// Read data-frame from disk
val iris = DataFrame.readTSV("data/iris.txt")


// Create data-frame in memory
val df: DataFrame = dataFrameOf(
    "first_name", "last_name", "age", "weight")(
    "Max", "Doe", 23, 55,
    "Franz", "Smith", 23, 88,
    "Horst", "Keanes", 12, 82
)

// Or from csv
// val otherDF = DataFrame.readCSV("path/to/file")

// Print rows
df                              // with implict string conversion using default options
df.print(colNames = false)      // with custom printing options

// Print structure
df.schema()


// Add columns with mutate
// by adding constant values as new column
df.addColumn("salary_category") { 3 }

// by doing basic column arithmetics
df.addColumn("age_3y_later") { it["age"] + 3 }

// Note: krangl dataframes are immutable so we need to (re)assign results to preserve changes.
val newDF = df.addColumn("full_name") { it["first_name"] + " " + it["last_name"] }

// Also feel free to mix types here since krangl overloads  arithmetic operators like + for dataframe-columns
df.addColumn("user_id") { it["last_name"] + "_id" + rowNumber }

// Create new attributes with string operations like matching, splitting or extraction.
df.addColumn("with_anz") { it["first_name"].asStrings().map { it!!.contains("anz") } }

// Note: krangl is using 'null' as missing value, and provides convenience methods to process non-NA bits
df.addColumn("first_name_initial") { it["first_name"].map<String>{ it.first() } }

// or add multiple columns at once
df.addColumns(
    "age_plus3" to { it["age"] + 3 },
    "initials" to { it["first_name"].map<String> { it.first() } concat it["last_name"].map<String> { it.first() } }
)


// Sort your data with sortedBy
df.sortedBy("age")
// and add secondary sorting attributes as varargs
df.sortedBy("age", "weight")
df.sortedByDescending("age")
df.sortedBy { it["weight"].asInts() }


// Subset columns with select
df.select2 { it is IntCol } // functional style column selection
df.select("last_name", "weight")    // positive selection
df.remove("weight", "age")  // negative selection
df.select({ endsWith("name") })    // selector mini-language


// Subset rows with vectorized filter
df.filter { it["age"] eq 23 }
df.filter { it["weight"] gt 50 }
df.filter({ it["last_name"].isMatching { startsWith("Do")  }})

// In case vectorized operations are not possible or available we can also filter tables by row
// which allows for scalar operators
df.filterByRow { it["age"] as Int > 5 }
df.filterByRow { (it["age"] as Int).rem(10) == 0 } // round birthdays :-)


// Summarize

// do simple cross tabulations
df.count("age", "last_name")

// ... or calculate single summary statistic
df.summarize("mean_age" to { it["age"].mean(true) })

// ... or multiple summary statistics
df.summarize(
    "min_age" to { it["age"].min() },
    "max_age" to { it["age"].max() }
)

// for sake of r and python adoptability you can also use `=` here
df.summarize(
    "min_age" `=` { it["age"].min() },
    "max_age" `=` { it["age"].max() }
)

// Grouped operations
val groupedDf: DataFrame = df.groupBy("age") // or provide multiple grouping attributes with varargs
val sumDF = groupedDf.summarize(
    "mean_weight" to { it["weight"].mean(removeNA = true) },
    "num_persons" to { nrow }
)

// Optionally ungroup the data
sumDF.ungroup().print()

// generate object bindings for kotlin.
// Unfortunately the syntax is a bit odd since we can not access the variable name by reflection
sumDF.printDataClassSchema("Person")

// This will generate and print the following conversion code:
data class Person(val age: Int, val mean_weight: Double, val num_persons: Int)

val records = sumDF.rows.map { row -> Person(row["age"] as Int, row["mean_weight"] as Double, row["num_persons"] as Int) }

// Now we can use the krangl result table in a strongly typed way
records.first().mean_weight

// Vice versa we can also convert an existing set of objects into
val recordsDF = records.asDataFrame()
recordsDF.print()

// to populate a data-frame with selected properties only, we can do
val deparsedDF = records.deparseRecords { mapOf("age" to it.age, "weight" to it.mean_weight) }