Documentation improvements (#455)

* Documentation improvements

* Standardize printing output in docs

* Remove outdated reference to XChart

docs/gettingstarted.md

@@ -35,9 +35,9 @@ To create a column you can use one of its static *create()* methods:
 ```java
 double[] numbers = {1, 2, 3, 4};
 DoubleColumn nc = DoubleColumn.create("Test", numbers);
-out(nc.print());
+System.out.println(nc.print());
 ```
-which produces: [^1]
+which produces:
 ```java
 Column: Test
 1.0
@@ -57,8 +57,9 @@ Tablesaw makes columns easy to work with. Operations that work on numbers in sta
 
 ```java
 DoubleColumn nc2 = nc.multiply(4);
+System.out.println(nc2.print());
 ```
-producing: [^1] 
+producing:
 
 ```java
 Column: Test * 4.0
@@ -123,7 +124,7 @@ If you have several columns of the same length as you would in a table of data,
 DoubleColumn result = firstColumn.where(someOtherColumn.startsWith("foo"));
 ```
 
-> **Key point:** Note the methods *startsWith(aString)*, *isLessThan(aNumber)*, and *isOdd()*. These were predefined for your use. There are many such methods that can be used in building queries. For StringColumn, they're defined in the [tech.tablesaw.columns.strings.StringFilters interface](http://www.javadoc.io/page/tech.tablesaw/tablesaw-core/latest/tech/tablesaw/columns/strings/StringFilters.html). It also includes *endsWith()*, *isEmpty()*, *isAlpha()*, *containsString()*[^2], etc. Each column has a similar set of filter operations. They can all be found in the filter interfaces located in sub-folders of tech.tablesaw.columns (e.g. tech.tablesaw.columns.dates.DateFilters). 
+> **Key point:** Note the methods *startsWith(aString)*, *isLessThan(aNumber)*, and *isOdd()*. These were predefined for your use. There are many such methods that can be used in building queries. For StringColumn, they're defined in the [tech.tablesaw.columns.strings.StringFilters interface](http://www.javadoc.io/page/tech.tablesaw/tablesaw-core/latest/tech/tablesaw/columns/strings/StringFilters.html). It also includes *endsWith()*, *isEmpty()*, *isAlpha()*, *containsString()*[^1], etc. Each column has a similar set of filter operations. They can all be found in the filter interfaces located in sub-folders of tech.tablesaw.columns (e.g. tech.tablesaw.columns.dates.DateFilters). 
 
 #### Map functions
 
@@ -463,10 +464,4 @@ See the section on [Cross Tabs](https://jtablesaw.github.io/tablesaw/userguide/c
 
 We've covered a lot of ground. To learn more, please take a look at the [User Guide](https://jtablesaw.github.io/tablesaw/userguide/toc) or API documentation ([Java Docs](http://www.javadoc.io/page/tech.tablesaw/tablesaw-core/latest/index)).
 
-[^1]: The method shown does not actually "produce" any output For that you would call *System.out.println()*. For brevity, this "faux" output will be shown indented by one tab beneath the code that produced it.
-[^2]: Note that containsString(String subString) is different from contains(). The first method looks at each string in the column to see if it conains the substring. The second method looks at every row in the column and returns true if any matches the entire string. In other words, contains is like contains as defined on List<String>. , etc.
-
-
-
-
-
+[^1]: Note that containsString(String subString) is different from contains(). The first method looks at each string in the column to see if it conains the substring. The second method looks at every row in the column and returns true if any matches the entire string. In other words, contains is like contains as defined on List<String>. , etc.

docs/userguide/BarsAndPies.md

@@ -14,10 +14,10 @@ Here we focus on some common plot types for working with univariate data:
 
 When you're exploring data, you need plot creation to be as easy as possible. With Tablesaw's simple plot API you can usually create and display new charts in a line or two of code. 
 
-First we load the Tornado dataset: 
+First we load the Tornado dataset:
 
 ```java
-Table tornadoes = Table.read().csv("Tornadoes.csv");
+Table tornadoes = Table.read().csv("data/tornadoes_1950-2014.csv");
 ```
 
 ### Univariate data: counts and distributions

docs/userguide/plotting.md

@@ -11,7 +11,7 @@ for every US tornado between 1950 and 2014. The code below loads the data, filte
 and renders the plot:
 
 ```java
-Table tornado = Table.createFromCsv("data/tornadoes_1950-2014.csv");
+Table tornado = Table.read().csv("data/tornadoes_1950-2014.csv");
 tornado = tornado.selectWhere(
         both(column("Start Lat").isGreaterThan(0),
              column("Scale").isGreaterThanOrEqualTo(0)));
@@ -20,7 +20,7 @@ Scatter.show("US Tornados 1950-2014",
         tornado.numberColumn("Start Lon"),
         tornado.numberColumn("Start Lat"));
 ```
-These plots provide instant visual feedback to the analyst while she’s working. They’re for discovery, rather than for presentation, and so ease of use is stressed over beauty. Behind the scenes, the scatter plots are created with Tim Molter’s awesome XChart library: https://github.com/timmolter/XChart.
+These plots provide instant visual feedback to the analyst while she’s working. They’re for discovery, rather than for presentation, and so ease of use is stressed over beauty.
 
 The following chart is taken from a baseball data set. It shows how to split a table on the values of one or more columns, 
 producing a series for each group. In this case, we color the mark differently if the team made the playoffs. 
@@ -30,7 +30,7 @@ winsByYear
 Here’s the code:
 
 ```java
-    Table baseball = Table.createFromCsv("data/baseball.csv");
+    Table baseball = Table.read().csv("data/baseball.csv");
     Scatter.show("Regular season wins by year",
         baseball.numericColumn("W"),
         baseball.numericColumn("Year"),