Converted all the types mentions to the links on the mentioned types pages (#278)

* Fixed the other

* Fixed the review

Author: zaleslaw

docs/StardustDocs/topics/DataColumn.md

@@ -156,7 +156,7 @@ df.filter { year > 2000 }
 
 <!---END-->
 
-To convert `ColumnAccessor` into [`DataColumn`](DataColumn.md) add values using `withValues` function:
+To convert [`ColumnAccessor`](columnAccessorsApi.md) into [`DataColumn`](DataColumn.md) add values using `withValues` function:
 
 <!---FUN columnAccessorToColumn-->
 

docs/StardustDocs/topics/KPropertiesApi.md

@@ -3,7 +3,8 @@
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.ApiLevels-->
 
 [`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).
+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-->
@@ -29,7 +30,8 @@ val passengers = DataFrame.read("titanic.csv")
 
 <!---END-->
 
-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:  
+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/adjustSchema.md

@@ -1,10 +1,12 @@
 [//]: # (title: Adjust schema)
 
-[`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.
+[`DataFrame`](DataFrame.md) interface has type argument `T` that doesn't affect contents of [`DataFrame`](DataFrame.md), 
+but marks [`DataFrame`](DataFrame.md) with a type that represents data schema that this [`DataFrame`](DataFrame.md) is supposed to have.
 This argument is used to generate [extension properties](extensionPropertiesApi.md) for typed data access. 
 
-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.
+Actual data in [`DataFrame`](DataFrame.md) may diverge from compile-time schema marker `T` due to dynamic nature of data inside [`DataFrame`](DataFrame.md). 
+However, at some points of code you may know exactly what [`DataFrame`](DataFrame.md) 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`.
+* [`cast`](cast.md) — change type argument of [`DataFrame`](DataFrame.md) to the expected schema without changing data in [`DataFrame`](DataFrame.md).
 * [`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 library we provide four different ways to access columns, and, while they are essentially different, they
+In the 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
@@ -141,16 +141,18 @@ df.add("weight") { ... } // add a new column `weight`, calculated by some expres
 We don't need to interrupt a function call chain and declare a column accessor or generate new properties.
 
 In contrast, generated [extension properties](extensionPropertiesApi.md) are the most convenient and the safest API. 
-Using it, you can always be sure that you work with correct data and types. But its bottleneck is the moment of generation. 
-To get new extension properties you have to run a cell in a notebook, which could lead to unnecessary variable declarations.
+Using it, you can always be sure that you work with correct data and types. 
+But its bottleneck is the moment of generation. 
+To get new extension properties you have to run a cell in a notebook, 
+which could lead to unnecessary variable declarations.
 Currently, we are working on compiler a plugin that generates these properties on the fly while typing!
 
 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`](DataFrame.md).
+It provides type-safe access to columns but doesn't ensure that the columns really exist in a particular data frame.
 
 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`](DataFrame.md).
+logic with fields that correspond columns of a data frame.
 
 <table>
     <tr>

docs/StardustDocs/topics/collectionsInterop.md

@@ -7,9 +7,10 @@ _Kotlin DataFrame_ and _Kotlin Collection_ represent two different approaches to
 * [`DataFrame`](DataFrame.md) stores data by fields/columns
 * `Collection` stores data by records/rows
 
-Although [`DataFrame`](DataFrame.md) doesn't implement `Collection` or `Iterable` interface, it has many similar operations, such as [`filter`](filter.md), [`take`](sliceRows.md#take), [`first`](first.md), [`map`](map.md), [`groupBy`](groupBy.md) etc.
+Although [`DataFrame`](DataFrame.md) doesn't implement [`Collection`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-collection/#kotlin.collections.Collection) or [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/) interface, it has many similar operations, 
+such as [`filter`](filter.md), [`take`](sliceRows.md#take), [`first`](first.md), [`map`](map.md), [`groupBy`](groupBy.md) etc.
 
-[`DataFrame`](DataFrame.md) has two-way compatibility with `Map` and `List`:
+[`DataFrame`](DataFrame.md) has two-way compatibility with [`Map`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-map/) and [`List`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-list/):
 * `List<T>` -> `DataFrame<T>`: [toDataFrame](createDataFrame.md#todataframe)
 * `DataFrame<T>` -> `List<T>`: [toList](toList.md)
 * `Map<String, List<*>>` -> `DataFrame<*>`: [toDataFrame](createDataFrame.md#todataframe)
@@ -68,11 +69,13 @@ val df2 = df.add("c") { a + b }
 
 <tip>
 
-To enable extension properties generation you should use [dataframe plugin](gradle.md) for Gradle or [Kotlin jupyter kernel](installation.md)
+To enable extension properties generation you should use [dataframe plugin](gradle.md) 
+for Gradle or [Kotlin jupyter kernel](installation.md)
 
 </tip>
 
-After data is transformed, [`DataFrame`](DataFrame.md) can be exported into `List` of another data class using [toList](toList.md) or [toListOf](toList.md#tolistof) extensions:
+After data is transformed, [`DataFrame`](DataFrame.md) can be exported 
+into [`List`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-list/) of another data class using [toList](toList.md) or [toListOf](toList.md#tolistof) extensions:
 
 <!---FUN listInterop4-->
 

docs/StardustDocs/topics/convertTo.md

@@ -1,7 +1,7 @@
 [//]: # (title: convertTo)
 <!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Modify-->
 
-[Converts](convert.md) columns in [`DataFrame`](DataFrame.md) to match given schema `Schema`
+[Converts](convert.md) columns in [`DataFrame`](DataFrame.md) to match given schema [`Schema`](schema.md).
 
 ```kotlin
 convertTo<Schema>(excessiveColumns = ExcessiveColumns.Keep)

docs/StardustDocs/topics/createColumn.md

@@ -84,7 +84,7 @@ values.toColumn("data", Infer.Nulls) // type: Any
 
 ### toColumnOf
 
-Converts `Iterable` of values into column of given type
+Converts [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/) of values into column of given type
 
 <!---FUN createValueColumnOfType-->
 

docs/StardustDocs/topics/createDataFrame.md

@@ -134,7 +134,7 @@ map.toDataFrame()
 
 <!---END-->
 
-`DataFrame` from `Iterable` of objects:
+[`DataFrame`](DataFrame.md) from [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/) of objects:
 
 <!---FUN readDataFrameFromObject-->
 
@@ -148,7 +148,8 @@ val df = persons.toDataFrame()
 
 <!---END-->
 
-Scans object properties using reflection and creates [ValueColumn](DataColumn.md#valuecolumn) for every property. Scope of properties for scanning is defined at compile-time by formal types of objects in `Iterable`, so properties of implementation classes will not be scanned.
+Scans object properties using reflection and creates [ValueColumn](DataColumn.md#valuecolumn) for every property. 
+Scope of properties for scanning is defined at compile-time by formal types of objects in [`Iterable`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-iterable/), so properties of implementation classes will not be scanned.
 
 Specify `depth` parameter to perform deep object graph traversal and convert nested objects into [ColumnGroups](DataColumn.md#columngroup) and [FrameColumns](DataColumn.md#framecolumn):
 

docs/StardustDocs/topics/cumSum.md

@@ -16,7 +16,7 @@ Returns a [`DataFrame`](DataFrame.md) or [`DataColumn`](DataColumn.md) containin
 **Available for:**
 * [`DataFrame`](DataFrame.md)
 * [`DataColumn`](DataColumn.md)
-* [`GroupBy`](groupBy.md) — cumulative sum per every data group
+* [`GroupBy DataFrame`](groupBy.md#transformation) — cumulative sum per every data group
 
 <!---FUN cumSum-->
 

docs/StardustDocs/topics/duplicate.md

@@ -5,7 +5,8 @@ Returns [`DataFrame`](DataFrame.md) with original [`DataRow`](DataRow.md) repeat
 DataRow.duplicate(n): DataFrame
 ```
 
-Returns [`FrameColumn`](DataColumn.md#framecolumn) with original [`DataFrame`](DataFrame.md) repeated `n` times. Resulting `FrameColumn` will have an empty [`name`](DataColumn.md#properties).
+Returns [`FrameColumn`](DataColumn.md#framecolumn) with original [`DataFrame`](DataFrame.md) repeated `n` times. 
+Resulting [`FrameColumn`](DataColumn.md#framecolumn) will have an empty [`name`](DataColumn.md#properties).
 ```text
 DataFrame.duplicate(n): FrameColumn
 ```