HW1
Bilton Fieldsend
2022-08-23
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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- My plots section of R studio has turned gray, other than that nothing has changed.
- 234 rows, 11 columns. Code:
mpg## # A tibble: 234 × 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manu… f 21 29 p comp…
## 3 audi a4 2 2008 4 manu… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto… f 21 30 p comp…
## 5 audi a4 2.8 1999 6 auto… f 16 26 p comp…
## 6 audi a4 2.8 1999 6 manu… f 18 26 p comp…
## 7 audi a4 3.1 2008 6 auto… f 18 27 p comp…
## 8 audi a4 quattro 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 quattro 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 quattro 2 2008 4 manu… 4 20 28 p comp…
## # … with 224 more rows
## # ℹ Use `print(n = ...)` to see more rows- The drv variable stands for the type of drive train, with f indicating front-wheel drive, r indicating real wheel drive, and 4 indicating 4wd.
ggplot(data = mpg) + geom_point(mapping = aes(x = cyl, y = hwy))
- The graph below is not very helpful as it doesn’t provide us with many points. As a result, we are unable to establish a relationship between the variables.
ggplot(mpg, aes(x = class, y = drv)) + geom_point()
3.3.1
The points are not blue because the part of code where color is associated with blue is being treated as an aesthetic. As a result, color is now a variable that has been assigned the value “blue”.
Categorical: manufacturer, class, trans, fl, drv, and model. Continuous: displ, hwy, cty, year, and cyl. When you run mpg,
indicates to you that the data in that column is categorical. or indicated that the data in that column is continuous.
mpg## # A tibble: 234 × 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manu… f 21 29 p comp…
## 3 audi a4 2 2008 4 manu… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto… f 21 30 p comp…
## 5 audi a4 2.8 1999 6 auto… f 16 26 p comp…
## 6 audi a4 2.8 1999 6 manu… f 18 26 p comp…
## 7 audi a4 3.1 2008 6 auto… f 18 27 p comp…
## 8 audi a4 quattro 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 quattro 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 quattro 2 2008 4 manu… 4 20 28 p comp…
## # … with 224 more rows
## # ℹ Use `print(n = ...)` to see more rows- The stroke aesthetic modifys the width of the border.
3.6.1
Line chart: gemo_line() #boxplot: geom_boxplot() #histogram: geom_histogram() #area chart: geom_area()
I predict this code will give us a scatter plot with displ as the x variable and hwy as the y variable. This part of my predication was accurate when I ran the code, however I was not expecting each different variable to be a different color.
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = drv)) +
geom_point() +
geom_smooth(se = FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
3.show.legend = FALSE hides the legend when the graph is produced. This was demonstrated when the author was talking about geom_smooth.
ggplot(data = mpg) +
geom_smooth(mapping = aes(x = displ, y = hwy, colour = drv),show.legend = FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
- When se is added to geom_smooth, it adds a visual to indicate standard error.
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = drv)) +geom_point() +geom_smooth(se = TRUE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
- These two graphs will looks the same, as part of the code written in code #2 is inherited in code #1, meaning that although it was specified in code #2, it didn’t have to be to reach the same result.
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot() +
geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'