rows and 5 variables:

session_id

Unique identifier for participants

sex

Sex of participant (f = female, m = male)

age

Age of participant in years

citizenship

Country code of citizenship

referrer

Location of data collection. Site abbreviations used here can be matched up to the full site name in the online supplement https://osf.io/wx7ck/

us_or_international

Was the study conducted on a US sample or international sample?

lab_or_online

Was the study conducted online or in-lab?

anchor

anchor, whether high or low

everest_feet

judged height of Mount Everest in feet. Converted from meters if given in meters.

everest_meters

judged height of Mount Everest in meters. Only contains values when judgment was actually given in meters.

Source

https://osf.io/pqf9r/. See also Klein, R. A., Ratliff, K. A., Vianello, M., Adams, R. B., Jr., Bahník, S., Bernstein, M. J., . . ., Nosek, B. A. (2014). Investigating variation in replicability: A "many labs" replication project. Social Psychology, 45(3), 142-152. doi:10.1027/1864-9335/a000178

Mean-centered values

Description

center computes mean-centered values. It is a convenience wrapper to scale, equal to scale(x, scale=FALSE)

Usage

center(x)

Arguments

Value

A numeric vector with mean-centered values

Examples

data(anchoring)
center(anchoring$everest_feet)

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

Description

\@format A data frame with 320 observations of 16 variables:

Participant

(factor) Participant ID

Age

(numeric) Participant age in years

Sex

(factor) Participant sex (Male or Female)

Task

(factor) Identical-Distractors, or Self-Distractors.

LineClickAccuracy

Measure of average response deviation from the visual analogue scale; scores > +/- 2.00 constitute exclusion.

Excluded

(numeric) Indicator whether participant was excluded from main analysis (0 = no, 1 = yes)

WhyExcluded

(character) explanation for exclusion

Item

(factor) Item description

Response

Attractiveness rating for the target face on a visual analogue scale ranging from “Very Unattractive” (0) to “Very Attractive” (100)

Usage

cheerleader

Format

An object of class data.frame with 192 rows and 9 columns.

Source

https://osf.io/je5u7/. 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 doi:10.1038/s41598-019-45789-6.

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.

Description

\@format A data frame with 400 observations of 16 variables:

pid

Participant ID

exptrNum

Experimenter Number

age

Participant age in years

gender

Participant self-reported gender

yearInUni

Year in University

ethnicity

Self-reported ethnicity

englishFluency

Self-reported English fluency (1=beginner; 7=native language)

experimenterBelief

Experimenter Belief (H: High or L: Low)

primeCond

Actual Prime Condition (HPP: High-power prime or LPP: low-power prime)

powerPRE

Self-reported power BEFORE the manipulation

powerPOST

Self-reported power AFTER the manipulation

ApproachAdvantage

Approach advantage (Avoid RT - Approach RT; see manuscript)

attractive

Rating of experimenter ATTRACTIVENESS

competent

Rating of experimenter COMPETENCE

friendly

Rating of experimenter FRIENDLINESS

trustworthy

Rating of experimenter TRUSTWORTHINESS

Usage

expBelief

Format

An object of class data.frame with 400 rows and 16 columns.

Source

https://osf.io/un4h6/. See also Gilder, T. S. E., & Heerey, E. A. (2018). The Role of Experimenter Belief in Social Priming. Psychological Science, 29(3), 403–417. doi:10.1177/0956797617737128.

Expand all contrast terms in car::Anova

Description

expand_Anova is an experimental function to add more detailed results to those returned by car::Anova. In particular, expand_Anova aims to provide test results for all individual contrasts assigned to the factors in a linear model, in addition to the omnibus tests returned by car::Anova.

Usage

expand_Anova(mod, type = c("III", "II", 3, 2), ...)

Arguments

Details

This is an experimental function

Value

Object of class anova returned by car::Anova

See Also

car::Anova() for more information about the Anova tables, and stats::lm() for information about how to specify the model

Examples

data("tetris2015")
mod <- lm(Days_One_to_Seven_Number_of_Intrusions ~ Condition, data=tetris2015)
car::Anova(mod,type=3) # default type III Anova table
expand_Anova(mod,type=3)

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

Description

A dataset containing the predictions and outcomes of matches in the 2010 FIFA European Cup.

Usage

fifa2010

Format

A data frame with 8 rows and 4 variables:

Match

countries playing

Prediction

country predicted to win

Result

score at the end of the match

Outcome

whether Paul was correct or incorrect

Source

https://en.wikipedia.org/wiki/Paul_the_Octopus

FIFA 2010 team statistics

Description

Statistics for all teams playing in the 2010 FIFA world cup.

Usage

fifa2010teams

Format

A data frame with 11 variables and 32 rows

nr

Unique numeric identifier for each team

team

Name of the team (i.e. country)

matches_played

Number of matches played

goals_for

Total goals counted against their opponents

goals_scored

Total goals scored against their opponents

goals_against

Goals counted against the team

penalty_goal

Number of penalty goals scored

own_goals_for

Number of own goals

yellow_cards

Number of yellow cards

indirect_red_cards

Number of indirect red cards

direct_red_cards

Number of direct red cards

Source

FIFA website. https://www.fifa.com/worldcup/archive/southafrica2010/statistics/teams/goal-scored and https://www.fifa.com/worldcup/archive/southafrica2010/statistics/teams/disciplinary

Half violin plot

Description

Half violin plot

Usage

geom_flat_violin(
  mapping = NULL,
  data = NULL,
  stat = "ydensity",
  position = "dodge",
  trim = TRUE,
  scale = "area",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

Value

A layer for a ggplot2::ggplot object, similar to e.g. ggplot2::geom_violin.

Source

urlhttps://gist.github.com/dgrtwo/eb7750e74997891d7c20

See Also

ggplot2::geom_violin(), which provided the basis of this function.

Examples

library(ggplot2)
data(diamonds)
ggplot(diamonds, aes(cut, carat)) + geom_flat_violin() + coord_flip()

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 doi:10.1111/lnc3.12439

Description

\@format A data frame with 54 observations of 6 variables:

ID

(factor) Participant ID

context

(factor) Whether talking to a friend or professor

duration

(numeric) Duration of the interaction

language

(factor) Language spoken: Catalan or Korean

gender

(factor) Participant gender (M = male, F = female)

gestures

(numeric) number of gestures in the interaction.

Usage

gestures

Format

An object of class data.frame with 54 rows and 6 columns.

Source

https://osf.io/6j8kc.

Legacy motives and pro-environmental behaviour

Description

Legacy motives and pro-environmental behaviour

Usage

legacy2015

Format

A data frame with 245 rows and 9 variables:

id

(numeric) ID variable relating to the original dataset

sex

(character) biological sex of participant (male or female)

age

(numeric) age in years

legacy

(numeric) Sverage of 8 items reflecting legacy motivation, on a scale from 1 (Not at all) to 6 (A great amount)

belief

(numeric) average of 5 items reflecting belief in climate change, on a scale from 1 (Strongly Disagree) to 7 (Strongly Agree)

intention

(numeric) average of 8 items reflecting intention to act in a pro-environmental way, on a scale from 1 (Never) to 6 (All the time)

education

(numeric) Level of education, 1 = 8th grade or less, 2 = Some high school, 3 = Graduated high school, 4 = Some college or technical school, 5 = Graduated college or technical school, 6 = Post-graduate

income

(numeric) Approximate household income, 1 = less than $20K, 2 = $20K-$35K, 3 = $35K-$50K 4 = $50K-$75K, 5 = $75K-100K, 6 = more than 100K

donation

(numeric) Donation of possible bonus payment, between $0 and $10

Source

Harvard DataVerse https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/27740&version=1.0

Examples

## Not run: 
 # this dataset was processed from the raw data as follows:
 tdat <- read.csv("legacy study - pilot_data.csv")

## End(Not run)

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 doi:10.3389/fpsyg.2016.00591

Description

\@format A data frame with 7560 observations of 10 variables:

id

(factor) Participant ID

age

(numeric) Participant age in years

sex

(factor) Participant sex (male or female)

block

(numeric) number of the test block (from 1 to 9). Practice block is excluded.

trial

(numeric) number of trial (between 1 and 42) within a block.

tilt

(numeric) whether grating is horizontal (0) or vertical (90)

contrast

(numeric) contrast of grating shown

correct

(numeric) Whether identified title was correct (1) or not (0)

visibility

(numeric) Rated visibility of the stimulus, on a scale between 0 () and 100 ()

confidence

(numeric) Rated confidence in tilt identification, on a scale between 0 () and 100 ()

Usage

metacognition

Format

An object of class data.frame with 7560 rows and 10 columns.

Source

https://osf.io/vk6fe/. Rausch, M. & Zehetleitner, M. (2016) Visibility is not equivalent to confidence in a low contrast orientation discrimination task. Frontiers in Psychology, 7, p. 591 doi:10.3389/fpsyg.2016.00591.

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.

Description

\@format A data frame with 90 observations of 3 variables:

newspaper

(factor) Reported newspaper read most often

vote

(factor) Which party voted on (including "did not vote")

n

(numeric) Number of people in the survey who responded with that combination of newspaper and vote

Usage

papervotes

Format

An object of class data.frame with 90 rows and 3 columns.

Source

https://d25d2506sfb94s.cloudfront.net/cumulus_uploads/document/smo1w49ph1/InternalResults_170613_2017Election_Demographics_W.pdf.

Q-Q plots with distributions in the margins

Description

plot_qq_marginals creates an enhanced Q-Q plot with the observed and theoretical distributions shown in the margins of the plot.

Usage

plot_qq_marginals(
  x,
  breaks = "Sturges",
  newpage = TRUE,
  xlab = "Observed Quantiles",
  ylab = "Theoretical quantiles",
  xlim = grDevices::extendrange(c(min(x), max(x))),
  ylim = NULL,
  main = NULL,
  sub = NULL,
  axes = TRUE,
  border = TRUE,
  ...
)

Arguments

Value

No return value. The function adds a plot to the active graphics window.

Examples

data(anchoring)
plot_qq_marginals(anchoring$everest_feet)

Create a raincloud plot

Description

plot_raincloud creates a raincloud plot to display the distribution of data by a combination of a a boxplot, a kernel density plot, and a scatterplot. The boxplot includes the median (displayed as a horizontal line) and the mean (displayed as a point). It does not indicate potential outliers, as these can be seen in the scatter plot. The kernel density plot provides a nonparametric estimate of the distribution. The scatterplot depicts all values in y with random jittering on the x-axis. The data can be grouped by supplying a grouping factor in the groups argument, in which case multiple raincloud plots are shown side by side. As plot_raincloud provides a ggplot2::ggplot object, it can be combined with further layers and functionality from the ggplot2 package.

Usage

plot_raincloud(data, y, horizontal = FALSE, groups, point_size = 0.5, ...)

Arguments

Value

An object of class gg, i.e. a ggplot object from the ggplot2 package

Source

Allen M, Poggiali D, Whitaker K et al. Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Res 2019, 4:63 (doi:10.12688/wellcomeopenres.15191.1)

See Also

ggplot2::ggplot() for information about ggplot objects, ggplot2::theme() for information about changing various aspects of the plot, and ggplot2::facet_wrap() and ggplot2::facet_grid() for creating multiple raincloud plots for different levels of grouping factors beyond those specified in groups.

Examples

data(anchoring)
plot_raincloud(anchoring,y=everest_feet)
plot_raincloud(anchoring,y=everest_feet,groups=anchor)
plot_raincloud(anchoring,y=everest_feet,groups=anchor) + 
    ggplot2::facet_wrap(~us_or_international) + 
    ggplot2::ylab("How high is Mount Everest (in feet)?")

Simultaneously nudge and jitter

Description

Simultaneously nudge and jitter

Usage

position_jitternudge(
  jitter.width = NULL,
  jitter.height = 0,
  nudge.x = 0,
  nudge.y = 0,
  seed = NA
)

Arguments

Value

Positions for data in a ggplot2::ggplot object, similar to e.g. ggplot2::position_jitter

See Also

ggplot2::position_jitter(), which is the basis of this function.

Examples

library(ggplot2)
dsub <- diamonds[ sample(nrow(diamonds), 1000), ]
ggplot(dsub, aes(x = cut, y = carat, fill = clarity)) +
  geom_boxplot(outlier.size = 0) +
  geom_point(pch = 21, position = position_jitterdodge())

Redistribution of wealth

Description

It is generally found that wealthy people tend to be more opposed to policies to reduce wealth inequalities. This may be unsurprising from a classical economic standpoint, because the material burden of the redistribution of wealth will fall on wealthier people. Wealthier people are also more likely than poorer people to adopt political ideologies that oppose redistribution policies. Dawtry, Sutton, and Sibley (2015) investigated whether, in addition to such factors, “social-sampling processes” lead wealthier people to oppose redistribution policies. Social sampling is the idea that people (partly) base inferences on their social surroundings. Wealthier people tend to live in more affluent areas and move in wealthier social circles. This may bias their view of the world, where wealthier people estimate the general population to be wealthier (with less of a gap between the wealthy and the poor) than it really is.

Usage

redist2015

Format

A data frame with 305 rows and 12 variables:

id

unique ID number for each participant

gender

only "male" or "female" could be answered by the looks of it

age

participant age in years

income

yearly household income (in units of $1,000)

pol_att

political leaning from 1="Extremely Liberal" to 9="Extremely Conservative"

PD_mean

estimate average household income in the general US population

PD_gini

GINI index computed for a subjective distribution of wealth in the general US population. The GINI index is a measure of wealth inequality; higher numbers mean more inequality

PD_fair

Answer to the question "To what extent do you feel that household incomes are fairly–unfairly distributed across the US population?" on a scale from 1="Extremely Fair" to 9 = "Extremely Unfair".

PD_sat

Answer to the question "How satisfied–dissatisfied are you with the way in which household incomes are distributed across the US population?" on a scale from 1="Extremely satisfied" to 9="Extremely dissatisfied".

SC_mean

estimate average household income in the participant's social circles

SC_gini

(subjective) inequality in the participant's social circles

redist

support for wealth redistribution policies (average of four items, higher scores indicate stronger support).

Details

In Experiment 1a of Dawtry, Sutton, and Sibley (2015), they assessed income and opinions for n=305 online U.S. participants recruited via Amazon’s Mechanical Turk.

Source

https://osf.io/3mftr/. See also Dawtry, Rael J., Robbie M. Sutton, and Chris G. Sibley. 2015. “Why Wealthier People Think People Are Wealthier, and Why It Matters: From Social Sampling to Attitudes to Redistribution.” Psychological Science 26 (9): 1389–1400. doi:10.1177/0956797615586560.

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 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.

Description

\@format A data frame with 2600 observations of 6 variables:

id

(factor) Participant ID

ai_strategy

(factor) Strategy adopted by AI player

round

(numeric) Round number (between 1 and 50)

human_action

(factor) Action taken by human (rock, paper, or scissors)

ai_action

(factor) Action taken by AI (rock, paper, or scissors)

score

(numeric) Outcome for human player, where 1 indicates a win, -1 a loss, and 0 a tie

Usage

rps

Format

An object of class data.frame with 2600 rows and 6 columns.

Source

Guennouni, I., Speekenbrink, M. (2022). Transfer of learned opponent models in repeated games. Computational Brain and Behaviour, 5, 326–342. doi:10.1007/s42113-022-00133-6

Compute a sample mode

Description

sample_mode computes the sample mode, i.e. the value in x with the highest frequency of occurrence. If there are multiple modes, the mode that occurs first in x is returned, with a warning that lists the other modes found.

Usage

sample_mode(x)

Arguments

Value

A single numeric value equal to the sample mode

Examples

data(anchoring)
sample_mode(anchoring$everest_feet)
# Multiple modes give a warning:
sample_mode(c(3,3,3,1,1,1,2,2,2))

Compute the sample standard deviation

Description

sample_sd computes the sample standard deviation, i.e. the square root of the sum of squared deviations of x from the mean divided by the total number of observations. This is in contrast to sd, which computes an unbiased estimate of the standard deviation (i.e. it divides the sum of squared deviations by n - 1).

Usage

sample_sd(x, na.rm = FALSE)

Arguments

Value

A single numeric value equal to the sample variance

Examples

data(anchoring)
sample_sd(anchoring$everest_feet)

Compute the sample variance

Description

sample_var computes the sample variance, i.e. the sum of squared deviations of x from the mean divided by the total number of observations. This is in contrast to var, which computes an unbiased estimate of the variance (i.e. it divides the sum of squared deviations by n - 1).

Usage

sample_var(x, na.rm = FALSE)

Arguments

Value

A single numeric value equal to the sample variance

Examples

data(anchoring)
sample_var(anchoring$everest_feet)

Speed dating

Description

A subset of cases (wave 6-9) and variables (see below) from an experiment on speed dating. by Columbia Business School professors Ray Fisman and Sheena Iyengar for their paper Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment.

Usage

speeddate

Format

A data frame with 1562 rows and 32 variables:

iid

(numeric) unique ID variable of participant

pid

(numeric) unique ID variable of date partner

gender

(character) gender of participant (male or female)

age

(numeric) age in years

date_like

(numeric) how much they like their date partner in general (between 1 and 10)

other_like

(numeric) how much their date partner likes them (between 1 and 10)

date_want

do they want to go on another date with their date partner? (1 = yes, 0 = no)

other_want

does their date partner want to go on another date with them? (1 = yes, 0 = no)

match

do they both want to go on another date with each other? (1 = yes, 0 = no)

self_attr

how attractive do they think they are? (between 1 and 10)

self_sinc

how sincere do they think they are? (between 1 and 10)

self_intel

how intelligent do they think they are? (between 1 and 10)

self_fun

how much fun do they think they are? (between 1 and 10)

self_amb

how ambitious do they think they are? (between 1 and 10)

other_attr,other_sinc,other_intel,other_fun,other_amb

how attractive etc does their date partner think they are? (between 1 and 10)

other_shar

how much does their date partner think they share hobbies and interests? (between 1 and 10)

date_attr,date_sinc,date_intel,date_fun,date_amb,date_shar

how do they rate their date partner's attractiveness etc? (between 1 and 10)

self_imp_attr,self_imp_sinc,self_imp_intel,self_imp_fun,self_imp_amb,self_imp_shar

how important do they find attractiveness etc in a partner? (between 1 and 10)

other_imp_attr,other_imp_sinc,other_imp_intel,other_imp_fun,other_imp_amb,other_imp_shar

how important does their date partner find attractiveness etc? (between 1 and 10)

Source

Kaggle https://www.kaggle.com/annavictoria/speed-dating-experiment

Tetris and intrusive memories

Description

Tetris and intrusive memories

Usage

tetris2015

Format

A data frame with 72 rows and 28 variables:

Condition

(factor) Condition: Control, Tetris_Reactivation, Tetris, or Reactivation

Time_of_Day

Time of day participant commenced experiment, either "morning” or “afternoon”

BDI_II

Beck Depression Inventory-II (BDI-II): Total score

STAI_T

Spielberger State-Trait Anxiety Trait scale (STAI): Total score

pre_film_VAS_Sad

Self-rated level of Sadness: Pre-film VAS mood. VAS = visual analogue scale. All VAS mood scales anchored from “not at all” to “extremely” in response to the question “Right at this very moment I am feeling”. Composite for pre-film mood calculated by summing the six pre-film VAS mood ratings

pre_film_VAS_Hopeless

Self-rated level of Hopelessness: Pre-film VAS mood

pre_film_VAS_Depressed

Self-rated level of Depressed: Pre-film VAS mood

pre_film_VAS_Fear

Self-rated level of Fear: Pre-film VAS mood

pre_film_VAS_Horror

Self-rated level of Horror: Pre-film VAS mood

pre_film_VAS_Anxious

Self-rated level of Anxiousness: Pre-film VAS mood

post_film_VAS_Sad

Self-rated level of Sadness: Post-film VAS mood. Composite for post-film mood calculated by summing the six post-film VAS mood ratings

post_film_VAS_Hopeless

Self-rated level of Hopelessness: Post-film VAS mood

post_film_VAS_Depressed

Self-rated level of Depressed: Post-film VAS mood

post_film_VAS_Fear

Self-rated level of Fear: Post-film VAS mood

post_film_VAS_Horror

Self-rated level of Horror: Post-film VAS mood

post_film_VAS_Anxious

Self-rated level of Anxious: Post-film VAS mood

Attention_Paid_to_Film

Attention paid to the film rating: How much attention did you pay to the film from 0-not at all to 10-extremely

Post_film_Distress

Post film distress rating: How distressing did you find the film from 0-not at all to 10-extremely

Day_Zero_Number_of_Intrusions

Day 0: Number of image-based intrusive memories in the Intrusion Diary (pre-intervention)

Days_One_to_Seven_Number_of_Intrusions

Days 1-7: Number of image-based intrusive memories in the Intrusion Diary (post-intervention)

Visual_Recognition_Memory_Test

Visual recognition memory test score: Number of correct responses (out of 22)

Verbal_Recognition_Memory_Test

Verbal recognition memory test score: Number of correct responses (out of 32)

Number_of_Provocation_Task_Intrusions

Intrusion Provocation Task (IPT): Number of image-based intrusive memories during 2min laboratory task on Day 7

Diary_Compliance

Diary compliance rating - indicate how accurate you think your diary is from 1 - not at all accurate to 10 extremely

IES_R_Intrusion_subscale

Impact of Event Scale-Revised (IES-R): Intrusion Subscale

Tetris_Total_Score

Tetris game play computer score total - cumulative (sum of all games). Only participants who played Tetris have data relating to Tetris_Total_Score

Self_Rated_Tetris_Performance

Self-rated Tetris performance: How difficult or easy did you find the game you just played. Only participants who played Tetris have data relating to Self_Rated_Tetris_Performance.

Tetris_Demand_Rating

Demand rating: How much did you think Tetris after a distressing film would increase or decrease intrusive memories of the film: -10: extremely decrease, to +10: extremely increase

Source

https://osf.io/ideta/. See also James et al., 'Computer Game Play Reduces Intrusive Memories of Experimental Trauma via Reconsolidation-Update Mechanisms'.

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

Description

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

Usage

trump2016

Format

A data frame with 4632 rows and 5 variables:

state

Name of the state

hate_groups

Number of hate groups in the state in 2016 as reported by the Southern Poverty Law Center (https://www.splcenter.org/hate-map)

population

Number of citizens in the state in 2016

hate_groups_per_million

Number of hate groups per million citizens

percent_bachelors_degree_or_higher

Percentage of citizens with a bachelor's degree of higher

percent_in_poverty

Percentage of citizens below the poverty threshold

percent_Trump_votes

Percentage of votes for Trump in the 2016 elections

Source

CSI Without Dead Bodies "Hate Groups and Trump's Vote%: Predictive effect present when education and poverty are considered" https://web.archive.org/web/20210414051437/https://www.csiwithoutdeadbodies.com/2017/02/hate-groups-and-trumps-vote-predictive.html

Predictions by Paul the Octopus in the 2008 UEFA Cup.

Description

A dataset containing the predictions and outcomes of matches in the 2008 UEFA European Cup.

Usage

uefa2008

Format

A data frame with 6 rows and 4 variables:

Match

countries playing

Prediction

country predicted to win

Result

score at the end of the match

Outcome

whether Paul was correct or incorrect

Source

https://en.wikipedia.org/wiki/Paul_the_Octopus