update ps03

CS_03.Rmd

@@ -51,7 +51,7 @@ library(ggplot2)
 library(scales)
 
 gapminder <- gapminder %>% filter(country != "Kuwait")
-gapminder.continent <- gapminder %>% 
+gapminder_continent <- gapminder %>% 
                           group_by(continent, year) %>% 
                           summarise(gdpPercap = weighted.mean(x = gdpPercap, w = pop), 
                                     pop = sum(as.numeric(pop)))
@@ -66,7 +66,8 @@ p1=ggplot(data = gapminder, aes(color = continent, x = lifeExp, y = gdpPercap, s
 p2=ggplot(data = gapminder, aes(color = continent, x = year, y = gdpPercap)) + 
   geom_point(aes(size = pop/100000)) + 
   geom_line(aes(group = country)) +
-  geom_point(data = gapminder.continent, color = "black", aes(size = pop/100000)) +  geom_line(data = gapminder.continent, color = "black") +
+  geom_point(data = gapminder_continent, color = "black", aes(size = pop/100000)) +
+  geom_line(data = gapminder_continent, color = "black") +
   facet_wrap(~continent, nrow = 1) +
   theme_bw() + 
   labs(x = "Year", y = "GDP per capita", color = "Continent", size = "Population (100k)")
@@ -92,13 +93,14 @@ p2
       * Use `group_by()` to group by `continent` and `year`
       * Use `summarize()` with the below commands to calculate the data for the black continent average line on the second plot:
          * `gdpPercapweighted = weighted.mean(x = gdpPercap, w = pop)`
-         * `pop = sum(as.numeric(pop))` 
+         * `pop = sum(as.numeric(pop))`
+      * Save this aggregated data as an object called `gapminder_continent`
 4. Plot #2 (the second row of plots) 
       * Use `ggplot()` and the `theme_bw()` to duplicate the second plot. In this plot you will add elements from both the raw gapminder dataset and your dataset summarized by continent. You will need to use the new data you summarized to add the black lines and dots showing the continent average. So it will look something like this:
      ` ggplot(gapminder,...) + geom_line() + geom_point() + geom_line(data=newdata,...) + ` `geom_point(data=newdata,...) + facet_wrap() + theme_bw() + labs(...)`
 5. Use `ggsave()` or `png()` and save each plot as a `.png` with a width of 15 inches
 6. Click `Source` to confirm that your script runs from start to finish without errors and saves the graphics.
-7. Save your script as an .R or .Rmd in your course repository.
+7. Save your script as `case_study_03.R` or `case_study_03.Rmd` (if you are starting to play with R Markdown) in your course repository for week 3.
 
 ## Next steps
 

CS_03.md

@@ -59,13 +59,14 @@ In this exercise you will recreate the two graphics shown below using `gapminder
       * Use `group_by()` to group by `continent` and `year`
       * Use `summarize()` with the below commands to calculate the data for the black continent average line on the second plot:
          * `gdpPercapweighted = weighted.mean(x = gdpPercap, w = pop)`
-         * `pop = sum(as.numeric(pop))` 
+         * `pop = sum(as.numeric(pop))`
+      * Save this aggregated data as an object called `gapminder_continent`
 4. Plot #2 (the second row of plots) 
       * Use `ggplot()` and the `theme_bw()` to duplicate the second plot. In this plot you will add elements from both the raw gapminder dataset and your dataset summarized by continent. You will need to use the new data you summarized to add the black lines and dots showing the continent average. So it will look something like this:
      ` ggplot(gapminder,...) + geom_line() + geom_point() + geom_line(data=newdata,...) + ` `geom_point(data=newdata,...) + facet_wrap() + theme_bw() + labs(...)`
 5. Use `ggsave()` or `png()` and save each plot as a `.png` with a width of 15 inches
 6. Click `Source` to confirm that your script runs from start to finish without errors and saves the graphics.
-7. Save your script as an .R or .Rmd in your course repository.
+7. Save your script as `case_study_03.R` or `case_study_03.Rmd` (if you are starting to play with R Markdown) in your course repository for week 3.
 
 ## Next steps
 

Schedule.md

@@ -25,7 +25,7 @@ We will refer to the following texts (all available online):
 |:-|:-|:-:|:---:|:---:|:---:|
 |  1 |  8/31/20 |  [<i class='fas fa-desktop'>   </i>](pres/PS_01.html){target='_blank'} |  [Getting Started](./TK_01.html) |  [Your first script](./CS_01.html) |  Introduction to R |
  |  2 |  9/7/20 |  [<i class='fas fa-desktop'>   </i>](pres/PS_02.html){target='_blank'} |  [Graphics Background and Getting started with RStudio](./TK_02.html) |  [My grandfather says climate is cooling](./CS_02.html) |  Introduction to Data Visualization with ggplot |
- |  3 |  9/14/20 |  [<i class='fas fa-desktop'>   </i>](https://docs.google.com/presentation/d/e/2PACX-1vQz-fvEBOO92SUMjbdfmlr3aQ4AYVTrblX1R33DTOxMYJIF5icvGhzOinvf4cdAgqmc2NFA7gFf_Wmx/pub?start=false&loop=false&delayms=3000){target='_blank'} |  [Git Repository](./TK_03.html) |  [Wealth over time](./CS_03.html) |  Introduction to the Tidyverse |
+ |  3 |  9/14/20 |  [<i class='fas fa-desktop'>   </i>](pres/PS_03_Git.html){target='_blank'} |  [Git Repository](./TK_03.html) |  [Wealth over time](./CS_03.html) |  Introduction to the Tidyverse |
  |  4 |  9/21/20 |  [<i class='fas fa-desktop'>   </i>](https://docs.google.com/presentation/d/e/2PACX-1vQGwl7rKVV13salaZ9-wTf4hLggWIV4ydTjKlkMSm8LA6oO10tusMBtzaGIaY_vPVD1wf-QO9Y_tqHW/pub?start=false&loop=false&delayms=3000){target='_blank'} |  [Data Wrangling 2](./TK_04.html) |  [Farthest airport from New York City](./CS_04.html) |  Communicating with Data in the Tidyverse |
  |  5 |  9/28/20 |  [<i class='fas fa-desktop'>   </i>](pres/PS_05.html){target='_blank'} |  [Spatial Vector Data](./TK_05.html) |  [Beware the Canadians!](./CS_05.html) |  Visualizing Geospatial Data in R |
  |  6 |  10/5/20 |  [<i class='fas fa-desktop'>   </i>](pres/PS_06_raster.html){target='_blank'} |  [Spatial Raster Data](./TK_06.html) |  [Find hottest country on each continent](./CS_06.html) |  Spatial Analysis in R with sf and raster |

TK_04.Rmd

@@ -10,7 +10,7 @@ tasks:
   - Briefly describe functions that seem especially useful in the README.md file for this week.
   - Confirm that all tasks and case studies you have completed have been committed in your course repository and pushed to Github. 
   - Look ahead at Task 6 (Project Proposal) and start thinking about possibilities
-  - Post at least one rough project idea (with links, etc.) to Slack in the [Project Channel](https://ubspatialdatascience.slack.com/messages/CDNEG4WN8).
+  - Post at least one rough project idea (with links, etc.) to Slack in the Projects Channel.
 ---
 
 # Reading

docs/CS_03.html

@@ -474,14 +474,15 @@ <h2>Detailed Steps</h2>
 <li><code>gdpPercapweighted = weighted.mean(x = gdpPercap, w = pop)</code></li>
 <li><code>pop = sum(as.numeric(pop))</code></li>
 </ul></li>
+<li>Save this aggregated data as an object called <code>gapminder_continent</code></li>
 </ul></li>
 <li>Plot #2 (the second row of plots)
 <ul>
 <li>Use <code>ggplot()</code> and the <code>theme_bw()</code> to duplicate the second plot. In this plot you will add elements from both the raw gapminder dataset and your dataset summarized by continent. You will need to use the new data you summarized to add the black lines and dots showing the continent average. So it will look something like this: <code>ggplot(gapminder,...) + geom_line() + geom_point() + geom_line(data=newdata,...) +</code> <code>geom_point(data=newdata,...) + facet_wrap() + theme_bw() + labs(...)</code></li>
 </ul></li>
 <li>Use <code>ggsave()</code> or <code>png()</code> and save each plot as a <code>.png</code> with a width of 15 inches</li>
 <li>Click <code>Source</code> to confirm that your script runs from start to finish without errors and saves the graphics.</li>
-<li>Save your script as an .R or .Rmd in your course repository.</li>
+<li>Save your script as <code>case_study_03.R</code> or <code>case_study_03.Rmd</code> (if you are starting to play with R Markdown) in your course repository for week 3.</li>
 </ol>
 </div>
 <div id="next-steps" class="section level2">