Package 'coursekata'

Ames, Iowa housing data

Description

Data describing all residential home sales in Ames, Iowa from the years 2006–2010 as reported by the Ames City Assessor's Office and compiled by De Cock (2011). Ames is located about 30 miles north of Des Moines (the stats capitol) and is home to Iowa State University (the largest university in the state). Each row represents the latest sale of a home (one row per home in the dataset). Columns represent home features and sale prices (outcome). The original dataset includes a uniquely detailed (81 features per home) and comprehensive look at the housing market. The data included here are only a subset used for examples in CourseKata course material. See the references and data source for the full dataset.

Pedagogical Modifications

To simplify the dataset for instructional purposes, the data were filtered to include only single family homes, residential zoning, 1-2 story homes, homes with brick, cinder block, or concrete foundations, and average to excellent kitchen qualities. Further, the descriptive variables were reduced to the subset described in the format section.

Usage

Ames

Format

A data frame with 2930 observations on the following 80 variables:

YearBuilt

Year home was built (YYYY).

YearSold

Year of home sale (YYYY). Note: all home sales in this dataset occurred between 2006 - 2010. If a home was sold more than once between 2006 - 2010, only its latest sale is included in dataset.

Neighborhood

One of two neighborhoods in Ames county:

• College Creek (CollegeCreek), a neighborhood located adjacent to Iowa State University (the largest University in the state).

• Old Town (OldTown), a nationally designated historic district in Ames. The old neighborhood is located just north of the central business district.

HomeSizeR

Raw above-ground area of home, measured in square feet.

HomeSizeK

Above-ground area of home, measured in thousands of square feet.

LotSizeR

Raw total property lot size, measured in square feet.

LotSizeK

Total property lot size, in thousands of square feet.

Floors

Number of above-ground floors (1 story or 2 story).

BuildQuality

Assessor's rating of overall material and finish of the house.

• 10: Very Excellent

• 9: Excellent

• 8: Very Good

• 7: Good

• 6: Above Average

• 5: Average

• 4: Below Average

• 3: Fair

• 2: Poor

• 1: Very Poor

Foundation

Type of foundation (ground material underneath the house).

• Brick&Tile: Brick and Tile

• CinderBlock: Cinder Blocks

• PouredConcrete: Poured Concrete

HasCentralAir

Indicator if home contains central air conditioning (0 = No, 1 = Yes).

Bathrooms

Number of full above-ground bathrooms.

Bedrooms

Number of full above-ground bedrooms.

TotalRooms

Number of above-ground rooms in home, excluding bathrooms.

KitchenQuality

Assessor's rating of kitchen material quality.

• Excellent

• Good

• Average

HasFireplace

Indicator if home contains at least one fireplace (0 = No, 1 = Yes).

GarageType

Type of garage.

• Attached: includes attached, built-in, basement, and dual-type garages

• Detached: includes detached and carport garages

• None: home does not have a garage or carport

GarageCars

Number of cars that can fit in garage.

PriceR

Sale price of home, in raw USD ($)

PriceK

Sale price of home, in thousands of USD ($)

TinySet

(Ignore) Whether or not this row is in ames_tiny.csv

Source

https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data

References

De Cock, Dean, (2011). Ames, Iowa: Alternative to the Boston Housing Data as an end of semester regression project, Journal of Statistics Education, 19(3). doi:10.1080/10691898.2011.11889627

---

Generated "class data" for exploring pairwise tests

Description

These data were generated as outcomes for "students" for three different "instructors" named A, B, and C. The outcome have means such that C > B > A, but the difference is only clearly significant for C > A, and borderline for the others.

Usage

class_data

Format

An object of class tbl_df (inherits from tbl, data.frame) with 105 rows and 2 columns.

Details

outcome

A hypothetical, numerical outcome of an intervention.

teacher

Either "A", "B", or "C", associating the outcome to a teacher.

---

Attach the CourseKata course packages

Description

Attach the CourseKata course packages

Usage

coursekata_attach(do_not_ask = FALSE, quietly = FALSE)

Arguments

do_not_ask	Prevent asking the user to install missing packages (they are skipped).
quietly	Whether to suppress messages.

Value

A named logical vector indicating which packages were attached.

Examples

coursekata_attach()

---

Install or update all CourseKata packages.

Description

Install or update all CourseKata packages.

Usage

coursekata_install(...)

coursekata_update(...)

Arguments

...	Arguments passed on to remotes::install_cran or remotes::install_github depending on whether the package appears to be from CRAN or GitHub.

Value

The state of all the packages after any updates have been performed.

---

Utility function for loading all themes.

Description

This function is called at package start-up and should rarely be needed by the user. The exception is when the user has called coursekata_unload_theme() and wants to go back to the CourseKata look and feel. When run, this function sets the CourseKata color palettes coursekata_palette(), sets the default theme to theme_coursekata(), and tweaks some default settings for specific plots. To restore the original ggplot2 settings, run coursekata_unload_theme().

Usage

coursekata_load_theme()

Value

No return value, called to adjust the global state of ggplot2.

See Also

coursekata_palette theme_coursekata scale_discrete_coursekata coursekata_unload_theme

---

List all CourseKata course packages

Description

List all CourseKata course packages

Usage

coursekata_packages(check_remote_version = FALSE)

Arguments

check_remote_version

Should the remote version number be checked? Requires internet, and will take longer.

Value

A data frame with three variables: the name of the package package, the version, and whether it is currently attached.

Examples

coursekata_packages()

---

The color palettes used in our theme system

Description

The color palettes used in our theme system

Usage

coursekata_palette(indices = integer(0))

Arguments

indices

The indices of the colors to pull (or all colors if no indices are given).

Value

A named list of the requested colors in the palette.

---

Create a function that provides a colorblind palette.

Description

Create a function that provides a colorblind palette.

Usage

coursekata_palette_provider()

Value

A function that accepts one argument n, which is the number of colors you want to use in the plot. This function is used by scales like scale_color_discrete to provide colorblind- safe palettes. Where possible, the function will use the hand-picked colors from coursekata_palette(), and when more colors are needed than are available, it will use the viridisLite::viridis() palette.

See Also

scale_discrete_coursekata

---

Get repositories for the packages.

Description

Ensures a default CRAN is set if one is not already set, and adds the repository for fivethirtyeightdata.

Usage

coursekata_repos(repos = getOption("repos"))

Arguments

repos

Optionally set a repository character vector to augment.

Value

A set of repositories that can be used to install or update the CourseKata packages.

Examples

coursekata_repos()

---

Restore ggplot2 default settings

Description

This function will restore all of the tweaks to themes and plotting to the original ggplot2 defaults. If you want to go back to the CourseKata look and feel, run coursekata_load_theme().

Usage

coursekata_unload_theme()

Value

No return value, called to restore the global state of ggplot2.

See Also

coursekata_load_theme

---

Emergency room canine therapy

Description

Data from: Controlled clinical trial of canine therapy versus usual care to reduce patient anxiety in the emergency department.

Abstract

Objective

Test if therapy dogs can reduce anxiety in emergency department (ED) patients.

Methods

In this controlled clinical trial (NCT03471429), medically stable, adult patients were approached if the physician believed that the patient had “moderate or greater anxiety.” Patients were allocated on a 1:1 ratio to either 15 min exposure to a certified therapy dog and handler (dog), or usual care (control). Patient reported anxiety, pain and depression were assessed using a 0-10 scale (10=worst). Primary outcome was change in anxiety from baseline (T0) to 30 min and 90 min after exposure to dog or control (T1 and T2 respectively); secondary outcomes were pain, depression and frequency of pain medication.

Results

Among 98 patients willing to participate in research, 7 had aversions to dogs, leaving 91 (93%) were willing to see a dog; 40 patients were allocated to each group (dog or control). No data were normally distributed. Median baseline anxiety, pain and depression were similar between groups. With dog exposure, anxiety decreased significantly from T0 to T1: 6 (IQR 4-9.75) to T1: 2 (0-6) compared with 6 (4-8) to 6 (2.5-8) in controls (P<0.001, for T1, Mann-Whitney U). Dog exposure was associated with significantly lower anxiety at T2 and a significant overall treatment effect on two-way repeated measures ANOVA for anxiety, pain and depression. After exposure, 1/40 in the dog group needed pain medication, versus 7/40 in controls (P=0.056, Fisher’s).

Conclusions

Exposure to therapy dogs plus handlers significantly reduced anxiety in ED patients.

Usage

er

Format

A data frame with 84 observations on the following 53 variables:

id

Subject ID

condition

Whether the subject saw a Dog or was in the Control group

age

Subject's age in years

gender

Subject's self-identified gender

race

Subject's self-identified race

veteran

Is the subject a veteran?

disabled

Is the subject disabled?

dog_name

The name of the therapy dog

base_pain

Subject's self reported pain before the intervention (T0)

base_depression

Subject's self reported depression before the intervention (T0)

base_anxiety

Subject's self reported anxiety before the intervention (T0)

base_total

The sum of the subject's ⁠base_*⁠ scores

later_pain

Subject's self reported pain after the intervention (T1)

later_depression

Subject's self reported depression after the intervention (T1)

later_anxiety

Subject's self reported anxiety after the intervention (T1)

later_total

The sum of the subject's ⁠later_*⁠ scores

last_pain

Subject's self reported pain after the intervention (T2)

last_depression

Subject's self reported depression after the intervention (T2)

last_anxiety

Subject's self reported anxiety after the intervention (T2)

last_total

The sum of the subject's ⁠last_*⁠ scores

change_pain

The change in subject's pain from before the intervention to after

change_depression

The change in subject's depression from before the intervention to after

change_anxiety

The change in subject's anxiety from before the intervention to after

change_total

The sum of the subject's ⁠change_*⁠ scores

provider_male

Was the health care provider male?

provider

The health care provider's status: either an ⁠Advanced Practitioner⁠, Resident physician, or Attending physician

heart_rate

The subject's heart rate at baseline (T0)

resp_rate

The subject's respiratory rate at baseline (T0)

sp_o2

The subject's SpO2 at baseline (T0)

bp_syst

The subject's systolic blood pressure at baseline (T0)

bp_diast

The subject's diastolic blood pressure at baseline (T0)

med_given

Was the subject given medication prior to the study? (T0)

mh_none

None of the other medical history items were indicated

mh_asthma

Medical history: asthma

mh_smoker

Medical history: smoker

mh_cad

Medical history: coronary artery disease

mh_diabetes

Medical history: diabetes mellitus

mh_hypertension

Medical history: hypertension

mh_stroke

Medical history: prior stroke

mh_chronic_kidney

Medical history: chronic kidney disease

mh_copd

Medical history: chronic obstructive pulmonary disease

mh_hyperlipidemia

Medical history: hyperlipidemia

mh_hiv

Medical history: HIV

mh_other

Medical history: other (write-in)

ph_adhd

Psychiatric history: attention-deficit/hyperactivity disorder

ph_anxiety

Psychiatric history: anxiety

ph_bipolar

Psychiatric history: bipolar

ph_borderline

Psychiatric history: borderline personality disorder

ph_depression

Psychiatric history: depression

ph_schizophrenia

Psychiatric history: schizophrenia

ph_ptsd

Psychiatric history: PTSD

ph_none

None of the other psychiatric history items were indicated

ph_other

Psychiatric history: other (write-in)

References

Kline, J. A., Fisher, M. A., Pettit, K. L., Linville, C. T., & Beck, A. M. (2019). Controlled clinical trial of canine therapy versus usual care to reduce patient anxiety in the emergency department. PloS One, 14(1), e0209232. doi:10.1371/journal.pone.0209232

---

Extract estimates/statistics from a model

Description

This collection of functions is useful for extracting estimates and statistics from a fitted model. They are particularly useful when estimating many models, like when bootstrapping confidence intervals. Each function can be used with an already fitted model as an lm object, or a formula and associated data can be passed to it. All of these assume the comparison is the empty model.

Usage

b0(object, data = NULL)

b1(object, data = NULL)

b(object, data = NULL, all = FALSE, predictor = character())

f(object, data = NULL, all = FALSE, predictor = character(), type = 3)

pre(object, data = NULL, all = FALSE, predictor = character(), type = 3)

p(object, data = NULL, all = FALSE, predictor = character(), type = 3)

fVal(object, data = NULL, all = FALSE, predictor = character(), type = 3)

PRE(object, data = NULL, all = FALSE, predictor = character(), type = 3)

Arguments

object	A lm object, or formula.
data	If object is a formula, the data to fit the formula to as a data.frame.
all	If TRUE, return a named list of all related terms (e.g. all F-values).The name for the full model value is the name of the function (e.g. "f"), and the names for the constituent terms are the term names prefixed by the function name (e.g. "f_a:b" for the F-value of the a:b interaction term).
predictor	Filter the output down to just the statistics for these terms (e.g. "hp" to just get the statistics for that term in the model). This argument is flexible: you can pass a character vector of terms (c("hp", "hp:cyl")), a one-sided formula (~hp), or a list of formulae (c(~hp, ~hp:cyl)).
type	The type of sums of squares to calculate (see generate_models()). Defaults to the widely used Type III SS.

Details

• b0: The intercept from the full model.

• b1: The slope b1 from the full model.

• b: The coefficients from the full model.

• f: The F value from the full model.

• pre: The Proportional Reduction in Error for the full model.

• p: The p-value from the full model.

• sse: The SS Error (SS Residual) from the model.

• ssm: The SS Model (SS Regression) for the full model.

• ssr: Alias for SSM.

Value

The value of the estimate as a single number.

References

Judd, C. M., McClelland, G. H., & Ryan, C. S. (2017). Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond (3rd ed.). New York: Routledge. ISBN:879-1138819832

Examples

supernova(lm(mpg ~ disp, data = mtcars))

change_p_decimals <- supernova(lm(mpg ~ disp, data = mtcars))
print(change_p_decimals, pcut = 8)

---

Forced Expiratory Volume (FEV) Data

Description

Data from: Fundamentals of Biostatistics Notes from: Kahn, M.

Abstract

Sample of 654 youths, aged 3 to 19, in the area of East Boston during middle to late 1970's. Interest concerns the relationship between smoking and FEV. Since the study is necessarily observational, statistical adjustment via regression models clarifies the relationship.

Pedagogical Notes:

This is a versatile dataset that can be used throughout an introductory statistics course as well as an introductory modeling course. It includes many issues from statistical adjustment in observational studies, to subgroup analysis, quadratic regression and analysis of covariance.

Usage

fevdata

Format

A data frame with 654 observations on the following 5 variables:

AGE

Age, in years

FEV

Forced expiratory volume, in liters

HEIGHT

Height, in inches

SEX

0 = Female, 1 = Male

SMOKE

0 = Non-smoker, 1 = Smoker

References

Kahn,M. (2003). Data Sleuth, STATS, 37, 24. https://jse.amstat.org/datasets/fev.txt Rosner, B. (1999). Fundamentals of Biostatistics, Pacific Grove, CA: Duxbury

---

Data from introductory statistics students at a university.

Description

Students at a university taking an introductory statistics course were asked to complete this survey as part of their homework.

Usage

Fingers

Format

A data frame with 157 observations on the following 16 variables:

Gender

Gender of participant.

RaceEthnic

Racial or ethnic background.

FamilyMembers

Members of immediate family (excluding self).

SSLast

Last digit of social security number (NA if no SSN).

Year

Year in school: 1=First, 2=Second, 3=Third, 4=Fourth, 5=Other

Job

Current employment status: 1=Not Working, 2=Part-time Job, 3=Full-time Job

MathAnxious

Agreement with the statement "In general I tend to feel very anxious about mathematics": 1=Strongly Disagree, 2=Disagree, 3=Neither Agree nor Disagree, 4=Agree, 5=Strongly Agree

Interest

Interest in statistics and the course: 1=No Interest, 2=Somewhat Interested, 3=Very Interested

GradePredict

Numeric prediction for final grade in the course. The value is converted from the student's letter grade prediction. 4.0=A, 3.7=A-, 3.3=B+, 3.0=B, 2.7=B-, 2.3=C+, 2.0=C, 1.7=C-, 1.3=Below C-

Thumb

Length in mm from tip of thumb to the crease between the thumb and palm.

Index

Length in mm from tip of index finger to the crease between the index finger and palm.

Middle

Length in mm from tip of middle finger to the crease between the middle finger and palm.

Ring

Length in mm from tip of ring finger to the crease between the middle finger and palm.

Pinkie

Length in mm from tip of pinkie finger to the crease between the pinkie finger and palm.

Height

Height in inches.

Weight

Weight in pounds.

Sex

Sex of participant.

---

Raw data from introductory statistics students at a university.

Description

This is the Fingers dataset before it was cleaned. In the cleaning process, we converted the values from numbers to appropriate types (where applicable), removed outliers that suggested data was input incorrectly, and we removed incomplete cases. The description for the dataset is: Students at a university taking an introductory statistics course were asked to complete this survey as part of their homework. (This is the same data set as the Fingers data)

Usage

FingersMessy

Format

A data frame with 157 observations on the following 16 variables:

Gender

Gender of participant.

RaceEthnic

Racial or ethnic background.

FamilyMembers

Members of immediate family (excluding self).

SSLast

Last digit of social security number (NA if no SSN).

Year

Year in school: 1=First, 2=Second, 3=Third, 4=Fourth, 5=Other

Job

Current employment status: 1=Not Working, 2=Part-time Job, 3=Full-time Job

MathAnxious

Agreement with the statement "In general I tend to feel very anxious about mathematics": 1=Strongly Disagree, 2=Disagree, 3=Neither Agree nor Disagree, 4=Agree, 5=Strongly Agree

Interest

Interest in statistics and the course: 1=No Interest, 2=Somewhat Interested, 3=Very Interested

GradePredict

Numeric prediction for final grade in the course. The value is converted from the student's letter grade prediction. 4.0=A, 3.7=A-, 3.3=B+, 3.0=B, 2.7=B-, 2.3=C+, 2.0=C, 1.7=C-, 1.3=Below C-

Thumb

Length in mm from tip of thumb to the crease between the thumb and palm.

Index

Length in mm from tip of index finger to the crease between the index finger and palm.

Middle

Length in mm from tip of middle finger to the crease between the middle finger and palm.

Ring

Length in mm from tip of ring finger to the crease between the middle finger and palm.

Pinkie

Length in mm from tip of pinkie finger to the crease between the pinkie finger and palm.

Height

Height in inches.

Weight

Weight in pounds.

Sex

Sex of participant.

---

Test the fit of a model on a train and test set.

Description

Test the fit of a model on a train and test set.

Usage

fit_stats(model, df_train, df_test)

fitstats(model, df_train, df_test)

Arguments

model	An lm model.
df_train	A data frame with the training data.
df_test	A data frame with the test data.

Value

A data frame with the fit statistics.

---

Simulated math game data.

Description

The simulated results of a small study comparing the effectiveness of three different computer- based math games in a sample of 105 fifth-grade students. All three games focused on the same topic and had identical learning goals, and none of the students had any prior knowledge of the topic.

Usage

game_data

Format

A data frame with 105 observations on the following 2 variables:

game

The game the student was randomly assigned to, coded as "A", "B", or "C".

outcome

Each student's score on the outcome test.

---

Add a model to a plot

Description

When teaching about regression it can be useful to visualize the data as a point plot with the outcome on the y-axis and the explanatory variable on the x-axis. For regression models, this is most easily achieved by calling ggformula::gf_lm(), with empty models ggformula::gf_hline() using the mean, and a more complicated call to ggformula::gf_segment() for group models. This function simplifies this by making a guess about what kind of model you are plotting (empty/null, regression, group) and then making the appropriate plot layer for it.

Usage

gf_model(object, model, ...)

Arguments

object	A plot created with the ggformula package.
model	A linear model fit by either lm() or aov().
...	Additional arguments. Typically these are (a) ggplot2 aesthetics to be set with attribute = value, (b) ggplot2 aesthetics to be mapped with attribute = ~ expression, or (c) attributes of the layer as a whole, which are set with attribute = value.

Details

This function only works with models that have a continuous outcome measure.

Value

a gg object (a plot layer) that can be added to a plot.

---

Add Residual Lines to a Plot

Description

This function adds vertical lines representing residuals from a linear model to a ggformula plot. The residuals are drawn from the observed data points to the predicted values from the model.

Usage

gf_resid(plot, model, linewidth = 0.2, ...)

Arguments

plot	A ggformula plot object, typically created with gf_point().
model	A fitted linear model object created using lm().
linewidth	A numeric value specifying the width of the residual lines. Default is 0.2.
...	Additional aesthetics passed to geom_segment(), such as color, alpha, linetype.

Value

A ggplot object with residual lines added.

Examples

Height_model <- lm(Thumb ~ Height, data = Fingers)
gf_point(Thumb ~ Height, data = Fingers) %>%
  gf_model(Height_model) %>%
  gf_resid(Height_model, color = "red", alpha = 0.5)

---

Add Residual Lines from a Function to a Plot

Description

Usage

gf_resid_fun(plot, fun, linewidth = 0.2, ...)

Arguments

plot	A ggformula/ggplot object, typically created with gf_point().
fun	A function that takes a numeric vector x and returns predicted y.
linewidth	Numeric width of the residual lines. Default 0.2.
...	Additional aesthetics passed to ggplot2::geom_segment(), e.g., color, alpha, linetype.

Details

Draws vertical residual lines from observed points to predicted values computed by a user-supplied function of x (e.g., the function plotted with gf_function()).

Value

A ggplot object with residual segments added.

Examples

set.seed(1)
df <- data.frame(X = 1:10, Y = 2 + 3 * (1:10) + rnorm(10))
my_fun <- function(x) 2 + 3 * x

gf_point(Y ~ X, data = df) %>%
  gf_function(my_fun) %>%
  gf_resid_fun(my_fun, color = "red", alpha = 0.5)

---

Add a Standard Deviation Ruler to a Plot

Description

Usage

gf_sd_ruler(
  p,
  y = NULL,
  data = NULL,
  x = NULL,
  where = c("middle", "mean", "median"),
  color = "red",
  size = 0.8,
  ...
)

Arguments

p	A ggplot object (typically from gf_point() or gf_jitter()).
y	The y-variable (bare name or string). Defaults to the plot's mapped y aesthetic if omitted.
data	Dataset. Defaults to p$data.
x	The x-variable for placement. Defaults to the plot's mapped x.
where	Where on the x-axis to place the ruler: "middle" (midpoint of x range), "mean", or "median".
color	Segment color. Default "red".
size	Segment linewidth. Default 0.8.
...	Additional arguments passed to ggplot2::geom_segment().

Details

Adds a vertical segment showing one standard deviation of a variable, placed at a specified x position. Works for both numeric x (scatter) and categorical x (jitter) plots.

Value

A ggplot object with the SD ruler segment added.

Examples

gf_jitter(Thumb ~ Height, data = Fingers) %>%
  gf_model(lm(Thumb ~ NULL, data = Fingers)) %>%
  gf_sd_ruler()

---

Add Squared Residual Visualization to a Plot

Description

gf_squaresid() was renamed to gf_square_resid() for naming consistency and is now deprecated.

Usage

gf_square_resid(plot, model, aspect = 4/6, alpha = 0.1, ...)

gf_squaresid(plot, model, aspect = 4/6, alpha = 0.1, ...)

Arguments

plot	A ggformula plot object, typically created with gf_point().
model	A fitted linear model object created using lm().
aspect	A numeric value controlling the square's aspect ratio. Default is 4/6.
alpha	A numeric value specifying the transparency of the square's fill. Default is 0.1.
...	Additional aesthetics passed to geom_polygon(), such as color and fill.

Details

This function adds squared residual representations to a ggformula plot, illustrating squared error as a polygon. The function dynamically adjusts the aspect ratio to ensure proper scaling of squares.

Value

A ggplot object with squared residuals added.

Examples

Height_model <- lm(Thumb ~ Height, data = Fingers)
gf_point(Thumb ~ Height, data = Fingers) %>%
  gf_model(Height_model) %>%
  gf_square_resid(Height_model, color = "blue", alpha = 0.5)

---

Add Squared Residual Visualization from a Function to a Plot

Description

Usage

gf_square_resid_fun(plot, fun, aspect = 4/6, alpha = 0.1, ...)

Arguments

plot	A ggformula/ggplot object, typically created with gf_point().
fun	A function that takes a numeric vector x and returns predicted y.
aspect	A numeric value controlling the square's aspect ratio. Default is 4/6.
alpha	Transparency of the filled squares. Default 0.1.
...	Additional aesthetics passed to ggplot2::geom_polygon(), e.g., color, fill, linetype.

Details

Draws squared residual polygons between observed points and predicted values computed by a user-supplied function of x.

Value

A ggplot object with squared residual polygons added.

Examples

set.seed(1)
df <- data.frame(X = 1:10, Y = 2 + 3 * (1:10) + rnorm(10))
my_fun <- function(x) 2 + 3 * x

gf_point(Y ~ X, data = df) %>%
  gf_function(my_fun) %>%
  gf_square_resid_fun(my_fun, color = "red", alpha = 0.3)

---

Countable-Rectangle Histogram

Description

Usage

gf_squareplot(
  x,
  data = NULL,
  binwidth = NULL,
  origin = NULL,
  boundary = NULL,
  fill = "#7fcecc",
  color = "black",
  alpha = 1,
  na.rm = TRUE,
  mincount = NULL,
  bars = c("none", "outline", "solid"),
  xbreaks = NULL,
  xrange = NULL,
  show_dgp = FALSE,
  show_mean = FALSE,
  auto_subdivide = FALSE
)

Arguments

x	Formula (~variable) or numeric vector.
data	Data frame (required if x is a formula).
binwidth	Width of histogram bins. Auto-calculated if NULL.
origin	Starting position for bins.
boundary	Alias for origin.
fill	Rectangle fill color. Default "#7fcecc".
color	Rectangle border color. Default "black".
alpha	Transparency. Default 1.
na.rm	Remove NA values. Default TRUE.
mincount	Minimum y-axis height for consistent scaling.
bars	Display style: "none" (squares only), "outline", or "solid".
xbreaks	Number of x-axis breaks or vector of specific positions.
xrange	X-axis limits as c(min, max).
show_dgp	Show DGP annotation overlay. Default FALSE.
show_mean	Show dashed mean line. Default FALSE.
auto_subdivide	Split bins with >75 observations into sub-columns. Default FALSE.

Details

Creates histograms where individual data points are visible as stacked unit rectangles, making counts easy to visualize. Designed for teaching statistical concepts, particularly sampling distributions.

Value

A ggplot object with S3 class c("gf_squareplot", "gg", "ggplot").

Examples

gf_squareplot(~Thumb, data = Fingers)
gf_squareplot(~Thumb, data = Fingers, bars = "outline")

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Find a percentage of a distribution

Description

Given a distribution, find which values lie in the upper, lower, or middle proportion of the distribution. Useful when you want to do something like shade in the middle 95% of a plot. This is a greedy operation, meaning that if the cutoff point is between two whole numbers the specified region will suck up the extra space. For example, the requesting the upper 30% of the ⁠[1 2 3 4]⁠ will return ⁠[FALSE FALSE TRUE TRUE]⁠ because the 30% was greedy.

outer() marks values in both outer tails of a distribution. It is the complement of middle(): outer(x, prop) is equivalent to tails(x, 1 - prop).

Usage

middle(x, prop = 0.95, greedy = TRUE)

tails(x, prop = 0.95, greedy = TRUE)

outer(x, prop)

lower(x, prop = 0.025, greedy = TRUE)

upper(x, prop = 0.025, greedy = TRUE)

Arguments

x	The distribution of values to check.
prop	The total proportion in both tails combined, must be in (0, 1).
greedy	Whether the function should be greedy, as per the description above.

Details

Note that NA values are ignored, i.e. they will always return FALSE.

Value

A logical vector indicating which values are in the specified region.

Examples

upper(1:10, .1)
lower(1:10, .2)
middle(1:10, .5)
tails(1:10, .5)

sampling_distribution <- do(1000) * mean(rnorm(100, 5, 10))
sampling_distribution %>%
  gf_histogram(~mean, data = sampling_distribution, fill = ~ middle(mean, .68)) %>%
  gf_refine(scale_fill_manual(values = c("blue", "coral")))

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A modified form of the palmerpenguins::penguins data set.

Description

The modifications are to select only a subset of the variables, and convert some of the units.

Usage

penguins

Format

A data frame with 333 observations on the following 7 variables:

species

The species of penguin, coded as "Adelie", "Chinstrap", or "Gentoo".

gentoo

Whether the penguin is a Gentoo penguin (1) or not (0).

body_mass_kg

The mass of the penguin's body, in kilograms.

flipper_length_m

The length of the penguin's flipper, in m.

bill_length_cm

The length of the penguin's bill, in cm.

female

Whether the penguin is female (1) or not (0).

island

The island where the penguin was observed, coded as "Biscoe", "Dream", or "Torgersen".

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A discrete color scale constructor with colorblind-safe palettes.

Description

See coursekata_palette() for more information.

Usage

scale_discrete_coursekata(...)

Arguments

...	Additional parameters passed on to the scale type.

Value

A discrete color scale.

See Also

coursekata_palette

---

Add Cutoff Markers to a Histogram

Description

Usage

show_cutoffs(plot, color = "#1e3a8a", size = 4, labels = FALSE)

Arguments

plot	A ggplot histogram with fill mapped to a distribution part function, e.g., fill = ~middle(Thumb, .95).
color	Marker/line color. Default "#1e3a8a".
size	Marker size. Default 4.
labels	Whether to add text annotations explaining the cutoffs. Default FALSE.

Details

Adds downward-pointing triangle markers at the empirical quantile cutoffs on a histogram that uses a distribution part function (middle(), tails(), upper(), lower(), or outer()) in its fill aesthetic.

Value

A ggplot object with cutoff markers and optional labels.

Examples

gf_histogram(~Thumb, data = Fingers, fill = ~middle(Thumb, .95)) %>%
  show_cutoffs(labels = TRUE)

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Simulated housing data

Description

These data are simulated to be similar to the Ames housing data, but with far fewer variables and much smaller effect sizes.

Usage

Smallville

Format

A data frame with 32 observations on the following 4 variables:

PriceK

Price the home sold for (in thousands of dollars)

Neighborhood

The neighborhood the home is in (Eastside, Downtown)

HomeSizeK

The size of the home (in thousands of square feet)

HasFireplace

Whether the home has a fireplace (0 = no, 1 = yes)

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Split data into train and test sets.

Description

Split data into train and test sets.

Usage

split_data(data, prop = 0.7)

Arguments

data	A data frame.
prop	The proportion of rows to assign to the training set.

Value

A list with two data frames, train and test.

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Students at a university were asked to enter a random number between 1-20 into a survey.

Description

Students at a university taking an introductory statistics course were asked to complete this survey as part of their homework.

Usage

Survey

Format

A data frame with 211 observations on the following 1 variable:

Any1_20

The random number between 1 and 20 that a student thought of.

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Tables data

Description

Data about tips collected from an experiment with 44 tables at a restaurant.

Usage

Tables

Format

A data frame with 44 observations on the following 2 variables.

TableID

A number assigned to each table.

Tip

How much the tip was.

---

A simple theme built on top of ggplot2::theme_bw

Description

The coursekata package automatically loads this theme when the package is loaded. This is in addition to a number of other plot tweaks and option settings. To just restore the theme to the default, you can run set_theme(theme_grey). If you want to restore all plot related settings and/or prevent them when loading the package, see coursekata_unload_theme.

Usage

theme_coursekata()

Value

A gg theme object

Examples

gf_boxplot(Thumb ~ RaceEthnic, data = Fingers, fill = ~RaceEthnic)

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Simulated data for an experiment about smiley faces and tips

Description

These are simulated data that are similar to the TipExperiment data. Hypothetical tables were randomly assigned to receive checks that either included or did not include a drawing of a smiley face, either from a male or a female server.

Usage

tip_exp

Format

A data frame with 44 observations on the following 3 variables.

gender

Whether the server was female or male

condition

Whether the check had a ⁠smiley face⁠ or not (control)

tip_percent

The size of the tip as a percentage of the price of the meal

---

Data from an experiment about smiley faces and tips

Description

Tables were randomly assigned to receive checks that either included or did not include a drawing of a smiley face. Data was collected from 44 tables in an effort to examine whether the added smiley face would cause more generous tipping.

Usage

TipExperiment

Format

A data frame with 44 observations on the following 3 variables.

TableID

A number assigned to each table.

Tip

How much the tip was.

Condition

Which experimental condition the table was randomly assigned to.

Check

(Simulated) The amount of money the table paid for their meal.

FoodQuality

(Simulated) The perceived quality of the food.

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Data on countries from the Happy Planet Index project.

Description

These data have been updated with some historical height data (from Our World in Data), drinking data (collected by the World Health Organization featured in fivethirtyeight), population and land characteristics, and vaccination data (from March 2023).

Usage

World

Format

A data frame with 130 observations on the following 14 variables:

Country

Name of country

Region

One of 5 UN defined regions: Africa, Americas, Asia, Europe, Oceania

Code

Three-letter country codes defined by the International Organization for Standardization (ISO) to represent countries in a way that avoids errors since a country’s name changes depending on the language being used.

LifeExpectancy

Average life expectancy (in years)

GirlsH1900

The average of 18-year-old girls heights in 1900 (in cm)

GirlsH1980

The average of 18-year-old girls heights in 1980 (in cm)

Happiness

Score on a 0-10 scale for average level of happiness (10 being happiest)

GDPperCapita

Gross Domestic Product (per capita)

FertRate

The average number of children that will be born to a woman over her lifetime

PeopleVacc

Total number of people vaccinated in the country

PeopleVacc_per100

Total number of people vaccinated in the country (in percent)

Population2010

Population (in millions) in 2010

Population2020

Population (in millions) in 2020

WineServ

Average wine consumption per capita for those age 15 and over per week (collected by WHO)

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