CS201/cars_analysis.qmd at main · AGreb21/CS201 · GitHub

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---
title: "Cars Analysis"
format: html
editor: visual
---

#### Load in Libraries

```{r}
library(tidyverse)
library(plotly)
library(ggpubr)
```

#### Read in Dataset

```{r}
mtcars <- datasets::mtcars
```

```{r}
?mtcars
```

Lets examine the relationship between weight and mpg

```{r}
cars_wt_mpg <- ggplot(mtcars, aes(x = mpg, y = wt)) +
  geom_point()
cars_wt_mpg
```

```{r}
ggplotly(cars_wt_mpg)
```

```{r}
mtcars <- mtcars %>%
  tibble::rownames_to_column("model")
```

```{r}
cars_wt_mpg <- ggplot(mtcars, aes(x = mpg, y = wt, label = model, color = hp)) +
  geom_point() +
  geom_smooth() +
  stat_cor()
cars_wt_mpg
```

```{r}
ggplotly(cars_wt_mpg)
```

```{r}
mtcars_split <- mtcars %>%
  separate(model, into = c("make", "model"), sep = " ", extra = "merge")
```

### Challenge

What is the average mpg for each car manufacturer.

Please plot the results.

```{r}
average_manufacturer_mpg <- mtcars_split %>%
  group_by(make) %>%
  summarise(avg_mpg = mean(mpg), na.rm = TRUE)
```

```{r}
ggplot(average_manufacturer_mpg, aes(y = reorder(make, avg_mpg), x = avg_mpg, fill = avg_mpg)) +
  geom_bar(stat = "identity")
```