Remove need for tidy evaluation from trees-and-tibbleations

exercises/concept/trees-and-tibbleations/.docs/hints.md

@@ -15,13 +15,10 @@
 ## 3. Orchard copy of dataset
 
 - You need to move columns around and sort rows (see the Introduction).
-- The [`all_of()`][ref-allof] function may be useful for moving columns.
-- See caution in the introduction about [data-masking][ref-datamask] which mentions [`pick()`][ref-pick].
 
 ## 4. Customer copy of dataset
 
 - You will need to take a subset of columns.
-- The [`all_of()`][ref-allof] function may be useful for subsetting.
 - Only the trees between the minimum and maximum height *and* below the maximum weight should be in the copy.
 
 [ref-allof]: https://tidyselect.r-lib.org/reference/all_of.html

exercises/concept/trees-and-tibbleations/.docs/instructions.md

@@ -60,49 +60,42 @@ girth_n_weight(tree_data, 1) |> head(3)
 #>      8.8	     63	   10.2	  27.6	 357.0
 ```
 
-**Note:** For testing, the input dataset for `girth_n_weight` can be assumed to have `Diameter` and `Volume` columns.
-
 ## 3. Orchard copy of dataset
 
 For each potential customer, the orchard keeps a special copy of the dataset to help with the sale.
-This dataset has important columns moved to the front and is sorted by that leading column.
+This dataset has the most relevant columns, `Height` and `Weight`, moved to the front and is sorted by `Height`.
 
-Define the function `orchard_copy(data, important_cols)` which takes a dataframe and a vector of column names.
-This should return a new dataframe with the important columns moved to the front and the rows sorted by the first column of that rearrangement.
+Define the function `orchard_copy(data, important_cols)` which takes a dataframe as input.
+This should return a new dataframe with `Height` and `Weight` moved to the front and the rows sorted by `Height`.
 
 ```R
-orchard_copy(tree_data, c('Height', 'Volume')) |> head(3)
-#>      A tibble: 3 × 3 
-#> Height	Volume	 Diameter
-#>  <dbl>	 <dbl>	    <dbl>
-#>     63	  10.2	      8.8
-#>     64	  24.9	     13.8
-#>     65	  10.3	      8.6
+tree_data |> girth_n_weight() |> orchard_copy() |> head(3)
+#>            A tibble: 3 × 5 
+#> Weight	Height	Diameter	Volume	Girth
+#>  <dbl>	 <dbl>	   <dbl>	 <dbl>	<dbl>
+#>    357	    63	       9	    10	   28
+#>    360	    70	       8	    10	   26
+#>    360	    65	       9	    10	   27
 ```
 
 ## 4. Customer copy of dataset
 
 Each potential customer will get a personal version of the dataset based on their preferences.
-To this end, the customer may specify two extra things:
-
-1. The minimum height, the maximum height and the maximum weight of any single tree.
-2. A list of attributes that are of interest.
+To this end, the customer may specify: the minimum height, the maximum height and the maximum weight of any single tree.
 
-Define the `customer_copy(data, attrbutes, min_height, max_height, max_weight)` function.
-Return a customer copy of the dataframe with the requested attributes and within the constraints.
+Define the `customer_copy(data, min_height, max_height, max_weight)` function.
+Return a customer copy of the dataframe with `Height`, `Weight`, `Diameter` and `Girth` columns with rows that are within the constraints.
 
 ```R
 tree_data |> 
     girth_n_weight(1) |> 
-    orchard_copy(c('Height', 'Weight', 'Volume')) |>
-    customer_copy(c('Height', 'Weight', 'Girth'), 65, 75, 1500) |> 
+    orchard_copy() |>
+    customer_copy(65, 75, 1500) |> 
     head(3)
-#>     A tibble: 3 × 3 
-#> Height	Weight   Girth
-#>  <dbl>	 <dbl>	 <dbl>
-#>     65	 360.5	  27.0
-#>     66	 546.0	  34.6
-#>     69	 745.5	  36.8
+#>        A tibble: 3 × 4 
+#> Height	Weight	Diameter	Girth
+#>  <dbl>	 <dbl>	   <dbl>	<dbl>
+#>     70	 360.5	     8.3	 26.1
+#>     65	 360.5	     8.6	 27.0
+#>     66	 546.0	    11.0	 34.6
 ```
-
-**Note:** For testing, the input dataset for `customer_copy` can be assumed to have `Height` and `Weight` columns.

exercises/concept/trees-and-tibbleations/.docs/introduction.md

@@ -302,31 +302,3 @@ tbl |> arrange(languages)
 #> 3 Python       1991 TRUE        
 #> 4 R            1993 TRUE     
 ```
-
-~~~~exercism/caution
-Many functions, such as `arrange()`, `filter()` and `mutate()` are data-masking and require data-masking variables.
-For this reason, non-data-masking arguments (e.g. character vectors) need to be converted to be used in data-masking functions.
-
-A full treatment of how data-masking works in R is beyond the scope of this concept, but it's useful to know there are ways of making this conversion which include options such as: `pick()`, `.data[[]]` and `!!sym()`.
-
-```R
- # arrange() with string input fails silently
-tbl |> arrange("languages")
-#>      A tibble: 4 × 3
-#>   languages created has.syllabus
-#>   <chr>       <dbl> <lgl>       
-#> 1 Fortran      1957 FALSE       
-#> 2 R            1993 TRUE        
-#> 3 Python       1991 TRUE        
-#> 4 Julia        2012 TRUE 
-
-tbl |> arrange(pick("languages"))
-#>       A tibble: 4 × 3
-#>   languages created has.syllabus
-#>   <chr>       <dbl> <lgl>       
-#> 1 Fortran      1957 FALSE       
-#> 2 Julia        2012 TRUE        
-#> 3 Python       1991 TRUE        
-#> 4 R            1993 TRUE     
-```
-~~~~

exercises/concept/trees-and-tibbleations/.meta/design.md

@@ -12,6 +12,7 @@ The goal of this exercise is to teach the student how to use dataframes with the
 
 ## Out of scope
 
+- Tidy evaluation
 - Merging dataframes
 - Dimension collapsing operations
 - Data operations / data analysis

exercises/concept/trees-and-tibbleations/.meta/exemplar.R

@@ -9,14 +9,14 @@ girth_n_weight <- function(data, rnd_digits) {
     round(rnd_digits)
 }
 
-orchard_copy <- function(data, important_cols) {
+orchard_copy <- function(data) {
   data |>
-    relocate(all_of(important_cols)) |>
-    arrange(pick(important_cols[1]))
+    relocate(c(Weight, Height)) |>
+    arrange(Weight)
 }
 
-customer_copy <- function(data, attributes, min_height, max_height, max_weight) {
+customer_copy <- function(data, min_height, max_height, max_weight) {
   data |> 
-    select(all_of(attributes)) |>
+    select(c(Height, Weight, Diameter, Girth)) |>
     filter(between(Height, min_height, max_height) & Weight < max_weight)
 }

exercises/concept/trees-and-tibbleations/test_trees-and-tibbleations.R

@@ -16,19 +16,19 @@ test_that("2. Add girth and weight columns", {
 })
 
 test_that("3. Orchard copy of dataset", {
-  test_data <- tree_data |> girth_n_weight(1) |> orchard_copy(c('Height', 'Weight'))
+  test_data <- tree_data |> girth_n_weight(1) |> orchard_copy()
 
-  expect_setequal(names(test_data), c('Height', 'Weight', 'Diameter', 'Volume', 'Girth'))
-  expect_equal(names(test_data)[1:2], c('Height', 'Weight'))
-  expect_equal(test_data$Height[15:18], c(76, 76, 77, 78))
-  expect_equal(test_data$Girth[21:24], c(44.6, 56.2, 56.5, 56.5))
-  expect_equal(test_data$Weight[5:9], c(745.5, 360.5, 899.5, 574.0, 1340.5))
+  expect_setequal(names(test_data), c('Weight', 'Height', 'Diameter', 'Volume', 'Girth'))
+  expect_equal(names(test_data)[1:2], c('Weight', 'Height'))
+  expect_equal(test_data$Height[15:18], c(80, 79, 64, 71))
+  expect_equal(test_data$Girth[21:24], c(40.5, 44.0, 45.6, 50.3))
+  expect_equal(test_data$Weight[5:9], c(574.0, 637.0, 658.0, 668.5, 689.5))
 })
 
 test_that("4. Customer copy of dataset", {
-  test_data <- tree_data |> girth_n_weight(1) |> customer_copy(c('Weight', 'Height', 'Girth'), 70, 75, 1000)
+  test_data <- tree_data |> girth_n_weight(1) |> customer_copy(70, 75, 1000)
 
-  expect_equal(names(test_data), c('Weight', 'Height', 'Girth'))
+  expect_equal(names(test_data), c('Height', 'Weight', 'Diameter', 'Girth'))
   expect_equal(test_data$Weight[2:5], c(574.0, 637.0, 696.5, 668.5))
   expect_equal(test_data$Height[4:7], c(75, 75, 74, 71))
   expect_equal(test_data$Girth[1:4], c(26.1, 33.0, 34.6, 35.2))

exercises/concept/trees-and-tibbleations/trees-and-tibbleations.R

@@ -6,10 +6,10 @@ girth_n_weight <- function(data, rnd_digits) {
 
 }
 
-orchard_copy <- function(data, rearrangement) {
+orchard_copy <- function(data) {
 
 }
 
-customer_copy <- function(data, attributes, min_height, max_height, max_weight) {
+customer_copy <- function(data, min_height, max_height, max_weight) {
 
 }