user_sample_1

In ubair: Effects of External Conditions on Air Quality

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/)

# 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)

1. Set sample variables.

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

1. create a params.yaml

2. either

3. by copying the default to your working dir, update the new params.yaml file and load it or

4. load the default and adapt the params programmatically

params <- load_params()
# adapt params programatically
params$target <- target
params$meteo_variables <- meteo_variables

1. Prepare data of station for training.

See function documentation for further details

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
)

1. Run counterfactual scenario (training and prediction)

res <- run_counterfactual(split_data,
  params,
  detrending_function = trend,
  model_type = model_type,
  alpha = 0.9,
  log_transform = FALSE
)
predictions <- res$prediction

1. Plot counterfactual run and optionally save to data_dir

counterfactual_plot <- plot_counterfactual(predictions, params,
  window_size = window_size,
  date_effect_start,
  buffer = buffer
)
counterfactual_plot

1. Evaluate model and effect

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"

ubair documentation built on April 12, 2025, 2:12 a.m.