corrected error

Author: venom1204

vignettes/datatable-joins.Rmd

@@ -576,61 +576,46 @@ When performing non-equi joins (<, >, <=, >=), it's important to understand how
 - The left operand (`x` column) determines the column name in the result.
 - The right operand (`i` column) contributes values but does not retain its original name.
 - By default, `data.table` does not retain the `i` column used in the join condition unless explicitly requested.
-`In non-equi joins, the resulting column inherits the name from the left (x) table but contains values from the right (i) table.`
 
-This can cause confusion when `x` and `i` have different column names.
+In non-equi joins, the left side of the operator (e.g., `A` in `A >= B`) must be a column from `x`,  
+and the right side (e.g., `B`) must be a column from `i`.  Non-equi join does not support  arbitrary expressions. For example, `on = .(x_col >= i_col)` is valid, but `on = .(x_col >= i_col + 1)` is not.  
 
-**Important**: Non-equi join conditions must use column names from `x` and `i`, *not arbitrary expressions*.  
-For example, `on = .(x_col >= i_col)` is valid, but `on = .(x_col >= i_col + 1)` is not.  
-
-In non-equi joins, the left side of the operator (e.g., `A` in `A >= B`) *must be a column from `x`*,  
-and the right side (e.g., `B`) *must be a column from `i`*.
-
-To use expressions, create temporary columns first (see example below).
+Arbitrary comparisons can be accomplished by create temporary columns first. For example:
 
 ```{r}
-x <- data.table(A = 1:5, value_x = letters[1:5])
-i <- data.table(B = c(2, 4, 5), value_i = LETTERS[1:3])
-x[i, on = .(A >= B)]
+ProductReceived[ProductSales, 
+                on = .(product_id, received_date <= sales_date)]
 ```
-In data.table, when using a non-equi join condition (>=, <, etc.), the column from x is retained in the result, while the column from i is not retained unless explicitly selected.
+```In data.table, when using a non-equi join condition (>=, <, etc.), the column from x is retained in the result, while the column from i is not retained unless explicitly selected.```
 
 Expected Output
-   A    value_x  value_i
-1: 2       b       A
-2: 4       d       B
-3: 5       e       C
-4: 5       e       C
+   product_id received_date i.sales_date i.sales_count
+1:          2   2024-02-01   2024-02-05           100
+2:          2   2024-02-03   2024-02-05           100
+3:          2   2024-02-01   2024-02-10           150
 
 If multiple rows in x satisfy the join condition with a single row in i, those rows will be duplicated in the result.
 
-Notice that A appears in the result, but B from i is missing.
-**Note for SQL Users**: Unlike SQL, `data.table` non-equi joins:  
-- Do not retain the `i` column used in the join condition unless explicitly selected.  
-- Use the `x` column name in the result (e.g., `A` instead of `B` in `A >= B`).
-
-This is because B was only used for filtering and is not retained unless explicitly selected.
-However, columns from i that are not used in the join condition (e.g., value_i) are automatically included in the output by default, since data.table keeps all non-matching columns from i.
-
-`If you want to keep the B column from i, you need to explicitly select it in the result:`
+If you want to keep the received_date column from i, you need to explicitly select it:
 
 ```{r}
-x[i, on = .(A >= B), .(B, A, value_x, value_i)]
+ProductReceived[ProductSales, 
+                on = .(product_id, received_date <= sales_date), 
+                .(product_id, received_date, sales_date = i.sales_date, sales_count = i.sales_count)]
 ```
 Updated Output
-   B  A   value_x  value_i
-1: 2  2       b       A
-2: 4  4       d       B
-3: 5  5       e       C
-4: 5  5       e       C
-
-Now, B from i is explicitly retained in the final table.
+      product_id received_date sales_date  sales_count
+1:          2     2024-02-01   2024-02-05         100
+2:          2     2024-02-03   2024-02-05         100
+3:          2     2024-02-01   2024-02-10         150
 
-`important Consideration: nomatch = NULL in Non-Equi Joins`
 If you want to exclude unmatched rows, you should use nomatch = NULL:
 
 ```{r}
-x[i, on = .(A >= B), .(B, A, value_x, value_i), nomatch = NULL]
+ProductReceived[ProductSales, 
+                on = .(product_id, received_date <= sales_date), 
+                .(product_id, received_date, sales_date = i.sales_date, sales_count = i.sales_count), 
+                nomatch = NULL]
 ```
 
 ## 5. Rolling join