Merge pull request #158 from fhdsl/update-data-output-lab

[Data Output] Changing the dataset for the lab and a few other Data Output Updates

modules/Data_Input/Data_Input.Rmd

@@ -351,9 +351,9 @@ Don't forget to use `<-` to assign your data to an object!
 
 💻 [Data Input Lab](https://daseh.org/modules/Data_Input/lab/Data_Input_Lab.Rmd)
 
-❓ [Posit's Data Import Cheatsheet](https://rstudio.github.io/cheatsheets/data-import.pdf)
+📃 [Posit's Data Import Cheatsheet](https://rstudio.github.io/cheatsheets/data-import.pdf)
 
-❓ [Day 2 Cheatsheet](https://daseh.org/modules/cheatsheets/Day-2.pdf)
+📃 [Day 2 Cheatsheet](https://daseh.org/modules/cheatsheets/Day-2.pdf)
 
 ```{r, fig.alt="The End", out.width = "50%", echo = FALSE, fig.align='center'}
 knitr::include_graphics(here::here("images/the-end-g23b994289_1280.jpg"))

modules/Data_Output/Data_Output.Rmd

@@ -63,6 +63,17 @@ write_delim(dat, file = "CO_ER_heat_newNames.csv", delim = ",")
 ```
 
 
+## GUT CHECK!
+
+What does `write_csv()` do? Saves data to..
+
+A. R's memory
+
+B. A file on your hard drive
+
+C. A ggplot
+
+
 ## R binary file
 
 `.rds` is an extension for R native file format.
@@ -110,6 +121,21 @@ knitr::include_graphics(here::here("images/subset_objects_in_environment.png"))
 knitr::include_graphics(here::here("images/save_environment.png"))
 ```
 
+
+## REMINDER: Saving a ggplot to file
+
+A few options:
+
+-   RStudio \> Plots \> Export \> Save as image / Save as PDF
+-   RStudio \> Plots \> Zoom \> [right mouse click on the plot] \> Save image as
+-   In the code
+
+```{r, eval = FALSE}
+ggsave(filename = "saved_plot.png",  # will save in working directory
+       plot = rp_fac_plot,
+       width = 6, height = 3.5)               # by default in inches
+```
+
 ## Summary {.small}
 
 - Use `write_csv()` and `write_delim()` from the `readr` package to write your (modified) data
@@ -120,7 +146,11 @@ knitr::include_graphics(here::here("images/save_environment.png"))
 
 💻 [Data Output Lab](https://daseh.org/modules/Data_Output/lab/Data_Output_Lab.Rmd)
 
-```{r, fig.alt="The End", out.width = "50%", echo = FALSE, fig.align='center'}
+📃 [Posit's Data Import Cheatsheet](https://rstudio.github.io/cheatsheets/data-import.pdf)
+
+📃 [Day 8 Cheatsheet](https://daseh.org/modules/cheatsheets/Day-2.pdf)
+
+```{r, fig.alt="The End", out.width = "30%", echo = FALSE, fig.align='center'}
 knitr::include_graphics(here::here("images/the-end-g23b994289_1280.jpg"))
 ```
 

modules/Data_Output/lab/Data_Output_Lab.Rmd

@@ -12,10 +12,9 @@ Remember anything you type here can be "sent" to the console with Cmd-Enter (OS-
 # Part 1
 
 ### 1.1
-
-Read in the SARS-CoV-2 wastewater data from URL 
-https://daseh.org/data/SARS-CoV-2_Wastewater_Data.csv
-and assign it to an object named `covid`. 
+Load the tidyverse package. Then read in the CalEnviroScreen dataset from 
+https://daseh.org/data/CalEnviroScreen_data.csv
+and assign it to an object named `ces. 
 
 ```
 # General format
@@ -29,7 +28,7 @@ library(readr)
 
 ### 1.2
 
-Filter the dataset so that the "reporting_jurisdiction" column is equal to "Maryland". Store the modified dataset as `covid_filtered`.
+Filter the dataset so that the `CaliforniaCounty` column is equal to "Yuba". Store the modified dataset as `ces_Yuba`.
 
 ```
 # General format
@@ -42,7 +41,7 @@ NEW_OBJECT <- OBJECT %>% filter(COLUMNNAME == CRITERIA)
 
 ### 1.3
 
-Write out the `covid_filtered` object as a CSV file calling it "covid_filtered.csv", using `write_csv()`:
+Write out the `ces_Yuba` object as a CSV file calling it "ces_Yuba.csv", using `write_csv()`:
 
 ```{r 1.3response}
 
@@ -60,7 +59,6 @@ Copy your code from problem 1.3 and modify it to write to the data directory ins
 ```
 
 ### P.2
-
 Write one of the objects in your Environment to your working directory in `rds` format. Call the file `my_variable.rds`.
 
 ```{r P.2response}

modules/Data_Output/lab/Data_Output_Lab_Key.Rmd

@@ -12,41 +12,40 @@ Remember anything you type here can be "sent" to the console with Cmd-Enter (OS-
 # Part 1
 
 ### 1.1
-
-Read in the SARS-CoV-2 wastewater data from URL 
-https://daseh.org/data/SARS-CoV-2_Wastewater_Data.csv
-and assign it to an object named `covid`. 
+Load the tidyverse package. Then read in the CalEnviroScreen dataset from 
+https://daseh.org/data/CalEnviroScreen_data.csv
+and assign it to an object named `ces`. 
 
 ```
 # General format
-library(readr)
-# OBJECT <- read_csv(FILE)
+library(tidyverse)
+OBJECT <- read_csv(FILE)
 ```
 
 ```{r 1.1response}
 library(tidyverse)
-covid <- read_csv(file = "https://daseh.org/data/SARS-CoV-2_Wastewater_Data.csv")
+ces <- read_csv(file = "https://daseh.org/data/CalEnviroScreen_data.csv")
 ```
 
 ### 1.2
 
-Filter the dataset so that the "reporting_jurisdiction" column is equal to "Maryland". Store the modified dataset as `covid_filtered`.
+Filter the dataset so that the `CaliforniaCounty` column is equal to "Yuba". Store the modified dataset as `ces_Yuba`.
 
 ```
 # General format
 NEW_OBJECT <- OBJECT %>% filter(COLUMNNAME == CRITERIA)
 ```
 
 ```{r 1.2response}
-covid_filtered <- covid %>% filter(reporting_jurisdiction == "Maryland")
+ces_Yuba <- ces %>% filter(CaliforniaCounty == "Yuba")
 ```
 
 ### 1.3
 
-Write out the `covid_filtered` object as a CSV file calling it "covid_filtered.csv", using `write_csv()`:
+Write out the `ces_Yuba` object as a CSV file calling it "ces_Yuba.csv", using `write_csv()`:
 
 ```{r 1.3response}
-write_csv(covid_filtered, file = "covid_filtered.csv")
+write_csv(ces_Yuba, file = "ces_Yuba.csv")
 ```
 
 
@@ -57,9 +56,8 @@ write_csv(covid_filtered, file = "covid_filtered.csv")
 Copy your code from problem 1.3 and modify it to write to the data directory inside your R Project. *Note: you may need to make a new folder named "data" if it doesn't already exist.*
 
 ```{r eval = FALSE, label = 'P.1response'}
-getwd()
-dir.create("data")
-write_csv(covid_filtered, file = "data/covid_filtered.csv")
+getwd() # Check -- are you in the project directory?
+write_csv(ces_Yuba, file = "data/ces_Yuba.csv")
 ```
 
 ### P.2
@@ -73,7 +71,7 @@ write_rds(y, file = "my_variable.rds")
 
 ### P.3
 
-Read the RDS file from your working directory back into your Environment. Call the file `z`.
+Read the RDS file you just created from your working directory back into your Environment. Call the file `z`.
 
 ```{r P.3response}
 z <- read_rds(file = "my_variable.rds")