Merge pull request #98 from fhdsl/manipulating-data

Manipulating Data - minor changes

modules/Manipulating_Data_in_R/Manipulating_Data_in_R.Rmd

@@ -3,7 +3,8 @@ title: "Manipulating Data in R"
 output:
   ioslides_presentation:
     css: ../../docs/styles.css
-    widescreen: yes
+    widescreen: true
+  beamer_presentation: default
 ---
 
 ```{r, echo = FALSE, include=FALSE}
@@ -188,17 +189,19 @@ long_vacc
 
 Newly created column names are enclosed in quotation marks.
 
-## Data used: CO Heat-related ER visits
+## Data used: Nitrate exposure 
 
-https://daseh.org/data/CO_heat_er_visits_DenverBoulder_wide.csv
+Nitrate exposure by quarter for populations on public water systems in the state of Washington for 1999-2020.
+
+https://daseh.org/data/Nitrate_Exposure_for_WA_Public_Water_Systems_byquarter_data.csv
 
 ```{r, message = FALSE}
 library(dasehr)
 wide <- nitrate
 head(nitrate)
 ```
 
-## Mission: Taking the average proportion of the population exposed by concentration
+## Mission: Average population exposed by concentration
 
 Let's imagine we want to see what proportion of population exposed to different nitrate concentrations. Results should look something like:
 
@@ -210,11 +213,18 @@ example <- tibble(
 example
 ```
 
+## Remove some columns we don't need
+
+```{r}
+wide <- wide %>%
+  select(!ends_with("exceedances"))
+wide
+```
+
 ## Reshaping data from **wide to long**
 
 ```{r}
 long <- wide %>%
-  select(!ends_with("exceedances")) %>% 
   pivot_longer(!c(year, quarter, pop_on_sampled_PWS),
                names_to = "conc_cat",
                values_to = "conc_count")
@@ -223,13 +233,13 @@ long
 
 ## Reshaping data from **wide to long**
 
-Un-pivoted columns (`year`, `quarter`, `pop_on_sampled_PWS`) are similar
+Un-pivoted columns (`year`, `quarter`, `pop_on_sampled_PWS`) are still columns.
 
 ```{r}
 long
 ```
 
-## Cleaning up long data
+## Cleaning up long data{.codesmall} 
 
 Let's make the `conc_count` into a proportion.
 
@@ -239,7 +249,7 @@ long <- long %>%
 long
 ```
 
-## Mission: Taking the average proportion of the population exposed by concentration
+## Mission: Average population exposed by concentration
 
 Now our data is more tidy, and we can take the averages easily!
 
@@ -280,13 +290,15 @@ wide_vacc <- long_vacc %>% pivot_wider(names_from = "Month",
 wide_vacc
 ```
 
-## Reshaping CO Heat-related ER Visits data
+## Reshaping nitrate exposure data{.codesmall} 
+
+What if we wanted different columns for each quarter?
 
 ```{r}
 long
 ```
 
-## Reshaping CO Heat-related ER Visits data
+## Reshaping nitrate exposure data
 
 ```{r}
 wide <- long %>%
@@ -295,18 +307,6 @@ wide <- long %>%
 wide
 ```
 
-## Reshaping Summary `tibbles`
-
-Reshaping can be helpful for your assessment of two `group_by()` categories.
-
-```{r}
-long %>% 
-  group_by(conc_cat, quarter) %>% 
-  summarize("avg_prop" = mean(conc_prop)) %>% 
-  pivot_wider(names_from = "quarter", values_from = "avg_prop")
-```
-
-
 ## Summary
 
 - `tidyr` package helps us convert between wide and long data

modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab.Rmd

@@ -9,12 +9,12 @@ editor_options:
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
-Data in this lab comes from the OCS "Exploring CO2 emissions across time" activity (https://www.opencasestudies.org/ocs-bp-co2-emissions/) and the CO Department of Health (https://coepht.colorado.gov/heat-related-illness). Both datasets are available in the `dasehr` package.
+Some data in this lab comes from the OCS "Exploring CO2 emissions across time" activity (https://www.opencasestudies.org/ocs-bp-co2-emissions/. This dataset is available in the `dasehr` package.
+
+Additional data about climate change disasters can be found at "https://daseh.org/data/Yearly_CC_Disasters.csv".
 
 ```{r message=FALSE}
-library(readr)
-library(dplyr)
-library(tidyr)
+library(tidyverse)
 library(dasehr)
 ```
 

modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab.html

@@ -353,13 +353,11 @@ <h1 class="title toc-ignore">Manipulating Data in R Lab</h1>
 </div>
 
 
-<p>Data in this lab comes from the OCS “Exploring CO2 emissions across
-time” activity (<a href="https://www.opencasestudies.org/ocs-bp-co2-emissions/" class="uri">https://www.opencasestudies.org/ocs-bp-co2-emissions/</a>)
-and the CO Department of Health (<a href="https://coepht.colorado.gov/heat-related-illness" class="uri">https://coepht.colorado.gov/heat-related-illness</a>). Both
-datasets are available in the <code>dasehr</code> package.</p>
-<pre class="r"><code>library(readr)
-library(dplyr)
-library(tidyr)
+<p>Some data in this lab comes from the OCS “Exploring CO2 emissions
+across time” activity (<a href="https://www.opencasestudies.org/ocs-bp-co2-emissions/" class="uri">https://www.opencasestudies.org/ocs-bp-co2-emissions/</a>.
+This dataset is available in the <code>dasehr</code> package.</p>
+<p>Additional data about climate change disasters can be found at “<a href="https://daseh.org/data/Yearly_CC_Disasters.csv" class="uri">https://daseh.org/data/Yearly_CC_Disasters.csv</a>”.</p>
+<pre class="r"><code>library(tidyverse)
 library(dasehr)</code></pre>
 <div id="part-1" class="section level1">
 <h1>Part 1</h1>

modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab_Key.Rmd

@@ -9,12 +9,12 @@ editor_options:
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
-Data in this lab comes from the OCS "Exploring CO2 emissions across time" activity (https://www.opencasestudies.org/ocs-bp-co2-emissions/) and the CO Department of Health (https://coepht.colorado.gov/heat-related-illness). Both datasets are available in the `dasehr` package.
+Some data in this lab comes from the OCS "Exploring CO2 emissions across time" activity (https://www.opencasestudies.org/ocs-bp-co2-emissions/. This dataset is available in the `dasehr` package.
+
+Additional data about climate change disasters can be found at "https://daseh.org/data/Yearly_CC_Disasters.csv".
 
 ```{r message=FALSE}
-library(readr)
-library(dplyr)
-library(tidyr)
+library(tidyverse)
 library(dasehr)
 ```
 

modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab_Key.html

@@ -353,13 +353,11 @@ <h1 class="title toc-ignore">Manipulating Data in R Lab - Key</h1>
 </div>
 
 
-<p>Data in this lab comes from the OCS “Exploring CO2 emissions across
-time” activity (<a href="https://www.opencasestudies.org/ocs-bp-co2-emissions/" class="uri">https://www.opencasestudies.org/ocs-bp-co2-emissions/</a>)
-and the CO Department of Health (<a href="https://coepht.colorado.gov/heat-related-illness" class="uri">https://coepht.colorado.gov/heat-related-illness</a>). Both
-datasets are available in the <code>dasehr</code> package.</p>
-<pre class="r"><code>library(readr)
-library(dplyr)
-library(tidyr)
+<p>Some data in this lab comes from the OCS “Exploring CO2 emissions
+across time” activity (<a href="https://www.opencasestudies.org/ocs-bp-co2-emissions/" class="uri">https://www.opencasestudies.org/ocs-bp-co2-emissions/</a>.
+This dataset is available in the <code>dasehr</code> package.</p>
+<p>Additional data about climate change disasters can be found at “<a href="https://daseh.org/data/Yearly_CC_Disasters.csv" class="uri">https://daseh.org/data/Yearly_CC_Disasters.csv</a>”.</p>
+<pre class="r"><code>library(tidyverse)
 library(dasehr)</code></pre>
 <div id="part-1" class="section level1">
 <h1>Part 1</h1>