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[Simulate dataset, duplicate, and modify with a distribution] #R
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| # This script simulates a dataset, duplicates it over time, and modifies it to | |
| # create a bell curve-like distribution. | |
| # Set up | |
| library(tidyverse) | |
| library(uuid) | |
| # Simulate dataset | |
| temp_df <- | |
| tibble( | |
| PersonId = uuid::UUIDgenerate(100, use.time = FALSE), | |
| Emails_sent = rnorm(100, mean = 30, sd = 5) %>% round() %>% abs() | |
| ) | |
| # Simulate dates | |
| seq_dates <- seq( | |
| from = as.Date("2023-01-01"), | |
| to = as.Date("2023-12-31"), | |
| by = "week" | |
| ) | |
| # Bind `temp_df` together based on dates | |
| full_df <- | |
| seq_dates %>% | |
| map_dfr(function(x){ | |
| temp_df %>% | |
| mutate(MetricDate = x) %>% | |
| select(PersonId, MetricDate, Emails_sent) | |
| }) | |
| # Add random noise to the dataset by PersonId | |
| full_df_with_noise <- | |
| full_df %>% | |
| group_split(PersonId) %>% | |
| map_dfr(function(x){ | |
| total_rows <- nrow(x) | |
| set.seed(total_rows) | |
| x$normdist <- rnorm(total_rows, mean = 0.5, sd = 2) %>% | |
| scales::rescale(to = c(-.2, .2)) | |
| print(summary(x$normdist)) | |
| x %>% | |
| mutate(Emails_sent_2 = Emails_sent * normdist) %>% | |
| select(-normdist) | |
| }) | |
| # Plot differences of `Emails_sent` and `Emails_sent_2` over time | |
| full_df_with_noise %>% | |
| group_by(MetricDate) %>% | |
| summarise( | |
| Emails_sent = mean(Emails_sent), | |
| Emails_sent_2 = mean(Emails_sent_2) | |
| ) %>% | |
| pivot_longer( | |
| cols = c(Emails_sent, Emails_sent_2), | |
| names_to = "Metric", | |
| values_to = "Value" | |
| ) %>% | |
| ggplot(aes(x = MetricDate, y = Value, color = Metric)) + | |
| geom_point() + | |
| geom_line() | |
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