Merge pull request #670 from jhudsl/manip-esquisse-updates

Small tweaks to appearance/libs

modules/Esquisse_Data_Visualization/Esquisse_Data_Visualization.Rmd

@@ -9,7 +9,6 @@ output:
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
-library(here)
 library(tidyverse)
 ```
 
@@ -31,9 +30,9 @@ It's super **nifty**!
 knitr::include_graphics("https://c.tenor.com/DNUSO9MjrTEAAAAC/bob-ross.gif")
 ```
 
-## First, get some data..
+## First, get some data..  {.codesmall}
 
-We can use the CO heat-related ER visits dataset. This dataset contains information about the number and rate of visits for heat-related illness to ERs in Colorado from 2011-2022, adjusted for age.
+We can use the CO heat-related ER visits dataset. This dataset contains information about the number and rate of visits for heat-related illness to Emergency rooms in Colorado from 2011-2022, adjusted for age.
 
 ```{r message=FALSE}
 er <-
@@ -161,15 +160,15 @@ glimpse(long_er)
 
 ## Wide Data
 
-As a comparison, let's also load a wide version of this dataset.
+As a comparison, let's also load a wide version of this dataset.  {.codesmall}
 
 ```{r}
 
 wide_er <- read_csv(file =
     "https://jhudatascience.org/intro_to_r/data/CO_heat_er_visits_DenverBoulder_wide.csv")
 ```
 
-## Wide vs Long Data
+## Wide vs Long Data: Which is better for plotting?
 
 ```{r}
 head(long_er)
@@ -201,7 +200,9 @@ C. Both of these!
 
 ## Summary
 
-* Use the `esquisser()` function on a dataset
+* Use Esquisse:  
+    * `library(esquisse)`  
+    * `esquisser()` function on a dataset  
 * Use the `viewer = "browser"` argument to launch in your browser.
 * Code from Esquisse can copied into code chunks to be generated in the "Plots" pane
 * It's easier if your code is in "long" form!
@@ -214,7 +215,7 @@ C. Both of these!
 
 📃 [Day 6 Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-6.pdf)
 
-```{r, fig.alt="The End", out.width = "50%", echo = FALSE, fig.align='center'}
+```{r, fig.alt="The End", out.width = "40%", echo = FALSE, fig.align='center'}
 knitr::include_graphics(here::here("images/the-end-g23b994289_1280.jpg"))
 ```
 

modules/Esquisse_Data_Visualization/lab/Esquisse_Data_Visualization_Lab_Key.Rmd

@@ -12,7 +12,7 @@ install.packages("ggplot2")
 
 ```{r, comment = FALSE}
 library(esquisse)
-library(ggplot2)
+library(tidyverse)
 ```
 
 ### 1.1

modules/Manipulating_Data_in_R/Manipulating_Data_in_R.Rmd

@@ -31,7 +31,7 @@ library(tidyverse)
     - `separate()` can split columns into additional columns
     - `unite()` can combine columns
 
-📃[Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-5.pdf)
+📃[Day 5 Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-5.pdf)
 
 ## Manipulating Data 
 
@@ -123,7 +123,7 @@ Long: **Easier for R to make plots & do analysis**
 ex_long
 ```
 
-## Pivoting using `tidyr` package
+## Pivoting using `tidyr` package (part of `tidyverse`)
 
 `tidyr` allows you to "tidy" your data.  We will be talking about:
 
@@ -181,10 +181,11 @@ ex_long <- ex_wide %>% pivot_longer(cols = ends_with("rate"),
 ex_long
 ```
 
-## Data used: Charm City Circulator
+## Data used: Charm City Circulator  {.codesmall}
 
 ```{r, message = FALSE}
-circ <- read_csv("http://jhudatascience.org/intro_to_r/data/Charm_City_Circulator_Ridership.csv")
+circ <- 
+  read_csv("http://jhudatascience.org/intro_to_r/data/Charm_City_Circulator_Ridership.csv")
 head(circ, 5)
 ```
 
@@ -213,6 +214,7 @@ Filter by Boardings only..
 
 ```{r}
 long <- long %>% filter(str_detect(name, "Boardings"))
+long
 ```
 
 ## Mission: Taking the average boardings by line
@@ -279,7 +281,7 @@ wide
 
 ## Summary
 
-- `tidyr` package helps us convert between wide and long data
+- `tidyr` package (part of `tidyverse`) helps us convert between wide and long data
 - `pivot_longer()` goes from wide -> long
      - Specify columns you want to pivot
      - Specify `names_to = ` and `values_to = ` for custom naming
@@ -537,14 +539,18 @@ C. Using one data source is too easy and we want our analysis ~ fancy ~
 
 💻 [Lab](https://jhudatascience.org/intro_to_r/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab.Rmd)
 
+~
+
 📃 [Day 6 Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-6.pdf)
 
 📃 [Posit's `tidyr` Cheatsheet](https://rstudio.github.io/cheatsheets/tidyr.pdf)
 
 📃 [Posit's `dplyr` Cheatsheet](https://rstudio.github.io/cheatsheets/data-transformation.pdf)
 
+🔎️ [Joining Open Case Study](https://www.opencasestudies.org/ocs-bp-diet/)
+
 
-```{r, fig.alt="The End", out.width = "50%", echo = FALSE, fig.align='center'}
+```{r, fig.alt="The End", out.width = "20%", echo = FALSE, fig.align='center'}
 knitr::include_graphics(here::here("images/the-end-g23b994289_1280.jpg"))
 ```
 

modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab_Key.Rmd

@@ -12,9 +12,7 @@ knitr::opts_chunk$set(echo = TRUE)
 Data in this lab comes from the CDC (https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-total-admin-rate-total - snapshot from January 12, 2022) and the Bureau of Economic Analysis (https://www.bea.gov/data/income-saving/personal-income-by-state).
 
 ```{r message=FALSE}
-library(readr)
-library(dplyr)
-library(tidyr)
+library(tidyverse)
 ```
 
 # Part 1