---
title: "Numbers"
author: "Jeffrey R. Stevens"
date: "2023-03-06"
output: html_document
execute:
  freeze: auto
---

For these exercises, we'll use the dog breed traits and dog breed popularity rankings data sets. 

1. Load tidyverse and import [`dog_breed_traits_clean.csv`](https://jeffreyrstevens.quarto.pub/dpavir/data/dog_breed_traits_clean.csv) to `traits`.

```{r}
# >
```


2. Create a column of per-row means over all rating columns called _mean_ratings_ and assign to `traits2`.

```{r}
# >
```

3. Convert _mean_ratings_ to a proportion in a column called _pmean_ratings_ and add to `traits2`.

```{r}
# >
```

4. Apply a natural log transformation to the _pmeans_ratings_ vector.

```{r}
# >
```

5. Round _pmean_ratings_ to two decimal places.

```{r}
# >
```

6. Convert _pmean_ratings_ to scientific notation.

```{r}
# >
```

7. Sum up the total grooming ratings for each coat type.

```{r}
# >
```


8. Add inline R code to the following sentence in R Markdown to say how many rows have `NA` for _grooming_:

We are missing grooming data for [insert inline R code] breeds.
