settings <- list(
# Top-level control settings -----
app_name = "climater",
# -> Currently not used/important
overwrite = list(
tidy = FALSE,
transform = FALSE
),
# -> controls which parts of the data processing should be overwritten
output = list(
show = FALSE
),
# -> controls if modeling results are shown via a temporary HTML file
versions = list(
# data_version = "v1"
# data_version = "v2"
# -> normalization of station IDs (4-digit to 5-digit)
data_version = "v3",
# -> imputation of missing values (via simputation::impute_lm with
# sequential models)
frontend_version = "v0.0.0.9005"
),
data_repo = list(
repo_1 = "C:/users/janko/dropbox (personal)/data/climater",
repo_2 = "C:/users/Sebastian/climater"
),
# -> sets the data version to be used
scaling = list(
# distance_1 = 0
# distance_1 = 0.001
distances = list(
0.0005,
0.001,
0.002,
0.003,
0.004,
0.005,
0.006,
0.007
),
time = list(
7,
6,
5,
4,
3,
2,
1,
0
)
),
# Number of recommendations to show for dream locations -----
number_of_recommendations = 1,
# Dev mode yes/no. If yes, additional tabs/infos are shown -----
dev_mode = FALSE,
# Present results in tabs yes/no -----
ui_mode_tabs = FALSE,
# Use map(s) yes/no -----
ui_mode_map = TRUE,
# Expand weight grid yes/no -----
expand_weight_grid = FALSE,
##############################################################################
# Backend settings >> DO NOT CHANGE -----
##############################################################################
# TODO 20181122: align with settings approach from FVA project
data = list(
# dir_raw = "inst/app/data/raw",
# dir_tidy = "inst/app/data/tidy",
dir_raw = "data/raw",
dir_tidy = "data/tidy",
cons = list(
station = list(
raw = "stations_list_CLIMAT_data.txt",
tidy = "station.rds"
),
temperature_min = list(
raw = "multi-annual/dailyTmin_1961_1990.txt",
tidy = "temp_min_1961_1990.rds"
),
temperature_max = list(
raw = "multi-annual/dailyTmax_1961_1990.txt",
tidy = "temp_max_1961_1990.rds"
),
sunshine_duration = list(
raw = "multi-annual/sunshine_duration_1961_1990.txt",
tidy = "sundur_avg_1961_1990.rds"
),
precipitation_historical = list(
raw = "precipGE1mm_days/historical",
tidy = "precip_avg_hist.rds"
),
precipitation_recent = list(
raw = "precipGE1mm_days/recent",
tidy = "precip_avg_recent.rds"
),
temperature_comb = list(
tidy = "temp_comb_1961_1990.rds"
),
precipitation_comb = list(
tidy = "precip_avg_comb.rds"
),
join_full = list(
tidy = "join_full.rds"
),
join_inner = list(
tidy = "join_inner.rds"
),
db = list(
tidy = "db.rds"
),
db_msr = list(
tidy = "db_msr.rds"
),
distance = list(
tidy = "distance.rds"
)
)
),
name_mapping = list(
station = list(
key = dplyr::quo(station),
label = list(
label_1 = "Location"
)
),
station_ref = list(
key = dplyr::quo(station_ref),
label = list()
),
time_start = list(
key = dplyr::quo(time_start),
label = list()
),
time_stop = list(
key = dplyr::quo(time_stop),
label = list()
),
time_type = list(
key = dplyr::quo(time_type),
label = list()
),
msr_precip_avg = list(
key = dplyr::quo(msr_precip_avg),
label = list(
label_1 = "Rain days per month (avg.)"
)
),
precip = list(
key = dplyr::quo(precip),
label = list(
label_1 = "Rain days per month"
)
),
temp = list(
key = dplyr::quo(temp),
label = list()
),
temp_min = list(
key = dplyr::quo(temp_min),
label = list(
label_1 = "Min. temperature"
)
),
temp_max = list(
key = dplyr::quo(temp_max),
label = list(
label_1 = "Max. temperature"
)
),
sundur = list(
key = dplyr::quo(sundur),
label = list(
label_1 = "Sunshine hours per day"
)
),
latitude = list(
key = dplyr::quo(latitude),
label = list(
label_1 = "Latitude"
)
),
longitude = list(
key = dplyr::quo(longitude),
label = list(
label_1 = "Longitude"
)
),
distance = list(
key = dplyr::quo(distance),
label = list(
label_1 = "Distance"
)
),
# Restart 2018-07-08 -----
input = list(
key = dplyr::quo(input),
label = list(
label_1 = "Input ID"
)
),
dim_rank = list(
key = dplyr::quo(dim_rank),
label = list(
label_1 = "Rank"
)
),
dim_latitude = list(
key = dplyr::quo(dim_latitude),
label = list(
label_1 = "Latitude"
)
),
dim_longitude = list(
key = dplyr::quo(dim_longitude),
label = list(
label_1 = "Longitude"
)
),
dim_country = list(
key = dplyr::quo(dim_country),
label = list(
label_1 = "Country"
)
),
dim_station_name = list(
key = dplyr::quo(dim_station_name),
label = list(
label_1 = "Location"
)
),
msr_distance = list(
key = dplyr::quo(msr_distance),
label = list(
label_1 = "Distance"
)
),
time_month = list(
key = dplyr::quo(time_month),
label = list(
label_1 = "Month"
)
),
diff_time_month = list(
key = dplyr::quo(time_month_diff),
label = list(
label_1 = "Month delta"
)
),
msr_temp_min = list(
key = dplyr::quo(msr_temp_min),
label = list(
label_1 = "Min. temperature"
)
),
diff_msr_temp_min = list(
key = dplyr::quo(msr_temp_min_diff),
label = list(
label_1 = "Min. temperature delta"
)
),
msr_temp_max = list(
key = dplyr::quo(msr_temp_max),
label = list(
label_1 = "Max. temperature"
)
),
diff_msr_temp_max = list(
key = dplyr::quo(msr_temp_max_diff),
label = list(
label_1 = "Max. temperature delta"
)
),
msr_temp_avg = list(
key = dplyr::quo(msr_temp_avg),
label = list(
label_1 = "Avg. temperature"
)
),
diff_msr_temp_avg = list(
key = dplyr::quo(msr_temp_avg_diff),
label = list(
label_1 = "Avg. temperature delta"
)
),
msr_precip_avg = list(
key = dplyr::quo(msr_precip_avg),
label = list(
label_1 = "Rain days per month"
)
),
diff_msr_precip_avg = list(
key = dplyr::quo(msr_precip_avg_diff),
label = list(
label_1 = "Rain days per month delta"
)
),
msr_sundur_avg = list(
key = dplyr::quo(msr_sundur_avg),
label = list(
label_1 = "Sunshine hours per day"
)
),
diff_msr_sundur_avg = list(
key = dplyr::quo(msr_sundur_avg_diff),
label = list(
label_1 = "Sunshine hours per day delta"
)
),
# fct_scaling = list(
# key = dplyr::quo(fct_scaling),
# label = list(
# label_1 = "Scaling factor"
# )
# )
id = list(
key = dplyr::quo(id),
label = list(
label_1 = "ID"
)
),
msr_co2 = list(
key = dplyr::quo(msr_co2),
label = list(
label_1 = "CO2-equivalents (kg)"
)
)
),
api_keys = list(
google_maps = "AIzaSyB4GFcTvkAc9IsVLaWzS27vIsLtqu-1MxE"
)
)
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