---
title: "user_sample_1"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{user_sample_1}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
``` r
library(ubair)
```
## Load data for one station and run counterfactual
Investigate effect of 9 Euro ticket for station Köln.
### Prerequisites
Installation for user as described in README
### Steps
1. adapt the data_dir (in windows e.g. //de-inf-008/\$/Eigene Dateien/ubair-master/data/)
``` r
# set data_dir where the data is stored
data_dir <- "../../Daten/user_sample_data/"
# This might take a few seconds for large files
data <- load_uba_data_from_dir(data_dir = data_dir)
```
2. Set sample variables.
``` r
target <- "NO2"
station <- "DENW006"
station_name <- "Köln"
meteo_variables <- c("TMP", "RFE", "WIG", "WIR", "LDR")
# dates for 9 Euro effect
application_start <- lubridate::ymd("20220301") # = start reference time
date_effect_start <- lubridate::ymd_hm("20220601 00:00")
application_end <- lubridate::ymd("20220831") # = end effect time
buffer <- 0 # number of data points to be ignored before effect
trend <- "linear"
model_type <- "rf"
window_size <- 14 # days of data to calculate the mean in prediction results
```
3. create a params.yaml
4. either
- by copying the default to your working dir, update the new params.yaml file and load it or
- load the default and adapt the params programmatically
``` r
params <- load_params()
# adapt params programatically
params$target <- target
params$meteo_variables <- meteo_variables
```
5. Prepare data of station for training.
See function documentation for further details
``` r
env_data <- clean_data(data, station = station)
dt_prepared <- prepare_data_for_modelling(env_data, params)
dt_prepared <- dt_prepared[complete.cases(dt_prepared)]
split_data <- split_data_counterfactual(
dt_prepared,
application_start = application_start,
application_end = application_end
)
```
6. Run counterfactual scenario (training and prediction)
``` r
res <- run_counterfactual(split_data,
params,
detrending_function = trend,
model_type = model_type,
alpha = 0.9,
log_transform = FALSE
)
predictions <- res$prediction
```
7. Plot counterfactual run and optionally save to data_dir
``` r
counterfactual_plot <- plot_counterfactual(predictions, params,
window_size = window_size,
date_effect_start,
buffer = buffer
)
counterfactual_plot
```
8. Evaluate model and effect
``` r
round(calc_performance_metrics(predictions,
date_effect_start,
buffer = buffer
), 2)
#> RMSE MSE MAE MAPE Bias R2
#> 8.69 75.45 5.83 0.36 -2.16 0.67
#> Coverage lower Coverage upper Coverage Correlation MFB FGE
#> 1.00 0.98 0.98 0.84 -0.10 0.41
round(calc_summary_statistics(predictions,
date_effect_start,
buffer = buffer
), 2)
#> true prediction
#> min 0.63 -1.02
#> max 92.58 57.05
#> var 228.50 111.89
#> mean 17.76 15.61
#> 5-percentile 3.24 2.31
#> 25-percentile 6.94 7.16
#> median/50-percentile 11.91 13.02
#> 75-percentile 24.65 22.92
#> 95-percentile 48.59 34.90
paste("effect size:", estimate_effect_size(predictions,
date_effect_start,
buffer = buffer,
verbose = FALSE
))
#> [1] "effect size: -0.393826299501436" "effect size: -0.0288"
```