Add data.table seal of approval

README.Rmd

@@ -23,7 +23,7 @@ knitr::opts_chunk$set(
 
 ## Overview
 
-dtplyr provides a [data.table](http://r-datatable.com/) backend for dplyr. The goal of dtplyr is to allow you to write dplyr code that is automatically translated to the equivalent, but usually much faster, data.table code.
+<a href="https://rdatatable-community.github.io/The-Raft/posts/2024-08-01-seal_of_approval-dtplyr/"><img src='man/figures/dt-seal.png' align="right" width="200" height="157" alt="data.table seal of approval"/></a>dtplyr provides a [data.table](http://r-datatable.com/) backend for dplyr. The goal of dtplyr is to allow you to write dplyr code that is automatically translated to the equivalent, but usually much faster, data.table code.
 
 See `vignette("translation")` for details of the current translations, and  [table.express](https://github.com/asardaes/table.express) and [rqdatatable](https://github.com/WinVector/rqdatatable/) for related work.
 
@@ -52,7 +52,7 @@ library(dtplyr)
 library(dplyr, warn.conflicts = FALSE)
 ```
 
-Then use `lazy_dt()` to create a "lazy" data table that tracks the operations performed on it. 
+Then use `lazy_dt()` to create a "lazy" data table that tracks the operations performed on it.
 
 ```{r}
 mtcars2 <- lazy_dt(mtcars)
@@ -61,35 +61,35 @@ mtcars2 <- lazy_dt(mtcars)
 You can preview the transformation (including the generated data.table code) by printing the result:
 
 ```{r}
-mtcars2 %>% 
-  filter(wt < 5) %>% 
+mtcars2 %>%
+  filter(wt < 5) %>%
   mutate(l100k = 235.21 / mpg) %>% # liters / 100 km
-  group_by(cyl) %>% 
+  group_by(cyl) %>%
   summarise(l100k = mean(l100k))
 ```
 
 But generally you should reserve this only for debugging, and use `as.data.table()`, `as.data.frame()`, or `as_tibble()` to indicate that you're done with the transformation and want to access the results:
 
 ```{r}
-mtcars2 %>% 
-  filter(wt < 5) %>% 
+mtcars2 %>%
+  filter(wt < 5) %>%
   mutate(l100k = 235.21 / mpg) %>% # liters / 100 km
-  group_by(cyl) %>% 
-  summarise(l100k = mean(l100k)) %>% 
+  group_by(cyl) %>%
+  summarise(l100k = mean(l100k)) %>%
   as_tibble()
 ```
 
 ## Why is dtplyr slower than data.table?
 
 There are two primary reasons that dtplyr will always be somewhat slower than data.table:
 
-* Each dplyr verb must do some work to convert dplyr syntax to data.table 
-  syntax. This takes time proportional to the complexity of the input code, 
+* Each dplyr verb must do some work to convert dplyr syntax to data.table
+  syntax. This takes time proportional to the complexity of the input code,
   not the input _data_, so should be a negligible overhead for large datasets.
-  [Initial benchmarks][benchmark] suggest that the overhead should be under 
+  [Initial benchmarks][benchmark] suggest that the overhead should be under
   1ms per dplyr call.
 
-* To match dplyr semantics, `mutate()` does not modify in place by default. 
+* To match dplyr semantics, `mutate()` does not modify in place by default.
   This means that most expressions involving `mutate()` must make a copy
   that would not be necessary if you were using data.table directly.
   (You can opt out of this behaviour in `lazy_dt()` with `immutable = FALSE`).

README.md

@@ -14,10 +14,10 @@ coverage](https://codecov.io/gh/tidyverse/dtplyr/branch/main/graph/badge.svg)](h
 
 ## Overview
 
-dtplyr provides a [data.table](http://r-datatable.com/) backend for
-dplyr. The goal of dtplyr is to allow you to write dplyr code that is
-automatically translated to the equivalent, but usually much faster,
-data.table code.
+<a href="https://rdatatable-community.github.io/The-Raft/posts/2024-08-01-seal_of_approval-dtplyr/"><img src='man/figures/dt-seal.png' align="right" width="200" height="157" alt="data.table seal of approval"/></a>dtplyr
+provides a [data.table](http://r-datatable.com/) backend for dplyr. The
+goal of dtplyr is to allow you to write dplyr code that is automatically
+translated to the equivalent, but usually much faster, data.table code.
 
 See `vignette("translation")` for details of the current translations,
 and [table.express](https://github.com/asardaes/table.express) and
@@ -62,10 +62,10 @@ You can preview the transformation (including the generated data.table
 code) by printing the result:
 
 ``` r
-mtcars2 %>% 
-  filter(wt < 5) %>% 
+mtcars2 %>%
+  filter(wt < 5) %>%
   mutate(l100k = 235.21 / mpg) %>% # liters / 100 km
-  group_by(cyl) %>% 
+  group_by(cyl) %>%
   summarise(l100k = mean(l100k))
 #> Source: local data table [3 x 2]
 #> Call:   `_DT1`[wt < 5][, `:=`(l100k = 235.21/mpg)][, .(l100k = mean(l100k)), 
@@ -85,11 +85,11 @@ But generally you should reserve this only for debugging, and use
 you’re done with the transformation and want to access the results:
 
 ``` r
-mtcars2 %>% 
-  filter(wt < 5) %>% 
+mtcars2 %>%
+  filter(wt < 5) %>%
   mutate(l100k = 235.21 / mpg) %>% # liters / 100 km
-  group_by(cyl) %>% 
-  summarise(l100k = mean(l100k)) %>% 
+  group_by(cyl) %>%
+  summarise(l100k = mean(l100k)) %>%
   as_tibble()
 #> # A tibble: 3 × 2
 #>     cyl l100k