A small simulated dataset for demonstrating margot functionality. This dataset mimics the structure of longitudinal panel data but contains entirely simulated data with no real participant information.
Format
A data frame with 300 rows (100 participants × 3 waves) and 15 variables:
- id
Participant identifier
- wave
Measurement wave (0, 1, 2)
- year
Year of measurement (2018, 2019, 2020)
- male
Binary gender indicator (0 = not male, 1 = male)
- age
Age in years
- partner
Relationship status (0 = no partner, 1 = has partner)
- eth_cat
Ethnicity category (euro, maori, pacific, asian)
- sample_weights
Survey sampling weights
- forgiveness
Forgiveness scale score (1-7)
- gratitude
Gratitude scale score (1-7)
- modesty
Modesty scale score (1-7)
- alcohol_frequency
Frequency of alcohol consumption (0-7)
- alcohol_intensity
Intensity of alcohol consumption (0-7)
- hours_exercise
Hours of exercise per week
- ego_rubin
Ego resilience scale (1-7)
Details
This is a lightweight example dataset suitable for package examples and testing. For larger, more comprehensive simulated datasets that better reflect real longitudinal studies, use `fetch_margot_data()` with version = "v1" or "v2".
See also
fetch_margot_data for accessing larger datasets
list_margot_data to see all available datasets
Examples
# load the example data
data(df_margot_example)
# check structure
str(df_margot_example)
#> tibble [300 × 15] (S3: tbl_df/tbl/data.frame)
#> $ id : int [1:300] 1 1 1 2 2 2 3 3 3 4 ...
#> $ wave : int [1:300] 0 1 2 0 1 2 0 1 2 0 ...
#> $ year : int [1:300] 2018 2019 2020 2018 2019 2020 2018 2019 2020 2018 ...
#> $ male : int [1:300] 1 1 1 0 0 0 1 1 1 0 ...
#> $ age : num [1:300] 51 52 53 27 28 29 43 44 45 61 ...
#> $ partner : num [1:300] 1 1 0 0 0 1 0 1 0 1 ...
#> $ eth_cat : chr [1:300] "euro" "euro" "euro" "euro" ...
#> $ sample_weights : num [1:300] 0.971 0.982 0.723 1.055 0.917 ...
#> $ forgiveness : num [1:300] 3.93 4.12 4.54 1.55 1.41 ...
#> $ gratitude : num [1:300] 4.48 3.88 4.19 4.39 5.04 ...
#> $ modesty : num [1:300] 3.76 4 3.93 2.77 3.11 ...
#> $ alcohol_frequency: num [1:300] 7 6 6 4 2 7 7 5 5 6 ...
#> $ alcohol_intensity: num [1:300] 4 1 0 6 2 3 3 0 NA 2 ...
#> $ hours_exercise : num [1:300] 9.6 1 0.2 3.3 4.1 NA 3.6 9.6 NA 1.6 ...
#> $ ego_rubin : num [1:300] 5.44 5.22 5.43 4.56 4.75 ...
# basic summary by wave
table(df_margot_example$wave)
#>
#> 0 1 2
#> 100 100 100