Start Final Project

EDA_Project/EDA_Project.rmd

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+---
+title: "EDA_Project"
+author: "Dusty P"
+date: "May 31, 2018"
+output: html_document
+---
+
+```{r echo=FALSE, message=FALSE, warning=FALSE, setup}
+knitr::opts_knit$set(root.dir = normalizePath("C:/Users/Dusty/Documents/coding/projects/Udacity/Data Analysis/eda/EDA_Project"))
+
+# load the ggplot graphics package and the others
+library(ggplot2)
+library(GGally)
+library(scales)
+library(memisc)
+library(gridExtra)
+library(RColorBrewer)
+library(bitops)
+library(RCurl)
+
+cuberoot_trans = function() trans_new('cuberoot', transform = function(x) x^(1/3),
+                                      inverse = function(x) x^3)
+```
+
+# Exploration of White Wines by Dustin Pianalto
+
+This report explores a dataset containing chemical information and ratings on almost 4900 white wine tastings.
+
+```{r echo=FALSE, message=FALSE, warning=FALSE, Load_the_Data}
+# Load the Data
+wqw <- read.csv('wineQualityWhites.csv')
+```
+
+# Univariate Plots Section
+
+> **Tip**: In this section, you should perform some preliminary exploration of
+your dataset. Run some summaries of the data and create univariate plots to
+understand the structure of the individual variables in your dataset. Don't
+forget to add a comment after each plot or closely-related group of plots!
+There should be multiple code chunks and text sections; the first one below is
+just to help you get started.
+
+```{r echo=FALSE, Univariate_Plots}
+
+```
+
+> **Tip**: Make sure that you leave a blank line between the start / end of
+each code block and the end / start of your Markdown text so that it is
+formatted nicely in the knitted text. Note as well that text on consecutive
+lines is treated as a single space. Make sure you have a blank line between
+your paragraphs so that they too are formatted for easy readability.
+
+# Univariate Analysis
+
+> **Tip**: Now that you've completed your univariate explorations, it's time to
+reflect on and summarize what you've found. Use the questions below to help you
+gather your observations and add your own if you have other thoughts!
+
+### What is the structure of your dataset?
+
+### What is/are the main feature(s) of interest in your dataset?
+
+### What other features in the dataset do you think will help support your \
+investigation into your feature(s) of interest?
+
+### Did you create any new variables from existing variables in the dataset?
+
+### Of the features you investigated, were there any unusual distributions? \
+Did you perform any operations on the data to tidy, adjust, or change the form \
+of the data? If so, why did you do this?
+
+
+# Bivariate Plots Section
+
+> **Tip**: Based on what you saw in the univariate plots, what relationships
+between variables might be interesting to look at in this section? Don't limit
+yourself to relationships between a main output feature and one of the
+supporting variables. Try to look at relationships between supporting variables
+as well.
+
+```{r echo=FALSE, Bivariate_Plots}
+
+```
+
+# Bivariate Analysis
+
+> **Tip**: As before, summarize what you found in your bivariate explorations
+here. Use the questions below to guide your discussion.
+
+### Talk about some of the relationships you observed in this part of the \
+investigation. How did the feature(s) of interest vary with other features in \
+the dataset?
+
+### Did you observe any interesting relationships between the other features \
+(not the main feature(s) of interest)?
+
+### What was the strongest relationship you found?
+
+
+# Multivariate Plots Section
+
+> **Tip**: Now it's time to put everything together. Based on what you found in
+the bivariate plots section, create a few multivariate plots to investigate
+more complex interactions between variables. Make sure that the plots that you
+create here are justified by the plots you explored in the previous section. If
+you plan on creating any mathematical models, this is the section where you
+will do that.
+
+```{r echo=FALSE, Multivariate_Plots}
+
+```
+
+# Multivariate Analysis
+
+### Talk about some of the relationships you observed in this part of the \
+investigation. Were there features that strengthened each other in terms of \
+looking at your feature(s) of interest?
+
+### Were there any interesting or surprising interactions between features?
+
+### OPTIONAL: Did you create any models with your dataset? Discuss the \
+strengths and limitations of your model.
+
+------
+
+# Final Plots and Summary
+
+> **Tip**: You've done a lot of exploration and have built up an understanding
+of the structure of and relationships between the variables in your dataset.
+Here, you will select three plots from all of your previous exploration to
+present here as a summary of some of your most interesting findings. Make sure
+that you have refined your selected plots for good titling, axis labels (with
+units), and good aesthetic choices (e.g. color, transparency). After each plot,
+make sure you justify why you chose each plot by describing what it shows.
+
+### Plot One
+```{r echo=FALSE, Plot_One}
+
+```
+
+### Description One
+
+
+### Plot Two
+```{r echo=FALSE, Plot_Two}
+
+```
+
+### Description Two
+
+
+### Plot Three
+```{r echo=FALSE, Plot_Three}
+
+```
+
+### Description Three
+
+------
+
+# Reflection
+
+> **Tip**: Here's the final step! Reflect on the exploration you performed and
+the insights you found. What were some of the struggles that you went through?
+What went well? What was surprising? Make sure you include an insight into
+future work that could be done with the dataset.
+
+> **Tip**: Don't forget to remove this, and the other **Tip** sections before
+saving your final work and knitting the final report!

EDA_Project/projecttemplate.rmd

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+TITLE by YOUR_NAME_HERE
+========================================================
+
+> **Tip**: You will see quoted sections like this throughout the template to
+help you construct your report. Make sure that you remove these notes before
+you finish and submit your project!
+
+> **Tip**: One of the requirements of this project is that your code follows
+good formatting techniques, including limiting your lines to 80 characters or
+less. If you're using RStudio, go into Preferences \> Code \> Display to set up
+a margin line to help you keep track of this guideline!
+
+```{r echo=FALSE, message=FALSE, warning=FALSE, packages}
+# Load all of the packages that you end up using in your analysis in this code
+# chunk.
+
+# Notice that the parameter "echo" was set to FALSE for this code chunk. This
+# prevents the code from displaying in the knitted HTML output. You should set
+# echo=FALSE for all code chunks in your file, unless it makes sense for your
+# report to show the code that generated a particular plot.
+
+# The other parameters for "message" and "warning" should also be set to FALSE
+# for other code chunks once you have verified that each plot comes out as you
+# want it to. This will clean up the flow of your report.
+
+library(ggplot2)
+```
+
+```{r echo=FALSE, Load_the_Data}
+# Load the Data
+
+```
+
+> **Tip**: Before you create any plots, it is a good idea to provide a short
+introduction into the dataset that you are planning to explore. Replace this
+quoted text with that general information!
+
+# Univariate Plots Section
+
+> **Tip**: In this section, you should perform some preliminary exploration of
+your dataset. Run some summaries of the data and create univariate plots to
+understand the structure of the individual variables in your dataset. Don't
+forget to add a comment after each plot or closely-related group of plots!
+There should be multiple code chunks and text sections; the first one below is
+just to help you get started.
+
+```{r echo=FALSE, Univariate_Plots}
+
+```
+
+> **Tip**: Make sure that you leave a blank line between the start / end of
+each code block and the end / start of your Markdown text so that it is
+formatted nicely in the knitted text. Note as well that text on consecutive
+lines is treated as a single space. Make sure you have a blank line between
+your paragraphs so that they too are formatted for easy readability.
+
+# Univariate Analysis
+
+> **Tip**: Now that you've completed your univariate explorations, it's time to
+reflect on and summarize what you've found. Use the questions below to help you
+gather your observations and add your own if you have other thoughts!
+
+### What is the structure of your dataset?
+
+### What is/are the main feature(s) of interest in your dataset?
+
+### What other features in the dataset do you think will help support your \
+investigation into your feature(s) of interest?
+
+### Did you create any new variables from existing variables in the dataset?
+
+### Of the features you investigated, were there any unusual distributions? \
+Did you perform any operations on the data to tidy, adjust, or change the form \
+of the data? If so, why did you do this?
+
+
+# Bivariate Plots Section
+
+> **Tip**: Based on what you saw in the univariate plots, what relationships
+between variables might be interesting to look at in this section? Don't limit
+yourself to relationships between a main output feature and one of the
+supporting variables. Try to look at relationships between supporting variables
+as well.
+
+```{r echo=FALSE, Bivariate_Plots}
+
+```
+
+# Bivariate Analysis
+
+> **Tip**: As before, summarize what you found in your bivariate explorations
+here. Use the questions below to guide your discussion.
+
+### Talk about some of the relationships you observed in this part of the \
+investigation. How did the feature(s) of interest vary with other features in \
+the dataset?
+
+### Did you observe any interesting relationships between the other features \
+(not the main feature(s) of interest)?
+
+### What was the strongest relationship you found?
+
+
+# Multivariate Plots Section
+
+> **Tip**: Now it's time to put everything together. Based on what you found in
+the bivariate plots section, create a few multivariate plots to investigate
+more complex interactions between variables. Make sure that the plots that you
+create here are justified by the plots you explored in the previous section. If
+you plan on creating any mathematical models, this is the section where you
+will do that.
+
+```{r echo=FALSE, Multivariate_Plots}
+
+```
+
+# Multivariate Analysis
+
+### Talk about some of the relationships you observed in this part of the \
+investigation. Were there features that strengthened each other in terms of \
+looking at your feature(s) of interest?
+
+### Were there any interesting or surprising interactions between features?
+
+### OPTIONAL: Did you create any models with your dataset? Discuss the \
+strengths and limitations of your model.
+
+------
+
+# Final Plots and Summary
+
+> **Tip**: You've done a lot of exploration and have built up an understanding
+of the structure of and relationships between the variables in your dataset.
+Here, you will select three plots from all of your previous exploration to
+present here as a summary of some of your most interesting findings. Make sure
+that you have refined your selected plots for good titling, axis labels (with
+units), and good aesthetic choices (e.g. color, transparency). After each plot,
+make sure you justify why you chose each plot by describing what it shows.
+
+### Plot One
+```{r echo=FALSE, Plot_One}
+
+```
+
+### Description One
+
+
+### Plot Two
+```{r echo=FALSE, Plot_Two}
+
+```
+
+### Description Two
+
+
+### Plot Three
+```{r echo=FALSE, Plot_Three}
+
+```
+
+### Description Three
+
+------
+
+# Reflection
+
+> **Tip**: Here's the final step! Reflect on the exploration you performed and
+the insights you found. What were some of the struggles that you went through?
+What went well? What was surprising? Make sure you include an insight into
+future work that could be done with the dataset.
+
+> **Tip**: Don't forget to remove this, and the other **Tip** sections before
+saving your final work and knitting the final report!