update codes to make them correct with kwargs

appB.jl

@@ -2,6 +2,11 @@
 
 # Codes for appendix B
 
+# the solutions for exercises from a given chapter assume that
+# there are packages loaded, variables and functions defined in the user's
+# Julia session in a state that reflects the point of computations
+# at the position of the chapter where a given exercise is formulated
+
 # Code for exercise 3.1
 
 using Statistics
@@ -57,10 +62,71 @@ end
 
 test_dice()
 
-
 # Code for exercise 3.3
 
-plot(scatter(data.set1.x, data.set1.y, legend=false),
-     scatter(data.set2.x, data.set2.y, legend=false),
-     scatter(data.set3.x, data.set3.y, legend=false),
-     scatter(data.set4.x, data.set4.y, legend=false))
+plot(scatter(data.set1.x, data.set1.y; legend=false),
+     scatter(data.set2.x, data.set2.y; legend=false),
+     scatter(data.set3.x, data.set3.y; legend=false),
+     scatter(data.set4.x, data.set4.y; legend=false))
+
+# Code for exercise 3.4
+
+parse.(Int, ["1", "2", "3"])
+
+# Code for exercise 4.1
+
+years_table = freqtable(years)
+plot(names(years_table, 1), years_table; legend=false,
+           xlabel="year", ylabel="# of movies")
+
+
+# Code for exercise 4.2
+
+s3 = Symbol.(s1)
+@benchmark sort($s3)
+@benchmark unique($s1)
+@benchmark unique($s2)
+@benchmark unique($s3)
+
+# Code for exercise 5.1
+
+v = ["1", "2", missing, "4"]
+[ismissing(x) ? missing : parse(Int, x) for x in v]
+map(v) do x
+    if ismissing(x)
+        return missing
+    else
+        return parse(Int, x)
+    end
+end
+using Missings
+passmissing(parse).(Int, v)
+
+# Code for exercise 5.2
+
+using Dates
+Date(2021, 1, 1):Month(1):Date(2021, 12, 1)
+collect(Date(2021, 1, 1):Month(1):Date(2021, 12, 1))
+
+# Code for exercise 6.1
+
+using BenchmarkTools
+@benchmark $puzzles."Rating"
+
+# Code for exercise 6.2
+
+using StatsBase
+summarystats(puzzles[puzzles.Popularity .== 100, "NbPlays"])
+summarystats(puzzles[puzzles.Popularity .== -100, "NbPlays"])
+
+# Code for exercise 6.3
+
+sum(length, values(rating_mapping))
+nrow(good)
+
+# Code for exercise 7.1
+
+using BenchmarkTools
+x = rand(10^6);
+@btime DataFrame(x=$x);
+@btime DataFrame(x=$x; copycols=false);

ch04.jl

@@ -120,13 +120,13 @@ sort!(table)
 
 years = [record.year for record in records]
 has_drama = ["Drama" in record.genres for record in records]
-drama_prop = proptable(years, has_drama, margins=1)
+drama_prop = proptable(years, has_drama; margins=1)
 
 # Code for listing 4.5
 
 using Plots
 
-plot(names(drama_prop, 1), drama_prop[:, 2], legend=false,
+plot(names(drama_prop, 1), drama_prop[:, 2]; legend=false,
      xlabel="year", ylabel="Drama probability")
 
 # Code for section 4.6.1

ch05.jl

@@ -188,7 +188,7 @@ std(skipmissing(rates))
 # Code for listing 5.8
 
 using FreqTables
-proptable(dayname.(dates), ismissing.(rates), margins=1)
+proptable(dayname.(dates), ismissing.(rates); margins=1)
 
 # Code showing how to specify a complex condition using broadcasting
 
@@ -201,11 +201,11 @@ dates[dayname.(dates) .== "Thursday" .&& ismissing.(rates)]
 # Codes for plotting exchange rate data
 
 using Plots
-plot(dates, rates, xlabel="day", ylabel="PLN/USD", legend=false)
+plot(dates, rates; xlabel="day", ylabel="PLN/USD", legend=false)
 
 rates_ok = .!ismissing.(rates)
 
-plot(dates[rates_ok], rates[rates_ok],
+plot(dates[rates_ok], rates[rates_ok];
      xlabel="day", ylabel="PLN/USD", legend=false)
 
 using Impute

ch06.jl

@@ -92,12 +92,13 @@ puzzles[!, :Rating]
 puzzles[!, 4]
 puzzles[!, col]
 
+using Plots
 plot(histogram(puzzles.Rating, label="Rating"),
      histogram(puzzles.RatingDeviation, label="RatingDeviation"),
      histogram(puzzles.Popularity, label="Popularity"),
      histogram(puzzles.NbPlays, label="NbPlays"))
 
-plot([histogram(puzzles[!, col], label=col) for
+plot([histogram(puzzles[!, col]; label=col) for
       col in ["Rating", "RatingDeviation",
               "Popularity", "NbPlays"]]...)
 
@@ -125,8 +126,8 @@ good = puzzles[row_selector, ["Rating", "Popularity"]]
 
 # Code for plotting histograms
 
-plot(histogram(good.Rating, label="Rating"),
-     histogram(good.Popularity, label="Popularity"))
+plot(histogram(good.Rating; label="Rating"),
+     histogram(good.Popularity; label="Popularity"))
 
 # Code for column selectors
 
@@ -236,7 +237,6 @@ mean_popularities = map(ratings) do rating
     return mean(popularities)
 end
 
-using Plots
 scatter(ratings, mean_popularities;
         xlabel="rating", ylabel="mean popularity", legend=false)
 
@@ -245,4 +245,4 @@ model = Loess.loess(ratings, mean_popularities);
 ratings_predict = float.(sort(ratings))
 popularity_predict = Loess.predict(model, ratings_predict)
 
-plot!(ratings_predict, popularity_predict, width=5, color="black")
+plot!(ratings_predict, popularity_predict; width=5, color="black")