Fleshed out the first half of lecture 2's slides

_freeze/materials/slides_2a/execute-results/html.json

@@ -1,7 +1,7 @@
 {
-  "hash": "f02ab99023d3b7aabd383255b056aaf6",
+  "hash": "c65fdb2924eff1b8d6d2666d9bcde8db",
   "result": {
-    "markdown": "---\ntitle: \"Intro to Data Science\"\nauthor: \"Lecture 2 -- Packages, Structure, & Conditionals\"\nengine: knitr\nformat: \n  revealjs: \n    slide-number: c\n    scrollable: false\n    code-overflow: wrap\n    code-line-numbers: false\n    code-copy: hover\n    theme: [night, slides.scss]\n    reference-location: document\n    footer: \"[Programming in R for Biologists](https://njlyon0.github.io/teach_r-for-biologists/)\"\n---\n\n\n## A Guide to Your Process\n\n### [Scheduling]{.blue}\n\n### [Learning Objectives]{.purple}\n\n### [Practice]{.pink}\n\n### [Supporting Information]{.orange}\n\n### [Class Discussion]{.gold}\n\n## [Today's Plan]{.blue}\n\n- R Packages\n- Class vs. Structure\n- Using Vectors\n- Conditionals\n\n## [Today's Learning Objectives]{.purple}\n\nAfter today's session you will be able to:\n\n. . .\n\n- Load and use an R package\n- Define the difference(s) between object <u>class</u> and <u>structure</u>\n- Create and manipulate vectors\n- Write conditional statements\n- Manage missing data in objects with conditionals\n\n## [R Package Background]{.orange}\n\n\n\n## [Package Locations]{.orange}\n\n\n## [Using Packages]{.orange}\n\n\n## [Package Analogy]{.gold}\n\n\n## [Practice: Packages]{.pink}\n\n\n## [Package Namespacing]{.orange}\n\n\n## [Practice: Namespacing]{.pink}\n\n\n## [Class versus Structure]{.orange}\n\n\n## [Checking Class/Structure]{.orange}\n\n\n## [Practice: Structure]{.pink}\n\n\n## [Using Vectors: Coordinates]{.orange}\n\n\n## [Using Vectors: Coordinates Cont.]{.orange}\n\n\n## [Practice: Vector Navigation]{.pink}\n\n\n## [Temperature Check]{.purple}\n\n#### How are you Feeling?\n\n<p align=\"center\">\n<img src=\"comics/debugging.png\" alt=\"Comic-style graph depicting someone's emotional state as they debug code (from initial struggle and defeat to eventual triumph)\">\n</p>\n\n## [Conditionals]{.orange}\n\n\n## [Conditionals: EQUAL, OR, & AND]{.orange}\n\n\n## [Practice: Fundamental Conditionals]{.pink}\n\n\n## [Discussion: Conditionals]{.gold}\n\n\n## [Numeric Conditionals]{.orange}\n\n\n## [Practice: Numeric Conditionals]{.orange}\n\n## [Temperature Check]{.purple}\n\n#### How are you Feeling?\n\n<p align=\"center\">\n<img src=\"comics/debugging.png\" alt=\"Comic-style graph depicting someone's emotional state as they debug code (from initial struggle and defeat to eventual triumph)\">\n</p>\n\n## [Upcoming Due Dates]{.blue} {.smaller}\n\n::::{.columns}\n\n:::{.column width=\"50%\"}\n\n### Due before lab\n\n#### (By midnight)\n\n- Muddiest Point \\#x\n\n:::\n\n:::{.column width=\"50%\"}\n\n### Due before lecture\n\n#### (By midnight)\n\n- X\n\n:::\n\n::::\n\n# [Bonus Conditionals]{.purple}\n\n## [OR with >2 Options]{.orange}\n\n\n## [Conditionals: NOT]{.orange}\n\n",
+    "markdown": "---\ntitle: \"Intro to Data Science\"\nauthor: \"Lecture 2 -- Packages, Structure, & Conditionals\"\nengine: knitr\nformat: \n  revealjs: \n    slide-number: c\n    scrollable: false\n    code-overflow: wrap\n    code-line-numbers: false\n    code-copy: hover\n    theme: [night, slides.scss]\n    reference-location: document\n    footer: \"[Programming in R for Biologists](https://njlyon0.github.io/teach_r-for-biologists/)\"\n---\n\n\n## A Guide to Your Process\n\n### [Scheduling]{.blue}\n\n### [Learning Objectives]{.purple}\n\n### [Practice]{.pink}\n\n### [Supporting Information]{.orange}\n\n### [Class Discussion]{.gold}\n\n## [Today's Plan]{.blue}\n\n- R Packages\n- Class vs. Structure\n- Using Vectors\n- Conditionals\n\n## [Today's Learning Objectives]{.purple}\n\nAfter today's session you will be able to:\n\n. . .\n\n- Load and use an R package\n- Define the difference(s) between object <u>class</u> and <u>structure</u>\n- Create and manipulate vectors\n- Write conditional statements\n- Manage missing data in objects with conditionals\n\n## [R Package Background]{.orange} {.smaller}\n\n- R packages are suites of functions developed by users\n\n\\\n\n- Packages have no defined depth or breadth requirements\n    - A package could be a single, simple function\n    - Or a complex ecosystem of inter-related functions\n\n\\\n\n- Packages can be installed by any R user for _free!_\n\n\\\n\n- R is versatile and powerful (in part) because of contributed packages\n\n## [Package Locations]{.orange}\n\n- There are two main homes for R packages\n\n\\\n\n. . . \n\n1. Comprehensive R Archive Network\n    - A.k.a. \"CRAN\"\n    - [cran.rstudio.com](https://cran.rstudio.com/)\n\n2. GitHub\n    - [github.com](https://github.com/)\n\n## [Package Locations Cont.]{.orange} {.smaller}\n\n::::{.columns}\n\n:::{.column width=\"50%\"}\n\n#### CRAN\n\n- Currently >20,000 packages live here\n\n\\\n\n- Strict rules for packages to be allowed\n\n\\\n\n- These are \"official\" packages\n\n:::\n\n:::{.column width=\"50%\"}\n\n#### GitHub\n\n- Unknown number of packages here (no centralized record retained)\n\n\\\n\n- No mandatory quality control tests to be available here\n\n\\\n\n- Packages _usually_ work but they don't have the same quality control as CRAN packages\n\n:::\n\n::::\n\n## [Using Packages]{.orange}\n\n- In order to use a package, you must:\n\n\\\n\n1. <u>Install</u> the desired package\n    - Done once per computer\n\n\\\n\n2. <u>Load</u> the package into R\n    - Done _every_ time you re-open RStudio\n\n## [Using Packages: Specific Steps]{.orange}\n\n\\\n\n```{.r}\n# Install desired package\ninstall.packages(\"dplyr\")\n\n# Load that package\nlibrary(dplyr)\n```\n\n\\\n\n- `install.packages` <u>_requires_</u> the package name be in quotes\n- `library` <u>_requires_</u> requires **unquoted** names.\n\n## [Package Analogy]{.gold}\n\n. . . \n\n- `install.packages` = buying a set of tools from the store and putting them in your home\n\n\\\n\n. . .\n\n- `library` = moving the tools that you already own to your workbench\n\n\\\n\n. . .\n\n- You only _buy_ the tools **once** but _every time_ you start work you need to bring them back to your work area!\n\n## [Practice: Packages]{.pink} {.smaller}\n\n![](images/hex-penguins.png){.absolute top=0 left=1100 width=\"12%\"}\n\n- Make a new script for this week's lecture!\n    - Save it in your RStudio Project folder for this course\n    - Make sure it has \"lecture\" and \"2\" in the file name\n\n\\\n\n. . .\n\n- Install the `palmerpenguins` package\n    - Remember to put quotes around the package name!\n\n\\\n\n. . .\n\n- Load the package with the `library` function\n    - Once loaded, run `?palmerpenguins` to see the package-level help file\n\n## [Note on _Function_ Names]{.orange} {.smaller}\n\n- Functions <u>_are not_</u> required to have unique names across all packages\n\n\\\n\n- Risk of using a different function than intended\n    - Best case: creates an error and forces you to catch the mistake\n    - Worst case: _silently_ does something wrong\n\n\\\n\n- Functions can be \"namespaced\" to specify which package the function comes from\n\n## [Function Namespacing]{.orange}\n\n- Namespacing _guarantees_ you use the function from the desired package\n\n\\\n\n- Done with two colons (:) between the package and function names\n    - E.g., `package::function()`\n\n## [Practice: Namespacing]{.pink} {.smaller}\n\n![](images/hex-penguins.png){.absolute top=0 left=1100 width=\"12%\"}\n![](images/hex-dplyr.png){.absolute top=0 left=965 width=\"12%\"}\n\n- `palmerpenguins` includes an example dataset on penguins\n\n. . .\n\n- Run the following code\n    - `peng_df <- palmerpenguins::penguins`\n\n\\\n\n. . .\n\n- Install and load the `dplyr` package\n    - `dplyr` has a function called `glimpse` that shows you core structures of data\n\n\\\n\n. . .\n\n- Namespace `glimpse` and run it on `peng_df`\n\n## [Class versus Structure]{.orange} {.smaller}\n\n- Objects have both [class]{.blue} and [structure]{.gold}\n\n\\\n\n. . .\n\n- [Class]{.blue} = the type of object it is\n    - E.g., dataframe, integer, character, etc.\n\n\\\n\n. . .\n\n- [Structure]{.gold} = the dimensions and \"shape\" of the data\n    - E.g., Number of rows / columns, length, etc.\n\n\\\n\n. . .\n\n- _Both_ class and structure affect what you can do with or to a given object!\n\n## [Checking Class/Structure]{.orange} {.smaller}\n\n- Best to **check class and structure** of an object to ensure functions will work\n\n. . .\n\n::::{.columns}\n\n:::{.column width=\"50%\"}\n\n### Check Class\n\n- Use the `class` function\n\n\\\n\n```{.r}\n# Checking class of 'my_obj'\nclass(my_obj)\n```\n\n:::\n\n:::{.column width=\"50%\"}\n\n### Check Structure\n\n- Use _either_ the `str` function _or_ `dplyr::glimpse`\n\n```{.r}\n# Checking structure of 'my_obj' (with base R)\nstr(my_obj)\n\n# Checking structure of 'my_obj' (with `dplyr`)\ndplyr::glimpse(my_obj)\n```\n\n:::\n\n::::\n\n## [Practice: Structure]{.pink} {.smaller}\n\n- What is the [class]{.blue} of 'peng_df'?\n\n\\\n\n. . .\n\n- What is the [structure]{.gold}?\n    - What information is included when you check?\n\n\\\n\n. . .\n\n- What happens when you check the [class]{.blue} of a function?\n    - Run `class(class)`\n\n\\\n\n. . .\n\n- What happens when you check the [structure]{.gold} of a function?\n    - Run `str(str)`\n\n## [Using Vectors: Coordinates]{.orange} {.smaller}\n\n- Vector structure is expressed as \"length\"\n    - Vector length = number of elements in the vector\n    - Dataframe length = number of rows\n\n\\\n\n- Bracket notation can be used to navigate vectors\n\n\\\n\n- Running `my_vec[5]` will return the fifth element of `my_vec`\n\n## [Using Vectors: Coordinates Cont.]{.orange} {.smaller}\n\n- Bracket notation accepts vectors of coordinates\n\n\\\n\n. . .\n\n- Running `my_vec[c(1, 4, 7)]` returns the first, fourth, and seventh elements of `my_vec`\n\n\\\n\n. . .\n\n- You can also grab the same element _more than once!_\n\n\\\n\n. . .\n\n- Running `my_vec[c(1, 1, 1)]` returns the first element three times \n\n## [Practice: Vector Navigation]{.pink} {.smaller}\n\n- Base R has a built-in vector of letters called `letters`\n    - Assign `letters` to an object called `my_vec`\n\n\\\n\n- Check the length of `my_vec` using the `length` function\n\n\\\n\n. . .\n \n- Identify the 10th element of `my_vec` using bracket notation\n\n\\\n\n- Identify the 8^th^, 5^th^, 12^th^, 12^th^ (again), and 15^th^ elements of `my_vec`\n    - Use concatenation inside of the brackets (with `c`)!\n\n## [Temperature Check]{.purple}\n\n#### How are you Feeling?\n\n<p align=\"center\">\n<img src=\"comics/debugging.png\" alt=\"Comic-style graph depicting someone's emotional state as they debug code (from initial struggle and defeat to eventual triumph)\">\n</p>\n\n## [Conditionals]{.orange}\n\n\n## [Conditionals: EQUAL, OR, & AND]{.orange}\n\n\n## [Practice: Fundamental Conditionals]{.pink}\n\n\n## [Discussion: Conditionals]{.gold}\n\n\n## [Numeric Conditionals]{.orange}\n\n\n## [Practice: Numeric Conditionals]{.orange}\n\n## [Temperature Check]{.purple}\n\n#### How are you Feeling?\n\n<p align=\"center\">\n<img src=\"comics/debugging.png\" alt=\"Comic-style graph depicting someone's emotional state as they debug code (from initial struggle and defeat to eventual triumph)\">\n</p>\n\n## [Upcoming Due Dates]{.blue} {.smaller}\n\n::::{.columns}\n\n:::{.column width=\"50%\"}\n\n### Due before lab\n\n#### (By midnight)\n\n- Muddiest Point \\#x\n\n:::\n\n:::{.column width=\"50%\"}\n\n### Due before lecture\n\n#### (By midnight)\n\n- X\n\n:::\n\n::::\n\n# [Bonus Conditionals]{.purple}\n\n## [OR with >2 Options]{.orange}\n\n\n## [Conditionals: NOT]{.orange}\n\n",
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materials/slides_2a.qmd

@@ -45,45 +45,341 @@ After today's session you will be able to:
 - Write conditional statements
 - Manage missing data in objects with conditionals
 
-## [R Package Background]{.orange}
+## [R Package Background]{.orange} {.smaller}
 
+- R packages are suites of functions developed by users
 
+\
+
+- Packages have no defined depth or breadth requirements
+    - A package could be a single, simple function
+    - Or a complex ecosystem of inter-related functions
+
+\
+
+- Packages can be installed by any R user for _free!_
+
+\
+
+- R is versatile and powerful (in part) because of contributed packages
 
 ## [Package Locations]{.orange}
 
+- There are two main homes for R packages
+
+\
+
+. . . 
+
+1. Comprehensive R Archive Network
+    - A.k.a. "CRAN"
+    - [cran.rstudio.com](https://cran.rstudio.com/)
+
+2. GitHub
+    - [github.com](https://github.com/)
+
+## [Package Locations Cont.]{.orange} {.smaller}
+
+::::{.columns}
+
+:::{.column width="50%"}
+
+#### CRAN
+
+- Currently >20,000 packages live here
+
+\
+
+- Strict rules for packages to be allowed
+
+\
+
+- These are "official" packages
+
+:::
+
+:::{.column width="50%"}
+
+#### GitHub
+
+- Unknown number of packages here (no centralized record retained)
+
+\
+
+- No mandatory quality control tests to be available here
+
+\
+
+- Packages _usually_ work but they don't have the same quality control as CRAN packages
+
+:::
+
+::::
 
 ## [Using Packages]{.orange}
 
+- In order to use a package, you must:
+
+\
+
+1. <u>Install</u> the desired package
+    - Done once per computer
+
+\
+
+2. <u>Load</u> the package into R
+    - Done _every_ time you re-open RStudio
+
+## [Using Packages: Specific Steps]{.orange}
+
+\
+
+```{.r}
+# Install desired package
+install.packages("dplyr")
+
+# Load that package
+library(dplyr)
+```
+
+\
+
+- `install.packages` <u>_requires_</u> the package name be in quotes
+- `library` <u>_requires_</u> requires **unquoted** names.
 
 ## [Package Analogy]{.gold}
 
+. . . 
+
+- `install.packages` = buying a set of tools from the store and putting them in your home
+
+\
+
+. . .
+
+- `library` = moving the tools that you already own to your workbench
+
+\
+
+. . .
+
+- You only _buy_ the tools **once** but _every time_ you start work you need to bring them back to your work area!
+
+## [Practice: Packages]{.pink} {.smaller}
+
+![](images/hex-penguins.png){.absolute top=0 left=1100 width="12%"}
+
+- Make a new script for this week's lecture!
+    - Save it in your RStudio Project folder for this course
+    - Make sure it has "lecture" and "2" in the file name
+
+\
 
-## [Practice: Packages]{.pink}
+. . .
+
+- Install the `palmerpenguins` package
+    - Remember to put quotes around the package name!
+
+\
+
+. . .
+
+- Load the package with the `library` function
+    - Once loaded, run `?palmerpenguins` to see the package-level help file
+
+## [Note on _Function_ Names]{.orange} {.smaller}
+
+- Functions <u>_are not_</u> required to have unique names across all packages
+
+\
+
+- Risk of using a different function than intended
+    - Best case: creates an error and forces you to catch the mistake
+    - Worst case: _silently_ does something wrong
 
+\
 
-## [Package Namespacing]{.orange}
+- Functions can be "namespaced" to specify which package the function comes from
 
+## [Function Namespacing]{.orange}
 
-## [Practice: Namespacing]{.pink}
+- Namespacing _guarantees_ you use the function from the desired package
 
+\
 
-## [Class versus Structure]{.orange}
+- Done with two colons (:) between the package and function names
+    - E.g., `package::function()`
 
+## [Practice: Namespacing]{.pink} {.smaller}
 
-## [Checking Class/Structure]{.orange}
+![](images/hex-penguins.png){.absolute top=0 left=1100 width="12%"}
+![](images/hex-dplyr.png){.absolute top=0 left=965 width="12%"}
 
+- `palmerpenguins` includes an example dataset on penguins
 
-## [Practice: Structure]{.pink}
+. . .
+
+- Run the following code
+    - `peng_df <- palmerpenguins::penguins`
+
+\
+
+. . .
+
+- Install and load the `dplyr` package
+    - `dplyr` has a function called `glimpse` that shows you core structures of data
+
+\
+
+. . .
 
+- Namespace `glimpse` and run it on `peng_df`
 
-## [Using Vectors: Coordinates]{.orange}
+## [Class versus Structure]{.orange} {.smaller}
 
+- Objects have both [class]{.blue} and [structure]{.gold}
 
-## [Using Vectors: Coordinates Cont.]{.orange}
+\
 
+. . .
+
+- [Class]{.blue} = the type of object it is
+    - E.g., dataframe, integer, character, etc.
+
+\
+
+. . .
+
+- [Structure]{.gold} = the dimensions and "shape" of the data
+    - E.g., Number of rows / columns, length, etc.
+
+\
+
+. . .
+
+- _Both_ class and structure affect what you can do with or to a given object!
+
+## [Checking Class/Structure]{.orange} {.smaller}
+
+- Best to **check class and structure** of an object to ensure functions will work
+
+. . .
+
+::::{.columns}
+
+:::{.column width="50%"}
+
+### Check Class
+
+- Use the `class` function
+
+\
+
+```{.r}
+# Checking class of 'my_obj'
+class(my_obj)
+```
+
+:::
+
+:::{.column width="50%"}
+
+### Check Structure
+
+- Use _either_ the `str` function _or_ `dplyr::glimpse`
+
+```{.r}
+# Checking structure of 'my_obj' (with base R)
+str(my_obj)
+
+# Checking structure of 'my_obj' (with `dplyr`)
+dplyr::glimpse(my_obj)
+```
+
+:::
+
+::::
+
+## [Practice: Structure]{.pink} {.smaller}
+
+- What is the [class]{.blue} of 'peng_df'?
+
+\
+
+. . .
+
+- What is the [structure]{.gold}?
+    - What information is included when you check?
+
+\
+
+. . .
+
+- What happens when you check the [class]{.blue} of a function?
+    - Run `class(class)`
+
+\
+
+. . .
+
+- What happens when you check the [structure]{.gold} of a function?
+    - Run `str(str)`
+
+## [Using Vectors: Coordinates]{.orange} {.smaller}
+
+- Vector structure is expressed as "length"
+    - Vector length = number of elements in the vector
+    - Dataframe length = number of rows
+
+\
+
+- Bracket notation can be used to navigate vectors
+
+\
+
+- Running `my_vec[5]` will return the fifth element of `my_vec`
+
+## [Using Vectors: Coordinates Cont.]{.orange} {.smaller}
+
+- Bracket notation accepts vectors of coordinates
+
+\
+
+. . .
+
+- Running `my_vec[c(1, 4, 7)]` returns the first, fourth, and seventh elements of `my_vec`
+
+\
+
+. . .
+
+- You can also grab the same element _more than once!_
+
+\
+
+. . .
+
+- Running `my_vec[c(1, 1, 1)]` returns the first element three times 
+
+## [Practice: Vector Navigation]{.pink} {.smaller}
+
+- Base R has a built-in vector of letters called `letters`
+    - Assign `letters` to an object called `my_vec`
+
+\
+
+- Check the length of `my_vec` using the `length` function
+
+\
+
+. . .
+
+- Identify the 10th element of `my_vec` using bracket notation
 
-## [Practice: Vector Navigation]{.pink}
+\
 
+- Identify the 8^th^, 5^th^, 12^th^, 12^th^ (again), and 15^th^ elements of `my_vec`
+    - Use concatenation inside of the brackets (with `c`)!
 
 ## [Temperature Check]{.purple}