Merge pull request #71 from fhdsl/rstudio-update

Rstudio update

modules/RStudio/RStudio.Rmd

@@ -11,10 +11,6 @@ library(knitr)
 opts_chunk$set(comment = "")
 ```
 
-## Help! Office hours
-
-Office hours will always be held at the _same Zoom link_.
-
 ## Working with R -- RStudio
 
 RStudio is an Integrated Development Environment (IDE) for R

modules/RStudio/lab/RStudio_Lab_Key.Rmd

@@ -32,25 +32,22 @@ You may be asked a question in the console when you do this. If so, answer by ty
 ### 0.3
 
 Now install a package that the instructors made that is not on CRAN but on GitHub, by copy and pasting the code into the console. We still don't want to copy the backticks.<br>
-`install_github("jhudsl/jhur")`<br>
+`install_github("fhdsl/dasehr")`<br>
 You may be asked a question in the console when you do this. If so, answer by typing Yes into the console.
 
 
 The gray area below is a code chunk that will set up our packages and data (this will not show up in the rendered report when we press knit). You can also run the code within the editor area by pressing the green play button. Don't worry right now about what the code is doing, we will cover this later. We just want you to get used to RStudio and RMarkdowns.
 
 ```{r setup, message=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
-library(jhur)
+library(dasehr)
 library(ggplot2)
 library(dplyr)
 
-long <- read_circulator_long()
-## take just average ridership per day
-avg <- long %>%
-  filter(type == "Average")
+ER <- CO_heat_ER_bygender
 # keep non-missing data
-avg <- avg %>%
-  filter(!is.na(number))
+ER_2 <- ER %>%
+  filter(!is.na(rate))
 ```
 
 When you click the **Knit** button (at the top of RStudio), a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. 
@@ -67,39 +64,39 @@ Here is code that will make a plot of the average daily ridership in Baltimore C
 
 Here we plot a few days. You can embed an R code chunk like this: Try pressing the green play button to see what happens. Make sure you have run the previous code chunk first by pressing the green play button in that chunk.
 
-```{r plot_circ, out.width = "100%"}
+```{r plot, out.width = "100%"}
 # keep only some days
-avg <- avg %>%
-  filter(day %in% c("Monday", "Tuesday", "Friday", "Saturday"))
+ER_3 <- ER_2 %>%
+  filter(county %in% c("Arapahoe", "Denver", "Jefferson", "Larimer"))
 
 palette <- c(
-  banner = "blue",
-  green = "darkgreen",
-  orange = "orange",
-  purple = "purple"
+  Arapahoe = "blue",
+  Denver = "darkgreen",
+  Jefferson = "orange",
+  Larimer = "purple"
 )
 
-ggplot(aes(x = date, y = number, colour = line), data = avg) +
+ggplot(aes(x = year, y = rate, colour = county, group = county), data = ER_3) +
   geom_line() +
-  facet_wrap(~day) +
+  facet_wrap(~gender) +
   scale_colour_manual(values = palette)
 ```
 
 ```{r out.width = "100%", label = '0response'}
 # keep only some days
-avg <- avg %>%
-  filter(day %in% c("Monday", "Tuesday", "Friday", "Sunday"))
+ER_3 <- ER_2 %>%
+  filter(county %in% c("Arapahoe", "Denver", "Jefferson", "Larimer"))
 
 palette <- c(
-  banner = "red",
-  green = "darkgreen",
-  orange = "orange",
-  purple = "purple"
+  Arapahoe = "red",
+  Denver = "darkgreen",
+  Jefferson = "orange",
+  Larimer = "salmon"
 )
 
-ggplot(aes(x = date, y = number, colour = line), data = avg) +
+ggplot(aes(x = year, y = rate, colour = county, group = county), data = ER_3) +
   geom_line() +
-  facet_wrap(~day) +
+  facet_wrap(~gender) +
   scale_colour_manual(values = palette)
 ```