DATA VISUALIZATION WITH R

 

DATA VISUALIZATION WITH R

GGPLOT2

Data has an important place today. People, companies, and governments who have data and use it effectively on timely manner ,will have a say in the future of the world.

 

There is a data chaos in the world. While millions of data are produced per second, the answer to how we can use this data actually creates new strategies. In order not to be deprived of these strategies, we must find a way to make sense of the data. Data visualization is one of the methods that allows us to get out of this chaos or guide us to get rid of this chaos. We understand more easily what a dataset is trying to tell us when we visualize it.

 

In this blog post, I will tell you how to visualize data with R ggplot2.

“R has several systems for making graphs, but ggplot2 is one of the most elegant and most versatile.”*

First we will use data, aes and geom to visualize the data with ggplot.

Data----à dataset

Aes-----àAesthetic properties of data. We will specify the color, size, shape, labels, transparency of the images and many more arguments in aes

Geom--à kind of the chart

#I have activated the ggplot2 package

library(ggplot2)

#I looked through the general properties of the diabetes data that we will visualize with the str().

> str(diabetes)

'data.frame':  145 obs. of  4 variables:

 $ class  : Factor w/ 3 levels "Chemical","Normal",..: 2 2 2 2 2 2 2 2 2 2 ...

 $ glucose: num  80 97 105 90 90 86 100 85 97 97 ...

 $ insulin: num  356 289 319 356 323 381 350 301 379 296 ...

 $ sspg   : num  124 117 143 199 240 157 221 186 142 131 ...

# I visualized the insulin and glucose features in the diabetes dataset, which have numeric values, with a point graph.

ggplot(data = diabetes)+

  geom_point(mapping = aes(x = glucose, y = insulin), color = "red")



 

#I can also get the same image without using data and mapping.

ggplot(diabetes)+

  geom_point(aes(x = glucose, y = insulin), color = "red")



#It can also be used this way.

ggplot(diabetes,aes(x = glucose, y = insulin))+

  geom_point(color = "blue")

or

ggplot(diabetes,aes(glucose, insulin))+

  geom_point(color = "blue")

 




#When I remove the color, it will use black color by default.

ggplot(diabetes)+

  geom_point(aes(x = glucose, y = insulin))



 

#I created a dot chart with the color I want by writing the color outside of aes in the previous chart. Now I will write the color in aes and examine the breakdowns of the feature(class) I want.

ggplot(diabetes)+

  geom_point(aes(x = glucose, y = insulin, color = class))



#If the data was continuous  then I would use size instead of color. Using size for a discrete variable is not advised.But I used the size. 

ggplot(diabetes)+

  geom_point(aes(x = glucose, y = insulin, size = class))

Warning message:

Using size for a discrete variable is not advised.



 

#shape makes the points on the chart appear in different shapes (triangle, square, etc.). In addition, more than 6 variables cannot be displayed on the chart with shape. That is, if the class had 8 variables instead of 3, only 6 would be visible and the other 2 would not.

ggplot(diabetes)+

  geom_point(aes(x = glucose, y = insulin, shape = class), color = "blue")



# alpha is used to make it transparent. However, it is not recommended to use categorical and discrete variables as in the size command.

ggplot(diabetes)+

  geom_point(aes(x = glucose, y = insulin, alpha = class), color = "purple")

Warning message:

Using alpha for a discrete variable is not advised.



 

# I think what comes to mind is what comes to mind for you too. So what happens if we use all the features at the same time? I think it's awesome.

 

ggplot(diabetes)+

  geom_point(aes(x = glucose, y = insulin, alpha = class, color = class, shape=class), color = "purple")



# glucose- sspg- class

ggplot(diabetes)+

  geom_point(aes(x = glucose, y = sspg, alpha = class, color = class, shape=class), color = "purple")



# Even if we don't specify x and y, it will detect it as default.

ggplot(diabetes)+

  geom_point(aes(glucose, sspg, color = class, alpha = class, shape=class), color = "purple")



# you can change the variables and I used the size

ggplot(diabetes)+

  geom_point(aes(glucose*2, insulin/sspg, size = class, alpha = class, shape=class), color = "purple")



# I think I caught your attention in terms of design. It can be used this way in long codes.

# we can also draw a line graph.

 

ggplot(diabetes, aes(glucose, insulin,

                                  color = class,

                                  alpha = class,

                                  shape=class))+

  geom_point()+

  geom_line()



 

In this blog post, I mentioned the importance of data and data visualization. With the ggplot2 package, I did various data visualization exercises on the diabetes dataset. See you in another blog post.

 

You can follow me on Linkedin and github.

https://www.linkedin.com/in/adem-bak%C4%B1rc%C4%B1/

https://github.com/edmbkrc

 

 


*Hadley Wickham & Garrett Grolemund | R for Data Science

 


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