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typos and fine-tuning

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aaronshaw
2020-10-13 11:13:27 -05:00
parent 68275101f1
commit c0a584cb21
6 changed files with 53 additions and 50 deletions
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r_tutorials/w05a-R_tutorial.html
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r_tutorials/w05a-R_tutorial.rmd
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@@ -38,7 +38,7 @@ d <- read_csv(data_url)
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)