updates to JFDA.jl

LearnJulia/src/JFDA.jl

@@ -3,6 +3,7 @@
 using CSV
 using DataFrames
 using Plots
+using SQLite
 
 puzzlespath = joinpath("data", "puzzles.csv");
 
@@ -13,4 +14,31 @@ show(describe(puzzles))# use to show summary stats
 # 8.3.4 Quick Histogram
 
 plot([histogram(puzzles[!,col]; label = col) for 
-    col in ["Rating", "RatingDeviation", "Popularity", "NbPlays"]]...)
+    col in ["Rating", "RatingDeviation", "Popularity", "NbPlays"]]...)
+
+
+# 8.4.2 SQLITE
+db = SQLite.DB(joinpath("data", "puzzles.db"));
+SQLite.load!(puzzles, db, "puzzles") # add puzzles to Database into a table named Puzzles
+
+query = DBInterface.execute(db, "SELECT * FROM puzzles")
+
+puzzles_db = DataFrame(query);
+
+close(db)
+
+
+# Chapter 9
+
+using Statistics
+
+plays_lo = median(puzzles.NbPlays);
+rating_lo = 1500;
+rating_hi = quantile(puzzles.Rating, 0.99);
+
+row_selector = (puzzles.NbPlays .> plays_lo) .&&
+  (rating_lo .< puzzles.Rating .< rating_hi);
+
+good = puzzles[row_selector, ["Rating", "Popularity"]]
+
+plot(histogram(good.Rating; label = "Rating"), histogram(good.Popularity; label = "Popularity"))

Project.toml

@@ -2,3 +2,4 @@
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+SQLite = "0aa819cd-b072-5ff4-a722-6bc24af294d9"