thisisnic commented on code in PR #14514:
URL: https://github.com/apache/arrow/pull/14514#discussion_r1022706108
##########
r/vignettes/data_wrangling.Rmd:
##########
@@ -0,0 +1,172 @@
+---
+title: "Data analysis with dplyr syntax"
+description: >
+ Learn how to use the `dplyr` backend supplied by `arrow`
+output: rmarkdown::html_vignette
+---
+
+The `arrow` package provides a `dplyr` back end that allows users to
manipulate tabular Arrow data (`Table` and `Dataset` objects) using familiar
`dplyr` syntax. To use this functionality, make sure that the `arrow` and
`dplyr` packages are both loaded. In this article we will take the `starwars`
data set included in `dplyr`, convert it to an Arrow Table, and then analyze
this data. Note that, although these examples all use an in-memory `Table`
object, the same functionality works for an on-disk `Dataset` object with only
minor differences in behavior (documented later in the article).
Review Comment:
Could we use a different term to "back end" here? I've heard different
people use the terms "back end", "frontend", "API" and other terms, and I think
this can sound a bit ambiguous.
##########
r/vignettes/data_wrangling.Rmd:
##########
@@ -0,0 +1,172 @@
+---
+title: "Data analysis with dplyr syntax"
+description: >
+ Learn how to use the `dplyr` backend supplied by `arrow`
+output: rmarkdown::html_vignette
+---
+
+The `arrow` package provides a `dplyr` back end that allows users to
manipulate tabular Arrow data (`Table` and `Dataset` objects) using familiar
`dplyr` syntax. To use this functionality, make sure that the `arrow` and
`dplyr` packages are both loaded. In this article we will take the `starwars`
data set included in `dplyr`, convert it to an Arrow Table, and then analyze
this data. Note that, although these examples all use an in-memory `Table`
object, the same functionality works for an on-disk `Dataset` object with only
minor differences in behavior (documented later in the article).
+
+To get started let's load the packages and create the data:
+
+```{r}
+library(dplyr, warn.conflicts = FALSE)
+library(arrow, warn.conflicts = FALSE)
+
+sw <- arrow_table(starwars, as_data_frame = FALSE)
+```
+
+## One-table dplyr verbs
+
+The `arrow` package provides support for the `dplyr` one-table verbs, allowing
users to construct data analysis pipelines in a familiar way. The example below
shows the use of `filter()`, `rename()`, `mutate()`, `arrange()` and `select()`:
+
+```{r}
+result <- sw %>%
+ filter(homeworld == "Tatooine") %>%
+ rename(height_cm = height, mass_kg = mass) %>%
+ mutate(height_in = height_cm / 2.54, mass_lbs = mass_kg * 2.2046) %>%
+ arrange(desc(birth_year)) %>%
+ select(name, height_in, mass_lbs)
+```
+
+It is important to note that `arrow` users lazy evaluation to delay
computation until the result is explicitly requested. This speeds up processing
by enabling the Arrow C++ library to perform multiple computations in one
operation. As a consequence of this design choice, we have not yet performed
computations on the `sw` data have been performed. The `result` variable is an
object with class `arrow_dplyr_query` that represents all the computations to
be performed:
Review Comment:
```suggestion
It is important to note that `arrow` uses lazy evaluation to delay
computation until the result is explicitly requested. This speeds up processing
by enabling the Arrow C++ library to perform multiple computations in one
operation. As a consequence of this design choice, we have not yet performed
computations on the `sw` data. The `result` variable is an object with class
`arrow_dplyr_query` that represents all the computations to be performed:
```
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