minor_updates_slides

gis/gis1/slides_gis1.Rmd

@@ -290,44 +290,7 @@ knitr::include_graphics(xfun::relative_path(here::here("images", "welcome", "tea
 ]
 
 
-We too have been working overtime on COVID response...<br>*and we believe that accelerating public health's transition to R is really important.*
-
----
-# A global grassroots movement
-
-Some faces from our broader team
-
-```{r, eval = TRUE, echo = FALSE, out.width = "120%"}
-# Using here() alone won't work because Rmd folder becomes the rendering root.
-# adding xfun::relative_path() creates a dynamic file path between the Rmd location and the here() path.
-# It dynamically creates the ../../etc filepath.
-
-knitr::include_graphics(xfun::relative_path(here::here("images", "welcome", "team_faces.png")))
-```
-
-
----
-# Partnerships
-
-We work closely with local and global entities to support people doing the hard work of public health practice
-
-
-```{r, eval = TRUE, echo = FALSE, out.width = "80%"}
-# Using here() alone won't work because Rmd folder becomes the rendering root.
-# adding xfun::relative_path() creates a dynamic file path between the Rmd location and the here() path.
-# It dynamically creates the ../../etc filepath.
-
-knitr::include_graphics(xfun::relative_path(here::here("images", "welcome", "partners-blur.jpg")))
-```
-
-
-
-???
-- What makes us different is that we focus on the challenges of applied epi, not academic epi. We emphasize the skills used every day by ground-level epidemiologists. 
-
-
-
-
+We too worked overtime on COVID response...<br>*and we believe that accelerating public health's transition to R is really important.*
 
 ---
 
@@ -358,12 +321,6 @@ class: inverse, center, middle
 
 
 
----
-# Your perceptions, hopes, and fears
-
-
-
-
 ---
 # Course objectives
 
@@ -446,78 +403,21 @@ Most learning happens when you are coding, and have rapid on-hand assistance
 
 --
 
-- **Coding assistance** raise your hand during the exercise for 1-on-1 support
+- **Coding assistance**: raise your hand during the exercise for 1-on-1 support
 
 --
 
 - **Screen share** helps instructors catch your errors early
 
 --
 
-- **Screen fatigue is real**  
-
---
-
-- **The exercises are long** - it is OK if you do not finish. The complete code is available to you in the "scripts/backup" folder  
+- **The exercises are long** - it is OK if you do not finish. The complete code is available to you in the **"scripts/backup"** folder  
 
 --
 
 - **Extra support** is available if you are confused  
 
 
-
-
-
----
-# Agreement with each other 
-
-**Our team will:**  
-
---
-
-- Create an affirming, supportive learning space
-
---
-
-- Provide personalized attention, as much as possible  
-
---
-
-- Allow you space to explore and "tinker" as you learn
-
---
-
-- Appreciate your expertise
-
---
-
-**We hope that you will:**  
-
---
-
-- Reach outside of your comfort zone *(you might discover a new passion!)*  
-
---
-
-- Try to attend all the sessions, and catch-up if you miss  
-
---
-
-- Keep trying  
-
---
-
-- Tell us if you do not understand  
-
---
-
-- Be supportive of fellow participants  
-
-???
-We are in this course to succeed, together
-
-
-
 ---
 # This session: objectives & schedule  
 
@@ -914,7 +814,7 @@ knitr::include_graphics(xfun::relative_path(here::here("images", "gis", "standar
 .footnote[Image source: https://www.mdpi.com/ijgi/ijgi-09-00196/article_deploy/html/images/ijgi-09-00196-g005.png
 ]
 ---
-# Other key parts of maps to consider
+# Other key parts of map
 
 .pull-left[
 - Compass or North arrows

gis/gis2/slides_gis2.Rmd

@@ -194,7 +194,7 @@ knitr::include_graphics(xfun::relative_path(here::here("images", "gis", "dh_nn.p
 ## In practice: 
 
 - Create a **new object** to join layers (cases + health facilities) for NN analysis 
-- Use `join = st_nearest_feature`argument for `st_join()`#
+- Use `join = st_nearest_feature` argument for `st_join()`
     - Calculates and appends nearest health facility to case
 
 --
@@ -429,7 +429,7 @@ We can combine buffers via a **union**
 
 ```{r eval = F, echo=T}
 sle_hf_outbreak_1k <- sle_hf_outbreak %>%
-  st_buffer(dist = 1000) %>% # lengths in meters
+  st_buffer(dist = 1000) %>% # length in meters
   st_union() %>%             # combine overlaps into a single shape
   st_as_sf()                 # convert to sf class
 ```
@@ -453,11 +453,11 @@ class: inverse, center, middle
 ---
 # Interactive maps
 
-R has excellent **interactive* GIS capabilities
+R has excellent **interactive** GIS capabilities
 
 - {leaflet}, {tmap}, {mapview} can all be used to produce interactive maps
-- these maps allow users to pan, scroll, zoom, select, etc. 
-- interactive maps can be shared in HTML outbreak situation reports, dashboards, shiny apps, etc. 
+- These maps allow users to pan, scroll, zoom, select, etc. 
+- Interactive maps can be shared in HTML outbreak situation reports, dashboards, shiny apps, etc. 
 
 The exercises today will provide an opportunity to work through an example using {lealet}. 
 

gis/gis3/slides_gis3.Rmd

@@ -172,14 +172,14 @@ There are over 500 cities within the state of Georgia
 
 In the USA, census data often include data on *Race* and *Ethnicity* as two separate columns. 
 
-Ethnicity options are typically:  
+Ethnicity options can include:  
 
-* "Hispanic or Latino" or,
+* "Hispanic or Latino" 
 * "Not Hispanic or Latino"  
 
 --
 
-Race options often include:  
+Race options can include:  
 
 * "American Indian or Alaska Native" (sometimes abbreviated as AI/AN)  
 * "Asian"
@@ -192,13 +192,13 @@ Race options often include:
 
 # Surveillance periods 
 
-We also might want to think about viewing *case counts over time* using **surveillance periods** 
+We also might want to think about viewing **case counts over time** using **surveillance periods**. 
 
 Why might this be useful? 
 
-- Compare *incidence* across **time periods** (e.g. week to week)
+- Compare **incidence** across **time periods** (e.g. week to week)
 - Observe how and where the outbreak is growing fastest
-- How might we *create surveillance periods* in R for our analysis? 
+- How might we **create surveillance periods** in R for our analysis? 
    - `floor_date()` from {lubridate}