### Install dtplyr Development Version

Source: https://github.com/tidyverse/dtplyr/blob/main/README.md

Install the latest development version of dtplyr from GitHub using the 'pak' package manager.

```r
# install.packages("pak")
pak::pak("tidyverse/dtplyr")
```

--------------------------------

### Install dtplyr from CRAN

Source: https://github.com/tidyverse/dtplyr/blob/main/README.md

Use this command to install the stable version of dtplyr from the Comprehensive R Archive Network (CRAN).

```r
install.packages("dtplyr")
```

--------------------------------

### R Error Output from revdepcheck

Source: https://github.com/tidyverse/dtplyr/blob/main/revdep/problems.md

This output shows an error encountered during the 'checking examples' phase of the revdepcheck for the rFIA package. The error originates from within the package's examples and involves a chain of calls through `tidyselect` and `vctrs` packages, culminating in an out-of-bounds subscript error.

```R
Running examples in ‘rFIA-Ex.R’ failed
The error most likely occurred in:

> ### Name: area
> ### Title: Estimate land area from FIADB
> ### Aliases: area
> 
> ### ** Examples
> 
> ## Load data from the rFIA package
... 
     14. │             └─tidyselect:::walk_data_tree(new, data_mask, context_mask)
     15. │               └─tidyselect:::as_indices_sel_impl(...)
     16. │                 └─tidyselect:::as_indices_impl(...)
     17. │                   └─tidyselect:::chr_as_locations(x, vars, call = call, arg = arg)
     18. │                     └─vctrs::vec_as_location(...)
     19. └─vctrs (local) `<fn>`()
     20.   └─vctrs:::stop_subscript_oob(...)
     21.     └─vctrs:::stop_subscript(...)
     22.       └─rlang::abort(...)
Execution halted
```

--------------------------------

### Error: Cannot supply both .before and .after in relocate()

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-colorder-relocate.md

When using relocate(), you cannot specify both the .before and .after arguments simultaneously. This condition will raise an error.

```R
relocate(dt, y, .before = x, .after = x)
```

--------------------------------

### Provide 'order_by' argument for slice_min/slice_max

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-subset-slice.md

The slice_min and slice_max functions require the 'order_by' argument to be supplied. If 'order_by' is absent, an error will occur.

```R
slice_min(dt)
```

```R
slice_max(dt)
```

--------------------------------

### Print Lazy Data Table with n

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step.md

Demonstrates printing a lazy data table with a specified number of rows using the `n` argument. This controls the number of rows displayed in the output.

```R
dt <- letters %>% lapply(function(.x) tibble(!!.x := 1:10)) %>% bind_cols() %>%
  lazy_dt("DT")
print(dt, n = 3)
```

--------------------------------

### Show query for names_glue

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-call-pivot_wider.md

Displays the generated data.table code when names_glue is used to format output column names.

```R
show_query(step)
```

--------------------------------

### Load dtplyr and dplyr

Source: https://github.com/tidyverse/dtplyr/blob/main/README.md

Load the necessary libraries, including data.table, dtplyr, and dplyr, to begin using dtplyr functionalities. warn.conflicts = FALSE prevents messages about conflicting function names.

```r
library(data.table)
library(dtplyr)
library(dplyr, warn.conflicts = FALSE)
```

--------------------------------

### Using summarise() with default .groups in dtplyr

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-subset-summarise.md

Demonstrates the default behavior of summarise() when no .groups argument is specified. It shows how dtplyr translates the operation to SQL and the resulting message about grouping.

```R
eval_bare(expr(lazy_dt(data.frame(x = 1, y = 2), "DT") %>% group_by(x, y) %>%
        dplyr::summarise() %>% show_query()), env(global_env()))
```

--------------------------------

### rename_with generates minimal spec

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-call.md

Shows how `rename_with` with `toupper` generates a minimal `setnames` specification for renaming columns. This is useful for simple, direct column renaming.

```R
dt %>% rename_with(toupper) %>% show_query()
```

--------------------------------

### Print Lazy Data Table with max_footer_lines

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step.md

Illustrates printing a lazy data table with a limited number of footer lines using `max_footer_lines`. This controls how many lines are shown after the main data, including row counts and variable information.

```R
print(dt, max_footer_lines = 1)
```

--------------------------------

### Group Lazy Data Table with dtplyr

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step.md

Demonstrates grouping a lazy data table by specified columns using `group_by()`. The output indicates that the data is grouped and shows a preview, similar to the initial lazy data table.

```R
dt %>% group_by(vs, am)
```

--------------------------------

### Print Lazy Data Table with max_extra_cols

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step.md

Shows how to print a lazy data table when it has many columns, using `max_extra_cols` to limit the number of columns displayed. This is useful for wide tables.

```R
print(dt, max_extra_cols = 3)
```

--------------------------------

### Preview Transformations and Generated data.table Code

Source: https://github.com/tidyverse/dtplyr/blob/main/README.md

Print a lazy data table after applying dplyr verbs to preview the transformations and see the generated data.table code. This is useful for debugging.

```r
mtcars2 %>%
  filter(wt < 5) %>%
  mutate(l100k = 235.21 / mpg) %>%
  group_by(cyl) %>%
  summarise(l100k = mean(l100k))
```

--------------------------------

### Supply exactly one of 'n' or 'prop' for slice_head

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-subset-slice.md

For the slice_head function, you must supply exactly one of the 'n' or 'prop' arguments. Providing both or neither will result in an error.

```R
slice_head(dt, n = 1, prop = 1)
```

--------------------------------

### Error in rename_with with non-function

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-call.md

Demonstrates an error condition when `rename_with` is provided with a non-function argument. The `.fn` argument must be a function name or formula.

```R
dt %>% rename_with(1)
```

--------------------------------

### Compute distinct computed variables

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-call.md

Demonstrates computing distinct values for a new variable derived from existing columns. This uses `unique` with `:=` for assignment and subsequent column removal.

```R
dt %>% distinct(z = x + y) %>% show_query()
```

--------------------------------

### Explicitly name 'n' argument for slice_*() functions

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-subset-slice.md

When using slice_*() functions like slice_head, slice_tail, slice_min, slice_max, and slice_sample, the 'n' argument must be explicitly named. Failure to do so will result in an error.

```R
slice_head(dt, 5)
```

```R
slice_tail(dt, 5)
```

```R
slice_min(dt, x, 5)
```

```R
slice_max(dt, x, 5)
```

```R
slice_sample(dt, 5)
```

--------------------------------

### Pivot wider with name repair

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-call-pivot_wider.md

Applies pivot_wider and demonstrates the error when duplicate names are encountered without repair. It then shows how to use names_repair = "unique" to resolve these conflicts.

```R
pivot_wider(df, names_from = lab, values_from = val)
```

```R
pivot_wider(df, names_from = lab, values_from = val, names_repair = "unique")
```

--------------------------------

### rename_with with column selection

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-call.md

Illustrates `rename_with` used with specific column selection, generating a `setnames` call that renames a subset of columns. This allows for targeted renaming.

```R
dt %>% rename_with(toupper, 1:3) %>% show_query()
```

--------------------------------

### Error: `into` must be a character vector

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-subset-separate.md

This error is raised when the `into` argument in `separate()` is not a character vector. Verify that `into` is provided as a character type.

```R
separate(dt, x, FALSE)
```

--------------------------------

### dtplyr full_join for Cross Join

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-join.md

Performs a full join that results in a cross join when `by` is an empty character vector. Use `as.data.table()`, `as.data.frame()`, or `as_tibble()` to access the results.

```r
full_join(dt1, dt2, by = character())
```

--------------------------------

### Error Handling for Unbalanced Datasets in pivot_longer

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-call-pivot_longer.md

This code demonstrates that `pivot_longer` will error if the dataset is unbalanced, as `data.table::melt` does not support this. Ensure your data is balanced before pivoting.

```R
pivot_longer(dt, everything(), names_to = c(".value", "id"), names_sep = "_")
```

--------------------------------

### Ensure 'prop' is a single number for slice_head

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-subset-slice.md

The 'prop' argument in slice_head must be a single number. Providing a non-numeric value or NA will cause an error.

```R
slice_head(dt, prop = "a")
```

```R
slice_head(dt, prop = NA)
```

--------------------------------

### Mutate and Compute Lazy Data Table with dtplyr

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step.md

Shows how to add a new column (`y = 10`) to a lazy data table using `mutate()` and then compute the result into a new table named 'DT2' using `compute()`. The output displays the new table with the added column and the underlying `data.table` code used for computation.

```R
dt %>% mutate(y = 10) %>% compute("DT2")
```

--------------------------------

### Create Lazy Data Table with dtplyr

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step.md

Use `lazy_dt()` to create a lazy data table from an existing data frame. The output shows the structure and a preview of the data, along with a note to use conversion functions like `as.data.table()` to access the results.

```R
dt <- lazy_dt(mtcars, "DT")
dt
```

--------------------------------

### Error when selecting non-existent columns with drop_na

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-call.md

Highlights an error that occurs when `drop_na` attempts to select a column that does not exist in the data.table. This ensures data integrity by preventing operations on invalid columns.

```R
collect(drop_na(dt, "z"))
```

--------------------------------

### if_all() gives informative errors for invalid .fns

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/tidyeval-across.md

Similar to `across()`, `dtplyr::if_all()` provides informative errors when the `.fns` argument is invalid, ensuring correct usage.

```R
capture_if_all(dt, if_all(a, 1))
```

```R
capture_if_all(dt, if_all(a, list(1)))
```

--------------------------------

### dtplyr left_join for Cross Join

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-join.md

Performs a left join that results in a cross join when `by` is an empty character vector. Use `as.data.table()`, `as.data.frame()`, or `as_tibble()` to access the results.

```r
left_join(dt1, dt2, by = character())
```

--------------------------------

### across() does not support formulas with dots

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/tidyeval-across.md

When using `dtplyr::across()` with a purrr-style lambda in `.fns`, `...` is not supported. Use a lambda instead or inline them.

```R
capture_across(dt, across(a:b, ~ log(.x, base = .y), base = 2))
```

```R
capture_across(dt, across(a:b, list(~ log(.x, base = .y)), base = 2))
```

--------------------------------

### dtplyr right_join for Cross Join

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-join.md

Performs a right join that results in a cross join when `by` is an empty character vector. Use `as.data.table()`, `as.data.frame()`, or `as_tibble()` to access the results.

```r
right_join(dt1, dt2, by = character())
```

--------------------------------

### dtplyr inner_join for Cross Join

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-join.md

Performs an inner join that results in a cross join when `by` is an empty character vector. Use `as.data.table()`, `as.data.frame()`, or `as_tibble()` to access the results.

```r
inner_join(dt1, dt2, by = character())
```

--------------------------------

### Mutate with unnamed argument

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-mutate.md

Unnamed arguments in mutate are ignored. If an object is not found, an error will occur.

```R
mutate(dt, y)

```

--------------------------------

### Ensure 'n' is a single number for slice_head

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-subset-slice.md

The 'n' argument in slice_head must be a single number. Providing a non-numeric value or NA will cause an error.

```R
slice_head(dt, n = "a")
```

```R
slice_head(dt, n = NA)
```

--------------------------------

### Error: `sep` must be a character vector

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-subset-separate.md

This error occurs when the `sep` argument in `separate()` is not a character vector. Ensure `sep` is correctly specified as a character type.

```R
separate(dt, x, "x", FALSE)
```

--------------------------------

### Unsupported lag() order_by in dtplyr

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/tidyeval.md

The `order_by` argument for the `lag()` function is not supported by dtplyr. Use with caution or consider alternative approaches.

```R
The `order_by` argument of `lag()` is not supported by dtplyr
```

--------------------------------

### Unsupported Feature: names_transform in pivot_longer

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-call-pivot_longer.md

dtplyr does not support the `names_transform` argument in `pivot_longer`. Use this to check for unsupported arguments and ensure compatibility.

```R
dt %>% pivot_longer(names_transform = list())
```

--------------------------------

### Unsupported Feature: names_ptypes in pivot_longer

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-call-pivot_longer.md

dtplyr does not support the `names_ptypes` argument in `pivot_longer`. Use this to check for unsupported arguments and ensure compatibility.

```R
dt %>% pivot_longer(names_ptypes = list())
```

--------------------------------

### Access Results as a Tibble

Source: https://github.com/tidyverse/dtplyr/blob/main/README.md

Convert the results of dplyr operations on a lazy data table back into a standard tibble using as_tibble(). This triggers the computation of the data.table code.

```r
mtcars2 %>%
  filter(wt < 5) %>%
  mutate(l100k = 235.21 / mpg) %>%
  group_by(cyl) %>%
  summarise(l100k = mean(l100k)) %>%
  as_tibble()
```

--------------------------------

### Empty select() after group_by()

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-subset-select.md

When an empty select() is applied after grouping with group_by(), dtplyr adds the grouping variables back. This behavior is consistent whether the data is copied or not.

```R
out <- lz %>% group_by(x) %>% select()

```

```R
out <- lz %>% group_by(x) %>% select()

```

--------------------------------

### Unsupported Feature: values_ptypes in pivot_longer

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-call-pivot_longer.md

dtplyr does not support the `values_ptypes` argument in `pivot_longer`. Use this to check for unsupported arguments and ensure compatibility.

```R
dt %>% pivot_longer(values_ptypes = list())
```

--------------------------------

### Unsupported Feature: values_transform in pivot_longer

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-call-pivot_longer.md

dtplyr does not support the `values_transform` argument in `pivot_longer`. Use this to check for unsupported arguments and ensure compatibility.

```R
dt %>% pivot_longer(values_transform = list())
```

--------------------------------

### across() gives informative errors for invalid .fns

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/tidyeval-across.md

dtplyr's `across()` provides informative errors when the `.fns` argument is not a NULL, function, formula, or list, or when list elements are not functions or formulas.

```R
capture_across(dt, across(a, 1))
```

```R
capture_across(dt, across(a, list(1)))
```

--------------------------------

### Create a Lazy Data Table

Source: https://github.com/tidyverse/dtplyr/blob/main/README.md

Use lazy_dt() to wrap a data frame (e.g., mtcars) into a 'lazy' data table object. This object tracks subsequent dplyr operations without immediately computing them.

```r
mtcars2 <- lazy_dt(mtcars)
```

--------------------------------

### desc() argument check in dtplyr

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/tidyeval.md

The `desc()` function in dtplyr checks for exactly one argument. Providing more than one argument will result in an error.

```R
capture_dot(df, desc(a, b))
```

--------------------------------

### Error when using .groups = "rowwise" with summarise() in dtplyr

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-subset-summarise.md

Illustrates that the .groups argument cannot be set to "rowwise" when using summarise() in dtplyr. It highlights the allowed values for .groups.

```R
i Possible values are NULL (default), "drop_last", "drop", and "keep"
```

--------------------------------

### if_all() disallows renaming variables

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/tidyeval-across.md

The `dtplyr::if_all()` function does not permit renaming variables within its arguments, which is enforced by an informative error.

```R
expect_error(capture_if_all(dt, if_all(c(a = x, b = y))))
```

--------------------------------

### Error with where() in dtplyr

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/tidyeval.md

The tidyselect interface for `where()` is not supported by dtplyr, leading to errors. This is due to limitations in predicate support.

```R
This tidyselect interface doesn't support predicates.
```

```R
This tidyselect interface doesn't support predicates.
```

--------------------------------

### dtplyr count() with invalid name

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/count.md

When using count() with a non-string value for the name argument, an error is raised. Ensure the name is a character string.

```R
dt %>% count(name = 10) %>% collect()
```

--------------------------------

### Named Input Error in dtplyr Filter

Source: https://github.com/tidyverse/dtplyr/blob/main/tests/testthat/_snaps/step-subset-filter.md

This error occurs when a named argument is used in the filter function instead of a comparison. Ensure you use '==' for comparisons.

```R
filter(dt, x = 1)
```

```R
filter(dt, y > 1, x = 1)
```

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