Create some of the most routinely used plots to explore data using the geom_*
functions:
library(ggplot2)
library(readr)
ecom <- read_csv('https://raw.githubusercontent.com/rsquaredacademy/datasets/master/web.csv')
## # A tibble: 1,000 x 11
## id referrer device bouncers n_visit n_pages duration country
## <int> <chr> <chr> <chr> <int> <dbl> <dbl> <chr>
## 1 1 google laptop true 10 1 693 Czech Republic
## 2 2 yahoo tablet true 9 1 459 Yemen
## 3 3 direct laptop true 0 1 996 Brazil
## 4 4 bing tablet false 3 18 468 China
## 5 5 yahoo mobile true 9 1 955 Poland
## 6 6 yahoo laptop false 5 5 135 South Africa
## 7 7 yahoo mobile true 10 1 75 Bangladesh
## 8 8 direct mobile true 10 1 908 Indonesia
## 9 9 bing mobile false 3 19 209 Netherlands
## 10 10 google mobile true 6 1 208 Czech Republic
## # ... with 990 more rows, and 3 more variables: purchase <chr>,
## # order_items <dbl>, order_value <dbl>
ggplot(mtcars, aes(x = disp, y = mpg)) +
geom_point()
geom_abline()
geom_smooth()
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_abline(intercept = 37.285, slope = -5.344)