Replace summarise() with reframe() for dplyr 1.2.0

.Rbuildignore

@@ -7,3 +7,5 @@
 README.Rmd
 vignettes/*_cache*
 vignettes/*_files*
+^doc$
+^Meta$

.gitignore

@@ -7,3 +7,6 @@ data/
 *.Rproj
 *.rda
 inst/doc
+/doc/
+/Meta/
+.DS_Store

DESCRIPTION

@@ -2,7 +2,7 @@ Type: Package
 Package: gratis
 Title: Generating Time Series with Diverse and Controllable
     Characteristics
-Version: 1.0.7
+Version: 1.0.8
 Authors@R: c(
     person("Yanfei", "Kang", , "yanfeikang@buaa.edu.cn", role = "aut",
            comment = c(ORCID = "0000-0001-8769-6650")),
@@ -54,4 +54,4 @@ Encoding: UTF-8
 LazyLoad: yes
 NeedsCompilation: no
 Repository: CRAN
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.3

NEWS.md

@@ -1,3 +1,8 @@
+# gratis 1.0.8
+
+* simulate_target(): examples updated to use dplyr::reframe() instead of summarise()
+  to comply with dplyr 1.2.0.
+
 # gratis 1.0.7
 
 * Fixed dependency issue on CRAN.

R/simulate_target.R

@@ -42,9 +42,10 @@
 #' df %>% 
 #'  as_tibble() %>%
 #'  group_by(key) %>%
-#'  dplyr::summarise(value = my_features(value), 
-#'            feature=c("entropy","acf1", "acf2"),
-#'            .groups = "drop")
+#'  dplyr::reframe(
+#'            value = my_features(value), 
+#'            feature=c("entropy","acf1", "acf2")
+#'            )
 #' autoplot(df)
 #' # Simulate time series similar to an existing series
 #' my_features <- function(y) {

README.Rmd

@@ -44,7 +44,7 @@ devtools::install_github("ykang/gratis")
 
 ### Tutorial video
 
-Watch [this Youtube video](https://www.youtube.com/watch?v=F3lWECtFa44) provided by [Prof. Rob Hyndman](https://robjhyndman.com/).
+Watch [this YouTube video](https://www.youtube.com/watch?v=F3lWECtFa44) provided by [Prof. Rob Hyndman](https://robjhyndman.com/).
 
 ### Load the package
 
@@ -56,7 +56,7 @@ library(feasts)
 ### Generate diverse time series
 
 ```{r}
-set.seed(1)
+set.seed(27)
 mar_model(seasonal_periods=12) %>%
   generate(length=120, nseries=2) %>%
   autoplot(value)
@@ -83,12 +83,12 @@ my_features <- function(y) {
 df <- generate_target(
   length = 60, feature_function = my_features, target = c(0.5, 0.9, 0.8)
 )
-df %>% 
+df %>%
  as_tibble() %>%
  group_by(key) %>%
- summarise(value = my_features(value), 
-           feature=c("entropy","acf1", "acf2"),
-           .groups = "drop")
+ reframe(value = my_features(value), 
+           feature=c("entropy","acf1", "acf2")
+           )
 autoplot(df)
 ```
 

README.md

@@ -48,7 +48,7 @@ library(feasts)
 ### Generate diverse time series
 
 ``` r
-set.seed(1)
+set.seed(27)
 mar_model(seasonal_periods=12) %>%
   generate(length=120, nseries=2) %>%
   autoplot(value)
@@ -82,23 +82,23 @@ df <- generate_target(
 df %>%
  as_tibble() %>%
  group_by(key) %>%
- summarise(value = my_features(value),
-           feature=c("entropy","acf1", "acf2"),
-           .groups = "drop")
+ reframe(value = my_features(value), 
+           feature=c("entropy","acf1", "acf2")
+           )
 #> # A tibble: 30 × 3
 #>    key       value feature
-#>    <chr>     <dbl> <chr>
-#>  1 Series 1  0.533 entropy
-#>  2 Series 1  0.850 acf1
-#>  3 Series 1  0.735 acf2
-#>  4 Series 10 0.478 entropy
-#>  5 Series 10 0.880 acf1
-#>  6 Series 10 0.764 acf2
-#>  7 Series 2  0.507 entropy
-#>  8 Series 2  0.890 acf1
-#>  9 Series 2  0.899 acf2
-#> 10 Series 3  0.454 entropy
-#> # … with 20 more rows
+#>    <chr>     <dbl> <chr>  
+#>  1 Series 1  0.509 entropy
+#>  2 Series 1  0.906 acf1   
+#>  3 Series 1  0.787 acf2   
+#>  4 Series 10 0.465 entropy
+#>  5 Series 10 0.896 acf1   
+#>  6 Series 10 0.775 acf2   
+#>  7 Series 2  0.483 entropy
+#>  8 Series 2  0.901 acf1   
+#>  9 Series 2  0.812 acf2   
+#> 10 Series 3  0.504 entropy
+#> # ℹ 20 more rows
 autoplot(df)
 ```
 
@@ -117,15 +117,15 @@ APP](https://ebsmonash.shinyapps.io/tsgeneration/)
 
 ## See also
 
--   R package `tsfeatures` from [GitHub
-    Repository](https://github.com/robjhyndman/tsfeatures).
+- R package `tsfeatures` from [GitHub
+  Repository](https://github.com/robjhyndman/tsfeatures).
 
 ## References
 
--   Kang, Y., Hyndman, R., and Li, F. (2020). **GRATIS**:
-    **G**ene**RA**ting **TI**me **S**eries with diverse and controllable
-    characteristics. [Statistical Analysis and Data
-    Mining](https://doi.org/10.1002/sam.11461).
+- Kang, Y., Hyndman, R.J, and Li, F. (2020). **GRATIS**:
+  **G**ene**RA**ting **TI**me **S**eries with diverse and controllable
+  characteristics. [Statistical Analysis and Data
+  Mining](https://doi.org/10.1002/sam.11461).
 
 ## License
 

vignettes/QuickStart.Rmd

@@ -114,9 +114,9 @@ df <- generate_target(
 df %>%
  as_tibble() %>%
  group_by(key) %>%
- summarise(value = my_features(value),
-           feature=c("entropy","acf1", "acf2"),
-           .groups = "drop")
+ reframe(value = my_features(value),
+           feature=c("entropy","acf1", "acf2")
+        )
 df %>% autoplot(value)
 ```