I have seen a lot of posts about a package called tidyplots. How does it compare to ggplot2? Is it like another type of graphic package similar to ggplot? Or is it like a package that you combine with gg? I’m confused 🤔 #rstats #ggplot2 #tidyplots

2025keo Euskal Autonomia Erkidegoko biztanleria-piramidea

2025keo Donostialdeako biztanleria-piramidea

2025keo Donostiako biztanleria-piramidea

2025keo Gipuzkoako biztanleria-piramidea

Azken urteotan bezala Eustateko datuak baliatuta EAEko lurralde eremu desberdinetako biztanleria-piramideak paratu ditut. Aurtengoan adin taldeetako bilakaera ere gehitu dut, saileko lehen urtearekiko (2001) ratio gisa emanda.

#dataviz #ggplot2 #demografia

Check out the Statistics Globe Hub: statisticsglobe.com/hub

The Statistics Globe Hub is an ongoing learning program that helps you stay up to date with statistics, data science, AI, and programming using R and Python.

#tidyplots #datavisualization #rstats #ggplot2 #datascience #statisticsglobehub

Two-panel visualization titled 'The Ocean Has a Memory.' Top panel: a heatmap showing multi-year average daily ocean temperature by depth (2m to 40m) and day of year at a Nova Scotia coastal monitoring station. Cold blue tones dominate winter and deeper depths; warm yellow tones appear at the surface from June through September, with stratification clearly visible as shallower depths warm faster. Bottom panel: a time series from 2018 to 2026 showing thermocline depth — the point of steepest temperature gradient — which shoals to 5–10 metres each summer and deepens or disappears in winter when the water column mixes. Scatter points at low opacity show daily variability; a loess smooth highlights the repeating annual pattern. An annotation notes that the thermocline shoals to approximately 5–10 metres in late summer as surface waters warm.

📊 #TidyTuesday – 2026 W13 | Coastal Ocean Temperature by Depth
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

plot that shows dots and a boxplot for 2 of 4 conditions

FridayNight coding; a #ggplot2 extension that drops the boxplot when n is too low

Introducing ggauto: automating better charts – Nicola Rennie The ggauto package is an opinionated ggplot2 extension package that aims to help people make better charts by default. This blog post explains why it exists and how it works.

🎉 ggauto is now on CRAN 🎉

An #RStats package that selects better chart types, and provides more accessible styling for #ggplot2 plots 📊

Blog post explaining why I made it and how it works: nrennie.rbind.io/blog/introdu...

#DataViz

#TidyTuesday 2026-03-24 One Million Digits of Pi.
Code: karel_fiser.gitlab.io/tidytuesday_...

#DataViz #RStats #ggplot2

3D bar chart of 2,000 digits of π arranged in a spiral, rendered with the rayshader r-package. Each bar's height and color represent a digit from 0 to 9. Data source: tidytuesday 2026 week 12 - One Million Digits of Pi (Eve Anderson collection)

I wanted to create a maze using #TidyTuesday 2026 w10 data about π digits at first but ended up having fun with #rayshader

Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2

Some progress on the model exploration visuals using #tidyplots. Patterns are starting to lock in.

#dataviz #ggplot2 #rstats

Line chart showing estimated unemployed counts (thousands) in London from 2019 to early 2026, using 4-quarter rolling averages. Two lines are plotted: 16+ overall unemployment (dark navy) and 16–24 youth unemployment (dark red). Both lines declined through 2022–2023 before rising sharply after late 2024. The overall 16+ count reached 390k, and the youth 16–24 count reached 125.4k in the most recent period, leaving a gap of 264.5k — the largest in that period. Two shaded vertical bands mark the October 2024 Budget announcement and the April 2025 implementation of NIC and minimum wage changes, both of which coincide with the acceleration in unemployment counts.

📊 #MakeoverMonday – 2026 W12 | London Estimated Unemployment Rates
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

A circle formed by many small tiles (as pixels) in the gold to orange color spectrum, resembling a sun, with the Pi symbol vaguely visible at the center.

1 million decimals of pi
#TidyTuesday

Polar coordinates of each digit as ordered tiles, digit value corresponding to color 🟡🌞
#R4DS #DataViz #ggplot2

Code: github.com/borstell/tid...

A spiral graph showing the first one thousand digits of pi. The spiral begins inward with the first number of pi (3) and gradually grows outward fading slowly until it reaches the final number (8).

A #PiDay visualisation for this weeks #TidyTuesday

🔗 Code: github.com/C-Monaghan/t...

#RStats #dataviz #PiDay #ggplot2

Another #PiDay themed visualisation for #TidyTuesday this week!

📊 Made with #ggplot2
🛠️ Function to shown first n digits and highlight specific ones
🪄 Gif created with {magick}

Code: github.com/nrennie/tidy...

#DataViz #RStats

Dot Plots in R The "dot plots in R" typically refers to two distinct types of visualizations: (i) Cleveland dot plots (for comparing categories) and (ii) Stacked (Wilkinson) dot plots (for showing distributions). A dot plot is a graphical representation that breaks the range of data into many small equal-width intervals and counts the number of observations in each interval. The interval count is superimposed on the number line at the interval midpoint as a series of dots (stacked if repeated), usually one for each observation.

The "dot plots in R" typically refers to two distinct types of visualizations: (i) Cleveland dot plots (for comparing categories) and (ii) Stacked (Wilkinson) dot plots (for showing distributions).
#EDAinR #RGraphics #Rvisualization #RDataAnalysis #RLanguage #RProgramming #RQuiz #ggplot2 #RAnalysis

Drei Liniendiagramme, die jeweils die Entwicklung der Umfragewerte von SPD und CDU 6 Monate vor der Landtagswahlen 2016, 2021 und 2026 zeigen. Die Linien sind geglättet, um die generellen Trends zu zeigen. In allen drei Wahlkämpfen konnte die SPD große Rückstände aufholen, besonders stark 2021.

Etwa 6 Stunden bis zur ersten Prognose für die Landtagswahl in Rheinland-Pfalz.

Die SPD 🔴 konnte einen großen Rückstand auf die CDU ⚫️ in den Umfragen bis auf wenige Prozentpunkte aufholen.

Wiederholt sich rheinland-pfälzische Wahlkampf-Geschichte?

#ltwrlp #ggplot2 📊

A dark navy background shows the random walk of π's first 10,000 digits as a gold fractal-like path. Each digit (0–9) maps to one of ten compass directions, producing a wandering trail with no apparent pattern. The path begins near the center-right (marked "Start – 3.14159…") and ends in the upper-left (marked "Step 10,000"), colored from near-black at the start to bright gold at the end. An inset in the upper-right shows the full 1,000,000-step walk as a dense cloud — drifting nowhere in particular, confirming π's statistical randomness at scale.

📊 #TidyTuesday – 2026 W12 | One Million Digits of Pi
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

A few more tweaks to chart types, styling, and syntax for {ggauto}:

📊 Lines/points/text all scale together
➡️ Pipe in unquoted variable names
🎨 Better styling for line and scatter plots

#ggplot2 #DataViz #RStats

Salmonid Mortality Data for #TidyTuesday, wk 11.

#Rstats #Dataviz #ggplot2

A scatter plot of the Aizawa attractor projected into 2D. The image shows looping, intertwined curves forming a complex, symmetric structure

The Aizawa attractor plotted directly with #ggplot2
This is what the structure looks like before applying any effects.

50 energy visualizations. 12 chart types. 1 dataset. All R.

Bar, lollipop, line, area, stream, bump, dumbbell, treemap, heatmap, scatter, stacked bar, grouped bar.

R can do it all.

energtx.com/visualizations

#RStats #ggplot2 #DataViz #DataVisualization #TidyTuesday #rstats

The tidyverse pipeline for energy data:

read_csv() → filter() → select() → mutate() → group_by() → summarise() → ggplot()

From raw OWID data to finished chart in ~20 lines.

energtx.com/visualizations

#RStats #tidyverse #ggplot2 #DataViz #TidyTuesday

Stacked area charts in ggplot2 tell the full energy story.

Coal, oil, gas, nuclear, hydro, wind, solar — all in one chart, from 1965 to today.

geom_area(alpha = 0.85) + scale_fill_manual()

energtx.com/visualizations

#RStats #ggplot2 #DataViz #EnergyMix #TidyTuesday

coord_flip(clip = "off") — a small detail that matters.

Without clip = "off", labels get cut at the panel edge. With it, they breathe.

Every horizontal bar chart on energtx uses this.

energtx.com/visualizations

#RStats #ggplot2 #DataViz #TidyTuesday #rstats

Scatter plot tip: use scale_x_log10() for GDP data.

Linear scale compresses most countries into a corner. Log scale spreads them out and reveals the real pattern.

energtx.com/visualizations

#RStats #ggplot2 #DataViz #ScatterPlot #TidyTuesday

ggrepel saves hours of manual label adjustment.

I use it on every multi-line chart — country labels that never overlap.

geom_text_repel(direction = "y", segment.color = "grey30")

energtx.com/visualizations

#RStats #ggplot2 #ggrepel #DataViz #TidyTuesday

Lollipop charts > bar charts for rankings.

Less ink, more clarity. geom_segment + geom_point.

I use them across energtx for per-capita metrics and share comparisons.

energtx.com/visualizations

#RStats #ggplot2 #DataViz #LollipopChart #TidyTuesday

170,000+ energy records visualized with R.

ggplot2 handles it effortlessly. Filter, aggregate, plot — the tidyverse pipeline just works.

energtx.com/datasets

#RStats #ggplot2 #tidyverse #DataScience #TidyTuesday #rstats

My energtx dark theme for ggplot2:

• Background: #0B1120
• Panel: #0F172A
• Grid: #1E293B
• Text: #E2E8F0
• Accent: #00E5A0

Dark themes make data pop. Every chart on energtx uses this palette.

energtx.com/visualizations

#RStats #ggplot2 #DataViz #TidyTuesday #rstats

showtext + Google Fonts changed my R plots.

Inter for body text. Space Grotesk for titles. Clean, modern typography — zero hassle.

font_add_google("Inter", "inter")
showtext_auto()

energtx.com/visualizations

#RStats #ggplot2 #showtext #DataViz #TidyTuesday

Heatmaps with geom_tile in ggplot2.

I mapped renewable electricity share across 15 countries from 2000 to 2025.

You can literally watch the energy transition unfold.

energtx.com/visualizations

#RStats #ggplot2 #Heatmap #DataViz #Renewables #TidyTuesday