prepping for CRAN

spsanderson · Jun 8, 2022 · d45bc0e · d45bc0e
1 parent c4d6f76
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Showing 10 changed files with 13 additions and 57 deletions.
diff --git a/R/boilerplate-theta.R b/R/boilerplate-theta.R
@@ -22,11 +22,8 @@
 #' @param .data The data being passed to the function. The time-series object.
 #' @param .date_col The column that holds the datetime.
 #' @param .value_col The column that has the value
-#' @param .rsamp_obj The rsample splits object
+#' @param .rsamp_obj The splits object
 #' @param .prefix Default is `ts_theta`
-#' @param .cv_assess How many observations for assess. See [timetk::time_series_cv()]
-#' @param .cv_skip How many observations to skip. See [timetk::time_series_cv()]
-#' @param .cv_slice_limit How many slices to return. See [timetk::time_series_cv()]
 #' @param .bootstrap_final Not yet implemented.
 #'
 #' @examples
@@ -62,19 +59,11 @@
 #'
 
 ts_auto_theta <- function(.data, .date_col, .value_col, .rsamp_obj,
-                          .prefix = "ts_theta", .grid_size = 10, .cv_assess = 12,
-                          .cv_skip = 3, .cv_slice_limit = 6,
-                          .bootstrap_final = FALSE){
+                          .prefix = "ts_theta", .bootstrap_final = FALSE){
 
     # Tidyeval ----
     date_col_var_expr <- rlang::enquo(.date_col)
     value_col_var_expr <- rlang::enquo(.value_col)
-    sampling_object <- .rsamp_obj
-
-    # Cross Validation
-    cv_assess = as.numeric(.cv_assess)
-    cv_skip   = as.numeric(.cv_skip)
-    cv_slice  = as.numeric(.cv_slice_limit)
 
     # Data and splits
     splits <- .rsamp_obj

diff --git a/R/ts_functions.R → R/ts-compare-data.R b/R/ts_functions.R → R/ts-compare-data.R
@@ -40,6 +40,9 @@
 #' suppressPackageStartupMessages(library(dplyr))
 #' suppressPackageStartupMessages(library(timetk))
 #'
+#' data_tbl <- ts_to_tbl(AirPassengers) %>%
+#'   select(-index)
+#'
 #' ts_compare_data(
 #'   .data           = data_tbl
 #'   , .date_col     = date_col

diff --git a/Rplot.png b/Rplot.png
diff --git a/Rplot01.png b/Rplot01.png
diff --git a/man/color_blind.Rd b/man/color_blind.Rd
diff --git a/man/ts_auto_theta.Rd b/man/ts_auto_theta.Rd
diff --git a/man/ts_compare_data.Rd b/man/ts_compare_data.Rd
diff --git a/man/ts_scale_color_colorblind.Rd b/man/ts_scale_color_colorblind.Rd
diff --git a/man/ts_scale_fill_colorblind.Rd b/man/ts_scale_fill_colorblind.Rd
diff --git a/vignettes/using-tidy-fft.Rmd b/vignettes/using-tidy-fft.Rmd
@@ -71,24 +71,13 @@ Let us now work through a simple example.
 Lets get started with some data.
 
 ```{r example_data}
-suppressPackageStartupMessages(library(healthyR.data))
 suppressPackageStartupMessages(library(dplyr))
 suppressPackageStartupMessages(library(ggplot2))
 suppressPackageStartupMessages(library(timetk))
 
-data_tbl <- healthyR_data%>%
-    filter(ip_op_flag == 'I') %>%
-    summarise_by_time(
-        .date_var = visit_end_date_time,
-        .by = "month",
-        value = n()
-    ) %>%
-    filter_by_time(
-        .date_var = visit_end_date_time,
-        .start_date = "2015",
-        .end_date = "2019"
-   ) %>%
-  rename(date_col = visit_end_date_time)
+data_tbl <- AirPassengers %>%
+    ts_to_tbl() %>%
+    select(-index)
 ```
 
 Now that we have our sample data, let's check it out.