update some examples

R/as-tsibble.R

@@ -21,13 +21,13 @@
 #' @seealso [build_tsibble]
 #'
 #' @examples
-#' # create a tsibble w/o a key ----
+#' # create a tsibble w/o a key
 #' tsibble(
 #'   date = as.Date("2017-01-01") + 0:9,
 #'   value = rnorm(10)
 #' )
 #'
-#' # create a tsibble with one key ----
+#' # create a tsibble with one key
 #' tsibble(
 #'   qtr = rep(yearquarter("201001") + 0:9, 3),
 #'   group = rep(c("x", "y", "z"), each = 10),
@@ -61,16 +61,16 @@ tsibble <- function(..., key = NULL, index, regular = TRUE, .drop = TRUE) {
 #' @seealso [tsibble]
 #'
 #' @examples
-#' # coerce tibble to tsibble w/o a key ----
+#' # coerce tibble to tsibble w/o a key
 #' tbl1 <- tibble(
 #'   date = as.Date("2017-01-01") + 0:9,
 #'   value = rnorm(10)
 #' )
 #' as_tsibble(tbl1)
-#' # specify the index var
+#' # supply the index to suppress the message
 #' as_tsibble(tbl1, index = date)
 #'
-#' # coerce tibble to tsibble with one key ----
+#' # coerce tibble to tsibble with one key
 #' # "date" is automatically considered as the index var, and "group" is the key
 #' tbl2 <- tibble(
 #'   mth = rep(yearmonth("2017-01") + 0:9, 3),

R/dplyr-verbs.R

@@ -11,7 +11,7 @@
 #' * `unnest()` requires argument `key = NULL` to get back to a tsibble.
 #'
 #' @param .data A `tbl_ts`.
-#' @param ... same arguments accepted as its dplyr generic.
+#' @param ... Same arguments accepted as its dplyr generic.
 #' @inheritParams dplyr::arrange
 #' @details
 #' Column-wise verbs, including `select()`, `transmute()`, `summarise()`,
@@ -164,7 +164,7 @@ transmute.grouped_ts <- function(.data, ...) {
 
 #' @rdname tsibble-tidyverse
 #' @examples
-#' library(dplyr)
+#' library(dplyr, warn.conflicts = FALSE)
 #' # Sum over sensors ----
 #' pedestrian %>%
 #'   summarise(Total = sum(Count))

R/gaps.R

@@ -23,7 +23,7 @@ globalVariables(c(".", ".gaps"))
 #'   key = fruit, index = year
 #' )
 #'
-#' # gaps as default `NA` ----
+#' # gaps as default `NA`
 #' fill_gaps(harvest, .full = TRUE)
 #' full_harvest <- fill_gaps(harvest, .full = FALSE)
 #' full_harvest
@@ -33,26 +33,26 @@ globalVariables(c(".", ".gaps"))
 #'   group_by(fruit) %>% 
 #'   tidyr::fill(kilo, .direction = "down")
 #'
-#' # replace gaps with a specific value ----
+#' # replace gaps with a specific value
 #' harvest %>%
 #'   fill_gaps(kilo = 0L)
 #'
-#' # replace gaps using a function by variable ----
+#' # replace gaps using a function by variable
 #' harvest %>%
 #'   fill_gaps(kilo = sum(kilo))
 #'
-#' # replace gaps using a function for each group ----
+#' # replace gaps using a function for each group
 #' harvest %>%
 #'   group_by(fruit) %>%
 #'   fill_gaps(kilo = sum(kilo))
 #'
-#' # leaves existing `NA` untouched ----
+#' # leaves existing `NA` untouched
 #' harvest[2, 3] <- NA
 #' harvest %>%
 #'   group_by(fruit) %>%
 #'   fill_gaps(kilo = sum(kilo, na.rm = TRUE))
 #'
-#' # replace NA ----
+#' # replace NA
 #' pedestrian %>%
 #'   group_by(Sensor) %>%
 #'   fill_gaps(Count = as.integer(median(Count)))
@@ -160,6 +160,7 @@ scan_gaps.tbl_ts <- function(.data, .full = FALSE, ...) {
 #' @examples
 #' ped_gaps <- pedestrian %>% 
 #'   count_gaps(.full = TRUE)
+#' ped_gaps
 #' if (!requireNamespace("ggplot2", quietly = TRUE)) {
 #'   stop("Please install the ggplot2 package to run these following examples.")
 #' }

R/index-by.R

@@ -17,7 +17,7 @@
 #' * [yearweek]: weekly aggregation
 #' * [as.Date] or [lubridate::as_date]: daily aggregation
 #' * [lubridate::ceiling_date], [lubridate::floor_date], or [lubridate::round_date]: 
-#' sub-daily aggregation
+#' fine-resolution aggregation
 #' * other index functions from other packages
 #'
 #' @details
@@ -27,8 +27,8 @@
 #' @rdname index-by
 #' @export
 #' @examples
-#' # Monthly counts across sensors ----
-#' library(dplyr)
+#' # Monthly counts across sensors
+#' library(dplyr, warn.conflicts = FALSE)
 #' monthly_ped <- pedestrian %>% 
 #'   group_by(Sensor) %>% 
 #'   index_by(Year_Month = yearmonth(Date_Time)) %>%
@@ -39,7 +39,7 @@
 #' monthly_ped
 #' index(monthly_ped)
 #' 
-#' # Using existing variable ----
+#' # Using existing variable
 #' pedestrian %>% 
 #'   group_by(Sensor) %>% 
 #'   index_by(Date) %>%
@@ -54,7 +54,7 @@
 #'   index_by(Date_Time4 = lubridate::floor_date(Date_Time, "4 hour")) %>%
 #'   summarise(Total_Count = sum(Count))
 #'
-#' # Annual trips by Region and State ----
+#' # Annual trips by Region and State
 #' tourism %>% 
 #'   index_by(Year = lubridate::year(Quarter)) %>% 
 #'   group_by(Region, State) %>% 

R/period.R

@@ -24,19 +24,22 @@
 #' yearweek(x)
 #' yearmonth(x)
 #' yearmonth(yearweek(x))
-#' yearmonth("2018-07")
 #' yearquarter(x)
 #'
 #' # coerce yearmonths to yearquarter
 #' y <- yearmonth(x)
 #' yearquarter(y)
 #'
+#' # parse characters
+#' yearmonth(c("2018 Jan", "2018-01", "2018 January"))
+#' yearquarter(c("2018 Q1", "2018 Qtr1", "2018 Quarter 1"))
+#'
 #' # seq() and binary operaters
 #' wk1 <- yearweek("2017-11-01")
 #' wk2 <- yearweek("2018-04-29")
 #' seq(from = wk1, to = wk2, by = 2) # by two weeks
 #' wk1 + 0:9
-#' mth <- yearmonth("2017-11-01")
+#' mth <- yearmonth("2017-11")
 #' seq(mth, length.out = 5, by = 1) # by 1 month
 #' mth + 0:9
 #' seq(yearquarter(mth), length.out = 5, by = 1) # by 1 quarter

R/time-wise.R

@@ -17,7 +17,7 @@
 #' difference(x, lag = 2)
 #' difference(x, differences = 2)
 #' # Use order_by if data not already ordered (example from dplyr)
-#' library(dplyr)
+#' library(dplyr, warn.conflicts = FALSE)
 #' tsbl <- tsibble(year = 2000:2005, value = (0:5) ^ 2, index = year)
 #' scrambled <- tsbl %>% slice(sample(nrow(tsbl)))
 #'