We load the required packages and the data:
library("groundhog")
packages <- c(
  "here",
  "tidyverse", 
  "knitr",
  "patchwork", 
  "mediocrethemes"
  # "vincentbagilet/mediocrethemes"
)
# groundhog.library(packages, "2022-11-28")
lapply(packages, library, character.only = TRUE)
set_mediocre_all(pal = "leo")# load small sample simulation results
summary_evol_large <- readRDS(here("data", "simulations", "summary_evol_large.RDS")) %>%
  mutate(sample_size = "large")
# load large sample simulation results
summary_evol_small <- readRDS(here("data", "simulations", "summary_evol_small.RDS")) %>%
  mutate(sample_size = "small")
# prepare data for graphs
summary_evol_all <- summary_evol_large %>%
  bind_rows(summary_evol_small) %>%
  mutate(
    id_method = case_when(
      id_method == "IV" ~ "Instrumental Variable",
      id_method == "OLS" ~ "Standard Regression",
      id_method == "reduced_form" ~ "Reduced-Form",
      id_method == "RDD" ~ "Discontinuity Design"
    ),
    n_obs = n_days * n_cities
  ) %>%
  pivot_longer(
    cols = c("power", "type_m", "mean_f_stat"),
    names_to = "metrics",
    values_to = "stat_value"
  ) %>%
  mutate(
    metrics_name = case_when(
      metrics == "power" ~ "Statistical Power (%)",
      metrics == "type_m" ~ "Exaggeration Ratio",
      metrics == "mean_f_stat" ~ "F-Statistic",
    ),
    id_method_name = fct_relevel(
      id_method,
      "Standard Regression",
      "Reduced-Form",
      "Instrumental Variable"
    )
  )



| Metric | Non-Accidental Causes | Respiratory Causes | COPD Elderly | 
|---|---|---|---|
| Statistical Power (%) | 89.7 | 15.8 | 7.5 | 
| Exaggeration Ratio | 1.0 | 2.4 | 5.9 | 
| F-Statistic | 7980.3 | 8002.9 | 8045.9 | 


