psych350data 
Datasets for UNL PSYC 350 Labs. All datasets are ready to use in R with human-readable categorical values, and can be exported as fully labeled SPSS (.sav) files with a single function call.
Installation
Install from GitHub:
# install.packages("pak")
pak::pak("emmarshall/psych350data")Quick Start
library(psych350data)
# See all available datasets
list_datasets() [1] "superman" "superman_smes" "superman_movies"
[4] "superman_combined" "hotones" "hotones_sauces"
[7] "hotones_episodes" "tip_jokes" "mcu"
[10] "mock_jury" "candy" "candy_simple"
[13] "football" "huskers" "interpersonal_data"
[16] "self_descriptive_data" "parent_child_data" "hindsight_mg_data"
[19] "hindsight_wg_data" "cheese_data" "lpd_data"# Use a dataset directly in R
head(superman)# Export to SPSS
export_superman_sav("superman_data.sav")
export_football_sav("~/Desktop/football_data.sav")
# Export all datasets at once
export_all_sav(dir = "~/Desktop/PSYC350_SPSS/")Three Ways to Use the Data
Each dataset supports three workflows depending on what you need:
1. Raw data in R — categorical variables are human-readable character strings, ideal for exploration and plotting with ggplot2:
superman |> count(type) # "Film", "TV Series", "Serial"
football |> count(group) # "Control", "Football no concussion", ...2. Prep for numeric R analysis — prep_*() converts character categories to the same numeric codes used in SPSS, so your R output matches SPSS output exactly. This is also useful when working with psych350lab functions that expect numeric grouping variables:
superman_num <- prep_superman(superman)
superman_num |> count(type) # 1, 2, 33. Export to SPSS — export_*_sav() produces a fully labeled .sav file with numeric codes, value labels, variable labels, and -99 for missing values:
export_superman_sav("superman_data.sav")See vignette("getting-started") for a full walkthrough of all three workflows.
Available Datasets
| Dataset | Description | Source |
|---|---|---|
superman |
Superman actor data with ratings | Rotten Tomatoes, Letterboxd, IMDb |
superman_smes |
SMES ratings by height gap and age difference | Simulated |
superman_movies |
Superman film box office data | IMDb Box Office Mojo |
hotones |
Hot Ones guest data | First We Feast (YouTube) |
hotones_sauces |
Hot sauce data by season and position | First We Feast (YouTube) |
hotones_episodes |
Episode-level YouTube engagement metrics | First We Feast (YouTube) |
tip_jokes |
Tipping experiment | Gueguen (2002) |
mcu |
MCU films box office and ratings | IMDb via openintro |
mock_jury |
Mock jury sentencing | Plaster (1989) |
candy |
Candy power rankings — full | FiveThirtyEight (2017) |
candy_simple |
Candy power rankings — simplified | FiveThirtyEight (2017) |
football |
Football concussion brain measurements | Singh et al. (2014), JAMA |
huskers |
Nebraska football box scores (1962–2024) | Historical records |
cheese_data |
Cheese characteristics and nutrition | cheese.com via TidyTuesday |
lpd_data |
Lincoln Police Dept. traffic stops | LPD Open Data Portal |
parent_child_data |
Parent-child observational study | Simulated |
hindsight_mg_data |
Hindsight bias — between-groups (long) | Simulated |
hindsight_wg_data |
Hindsight bias — within-groups (wide) | Simulated |
interpersonal_data |
Interpersonal relationships survey scales | Simulated |
self_descriptive_data |
Personality survey scales | Simulated |
Exporting for Answer Keys vs. Student Data
Export the full dataset as an instructor answer key, and a subset as the student version:
library(dplyr)
# Full dataset — answer key with all variables
export_football_sav("football_answer_key.sav")
# Student version — only the variables they need
football |>
select(group, volume) |>
export_sav(path = "football_student.sav")R vs SPSS: What’s Different?
| Aspect | Raw R data | After prep_*() |
SPSS .sav export |
|---|---|---|---|
| Categorical values | Character strings ("Film", "Control") |
Numeric codes (1, 2, 3) | Numeric codes with value labels |
| Missing values | NA |
NA |
-99 with user-defined missing |
| Variable descriptions | Help files (?superman) |
Help files | Variable labels in Variable View |
Example: All Three Workflows
library(dplyr)
# Workflow 1: Raw data — human-readable, great for plots
football |>
count(group)# A tibble: 3 × 2
group n
<chr> <int>
1 Control 25
2 Football no concussion 25
3 Football with concussion 25# Workflow 2: Prep for numeric analysis matching SPSS
football_num <- prep_football(football)
football_num |>
count(group) # 1, 2, 3# A tibble: 3 × 2
group n
<dbl> <int>
1 1 25
2 2 25
3 3 25# Run ANOVA with numeric codes
model <- aov(volume ~ factor(group), data = football_num)
summary(model) Df Sum Sq Mean Sq F value Pr(>F)
factor(group) 2 44.35 22.174 31.47 1.51e-10 ***
Residuals 72 50.73 0.705
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Workflow 3: Export to SPSS with full labels and -99 for missing
export_football_sav("football_data.sav")
# Export a subset of variables
superman |>
select(num, media, type, clark_height, rt_critics_score) |>
export_sav(path = "superman_subset.sav")What’s Included in SPSS Exports?
- Variable labels describing each column
- Value labels for categorical variables (e.g., 1 = “Control”, 2 = “Football no concussion”)
- Missing values coded as
-99with SPSS missing value definitions - Proper SPSS formats for all variable types
Auditing an Export
export_huskers_sav("huskers_data.sav")
check_sav_export("huskers_data.sav")
# Show all variables, not just issues
check_sav_export("huskers_data.sav", show_all = TRUE)
# Read back for analysis
huskers_clean <- get_spss_data("huskers_data.sav")Documentation
vignette("getting-started")— Introduction and all three workflows, including psych350lab compatibilityvignette("exporting-spss")— Detailed SPSS export guide with answer key vs. student workflowsvignette("dataset-reference")— Complete reference for all datasets with source attribution?dataset_name— Help for any specific dataset
License
MIT