ready for cran release

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: tsibble
 Type: Package
 Title: Tidy Temporal Data Frames and Tools
-Version: 0.1.2.9999
+Version: 0.1.3
 Date: 2018-01-27
 Authors@R: c(
     person("Earo", "Wang", email = "[email protected]", role = c("aut", "cre")),

NEWS.md

@@ -1,4 +1,4 @@
-# tsibble 0.1.2.9999
+# tsibble 0.1.3
 
 ## New functions
 

R/tsummarise.R

@@ -158,7 +158,8 @@ tsum <- function(.data, first, remainder = NULL, FUN = summarise) {
   }
   if (is.null(remainder)) { # summarise_*()
     nonkey <- setdiff(flatten_key(key(.data)), flat_grps)
-    grped_data <- select(grped_data, - !! nonkey)
+    sel_cols <- setdiff(names(grped_data), nonkey)
+    grped_data <- select(grped_data, !! sel_cols)
     result <- FUN(grped_data)
   } else { # summarise()
     result <- FUN(grped_data, !!! remainder)

README.Rmd

@@ -73,7 +73,7 @@ full_weather
 
 ### `tsummarise()` to summarise over calendar periods
 
-`tsummarise()` and its scoped variants (including `_all()`, `_at()`, `_if()`) are introduced to aggregate interested variables over calendar periods. `tsummarise()` goes hand in hand with the index functions including `as.Date()`, `yearmonth()`, and `yearquarter()`, as well as other friends from *lubridate*, such as `year()` and `ceiling_date()`. For example, it would be of interest in computing average temperature and total precipitation per month, by applying `yearmonth()` to the hourly time index.
+`tsummarise()` and its scoped variants (including `_all()`, `_at()`, `_if()`) are introduced to aggregate interested variables over calendar periods. `tsummarise()` goes hand in hand with the index functions including `as.Date()`, `yearmonth()`, and `yearquarter()`, as well as other friends from *lubridate*, such as `year()`, `ceiling_date()`, `floor_date()` and `round_date()`. For example, it would be of interest in computing average temperature and total precipitation per month, by applying `yearmonth()` to the hourly time index.
 
 ```{r tsummarise}
 full_weather %>%
@@ -93,7 +93,7 @@ Temporal data often involves moving window calculations. Several functions in th
 
 * `slide()`: sliding window with overlapping observations.
 * `tile()`: tiling window without overlapping observations.
-* `stretch()`: fixing an initial window and expanding more observations.
+* `stretch()`: fixing an initial window and expanding to include more observations.
 
 For example, a moving average of window size 3 is carried out on hourly temperatures for each group (*origin*).
 

README.md

@@ -108,10 +108,10 @@ preserves time zones for date-times.
 `_if()`) are introduced to aggregate interested variables over calendar
 periods. `tsummarise()` goes hand in hand with the index functions
 including `as.Date()`, `yearmonth()`, and `yearquarter()`, as well as
-other friends from *lubridate*, such as `year()` and `ceiling_date()`.
-For example, it would be of interest in computing average temperature
-and total precipitation per month, by applying `yearmonth()` to the
-hourly time index.
+other friends from *lubridate*, such as `year()`, `ceiling_date()`,
+`floor_date()` and `round_date()`. For example, it would be of interest
+in computing average temperature and total precipitation per month, by
+applying `yearmonth()` to the hourly time index.
 
 ``` r
 full_weather %>%
@@ -144,7 +144,7 @@ windows using purrr-like syntax:
 
   - `slide()`: sliding window with overlapping observations.
   - `tile()`: tiling window without overlapping observations.
-  - `stretch()`: fixing an initial window and expanding more
+  - `stretch()`: fixing an initial window and expanding to include more
     observations.
 
 For example, a moving average of window size 3 is carried out on hourly