Move database transform stuff into own file

databases.Rmd

@@ -0,0 +1,24 @@
+---
+layout: default
+title: Databases
+---
+
+### Two-table verbs
+
+Each two-table verb has a straightforward SQL equivalent:
+
+| R                | SQL
+|------------------|--------
+| `inner_join()`   | `SELECT * FROM x JOIN y ON x.a = y.a`
+| `left_join()`    | `SELECT * FROM x LEFT JOIN y ON x.a = y.a`
+| `right_join()`   | `SELECT * FROM x RIGHT JOIN y ON x.a = y.a`
+| `full_join()`    | `SELECT * FROM x FULL JOIN y ON x.a = y.a`
+| `semi_join()`    | `SELECT * FROM x WHERE EXISTS (SELECT 1 FROM y WHERE x.a = y.a)`
+| `anti_join()`    | `SELECT * FROM x WHERE NOT EXISTS (SELECT 1 FROM y WHERE x.a = y.a)`
+| `intersect(x, y)`| `SELECT * FROM x INTERSECT SELECT * FROM y`
+| `union(x, y)`    | `SELECT * FROM x UNION SELECT * FROM y`
+| `setdiff(x, y)`  | `SELECT * FROM x EXCEPT SELECT * FROM y`
+
+`x` and `y` don't have to be tables in the same database. If you specify `copy = TRUE`, dplyr will copy the `y` table into the same location as the `x` variable. This is useful if you've downloaded a summarised dataset and determined a subset of interest that you now want the full data for. You can use `semi_join(x, y, copy = TRUE)` to upload the indices of interest to a temporary table in the same database as `x`, and then perform a efficient semi join in the database. 
+
+If you're working with large data, it maybe also be helpful to set `auto_index = TRUE`. That will automatically add an index on the join variables to the temporary table.

transform.Rmd

@@ -957,72 +957,3 @@ union(df1, df2)
 setdiff(df1, df2)
 setdiff(df2, df1)
 ```
-
-### Databases
-
-Each two-table verb has a straightforward SQL equivalent:
-
-| R                | SQL
-|------------------|--------
-| `inner_join()`   | `SELECT * FROM x JOIN y ON x.a = y.a`
-| `left_join()`    | `SELECT * FROM x LEFT JOIN y ON x.a = y.a`
-| `right_join()`   | `SELECT * FROM x RIGHT JOIN y ON x.a = y.a`
-| `full_join()`    | `SELECT * FROM x FULL JOIN y ON x.a = y.a`
-| `semi_join()`    | `SELECT * FROM x WHERE EXISTS (SELECT 1 FROM y WHERE x.a = y.a)`
-| `anti_join()`    | `SELECT * FROM x WHERE NOT EXISTS (SELECT 1 FROM y WHERE x.a = y.a)`
-| `intersect(x, y)`| `SELECT * FROM x INTERSECT SELECT * FROM y`
-| `union(x, y)`    | `SELECT * FROM x UNION SELECT * FROM y`
-| `setdiff(x, y)`  | `SELECT * FROM x EXCEPT SELECT * FROM y`
-
-`x` and `y` don't have to be tables in the same database. If you specify `copy = TRUE`, dplyr will copy the `y` table into the same location as the `x` variable. This is useful if you've downloaded a summarised dataset and determined a subset of interest that you now want the full data for. You can use `semi_join(x, y, copy = TRUE)` to upload the indices of interest to a temporary table in the same database as `x`, and then perform a efficient semi join in the database. 
-
-If you're working with large data, it maybe also be helpful to set `auto_index = TRUE`. That will automatically add an index on the join variables to the temporary table.
-
-### Coercion rules
-
-When joining tables, dplyr is a little more conservative than base R about the types of variable that it considers equivalent. This is mostly likely to surprise if you're working factors:
-
-  * Factors with different levels are coerced to character with a warning:
-    
-    ```{r}
-    df1 <- data_frame(x = 1, y = factor("a"))
-    df2 <- data_frame(x = 2, y = factor("b"))
-    full_join(df1, df2) %>% str()
-    ```
-
-  * Factors with the same levels in a different order are coerced to character 
-    with a warning:
-  
-    ```{r}
-    df1 <- data_frame(x = 1, y = factor("a", levels = c("a", "b")))
-    df2 <- data_frame(x = 2, y = factor("b", levels = c("b", "a")))
-    full_join(df1, df2) %>% str()
-    ```
-
-  * Factors are preserved only if the levels match exactly:
-    
-    ```{r}
-    df1 <- data_frame(x = 1, y = factor("a", levels = c("a", "b")))
-    df2 <- data_frame(x = 2, y = factor("b", levels = c("a", "b")))
-    full_join(df1, df2) %>% str()
-    ```    
-
-  * A factor and a character are coerced to character with a warning:
-    
-    ```{r}
-    df1 <- data_frame(x = 1, y = "a")
-    df2 <- data_frame(x = 2, y = factor("a"))
-    full_join(df1, df2) %>% str()
-    ```
-    
-Otherwise logicals will be silently upcast to integer, and integer to numeric, but coercing to character will raise an error:
-
-```{r, error = TRUE}
-df1 <- data_frame(x = 1, y = 1L)
-df2 <- data_frame(x = 2, y = 1.5)
-full_join(df1, df2) %>% str()
-
-df1 <- data_frame(x = 1, y = 1L)
-df2 <- data_frame(x = 2, y = "a")
-full_join(df1, df2) %>% str()
-```