In this lab you can use the interactive console to explore or **Knit** the document. 
Remember anything you type here can be "sent" to the console with Cmd-Enter (OS-X) or Ctrl-Enter (Windows/Linux) in an R code chunk.


# Part 1

### 1.1

Read in SARS-CoV-2 vaccination data from URL 
http://jhudatascience.org/intro_to_r/data/vaccinations.csv
and assign it to an object named `vacc`. 

```
# General format
library(readr)
# OBJECT <- read_csv(FILE)
```

```{r 1.1response}

```

### 1.2

Filter the dataset so that the "Location" column is equal to "US" (aka the entire USA). Store the modified dataset as `vacc_filtered`.

```
# General format
NEW_OBJECT <- OBJECT %>% filter(COLUMNNAME == CRITERIA)
```

```{r 1.2response}

```

### 1.3

Write out the `vacc_filtered` object as a CSV file calling it "vacc_filtered.csv", using `write_csv()`:

```{r 1.3response}

```


# Practice on Your Own!

### P.1

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'}

```

### 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}

```

### P.3

Read the RDS file from your working directory back into your Environment. Call the file `z`.

```{r P.3response}

```
