This week's assignment was to make visualizations in three different ways in R. 

I chose a humorous Beenie baby dataset to work with from the list chosen that is based around the age of the Beenie and its current price.  

Dataset can be located here: https://vincentarelbundock.github.io/Rdatasets/datasets.html

My first 2 plots were simple plots using built in R commands:

plot(beanie$age, beanie$value,

     main = "Beanie Value vs Age",

     xlab = "Age",

     ylab = "Value")

hist(beanie$value,

     main = "Distribution of Beanie Values",

     xlab = "Value")



Next I was told to make a plot using "lattice" 

xyplot(value ~ age, data = beanie,

       main = "Value vs Age",

       xlab = "Age",

       ylab = "Value",

       col = "blue")


bwplot(age ~ value, data = beanie,

       main = "Value by Age Group")




Finally I had to use ggplot 2 to plot one final visualization

ggplot(beanie, aes(x = age, y = value, color = age)) +

  geom_point(size = 3) +

  geom_smooth(method = "lm", se = FALSE) +

  labs(title = "Beanie Value vs Age",

       x = "Age",

       y = "Value",

       color = "Age Group") +

  theme_minimal()



The syntax between each only differs slightly but with each of lattice and ggplot you can see how much more detailed and clear the images are. As I have worked extensively with ggplot 2 and minimally with lattice I did have to look up how to actually use the commands but it worked itself out easily

Comments

Post a Comment

Popular posts from this blog

Assignment #10: Building Your Own R Package

The purpose of my blog this week is to build a  draft a proposal describing: The purpose and scope of your package (who will use it and why). The idea would be for small businesses to quickly sort and predict where volume of orders are being shipped most often. Key functions you plan to implement (name + brief description). clean address sort labels by region determine where most your key shipping locations are. How you chose the fields in  DESCRIPTION  (dependencies, license, authors). Package: Friedman Title: What the Package Does (One Line, Title Case) Version: 0.0.0.9000 Authors@R:      person("Zachary", "Wright", , "zakkwright@yahoo.com", role = c("aut", "cre")) Description: "Tools for organizing and analyzing shipping and order data for small collectible businesses.  With the intention of " License: `use_mit_license()`, `use_gpl3_license()` or friends to pick a     license Encoding: UTF-8 Roxygen: list(markdown = TRUE...
Read more

Module # 7 R Object: S3 vs. S4 assignment

  How do you tell what OO system (S3 vs. S4) an object is associated with?          Y ou can check an Objects class using  class(object_name) in order to determine if its S3 or S4. If an object has a formal class definition then it is S4. Most base R objects are S3 How do you determine the base type (like integer or list) of an object? You can use the command typeof() in order to get a readout of what type an object is. For instance class(mtcars) will give you a readout "data.frame" but typeof(mtcars) will give you the base type list.  What is a generic function? A generic function is a function that performs differently depending on the class. R actually decides what the function does for you in a process called method dispatch. methods() will list all the options the function could perform some examples of generics print() summary() plot() mean() head() tail() str() What are the main differences between S3 and S4? S3 is a simple and informa...
Read more

Week 3

Image
 This week is about comparing Election poll Results. To start with arranging the 3 variables into a data frame provides us with this. Name ABC_poll_results CBS_poll_results 1 Jeb 4 12 2 Donald 62 75 3 Ted 51 43 4 Marco 21 19 5 Carly 2 1 6 Hillary 14 21 7 Berine 15 19 We can look at the differences between the two polls. Name ABC_poll_results CBS_poll_results Difference 1 Jeb 4 12 -8 2 Donald 62 75 -13 3 Ted 51 43 8 4 Marco 21 19 2 5 Carly 2 1 1 6 Hillary 14 21 -7 7 Berine 15 19 -4 We can also clearly ...
Read more