Correlations

Pearson’s r with descriptives, APA tables, and write-ups

Overview

This guide shows how to use psych350lab to build a complete correlation lab—from running the analysis through generating student worksheets, answer keys, APA tables, write-ups, and interactive homework checkers.

Every code block below is a self-contained example you can copy into your own lab setup file or Quarto document.

1. Run the Analysis

corr_answers() computes a Pearson correlation and returns descriptive statistics for both variables. All numeric values are stored unrounded so that display functions can format them appropriately.

library(psych350lab)
library(psych350data)
library(dplyr)

# Load and prepare data
data(superman, package = "psych350data")

# Run the correlation
corr_result <- corr_answers(
  data = superman,
  var1 = "clark_height_in",
  var2 = "rt_audience_score"
)

# Inspect what comes back
str(corr_result)

What corr_answers() returns

Element	Contents
$Correlation	r, p_value, df, n
$Descriptives	Tibble with variable, mean, sd, n for each variable

2. Format Results for Worksheets

format_corr_results() creates markdown-formatted output with two modes: Key = TRUE fills in the answers; Key = FALSE leaves blanks for students.

Use these inside a Quarto chunk with results: asis so the markdown renders properly.

# Answer key version
corr_KEY <- format_corr_results(
  rh_name           = "RH1",
  vars              = c("clark_height_in", "rt_audience_score"),
  corr_results_list = corr_result,
  Key               = TRUE
)

# Student worksheet version (blanks)
corr_BLANK <- format_corr_results(
  rh_name           = "RH1",
  vars              = c("clark_height_in", "rt_audience_score"),
  corr_results_list = corr_result,
  Key               = FALSE
)

# In your Quarto document:
# cat(corr_KEY)   # for answer key
# cat(corr_BLANK) # for student worksheet

3. Create APA Correlation Tables

create_apa_corr_table() produces a publication-ready correlation matrix as a flextable. The KEY argument toggles between filled and blank versions.

# Answer key table
create_apa_corr_table(
  data       = superman,
  vars       = c("clark_height_in", "rt_audience_score"),
  var_labels = c("1. Actor Height (in)", "2. Audience Score"),
  KEY        = TRUE,
  table_title = "Table 1: Correlations Between Actor Height and Ratings"
)

# Blank table for worksheets (no data needed)
create_apa_corr_table(
  var_labels = c("1. Actor Height (in)", "2. Audience Score"),
  KEY        = FALSE
)

4. Generate APA Write-Ups

apa_corr_writeup() produces a complete statistical write-up with hypothesis evaluation, descriptive statistics, and interpretation.

writeup <- apa_corr_writeup(
  corr_results_list    = corr_result,
  var1_label           = "actor height",
  var2_label           = "audience score",
  hypothesis           = list(
    direction = "positive",
    rh_text   = "taller actors would receive higher audience scores"
  ),
  include_descriptives = TRUE,
  table_number         = 1
)

# Use in a Quarto chunk with results: asis
# cat(writeup)

Hypothesis directions

The direction field controls the write-up language:

• "positive" — expects r > 0
• "negative" — expects r < 0
• "none" — non-directional (two-tailed only)

5. Create Interactive Homework Checkers

create_corr_checker() produces an interactive HTML widget (using the webexercises package) where students type in their answers and get immediate feedback.

# In a Quarto HTML document:
create_corr_checker(
  rh_name           = "RH1",
  vars              = c("clark_height_in", "rt_audience_score"),
  corr_results_list = corr_result
)

Students see fill-in-the-blank boxes for means, SDs, N, r, df, and p. Correct answers turn green; incorrect answers turn red.

6. Inline APA Statistics

For quick inline reporting in Quarto text, use apa_inline_r():

# Returns something like: *r*(28) = .45, *p* = .012
apa_inline_r(corr_result)

7. SPSS-Style Scatterplots

Use ggplot2 with theme_SPSS() and number_SPSS for plots that match SPSS output:

library(ggplot2)

ggplot(superman, aes(x = clark_height_in, y = rt_audience_score)) +
  geom_point(
    shape = 21, fill = "#1192E8", color = "black",
    size = 2.5, stroke = 0.75, alpha = 0.7
  ) +
  theme_SPSS() +
  scale_x_continuous(labels = number_SPSS) +
  scale_y_continuous(labels = number_SPSS) +
  labs(x = "Actor Height (inches)", y = "Audience Score")

Complete Lab Setup Example

Here is a minimal but complete setup file for a correlation lab section, following the same pattern used in the actual course labs:

library(psych350lab)
library(psych350data)
library(dplyr)
library(ggplot2)

# ── Data ─────────────────────────────────────────────────
data(superman, package = "psych350data")

# ── Analysis ─────────────────────────────────────────────
RH1_corr <- corr_answers(
  data = superman,
  var1 = "clark_height_in",
  var2 = "rt_audience_score"
)

RH1_vars <- c("clark_height_in", "rt_audience_score")

# ── Worksheet Output ─────────────────────────────────────
RH1_corr_BLANK <- format_corr_results(
  rh_name = "RH1", vars = RH1_vars,
  corr_results_list = RH1_corr, Key = FALSE
)

RH1_corr_KEY <- format_corr_results(
  rh_name = "RH1", vars = RH1_vars,
  corr_results_list = RH1_corr, Key = TRUE
)

# ── APA Tables ───────────────────────────────────────────
RH1_table_BLANK <- create_apa_corr_table(
  var_labels = c("1. Actor Height (in)", "2. Audience Score"),
  KEY = FALSE
)

RH1_table_KEY <- create_apa_corr_table(
  data = superman, vars = RH1_vars,
  var_labels = c("1. Actor Height (in)", "2. Audience Score"),
  KEY = TRUE,
  table_title = "Table 1: Correlations Between Actor Height and Ratings"
)

# ── Write-Up ─────────────────────────────────────────────
RH1_writeup <- apa_corr_writeup(
  corr_results_list = RH1_corr,
  var1_label = "actor height",
  var2_label = "audience score",
  hypothesis = list(
    direction = "positive",
    rh_text = "taller actors would receive higher audience scores"
  ),
  include_descriptives = TRUE
)

# ── Scatterplot ──────────────────────────────────────────
RH1_plot <- ggplot(superman, aes(x = clark_height_in, y = rt_audience_score)) +
  geom_point(shape = 21, fill = "#1192E8", color = "black",
             size = 2.5, stroke = 0.75, alpha = 0.7) +
  theme_SPSS() +
  scale_x_continuous(labels = number_SPSS) +
  scale_y_continuous(labels = number_SPSS) +
  labs(x = "Actor Height (in)", y = "Audience Score")

Key Functions Reference

Function	Purpose
corr_answers()	Run Pearson correlation, get r, p, df, descriptives
format_corr_results()	Markdown output for worksheets (KEY/BLANK)
create_apa_corr_table()	APA-style correlation matrix (flextable)
apa_corr_writeup()	Full APA write-up with hypothesis evaluation
create_corr_checker()	Interactive HTML homework checker
apa_inline_r()	Inline APA-formatted r statistic
theme_SPSS() / number_SPSS	SPSS-style plot theme and axis labels