Ilya Kashnitsky

@ikashnitsky.phd

Demographer / Senior Researcher @dst.dk / Affiliate Member @oxforddemsci.bsky.social‬ / Board @demografi.dk ✨ @datavizartskill.ikashnitsky.phd 📝 https://ikashnitsky.phd 😍 #demography #dataviz #rstats 🙌 #openscience #bibliometrics 🔗 https://bio.link/ikx

Infographic titled "Education Spending as % of GDP," using a color-coded world map and two ranked tables to compare countries by education spending relative to GDP and the percentage of population aged 24 and under.

## Overall Layout

The image is a data infographic with a navy blue background, a legend of seven color-coded spending brackets (from "less than 2%" in dark gray to "greater than 7%" in dark blue-green), and a world map showing countries filled with these colors and labeled with specific percentages, such as Sweden 7.3%, U.S. 5.4%, Russia 4.2%, China 3.9%, and India 2.8%.

## Top and Bottom Tables

Below the map, two tables titled "The Highest and Lowest Among Top 40 Economies" list "10 Highest" and "10 Lowest" countries by education spending as % of GDP, each paired with a flag icon, a colored income-tier tag (High-Income in blue, Upper-Middle in yellow, Lower-Middle in red), and a column showing "% population aged 24 and below." Sweden tops the high list at 7.3% spending with 28.4% young population, while Japan sits lowest at 3.3% spending with 20.8% young population.

## Text and Sourcing

The top-right corner contains a paragraph explaining that education spending reflects national priorities, demographics, and fiscal capacity, and that higher spending can signal investment in future generations. A small source line at the bottom cites "UNESCO via OurWorldInData, Population Aged 24 and below: UN Population Division, 2025," with a copyright notice "© 2026 Iswardi Ishak."

April 18, 2026

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Friends Don't Let Friends Use Ollama

Ollama gained traction by being the first easy llama.cpp wrapper, then spent years dodging attribution, misleading users, and pivoting to cloud, all while riding VC money earned on someone else's engine. Here's the full history, and why the alternatives are better.

Tags: LLMs, local, open-source, opinion

Illustration chart of FIFA World Cup finals showing goals scored in each final from 1930 to 2022. A horizontal timeline runs along the bottom with years labeled (1930, 1934, 1938, 1950, 1954, 1958, 1962, 1966, 1970, 1974, 1978, 1982, 1986, 1990, 1994, 1998, 2002, 2006, 2010, 2014, 2018, 2022). Vertical lines rise from each year to a height indicating total goals, with circular markers showing the two finalist nations using their flags. Background is light beige with faint grid lines. Title at top reads “All 22 FIFA World Cup Finals.” Left side text reads “Goals scored in the final.” Colors are muted pastels; flags provide small bursts of color. Overall layout is wide and evenly spaced.

Column plot of the total number of journal included in Scopus database, 1999-2025. There is a steady growth in the global academic output, yet we see a sharp drop in 2021, which is a result of fraud purging

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Diagram divided into four quadrants by two axes — a horizontal axis labeled "JUDGEMENT" on the left and "INTELLIGENCE" on the right, and a vertical axis labeled "OUTSOURCED" at the top and "INSOURCED" at the bottom. Each quadrant lists professional service sectors with estimated market sizes in US dollars.

**Top-left quadrant, labeled "COPILOT TERRITORY"**, contains open-circle bullet points indicating lower automation readiness: Management consulting $300B+, Graphic / UX design $30B+, Executive search $20B+, PR & comms $20B+.

**Top-right quadrant, labeled "AUTOPILOT TERRITORY"**, contains filled-circle bullet points indicating higher automation readiness: Insurance brokerage $140–200B, IT managed services $100B+, Payroll & compliance $50–70B, Claims adjusting $50–80B, Accounting & audit $50–80B, Healthcare rev cycle $50–80B, Mortgage origination $30–50B, KYC/AML $30–50B, Paralegal / LPO $36B, Tax advisory $30–35B, Legal transactional $20–25B, Real estate closing $20–25B, Cost estimation $16B.

**Bottom-left quadrant, labeled "WATCH"**, contains open-circle bullet points: Recruitment $200B+, Advertising $100B+, Freight brokerage $100B+, Admin assistants $80B+, Clinical trials / CRO $80B+, SEO / SEM $50B+, ERP implementation $50B+, Corporate training $50B+, Market research $45B, Cybersecurity $30B+, Architecture $25B+, Patent / IP $15–20B, Travel mgmt $15B+.

**Bottom-right quadrant, labeled "NEXT WAVE"**, contains filled-circle bullet points: Supply chain & procurement $200B+, Pharmacy back-office $30B+, Wealth mgmt ops $30B+, Medical admin $20B+, Fund administration $15–20B.

User
can human survive on a motorbike at 250 km/h?
talkie-1930
No; it is impossible to survive at such a speed for any length of time. The human organism cannot keep pace with a speed greater than 150 km/h, and to maintain even this pace for any considerable time would severely tax the heart and lungs. At 250 km/h the air resistance opposing the forward motion of the rider would be equal to about 44 lb, and the power necessary to overcome this would be 4½ H.P. The task of providing this power would soon become prohibitive, and the rider would be compelled to walk. On the other hand, if the speed were reduced to 100 km/h the air resistance would be only 22 lb, and the power necessary to overcome it 2½ H.P. In other words, a man can ride a motorbike at 100 km/h with comparative comfort, but cannot survive at a greater speed.

Scatter plot with a fitted regression line illustrating the near-perfect negative linear relationship between year-on-year relative changes in life expectancy and year-on-year relative changes in total death counts for Italian males, covering the period 1950 to 2023.

The horizontal axis is labeled "Δ log D" (change in log total deaths), ranging from approximately −0.15 to beyond 0.15. The vertical axis is labeled "Δ log e₀" (change in log life expectancy at birth), ranging from below 0.00 to above 0.02. Reference lines cross at the origin (0.0, 0.00).

Dark teal circular data points are tightly clustered along a descending pink regression line, confirming a strong inverse correlation: years with rising death counts correspond to falling life expectancy, and vice versa. The majority of points fall near the origin, reflecting small year-on-year fluctuations. Two notable outliers appear in the upper-left quadrant (large life expectancy gain, large death count drop). One prominent outlier is labeled "2020" in pink, located far to the lower right, representing a large spike in deaths and a sharp drop in life expectancy during the COVID-19 pandemic. A second unlabeled point sits near it, slightly above and to the left.

The title reads: "A near-perfect linear link between changes in life expectancy and total death counts." The subtitle reads: "Year-on-year relative changes in life expectancy VS year-on-year relative changes in total death counts, Italy, males, 1950–2023." The data source is credited to UN World Population Prospects 2024 (wpp2024 R package), with attribution to Ilya Kashnitsky @ikashnitsky.phd, part of the #30DayChartChallenge 2026, Day 16, theme: causation.

Dot plot chart titled "Do rap musicians really die 40 years younger than jazz musicians?" with subtitle "Age at death by genre — but genre age is the real confound (right-censoring)." The horizontal x-axis spans from 30 years to 80 years, labeled in 10-year increments. Music genres are listed vertically on the y-axis from top to bottom: Gospel, Blues, Jazz, Country, R&B, New Age, Rock, Disco, Techno, Punk, Metal, and Rap/Hip-hop. Each genre has one or two data points representing average age at death, differentiated by sex: filled circles for Female and filled diamonds for Male. Points are color-coded by the decade the genre was founded, ranging from dark blue for 1910 to dark red for 1980. A vertical dashed gray line marks the US population average at approximately 70 years. Genres founded earlier, such as Gospel (light blue, ~1920), Blues and Jazz (dark blue, ~1910), cluster near or above 68–72 years. Newer genres fall progressively lower: Rock and Disco in the mid-50s, Punk and Metal in the low-to-mid 40s. Rap/Hip-hop, colored dark red for ~1980, shows a single male diamond at approximately 30 years, with a red arrow pointing left and an annotation reading "Rap/Hip-hop founded ~1980 — most artists still alive! Only early deaths recorded." A legend on the right shows genre founding decades and sex symbols. Source text at the bottom reads: "Data: Kenny & Asher (2015), Med Probl Perform Art; Calling Bullshit case study (Bergstrom & West) #30DayChartChallenge 2026 · Day 15 · Correlation · Ilya Kashnitsky @ikashnitsky.phd."

Streamgraph titled "Name Waves: the Ebb and Flow of American Baby Names," displaying the popularity of the top 10 baby names per sex in the USA from 1950 to 2022, sourced from the US Social Security Administration. Stream width reflects total births. Color encodes peak era: warm tones (yellow, orange, red) for early-peak names and cool tones (pink, purple, blue, teal, green) for recent-peak names.

The chart is split into two sections stacked vertically. The upper section, labeled "Girls" in bold teal, shows a wide stream that peaks broadly around the 1980s–1990s before narrowing toward 2022. Names labeled within the streams include Linda, Mary, Susan, Lisa (warm tones, prominent in the 1950s–1960s), Patricia and Jennifer (mid-era, orange to pink), and Sarah, Ashley, Jessica, Elizabeth (cooler tones, prominent from the 1980s onward). The lower section, labeled "Boys," shows a similarly shaped stream widest in the 1950s–1960s and tapering toward 2022. Names labeled include James, John, Robert, William, David, Michael (warm yellow-green tones, dominant in the 1950s–1970s), and Joseph, Matthew, Christopher, Daniel (cooler teal and purple tones, peaking in the 1980s–1990s). Credit text reads: "Data: US Social Security Administration via {babynames} · #30DayChartChallenge 2026 · Day 12 · FlowingData · Ilya Kashnitsky @ikashnitsky.phd."

Two-panel cartoon illustration comparing how UI designers and users perceive a baby mobile. The left panel, labelled "UI" in bold black text at the top, shows two smiling cartoon adults — a dark-haired person in yellow and a brown-haired person in blue — leaning happily over a cot railing. A speech bubble reads "I love it!" and another near the cot reads "Me too!" Small cute animal toys — a blue bear, pink pig, and orange cat — hang from the mobile above a contented baby lying face-up in light blue clothing. Musical notes float in the air. The right panel, labelled "Users" in bold black text, shows the baby's point-of-view perspective of the same mobile from below: a striped orange tiger, a blue elephant, a tan rabbit, and a large pink pig are seen upside-down and distorted, spinning chaotically with motion lines and musical notes scattered around. The watermark "VKCOM/PITERSKIS_PUNK_WALL" appears in small text in the upper right of the right panel.

$Illustration-style painting on a white background featuring a central hexagonal frame surrounded by colorful paint splashes and drips, displaying numerous Linux distribution logos arranged across the composition. At the center of the hexagon, a group of five painted penguins — three adults and two smaller ones — represent the Linux mascot (Tux), rendered in a realistic watercolor style in black, white, and yellow. Below the penguins, bold black text reads **LinuxColors**. Surrounding the central frame, the following Linux distribution logos are visible, each rendered in a painterly, dripping style: - **Fedora** — blue circular logo, upper center-left - **Arch Linux** — cyan/teal upward triangle with "tm" mark, upper center - **EndeavourOS** — purple and red triangle with text "ENDEAVOUROS", upper center - **openSUSE** — green chameleon logo with text "openSUSE", upper right - **Void Linux** — dark green circular logo with text "VOID", center-left - **Pop!\_OS** — yellow circular logo with "P!" symbol, lower center-left - **Linux Mint** — green "lm" stylized logo, lower center - **Ubuntu** — orange circular logo with three dots, lower right - **Kali Linux** — grey dragon/kite shape, left side - **Zorin OS** — blue circular "Z" logo, lower left - **Artix Linux** — blue circular "A" logo, bottom left - **Nix / NixOS** — blue snowflake-style logo, upper left Additional unidentified logos appear in dark navy (an "X" triangle, lower center) and teal/green shapes on the right side. Paint splatters in red, yellow, blue, green, and purple are scattered throughout.$

Scatter plot grid comparing the height (in cm) of ice hockey players against the general male population across eight European countries, organized in two rows of four panels. Each panel displays data by birth cohort on the x-axis (approximately 1870 to 2005) and height in centimeters on the y-axis (ranging from 160 to 190+ cm).

Each panel is identified by a circular national flag icon in the upper left: Austria (top row, first), Germany (top row, second), Denmark (top row, third), Finland (top row, fourth), France (bottom row, first), Italy (bottom row, second), Norway (bottom row, third), and Sweden (bottom row, fourth).

Two data series appear in each panel: small open navy circles represent the general male population height trend, forming a smooth upward curve from roughly 167 cm around 1870 to 180 cm by 2000; small green diamond markers represent individual ice hockey players born from approximately 1950 to 2005, clustered between 180 and 192 cm. A green diagonal trend line runs through the hockey player data points, showing a modest upward slope.

Across all eight panels, ice hockey players are consistently and substantially taller than the general male population of the same birth cohort, with a gap of roughly 5–8 cm. The hockey player cluster sits visibly above where the population curve ends.

The light blue background grid uses subtle gridlines. The y-axis label reads "Height (cm)" and the x-axis label reads "Birth cohort." The footer reads: "Data: IIHF data on ice hockey players; Hatton & Bray (2010) male population data · #30DayChartChallenge 2026 · Day 11 · Physical · Ilya Kashnitsky @ikashnitsky.phd."

Horizontal ridgeline (density) chart combining dot strip plots showing the age at first Billboard Hot 100 #1 hit, by music genre, spanning 1958 to 2021. Five genres are displayed as stacked rows: Country, Hip-Hop, Rock, Pop, and R&B.

Each genre has a filled density curve above a horizontal dot strip, where each dot represents one artist. A central filled circle marks the median age for each genre. A horizontal line extends through each density plot indicating the interquartile range.

**Country** (salmon/terracotta): Density skews right, with a wide spread from roughly age 22 to 45. Median sits near 37. Notable labeled artists: Carrie Underwood (approx. age 22) and Kenny Rogers (approx. age 43).

**Hip-Hop** (golden yellow): Density peaks sharply around ages 22–26, with a right tail to ~32. Median near 26. Labeled artists: Lil Nas X (approx. age 20) and Drake (approx. age 27).

**Rock** (light green): Broad, flat distribution from ~25 to beyond 50, with a long right tail. Median near 28. Labeled artists: The Monkees (approx. age 22) and Santana (approx. age 52).

**Pop** (sky blue): Concentrated bell shape peaking around ages 20–28. Median near 25. Labeled artists: Michael Jackson (approx. age 17) and Lionel Richie (approx. age 33).

**R&B** (muted purple-grey): Moderate spread from roughly 17 to 37. Median near 26. Labeled artists: Chris Brown (approx. age 17) and Earth, Wind & Fire (approx. age 32).

The x-axis runs from age 10 to 50. Text at bottom right reads: "Data: Billboard Hot 100 via TidyTuesday 2021 · Birth years: Wikipedia / For groups, lead/founding member's birth year used / #30DayChartChallenge 2026 · Day 10 · Pop Culture · Ilya Kashnitsky @ikashnitsky.phd".

Multi-panel dumbbell chart comparing how the share of net personal wealth changed from about 1985 to the latest available year for seven countries across three groups: Top 1%, Middle 40%, and Bottom 50%. Countries listed down the left are China, India, United States, France, Germany, Brazil, and United Kingdom; each panel has a horizontal percent axis from 0% to 40%, with light blue dots for “~1985” and dark blue dots for the latest year connected by gray lines, showing the top 1% generally gaining share while the middle 40% and bottom 50% often lose or stay low. Visible text includes: “Only the richest keep accumulating wealth”; “Share of net personal wealth — ~1985 -> latest available year”; “Top 1%”; “Middle 40%”; “Bottom 50%”; “Data: World Inequality Database (WID.world) via {wid}”; and “#30DayChartChallenge 2026 · Day 9 · Wealth · Ilya Kashnitsky @ikashnitsky.phd”.

Screenshot of a Gmail inbox on a dark-themed mobile device. The email subject line at the top reads "Invitation to review a manuscript for BMC Medical Education from Dr" followed by a partially redacted sender name with a green "Inbox" label. Below, an open email from "BMC Medi..." sent at 11:59 is addressed "to me." The email body reads: "Invitation to review 'What drives them home? Influencing factors of return migration to western China among clinical medicine master's graduates'." A second paragraph states: "Should you accept to review this manuscript, your report would be due within 7 days." The number "7" is circled and underlined in green handwriting with the word "(seven)" written next to it in green. The email closes with "Dear Dr Kashnitsky,". The overall mood conveys surprise or disbelief at the short 7-day review deadline.

Circular donut/radial bar chart titled "FOSS — Free of Sleep Surrender" displaying the percent of commits per hour across 8 major GitHub repositories, arranged as a 24-hour clock face on a light cyan background.

The chart uses three color-coded segments radiating outward from the center: golden yellow for working hours (9–17h), orange for twilight hours (6–8h and 18–20h), and deep purple for after hours (0–5h and 21–23h). The center of the donut displays bold text reading "39.6% commits outside working hours." Hour markers are labeled around the outer ring at 0 h midnight, 3 h, 6 h, 9 h, 12 h noon, 15 h, 18 h, and 21 h. The largest bars appear in the yellow working-hours arc from roughly 9 h to 17 h, peaking around 10–15 h. Purple after-hours bars cluster near the top of the clock (0–5 h and 21–23 h) and are notably smaller. Orange twilight bars bridge the transitions. Below the chart, a legend identifies the three color categories. Footer text reads: "Data: GitHub punch_card API — microsoft/vscode · torvalds/linux · facebook/react · vercel/next.js · tensorflow/tensorflow · rust-lang/rust · golang/go · kubernetes/kubernetes · #30DayChartChallenge 2026 · Day 8 · Circular · Ilya Kashnitsky @ikashnitsky.phd."

Bivariate choropleth world map titled "Humanity in transition, Demographic Transition," subtitled "Life expectancy (y) × Total fertility rate (x), 2023." Countries are colored using a 3×3 biscale grid encoding two variables simultaneously: life expectancy at birth (y-axis) and total fertility rate (x-axis).

Key regional patterns: sub-Saharan Africa is deep orange (low life expectancy, high fertility, 26.1% of countries); North America, Europe, and Australia are deep purple (high life expectancy, low fertility, 21.4%); central categories of mid life expectancy and mid fertility account for 15.0%. The Middle East and North Africa show mixed orange-brown tones; Russia and Central Asia display medium purple.

Footer reads: "Data: World Population Prospects via {wpp2024} · #30DayChartChallenge 2026 · Day 7 · Multiscale · Ilya Kashnitsky @ikashnitsky.phd." [help.siteimprove](https://help.siteimprove.com/support/solutions/articles/80000863904-accessibility-image-alt-text-best-practices)

Data visualization — a horizontal box plot chart titled **"Press Freedom is declining globally"** — showing RSF World Press Freedom Index scores for 180+ countries across 13 years (2013–2025), created by Ilya Kashnitsky (@ikashnitsky.phd) for the #30DayChartChallenge 2026, Day 06.

Each row represents one year on the vertical axis (labeled 2013 through 2025, reading top to bottom). The horizontal axis shows Press Freedom Score, ranging from approximately 0 on the left to just above 90 on the right, with gridlines at 25, 50, and 75. Individual country data points are rendered as small filled circles overlaid on white box-and-whisker outlines. Each circle is colored by world region: orange for Africa, green for Americas, red/pink for Asia, light blue for Europe, and purple for Oceania.

The box plots' interquartile ranges sit predominantly between scores of roughly 45 and 75, with long left-side whiskers reaching toward scores below 10, representing countries with severely restricted press freedom. The dense cluster of dots is heaviest between scores of 50 and 80. From 2013 to 2025, the entire distribution visibly shifts leftward — the boxes and median lines migrate from around 65–70 in 2013 toward approximately 55–60 by 2025 — illustrating a global decline in press freedom.

The box fill color transitions from teal-green (higher global average ~65) in 2013–2014 to orange-brown (lower global average ~55) by 2024–2025, encoded by a "Global average score" gradient color bar at the bottom, ranging from orange at 55.0 through yellow-green at 62.5 to teal at 65.0. European countries (light blue dots) consistently appear at the high-scoring right end, while Asian and African countries (red and orange dots) cluster toward the lower-scoring left end throughout all years. Data source credited as RSF World Press Freedom Index via Kaggle (vladyslavhubanov).

colored version

Watercolour-style illustrated map on a white background, depicting the same yard layout as the previous sketch, now rendered in soft colour washes.

The title **"EGG HUNT"** appears at the top in large bold letters, each letter in a different bright colour — red, orange, yellow, green, blue, and purple. Two decorated Easter eggs sit to the left of the title: one green with wavy stripes, one blue with a zigzag pattern. A pink flower doodle appears in the top-right corner of the title area.

Building footprints are filled with a warm sandy beige-brown wash and arranged in the same block layout as the original sketch. Green watercolour washes represent grass and open yard areas between and around the buildings. Pink-purple flower doodles are scattered across the map — top-left corner, top centre, and lower centre.

Six **"X"** marks in dark ink are distributed across the green yard areas, indicating egg hiding spots. A small **heart symbol** (♥) in pink marks a covered structure rendered in blue-grey stripes at the centre of the map.

A large rounded shape filled with muted blue-grey represents the **parking area**, labelled with the letter **"P"** in dark ink. To the right, a curved road or path is coloured in warm golden-yellow, with small beige building shapes alongside it.

The bottom-right area features soft blue watercolour washes with wavy lines suggesting a body of water — likely a river or harbour. In the bottom-left corner, a small illustrated **brown rabbit** sits beside a decorated Easter egg, surrounded by pink flower doodles.

The overall palette is soft and pastel. The mood is cheerful and festive.

Slope chart titled "Life expectancy increased in every country," created for #30DayChartChallenge Day 4 by Ilya Kashnitsky (@ikashnitsky.phd), using data from the {owidapi} R package. Two vertical axes represent the years 1960 (left) and 2020 (right), with the y-axis showing life expectancy at birth in years, ranging from approximately 25 to over 85. Each country is represented by a diagonal line connecting its 1960 value to its 2020 value, with a small circular flag icon at each endpoint. All lines slope upward from left to right, visually confirming that life expectancy increased in every country over this 60-year period. Lines are color-coded by world region: cyan/teal for the Americas, blue for Europe, green for Asia, pink/magenta for Africa, and orange for Oceania, as shown in a world map legend positioned at the lower center of the image. Country names are listed vertically along both axes. The chart background is light cyan. The title text is bold and dark teal, with subtitle text in smaller grey font reading "#30DayChartChallenge Day 4 · Slope | 1960 -> 2020 · Lines colored by world region." African nations cluster at the lower end of the 1960 axis, while European and high-income countries appear near the top of both axes, though with notably smaller gains. The most dramatic upward slopes are visible among Asian and African countries, indicating the largest absolute gains in life expectancy.

Marimekko (mosaic) chart titled "Africa's Linguistic Mosaic" with subtitle "Share of language family by region." The chart displays five horizontal rows representing African regions: North, West, Central, East, and Southern. Each row is subdivided into colored rectangular segments whose widths represent the proportional share of language-family-to-country pairs within that region.

The color-coded language families, shown in a legend at the bottom, are: Ubangian (dark purple), Other families (medium purple), Nilo-Saharan (steel blue), Niger-Congo (teal/dark cyan), Kx'a (bright teal), Khoe-Kwadi (light green), Indo-European (yellow-green), and Afroasiatic (golden yellow).

Row heights vary: West is the tallest, suggesting the largest share of language-country pairs; North is the shortest. Key patterns visible across rows: Niger-Congo (teal) dominates West, Central, and Southern Africa, occupying the largest segment in each. In the North row, Afroasiatic (yellow), Niger-Congo, and Nilo-Saharan (blue) share roughly equal thirds. East Africa shows a four-way split among Afroasiatic, Niger-Congo, Nilo-Saharan, and Other families. The Southern row features small segments for Afroasiatic, Indo-European, Khoe-Kwadi, and Kx'a on the left before a large Niger-Congo block. Nilo-Saharan appears as a notable segment in North, West, Central, and East rows.

A source note at bottom right reads: "Data: Languages of Africa · TidyTuesday 2026-01-13. Row width represents the share of language-country pairs. #30DayChartChallenge 2026 · Ilya Kashnitsky @ikashnitsky.phd."

Infographic on a light cyan background titled "The 6 Rules of Good Dataviz" in bold teal text, with subtitle "Day 2 · Pictogram | Each rule illustrated as a self-referential mini chart." Below are six mini charts arranged in two rows demonstrating each rule.

Top row, left to right: First panel (pink background) shows two bar charts side by side — a muted blue-purple bar labeled "mean bars only ✗" and a taller green bar, illustrating a don't. Second panel shows a scatter plot of blue and green dots labeled "show distribution ✓," demonstrating data spread. Third panel shows a vertical bar chart with diagonal rotated labels ("Another Cat," "Last One," "Third Grp," "Very Long Label") marked "rotated labels ✗." Fourth panel shows a horizontal bar chart with the same labels on the left, marked "flipped horizontal ✓," with green bars. Fifth panel (yellow border, cream background) is highlighted as Rule 2: "Readable base size · ≥ 14 for charts · ≥ 16 for social," showing three text examples: "8 pt — too small ✗" in pink, "14 pt — minimum ✓" in gray, and "16 pt — social ✓✓" in larger gray text.

Bottom row: Rule 3 panel (lavender background) titled "Colorblind-safe palettes · viridis default · no rainbow / red-green," showing a viridis color swatch (dark purple to yellow) labeled "colorblind-safe ✓" and a rainbow swatch labeled "inaccessible ✗." Rule 4 panel (pink border) titled "Gray out · saturate focal · ggforce callouts · color title words not legends," showing a scatter plot with gray dots and a dashed teal ellipse highlighting green focal points. Final panel shows a bar chart and a dot plot side by side, both using teal tones, with categories Delta, Beta, Epsilon, Alpha, and Gamma. Footer reads "#30DayChartChallenge 2026 · Ilya Kashnitsky @ikashnitsky.phd · Data: Author's personal dataviz principles."

Treemap chart titled "Women & 1st class first — how the Titanic's lifeboats were filled," created by Ilya Kashnitsky for #30DayChartChallenge 2026, Day 1. Data covers 2,201 Titanic passengers and crew from the built-in R {datasets} Titanic dataset. Tile area is proportional to passenger count; tile color encodes survival rate on a continuous scale from dark navy-purple (low, ~25%) to mint green (high, ~75%+).

The chart is split into two labeled sections: "Male" (large, left and bottom-center) and "Female" (smaller, right column). Males vastly outnumbered females aboard, reflected in tile sizes.

Male tiles are uniformly dark, indicating low survival: 3rd class (n=510, 17%), 2nd class (n=179, 14%), 1st class (n=180, 34%), and crew (n=862, 22%). The crew tile is the largest single tile in the chart.

Female tiles fill the right column in lighter mint-green tones, reflecting far higher survival: crew (n=23, 87%), 2nd class (n=106, 88%), 1st class (n=145, 97%), and 3rd class (n=196, 46%). Female 1st class at 97% is the highest-survival group; even female 3rd class (46%) outpaced every male group.

A horizontal color legend at the bottom runs from dark navy (~25%) through medium blue (~50%) to bright mint green (~75%+). The chart visually reinforces the "women and children first" lifeboat protocol and clear class-based survival disparities.

$screenshot of positron with a system-prompt.md document open, its visible part reads: # R Dataviz — #30DayChartChallenge You are an R dataviz assistant helping Ilya Kashnitsky (@ikashnitsky) produce daily #30DayChartChallenge contributions. Every output is a finished, shareable, attribution-stamped chart. ## Mandatory Setup (immediately after all library() calls) devtools::source_gist("653e1040a07364ae82b1bb312501a184") sysfonts::font_add_google("Atkinson Hyperlegible", family = "ah") showtext::showtext_auto() theme_set(theme_ik(base_family = "ah")) Never redefine theme_ik(). Always include {showtext} in library calls. ## Mandatory labs() labs( title = "[Punchy title — **bold**/*italics* via ggtext ok]", subtitle = "Day [N] · [Prompt] | [one-sentence data context]", caption = "#30DayChartChallenge 2025 · Ilya Kashnitsky @ikashnitsky · ikashnitsky.phd Data: [Source]" ) Caption must always contain: hashtag + handle + data source. Add plot.caption.position = "plot" to theme(). For markdown in title/subtitle, add to theme(): plot.title = ggtext::element_markdown(), plot.subtitle = ggtext::element_markdown(). ## The Rules **Rule 0** — Never show only summaries. Reveal distributions and raw patterns via layering or small-multiples. **Rule 1** — Text is horizontal. No rotated labels. Flip charts; use geomtextpath for lines, ggrepel for points,$