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Working smarter with {dplyr} 1.2.0

Isabella Velásquez — Wed, 18 Mar 2026 00:00:00 GMT

Details

👥 R-Ladies Rome
📆 18 March 2026 // 01:00 PM ET
💻️ Virtual

Description

The tidyverse keeps evolving, and {dplyr} 1.2.0 brings a set of new tools that make data manipulation even more expressive and flexible. Whether you regularly work with R and the tidyverse, or you’re looking to sharpen your data wrangling skills, this session will help you better understand the design philosophy behind the latest {dplyr} release and how to apply it effectively.

Slides

Recording

Summary

This summary was generated by Claude Sonnet 4.6 and reviewed by me.

Today’s data

We’ll use salmonid mortality data from TidyTuesday, published by the Norwegian Veterinary Institute. The dataset covers monthly mortality data from 2020.

“The data is going to be shared saved in an object called monthly_losses_data. And if we read in that CSV and take a look at the first few rows, you’ll notice that there are nine columns that cover things like species, date, geo group, region, losses, and a variety of variables that describe how those losses happened. And so just a really nice data set to practice some of the functions that we’re going to be taking a look at.”

monthly_losses_data <-
  readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-03-17/monthly_losses_data.csv')

Rows: 2808 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (3): species, geo_group, region
dbl  (5): losses, dead, discarded, escaped, other
date (1): date

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(monthly_losses_data)

# A tibble: 6 × 9
  species date       geo_group region losses   dead discarded escaped other
                             
1 salmon  2020-01-01 area      1       31425  28126      3299       0     0
2 salmon  2020-01-01 area      2      324116 277888     46113       0   115
3 salmon  2020-01-01 area      3      844829 776983     63770       0  4076
4 salmon  2020-01-01 area      4      676852 623159     51823       0  1870
5 salmon  2020-01-01 area      5      109269  97627     11424       0   218
6 salmon  2020-01-01 area      6      548921 531193     15710       0  2018

A quick dplyr refresher

dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges.

“dplyr is a core member of the tidyverse which is a curated collection of R packages specifically for data science. All the tidyverse packages share this opinionated philosophy that data should be in what’s called a tidy format — each variable is a column, each observation is a row, each value is a cell.”

Here are the core functions we’ll use as a foundation:

arrange() — changes the ordering of rows:

monthly_losses_data |> arrange(date)

select() — picks variables based on their names:

monthly_losses_data |>
  select(species, dead, discarded, escaped, other)

summarise() — reduces multiple values down to a single summary:

monthly_losses_data |>
  summarize(mean_losses = mean(losses))

group_by() — performs any operation “by group”:

monthly_losses_data |>
  group_by(region) |>
  summarize(mean = mean(losses))

mutate() — adds new variables that are functions of existing variables:

monthly_losses_data |>
  mutate(total = dead + discarded + escaped + other)

case_when() — checks each condition in order and assigns a value based on the first match:

monthly_losses_data |>
  mutate(loss_rating =
           case_when(losses > 100000 ~ "High",
                     losses < 100000 ~ "Low"))

filter() — picks cases based on their values:

monthly_losses_data |> filter(region == "1")

“dplyr is a grammar. This kind of syntax — taking your data frame, using a pipe, using a verb, and then specifying the columns — that’s the general syntax that we’ll see throughout this presentation.”

dplyr 1.2.0

Everything covered below is explored in great detail in the dplyr 1.2.0 blog post by Davis Vaughan.

`filter_out()`

The problem

Have you ever stared at filter(region == "1") and wondered: am I keeping Region 1 or dropping it?

“The original filter function was optimized for keeping rows. And often, because it was the quickest tool at our disposal or the only tool that we had, we may have forced it to drop rows using negative logic.”

Here’s what that looks like in practice. Say we have a small dataset with an NA in the losses column:

monthly_losses_NA

# A tibble: 5 × 3
  species region losses
        
1 salmon  1       31425
2 salmon  2      324116
3 salmon  3      844829
4 salmon  3      676852
5 salmon  3          NA

We want to drop rows where region == 3 and losses > 700000. Using the traditional approach:

monthly_losses_NA |>
  filter(!(region == 3 & losses > 700000))

# A tibble: 3 × 3
  species region losses
        
1 salmon  1       31425
2 salmon  2      324116
3 salmon  3      676852

The row with NA losses was silently dropped too — not what we wanted. To handle NAs correctly with filter(), we’d need:

monthly_losses_NA |>
  filter(
    !((region == 3 & !is.na(region)) &
             (losses > 700000 & !is.na(losses)))
    )

# A tibble: 4 × 3
  species region losses
        
1 salmon  1       31425
2 salmon  2      324116
3 salmon  3      676852
4 salmon  3          NA

The solution: `filter_out()`

“Rather than trying to think through negations and logic and things like that, just remember: use filter() to keep rows, use filter_out() to drop rows.”

monthly_losses_NA |>
  filter_out(region == 3 & losses > 700000)

# A tibble: 4 × 3
  species region losses
        
1 salmon  1       31425
2 salmon  2      324116
3 salmon  3      676852
4 salmon  3          NA

dplyr with filter_out() assumes that if a row is NA or unknown, you probably don’t want to drop it — so it only drops rows that definitively meet your criteria.

`when_any()` and `when_all()`

The problem

Combining multiple OR conditions with filter() leads to deeply nested, error-prone code:

monthly_losses_filters |>
  filter(
    (region %in% c("7", "8") & losses > 400000) |
           (region %in% c("2", "9") & losses > 300000)
    )

# A tibble: 4 × 3
# Groups:   region [4]
  species region losses
        
1 salmon  2      324116
2 salmon  7      475115
3 salmon  8      442659
4 salmon  9      311127

“It starts to indent. You have to remember where you put your parentheses because there are a lot of them. As you can imagine, it can just get very unwieldy to write something like this, especially if you have many OR statements.”

The solution: `when_any()` and `when_all()`

Use when_any() for OR conditions and when_all() for AND conditions:

monthly_losses_filters |>
  filter(
    when_any(
      (region %in% c("7", "8") & losses > 400000),
      (region %in% c("2", "9") & losses > 300000)
    )
  )

# A tibble: 4 × 3
# Groups:   region [4]
  species region losses
        
1 salmon  2      324116
2 salmon  7      475115
3 salmon  8      442659
4 salmon  9      311127

monthly_losses_filters |>
  filter(
    when_all(
      region %in% c("7", "8"),
      losses > 400000
    )
  )

# A tibble: 2 × 3
# Groups:   region [2]
  species region losses
        
1 salmon  7      475115
2 salmon  8      442659

“when_any() and when_all() continue this theme of intent-based coding — you lead with exactly what it is that you want. Your requirements are just separated by a comma, everything is indented at the same level, and hopefully much easier for a colleague to follow your logic.”

Both helpers also work with the new filter_out().

New recoding and replacing functions

Some background

Recoding used to involve choosing between if/else, case_when(), recode(), or recode() + rlang’s !!! splicing operator — each with its own quirky syntax.

“There are so many ways of recoding — if/else, case_when, recode, recode with rlang — and each one is very specific in how it works, each one brings their own set of syntax, and it could get really confusing.”

Recoding vs. replacing

Two important distinctions:

Recoding — creating an entirely new column using values from an existing column
Replacing — partially updating an existing column with new values

The new family

dplyr 1.2.0 introduces three new functions alongside case_when():

Task	Match by	Function
Recode	Conditions	`case_when()`
Recode	Values	`recode_values()`
Replace	Conditions	`replace_when()`
Replace	Values	`replace_values()`

`recode_values()`

po_mapping <- list(
  "1" = "Jæren", "2" = "Ryfylke", "3" = "Sotra",
  "4" = "Stadt", "5" = "Hustadvika", "6" = "Nordmøre",
  "7" = "Nord-Trøndelag", "8" = "Bodø", "9" = "Vestlfjorden",
  "10" = "Andfjorden", "11" = "Kvaløya",
  "12" = "Vest-Finnmark", "13" = "Øst-Finnmark"
)

monthly_losses_data |>
  select(species, geo_group, region, losses) |>
  filter(geo_group == "area") |>
  mutate(
    production_area = region |>
      recode_values(
        "1" ~ "Jæren",
        "2" ~ "Ryfylke",
        # ...
        "13" ~ "Øst-Finnmark",
        unmatched = "error"
      )
  )

The unmatched = "error" argument is particularly useful:

“Say I was unaware that my dataset actually has 14 regions instead of 13 and I didn’t give it something to recode for region 14. I could put unmatched = "error". And when I run this, it will actually stop the code and let me know immediately — hey, you didn’t match your list to everything. It’s a good way of cleaning up your pipeline and avoiding those kind of silent errors.”

You can also use a lookup table instead of listing values inline:

po_mapping_tibble <-
  tibble::enframe(po_mapping, name = "from", value = "to") |>
  unnest(to)

monthly_losses_data |>
  select(species, geo_group, region, losses) |>
  filter(geo_group == "area") |>
  mutate(production_area =
           recode_values(region,
                         from = po_mapping_tibble$from,
                         to = po_mapping_tibble$to)
         )

# A tibble: 1,512 × 5
   species geo_group region losses production_area
                         
 1 salmon  area      1       31425 Jæren          
 2 salmon  area      2      324116 Ryfylke        
 3 salmon  area      3      844829 Sotra          
 4 salmon  area      4      676852 Stadt          
 5 salmon  area      5      109269 Hustadvika     
 6 salmon  area      6      548921 Nordmøre       
 7 salmon  area      7      231487 Nord-Trøndelag 
 8 salmon  area      8      442659 Bodø           
 9 salmon  area      9      311127 Vestlfjorden   
10 salmon  area      10     288849 Andfjorden     
# ℹ 1,502 more rows

`replace_values()`

Use replace_values() when you want to update specific values within an existing column — all other values stay the same:

monthly_losses_data_po |>
  mutate(production_area = production_area |>
           replace_values(
             "Nordmøre" ~ "Nordmøre + Sør-Trøndelag",
             "Nord-Trøndelag" ~ "Nord-Trøndelag + Bindal"
             )
         )

# A tibble: 1,512 × 5
   species geo_group region losses production_area         
                                  
 1 salmon  area      1       31425 Jæren                   
 2 salmon  area      2      324116 Ryfylke                 
 3 salmon  area      3      844829 Sotra                   
 4 salmon  area      4      676852 Stadt                   
 5 salmon  area      5      109269 Hustadvika              
 6 salmon  area      6      548921 Nordmøre + Sør-Trøndelag
 7 salmon  area      7      231487 Nord-Trøndelag + Bindal 
 8 salmon  area      8      442659 Bodø                    
 9 salmon  area      9      311127 Vestlfjorden            
10 salmon  area      10     288849 Andfjorden              
# ℹ 1,502 more rows

`replace_when()`

case_when() requires a .default argument, otherwise unmatched rows become NA. replace_when() knows you only intend to replace some values — so no .default needed:

monthly_losses_data |>
  slice(1:4) |>
  select(species, region, losses) |>
  mutate(losses =
           replace_when(losses,
                        losses > 500000 ~ 500000)
         )

# A tibble: 4 × 3
  species region losses
        
1 salmon  1       31425
2 salmon  2      324116
3 salmon  3      500000
4 salmon  4      500000

Additional notes

case_match() has been soft deprecated — it has been fully replaced by recode_values() and replace_values().
Speed improvements — core functions like if_else() and case_when() have been rewritten in C using the vctrs framework, bringing speed gains across the tidyverse. See the dplyr performance blog post.
Updating your LLM — because dplyr 1.2.0 is so new, your AI assistant may not know about filter_out(), when_any(), or the new recode functions. Give it context by pasting the release notes or blog post into the conversation, adding a CLAUDE.md file, or using an MCP server.

Community feedback matters

“dplyr 1.2.0 is a direct result of community feedback. The authors are incredibly active on platforms like Bluesky and they don’t just post updates — they also ask for your thoughts. And the reason that dplyr is more efficient, faster, and everything is because the people building it are listening to the people using it. So if you ever find yourself at a point where you have a really messy workaround, I highly encourage you to share it — because you might just inspire the next big feature in 1.3.0.”

Discussions happen in the Tidyups repo on GitHub, where the tidyverse team proposes ideas and invites community comment.

Summary

dplyr 1.2.0 introduces:

filter_out() — the missing complement to filter(), for dropping rows cleanly and safely
when_any() and when_all() — helpers for expressing OR and AND conditions more clearly inside filter() and filter_out()
recode_values(), replace_values(), and replace_when() — a complete, coherent family for recoding and replacing values

To upgrade:

install.packages("pak")
pak::pak("dplyr")

Categorical Survey of Complex Survey Data in R

Rebecca Powell — Fri, 21 Nov 2025 00:00:00 GMT

Event Slides Repo

Details

👥 MAPOR 2025
📆 21 November 2025 // 08:30 AM CT
🌆️ Chicago, IL

Description

This interactive short course will introduce how to analyze categorical survey data in R. We will cover the R functions needed to create the survey design object to account for the sampling design, obtain weighted proportions from survey data, and conduct tests and simple linear regression models. Throughout the course, you will get hands-on experience using data from the American National Election Studies. We will be using Posit Cloud, so you do not need to have R or RStudio preinstalled on your computer. For the best learning experience, we recommend you have prior experience with R and the tidyverse, including familiarity with summarize() and group_by().

Slides

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Isabella Velásquez — Tue, 04 Nov 2025 00:00:00 GMT

Event Website Exercises Repo Recording

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👥 R/Pharma 2025
📆 04 November 2025 // 01:00 PM ET
💻️ Virtual

Description

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Website ↗

Find the materials, exercises, and slides on the website.

Recording

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Event Slides Repo Exercises

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👥 MAPOR Fall Webinar Series 2025
📆 24 October 2025 // 12:00 PM CT
💻️ Virtual

Description

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Isabella Velásquez — Thu, 09 Oct 2025 00:00:00 GMT

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👥 R-Ladies Gaborone
📆 09 October 2025 // 11:00 AM CT
💻️ Virtual

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Recording

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Isabella Velásquez — Tue, 16 Sep 2025 00:00:00 GMT

Event Website Repo Exercises

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👥 posit::conf(2025)
📆 16 September 2025 // 09:00 AM ET
🍑️ Atlanta, GA

Description

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You will learn how to create dynamic websites, professional PDF documents, engaging presentations, and interactive dashboards using Quarto. This workshop highlights Quarto’s powerful theming capabilities, including the new support for brand.yml, which ensures that your work maintains a professional and cohesive style across all formats.

By the end of the session, you’ll be equipped to:

Build and deploy Quarto websites.
Generate professional presentations and PDF reports.
Create interactive dashboards for data visualization and reporting.
Use brand.yml to define and apply consistent theming across all outputs.

Whether you’re looking to enhance your personal projects or streamline organizational outputs, this workshop will equip you with the tools to create polished, professional results.

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Stephanie Zimmer — Fri, 08 Aug 2025 00:00:00 GMT

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👥 useR! 2025
📆 08 August 2025 // 01:00 PM ET
💙️ Duke University

Description

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Event Website Exercises Repo Recording

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👥 Government Advances in Statistical Programming (GASP) 2025
📆 26 June 2025 // 10:30 AM ET
💻️ Virtual

Description

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📆 25 June 2025 // 11:00 AM ET
💻️ Virtual

Description

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Marketing Leadership, who need to identify top-performing platforms, find the mass amount of information in the report confusing. They constantly return to Isabella for clarification or, even worse, don’t reach back out and make misinformed decisions on critical budget allocation. Determined, Isabella decides it’s finally time to automate and tailor this critical dashboard, and save headaches for both Leadership and herself.

In this demo, you will learn how to:

Transform redundant, manual, error-prone data compilation into an automated lead performance dashboard with Shiny
Integrate diverse marketing data sources for a holistic view
Build tailored marketing KPI dashboards that address Leadership’s specific questions
Use drill-downs, data visualizations, and LLM-powered summaries to reduce data overload and clarify complex takeaways instantly
Provide flexibility in dashboard functionality to meet diverse preferences and needs, from pre-selected insights derived directly from the data to implementing an LLM-powered analytics option

Slides

Recording

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Isabella Velásquez — Thu, 10 Apr 2025 00:00:00 GMT

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🔔 ShinyConf 2025
📆 10 April 2025 // 07:10 AM CT
💻️ Virtual

Description

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Slides

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Isabella Velásquez — Wed, 12 Mar 2025 00:00:00 GMT

Event Slides Repo Recording

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🔔 R-Ladies Philly
📆 12 March 2025 // 11:00 AM CT
💻️ Virtual

Description

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Join us to discover how brand.yml can elevate your Quarto documents to a new level of professionalism to ensure that your work is more impactful and on-brand.

Slides

Recording

Tidy Data, Weighted Insights: Analyzing Complex Survey Data in R

Stephanie Zimmer — Tue, 25 Feb 2025 00:00:00 GMT

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👥 R-Ladies Amsterdam and R-Ladies RTP
📆 25 February 2025 // 11:00 AM CT
💻️ Virtual

Description

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Slides

Recording

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Isabella Velásquez — Fri, 21 Feb 2025 00:00:00 GMT

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🏛️ R-Ladies Rome
📆 21 February 2025 // 11:00 AM CT
💻️ Virtual

Description

Discover new ways to share R, Python, and Shiny projects using Quarto Live and Shinylive, no server required!

These tools bring R, Python, and Shiny applications to life directly in the browser. Your audience can open a web page and instantly run code or interact with Shiny apps without needing any setup.

Quarto Live allows you to embed interactive code blocks, exercises with hints and solutions, and dynamic visualizations within static HTML documents. Powered by WebAssembly, it brings interactivity to your Quarto documents.

Shinylive transforms Shiny apps into fully browser-based experiences.

In this talk, you’ll learn how to use these tools to create engaging, interactive documents, dashboards, and educational content that anyone can access, even if they do not have R or Python installed. I look forward to seeing you there!

Slides

Recording

From Notebooks to Dashboards with Quarto

Isabella Velásquez — Mon, 23 Sep 2024 00:00:00 GMT

Event Website Repo Exercises

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👥 Small Data SF 2024
📆 23 September 2024 // 1:00 PM PT
🌉️ San Francisco, CA

Description

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Isabella Velásquez — Wed, 28 Aug 2024 00:00:00 GMT

Event Slides Repo Recording

Details

👥 RStudio Enterprise Community Meetup
📆 28 August 2024 // 11:00 AM CT
💻️ Virtual

Description

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Slides

Recording

Quarto Dashboards

Isabella Velásquez — Thu, 18 Jul 2024 00:00:00 GMT

Event Slides Repo Recording

Details

👥 R-Ladies Rome
📆 18 July 2024 // 11:00 AM CT
💻️ Virtual

Description

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Slides

Recording

Automating your reporting with Quarto Dashboards and Posit Connect

Isabella Velásquez — Wed, 29 May 2024 00:00:00 GMT

Event Slides Repo Recording

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👥 End-to-End Workflow With Posit Team
📆 29 May 2024 // 11:00 AM ET
💻️ Virtual

Description

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You’ll learn how to build and automate Quarto Dashboards with Posit Connect. We’ll showcase a Python example, but the same principles apply to R, Julia, and Observable.

Recording

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Isabella Velásquez — Tue, 23 Jan 2024 00:00:00 GMT

Event Slides Repo Recording Part 1 Recording Part 2

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👥 R-Ladies Nairobi Meetup
📆 23 January 2024 // 9:00 AM PT
💻️ Virtual

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Recordings

Part 1:

Part 2:

The Medium is the Message: R Programmers as Content Creators

Isabella Velásquez — Sat, 19 Aug 2023 00:00:00 GMT

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Details

👥 Cascadia R Conference 2023
📆 19 August 2023 // 9:15 AM PT
🌲️ Seattle, WA

Slides

Script

Slide 1

How many of us have seen this diagram before? As R programmers, the topic of data wrangling is probably not new. We know that data can be inherently valuable, but can be complex and overwhelming in its raw form. We know our role is to take these data and transform them into something that others can consume. And our job doesn’t end there - we also need to actually communicate findings or model results or crosstabs or whatever we actually did with the data.

Slide 2

These were my primary responsibilities at my previous job as a data analyst. I worked with publicly available data such as government databases or online repositories, sometimes interacting with them through APIs. I had to download, organize, and maintain datasets from these multiple sources. Sometimes, I would run into issues like limitations with the data or unexpected formats. Once I had it processed and cleaned, then it was time to present my findings. Great.

Slide 3

When I first started, I would share my findings with my manager. She preferred using PowerPoint decks. These decks had headings summarizing the data’s main points and a big visualization with annotations. She’d ask for these decks through email and then set up one-on-one discussions with me. During these meetings, we’d cover how I organized the data, any insights, the main takeaways, and any important details she needed to know when using the deck.

Audience	Medium	Channel
Manager	PowerPoint decks	Email/one-on-one discussions

Slide 4

But then, as we became more familiar, my manager asked me to email the directors and deputies directly. The directors and deputies were enthusiastic about exploring the data themselves. They wanted to make their own tables and summaries and derive their own insights. So, while they liked the PowerPoint slides, they often wanted the raw data in the form of Excel sheets. I began attaching these sheets to emails and arranging meetings to discuss what they were interested in so we could do the analysis together.

Audience	Medium	Channel
Manager/Deputy/DDs	PowerPoint decks	Email/one-on-one discussions
Deputy/DDs	Excel spreadsheets	Email/data workshops

Slide 5

But I also had teammates who needed to access and edit my code. They preferred downloading everything at once. And then they would run their analysis, reproducing or expanding on what I had created. So, we used GitHub, a platform for sharing and collaborating with code. They could clone a project and get everything on their computer to work with. And in order for me to document what they needed to know, we started a Wiki with information on the data, the analysis, and reproducibility steps.

Audience	Medium	Channel
Manager/Deputy/DDs	PowerPoint decks	Email/one-on-one discussions
Deputy/DDs	Excel spreadsheets	Email/data workshops
Analysts	Code/data/output	GitHub/Wiki

Slide 6

But I also had colleagues curious about our analyses. We began sending email newsletters with links to the slide decks so they could also access and see what we were working on, and decide whether they were interested in perusing the deck.

Audience	Medium	Channel
Manager/Deputy/DDs	PowerPoint decks	Email/one-on-one discussions
Deputy/DDs	Excel spreadsheets	Email/data workshops
Analysts	Code/data/output	GitHub
Colleagues	PowerPoint decks	Email newsletters

Slide 7

These colleagues liked the slide decks but wanted reports tailored to their needs. I used R Markdown to make parameterized reports for different regions or demographics they cared about. These reports would have a heading, bullet points with key takeaways, then some narrative around the data being shared, and then crosstabs or tables specific to each group. Then, I shared these reports through an internal website made with blogdown. I made sure the website was user-friendly, so my colleagues could easily find the reports they needed, along with extra information about the data.

Audience	Medium	Channel
Manager/Deputy/DDs	PowerPoint deck	Email
Deputy/DDs	PowerPoint deck/CSVs	Email
Analysts	Code/notebooks/CSVs/output	GitHub
Colleagues	PowerPoint deck	Email newsletters
Colleagues	Parameterized Word reports	Internal website made with blogdown

Slide 8

But some colleagues didn’t want to click through many reports to compare different subgroups. They needed something quicker to switch between and regular reports didn’t cut it. Instead, for them, I made Shiny apps and created short videos to help them use and understand the dashboards.

Audience	Medium	Channel
Manager/Deputy/DDs	PowerPoint deck	Email
Deputy/DDs	PowerPoint deck/CSVs	Email
Analysts	Code/notebooks/CSVs/output	GitHub
Colleagues	PowerPoint deck	Email newsletters
Colleagues	Parameterized Word reports	Internal repository
Other colleagues	Shiny apps/video tutorials	Internal repository

Slide 9

Others from different teams joined the newsletter and used the internal repository. The communications team contacted me to write blog posts and create infographics to share externally. But they didn’t want to share the regular reports I had with colleagues. They asked for blog posts that told a story, with lots of pictures and visuals, to interest a bigger audience. These would go on the company blog and social media.

Audience	Medium	Channel
Manager/Deputy/DDs	PowerPoint deck	Email
Deputy/DDs	PowerPoint deck/CSVs	Email
Analysts	Code/notebooks/CSVs/output	GitHub
Colleagues	PowerPoint deck	Email newsletters
Colleagues	Parameterized Word reports	Internal repository
Other colleagues	Shiny apps/video tutorials	Internal repository
External interested parties	Blog posts and infographics	Company blog/social media

Slide 10

While my main focus was data analysis for internal use, I wanted to share what I was learning in R, like code snippets and workflow recommendations. Since this was more general, I was able to share it on my personal accounts. So, I began writing on my personal blog and sharing social media posts to share my insights.

Audience	Medium	Channel
Manager/Deputy/DDs	PowerPoint deck	Email
Deputy/DDs	PowerPoint deck/CSVs	Email
Analysts	Code/notebooks/CSVs/output	GitHub
Colleagues	PowerPoint deck	Email newsletters
Colleagues	Reports	Internal website
External interested parties	Text and infographics	Company blog/social media
External R programmers	Blog posts with narrative and code

And essentially, for each analysis I did, I considered all these audiences, activities, and channels and decided which ones I would take on and how. I was a data analyst for five years, and after a while, I reflected on my role and tasks, all the analyses I had done, along with everything I shared, and how all these activities were received, and the decisions that were made based on what I had created. And then I thought…

Slide 11

Am I an influencer?!

That is a bit of a joke, but really, it underlies what I wanted to talk about today. Sometimes we say we’re going to create a slide deck or dashboard as if those two things were interchangeable to the people receiving them. But one thing that I realized over time is that they are not.

Slide 12

Medium Matters: First, it really matters to the audience whether we are sending them a slide deck or a report or a dashboard or a spreadsheet. The medium we use influences how the message is perceived and understood.
Tailoring Communication: Different audiences have different preferences and needs. Adapting our communication style to suit the audience’s preferences can enhance the impact of our insights and findings. And it wasn’t just their personal preference, but sometimes specific contexts required one format or another.
Medium Shapes Visibility: The medium we choose has a direct impact on whether our work gets noticed at all.

Slide 13

Ever heard the phrase ‘The Medium is the Message’? This concept, coined by Marshall McLuhan, suggests that the medium used to convey information is just as important as the information itself. In essence, how you communicate shapes how your message is perceived. Whether you are using a dashboard, or a report, or a CSV, each has its own unique characteristics with different effects on people. At first, this concept might seem vague. But I can honestly say that understanding the meaning behind these words changed the way I approach data analysis; not just from a technical or procedural standpoint, but from a mental perspective on how R programmers should approach and design for an audience.

Slide 14

So, today, I’m here to delve deeper into the ‘communicate’ phase of the data wrangling workflow. This encompasses understanding the audience, selecting the right medium, choosing appropriate channels, and incorporating special features for effective communication. Or, in other words, content creation.

Slide 15

To define content, at its most basic level, it includes:

The information and/or experience
The medium/channel in which the information is delivered
Any beneficial features the medium adds to the information or experience

Slide 16

What exactly is a content creator? Through my extensive Google research, one definition is ‘A content creator is someone who crafts informative or engaging material to be communicated through diverse mediums or channels.’ In other words, every one of us! For R programmers, content creation involves shaping data insights and findings into compelling formats. This can include creating newsletters, emails, wikis, articles, reports, and more, all with the goal of connecting and resonating with the intended audience. Content itself encompasses a range of experiences, including workshops and webinars, to deliver information to end users. In the context of business, content becomes vital as it’s the substance from which users derive value.

Slide 17

One of my esteemed colleagues, Joseph Rickert, kindly pointed out that ‘content’ is a marketing term, and honestly, before my current role as a Sr. Product Marketing Manager, I never thought of my work as content creation. Traditionally, content creation is closely associated with marketing teams, strategically employed to boost sales and revenue by engaging external audiences to do things like visit your website, call your sales team, etc.

However, the principles of content creation hold significant value even within internal contexts. For R programmers predominantly working within an organization, content creation becomes a powerful tool for collaboration, understanding, and decision-making. By tailoring content to different teams and departments, we can ensure that our analyses resonate effectively. The goal of a content creator is to attract, engage, and delight - add value at every stage and build trust, credibility, and momentum. As people working with data, these are our goals as well.

Slide 18

The content that you create is important, of course. But the way you deliver the content is part of the whole - you need to champion knowledge through your content. And the way you do this is by understanding your audience.

Slide 19

A few months ago, I gave a talk to R-Ladies St. Louis called Intro to Quarto. Quarto is an open-source tool for creating documents using literate programming, or a mix of code and narrative. You can use Quarto to output to Word, PDFs, HTML, etc. from one source.

Consider walking through how to create a Quarto document by talking through some slides. (here I’d walk through creating a Quarto document with some slides)

Or, consider what it would be like to watch a live demo of creating a Quarto document. (here I’d walk through creating a Quarto document with a live demo)

Or, consider what it would mean to be given a Posit Cloud project where you can create your first Quarto document. (here I point to a Posit Cloud project)

Each of these are viable and effective ways of introducing Quarto to an interested group. When I was coming up with the talk, I thought about:

Level of Engagement: Tailor the level of engagement to match your audience’s preferences. Are they looking for a high-level overview or a deep dive into technical details? This might inform whether you would just send them an email with top-level takeaways or schedule a time to wade in the data together. This would be an online webinar that ran an hour. Therefore, there would be enough time to go into the concepts of Quarto and specific details on how it works.
Interactivity: Assess how interactive your content should be. Some audiences might prefer interactive tools like dashboards to explore data themselves, while others might prefer static visualizations that present the information clearly without requiring interaction. Since it was online, I wouldn’t be able to guide people individually and troubleshoot with them one-on-one. Therefore, I elected to instead give the deep dive and provide options for interactive use for after the webinar.
Intended Depth of Understanding: Gauge how deep your audience wants to delve into the subject matter. Determine whether they’re seeking a broad understanding of key insights or a detailed understanding of the methodology and data. This guides us in deciding whether to provide high-level summaries or comprehensive explanations. I wanted attendees to leave the webinar ready to create a Quarto document. Therefore, I needed to give them enough information such that they left confident in next steps to try without my help.

“The medium is the message” because it is the “medium that shapes and controls the scale and form of human association and action.” Because the media are inherently different, we experience the content in entirely different ways.

Slide 20

And as R users, we are better suited than most for content creation because of the tools we have.

Tools for creation

R Markdown/Quarto documents that can be exported to Word, PDFs, HTML
R Markdown/Quarto projects for websites, books, blogs, presentations
The {officer} package lets R users manipulate Word and PowerPoint documents
Shiny apps that make our R scripts into dashboards
{ggplot2} for beautiful visualizations
{writexl} for writing to Excel

Slide 12

Hopefully, I have convinced you that yes, R programmers can be content creators. So, how can we be effective content creators?

1. Roadmap: Every piece of content you create should be viewed as a valuable addition to a growing library of insights. Imagine your team becoming the go-to source for data-driven information that supports multiple teams across the organization. By developing a content roadmap, you’re not just creating standalone pieces; you’re building a resource that contributes to the long-term knowledge base of your organization.
1. Before you start crafting new content, take a moment to assess what you already have. This not only saves you hours of needless work but also helps you identify gaps and opportunities.
1. Effective content creation requires clear goals. Using the SMART framework—Specific, Measurable, Achievable, Relevant, and Time-bound—ensures that your goals are well-defined and achievable.
1. Content is most effective when it’s tailored to your audience. Identify the personas or groups within your organization that benefit from your content. Understand how and when they consume information.
1. It’s essential to measure the effectiveness of your content. Analyze metrics such as engagement rates, views, shares, and comments. This data provides valuable insights into what’s resonating with your audience and what might need adjustment. Continuous analysis allows you to refine your content strategy over time.
1. Creating content is just the first step. To maximize its impact, you need to promote it effectively. Share your content across appropriate channels and engage with your audience. Additionally, identify ‘influencers’ within your organization who can help amplify your message. Building partnerships with those who hold influence can significantly increase the reach of your insights.
1. Feedback is a goldmine of insights. Encourage your audience to provide feedback on your content—what they found valuable, what could be improved, and what topics they’d like to see covered. This input helps you iterate and enhance your content, ensuring that it consistently meets the needs and expectations of your audience.
1. And finally, remember to have fun! Content creation isn’t just about conveying information; it’s an opportunity to engage, inspire, and connect with your audience. Embrace your creativity, experiment with different formats, and enjoy the process of crafting meaningful content. When you’re passionate about what you’re creating, it shines through in your work and resonates with your audience. So, have fun with your content creation journey, and let your enthusiasm drive the impact of your insights!

Slide 22

As R programmers and content creators, we bear another significant responsibility—creating and sharing intellectual property. This responsibility goes beyond the initial presentation of our work. It delves into the realms of reproducibility and maintenance. If we don’t ensure that our creations can be reproduced and sustained, the value they hold can easily be lost over time. This concept echoes the essence of our discussion today: how the medium we choose to convey our insights isn’t just a vessel for information; it’s a foundation for longevity and impact.

Slide 23

It’s clear that picking the right way to share is more than just a decision — it conveys something important. As you create and share, remember that the medium you choose is a vital part of your message. Take a moment to consider your audience, the level of engagement, interactivity, and depth of understanding they seek. Today, we’re about to see incredible content from creators across Cascadia. So, think about how they’re doing it: what’s the tool they’re using to show their content, and what does it say? How do these two things work together to make an impact?

Embrace the ‘medium is the message’ philosophy and let it guide your own content creation. Let’s all be content creators who not only share knowledge but also shape how it’s received. Thank you.

Seven Essential Excel Data Visualization Tips (in R!)

Isabella Velásquez — Wed, 07 Jun 2023 00:00:00 GMT

Event Slides Repo Recording

Details

⛰️ Salt Lake City R User Group
📆 07 June 2023 // 12:00 PM MT
💻️ Virtual

Description

Have you ever seen a nifty Excel data visualization and wondered, “Can I do that with R?” The answer is likely yes! With R, you can create stunning, customizable visuals that elevate your data analysis. In this talk, we will cover seven data visualization tips that are achievable in Excel, but also demonstrate how (and why) to create them in R. From sparklines to heat maps, if Excel can do it, R can do it too! Best of all, your work will be reproducible and repeatable, allowing you to share your code and remake your charts anytime, making your work more transparent and streamlined. Join us to learn the tools for making impactful and reproducible dataviz in R!

%>% dreams

Working smarter with {dplyr} 1.2.0

Details

Description

Slides

Recording

Summary

Today’s data

A quick dplyr refresher

dplyr 1.2.0

filter_out()

The problem

The solution: filter_out()

when_any() and when_all()

The problem

The solution: when_any() and when_all()

New recoding and replacing functions

Some background

Recoding vs. replacing

The new family

recode_values()

replace_values()

replace_when()

Additional notes

Community feedback matters

Summary

Links

Categorical Survey of Complex Survey Data in R

Details

Description

Slides

Creating Polished, Branded Documents with Quarto

Details

Description

Website ↗

Recording

Automating Repetitive Reports with Quarto

Details

Description

Slides

Positron for RStudio Users: A Gentle Introduction

Details

Description

Slides

Recording

Branded Websites, Presentations, Dashboards, and PDFs with Quarto

Details

Description

Website

Complex Survey Data Analysis: A Tidy Introduction with {srvyr} and {survey}

Details

Description

Website

Quarto—To Tell Your Story with Data

Details

Description

Website

Recording

Build Automated, Tailored Marketing Dashboards for Optimized Marketing Spend

Details

Description

Slides

Recording

Choosing the right tool for interactive dashboards: flexdashboard, Quarto, and Shiny

Details

Description

Slides

Brand your Quarto projects with brand.yml

Details

Description

Slides

Recording

Tidy Data, Weighted Insights: Analyzing Complex Survey Data in R

Details

Description

Slides

Recording

Interactive R, Python, and Shiny in the Browser with Quarto and Shinylive

Details

Description

`filter_out()`

The solution: `filter_out()`

`when_any()` and `when_all()`

The solution: `when_any()` and `when_all()`

`recode_values()`

`replace_values()`

`replace_when()`