Merge pull request #478 from SebKrantz/development

Development

README.md

@@ -240,8 +240,8 @@ pdata <- EuStockMarkets %>% list(`A` = ., `B` = .) %>%
 L(pdata, -1:3, ~Id, ~Time)                   # Sequence of fully identified panel-lags (L is operator for flag) 
 pdata %>% fgroup_by(Id) %>% flag(-1:3, Time) # Same thing..
 
-# collapse supports pseries and pdata.frame's, provided by the plm package
-pdata <- plm::pdata.frame(pdata, index = c("Id", "Time"))         
+# collapse also supports indexed series and data frames (and plm panel data classes)
+pdata <- findex_by(pdata, Id, Time)         
 L(pdata, -1:3)          # Same as above, ...
 psacf(pdata)            # Multivariate panel-ACF
 psmat(pdata) %>% plot   # 3D-array of time series from panel data + plotting

vignettes/collapse_for_tidyverse_users.Rmd

@@ -136,7 +136,7 @@ To take full advantage of *collapse*, it is highly recommended to use the *Fast
 
 ## Writing Efficient Code
 
-It is also performance-critical to correctly sequence operations and limit excess computations. *tidyverse* code is often inefficient simply because the *tidyverse* allows you to do everything. For example, `mtcars |> group_by(cyl) |> filter(mpg > 13) |> arrange(mpg)` is permissible but inefficient code as it filters and reorders grouped data, requiring modifications to both the data frame and the attached grouping object. *collapse* does not allow calls to `fsubset()` on grouped data and messages about it `roworder()`, encouraging you to write more efficient code. 
+It is also performance-critical to correctly sequence operations and limit excess computations. *tidyverse* code is often inefficient simply because the *tidyverse* allows you to do everything. For example, `mtcars |> group_by(cyl) |> filter(mpg > 13) |> arrange(mpg)` is permissible but inefficient code as it filters and reorders grouped data, requiring modifications to both the data frame and the attached grouping object. *collapse* does not allow calls to `fsubset()` on grouped data, and messages about it in `roworder()`, encouraging you to write more efficient code. 
 
 The above example can also be optimized because we are subsetting the whole frame and then doing computations on a subset of columns. It would be more efficient to select all required columns during the subset operation: