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syntax updates for DB #6

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52 changes: 25 additions & 27 deletions from-dataframes-to-databases.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -149,16 +149,16 @@ end
@summarize(max_birthdate = maximum(birthdate),
max_deathdate = maximum(deathdate))
@collect()
@pivot_longer()
@pivot_longer(cols = everything())
end

# ╔═╡ 1f9d1d3d-6132-4cfe-a8ab-132aba378f96
todays_date = @chain begin
db_table(connect(duckdb()), "data/patients.csv")
patients
@summarize(max_birthdate = maximum(birthdate),
max_deathdate = maximum(deathdate))
@collect()
@pivot_longer()
@pivot_longer(cols = everything())
@filter(value == maximum(value))
@pull(value)
_[1] # 1
Expand Down Expand Up @@ -204,32 +204,32 @@ patients_age = @eval @chain begin
end

# ╔═╡ 005330a2-e7b1-4381-9d50-453ba1d2ce4e
@chain t(patients_age) begin
@chain patients_age begin
@mutate(age_category = if_else(age >= 18, "Adult", "Child"))
@count(age_category)
@collect()
end

# ╔═╡ 5d2390fb-c618-4d19-963b-9b2601021c80
@chain t(patients_age) begin
@chain patients_age begin
@mutate(age_category =
case_when(age >= 75, "Older adult",
age >= 18, "Adult",
age >= 4, "Child",
true, "Infant"))
case_when(age >= 75 => "Older adult",
age >= 18 => "Adult",
age >= 4 => "Child",
true => "Infant"))
@count(age_category)
@collect()
end

# ╔═╡ c8ccb7ec-ae75-4dbb-9448-0bfe853b5268
@chain t(patients_age) begin
@chain patients_age begin
@mutate(age_category =
case_when(age >= 75 && gender == "F", "Older adult female",
age >= 75 && gender == "M", "Older adult male",
age >= 18 && gender == "F", "Adult female",
age >= 18 && gender == "M", "Adult male",
age >= 4, "Child",
true, "Infant"))
case_when(age >= 75 && gender == "F" => "Older adult female",
age >= 75 && gender == "M" => "Older adult male",
age >= 18 && gender == "F" => "Adult female",
age >= 18 && gender == "M" => "Adult male",
age >= 4 => "Child",
true => "Infant"))
@count(age_category)
@collect()
end
Expand All @@ -241,34 +241,32 @@ patients = db_table(connect(duckdb()), "data/patients.csv");
meds = db_table(connect(duckdb()), "data/medications.csv");

# ╔═╡ 215ed9ca-6698-4f00-ac25-04fc08470981
@chain t(patients) @collect()
@chain patients @collect()

# ╔═╡ 2b7d151e-af7c-49ab-9b64-6b3d922c9312
@chain t(meds) @collect()
@chain meds @collect()

# ╔═╡ 917616d8-bc68-450f-bb72-0571985643af
@chain t(meds) begin
@chain meds begin
@filter(!ismissing(start), ismissing(stop)) # med is still active
@count(patient) # count unique rows at the patient level, save result to `n`
@arrange(desc(count)) # arrange in descending order of count
@arrange(desc(n)) # arrange in descending order of count
@collect()
end

# ╔═╡ b256e86c-c091-4125-bd33-867908b19b0a
meds_clean_count =
@chain t(meds) begin
@chain meds begin
@filter(!ismissing(start), ismissing(stop)) # med is still active
@count(patient) # count unique rows at the patient level, save result to `n`
@arrange(desc(count)) # arrange in descending order of count
@arrange(desc(n)) # arrange in descending order of count
end

# ╔═╡ 662ee3aa-1239-419d-b0a4-516ef8b47a15
@chain begin
@left_join(t(patients), t(meds_clean_count), id = patient)
@mutate(num_meds = replace_missing(count, 0))
@summarize(mean_num_meds = mean(num_meds),
min_num_meds = minimum(num_meds),
max_num_meds = maximum(num_meds))
@left_join(patients, meds_clean_count, id = patient)
@mutate(num_meds = replace_missing(n, 0))
@summarize(across(num_meds, (mean, minimum, maximum)))
@collect()
end

Expand Down
2 changes: 1 addition & 1 deletion recoding-data.jl
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Expand Up @@ -351,7 +351,7 @@ md"""

If you think of `if_else` and `case_when` as the workhorses of recoding data, `TidierCats.jl` provides a set of bespoke functions for achieving many of the same goals as our two handy go-to functions. Fancy, fancy!

Had we wanted to chop age into categories, we could've achieved this using TidierCats `cut` function. THe `extend = true` argument ensures that the values higher than the greatest threshold (i.e., those older than 75) are lumped in with the highest category (in this case, "Older adult").
Had we wanted to chop age into categories, we could've achieved this using CategoricalArrays `cut` function (reexported by TidierCats). THe `extend = true` argument ensures that the values higher than the greatest threshold (i.e., those older than 75) are lumped in with the highest category (in this case, "Older adult").

## `cut` for cutting continuous values into categories
"""
Expand Down