This shows examples of TextBlock, Notes, LinkList, CodeBlock.
TextBlock allows you to add rich text, HTML, and annotations to your reports.
You can include formatting, styled text, and even inline code.
See here for TextBlock examples.
Notes provides editable text areas for adding commentary to your visualizations. When using external data formats (parquet, csv_external, json_external), Notes creates text files that you can edit after generating the HTML. Your edits will appear in the document when refreshed.
The idea is sometimes you will want to make a report, interpret it, and then store your observations as part of the report. You can always do this directly by editing the HTML but note is designed to make it easier.
After generating the HTML, edit the text file in the notes/ folder and refresh the page to see your notes.
See the Notes example for a demonstration.
notes/tutorial_notes.txt
function generate_comprehensive_data()
rng = StableRNG(42)
# Time period: One year of daily data
start_date = Date(2024, 1, 1)
dates = start_date:Day(1):(start_date + Day(700))
n = length(dates)
# Product categories
products = ["Laptop", "Tablet", "Phone"]
regions = ["North", "South", "West"]
segments = ["Enterprise", "SMB", "Consumer"]
# Generate sales data
records = DataFrame[]
for (i, date) in enumerate(dates)
for product in products
for region in regions
# Base sales with trends and seasonality
base = 10000 + 1000 * sin(2π * i / 365)
trend = i * 10
# Product-specific multipliers
product_mult = Dict(
"Laptop" => 3.0, "Tablet" => 2.0, "Phone" => 4.0
)[product]
# Region-specific multipliers
region_mult = Dict("North" => 1.2, "South" => 1.0, "West" => 1.1)[region]
sales = (base + trend) * product_mult * region_mult * (1 + 0.2 * randn(rng))
quantity = round(Int, sales / (100 + 50 * randn(rng)))
cost = sales * (0.6 + 0.1 * randn(rng))
profit = sales - cost
# Customer metrics
customers = round(Int, quantity * (0.3 + 0.1 * randn(rng)))
satisfaction = 3.5 + 1.0 * randn(rng)
# Geographic coordinates
lat = 35.0 + Dict("North" => 10, "South" => -10, "West" => 0)[region] + randn(rng)
lon = -95.0 + Dict("North" => 0, "South" => 0, "West" => -15)[region] + randn(rng)
# Random segment assignment
segment = rand(rng, segments)
push!(records, DataFrame(
date = date,
product = product,
region = region,
segment = segment,
sales = max(0, sales),
quantity = max(0, quantity),
cost = max(0, cost),
profit = profit,
customers = max(0, customers),
satisfaction = clamp(satisfaction, 1, 5),
latitude = lat,
longitude = lon,
month = month(date),
quarter = (month(date) - 1) ÷ 3 + 1,
day_of_year = dayofyear(date)
))
end
end
end
return vcat(records...)
endData: no_data.parquet