version 1.1.1

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: evprof
 Title: Electric Vehicle Charging Sessions Profiling and Modelling
-Version: 1.1.0
+Version: 1.1.1
 Authors@R: 
     person("Marc", "Cañigueral", , "marc.canigueral@udg.edu", role = c("aut", "cre", "cph"),
            comment = c(ORCID = "0000-0001-9724-5829"))
@@ -24,9 +24,9 @@ Language: en-US
 LazyData: true
 RoxygenNote: 7.2.3
 NeedsCompilation: no
-Packaged: 2024-01-30 00:25:18 UTC; mcanigueral
+Packaged: 2024-02-05 15:56:41 UTC; mcanigueral
 Author: Marc Cañigueral [aut, cre, cph]
     (<https://orcid.org/0000-0001-9724-5829>)
 Maintainer: Marc Cañigueral <marc.canigueral@udg.edu>
 Repository: CRAN
-Date/Publication: 2024-01-30 00:40:02 UTC
+Date/Publication: 2024-02-05 22:50:06 UTC

MD5

@@ -1,19 +1,19 @@
-c96525ee428d6002e189b8e12635ff10 *DESCRIPTION
-9faa179c32209ba466db6d5d83bcb7fb *NAMESPACE
-be5eef26de13063cf313009e7ec1677e *NEWS.md
-b35c4c551a87ac9ceda5c0e8d2a9ce5e *R/clustering.R
-49bfe15a2e9f1bbc4b0b8b8cbcab7acc *R/data.R
-3fd6fe919593a7c2e481bd5c7ca4f27e *R/exploration.R
-794d2f0c4c7c91c618021836df1bc1bf *R/modelling.R
+9fe86adcd870df013505ee1f13e1ec8a *DESCRIPTION
+fcc2ca471a4b59355c21ce2c9d1f0a1a *NAMESPACE
+954b5b0ba2e926fc4d5474b87c1e78f8 *NEWS.md
+a383b1dc42e7950cfa6b998023d1fa4e *R/clustering.R
+a6d0c58e12a1f1ae92c074ebcf043701 *R/data.R
+63e957a463b4f586872143e27881fe3b *R/exploration.R
+35f12fae423dabc6f4489031545ffe3c *R/modelling.R
 77bf9b420ca315d87755518b6982c06a *R/package_utils.R
-65c8349534a85c122cb2f21f97a9ce27 *R/preprocessing.R
+9a9e27cb21aa5086d5387a17e8f342c8 *R/preprocessing.R
 31947b3126783aa2749dc2aef8855a9b *R/profiling.R
 d0c4b6446ee70d1a99bcdf9bd36f166e *README.md
 ca92612c359a079d6fbda517be2ed926 *build/vignette.rds
-9de514546ce47535410a55fdae4a27ac *data/california_GMM.rda
+b8f25843c7bbd51a8a95ac9af33b621e *data/california_GMM.rda
 80ccd1f35fdbc3bdc043ba254b86c8b8 *data/california_ev_model.rda
-07c23309b23d7f7aa9371219ac719731 *data/california_ev_sessions.rda
-7a2187e38d0480684f7ead3d753bb175 *data/california_ev_sessions_profiles.rda
+8fe97f9bd51af4b5355ef726042604d3 *data/california_ev_sessions.rda
+513fe88ce9cacc2756956e7649d73a37 *data/california_ev_sessions_profiles.rda
 66b3acfb7bac463b3fe35a5aa5b4414b *data/sessions_feature_names.rda
 465992bd9176d7911cc54633e6de098a *data/sessions_summary_feature_names.rda
 662cd0191c3d22289ccf758ecb4ce976 *inst/doc/evmodel.R
@@ -22,65 +22,63 @@ ca92612c359a079d6fbda517be2ed926 *build/vignette.rds
 7c173a67481270a68ff9101cb21badbd *inst/doc/sessions-format.R
 4b6cf81408beeb1904ea368a732a2463 *inst/doc/sessions-format.Rmd
 3f89a5e88ba6ec5e97f74c0b714b8ab6 *inst/doc/sessions-format.html
-20aff559b2c2c7c635300c0a8032ec75 *man/california_GMM.Rd
+da9639127203c34ae8b4aac1d8009b81 *man/california_GMM.Rd
 8278c42bd4275ab4664f81cdebb96c9b *man/california_ev_model.Rd
 8499fe2b32a67660c41d73c83744011d *man/california_ev_sessions.Rd
 6adc1aab217604d24f75040593c0d6b4 *man/california_ev_sessions_profiles.Rd
-f62a8f36258ce1e21d315be96ecd9fce *man/choose_k_GMM.Rd
-375930cb4e20b6232bf9842c542a9c8e *man/cluster_sessions.Rd
-1601ef1a40821ea75bf42414a4a0f271 *man/convert_time_dt_to_num.Rd
-b9826a4ac900d3b0c408fb2ba69944dd *man/convert_time_dt_to_plot_dt.Rd
-e5a89215616c892c14c59d07b20da3ac *man/convert_time_dt_to_plot_num.Rd
-cef86402013883c398c0fd22e6bda9d6 *man/convert_time_num_to_chr.Rd
-c795f8eda44f53c5f77d8d9f526df4ef *man/convert_time_num_to_period.Rd
-9dcb7d83ebc6e2055d716e50948190ed *man/convert_time_num_to_plot_num.Rd
-850b0fc612eb7f8df3458afefe5a4839 *man/cut_sessions.Rd
+ed59f13165776142a75ed61616edf056 *man/choose_k_GMM.Rd
+d01bcc2b8ed5bcd33d20e83885b188a7 *man/cluster_sessions.Rd
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+b9fea58b43a2780fea6e850fd3247fcc *man/convert_time_num_to_chr.Rd
+0699d10192af71ae60c53443d9a98460 *man/convert_time_num_to_period.Rd
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 7ba1a91adc1a79ccde559bfb3126e44a *man/define_clusters.Rd
-205d838747a3bcefa4cc9d6ce9679c02 *man/detect_outliers.Rd
-a4e9e863b1618e957c71256694e15f61 *man/divide_by_disconnection.Rd
-10dbf80742caa348f7514eac8762375f *man/divide_by_timecycle.Rd
-654de6c9344a1fb74eb1225db24340fc *man/drop_outliers.Rd
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 749ad31e388a934da20a17773306bbf9 *man/figures/logo.png
 236e6df60d74a8b9de2f4918add7beb8 *man/get_charging_rates_distribution.Rd
-063a5fb513b2e2312aee23be24aa1ae1 *man/get_connection_model_mclust_object.Rd
+acd1779ce44841587e7ae34f112ff1b0 *man/get_connection_model_mclust_object.Rd
 4c56fe2a8e10f788e1e25ee370653ace *man/get_connection_model_params.Rd
 ae08273d7d7d2214497b1b0db9b2190d *man/get_connection_models.Rd
 b90de871f3671e163b9f35499cea2cf0 *man/get_daily_avg_n_sessions.Rd
 09249f16969656bdb0cc23f76e0023eb *man/get_daily_n_sessions.Rd
-9c1552e45fb2bb48bee340444b3f7580 *man/get_dbscan_params.Rd
+8ea73b937f0f4e562649cb64d02502f4 *man/get_dbscan_params.Rd
 7afd96657ccf87b48f157263ac011d0c *man/get_division_line.Rd
 55c231dad0c46027b2141b475f0731bb *man/get_energy_model_mclust_object.Rd
-040c1f72de82cfb4c5ec69ce0c231e89 *man/get_energy_model_parameters.Rd
-aadb8f6bd871a7b3805c74d8b1dec09a *man/get_energy_models.Rd
+e9ec43500eb7d7da09ecff9ff7335095 *man/get_energy_model_parameters.Rd
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 55ecffca9becdbaf8baa60a82381c429 *man/get_ev_model.Rd
-9b5a555a216fe9dcc6683f78e92ed375 *man/mutate_to_log.Rd
-dd51a03c56934d7cdad757ec40ad239a *man/plot_bivarGMM.Rd
-9773a0801023f25142f2da012e9319c3 *man/plot_density_2D.Rd
-5bc059f1d15a61a60e2912e208618498 *man/plot_density_3D.Rd
-c817a28661229bf246d463b777b6c3bb *man/plot_division_lines.Rd
+0143ca8beba6195325e8918f141d5f09 *man/mutate_to_log.Rd
+971c13cc0467a5fc422e37d8bc68dd0a *man/plot_bivarGMM.Rd
+676e02a986292270491025f16c11cebb *man/plot_density_2D.Rd
+a9ecac5d7581642d7780cbf29612a43a *man/plot_density_3D.Rd
+de725c2cde1c0735c014e692ac7ea1db *man/plot_division_lines.Rd
 befd767e50965f616887ee3938cd6921 *man/plot_energy_models.Rd
 e93eb65d9f82eab786483e123bcca05a *man/plot_histogram.Rd
 88bc4d08d677dba1c0bb1f6e13ae2623 *man/plot_histogram_grid.Rd
-74acbeac8f0c85bd7ca36e649766a679 *man/plot_kNNdist.Rd
-d9222cd6a22185e3734e29291ad4a606 *man/plot_model_clusters.Rd
-fdcc22da4e043770c575466a204acaf3 *man/plot_outliers.Rd
-b028644e3ccf4f8f466d2471b4079b2f *man/plot_points.Rd
+7d342737216857878cac0c5aa9d55c85 *man/plot_kNNdist.Rd
+4c98a92553d6ca74ceb19eea5c237f80 *man/plot_model_clusters.Rd
+f3edd433b2b985efc121859668bb6bd4 *man/plot_outliers.Rd
+abf1941078340380fc5c29f938facacc *man/plot_points.Rd
 05ef1d2e16e791459e51059adb0aaf71 *man/print.evmodel.Rd
 9229b7aa474b7345c92595ff93e7a313 *man/read_ev_model.Rd
-ec4377fa1e8c87847d73f411706c94d2 *man/round_to_half.Rd
+4c93ce55df8c2d682d7621bf39aa56a2 *man/round_to_half.Rd
 dd0c09ca9e5c232accaac33bc91538aa *man/round_to_interval.Rd
-b04209585a8adf60ae2d6721e993afb4 *man/save_clustering_iterations.Rd
+e16c039a3190549be3404a3cd3ca9574 *man/save_clustering_iterations.Rd
 aedeb23173b2b4024312c6fdfd594dfb *man/save_ev_model.Rd
 2fd468136b57e6fc7d3ab59d9b2e978b *man/sessions_feature_names.Rd
 dad02fd42d30473859cd09f9089c8ef8 *man/sessions_summary_feature_names.Rd
 b9f563e33ab0137ef64ae0173e87b9eb *man/set_profiles.Rd
 e48e114ec50c3d992e337e72b9b52e81 *man/summarise_sessions.Rd
 0622a97a2aaa3c342f09636052c2d7f5 *tests/spelling.R
 c5a8ebd44a1983bb51d26baacb5767b1 *tests/testthat.R
-b90c0fffdc8012dc53be3ff3c6625eb1 *tests/testthat/test-clustering.R
-cfb040d89de464f2fede6660e8cc5371 *tests/testthat/test-exploration.R
-86bf6894d8da9b5b29b399061b97713b *tests/testthat/test-modelling.R
-ebd1ac6e2f58f6c696d02cc6d1b2e981 *tests/testthat/test-preprocessing.R
-665205e6b89005c0b133a08fef2714e5 *tests/testthat/test-profiling.R
+b67aee9da687822bb38595194ab7b0e8 *tests/testthat/test-clustering.R
+55c84d782d7d0e8a2191292c905a83b5 *tests/testthat/test-exploration.R
+5548b911f4dbfcd8ec493ce5a3660799 *tests/testthat/test-modelling.R
+442e65726d3cfb14c2c1aa56ee1a7e5f *tests/testthat/test-preprocessing.R
+9ce7fd1b15745e74e79b4afe359db068 *tests/testthat/test-profiling.R
 14a37235523ed1ee6dff1ce40c01f867 *vignettes/evmodel.Rmd
 4b6cf81408beeb1904ea368a732a2463 *vignettes/sessions-format.Rmd

NAMESPACE

@@ -14,7 +14,6 @@ export(get_connection_models)
 export(get_daily_avg_n_sessions)
 export(get_daily_n_sessions)
 export(get_dbscan_params)
-export(get_division_line)
 export(get_energy_models)
 export(get_ev_model)
 export(plot_bivarGMM)

NEWS.md

@@ -1,3 +1,9 @@
+# evprof 1.1.1
+
+* Improved the consistency of the provided example data sets
+* Function `plot_points` can configure `start` hour when `log = TRUE`
+
+
 # evprof 1.1.0
 
 * Energy GMM inside of `evmodel` also contain the `ratio` of every `charging_rate`

R/clustering.R

@@ -11,7 +11,6 @@
 #' @param log logical, whether to transform `ConnectionStartDateTime` and
 #' `ConnectionHours` variables to natural logarithmic scale (base = `exp(1)`).
 #' @param start integer, start hour in the x axis of the plot.
-#' This is only used when `log = FALSE`.
 #'
 #' @keywords internal
 #' @returns mclust object
@@ -21,9 +20,12 @@
 get_connection_model_mclust_object <- function(sessions, k, mclust_tol = 1e-8, mclust_itmax = 1e4,
                                                log = FALSE, start = getOption("evprof.start.hour")) {
   if (!log) {
-    sessions["ConnectionStartDateTime"] <- convert_time_dt_to_plot_num(sessions[["ConnectionStartDateTime"]], start)
+    sessions["ConnectionStartDateTime"] <- convert_time_dt_to_plot_num(
+      sessions[["ConnectionStartDateTime"]],
+      start = start
+    )
   } else {
-    sessions <- mutate_to_log(sessions)
+    sessions <- mutate_to_log(sessions, start)
   }
   sessions_cluster <- sessions[,c("ConnectionStartDateTime", "ConnectionHours")]
   Mclust(sessions_cluster, G = k, control = emControl(tol = mclust_tol, itmax = mclust_itmax))
@@ -51,6 +53,13 @@ get_connection_model_params <- function(mclust_obj) {
 
 #' Visualize BIC indicator to choose the number of clusters
 #'
+#' The Baysian Information Criterion (BIC) is the value of the maximized
+#' loglikelihood with a penalty on the number of parameters in the model,
+#' and allows comparison of models with differing parameterizations and/or
+#' differing numbers of clusters. In general the larger the value of the BIC,
+#' the stronger the evidence for the model and number of clusters
+#' (see, e.g. Fraley and Raftery 2002a).
+#'
 #' @param sessions tibble, sessions data set in evprof
 #' [standard format](https://mcanigueral.github.io/evprof/articles/sessions-format.html).
 #' @param k sequence with the number of clusters, for example 1:10, for 1 to 10 clusters.
@@ -59,7 +68,6 @@ get_connection_model_params <- function(mclust_obj) {
 #' @param log logical, whether to transform `ConnectionStartDateTime` and
 #' `ConnectionHours` variables to natural logarithmic scale (base = `exp(1)`).
 #' @param start integer, start hour in the x axis of the plot.
-#' This is only used when `log = FALSE`.
 #'
 #' @returns BIC plot
 #' @export
@@ -94,7 +102,6 @@ choose_k_GMM <- function(sessions, k, mclust_tol = 1e-8, mclust_itmax = 1e4,
 #' @param log logical, whether to transform `ConnectionStartDateTime` and
 #' `ConnectionHours` variables to natural logarithmic scale (base = `exp(1)`).
 #' @param start integer, start hour in the x axis of the plot.
-#' This is only used when `log = FALSE`.
 #'
 #' @returns list with two attributes: sessions and models
 #' @export
@@ -158,7 +165,6 @@ cluster_sessions <- function(sessions, k, seed, mclust_tol = 1e-8, mclust_itmax
 #' @param log logical, whether to transform `ConnectionStartDateTime` and
 #' `ConnectionHours` variables to natural logarithmic scale (base = `exp(1)`).
 #' @param start integer, start hour in the x axis of the plot.
-#' This is only used when `log = FALSE`.
 #'
 #' @export
 #' @returns nothing, but a PDF file is saved in the path specified by parameter `filename`
@@ -238,7 +244,6 @@ get_ellipse <- function(mu, sigma, alpha = 0.05, npoints = 200) {
 #' @param log logical, whether to transform `ConnectionStartDateTime` and
 #' `ConnectionHours` variables to natural logarithmic scale (base = `exp(1)`).
 #' @param start integer, start hour in the x axis of the plot.
-#' This is only used when `log = FALSE`.
 #'
 #' @returns ggplot2 plot
 #' @export
@@ -291,7 +296,7 @@ plot_bivarGMM <- function(sessions, models, profiles_names = seq(1, nrow(models)
   if (!log) {
     sessions["ConnectionStartDateTime"] <- convert_time_dt_to_plot_num(sessions[["ConnectionStartDateTime"]], start)
   } else {
-    sessions <- mutate_to_log(sessions)
+    sessions <- mutate_to_log(sessions, start)
   }
 
   plot <- ggplot(data = sessions, aes(x = .data$ConnectionStartDateTime, y = .data$ConnectionHours)) +

R/data.R

@@ -49,13 +49,14 @@
 #' More information about the development of the model in the evprof website:
 #' <https://mcanigueral.github.io/evprof/articles/california.html>
 #'
-#' @format list of two tibbles
+#' @format list
 #' \describe{
 #'   \item{connection_models}{Tibble with the parameters of the bi-variate
-#'   (connection start time and connection duration) GMM from the working days
-#'   sessions of the California data set}
+#'   (connection start time and connection duration) GMM from the working/weekend days
+#'   sessions of the California data set obtained from `get_connection_models`}
 #'   \item{energy_models}{Tibble with the parameters of the uni-variate
-#'   (energy) GMM from the working days sessions of the California data set}
+#'   (energy) GMM from the working/weekend days sessions of the California data set
+#'    obtained from `get_energy_models`}
 #' }
 #' @source <https://mcanigueral.github.io/evprof/articles/california.html>
 #' @keywords internal