Update R, packages, etc. and bug fixing

DESCRIPTION

@@ -41,7 +41,7 @@ Imports:
     crayon
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 Suggests: 
     knitr,
     rmarkdown,

R/plot_bar.R

@@ -101,8 +101,8 @@ plot_bar <- function(data,
   # Adjust annotations
   p <- p + labs(title = title)
   p <- p + labs(subtitle = subtitle)
-  p <- p + labs(x = if_else(is_empty(xlab), as_name(enquo(x)), xlab))
-  p <- p + labs(y = if_else(is_empty(ylab), as_name(enquo(y)), ylab))
+  p <- p + labs(x = xlab)
+  p <- p + labs(y = ylab)
   p <- p + labs(caption = caption)
 
   # Adjust ggplot2 theme

R/plot_density.R

@@ -90,8 +90,8 @@ plot_density <- function(data,
   # Adjust annotations
   p <- p + labs(title = title)
   p <- p + labs(subtitle = subtitle)
-  p <- p + labs(x = if_else(is_empty(xlab), as_name(enquo(x)), xlab))
-  p <- p + labs(y = if_else(is_empty(ylab), "Density", ylab))
+  p <- p + labs(x = xlab)
+  p <- p + labs(y = ylab)
   p <- p + labs(caption = caption)
 
   # Adjust ggplot2 theme

R/plot_histogram.R

@@ -89,8 +89,8 @@ plot_histogram <- function(data,
   # Adjust annotations
   p <- p + labs(title = title)
   p <- p + labs(subtitle = subtitle)
-  p <- p + labs(x = if_else(is_empty(xlab), as_name(enquo(x)), xlab))
-  p <- p + labs(y = if_else(is_empty(ylab), "Count", ylab))
+  p <- p + labs(x = xlab)
+  p <- p + labs(y = ylab)
   p <- p + labs(caption = caption)
 
   # Adjust ggplot2 theme

R/plot_line.R

@@ -87,8 +87,8 @@ plot_line <- function(data,
   # Adjust annotations
   p <- p + labs(title = title)
   p <- p + labs(subtitle = subtitle)
-  p <- p + labs(x = if_else(is_empty(xlab), as_name(enquo(x)), xlab))
-  p <- p + labs(y = if_else(is_empty(ylab), as_name(enquo(y)), ylab))
+  p <- p + labs(x = xlab)
+  p <- p + labs(y = ylab)
   p <- p + labs(caption = caption)
 
   # Adjust ggplot2 theme

R/plot_point.R

@@ -87,8 +87,8 @@ plot_point <- function(data,
   # Adjust annotations
   p <- p + labs(title = title)
   p <- p + labs(subtitle = subtitle)
-  p <- p + labs(x = if_else(is_empty(xlab), as_name(enquo(x)), xlab))
-  p <- p + labs(y = if_else(is_empty(ylab), as_name(enquo(y)), ylab))
+  p <- p + labs(x = xlab)
+  p <- p + labs(y = ylab)
   p <- p + labs(caption = caption)
 
   # Adjust ggplot2 theme

R/plot_qq.R

@@ -137,8 +137,8 @@ plot_qq <- function(data,
   # Adjust annotations
   p <- p + labs(title = title)
   p <- p + labs(subtitle = subtitle)
-  p <- p + labs(x = if_else(is_empty(xlab), "Theoretical quantile", xlab))
-  p <- p + labs(y = if_else(is_empty(ylab), "Sample quantile", ylab))
+  p <- p + labs(x = xlab)
+  p <- p + labs(y = ylab)
   p <- p + labs(caption = caption)
 
   # Adjust ggplot2 theme

README.md

@@ -47,14 +47,14 @@ prices in \[EUR/MWh\] from the ENTSO-E Transparency Platform. The data
 set contains hourly time series data from 2019-01-01 to 2020-12-31 for 8
 European bidding zones (BZN):
 
--   DE: Germany (including Luxembourg)
--   DK: Denmark
--   ES: Spain
--   FI: Finland
--   FR: France
--   NL: Netherlands
--   NO1: Norway 1 (Oslo)
--   SE1: Sweden 1 (Lulea)
+- DE: Germany (including Luxembourg)
+- DK: Denmark
+- ES: Spain
+- FI: Finland
+- FR: France
+- NL: Netherlands
+- NO1: Norway 1 (Oslo)
+- SE1: Sweden 1 (Lulea)
 
 In this vignette, we will use only four time series to demonstrate the
 functionality of the package (the data set is filtered to the bidding
@@ -66,6 +66,7 @@ The function `summarise_stats()` calculates descriptive statistics for
 each time series.
 
 ``` r
+
 series_id = "bidding_zone"
 value_id = "value"
 index_id = "time"
@@ -81,7 +82,7 @@ main_frame <- elec_price %>%
   filter(bidding_zone %in% c("DE", "FR", "NO1", "SE1"))
 
 main_frame
-#> # A tibble: 70,176 x 5
+#> # A tibble: 70,176 × 5
 #>    time                item            unit      bidding_zone  value
 #>    <dttm>              <chr>           <chr>     <chr>         <dbl>
 #>  1 2019-01-01 00:00:00 Day-ahead Price [EUR/MWh] DE            10.1 
@@ -94,7 +95,7 @@ main_frame
 #>  8 2019-01-01 07:00:00 Day-ahead Price [EUR/MWh] DE            -4.93
 #>  9 2019-01-01 08:00:00 Day-ahead Price [EUR/MWh] DE            -6.33
 #> 10 2019-01-01 09:00:00 Day-ahead Price [EUR/MWh] DE            -4.93
-#> # ... with 70,166 more rows
+#> # ℹ 70,166 more rows
 
 main_frame %>%
   plot_line(
@@ -113,31 +114,32 @@ main_frame %>%
 <img src="man/figures/README-clean_data-1.svg" width="100%" />
 
 ``` r
+
 summarise_data(
   .data = main_frame,
   context = context
 )
-#> # A tibble: 4 x 8
+#> # A tibble: 4 × 8
 #>   bidding_zone start               end                 n_obs n_missing
 #>   <chr>        <dttm>              <dttm>              <int>     <int>
 #> 1 DE           2019-01-01 00:00:00 2020-12-31 23:00:00 17544         0
 #> 2 FR           2019-01-01 00:00:00 2020-12-31 23:00:00 17544         0
 #> 3 NO1          2019-01-01 00:00:00 2020-12-31 23:00:00 17544         0
 #> 4 SE1          2019-01-01 00:00:00 2020-12-31 23:00:00 17544         0
-#> # ... with 3 more variables: pct_missing <dbl>, n_zeros <int>, pct_zeros <dbl>
+#> # ℹ 3 more variables: pct_missing <dbl>, n_zeros <int>, pct_zeros <dbl>
 
 summarise_stats(
   .data = main_frame,
   context = context
 )
-#> # A tibble: 4 x 11
+#> # A tibble: 4 × 11
 #>   bidding_zone  mean median  mode    sd     p0   p25   p75  p100 skewness
 #>   <chr>        <dbl>  <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>    <dbl>
 #> 1 DE            34.1   35.1 35.9   16.9 -90.0  26.3   43.8  200.   -0.776
 #> 2 FR            35.8   36   37.2   15.5 -75.8  26.6   44.9  200.    0.323
 #> 3 NO1           24.3   26.9  2.66  17.1  -1.73  6.95  38.8  109.    0.189
 #> 4 SE1           26.1   27.9 37.9   15.9  -1.73 11.4   38.5  189.    0.378
-#> # ... with 1 more variable: kurtosis <dbl>
+#> # ℹ 1 more variable: kurtosis <dbl>
 ```
 
 ## Split data into training and testing
@@ -154,6 +156,7 @@ options for splitting the data are available via `type` (see function
 reference for more details).
 
 ``` r
+
 # Setup for time series cross validation
 type = "first"
 value = 2400      # size for training window
@@ -180,7 +183,7 @@ split_frame <- split_frame %>%
   filter(split %in% c(1:50))
 
 split_frame
-#> # A tibble: 200 x 4
+#> # A tibble: 200 × 4
 #>    bidding_zone split train         test      
 #>    <chr>        <int> <list>        <list>    
 #>  1 DE               1 <int [2,400]> <int [24]>
@@ -193,7 +196,7 @@ split_frame
 #>  8 DE               8 <int [2,400]> <int [24]>
 #>  9 DE               9 <int [2,400]> <int [24]>
 #> 10 DE              10 <int [2,400]> <int [24]>
-#> # ... with 190 more rows
+#> # ℹ 190 more rows
 ```
 
 ## Training and forecasting
@@ -206,21 +209,22 @@ have to convert the data set `main_frame` from a `tibble` to a
 `tsibble`. Due to the sample size and computation time, only very simple
 benchmark methods are used:
 
--   `SNAIVE`: Seasonal naive model with weekly seasonality (from package
-    `fable`)
--   `STL-NAIVE`: STL-decomposition model and naive forecast. The series
-    is decomposed via STL and the seasonal adjusted series is predicted
-    via the naive approach. Afterwards, seasonal component is added to
-    the forecasts (from packages `fable` and `feasts`)
--   `SNAIVE2`: Variation of the seasonal naive approach. Mondays,
-    Saturdays and Sundays are treated with a weekly lag. Tuesdays,
-    Wednesdays, Thursdays and Fridays are treated with a daily lag.
--   `SMEDIAN`: Seasonal median model
+- `SNAIVE`: Seasonal naive model with weekly seasonality (from package
+  `fable`)
+- `STL-NAIVE`: STL-decomposition model and naive forecast. The series is
+  decomposed via STL and the seasonal adjusted series is predicted via
+  the naive approach. Afterwards, seasonal component is added to the
+  forecasts (from packages `fable` and `feasts`)
+- `SNAIVE2`: Variation of the seasonal naive approach. Mondays,
+  Saturdays and Sundays are treated with a weekly lag. Tuesdays,
+  Wednesdays, Thursdays and Fridays are treated with a daily lag.
+- `SMEDIAN`: Seasonal median model
 
 The functions `SMEDIAN()` and `SNAIVE2()` are extensions to the `fable`
 package
 
 ``` r
+
 # Slice training data from main_frame according to split_frame
 train_frame <- slice_train(
   main_frame = main_frame,
@@ -229,7 +233,7 @@ train_frame <- slice_train(
   )
 
 train_frame
-#> # A tibble: 480,000 x 6
+#> # A tibble: 480,000 × 6
 #>    time                item            unit      bidding_zone  value split
 #>    <dttm>              <chr>           <chr>     <chr>         <dbl> <int>
 #>  1 2019-01-01 00:00:00 Day-ahead Price [EUR/MWh] DE            10.1      1
@@ -242,7 +246,7 @@ train_frame
 #>  8 2019-01-01 07:00:00 Day-ahead Price [EUR/MWh] DE            -4.93     1
 #>  9 2019-01-01 08:00:00 Day-ahead Price [EUR/MWh] DE            -6.33     1
 #> 10 2019-01-01 09:00:00 Day-ahead Price [EUR/MWh] DE            -4.93     1
-#> # ... with 479,990 more rows
+#> # ℹ 479,990 more rows
 
 # Convert tibble to tsibble
 train_frame <- train_frame %>%
@@ -266,7 +270,7 @@ train_frame
 #>  8 2019-01-01 07:00:00 Day-ahead Price [EUR/MWh] DE            -4.93     1
 #>  9 2019-01-01 08:00:00 Day-ahead Price [EUR/MWh] DE            -6.33     1
 #> 10 2019-01-01 09:00:00 Day-ahead Price [EUR/MWh] DE            -4.93     1
-#> # ... with 479,990 more rows
+#> # ℹ 479,990 more rows
 
 # Model training via fabletools::model()
 model_frame <- train_frame %>%
@@ -292,7 +296,7 @@ model_frame
 #>  8 DE               8 <SNAIVE> <STL decomposition model> <SNAIVE2> <SMEDIAN>
 #>  9 DE               9 <SNAIVE> <STL decomposition model> <SNAIVE2> <SMEDIAN>
 #> 10 DE              10 <SNAIVE> <STL decomposition model> <SNAIVE2> <SMEDIAN>
-#> # ... with 190 more rows
+#> # ℹ 190 more rows
 
 # Forecasting via fabletools::forecast()
 fable_frame <- model_frame %>%
@@ -301,19 +305,20 @@ fable_frame <- model_frame %>%
 fable_frame
 #> # A fable: 19,200 x 6 [1h] <UTC>
 #> # Key:     bidding_zone, split, .model [800]
-#>    bidding_zone split .model time                     value .mean
-#>    <chr>        <int> <chr>  <dttm>                  <dist> <dbl>
-#>  1 DE               1 SNAIVE 2019-04-11 00:00:00 N(33, 367)  33  
-#>  2 DE               1 SNAIVE 2019-04-11 01:00:00 N(33, 367)  32.6
-#>  3 DE               1 SNAIVE 2019-04-11 02:00:00 N(34, 367)  34.1
-#>  4 DE               1 SNAIVE 2019-04-11 03:00:00 N(37, 367)  36.9
-#>  5 DE               1 SNAIVE 2019-04-11 04:00:00 N(45, 367)  44.7
-#>  6 DE               1 SNAIVE 2019-04-11 05:00:00 N(54, 367)  53.6
-#>  7 DE               1 SNAIVE 2019-04-11 06:00:00 N(60, 367)  59.9
-#>  8 DE               1 SNAIVE 2019-04-11 07:00:00 N(47, 367)  46.9
-#>  9 DE               1 SNAIVE 2019-04-11 08:00:00 N(48, 367)  48  
-#> 10 DE               1 SNAIVE 2019-04-11 09:00:00 N(47, 367)  47  
-#> # ... with 19,190 more rows
+#>    bidding_zone split .model time               
+#>    <chr>        <int> <chr>  <dttm>             
+#>  1 DE               1 SNAIVE 2019-04-11 00:00:00
+#>  2 DE               1 SNAIVE 2019-04-11 01:00:00
+#>  3 DE               1 SNAIVE 2019-04-11 02:00:00
+#>  4 DE               1 SNAIVE 2019-04-11 03:00:00
+#>  5 DE               1 SNAIVE 2019-04-11 04:00:00
+#>  6 DE               1 SNAIVE 2019-04-11 05:00:00
+#>  7 DE               1 SNAIVE 2019-04-11 06:00:00
+#>  8 DE               1 SNAIVE 2019-04-11 07:00:00
+#>  9 DE               1 SNAIVE 2019-04-11 08:00:00
+#> 10 DE               1 SNAIVE 2019-04-11 09:00:00
+#> # ℹ 19,190 more rows
+#> # ℹ 2 more variables: value <dist>, .mean <dbl>
 
 # Convert fable_frame (fable) to future_frame (tibble)
 future_frame <- make_future(
@@ -322,7 +327,7 @@ future_frame <- make_future(
   )
 
 future_frame
-#> # A tibble: 19,200 x 6
+#> # A tibble: 19,200 × 6
 #>    time                bidding_zone model  split horizon point
 #>    <dttm>              <chr>        <chr>  <int>   <int> <dbl>
 #>  1 2019-04-11 00:00:00 DE           SNAIVE     1       1  33  
@@ -335,7 +340,7 @@ future_frame
 #>  8 2019-04-11 07:00:00 DE           SNAIVE     1       8  46.9
 #>  9 2019-04-11 08:00:00 DE           SNAIVE     1       9  48  
 #> 10 2019-04-11 09:00:00 DE           SNAIVE     1      10  47  
-#> # ... with 19,190 more rows
+#> # ℹ 19,190 more rows
 ```
 
 ## Evaluation of forecast accuracy
@@ -344,19 +349,20 @@ To evaluate the forecast accuracy, the function `make_accuracy()` is
 used. You can define whether to evaluate the accuracy by `horizon` or by
 `split`. Several accuracy metrics are available:
 
--   `ME`: mean error
--   `MAE`: mean absolute error
--   `MSE`: mean squared error
--   `RMSE`: root mean squared error
--   `MAPE`: mean absolute percentage error
--   `sMAPE`: scaled mean absolute percentage error
--   `MPE`: mean percentage error
--   `rMAE`: relative mean absolute error (relative to some user-defined
-    benchmark method)
+- `ME`: mean error
+- `MAE`: mean absolute error
+- `MSE`: mean squared error
+- `RMSE`: root mean squared error
+- `MAPE`: mean absolute percentage error
+- `sMAPE`: scaled mean absolute percentage error
+- `MPE`: mean percentage error
+- `rMAE`: relative mean absolute error (relative to some user-defined
+  benchmark method)
 
 ### Forecast accuracy by forecast horizon
 
 ``` r
+
 # Estimate accuracy metrics by forecast horizon
 accuracy_horizon <- make_accuracy(
   future_frame = future_frame,
@@ -366,7 +372,7 @@ accuracy_horizon <- make_accuracy(
 )
 
 accuracy_horizon
-#> # A tibble: 2,688 x 6
+#> # A tibble: 2,688 × 6
 #>    bidding_zone model   dimension     n metric value
 #>    <chr>        <chr>   <chr>     <int> <chr>  <dbl>
 #>  1 DE           SMEDIAN horizon       1 MAE     4.84
@@ -379,7 +385,7 @@ accuracy_horizon
 #>  8 DE           SMEDIAN horizon       8 MAE     6.73
 #>  9 DE           SMEDIAN horizon       9 MAE     7.38
 #> 10 DE           SMEDIAN horizon      10 MAE     9.06
-#> # ... with 2,678 more rows
+#> # ℹ 2,678 more rows
 
 # Visualize results
 accuracy_horizon %>%
@@ -402,6 +408,7 @@ accuracy_horizon %>%
 ### Forecast accuracy by split
 
 ``` r
+
 # Estimate accuracy metrics by forecast horizon
 accuracy_split <- make_accuracy(
   future_frame = future_frame,
@@ -411,7 +418,7 @@ accuracy_split <- make_accuracy(
 )
 
 accuracy_split
-#> # A tibble: 5,600 x 6
+#> # A tibble: 5,600 × 6
 #>    bidding_zone model   dimension     n metric value
 #>    <chr>        <chr>   <chr>     <int> <chr>  <dbl>
 #>  1 DE           SMEDIAN split         1 MAE     2.80
@@ -424,7 +431,7 @@ accuracy_split
 #>  8 DE           SMEDIAN split         8 MAE     4.87
 #>  9 DE           SMEDIAN split         9 MAE    11.5 
 #> 10 DE           SMEDIAN split        10 MAE     5.02
-#> # ... with 5,590 more rows
+#> # ℹ 5,590 more rows
 
 # Visualize results
 accuracy_split %>%