including more import data practice but not through ocs_data

Author: carriewright11

modules/Subsetting_Data_in_R/Subsetting_Data_in_R.Rmd

@@ -113,49 +113,28 @@ See https://cran.r-project.org/web/packages/OCSdata/vignettes/instructions.html
 
 See https://www.opencasestudies.org/ocs-bp-opioid-rural-urban/ about this data.
 
-##  EXTRA PRACTICE - Get the data into your project directory
-
-```{r, results='hide', eval = FALSE}
-OCSdata::simpler_import_data("ocs-bp-opioid-rural-urban", 
-                             outpath = tempdir())
-```
-
-Where is the data?
-
-Simulates creating subdirectories to organize your data.
-
-```{r, fig.alt="dplyr", out.width = "55%", echo = FALSE, fig.align='center'}
-knitr::include_graphics("images/path.gif")
-```
 
 ## Import the data
-`here` function of the `here` package helps R start looking where your .Rproj file is.
 
-```{r, echo = FALSE, message=FALSE, comment= FALSE, results='hide'}
-install.packages("here", repos='http://cran.us.r-project.org')
-```
-
-```{r, eval = FALSE}
-#install.packages(here)
-library(here)
-annualDosage <- read_csv(file = 
-   here("OCS_data/data/simpler_import/county_annual.csv"))
-
-```
-
-
-## Or do this!
+Now we will get the data from the `OCSdata` package using the `load_imported_data()` function.
 
 ```{r}
-OCSdata::load_imported_data("ocs-bp-opioid-rural-urban")
+load_imported_data("ocs-bp-opioid-rural-urban")
 ```
 
 You will see a few new objects in your environment called:
 
-- `annualDosage` (number of shipments (count) of either oxycodone or hydrocodone pills (DOSAGE_UNIT))
+- `annualDosage` (number of shipments (count) of either oxycodone or hydrocodone pills (DOSAGE_UNIT)) - *we will work with this one for now*
 - `county_pop` (population per county)
 - `land` (land area per county)
 
+## Import the data - method 2
+
+```{r}
+annualDosage <- read_csv("https://jhudatascience.org/intro_to_r/data/annualDosage.csv")
+
+```
+
 ## Checking the data `dim()`
 
 The `dim()`, `nrow()`, and `ncol()` functions are good options to check the dimensions of your data before moving forward.
@@ -190,11 +169,11 @@ slice_sample(annualDosage, n = 2)
 
 ## Data frames
 
-An older version of data in tables is called a data frame. The iris dataset is an example of this. 
+An older version of data in tables is called a data frame. The mtcars dataset is an example of this. 
 
 ```{r}
-class(iris)
-head(iris)
+class(mtcars)
+head(mtcars)
 ```
 ## tibble
 
@@ -215,8 +194,8 @@ annualDosage
 If we wanted to create a `tibble` ("fancy" data frame), we can using the  `tibble()` function on a data frame.  
 
 ```{r}
-tbl_iris <- tibble(iris) 
-tbl_iris
+tbl_mtcars <- tibble(mtcars) 
+tbl_mtcars
 ```
 
 Note don't necessarily need to use `head()` with tibbles, as they conveniently print a portion of the data.

modules/Subsetting_Data_in_R/lab/Subsetting_Data_in_R_Lab_Key.Rmd

@@ -13,6 +13,10 @@ In this lab you can use the interactive console to explore but please record you
 
 # Part 1
 
+Now we will work with the `county_pop` data from the same case study.
+
+
+
 First let's get the OCSdata package if we haven't already. We added an argument here so you don't have to select a mirror. 
 
 ```{r}
@@ -26,8 +30,18 @@ library(tidyverse)
 library(OCSdata)
 ```
 
-Now let's load the opioid related datasets by running this chunk. 
 
+There are two ways to get the data- either one is fine. We give you two options in case you have challenges:
+1) importing the csv 
+2) using the OCSdata package
+
+```{r}
+
+county_pop <- read_csv("https://jhudatascience.org/intro_to_r/data/county_pop.csv")
+
+```
+
+Or we can load the data from this package:
 ```{r}
 OCSdata::load_imported_data("ocs-bp-opioid-rural-urban")
 ```