psych350lab

psych350lab provides helper functions for a Research Methods and Statistical Analysis lab course. It streamlines running common statistical analyses, generating APA-formatted tables, and creating interactive student worksheets — all from R and Quarto/RMarkdown.

Installation

You can install psych350lab using the pak package, which handles both CRAN and GitHub installations seamlessly.

Install from GitHub (recommended)

To install you need to first set up GitHub authentication first.

Step 1: Configure GitHub credentials (one-time setup)

# Install required packages if needed
install.packages(c("gitcreds", "usethis"))

# Check if credentials are already configured
gitcreds::gitcreds_get()

# If not configured, create a Personal Access Token:
usethis::create_github_token()  
# This opens GitHub with the correct scopes pre-selected.
# Generate the token, copy it, then run:

gitcreds::gitcreds_set()
# Paste your token when prompted

Step 2: Install the package

pak::pak("emmarshall/psych350lab")

Install from a local source

If you have a local copy of the package (e.g., a .tar.gz file or a cloned repo), you can install it with:

# From a .tar.gz file
pak::pak("local::path/to/psych350lab_0.1.0.tar.gz")

# From a local directory
pak::pak("local::path/to/psych350lab")

Troubleshooting authentication

If you encounter authentication errors:

# Check your current GitHub configuration
usethis::git_sitrep()

# Reset and reconfigure credentials
gitcreds::gitcreds_set()

Overview

The package is organized around several core analysis workflows, each with a computation function (*_answers()) and companion table/formatting functions.

Data Import

Function	Description
`get_spss_data()`	Read SPSS `.sav` files, convert missing values, and strip labels

Correlation Analysis

Function	Description
`corr_answers()`	Compute Pearson correlation with descriptives and hypothesis test
`create_corr_table()`	Compact flextable with r, p, df, means, SDs
`format_corr_results()`	Fill-in-the-blank or answer key text output
`create_apa_corr_table()`	APA correlation matrix with significance stars
`create_corr_matrix_checker()`	Interactive webexercise correlation matrix

Between-Groups ANOVA

Function	Description
`bg_anova_answers()`	One-way between-groups ANOVA with group descriptives
`format_bg_anova_results()`	Fill-in-the-blank or answer key text
`create_bg_anova_table()`	APA descriptive statistics table
`create_anova_source_table()`	APA ANOVA source table (SS, df, MS, F, p)
`create_bg_anova_combined_table()`	Combined ANOVA + descriptives flextable

Within-Groups (Repeated Measures) ANOVA

Function	Description
`wg_anova_answers()`	Repeated measures ANOVA for two conditions
`format_wg_anova_results()`	Fill-in-the-blank or answer key text
`create_wg_anova_table()`	APA descriptive statistics table
`create_wg_anova_combined_table()`	Combined ANOVA + descriptives flextable

Chi-Square Analysis

Function	Description
`chi_square_answers()`	Compute chi-square goodness-of-fit or 2×k test with effect size
`chi_square_kgroup_answers()`	K-group chi-square with pairwise comparisons and Bonferroni correction
`pr_chi_to_r()`	Convert 2×2 contingency cell counts to chi-square, p, and r
`format_chi2_results()`	Fill-in-the-blank or answer key text output
`create_rh_contingency()`	Display contingency table with observed/expected counts
`create_chi_crosstabs_table()`	Crosstabs table with counts and percentages
`create_chi_combined_table()`	Combined chi-square results and crosstabs table
`format_chi_omnibus_results()`	Formatted omnibus results with highlighting
`chisq_pairwise_KEY()`	Pairwise comparison results table

K-Group ANOVA

Function	Description
`anova_kgroup_answers()`	K-group one-way ANOVA with group descriptives and pairwise comparisons
`pr_means_to_r()`	Convert group means and n to between-groups effect size
`lsd_hsd_calculator()`	Compute LSD and HSD minimum mean differences
`create_rh_support_text()`	Formatted hypothesis support text
`anova_statistics_KEY()`	ANOVA statistics answer key display
`anova_descriptives_KEY()`	Group descriptive statistics answer key display

Factorial (Two-Way) ANOVA

Function	Description
`anova_factorial_answers()`	2×2 factorial ANOVA via `jmv` with EMMs and post-hoc
`lsd_hsd_calculator()`	Compute LSD and HSD minimum mean differences
`check_factor_alignment()`	Diagnostic check for factor level ordering

Multiple Regression

Function	Description
`regression_answers()`	Full regression with univariate, bivariate, and multivariate results plus interpretation categories (a–d)
`regression_model_statistics()`	Formatted model summary line (R, R², F, df, p)
`regression_model_evaluation()`	“Does the model work?” / “How well?” text
`create_regression_combined_table()`	Flextable with r, b, type, and result category per predictor
`regression_table_legend()`	Markdown legend for result categories
`regression_category_legend()`	Extended markdown legend
`create_correlation_interp_ft()`	Flextable with auto-generated correlation interpretations
`create_regression_weight_interp_ft()`	Flextable with regression weight interpretations

Interactive Webexercise Checkers (for Quarto)

Function	Description
`create_model_summary_checker()`	Fill-in-the-blank model summary (tinytable + webexercises)
`create_predictor_checker()`	Interactive predictor results table with MCQs
`create_correlation_interpretations()`	Interactive correlation interpretation table
`create_regression_weight_interpretations()`	Interactive regression weight interpretation table

SPSS-Style Plotting

Function	Description
`theme_SPSS()`	Complete ggplot2 theme mimicking SPSS charts (modern or legacy)
`scale_color_SPSS()`	SPSS discrete color scale
`scale_fill_SPSS()`	SPSS discrete fill scale
`palette_SPSS()`	Get SPSS hex color codes directly
`SPSS_pal()`	Palette function factory for ggplot2 scales
`number_SPSS()`	Format axis numbers without thousands separators

Formatting Functions

All display and worksheet functions use a shared set of formatting functions to ensure consistent APA-style rounding across the package.

Function	Description
`format_stat()`	Format a statistic to 2 decimal places (scalar)
`format_p_value()`	Format p-values in APA style (e.g., `"< .001"`, `"p = .023"`)
`format_p_md()`	Format p-values with markdown italic p (e.g., `"p < .001"`)
`format_r()`	Format correlation coefficient (3 decimal places, no leading zero)
`format_effect()`	Format effect sizes (3 decimal places)
`format_mean()`	Format means (2 decimal places)
`format_sd()`	Format standard deviations (2 decimal places)
`format_F()`	Format F statistics (2 decimal places)
`format_t()`	Format t statistics (2 decimal places)
`format_chi2()`	Format chi-square statistics (2 decimal places)
`format_mse()`	Format mean square error (2 decimal places)
`format_df()`	Format degrees of freedom (integer)
`format_n()`	Format sample sizes (integer)
`format_int()`	Format integers
`p_to_stars()`	Convert p-value to significance stars

APA Write-Up Generators

These functions produce paragraph-length APA-style results write-ups in markdown, suitable for Quarto documents.

Function	Description
`apa_corr_writeup()`	Correlation results write-up
`apa_chi_writeup()`	Chi-square test results write-up
`apa_kgroup_chi_writeup()`	K-group chi-square with pairwise results write-up
`apa_bg_anova_writeup()`	Between-groups ANOVA results write-up
`apa_wg_anova_writeup()`	Within-groups ANOVA results write-up
`apa_kgroup_anova_writeup()`	K-group ANOVA with pairwise results write-up
`apa_wg_kgroup_anova_writeup()`	Within-groups k-group ANOVA write-up
`apa_planned_comparisons_writeup()`	Planned comparisons write-up
`apa_wg_simple_contrasts_writeup()`	Within-groups simple contrasts write-up
`apa_2x2_bg_factorial_writeup()`	2×2 between-groups factorial ANOVA write-up
`apa_2x2_mixed_factorial_writeup()`	2×2 mixed factorial ANOVA write-up
`apa_2x2_wg_factorial_writeup()`	2×2 within-groups factorial ANOVA write-up

Inline APA Reporting

Helper functions for inline reporting in Quarto/RMarkdown text, returning markdown-formatted strings:

Function	Description
`apa_inline_r()`	Inline r(df) = .xx, p = .xxx
`apa_inline_chi2()`	Inline χ²(df) = x.xx, p = .xxx
`apa_inline_t()`	Inline t(df) = x.xx, p = .xxx
`apa_inline_F()`	Inline F(df1, df2) = x.xx, p = .xxx
`apa_inline_anova()`	Inline ANOVA result from answers object

Quick Examples

Correlation

library(psych350lab)
data(superman_data)

result <- corr_answers(superman_data, "clark_height_in", "rt_critics_score")
result$Correlation
result$Descriptives

# APA table
create_corr_table("RH1", c("clark_height_in", "rt_critics_score"), result)

Between-Groups ANOVA

result <- bg_anova_answers(superman_data, iv = "clark_grp", dv = "rt_critics_score")
result$ANOVA
result$Descriptives

# Formatted results (answer key)
cat(format_bg_anova_results("RH1", c("clark_grp", "rt_critics_score"),
  result, iv_labels = c("Under 6ft", "6ft+")))

Multiple Regression

sm <- superman_data[!is.na(superman_data$rt_critics_score) &
                    !is.na(superman_data$rt_audience_score), ]

result <- regression_answers(
  data = sm,
  criterion = "rt_critics_score",
  quant_predictors = c("clark_height_in", "rt_audience_score"),
  quant_labels = c("Clark Height (in)", "Audience Score"),
  criterion_label = "Critics Score"
)

result$Model
result$Regression_Weights

# Combined predictor table
create_regression_combined_table(result, KEY = TRUE)

Chi-Square

result <- chi_square_answers(superman_data, "clark_grp", "gender",
  iv_labels = c("Under 6ft", "6ft+"), dv_labels = c("Male", "Female"))
result$ChiSquare
result$ContingencyTable

# Formatted results
cat(format_chi2_results("RH1", c("clark_grp", "gender"), result))

APA Write-Up

# Generate a paragraph-length APA results section
corr_result <- corr_answers(superman_data, "clark_height_in", "rt_critics_score")
writeup <- apa_corr_writeup(
  corr_result,
  iv_label = "Clark Kent's height",
  dv_label = "critics' score"
)
cat(writeup)

SPSS-Style Plot

library(ggplot2)

ggplot(mtcars, aes(x = factor(cyl), y = mpg, fill = factor(cyl))) +
  geom_boxplot() +
  scale_fill_SPSS() +
  theme_SPSS(scale.x = "discrete") +
  labs(x = "Cylinders", y = "MPG")

Formatting and Inline Reporting

# Consistent APA formatting
format_p_value(0.00012)                  # "< .001"
format_p_value(0.034, include_p = TRUE)  # "p = .034"
format_p_md(0.00012)                     # "*p* < .001"
format_r(0.4567)                         # ".457"
format_mean(3.1)                         # "3.10"

# Inline reporting in Quarto text
# `r apa_inline_r(result)` → *r*(48) = .46, *p* < .001

Answer Key vs. Student Worksheet

Most table and formatting functions include a Key (or KEY) argument. Set Key = TRUE for a filled answer key, or Key = FALSE for a blank student worksheet — making it easy to generate both versions from the same code.

# Answer key
cat(format_bg_anova_results("RH1", vars, result, iv_labels = labels, Key = TRUE))

# Student worksheet
cat(format_bg_anova_results("RH1", vars, result, iv_labels = labels, Key = FALSE))

Interactive Webexercises (Quarto)

Several functions generate interactive fill-in-the-blank and multiple-choice tables powered by webexercises and tinytable. These are designed for use in Quarto HTML documents:

# In a Quarto document
create_model_summary_checker(result)
create_predictor_checker(result)

Sample Data

The package includes superman_data, a dataset used throughout the examples and course materials.

data(superman_data)
str(superman_data)