![]() Among many interesting things at the conference, one of the biggest announcements was that “ RStudio has become a Public Benefit Corporation“. doi.RStudio Conference 2020RStudio Conference 2020, one of the biggest R/data science conferences ended this week. SBC LTER: Santa Cruz Island: Cover of Algae, Invertebrates and Benthic Substrate. Description: Algal cover, invertebrates and substrates near Santa Cruz Island.Source: Fisheries Statistics Division of the NOAA Fisheries.Description: NOAA Commercial Fisheries Landing data (1950 - 2017).SBC LTER: Reef: Abundance, size and fishing effort for California Spiny Lobster (Panulirus interruptus), ongoing since 2012. Description: Lobster size, abundance and fishing pressure (SB coast). ![]() SBC LTER: Reef: Kelp Forest Community Dynamics: Abundance and size of Giant Kelp (Macrocystis Pyrifera), ongoing since 2000. Description: Giant kelp abundance and size, SB coast.SBC LTER: Reef: Kelp Forest Community Dynamics: Invertebrate and algal density. Description: Invertebrate counts, SB coast.SBC LTER: Reef: Kelp Forest Community Dynamics: Fish abundance. Description: Reef fish abundance, SB coast.We use the following data from the Santa Barbara Coastal Term Ecological Research and National Oceanic and Atmospheric Administration in this workshop: She is also Artist in Residence at RStudio! Julie Lowndes is a Senior Fellow and Director of Openscapes at the National Center for Ecological Analysis and Synthesis.Īllison Horst is a Lecturer of Data Science & Statistics at the Bren School of Environmental Science and Management. We both work at the University of California Santa Barbara, USA. We are environmental scientists who use and teach R in our daily work. It is being fine-tuned but the most recent version is always available: And also, awesomely, it’s created with the same tools and practices we will be talking about: R and RStudio - specifically bookdown - and GitHub. This book is written to be used as a reference, to teach, or as self-paced learning. It is a friendly intro to becoming a modern R user, full of tidyverse, RMarkdown, GitHub, collaboration & reproducibility. This course is for Excel users who want to add or integrate R and RStudio into their existing data analysis toolkit. Hello! This is a course taught by Dr. Julie Stewart Lowndes and Dr. Allison Horst at the RStudio Conference: January 27-28 in San Francisco, California. 10.8 Add an image to your partner’s document.10.5 Make a graph of US commercial fisheries value by species over time with ggplot2.10.4 Find total annual US value ($) for each salmon subgroup.10.3 Some data cleaning to get salmon landings by species.10.2 Attach packages, read in and explore the data.9.6.6 How do you avoid merge conflicts?.9.6.4 Sync attempts & fixes (Partner 1).9.6.2 Create a conflict (Partners 1 and 2).9.5.5 Clone to a new R Project (Partner 2).9.5.4 Clone to a new R Project (Partner 1).9.5.3 Give your collaborator privileges (Partner 1 and 2).9.5.2 Create a gh-pages branch (Partner 1).8.5 An HTML table with kable() and kableExtra.8.4.4 filter() and join() in a sequence.8.4.3 inner_join() to merge data frames, only keeping observations with a match in both.8.4.2 left_join(x,y) to merge data frames, keeping everything in the ‘x’ data frame and only matches from the ‘y’ data frame.8.4.1 full_join() to merge data frames, keeping everything.8.4 dplyr::*_join() to merge data frames.8.3.6 stringr::str_detect() to filter by a partial pattern.8.3.5 Activity: combined filter conditions.8.3.4 Filter to return observations that match this AND that.8.3.3 Filter to return rows that match this OR that OR that.8.3.2 Filter rows based on numeric conditions.8.3.1 Filter rows by matching a single character string.8.3 dplyr::filter() to conditionally subset by rows.7.7 stringr::str_replace() to replace a pattern.7.6.2 tidyr::separate() to separate information into multiple columns.7.6.1 tidyr::unite() to merge information from separate columns.7.6 tidyr::unite() and tidyr::separate() to combine or separate information in column(s).7.5 janitor::clean_names() to clean up column names.7.4 tidyr::pivot_wider() to convert from longer-to-wider format.7.3 tidyr::pivot_longer() to reshape from wider-to-longer format.7.2.2 read_excel() to read in data from an Excel worksheet.7.2.1 Create a new R Markdown and attach packages.6.5.1 Knit, push, & show differences on GitHub.3.8 Assigning objects with % summarize().3.4.3 Writing code in a file vs. Console.2.2 Guiding principles / recurring themes.
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