R Notebook

Książki i inne rzeczy

1. R for Data Science
2. ggplot2: Elegant Graphics for Data Analysis
3. Colors in R

Praca ggplot2

Czyścimy środowisko

rm(list=ls())

R for Data Science

library(tidyverse)

Rysunki w oparciu o kody z książki R for Data Science.

mpg

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = cty))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = year))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = as.character(year)))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = as.factor(year)))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = class))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = year, color = class))

# Left
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = class))

# Right
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))

# Left
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = year))

# Right
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class, color = class, size = class))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 3)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, ncol = 2)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ year, nrow = 2)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class)) + 
  facet_grid(drv ~ cyl)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class, shape = fl)) + 
  facet_grid(drv ~ cyl)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class), position = "jitter")

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class), position = "jitter") + 
  facet_grid(drv ~ cyl)

summary(mpg)

 manufacturer          model               displ            year           cyl       
 Length:234         Length:234         Min.   :1.600   Min.   :1999   Min.   :4.000  
 Class :character   Class :character   1st Qu.:2.400   1st Qu.:1999   1st Qu.:4.000  
 Mode  :character   Mode  :character   Median :3.300   Median :2004   Median :6.000  
                                       Mean   :3.472   Mean   :2004   Mean   :5.889  
                                       3rd Qu.:4.600   3rd Qu.:2008   3rd Qu.:8.000  
                                       Max.   :7.000   Max.   :2008   Max.   :8.000  
    trans               drv                 cty             hwy             fl           
 Length:234         Length:234         Min.   : 9.00   Min.   :12.00   Length:234        
 Class :character   Class :character   1st Qu.:14.00   1st Qu.:18.00   Class :character  
 Mode  :character   Mode  :character   Median :17.00   Median :24.00   Mode  :character  
                                       Mean   :16.86   Mean   :23.44                     
                                       3rd Qu.:19.00   3rd Qu.:27.00                     
                                       Max.   :35.00   Max.   :44.00                     
    class          
 Length:234        
 Class :character  
 Mode  :character  
                   
                   
                   

mpg$year %>% as.factor %>% summary

1999 2008 
 117  117 

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class)) + 
  facet_grid(drv ~ cyl)

Praca domowa

z dnia 10 marca 2020r.

Wykonać 10 wykresóW analogicznych jak na ćwiczeniach z wykorzystaniem danych z plik acsNew.csv ze strony Jareda P. Landera