Dallas Novakowski's Avatar

Dallas Novakowski

@dallasnova.bsky.social

Behavioural Scientist, Thought-doer; Survey and Research Analyst @ College of New Caledonia. https://dallasnova.rbind.io/ Interested in competitive and cooperative consumption, using R and Open Science Opinions hopefully my own

142 Followers  |  664 Following  |  8 Posts  |  Joined: 14.02.2025  |  2.0235

Latest posts by dallasnova.bsky.social on Bluesky

A comic where a duck asks the WTP to cross the bridge

A comic where a duck asks the WTP to cross the bridge

WTP #econsky
Credit: www.exocomics.com

14.10.2025 20:12 β€” πŸ‘ 10    πŸ” 3    πŸ’¬ 1    πŸ“Œ 0

They should be publicly available now - just take them with a grain of salt as they are still drafts for a Canadian context :)

14.10.2025 19:24 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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socia_service_blueprint Zoom - Google Drive/Microsoft 365 QuickBooks - Toggl - HubSpot CRM - Trello Wave /quickbooks - Harvest - HubSpot CRM (free) - ClickUp or Asana IP ownership, payment terms, cancellation, dispute resolu...

Thanks for sharing, Crystal! In this same spirit - here's a draft sales and service blueprint I've been working on for my own research and strategy contracting docs.google.com/presentation...

14.10.2025 19:14 β€” πŸ‘ 2    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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GitHub - Cghlewis/freelancing_resources: Resources for data freelancers Resources for data freelancers. Contribute to Cghlewis/freelancing_resources development by creating an account on GitHub.

Over the last 3 years I've been collecting resources to share with people interested in data freelancing. I've recently compiled those resources in a GitHub Repository.

If you are interested in data freelancing, these resources may help you navigate that transition.

github.com/Cghlewis/fre...

14.10.2025 16:35 β€” πŸ‘ 49    πŸ” 14    πŸ’¬ 1    πŸ“Œ 0
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Simplifying Transparent Data Visualizations Using Faded Dotplots and Shadeplots | Dallas Novakowski Building off of raincloud and fadecloud plots, I introduce two new styles for plotting data transparently: (1) the faded dotplot and (2) the shadeplot. Both leverage the r package ggdist to present st...

some side-by-side comparisons of Boxplot and other group comparion visualization methods: dallasnova.rbind.io/post/creatin...

10.10.2025 21:02 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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It's been a while since I've reminded people that DMLSER has an entire chapter to help guide you in creating standardized rules for your team/projects in regards to file structures, file naming, variable naming and more! πŸ“

datamgmtinedresearch.com/style

27.08.2025 13:10 β€” πŸ‘ 28    πŸ” 5    πŸ’¬ 2    πŸ“Œ 1
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WeRTogether-useR!2025Keynote We R Together Yanina Bellini Saibene useR! 2025 Duke University How to learn, use and improve a programming language as a community

1/

I had the honor of giving a keynote at #useR2025 πŸŽ‰ at Duke University: β€œWe R Together – How to learn, use, and improve a programming language as a community” πŸ’œ

Slides here πŸ‘‰ docs.google.com/presentation...

Video here: www.youtube.com/live/CTTvTQ-...

#rstats #useR2025

12.08.2025 12:26 β€” πŸ‘ 25    πŸ” 12    πŸ’¬ 1    πŸ“Œ 1
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Simplifying Transparent Data Visualizations Using Faded Dotplots and Shadeplots | Dallas Novakowski Building off of raincloud and fadecloud plots, I introduce two new styles for plotting data transparently: (1) the faded dotplot and (2) the shadeplot. Both leverage the r package ggdist to present st...

I also have been drawn to raincloud plots - I've also done a bit of work trying to combine the boxplot with the violin geom - with the goal of presenting equivalent information with fewer plotted objects
dallasnova.rbind.io/post/efficie...

12.08.2025 16:24 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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Thought-doing | Dallas Novakowski A scientific blog and portfolio by Dallas Novakowski

Always great to have collections of resources! If it's of any value for your list, I also have a few posts mostly on #DataViz in #RStats dallasnova.rbind.io/post/

12.08.2025 15:58 β€” πŸ‘ 3    πŸ” 1    πŸ’¬ 0    πŸ“Œ 0
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Depending on your data type, use the right plot to tell the story.
A nice interactive app to determine what plots you need
www.data-to-viz.com/

08.08.2025 13:45 β€” πŸ‘ 15    πŸ” 2    πŸ’¬ 0    πŸ“Œ 0
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"Does LED Street Lighting Reduce Crime? Evidence From a Staggered Large-Scale Retrofitting Program"

papers.ssrn.com/sol3/papers....

06.08.2025 16:27 β€” πŸ‘ 4    πŸ” 1    πŸ’¬ 1    πŸ“Œ 0

I'm back home with my dad and have thoughts about being an Indigenous scientist and academic. I'm writing this in real time so it might get disrupted and will have typos.

I am fairly successful by academic standards. I have a tenure track job, wrote papers, have grant funding, mentor students.

21.07.2025 21:18 β€” πŸ‘ 379    πŸ” 107    πŸ’¬ 3    πŸ“Œ 8

This is great! My current to-do is to get a workflow for making a good data dictionary from SPSS (.sav) files

11.07.2025 23:18 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

Do you clean data in R?
Stop renaming, reordering, or dropping variables manually. Automate the work with your data dictionary. #rstats
cghlewis.com/blog/dict_cl...

18.11.2023 13:54 β€” πŸ‘ 46    πŸ” 25    πŸ’¬ 4    πŸ“Œ 1
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Posit We’re happy to announce that we’re supporting Typst by funding one of their full-time engineers.

At Posit, we love @typst.app:

⚑ Make PDFs in milliseconds, not minutes
✨ The power of LaTeX with today's technologies
✍️ Modern typography (including emojis!)
🧠 Clear mental model

So I'm thrilled to announce that we're now supporting its development: posit.co/blog/posit-a...

#rstats

03.06.2025 13:47 β€” πŸ‘ 241    πŸ” 52    πŸ’¬ 5    πŸ“Œ 11
library(tidyplots)

x <- c(2.3, 4.5, 6.3, 3.4, 7.8, 6.7)
df <- data.frame(
  x = c(x, x + c(0.8, 0.75)),
  group = paste0("g", rep(c(1, 2), each = 6)),
  batch = paste0("b", c(1:6, 1:6)),
  shuffle = paste0("c", c(1:6, 6:1))
)

df |>
  tidyplot(group, x, color = group) |>
  add_boxplot() |>
  add_data_points() |>
  add_test_pvalue(paired_by = batch) |>
  add_line(group = batch, color = "black")

library(tidyplots) x <- c(2.3, 4.5, 6.3, 3.4, 7.8, 6.7) df <- data.frame( x = c(x, x + c(0.8, 0.75)), group = paste0("g", rep(c(1, 2), each = 6)), batch = paste0("b", c(1:6, 1:6)), shuffle = paste0("c", c(1:6, 6:1)) ) df |> tidyplot(group, x, color = group) |> add_boxplot() |> add_data_points() |> add_test_pvalue(paired_by = batch) |> add_line(group = batch, color = "black")

This is how you can do paired testing in #tidyplots 0.3.1 πŸ’

#rstats #dataviz #phd

07.07.2025 16:18 β€” πŸ‘ 15    πŸ” 2    πŸ’¬ 0    πŸ“Œ 0
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03.07.2025 19:20 β€” πŸ‘ 2    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
Bootstrap inference made easy: p-values and confidence intervals in one line of code
YouTube video by R Consortium Bootstrap inference made easy: p-values and confidence intervals in one line of code

I really enjoyed attending and speaking at R/Medicine this year! I learned a lot. Huge thanks to the organisers! My talk "Bootstrap inference made easy" is now available online: www.youtube.com/watch?v=EeAt...

#Rstats #Statsky

02.07.2025 06:47 β€” πŸ‘ 7    πŸ” 2    πŸ’¬ 0    πŸ“Œ 0
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22.06.2025 13:33 β€” πŸ‘ 27    πŸ” 9    πŸ’¬ 4    πŸ“Œ 4
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Tired of manually styling PDFs, HTML docs, and Shiny apps to fit your brand? Join Garrick Aden-Buie on R for the Rest of Us podcast to learn about brand.ymlβ€”a single YAML file for consistent styling.

🎧 Watch the episode and demo: buff.ly/Ybu6rF9

#rstats #DataViz

12.06.2025 14:12 β€” πŸ‘ 5    πŸ” 4    πŸ’¬ 0    πŸ“Œ 0
Screenshot of a slide titled "Subgrouping" from a meta-analysis presentation. The slide displays a forest plot showing individual study risk ratios with 95% confidence intervals, grouped by whether the study used random allocation ("yes" or "no"). Each study is listed with author(s) and year in the leftmost column, followed by a "Random" column indicating randomization, and a "Risk Ratio [95% CI]" column with horizontal bars for each study. At the bottom, two pooled estimates are shown: one for non-random allocation (risk ratio = 0.62 [0.40, 0.95]) and one for random allocation (risk ratio = 0.38 [0.22, 0.65]), each with a corresponding 95% predictive distribution interval displayed as a shaded curve. A speaker (Wolfgang Viechtbauer) is visible in the upper right corner of the screenshot.

Screenshot of a slide titled "Subgrouping" from a meta-analysis presentation. The slide displays a forest plot showing individual study risk ratios with 95% confidence intervals, grouped by whether the study used random allocation ("yes" or "no"). Each study is listed with author(s) and year in the leftmost column, followed by a "Random" column indicating randomization, and a "Risk Ratio [95% CI]" column with horizontal bars for each study. At the bottom, two pooled estimates are shown: one for non-random allocation (risk ratio = 0.62 [0.40, 0.95]) and one for random allocation (risk ratio = 0.38 [0.22, 0.65]), each with a corresponding 95% predictive distribution interval displayed as a shaded curve. A speaker (Wolfgang Viechtbauer) is visible in the upper right corner of the screenshot.

From @wviechtb.bsky.social talk @ #ESMARConf2025 on showing heterogeneity in meta-analysis. Learned that 95% prediction interval can be visualized in a {metafor} forest plot using predictive distributions. I find it especially useful for illustrating subgroup differences! #RStats

11.06.2025 15:39 β€” πŸ‘ 5    πŸ” 2    πŸ’¬ 1    πŸ“Œ 0
A workflow diagram of how to make grilled cheese. Slice cheese and butter bread. Put them together, then heat. The end result is grilled cheese.

A workflow diagram of how to make grilled cheese. Slice cheese and butter bread. Put them together, then heat. The end result is grilled cheese.

Here's the grilled cheese workflow diagram. It's just made in Google Slides because it needed to be quick. I use LucidChart frequently for work projects or Inkscape to make prettier ones.

17.04.2025 22:06 β€” πŸ‘ 10    πŸ” 2    πŸ’¬ 3    πŸ“Œ 0
plot of 2 regression lines where the middle 50% of the predictor vars is much thicker than the rest of lines. one line is labeled "vowels". it is flatter and higher up than the other line. the other line is labeled "consonants". there is an annotation that says "expected intelligibility for typical 4-year-old on typical item with other predictors fixed to means".

plot of 2 regression lines where the middle 50% of the predictor vars is much thicker than the rest of lines. one line is labeled "vowels". it is flatter and higher up than the other line. the other line is labeled "consonants". there is an annotation that says "expected intelligibility for typical 4-year-old on typical item with other predictors fixed to means".

still trying to make regression boxplots a thing

06.06.2025 20:14 β€” πŸ‘ 41    πŸ” 8    πŸ’¬ 10    πŸ“Œ 1
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Scaling variables in R A walkthrough of a tip I recently learned

I used to wrap scale() in as.numeric() and call it a day. Now I know better: drop() preserves attributes and behaves cleanly.

Full walkthrough (with mtcars, lm, ggplot2): mattkmiecik.substack.com/p/scaling-va...

#rstats #DataViz #OpenScience

06.06.2025 15:14 β€” πŸ‘ 4    πŸ” 1    πŸ’¬ 0    πŸ“Œ 0
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How to add citations from Zotero to Quarto documents – Matti’s homepage The vscode-zotero extension allows quickly inserting citation keys from Zotero to your Quarto documents, and updating the associated .bib file with the citation’s biblatex entry. Here’s how to install...

Here's a little piece on adding references to #quartopub documents & their bibfile directly from Zotero: vuorre.com/posts/zotero...

tl;dr: Install github.com/mvuorre/vsco..., insert citation, profit.

06.06.2025 14:56 β€” πŸ‘ 7    πŸ” 1    πŸ’¬ 1    πŸ“Œ 1
When exporting long char vars to .sav from R, the vars will be truncated at 255 chars and a new var will be created that contains the rest of the string. One solution that also maintains your labels is to export data to a .dta and import this file into SPSS!

When exporting long char vars to .sav from R, the vars will be truncated at 255 chars and a new var will be created that contains the rest of the string. One solution that also maintains your labels is to export data to a .dta and import this file into SPSS!

Thank you, past me, for sharing a solution to this #rstats / SPSS issue. πŸ™

21.05.2025 12:40 β€” πŸ‘ 19    πŸ” 1    πŸ’¬ 2    πŸ“Œ 0
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Datamethods Discussion Forum This is a place for discussions and Q&A about data-related issues and quantitative methods including study design, data analysis, and interpretation.

datamethods.org is a place where methodologists meet subject matter experts for in-depth discussions + Q&A related to study design, measurement, data analysis, interpretation, and more. Now on a new server with the latest version of discourse software. Come join us there. #StatsSky

24.04.2025 14:42 β€” πŸ‘ 59    πŸ” 18    πŸ’¬ 0    πŸ“Œ 1

πŸ‘‹ Join us for a hands-on session πŸ› οΈ exploring how good Git practices can transform your workflowβ€”whether you’re coding or writing with R. Bring your questions, share your tips, and let’s level up our version control together!

@maellesalmon.bsky.social @rladiesrome.bsky.social

21.05.2025 08:55 β€” πŸ‘ 11    πŸ” 10    πŸ’¬ 0    πŸ“Œ 0
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I'm often asked to recode multiple dummy coded race variables into a single variable so I thought I would share 2 different ways you might do this in #rstats. I'm sure there are many more ways. πŸ˜‰

21.05.2025 02:28 β€” πŸ‘ 45    πŸ” 6    πŸ’¬ 1    πŸ“Œ 2

@dallasnova is following 20 prominent accounts