Converted all the data type mentions to the links on the mentioned types (#273)

* Added uncompiled version

* Set up the Java 11 for sources of this example due to the lowest JDK 11 in KotlinDL

* Fixed the DataFrame links

* Fixed the DataRow

* Fixed the other

* Fixed the other

Author: zaleslaw

docs/StardustDocs/topics/ColumnSelectors.md

@@ -2,7 +2,7 @@
 
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Access-->
 
-`DataFrame` provides DSL for selecting arbitrary set of columns.
+[`DataFrame`](DataFrame.md) provides DSL for selecting arbitrary set of columns.
 
 Column selectors are used in many operations:
 

docs/StardustDocs/topics/DataColumn.md

@@ -1,7 +1,7 @@
 [//]: # (title: DataColumn)
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Create-->
 
-`DataColumn` represents a column of values. It can store objects of primitive or reference types, or other [`DataFrames`](DataFrame.md).
+[`DataColumn`](DataColumn.md) represents a column of values. It can store objects of primitive or reference types, or other [`DataFrames`](DataFrame.md).
 
 See [how to create columns](createColumn.md)
 
@@ -14,7 +14,7 @@ See [how to create columns](createColumn.md)
 * `size: Int` — number of elements in the column
 
 ### Column kinds
-`DataColumn` instances can be one of three subtypes: `ValueColumn`, `ColumnGroup` or `FrameColumn`
+[`DataColumn`](DataColumn.md) instances can be one of three subtypes: `ValueColumn`, [`ColumnGroup`](DataColumn.md#columngroup) or [`FrameColumn`](DataColumn.md#framecolumn)
 
 #### ValueColumn
 
@@ -30,13 +30,13 @@ Container for nested columns. Is used to create column hierarchy.
 
 Special case of [`ValueColumn`](#valuecolumn) that stores other [`DataFrames`](DataFrame.md) as elements. 
 
-`DataFrames` stored in `FrameColumn` may have different schemas. 
+[`DataFrames`](DataFrame.md) stored in [`FrameColumn`](DataColumn.md#framecolumn) may have different schemas. 
 
-`FrameColumn` may appear after [reading](read.md) from JSON or other hierarchical data structures, or after grouping operations such as [groupBy](groupBy.md) or [pivot](pivot.md).  
+[`FrameColumn`](DataColumn.md#framecolumn) may appear after [reading](read.md) from JSON or other hierarchical data structures, or after grouping operations such as [groupBy](groupBy.md) or [pivot](pivot.md).  
 
 ## Column accessors
 
-`ColumnAccessors` are used for [typed data access](columnAccessorsApi.md) in `DataFrame`. `ColumnAccessor` stores column [`name`](#properties) (for top-level columns) or column path (for nested columns), has type argument that corresponds to [`type`](#properties) of thep column, but it doesn't contain any actual data.
+`ColumnAccessors` are used for [typed data access](columnAccessorsApi.md) in [`DataFrame`](DataFrame.md). `ColumnAccessor` stores column [`name`](#properties) (for top-level columns) or column path (for nested columns), has type argument that corresponds to [`type`](#properties) of thep column, but it doesn't contain any actual data.
 
 <!---FUN columnAccessorsUsage-->
 

docs/StardustDocs/topics/DataFrame.md

@@ -1,6 +1,6 @@
 [//]: # (title: DataFrame)
 
-`DataFrame` represents a list of [`DataColumn`](DataColumn.md).
+[`DataFrame`](DataFrame.md) represents a list of [`DataColumn`](DataColumn.md).
 
 Columns in dataframe must have equal size and unique names.
 

docs/StardustDocs/topics/DataRow.md

@@ -5,7 +5,7 @@
 
 ## Row functions
 
-* `index(): Int` — sequential row number in `DataFrame`, starts from 0
+* `index(): Int` — sequential row number in [`DataFrame`](DataFrame.md), starts from 0
 * `prev(): DataRow?` — previous row (`null` for the first row)
 * `next(): DataRow?` — next row (`null` for the last row)
 * `diff { rowExpression }: T` — difference between results of [row expression](#row-expressions) calculated for current and previous rows
@@ -18,14 +18,14 @@
 * `namedValuesOf<T>(): List<NameValuePair<T>>` — list of name-value pairs where value has given type 
 * `transpose(): DataFrame<NameValuePair<*>>` — dataframe of two columns: `name: String` is column names and `value: Any?` is cell values
 * `transposeTo<T>(): DataFrame<NameValuePair<T>>`— dataframe of two columns: `name: String` is column names and `value: T` is cell values
-* `getRow(Int): DataRow` — row from `DataFrame` by row index
+* `getRow(Int): DataRow` — row from [`DataFrame`](DataFrame.md) by row index
 * `getRows(Iterable<Int>): DataFrame` — dataframe with subset of rows selected by absolute row index. 
 * `relative(Iterable<Int>): DataFrame` — dataframe with subset of rows selected by relative row index: `relative(-1..1)` will return previous, current and next row. Requested indices will be coerced to the valid range and invalid indices will be skipped
 * `get(column): T` — cell value by this row and given `column`
-* `df()` — `DataFrame` that current row belongs to
+* `df()` — [`DataFrame`](DataFrame.md) that current row belongs to
 
 ## Row expressions
-Row expressions provide a value for every row of `DataFrame` and are used in [add](add.md), [filter](filter.md), [forEach](iterate.md), [update](update.md) and other operations.
+Row expressions provide a value for every row of [`DataFrame`](DataFrame.md) and are used in [add](add.md), [filter](filter.md), [forEach](iterate.md), [update](update.md) and other operations.
 
 <!---FUN expressions-->
 
@@ -75,7 +75,7 @@ The following [statistics](summaryStatistics.md) are available for `DataRow`:
 * `rowMedian`
 
 These statistics will be applied only to values of appropriate types and incompatible values will be ignored.
-For example, if `DataFrame` has columns of type `String` and `Int`, `rowSum()` will successfully compute sum of `Int` values in a row and ignore `String` values.
+For example, if [`DataFrame`](DataFrame.md) has columns of type `String` and `Int`, `rowSum()` will successfully compute sum of `Int` values in a row and ignore `String` values.
 
 To apply statistics only to values of particular type use `-Of` versions:
 * `rowMaxOf<T>`

docs/StardustDocs/topics/KPropertiesApi.md

@@ -2,7 +2,8 @@
 
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.ApiLevels-->
 
-`Kotlin Dataframe` can be used as an intermediate structure for data transformation between two data formats. If either source or destination is a Kotlin object, e.g. data class, it is convenient to use its properties for typed data access in `DataFrame`.
+[`DataFrame`](DataFrame.md) can be used as an intermediate structure for data transformation between two data formats. 
+If either source or destination is a Kotlin object, e.g. data class, it is convenient to use its properties for typed data access in [`DataFrame`](DataFrame.md).
 This can be done using `::` expression that provides [property references](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.reflect/-k-property/)
 
 <!---FUN kproperties1-->
@@ -28,7 +29,7 @@ val passengers = DataFrame.read("titanic.csv")
 
 <!---END-->
 
-By default, `DataFrame` uses `name` and `returnType` of `KProperty` for typed access to data. When column name differs from property name, use `ColumnName` annotation:  
+By default, [`DataFrame`](DataFrame.md) uses `name` and `returnType` of `KProperty` for typed access to data. When column name differs from property name, use `ColumnName` annotation:  
 
 <!---FUN kproperties2-->
 

docs/StardustDocs/topics/add.md

@@ -2,9 +2,10 @@
 
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Modify-->
 
-Returns `DataFrame` which contains all columns from original `DataFrame` followed by newly added columns. Original `DataFrame` is not modified.
+Returns [`DataFrame`](DataFrame.md) which contains all columns from original [`DataFrame`](DataFrame.md) followed by newly added columns. 
+Original [`DataFrame`](DataFrame.md) is not modified.
 
-## Create new column and add it to `DataFrame`
+## Create new column and add it to [`DataFrame`](DataFrame.md)
 
 ```text
 add(columnName: String) { rowExpression }
@@ -55,7 +56,7 @@ df.add("fibonacci") {
 
 <!---END-->
 
-## Create and add several columns to `DataFrame`
+## Create and add several columns to [`DataFrame`](DataFrame.md)
 
 ```kotlin
 add { 
@@ -191,7 +192,7 @@ val personWithCityInfo = df.add {
 </tab></tabs>
 <!---END-->
 
-## Add existing column to `DataFrame`
+## Add existing column to [`DataFrame`](DataFrame.md)
 
 <!---FUN addExisting-->
 
@@ -204,7 +205,7 @@ df + score
 
 <!---END-->
 
-## Add all columns from another `DataFrame`
+## Add all columns from another [`DataFrame`](DataFrame.md)
 
 <!---FUN addDfs-->
 
@@ -216,7 +217,7 @@ df.add(df1, df2)
 
 ## addId
 
-Adds column with sequential values 0, 1, 2,... New column will be added in the beginning of columns list and will become the first column in `DataFrame`.
+Adds column with sequential values 0, 1, 2,... New column will be added in the beginning of columns list and will become the first column in [`DataFrame`](DataFrame.md).
 
 ```
 addId(name: String = "id")

docs/StardustDocs/topics/addDf.md

@@ -2,7 +2,7 @@
 
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Modify-->
 
-Returns `DataFrame` with union of columns from several given `DataFrames`.
+Returns [`DataFrame`](DataFrame.md) with union of columns from several given [`DataFrames`](DataFrame.md).
 
 <!---FUN addDfs-->
 

docs/StardustDocs/topics/addRemove.md

@@ -1,5 +1,5 @@
 [//]: # (title: Add/map/remove columns)
 
-* [`add`](add.md) columns to `DataFrame`
-* [`map`](map.md) columns to new `DataFrame` or `DataColumn`
-* [`remove`](remove.md) columns from `DataFrame`
+* [`add`](add.md) columns to [`DataFrame`](DataFrame.md)
+* [`map`](map.md) columns to new [`DataFrame`](DataFrame.md) or [`DataColumn`](DataColumn.md)
+* [`remove`](remove.md) columns from [`DataFrame`](DataFrame.md)

docs/StardustDocs/topics/adjustSchema.md

@@ -1,10 +1,10 @@
 [//]: # (title: Adjust schema)
 
-`DataFrame` interface has type argument `T` that doesn't affect contents of `DataFrame`, but marks `DataFrame` with a type that represents data schema that this `DataFrame` is supposed to have.
+[`DataFrame`](DataFrame.md) interface has type argument `T` that doesn't affect contents of `DataFrame`, but marks `DataFrame` with a type that represents data schema that this `DataFrame` is supposed to have.
 This argument is used to generate [extension properties](extensionPropertiesApi.md) for typed data access. 
 
-Actual data in `DataFrame` may diverge from compile-time schema marker `T` due to dynamic nature of data inside `DataFrame`. However, at some points of code you may know exactly what `DataFrame` schema is expected.
-To match your knowledge with expected real-time `DataFrame` contents you can use one of two functions:
-* [`cast`](cast.md) — change type argument of `DataFrame` to the expected schema without changing data in `DataFrame`.
-* [`convertTo`](convertTo.md) — convert `DataFrame` contents to match the expected schema.
+Actual data in [`DataFrame`](DataFrame.md) may diverge from compile-time schema marker `T` due to dynamic nature of data inside `DataFrame`. However, at some points of code you may know exactly what `DataFrame` schema is expected.
+To match your knowledge with expected real-time [`DataFrame`](DataFrame.md) contents you can use one of two functions:
+* [`cast`](cast.md) — change type argument of [`DataFrame`](DataFrame.md) to the expected schema without changing data in `DataFrame`.
+* [`convertTo`](convertTo.md) — convert [`DataFrame`](DataFrame.md) contents to match the expected schema.
 

docs/StardustDocs/topics/apiLevels.md

@@ -6,7 +6,7 @@ By nature data frames are dynamic objects, column labels depend on the input sou
 or deleted while wrangling. Kotlin, in contrast, is a statically typed language and all types are defined and verified
 ahead of execution. That's why creating a flexible, handy, and, at the same time, safe API to a data frame is tricky.
 
-In `Kotlin DataFrame` we provide four different ways to access columns, and, while they are essentially different, they
+In Kotlin DataFrame library we provide four different ways to access columns, and, while they are essentially different, they
 look pretty similar in the data wrangling DSL.
 
 ## List of Access APIs
@@ -147,10 +147,10 @@ Currently, we are working on compiler a plugin that generates these properties o
 
 The [Column Accessors API](columnAccessorsApi.md) is a kind of trade-off between safety and needs to be written ahead of
 the execution type declaration. It was designed to better be able to write code in an IDE without a notebook experience. 
-It provides type-safe access to columns but doesn't ensure that the columns really exist in a particular dataframe.
+It provides type-safe access to columns but doesn't ensure that the columns really exist in a particular [`DataFrame`](DataFrame.md).
 
 The [KProperties API](KPropertiesApi.md) is useful when you already have declared classed in your application business
-logic with fields that correspond columns of dataframe.
+logic with fields that correspond columns of [`DataFrame`](DataFrame.md).
 
 <table>
     <tr>