Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Anthropometric data from US Army Personnel

Description

Data on the height, weight, handedness from men and women of different ages and different races.

Usage

ansur

Format

A data frame with 6068 observations from 9 variables.

subjectid

Unique ID of the person

gender

Binary variable indicating the subject's sex: male or female.

height

Height in centimeters.

weight

Weight in kilograms.

handedness

Categorical variable indicating if the person is left, or right handed, or both.

age

Age in years

race

Race, with categories like white, black, hispanic.

height_tercile

The tercile of the person's height.

age_tercile

The tercile of the person's weight.

Source

This data is a transformed version of data sets obtained the Anthropometric Survey of US Army Personnel (ANSUR 2 or ANSUR II).

Cohen's d and Hedges g effect size

Description

This is wrapper to the effsize::cohen.d() function.

Usage

cohen_d(...)

Arguments

Value

A list of class effsize as returned by effsize::cohen.d().

Examples

cohen_d(weight ~ gender, data = ansur)
cohen_d(age ~ gender, data = schizophrenia)

Calculate Cronbach's alpha for sets of psychometric scale items

Description

This function calculates the Cronbach alpha for one or more sets of psychometric scale items. Each item is a variable in a data frame. Each set of items is defined by a tidy selection of a set of items.

Usage

cronbach(.data, ..., .ci = 0.95)

Arguments

Value

A data frame whose rows are psychometric scales and for each scale, we have the Cronbach's alpha, and the lower and upper bound of the confidence interval on alpha.

Examples

 # Return the Cronbach alpha and 95% ci for two scales.
 # The first scale, named `x`, is identified by all items beginning with `x_`.
 # The second scale, named `y`, is identified by the consecutive items from `y_1` to `y_10`.
 cronbach(test_psychometrics,
          x = starts_with('x'),
          y = y_1:y_10)

A density plot

Description

This is a wrapper to the typical ggplot based density plot, i.e., using geom_density. A continuous variable, x, is required as an input. Optionally, a by categorical variable can be provided.

Usage

densityplot(
  x,
  data,
  by = NULL,
  position = "stack",
  facet = NULL,
  facet_type = "wrap",
  alpha = 1,
  xlab = NULL,
  ylab = NULL
)

Arguments

Value

A ggplot2::ggplot object, which may be modified with further ggplot2 commands.

Examples

densityplot(x = age, data = schizophrenia, by = gender)

Calculate descriptive statistics

Description

This function is a lightweight wrapper to dplyr's summarize function. It can be used to calculate any descriptive or summary statistic for any variable in the data set. Optionally, a by grouping variable can be used, and then the summary statistics are calculated for each subgroup defined by the different values of the by variable.

Usage

describe(data, by = NULL, ...)

Arguments

Value

A tibble data frame with each row providing descriptive statistics for selected variables for each value of the grouping by variable.

Examples

describe(faithfulfaces, avg = mean(faithful), stdev = sd(faithful))
describe(faithfulfaces, by = face_sex, avg = mean(faithful), stdev = sd(faithful))

Apply multiple descriptive functions to multiple variables

Description

This function is a wrapper to dplyr's summarize used with the across function. For each variable in a set of variables, calculate each summary statistic from a list of summary statistic functions. Optionally, group the variables by a grouping variable, and then calculate the statistics. Optionally, the tibble that is returned by default, which is in a wide format, can be pivoted to a long format.

Usage

describe_across(data, variables, functions, by = NULL, pivot = FALSE)

Arguments

Value

A tibble data frame. If pivot = F, which is the default, the data frames contains one row per value of the by variable, or just one row overall if there is no by variable. If pivot = T, there will be k + 1 columns if there is no by variable, or k + 2 columns if there is a by variable, where k is the number of functions.

Examples

describe_across(faithfulfaces, 
                variables = c(trustworthy, faithful), 
                functions = list(avg = mean, stdev = sd),
                pivot = TRUE)
describe_across(faithfulfaces, 
                variables = c(trustworthy, faithful), 
                functions = list(avg = mean, stdev = sd), 
                by = face_sex)
describe_across(faithfulfaces, 
                variables = c(trustworthy, faithful), 
                functions = list(avg = mean, stdev = sd), 
                by = face_sex,
                pivot = TRUE)

Analysis of variance

Description

This is wrapper to the ez::ezANOVA() function.

Usage

ez_anova(
  data,
  dv,
  wid,
  within = NULL,
  within_full = NULL,
  within_covariates = NULL,
  between = NULL,
  between_covariates = NULL,
  observed = NULL,
  diff = NULL,
  reverse_diff = FALSE,
  type = 2,
  white.adjust = FALSE,
  detailed = FALSE,
  return_aov = FALSE
)

Arguments

Value

A list containing one or more components as returned by ez::ezANOVA().

Examples

ez_anova(data = selfesteem2_long,
            dv = score,
            wid = id,
            within = c(time, treatment),
            detailed = TRUE,
            return_aov = TRUE)

Faithfulness from a Photo?

Description

Ratings from a facial photo and actual faithfulness.

Usage

faithfulfaces

Format

A data frame with 170 observations on the following 7 variables.

sex_dimorph

Rating of sexual dimorphism (masculinity for males, femininity for females)

attractive

Rating of attractiveness

cheater

Was the face subject unfaithful to a partner?

trustworthy

Rating of trustworthiness

faithful

Rating of faithfulness

face_sex

Sex of face (female or male)

rater_sex

Sex of rater (female or male)

Details

College students were asked to look at a photograph of an opposite-sex adult face and to rate the person, on a scale from 1 (low) to 10 (high), for attractiveness. They were also asked to rate trustworthiness, faithfulness, and sexual dimorphism (i.e., how masculine a male face is and how feminine a female face is). Overall, 68 students (34 males and 34 females) rated 170 faces (88 men and 82 women).

Source

This data set was taken from the Stats2Data R package. From the description in that package, the original is based on G. Rhodes et al. (2012), "Women can judge sexual unfaithfulness from unfamiliar men's faces," Biology Letters, November 2012. All of the 68 raters were heterosexual Caucasians, as were the 170 persons who were rated. (We have deleted 3 subjects with missing values and 16 subjects who were over age 35.)

Show the dummy code of a categorical variable

Description

For each value of a categorical variables, show the binary code used in a regression model to represent its value. This is wrapper to the fastDummies::dummy_cols() function.

Usage

get_dummy_code(Df, variable)

Arguments

Value

A data frame whose rows provide the dummy code for each distinct value of variable.

Examples

get_dummy_code(PlantGrowth, group)

A histogram

Description

This is a wrapper to the typical ggplot based histogram, i.e., using geom_histogram. A continuous variable, x, is required as an input. Optionally, a by categorical variable can be provided.

Usage

histogram(
  x,
  data,
  by = NULL,
  position = "stack",
  facet = NULL,
  facet_type = "wrap",
  bins = 10,
  alpha = 1,
  xlab = NULL,
  ylab = NULL
)

Arguments

Value

A ggplot2::ggplot object, which may be modified with further ggplot2 commands.

Examples

histogram(x= age, data = schizophrenia, by = gender, bins = 20)
histogram(x= age, data = schizophrenia, by = gender, position = 'identity', bins = 20, alpha = 0.7)
histogram(x= age, data = schizophrenia, by = gender, position = 'dodge', bins = 20)
histogram(x = weight, bins = 20, data = ansur, facet = height_tercile)
histogram(x = weight, bins = 20, data = ansur, 
          facet = c(height_tercile, age_tercile), facet_type = 'grid')

Make a interaction line plot

Description

Make a interaction line plot

Usage

interaction_line_plot(y, x, by, data, ylim = NULL, xlab = NULL, ylab = NULL)

Arguments

Value

A ggplot2::ggplot object, which may be modified with further ggplot2 commands.

Examples

interaction_line_plot(y = score, x = time, by = treatment, 
                      data = selfesteem2_long, ylim = c(70, 100))
interaction_line_plot(y = score, x = time, by = treatment, 
                      data = selfesteem2_long, 
                      xlab = 'measurement time',
                      ylab = 'self esteem score',
                      ylim = c(70, 100))

Job Satisfaction Data for Two-Way ANOVA

Description

Contains the job satisfaction score organized by gender and education level. This data set was taken from the datarium R package.

Usage

data("jobsatisfaction")

Format

A data frame with 58 rows and 3 columns.

Examples

data(jobsatisfaction)
jobsatisfaction

Paired samples t-test

Description

A wrapper to stats::t.test() with paired = TRUE.

Usage

paired_t_test(y1, y2, data, ...)

Arguments

Value

A list with class "htest" as returned by stats::t.test().

Examples

paired_t_test(y1, y2, data = pairedsleep)

Paired sleep data

Description

Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

Usage

pairedsleep

Format

A data frame with 10 observations on the following 3 variables.

ID

The patient ID.

y1

The increase in hours, relative to control, for drug 1.

y2

The increase in hours, relative to control, for drug 2.

Source

This data is a transformed version of datasets::sleep.

A pairs plot

Description

This is a wrapper to the GGally based pairs plot of a list of variables displayed as scatterplots for pairs of continuous variables, density functions in the diagonal, and boxplots for pairs of continuous and categorical variables. Optionally, a by categorical variable can be provided.

Usage

pairs_plot(variables, data, by = NULL)

Arguments

Value

A GGally::ggpairs plot.

Examples

# A simple pairs plot
pairs_plot(variables = c("sex_dimorph", "attractive"),
data = faithfulfaces)
# A pairs plot with grouping variable
pairs_plot(variables = c("sex_dimorph", "attractive"),
by = face_sex,
data = faithfulfaces)

Pairwise t-test

Description

This is wrapper to the pairwise.t.test function. The p-value adjustment is "bonferroni" by default. Other possible values are "holm", "hochberg", "hommel", "BH", "BY", "fdr", "none". See stats::p.adjust().

Usage

pairwise_t_test(formula, data, p_adj = "bonferroni")

Arguments

Value

An object of class pairwise.htest as returned by stats::pairwise.t.test().

Examples

data_df <- dplyr::mutate(vizverb, IV = interaction(task, response))
pairwise_t_test(time ~ IV, data = data_df)

Recode specified values by new values

Description

Recode specified values by new values

Usage

re_code(x, from, to)

Arguments

Value

A vector that is the input vector but with old values replaced by new ones.

Examples

# Replace any occurrence of 1 and 2 with 101 and 201, respectively
x <- c(1, 2, 3, 4, 5, 1, 2)
re_code(x, from = c(1, 2), to = c(101, 201))

A two dimensional scatterplot

Description

This function is a wrapper around the typical ggplot command to create two dimensional scatterplots, i.e. using geom_point. It provides the option of colouring point by a third variable, one that is usually, though not necessarily categorical. Also, it provides the option of placing the line of best fit on the scatterplot. If points are coloured by a categorical variable, the a different line of best for each value of the categorical variable is provided.

Usage

scatterplot(
  x,
  y,
  data,
  by = NULL,
  best_fit_line = FALSE,
  xlab = NULL,
  ylab = NULL
)

Arguments

Value

A ggplot2::ggplot object, which may be modified with further ggplot2 commands.

Examples

scatterplot(x = attractive, y = trustworthy, data = faithfulfaces)
scatterplot(x = attractive, y = trustworthy, data = faithfulfaces,
            xlab = 'attractiveness', ylab = 'trustworthiness')
scatterplot(x = attractive, y = trustworthy, data = faithfulfaces,
            by = face_sex)
scatterplot(x = trustworthy, y = faithful, data = faithfulfaces,
            by = face_sex, best_fit_line = TRUE)

Make a scatterplot matrix

Description

Make a scatterplot matrix

Usage

scatterplot_matrix(.data, ..., .by = NULL, .bins = 10)

Arguments

Value

A GGally::ggpairs plot.

Examples

data_df <- test_psychometrics %>%
              total_scores(x = starts_with('x_'), 
                           y = starts_with('y_'), 
                           z = starts_with('z_'))
scatterplot_matrix(data_df, x, y, z)

Age of Onset of Schizophrenia Data

Description

Data on sex differences in the age of onset of schizophrenia.

Usage

schizophrenia

Format

A data frame with 251 observations on the following 2 variables.

age

Age at the time of diagnosis.

gender

A categorical variable with values female and male

Details

A sex difference in the age of onset of schizophrenia was noted by Kraepelin (1919). Subsequently epidemiological studies of the disorder have consistently shown an earlier onset in men than in women. One model that has been suggested to explain this observed difference is known as the subtype model which postulates two type of schizophrenia, one characterised by early onset, typical symptoms and poor premorbid competence, and the other by late onset, atypical symptoms, and good premorbid competence. The early onset type is assumed to be largely a disorder of men and the late onset largely a disorder of women.

Source

This data set was taken from the HSAUR R package. From the description in that package, the original is E. Kraepelin (1919), Dementia Praecox and Paraphrenia. Livingstone, Edinburgh.

Self-Esteem Score Data for One-way Repeated Measures ANOVA

Description

The dataset contains 10 individuals' self-esteem score on three time points during a specific diet to determine whether their self-esteem improved.

One-way repeated measures ANOVA can be performed in order to determine the effect of time on the self-esteem score.

This data set was taken from the datarium R package.

Usage

data("selfesteem")

Format

A data frame with 10 rows and 4 columns.

Examples

data(selfesteem)
selfesteem

Self Esteem Score Data for Two-way Repeated Measures ANOVA

Description

Data are the self esteem score of 12 individuals enrolled in 2 successive short-term trials (4 weeks) - control (placebo) and special diet trials.

The self esteem score was recorded at three time points: at the beginning (t1), midway (t2) and at the end (t3) of the trials.

The same 12 participants are enrolled in the two different trials with enough time between trials.

Two-way repeated measures ANOVA can be performed in order to determine whether there is interaction between time and treatment on the self esteem score.

This data set was taken from the datarium R package.

Usage

data("selfesteem2")

Format

A data frame with 24 rows and 5 columns.

Examples

data(selfesteem2)
selfesteem2

Self Esteem Score Data for Two-way Repeated Measures ANOVA: Long format

Description

Data are the self esteem score of 12 individuals enrolled in 2 successive short-term trials (4 weeks) - control (placebo) and special diet trials.

The self esteem score was recorded at three time points: at the beginning (t1), midway (t2) and at the end (t3) of the trials.

The same 12 participants are enrolled in the two different trials with enough time between trials.

Two-way repeated measures ANOVA can be performed in order to determine whether there is interaction between time and treatment on the self esteem score.

This data set was converted from the selfesteem2 data taken from the datarium R package.

Usage

data("selfesteem2_long")

Format

A data frame with 72 rows and 4 columns.

id

Unique ID of the person

treatment

Binary variable indicating the treatment condition: Diet or ctr.

time

A categorical variable indicating the time of measurement: beginning (t1), midway (t2) and at the end (t3)

score

Self-esteem score

Examples

data(selfesteem2_long)
selfesteem2_long

Shapiro-Wilk normality test

Description

This function is a wrapper around stats::shapiro.test(). It implements the Shapiro-Wilk test that tests the null hypothesis that a sample of values is a sample from a normal distribution. Thie function can be applied to single vectors or groups of vectors.

Usage

shapiro_test(y, by = NULL, data)

Arguments

Value

A tibble data frame with one row for each value of the by variable, or one row overall if there is no by variable. For the y variable whose normality is being tested, for each subset of values corresponding to the values of they by variable, or for all values if there is no by variable, return the Shapiro-Wilk statistic, and the corresponding p-value.

Examples

shapiro_test(faithful, data = faithfulfaces)
shapiro_test(faithful, by = face_sex, data = faithfulfaces)

Descriptive statistics for variables with missing values

Description

Most descriptive statistic function like base::sum(), base::mean(), stats::median(), etc., do not skip NA values when computing the results and so always return NA if there is at least one NA in the input vector. The NA values can be skipped always by setting the na.rm argument to TRUE. While this is simply to do usually, in some cases, such as when a function is being passed to another function, setting na.rm = TRUE in that function requires creating a new anonymous function. The functions here, which all end in ⁠_xna⁠, are wrappers to common statistics functions, but with na.rm = TRUE.

Usage

sum_xna(...)

mean_xna(...)

median_xna(...)

iqr_xna(...)

sd_xna(...)

var_xna(...)

Arguments

Value

A numeric vector, usually with one element, that provides the result of a descriptive statistics function applied to a vector after the NA values have been removed.

Functions

• mean_xna: The arithmetic mean for vectors with missing values.

• median_xna: The median for vectors with missing values.

• iqr_xna: The interquartile range for vectors with missing values.

• sd_xna: The standard deviation for vectors with missing values.

• var_xna: The variance for vectors with missing values.

Examples

set.seed(10101)
# Make a vector of random numbers
x <- runif(10, min = 10, max = 20)
# Concatenate with a NA value
x1 <- c(NA, x)
sum(x)
sum(x1) # Will be NA
sum_xna(x1) # Will be same as sum(x)
stopifnot(sum_xna(x1) == sum(x))
stopifnot(mean_xna(x1) == mean(x))
stopifnot(median_xna(x1) == median(x))
stopifnot(iqr_xna(x1) == IQR(x))
stopifnot(sd_xna(x1) == sd(x))
stopifnot(var_xna(x1) == var(x))

Independent samples t-test

Description

A wrapper to stats::t.test() with var.equal = TRUE.

Usage

t_test(formula, data)

Arguments

Value

A list with class "htest" as returned by stats::t.test().

Examples

t_test(trustworthy ~ face_sex, data = faithfulfaces)
  

Psychometrics raw data from testing or demo purposes

Description

Typical psychometrics raw data files have multiple psychometric variables (scales), each with multiple constituent items. In this data set, there are three psychometric variables, each with 10 constituent items. The variables can be labelled x, y, and z. The constituent items of x, y and z are ⁠x_1, x_2 ... x_10⁠, ⁠y_1, y_2 ... y_10⁠, ⁠z_1, z_2 ... z_10⁠, respectively.

Usage

data('test_psychometrics')

Format

A data frame with 44 rows and 30 columns

Examples

data(test_psychometrics)
test_psychometrics

Calculate the total scores from sets of scores

Description

Calculate the total scores from sets of scores

Usage

total_scores(.data, ..., .method = "mean", .append = FALSE)

Arguments

Value

A new data frame with columns representing the total scores.

Examples

# Calculate the mean of all items beginning with `x_` and separately all items beginning with `y_`
total_scores(test_psychometrics, x = starts_with('x'), y = starts_with('y'))
# Calculate the sum of all items beginning with `z_` and separately all items beginning with `x_`
total_scores(test_psychometrics, .method = 'sum', z = starts_with('z'), x = starts_with('x_'))
# Calculate the mean of all items from `x_1` to `y_10`
total_scores(test_psychometrics, xy = x_1:y_10)

A Tukey box-and-whisker plot

Description

This function is a wrapper around a typical ggplot based box-and-whisker plot, i.e. using geom_boxplot, which implements the Tukey variant of the box-and-whisker plot. The y variable is the outcome variable whose distribution is represented by the box-and-whisker plot. If the x variable is missing, then a single box-and-whisker plot using all values of y is shown. If an x variable is used, this is used an the independent variable and one box-and-whisker plot is provided for each set of y values that correspond to each unique value of x. For this reason, x is usually a categorical variable. If x is a continuous numeric variable, it ideally should have relatively few unique values, so that each value of x corresponds to a sufficiently large set of y values.

Usage

tukeyboxplot(
  y,
  x,
  data,
  by = NULL,
  jitter = FALSE,
  box_width = 1/3,
  jitter_width = 1/5,
  xlab = NULL,
  ylab = NULL
)

Arguments

Value

A ggplot2::ggplot object, which may be modified with further ggplot2 commands.

Examples

# A single box-and-whisker plot
tukeyboxplot(y = time, data = vizverb)
# One box-and-whisker plot for each value of a categorical variable
tukeyboxplot(y = time, x = task, data = vizverb)
# Box-and-whisker plots with jitters
tukeyboxplot(y = time, x = task, data = vizverb,  jitter = TRUE)
# `tukeyboxplot` can be used with a continuous numeric variable too
tukeyboxplot(y = len, x = dose, data = ToothGrowth)
tukeyboxplot(y = len, x = dose, data = ToothGrowth,
             by = supp, jitter = TRUE, box_width = 0.5, jitter_width = 1)

Visual versus Verbal Perception and Responses

Description

An experiment studying the interaction between visual versus perception and visual versus verbal responses.

Usage

vizverb

Format

A data frame with 80 observations on the following 5 variables.

subject

Subject identifying number (s1 to s20)

task

Describe a diagram (visual) or a sentence (verbal)

response

Point response (visual) or say response (verbal)

time

Response time (in seconds)

Details

Subjects carried out two kinds of tasks. One task was visual (describing a diagram), and the other was classed as verbal (reading and describing a sentence sentences). They reported the results either by pointing (a "visual" response), or speaking (a verbal response). Time to complete each task was recorded in seconds.

Source

This data set was taken from the Stats2Data R package. From the description in that package, the original data appear to have been collected in a Mount Holyoke College psychology class based replication of an experiment by Brooks, L., R. (1968) "Spatial and verbal components of the act of recall," Canadian J. Psych. V 22, pp. 349 - 368.