d
```
-For the sake of my examples, I'm planning to work with the `date`, `state`, `cases`, and `deaths` variables. Notice that by using the `read_csv()` function to import the data, R already recognizes the `date` column as dates. Also notice that the column names for cases and deaths don't reflect the fact that both variables are *cumulative* counts. Also also, notice that it looks like I need to convert the state variable to a factor. I'll start there and then get a quick sense of how much data I have for each state with a univariate table.
+For the sake of my examples, I'm planning to work with the `date`, `state`, `cases`, and `deaths` variables. Notice that by using the `read_csv()` function to import the data, R already recognizes the `date` column as dates. Also notice that the column names for cases and deaths don't reflect the fact that both variables are *cumulative* counts. Also also, notice that it looks like I will want to convert the state variable to a factor (since that's a more accurate representation of the data and it will likely make my analysis/plotting work easier later on). I'll start there and then get a quick sense of how much data I have for each state with a univariate table.
```{r}
d$state <- factor(d$state)
table(d$state)