Update slide formatting + Minor edits to lesson snippet 1 + Materials…

… for lesson snippet 2

Teaching R slides.Rmd

@@ -21,7 +21,8 @@ knitr::opts_chunk$set(echo = TRUE)
 ```{r packages, include=FALSE}
 if(!require(pacman)) {install.packages("pacman")}
 if(!require(memer)) {devtools::install_github("sctyner/memer")}
-pacman::p_load(tidyverse, lubridate, gapminder, coefplot, memer)
+if(!require(emo)) {devtools::install_github("hadley/emo")}
+pacman::p_load(tidyverse, lubridate, gapminder, coefplot, memer, emo)
 ```
 
 ## Warm up
@@ -37,84 +38,84 @@ Let's do some polling! Go to [pollev.com/kumarr436](http://pollev.com/kumarr436)
 
 ## Outline
 
--   Preparing to teach
--   Determining the scope of your lesson
--   Building your lesson (nuts-and-bolts)
--   Structuring your lesson (what goes after what?)
--   Examples and Exercises
+- Preparing to teach
+- Determining the scope of your lesson
+- Building your lesson (nuts-and-bolts)
+- Structuring your lesson (what goes after what?)
+- Examples and Exercises
 
 # Preparing to teach
 
 ## Why should you learn to teach R?
 
-> -   Teaching is the best way to learn 🤓
-> -   Lots of opportunities to teach R workshops/etc at Northwestern 💸
-> -   Build your teaching portfolio 🗂
-> -   Training for certain non-academic career paths 👩‍💻
+> - Teaching is the best way to learn `r emo::ji("nerd")`
+> - Lots of opportunities to teach R workshops/etc at Northwestern `r emo::ji("teacher")`
+> - Build your teaching portfolio `r emo::ji("folder")`
+> - Training for certain non-academic career paths `r emo::ji("money with wings")`
 
 ## Questions to ask yourself
 
-> -   What are your **learning objectives** for the course/session/workshop?
-> -   What are the opportunities and constraints you'll have in your **teaching environment**?
-> -   How much **background** do your students have? How much do they need?
+> - What are your **learning objectives** for the course/session/workshop?
+> - What are the opportunities and constraints you'll have in your **teaching environment**?
+> - How much **background** do your students have? How much do they need?
 
 ## Learning objectives
 
-> -   Much of this will be determined by context: the students may need specific skills (e.g. regression, data viz) for a class, you may be teaching general-purpose skills for data analysis, etc.
-> -   I believe every R workshop should share these learning objectives: **Articulate questions about the [technique/method taught]** and **Identify resources to address those questions**.
-> -   In simpler language: you want students to walk away knowing how to ask for help when they run into problems.
+> - Much of this will be determined by context: the students may need specific skills (e.g. regression, data viz) for a class, you may be teaching general-purpose skills for data analysis, etc.
+> - I suggest that every R workshop should share two objectives: **Articulate questions about the [technique/method taught]** and **Identify resources to address those questions**.
+> - In simpler language: you want students to walk away knowing how to ask for help when they run into problems.
 
 ## Teaching environment
 
-> -   One-off workshop vs. series
-> -   Virtual vs. in-person
-> -   Solo teaching vs. group teaching
+> - One-off workshop vs. series 
+> - Virtual vs. in-person 
+> - Solo teaching vs. group teaching
 
 # Determining the scope of your lesson
 
 ## Background knowledge
 
--   What do the students already know? You may be able to answer this from program structure, or through a survey.
+- What do the students already know? You may be able to answer this from program structure, or through a survey.
 
--   Sometimes students will be coming in with different levels of background knowledge. You can try to address this before the session and/or adjust your teaching accordingly.
+- Sometimes students will be coming in with different levels of background knowledge. You can try to address this before the session and/or adjust your teaching accordingly.
 
--   Where can students get background knowledge before the session?
+- Where can students get background knowledge before the session?
 
-    -   Pre-session videos
-    -   DataQuest
-    -   `learnr` tutorials
+    - Pre-session videos
+    - DataQuest
+    - `learnr` tutorials
 
 ## Do you need to cover the basics? What *are* the basics?
 
-> -   Installing R and RStudio
-> -   Navigating RStudio
-> -   "code", "comments", "objects"
-> -   Syntax and data types
-> -   Data structures
-> -   Reading and writing files
+> - Installing R and RStudio
+> - Navigating RStudio
+> - "code", "comments", "objects"
+> - Syntax and data types
+> - Data structures
+> - Reading and writing files
 
 # Building your lesson
 
 ## Building your lesson
 
-> -   Befriend `RMarkdown` (and `RProjects`)
-> -   Consider how to store and share materials: Github, Box folder, something else, none of the above
-> -   Other types of tools: RStudio Cloud
-> -   Connect to other course materials, pre-workshop assignments, etc.
+> - Befriend `RMarkdown` (and `RProjects`)
+> - Consider how to store and share materials: Github, Box folder, something else, none of the above
+> - Other types of tools: RStudio Cloud
+> - Connect to other course materials, pre-workshop assignments, etc.
 
 # Structuring your lesson
 
 ## Where to start
 
 Motivation
 
--   Use the end point as motivation: show them what they will learn!
--   Pick some real data that the students are likely to be interested in
+- Use the end point as motivation: show them what they will learn!
+- Pick some real data that the students are likely to be interested in
 
 Help students feel comfortable
 
--   Remind them that they are learning a skill, which only comes with practice
--   Encourage your students to learn from *each other*
+- Remind them that they are learning a skill, which only comes with practice
+- Encourage your students to learn from *each other*
 
 ## Core content
 
@@ -127,9 +128,9 @@ I strongly suggest an **examples and exercises** approach to teaching skills in
 
 ## Building flexibility into your lesson
 
--   Plan for a little bit more material than you can teach
--   Provide data files that students can play around with
--   For complex skills, give yourself wiggle room to skip over exercises based on timing/interest
+- Plan for a little bit more material than you can teach
+- Provide data files that students can play around with
+- For complex skills, give yourself wiggle room to skip over exercises based on timing/interest
 
 ## Ending with encouragement
 
@@ -160,10 +161,10 @@ The following lesson snippets all use `.R` code files for the exercises. You can
 
 ## Components of a basic plot
 
--   **data**: a data frame, provided to the `ggplot()` function
--   **geometric objects**: the objects/shapes that you want to plot, indicated through one of the many available `geom` functions, such as `geom_point()` or `geom_hist()`
--   **aesthetic mapping**: the mapping from the data to the geometric objects, provided in an `aes()` function nested within `ggplot()` or a `geom` function
--   connected with the `+` operator
+- **data**: a data frame, provided to the `ggplot()` function
+- **geometric objects**: the objects/shapes that you want to plot, indicated through one of the many available `geom` functions, such as `geom_point()` or `geom_hist()`
+- **aesthetic mapping**: the mapping from the data to the geometric objects, provided in an `aes()` function nested within `ggplot()` or a `geom` function
+- connected with the `+` operator
 
 ```{r ggplot structure, eval=F}
 ggplot(data = <DATA FRAME>) + 
@@ -223,9 +224,9 @@ ggplot(gapminder) +
 
 Plot life expectancy as a function of GDP per capita for the year 2007, and add labels.
 
-> -   Step 1: Supply the data `gapminder07` to `ggplot()`
-> -   Step 2: Choose `geom_point()` + Supply `x=gdpPercap` and `y=lifeExp` to `aes()`
-> -   Step 3: Add `title`, `x`, and `y` in `labs()`
+> - Step 1: Supply the data `gapminder07` to `ggplot()`
+> - Step 2: Choose `geom_point()` + Supply `x=gdpPercap` and `y=lifeExp` to `aes()`
+> - Step 3: Add `title`, `x`, and `y` in `labs()`
 
 ## Your turn!
 
@@ -283,6 +284,10 @@ Plot the **life expectancy** of each **continent** in 2007.
 
 Look at the [ggplot cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/master/data-visualization-2.1.pdf) and decide which kind of geom to use.
 
+> - Step 1: Supply `gapminder07` to `ggplot()`
+> - Step 2: Choose a `geom` (e.g. `geom_boxplot()`) and supply appropriate aesthetics in nested `aes()` (e.g. `x=continent, y=lifeExp`)
+> - Step 3: Add some labels in `labs()`
+
 ## Life expectancy in each continent
 
 ```{r}
@@ -365,9 +370,116 @@ Choose your own adventure!
 
 Create a plot that includes **two geoms** and **facets**
 
-## Lesson snippet 2: working with dates
+# Lesson snippet 2: working with dates
+
+## Prepare libraries and data
+
+```{r}
+# Load tidyverse and lubridate
+library(tidyverse)
+library(lubridate)
+
+# Import vaccine data
+vaccines <- read_csv("data/chicago_vaccines_daily.csv")
+```
+
+```{r}
+# Let's glimpse the data
+glimpse(vaccines)
+```
+
+## Working with dates
+
+In your exercise file, check the class of the date variable.
+
+## Working with dates
+
+Check the class of the date variable: 
+
+```{r}
+class(vaccines$date)
+```
+
+This is just a character string. And it's not even ordered correctly!
+
+```{r}
+head(vaccines$date)
+```
+
+## Working with dates
+
+It looks like `as_date()` might be a helpful function from `lubridate`. But what happens when we use it?
+
+```{r}
+as_date(vaccines$date)
+```
+
+Whoops! Let's turn to the `lubridate` [cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/master/lubridate.pdf) for help. What function should we use? 
+
+## Converting dates
+
+![From lubridate cheatsheet](figures/help_mdy.png)
+
+## Converting dates
+
+We can use the tailored `mdy()` function:
+
+```{r}
+mdy(vaccines$date) %>% head()
+```
+Or we can speficy the format of the values in the character string using the `format=` argument in `as_date()`. See the help file for `strptime()` for how to define formats.
+
+```{r}
+as_date(vaccines$date, format="%m/%d/%y") %>% head()
+```
+
+## Converting dates
+
+```{r}
+# Replace the date variable in the dataset with the converted version
+vaccines$date <- mdy(vaccines$date)
+
+# Check the class of our converted variable
+class(vaccines$date)
+```
+
+## Utility of the Date format
+
+```{r}
+ggplot(vaccines, aes(x=date, y=doses)) + geom_col() 
+```
+
+## Converting dates
+
+Now, let's try to convert the `date` variable, which is in `Date` class, into a numeric value for month. 
+
+## Converting dates
+
+```{r}
+# Create a new variable for month. Refer to the cheatsheet for guidance.
+vaccines$month <- month(vaccines$date)
+
+# Repeat the plot, using month as the x-axis
+ggplot(vaccines, aes(x=month, y=doses)) + geom_col()
+```
+
+## Converting dates 
+
+How about if we want to get the *day of the week*? Identify the right function using the `lubridate` cheatsheet. Check the help file to see if there are any useful arguments. 
+
+```{r}
+# Create a new variable for day of the week, with label=TRUE
+vaccines$wday <- wday(vaccines$date, label=TRUE)
+
+# Repeat the plot, using day of the week as the x-axis
+ggplot(vaccines, aes(x=wday, y=doses)) + geom_col()
+```
+
+## Working with dates
+
+Bonus exercise if time permits: Can you calculate the number of days it took for Chicago to fully vaccinate 1 million people?
 
-## [under construction -- answers/exercise2_answers.R shows what will be covered]
+<small>Hint: You may need to ue the function `cumsum()`</small>
 
 # Lesson snippet 3: regression and coefplot
 
@@ -386,20 +498,20 @@ Create a plot that includes **two geoms** and **facets**
 
 Examples from NU:
 
--   [NUIT Research Computing Services workshops](https://github.com/nuitrcs)
--   [Masters of Science in Analytics boot camps](https://github.com/msia/bootcamp-2019)
--   [Amanda d'Urso's tutorials and workshops](https://sites.northwestern.edu/asdurso/r/)
+- [NUIT Research Computing Services workshops](https://github.com/nuitrcs)
+- [Masters of Science in Analytics boot camps](https://github.com/msia/bootcamp-2019)
+- [Amanda d'Urso's tutorials and workshops](https://sites.northwestern.edu/asdurso/r/)
 
 Other resources:
 
--   [Library of free learning resources](https://sites.northwestern.edu/researchcomputing/category/learning-resources/), from NUIT RCS
--   [Compilation of resources often used by social scientists](https://efarristcu.medium.com/teaching-myself-r-c03c52361bed), from Emily Farris
--   [`learnr` interactive tutorials](https://rstudio.github.io/learnr/), from RStudio
--   [Dataquest](https://www.it.northwestern.edu/research/campus-events/data-camp.html) (coursework license available from NUIT)
+- [Library of free learning resources](https://sites.northwestern.edu/researchcomputing/category/learning-resources/), from NUIT RCS
+- [Compilation of resources often used by social scientists](https://efarristcu.medium.com/teaching-myself-r-c03c52361bed), from Emily Farris
+- [`learnr` interactive tutorials](https://rstudio.github.io/learnr/), from RStudio
+- [Dataquest](https://www.it.northwestern.edu/research/campus-events/data-camp.html) (coursework license available from NUIT)
 
 Take it to the next level (suggestions from Christina Maimone):
 
--   ["Teaching Tech Together"](https://teachtogether.tech/) + [Carpentries Instructor Training](https://carpentries.github.io/instructor-training/)
+- ["Teaching Tech Together"](https://teachtogether.tech/) + [Carpentries Instructor Training](https://carpentries.github.io/instructor-training/)
 
 ## You can do it!
 

answers/exercise2_answers.R

@@ -12,7 +12,7 @@ vaccines <- read_csv("data/chicago_vaccines_daily.csv")
 # Let's glimpse the data
 glimpse(vaccines)
 
-# What happens if we try to plot the 
+# What happens if we try to plot the doses administered over time?
 ggplot(vaccines, aes(x=date, y=doses)) + geom_col() 
 
 #### Working with dates ####
@@ -26,7 +26,7 @@ class(vaccines$date)
 # Does it work?
 as_date(vaccines$date)
 
-# Whoops! Not quite. Let's take a look at the cheatsheet for help: https://raw.githubusercontent.com/rstudio/cheatsheets/master/lubridate.pdf 
+# Whoops! Not quite. Let's take a look at the cheatsheet for help: https://raw.githubusercontent.com/rstudio/cheatsheets/master/lubridate.pdf
 
 # Try to use a different function to convert the date variable
 mdy(vaccines$date)
@@ -54,7 +54,28 @@ class(vaccines$month)
 ggplot(vaccines, aes(x=month, y=doses)) + geom_col()
 
 # Create a new variable for day of the week
+# Note: Look at the help file for the function. Are there any arguments you want to use?
+# I used label=TRUE here, to create a factor variable (Mon, Tue, etc.) rather than a numeric one (1, 2, etc.)
 vaccines$wday <- wday(vaccines$date, label=TRUE)
 
 # Repeat the plot, using day of the week as the x-axis
 ggplot(vaccines, aes(x=wday, y=doses)) + geom_col()
+
+#### Bonus ####
+
+# Bonus question: Can you calculate the number of days it took for Chicago to fully vaccinate 1 million people?
+# Hint: You may need to use the function cumsum() 
+
+# Step 1: Create a cumulative sum of the final doses variable
+vaccines$final_doses_cumulative <- cumsum(vaccines$final_dose)
+
+# Step 2: Identify the first date when the cumulative final doses variable reaches 1,000,000
+date_million <- min(vaccines$date[vaccines$final_doses_cumulative>=1000000])
+
+# Step 3: Identify first date of vaccines being administered
+date_first <- min(vaccines$date)
+
+# Calculate difference
+date_million-date_first
+
+# Lubridate helpfully informs us: "Time difference of 149 days"!