update layout of all exercises
This commit is contained in:
Bogumił Kamiński
@@ -22,69 +22,8 @@ Create `matein2` data frame that will have only puzzles that have `"mateIn2"`
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in the `Themes` column.
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Use the `contains` function (check its documentation first).
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### Exercise 2
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What is the fraction of puzzles that are mate in 2 in relation to all puzzles
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in the `puzzles` data frame?
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### Exercise 3
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Create `small` data frame that holds first 10 rows of `matein2` data frame
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and columns `Rating`, `RatingDeviation`, and `NbPlays`.
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### Exercise 4
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Iterate rows of `small` data frame and print the ratio of
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`RatingDeviation` and `NbPlays` for each row.
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### Exercise 5
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Get names of columns from the `matein2` data frame that end with `n` (ignore case).
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### Exercise 6
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Write a function `collatz` that runs the following process. Start with a
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positive number `n`. If it is even divide it by two. If it is odd multiply
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it by 3 and add one. The function should return the number of steps needed to
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reach 1.
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Create a `d` dictionary that maps number of steps needed to a list of numbers from
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the range `1:100` that required this number of steps.
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### Exercise 7
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Using the `d` dictionary make a scatter plot of number of steps required
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vs average value of numbers that require this number of steps.
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### Exercise 8
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Repeat the process from exercises 6 and 7, but this time use a data frame
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and try to write an appropriate expression using the `combine` and `groupby`
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functions (as it was explained in the last part of chapter 9). This time
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perform computations for numbers ranging from one to one million.
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### Exercise 9
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Set seed of random number generator to `1234`. Draw 100 random points
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from the interval `[0, 1]`. Store this vector in a data frame as `x` column.
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Now compute `y` column using a formula `4 * (x - 0.5) ^ 2`.
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Add random noise to column `y` that has normal distribution with mean 0 and
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standard deviation 0.25. Call this column `z`.
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Make a scatter plot with `x` on x-axis and `y` and `z` on y-axis.
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### Exercise 10
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Add a line of LOESS regression of `x` explaining `z` plot to figure produced in exercise 10.
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# Solutions
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<details>
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<summary>Show!</summary>
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### Exercise 1
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Solution:
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<summary>Solution</summary>
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```
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julia> matein2 = puzzles[contains.(puzzles.Themes, "mateIn2"), :]
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@@ -104,9 +43,17 @@ julia> matein2 = puzzles[contains.(puzzles.Themes, "mateIn2"), :]
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1 column and 274127 rows omitted
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```
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</details>
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### Exercise 2
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Solution (two ways to do it):
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What is the fraction of puzzles that are mate in 2 in relation to all puzzles
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in the `puzzles` data frame?
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<details>
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<summary>Solution</summary>
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Two ways to do it:
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```
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julia> using Statistics
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@@ -118,9 +65,15 @@ julia> mean(contains.(puzzles.Themes, "mateIn2"))
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0.12852152542746353
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```
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</details>
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### Exercise 3
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Solution:
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Create `small` data frame that holds first 10 rows of `matein2` data frame
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and columns `Rating`, `RatingDeviation`, and `NbPlays`.
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<details>
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<summary>Solution</summary>
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```
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julia> small = matein2[1:10, ["Rating", "RatingDeviation", "NbPlays"]]
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@@ -140,9 +93,15 @@ julia> small = matein2[1:10, ["Rating", "RatingDeviation", "NbPlays"]]
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10 │ 979 144 14
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```
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</details>
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### Exercise 4
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Solution:
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Iterate rows of `small` data frame and print the ratio of
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`RatingDeviation` and `NbPlays` for each row.
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<details>
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<summary>Solution</summary>
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```
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julia> for row in eachrow(small)
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@@ -160,9 +119,16 @@ julia> for row in eachrow(small)
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10.285714285714286
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```
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</details>
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### Exercise 5
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Solution (several options):
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Get names of columns from the `matein2` data frame that end with `n` (ignore case).
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<details>
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<summary>Solution</summary>
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Several options:
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```
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julia> names(matein2, Cols(col -> uppercase(col[end]) == 'N'))
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2-element Vector{String}:
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@@ -180,9 +146,20 @@ julia> names(matein2, r"[nN]$")
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"RatingDeviation"
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```
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</details>
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### Exercise 6
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Solution:
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Write a function `collatz` that runs the following process. Start with a
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positive number `n`. If it is even divide it by two. If it is odd multiply
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it by 3 and add one. The function should return the number of steps needed to
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reach 1.
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Create a `d` dictionary that maps number of steps needed to a list of numbers from
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the range `1:100` that required this number of steps.
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<details>
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<summary>Solution</summary>
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```
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julia> function collatz(n)
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@@ -232,9 +209,15 @@ Dict{Int64, Vector{Int64}} with 45 entries:
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As we can see even for small `n` the number of steps required to reach `1`
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can get quite large.
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</details>
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### Exercise 7
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Solution:
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Using the `d` dictionary make a scatter plot of number of steps required
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vs average value of numbers that require this number of steps.
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<details>
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<summary>Solution</summary>
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```
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using Plots
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@@ -247,9 +230,17 @@ scatter(steps, mean_number, xlabel="steps", ylabel="mean of numbers", legend=fal
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Note that we needed to use `collect` on `keys` as `scatter` expects an array
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not just an iterator.
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</details>
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### Exercise 8
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Solution:
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Repeat the process from exercises 6 and 7, but this time use a data frame
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and try to write an appropriate expression using the `combine` and `groupby`
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functions (as it was explained in the last part of chapter 9). This time
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perform computations for numbers ranging from one to one million.
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<details>
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<summary>Solution</summary>
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```
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df = DataFrame(n=1:10^6);
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@@ -258,6 +249,8 @@ agg = combine(groupby(df, :collatz), :n => mean);
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scatter(agg.collatz, agg.n_mean, xlabel="steps", ylabel="mean of numbers", legend=false)
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```
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</details>
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### Exercise 9
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Set seed of random number generator to `1234`. Draw 100 random points
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@@ -267,7 +260,8 @@ Add random noise to column `y` that has normal distribution with mean 0 and
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standard deviation 0.25. Call this column `z`.
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Make a scatter plot with `x` on x-axis and `y` and `z` on y-axis.
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Solution:
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<details>
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<summary>Solution</summary>
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```
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using Random
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@@ -278,9 +272,14 @@ df.z = df.y + randn(100) / 4
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scatter(df.x, [df.y df.z], labels=["y" "z"])
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```
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</details>
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### Exercise 10
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Solution:
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Add a line of LOESS regression of `x` explaining `z` plot to figure produced in exercise 10.
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<details>
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<summary>Solution</summary>
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```
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using Loess
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