Copyedits

Author: devnich

README.md

@@ -7,7 +7,7 @@
 -   [Accessing data with queries](#accessing-data-with-queries)
     -   [Basic queries](#basic-queries)
     -   [Filtering](#filtering)
-    -   [**Challenge 1**: Large bois](#challenge-1-large-bois)
+    -   [**Challenge 1**](#challenge-1)
     -   [Building complex queries](#building-complex-queries)
     -   [Sorting](#sorting)
     -   [**Challenge 2**](#challenge-2)
@@ -95,9 +95,9 @@ and atomic transactions. It eliminates ambiguity by forbidding NULLs.
 
 4.  Solution: Normalize the data by breaking it into multiple tables
 
-      | Animals                      | Sightings                      |
-      |------------------------------|--------------------------------|
-      | ![](images/animals_half.png) | ![](images/sightings_half.png) |
+      Animals                        Sightings
+      ------------------------------ --------------------------------
+      ![](images/animals_half.png)   ![](images/sightings_half.png)
 
     -   Every row of every table contains unique information
     -   Normalization is a continuum. We could normalize this data
@@ -214,7 +214,7 @@ and atomic transactions. It eliminates ambiguity by forbidding NULLs.
     WHERE (species_id = 'DM') OR (species_id = 'DO') OR (species_id = 'DS');
     ```
 
-## **Challenge 1**: Large bois
+## **Challenge 1**
 
 Get all of the individuals in Plot 1 that weighed more than 75 grams,
 telling us the date, species id code, and weight (in kg).
@@ -558,7 +558,7 @@ sqlite3     # enter sqlite prompt
     ```
 
     ``` sql
-    select * from species where taxa == 'Rodent';
+    SELECT * FROM species WHERE taxa = 'Rodent';
     ```
 
 2.  Output to .csv file
@@ -569,7 +569,7 @@ sqlite3     # enter sqlite prompt
     ```
 
     ``` sql
-    select * from species where taxa == 'Rodent';
+    SELECT * FROM species WHERE taxa = 'Rodent';
     ```
 
     ``` bash
@@ -588,47 +588,47 @@ sqlite3     # enter sqlite prompt
 
 2.  Import libraries
 
-    ``` {.r org-language="R"}
+    ``` r
     library("DBI")
     library("RSQLite")
     ```
 
 3.  FYI, use namespaces explicitly
 
-    ``` {.r org-language="R"}
+    ``` r
     con <- DBI::dbConnect(RSQLite::SQLite(), "../data/portal_mammals.sqlite")
     ```
 
 4.  Show tables
 
-    ``` {.r org-language="R"}
+    ``` r
     dbListTables(con)
     ```
 
 5.  Show column names
 
-    ``` {.r org-language="R"}
+    ``` r
     dbListFields(con, "species")
     ```
 
 6.  Get query results at once
 
-    ``` {.r org-language="R"}
+    ``` r
     df <- dbGetQuery(con, "SELECT * FROM surveys WHERE year = 2000")
     head(df)
     ```
 
 7.  Use parameterized queries
 
-    ``` {.r org-language="R"}
+    ``` r
     df <- dbGetQuery(con, "SELECT * FROM surveys WHERE year = ? AND (month > ? AND month < ?)",
                      params = c(2000, 4, 10))
     head(df)
     ```
 
 8.  Disconnect
 
-    ``` {.r org-language="R"}
+    ``` r
     dbDisconnect(con)
     ```
 

README.org

@@ -6,6 +6,12 @@
 #+BEGIN_SRC sql
 #+END_SRC
 
+* COMMENT Fix R formatting in final version
+* COMMENT Add Python example code
+* COMMENT Rethink narrative: Start by building database from CSVs?
+e.g. http://swcarpentry.github.io/sql-novice-survey/09-create/index.html
+* COMMENT Is there a better database (for me)?
+
 * COMMENT SQL interaction in Emacs
 1. Start SQLite inferior process
    ~M-x sql-sqlite~
@@ -37,8 +43,8 @@ There are two aspects of "correctness": Enforcing consistency and eliminating am
 
 4. Solution: Normalize the data by breaking it into multiple tables
 
-   | Animals                          | Sightings                          |
-   |----------------------------------+------------------------------------+
+   | Animals                      | Sightings                      |
+   |------------------------------+--------------------------------|
    | [[file:images/animals_half.png]] | [[file:images/sightings_half.png]] |
 
    - Every row of every table contains unique information
@@ -136,7 +142,7 @@ There are two aspects of "correctness": Enforcing consistency and eliminating am
    WHERE (species_id = 'DM') OR (species_id = 'DO') OR (species_id = 'DS');
    #+END_SRC
 
-** *Challenge 1*: Large bois
+** *Challenge 1*
 Get all of the individuals in Plot 1 that weighed more than 75 grams, telling us the date, species id code, and weight (in kg).
 
 ** Building complex queries
@@ -425,7 +431,7 @@ sqlite3     # enter sqlite prompt
    #+END_SRC
 
    #+BEGIN_SRC sql
-   select * from species where taxa == 'Rodent';
+   SELECT * FROM species WHERE taxa = 'Rodent';
    #+END_SRC
 
 2. Output to .csv file
@@ -435,7 +441,7 @@ sqlite3     # enter sqlite prompt
    #+END_SRC
 
    #+BEGIN_SRC sql
-   select * from species where taxa == 'Rodent';
+   SELECT * FROM species WHERE taxa = 'Rodent';
    #+END_SRC
 
    #+BEGIN_SRC bash
@@ -573,7 +579,8 @@ pandoc -f markdown --toc --toc-depth=2 -s tmp.md -o README.md
 
 ** Find and replace code block markers in final document (if applicable)
 #+BEGIN_EXAMPLE
-M-x qrr " {.python}" "python"
+M-x qrr " {.r org-language="R"}" "r"
+M-x qrr " {.r}" "r"
 M-x qrr " {.bash}" "bash"
 #+END_EXAMPLE
 

images/database_alignment.jpg

@@ -22,14 +22,14 @@
     c = conn.execute(query, args)
     results = c.fetchall()
 
-# Parameter substitution with named parameters; don't create intermediate
+# Parameter substitution with named parameters; doesn't create intermediate
 # cursor variable.
 with conn:
-    query_named = "SELECT * FROM surveys WHERE species_id = :id AND year > :year ORDER BY hindfoot_length;"
-    args_named = {"id": "DM",
+    query = "SELECT * FROM surveys WHERE species_id = :id AND year > :year ORDER BY hindfoot_length;"
+    named_args = {"id": "DM",
                   "year": 1995}
 
-    results = conn.execute(query_named, args_named).fetchall()
+    results = conn.execute(query, named_args).fetchall()
 
 # Iteration on results from implicit cursor
 with conn: