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+    "markdown": "---\ntitle: \"Overview\"\n---\n\n\n### Welcome!\n\nThis workshop provides an overview of many of the packages included in the Tidyverse suite of packages for the R programming language. The Tidyverse is a veritable universe of tools though that no single workshop could hope to cover so **we are focusing here on an introductory approach that focuses primarily on some fundamentals to tidying data in R**. We are always happy to improve workshop content so please don't hesitate to [post an Issue](https://github.com/lter/workshop-tidyverse/issues) on our GitHub repository if you see clear areas for improvement!\n\n<img src = \"images/hex_tidyverse.png\" align = \"right\" width = \"17%\" />\n\nTo maximize the value of this workshop to you, we recommend that you take the following steps **before the day of the workshop**. If anything is unclear, feel free to reach out to us; our contact information can be found in the \"Content Creators\" tab.\n\n## Programs to Install\n\n### R & RStudio\n\n**Install [R](https://www.r-project.org/) and its more convenient (in our opinion) user-interface: [RStudio](https://www.rstudio.com/products/rstudio/download/)**.\n\nIf you already have R, check that you have at least version 4.0.0 by running the following code:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nversion$version.string\n```\n:::\n\n\nIf your version starts with a 3 (e.g., the above code returns \"R version 3...\"), please update R to make sure all packages behave as expected.\n\n### R Packages\n\n**Install the `tidyverse` and `palmerpenguins` R packages** using the following code:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ninstall.packages(c(\"tidyverse\", \"palmerpenguins\"))\nlibrary(tidyverse)\nlibrary(palmerpenguins)\n```\n:::\n\n\n**Please run the above code even if you already have these packages** to update these packages and ensure that your code aligns with the examples and challenges introduced during the workshop.\n\n## Penguin Data\n\nThe data we'll be using for this workshop comes from the `palmerpenguins` package, maintained by [Allison Horst](mailto:ahorst@ucsb.edu). The \"penguins\" dataset from this package contains size measurements for adult foraging penguins near Palmer Station, Antarctica. Data were collected and made available by Dr. Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) Program. Let's take a look at it!\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\npenguins\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 344 × 8\n   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g\n   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>\n 1 Adelie  Torgersen           39.1          18.7               181        3750\n 2 Adelie  Torgersen           39.5          17.4               186        3800\n 3 Adelie  Torgersen           40.3          18                 195        3250\n 4 Adelie  Torgersen           NA            NA                  NA          NA\n 5 Adelie  Torgersen           36.7          19.3               193        3450\n 6 Adelie  Torgersen           39.3          20.6               190        3650\n 7 Adelie  Torgersen           38.9          17.8               181        3625\n 8 Adelie  Torgersen           39.2          19.6               195        4675\n 9 Adelie  Torgersen           34.1          18.1               193        3475\n10 Adelie  Torgersen           42            20.2               190        4250\n# ℹ 334 more rows\n# ℹ 2 more variables: sex <fct>, year <int>\n```\n\n\n:::\n:::\n\n\nThe \"penguins\" dataset has 344 rows and 8 columns.\n\nThe columns are as follows:\n\n`species`: a factor denoting penguin species (Adélie, Chinstrap and Gentoo)\n\n`island`: a factor denoting island in Palmer Archipelago, Antarctica (Biscoe, Dream or Torgersen)\n\n`bill_length_mm`: a number denoting bill length (millimeters)\n\n`bill_depth_mm`: a number denoting bill depth (millimeters)\n\n`flipper_length_mm`: an integer denoting flipper length (millimeters)\n\n`body_mass_g`: an integer denoting body mass (grams)\n\n`sex`: a factor denoting penguin sex (female, male)\n\n`year`: an integer denoting the study year (2007, 2008, or 2009)\n\nThis dataset is an example of **tidy data**, which means that each **variable** is in its own **column** and each **observation** is in its own **row**. Generally speaking, functions from packages in the Tidyverse expect tidy data though they can be used in some cases to help get data into tidy format! Regardless, the penguins dataset is what we'll use for all examples in this workshop so be sure that you install the `palmerpenguins` R package. The examples on this page were adapted from [Allison Horst's `dplyr` tutorial](https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome)!\n\n## Websites to Visit\n\n### Supplemental Material\n\nWhile not technically necessary to attend the workshop, if you'd like you can see the content that created the workshop website you are viewing by visiting our [GitHub repository here](https://github.com/lter/workshop-tidyverse).\n\nAlso, check out **NCEAS' [Learning Hub](https://www.nceas.ucsb.edu/learning-hub)** for a complete list of workshops and trainings offered by NCEAS.\n",
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+    "markdown": "---\ntitle: \"Join\"\n---\n\n\n## Module Learning Objectives\n\nBy the end of this module, you will be able to:\n\n- <u>Differentiate</u> `dplyr`'s various `join` functions from each other\n- <u>Use</u> `dplyr`'s `left_join`, `right_join`, `inner_join`, `full_join`, and `anti_join` functions to manipulate two dataframes\n\n\n::: {.cell}\n\n:::\n\n\n## Combining data\n\nNow that we know how to manipulate a single dataframe, how do we manipulate multiple dataframes? If we have multiple sources of data and we want to combine them together into one dataframe or table, we can **join** them through any shared column(s)! Data you'll be joining can be called \"relational data\", because there is some kind of relationship between the dataframes that you’ll be leveraging. In the `tidyverse`, combining data that has a relationship is called \"joining\". Let's look at some of `dplyr`'s many `join` functions!\n\nIn each of the following `join` functions, you provide two dataframes, the one you arbitrarily provide first is called the \"left\" dataframe while the other is called the \"right\" dataframe. This is important because each of the different `join` functions brings the columns from one of the dataframes into the other depending on (1) which dataframe is left and which is right and (2) what type of `join` you specify.\n\nThis becomes somewhat more intuitive when looking at tangible examples so let's prepare some data to `join` in different ways!\n\n### `join` Data Preparation\n\nFor demonstration purposes, let's add a new column called `record_number` to our penguins data and call the new dataframe `penguins_tidy`. As you can see below, each row is now numbered from 1 to the length of the dataframe. \n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Add a column called `record_number` to our penguins dataset\npenguins_tidy <- penguins %>%\n    dplyr::mutate(record_number = 1:n(), .before = dplyr::everything())\n\ndplyr::glimpse(penguins_tidy)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 344\nColumns: 9\n$ record_number     <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1…\n$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…\n$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…\n$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …\n$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …\n$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…\n$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …\n$ sex               <fct> male, female, female, NA, female, male, female, male…\n$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…\n```\n\n\n:::\n:::\n\n\nThe `palmerpenguins` package also has a \"penguins_raw\" dataset with additional, raw information on the same penguins, such as their sampling region, unique identifier, and the date when their nest was observed. Again, for demonstration purposes, let's add a new column called `record_number` and call this new dataframe `penguins_extra`.  \n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Add a column called `record_number` to our raw penguins dataset\npenguins_extra <- penguins_raw %>%\n  dplyr::mutate(record_number = 1:n()) %>%\n  # Also keep only desired columns to avoid unnecessary complexity\n  dplyr::select(record_number, Region, `Individual ID`, `Date Egg`)\n\ndplyr::glimpse(penguins_extra)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 344\nColumns: 4\n$ record_number   <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,…\n$ Region          <chr> \"Anvers\", \"Anvers\", \"Anvers\", \"Anvers\", \"Anvers\", \"Anv…\n$ `Individual ID` <chr> \"N1A1\", \"N1A2\", \"N2A1\", \"N2A2\", \"N3A1\", \"N3A2\", \"N4A1\"…\n$ `Date Egg`      <date> 2007-11-11, 2007-11-11, 2007-11-16, 2007-11-16, 2007-…\n```\n\n\n:::\n:::\n\n\nNow that we have two dataframes that both have a column called `record_number`, we can `join` them together to combine information in various ways!\n\nAlso, note that if column names include spaces (as in `Individual ID` and `Date Egg`) they need to have a \"backtick\" (\\`) on either side. On your keyboard, a backtick (\\`) is on the left just below the \"escape\" key, and shares a button with the tilde (~).\n\n### `left_join` Example: Prioritize the \"Left\" Dataframe\n\n:::callout-note\n## Example\n\nIn a `left_join`, we bring the columns from the right dataframe that match rows found in the specified column(s) of the left dataframe.\n\n<img src=\"images/join-left.png\" align=\"center\" width=\"50%\" />\n\nWe can specify the column that we want to join based on with `by = ...`. If we don't provide this argument, then `dplyr` will automatically join on **all** matching columns between the left and right dataframes. In our case, we want to `left_join` by `record_number`.\n\nTo better demonstrate that only rows found in the left dataframe will be joined from the right dataframe, we'll use the pipe `%>%` to `filter` the left dataframe before `join`ing. \n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Left-join the two dataframes together on the shared column!\npenguins_left_joined <- penguins_tidy %>%\n  dplyr::filter(record_number < 5) %>%\n  dplyr::left_join(y = penguins_extra, by = \"record_number\")\n\ndplyr::glimpse(penguins_left_joined)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 4\nColumns: 12\n$ record_number     <int> 1, 2, 3, 4\n$ species           <fct> Adelie, Adelie, Adelie, Adelie\n$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen\n$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA\n$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA\n$ flipper_length_mm <int> 181, 186, 195, NA\n$ body_mass_g       <int> 3750, 3800, 3250, NA\n$ sex               <fct> male, female, female, NA\n$ year              <int> 2007, 2007, 2007, 2007\n$ Region            <chr> \"Anvers\", \"Anvers\", \"Anvers\", \"Anvers\"\n$ `Individual ID`   <chr> \"N1A1\", \"N1A2\", \"N2A1\", \"N2A2\"\n$ `Date Egg`        <date> 2007-11-11, 2007-11-11, 2007-11-16, 2007-11-16\n```\n\n\n:::\n:::\n\n\nWhat we have in the end is `penguins_left_joined`, a dataframe with information from both `penguins_tidy` and `penguins_extra`! *All* of the rows in our `filter`ed `penguins_tidy` are kept but only the rows from `penguins_extra` that have a matching `record_number` in `penguins_tidy` are included.\n:::\n\n### `right_join` Example: Prioritize the \"Right\" Dataframe\n\n:::callout-note\n## Example\n\nIn a `right_join`, we bring rows from the left dataframe into the right dataframe based on the values in the specified column(s) of the right dataframe.\n\n<img src=\"images/join-right.png\" align=\"center\" width=\"50%\" />\n\nAs the names imply, a `right_join` is the opposite of a `left_join`.\n:::\n\n### `inner_join` Example: Keep Rows Found in *Both* Dataframes\n\n:::callout-note\n## Example\n\nIn an `inner_join`, we keep only the rows where the values in the column we are joining `by` are found in both dataframes. \n\n<img src=\"images/join-inner.png\" align=\"center\" width=\"50%\" />\n\nThis can be really useful when one of the dataframes includes supplementary data that has incomplete coverage on the other dataframe and you want to simultaneously combine the dataframes and remove the inevitable `NA`s that will be created.\n\nFor example, imagine that you have a dataframe of 100 study sites with information on plant growth and a second dataframe of soil chemistry information. Your grant budget was really tight though so you needed to prioritize sample processing and you only have soil chemistry for 20 of the sites where you have plant growth data.\n\nIf you use `inner_join` on your plant growth and soil chemistry datasets, you will create a single dataframe with both chemistry and plant data that only has the sites (i.e., rows) where you had data for both. This dataframe then would likely be ready for analysis because you'd have complete data for every site in the new `join`ed dataframe!\n\nNote that in an `inner_join` it doesn't matter which dataframe is \"left\" and which is \"right\" because either way you're only keeping the rows that are found in both dataframes.\n:::\n\n### `full_join` Example: Combine *All* Data in Both Dataframes\n\n:::callout-note\n## Example\n\nIn a `full_join`, we keep all values and all rows. \n\n<img src=\"images/join-full.png\" align=\"center\" width=\"50%\" />\n\nA `full_join` is \"smart\" enough to fill with `NA`s in all rows that don't match between the two dataframes. Also, just like an `inner_join`, a `full_join` doesn't care about which dataframe is \"left\" and which is \"right\" because all columns are getting combined regardless of which is left vs. right.\n:::\n\n### `anti_join` Example: Keep Only Columns that *Aren't* Shared\n\n:::callout-note\n## Example\n\nIn an `anti_join`, we return rows of the left dataframe that do not have a match in the right dataframe. This can be used to see what will **not** be included in a join. \n\n<img src=\"images/join-anti.png\" align=\"center\" width=\"50%\" />\n\nOne case where an `anti_join` is particularly useful is that of \"text mining\" where you have one dataframe with a column of individual words that you've split apart from a larger block of free text. If you also have a dataframe of one column that contains words that you want to remove from your \"actual\" data (e.g., \"and\", \"not\", \"I\", \"me\", etc.), you can `anti_join` the two dataframes to quickly remove all of those unwanted words from your text mining dataframe.\n:::\n\n### Additional Notes\n\n- If we want to join by more than one matching column, we can specify multiple columns with a vector like so: `by = c(\"column1\", \"column2\")`.\n\n- We can also use a named vector, `by = c(\"column_a\" = \"COLUMN_A\")` to match on columns that have different names in each dataframe. \n",
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