```{r message=FALSE, warning=FALSE}
#LOAD PACKAGES 
library(tidyverse)

#Load gapminder data
library(gapminder)
data(gapminder)
```

# 0.
Download the `co2.csv` file from Canvas. Import the dataset into this assignment in a dataframe called `co2`. 

```{r}

```

# 1.
Use your brain and skills to figure out what the code below is doing. Add a sentence describing what this does. 

```{r}
country_continent <- gapminder %>% 
  select(country, continent) %>% 
  distinct()
```

# 2.
What columns do `country_continent` and `co2` have in common? (*This is your key*)

# 3.
What columns does `country_continent` have that `co2` doesn't? 

# 4.
What columns does `co2` have that `country_continent` doesn't? 

# 5.
Create a new table called `co2_joined`, which includes all columns from `co2` in addition to the column `continent`. To start, use `inner_join()`. Make a note of how many rows you have in the new table, and compare it with the original tables.

```{r}

```

# 6.
Create a new table called `co2_joined_left`, which includes all columns from `co2` in addition to the column `continent` but this time use `left_join()` instead of `inner_join()`. Make a note of how many rows you have in the new table, and compare it with the original tables.

```{r}

```


# 7. 
What columns do `gapminder` and `co2` have in common? 

# 8.
What columns does `gapminder` have that `co2` doesn't? 

# 9.
What columns does `co2` have that `gapminder` doesn't? 

# 10. 
Create a new table called `gapminder_joined`, which includes all rows from both tables joined together by their common variable keys (use `inner_join()`). Make a note of how many rows you have in the new table, and compare it with the original tables.

```{r}

```

# 11. 
Use the `anti_join()` function to identify which rows (if any) in the `gapminder` dataset do NOT have a match in out `co2` dataset. 

```{r}

```

# 12. 
Use the `anti_join()` function to identify which rows (if any) in the `co2` dataset do NOT have a match in out `gapminder` dataset.

```{r}

```


# 13.
Use `gapminder_joined` to create a scatterplot of  `gdpPercap` vs. `co2_emissions_pc`. 

```{r}

```



