R-WORKSHOP.Rmd

---
title: "Data Science Project"
output: html_document
date: "2023-01-10"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## R Markdown

Exploratory data analysis is a data analysis approach that involves building an intuitive understanding of the dataset at hand. That is what you're being asked to do in this project! 

Requirements

Please pick a dataset you will use for your analysis. Please choose from the following (I know these work and are good):




Explore your data variables and decide what questions you want to ask and answer.


 
1. Upload your data set to Kaggle, Radiant or R studio OR VIEW data sets.

2. Ask 2 questions that can be answered using a correlation analyses. Make a copy of this document and fill out information for each correlation.


Correlation analysis Template

https://docs.google.com/document/d/1h2zhx6STiFp7SpXzy_Q0cHm6rQIntbxO2vEel9_juaY/edit?usp=sharing


3. Ask questions that can be answered using a T-Test or ANOVA. Find the p value for your questions.
Complete 3 ANOVA Analyses Choose your own data sets.

Explanation Template -https://docs.google.com/document/d/1WVw8JKT13BRnZZlCbNMTfIG1GTDQM4puHbMB9QMqQs4/edit?usp=sharing

4. Create 1 visuals per hypothesis or question. 

5. Get a basic statistics summary to explore your data. There is not only one right answer for how to explore your data. 


1. Choose 1-3 datasets.
2. Lets look at the column names
3. You can look at the names of the columns in the dataset with the `names()` function
4. View the entire dataset in a new window
5. Find the mean and max of a variable in your dataset
6.  Create a scatterplot
7.  make another pirateplot showing the relationship between 2 variables using a different plotting theme and the `"pony"` color palette:
8.  Do 2 fifferent t-tests to test a 2 hypothesis ?
9.  Complete a ANOVA 
10. Create a linear regression model



Below we will first load some libraries and load a bunch of data sets to choose from.

```{r cars}
library(yarrr)
library("datasets")

library(tidyverse) # metapackage of all tidyverse packages
install.packages('yarrr')

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
​
list.files(path = "../input")
library(yarrr)
```

## Library datasets



```{r pressure, echo=FALSE}
library(help = "datasets")
```

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.
```{r pressure, echo=FALSE}
?sleep
```


Lets look at the column names
```{r}
head(sleep)
```

## Exploring data

Next, we'll look at the help menu for the pirates dataset using the question mark `?pirates`. When you run this, you should see a small help window open up in RStudio that gives you some information about the dataset.

```{r, eval = FALSE}
?pirates
```


First, let's take a look at the first few rows of the dataset using the `head()` function. This will show you the first few rows of the data.

```{r}
# Look at the first few rows of the data
head(pirates)
```

You can look at the names of the columns in the dataset with the `names()` function

```{r}
# What are the names of the columns?
names(pirates)
```

Finally, you can also view the entire dataset in a separate window using the `View()` function:

```{r eval = FALSE}
# View the entire dataset in a new window
View(pirates)
```


## Descriptive statistics

Now let's calculate some basic statistics on the entire dataset. We'll calculate the mean age, maximum height, and number of pirates of each sex:

```{r}
# What is the mean age?
mean(pirates$age)

# What was the tallest pirate?
max(pirates$height)

# How many pirates are there of each sex?
table(pirates$sex)
```

Now, let's calculate statistics for different groups of pirates. For example, the following code will use the `aggregate()` function to calculate the mean age of pirates, separately for each sex.

```{r}
# Calculate the mean age, separately for each sex
aggregate( age ~ sex,
          data = pirates,
          FUN = mean)
```


## Plotting

Cool stuff, now let's make a plot! We'll plot the relationship between pirate's height and weight using the `plot()` function

```{r}
# Create scatterplot
plot(x = pirates$height,        # X coordinates
     y = pirates$weight)        # y-coordinates
```
```{r}

```
Now let's make a fancier version of the same plot by adding some customization 

```{r}
# Create scatterplot
plot(x = pirates$height,        # X coordinates
     y = pirates$weight,        # y-coordinates
     main = 'My first scatterplot of pirate data!',
     xlab = 'Height (in cm)',   # x-axis label
     ylab = 'Weight (in kg)',   # y-axis label
     pch = 16,                  # Filled circles
     col = gray(.0, .1))        # Transparent gray
```

Now let's make it even better by adding gridlines and a blue regression line to measure the strength of the relationship.

```{r}
# Create scatterplot
plot(x = pirates$height,        # X coordinates
     y = pirates$weight,        # y-coordinates
     main = 'My first scatterplot of pirate data!',
     xlab = 'Height (in cm)',   # x-axis label
     ylab = 'Weight (in kg)',   # y-axis label
     pch = 16,                  # Filled circles
     col = gray(.0, .1))        # Transparent gray

grid()        # Add gridlines

# Create a linear regression model
model <- lm(formula = weight ~ height, 
            data = pirates)

abline(model, col = 'blue')      # Add regression to plot
```


Scatterplots are great for showing the relationship between two continuous variables, but what if your independent variable is not continuous? In this case, pirateplots are a good option. Let's create a pirateplot using the `pirateplot()` function to show the distribution of pirate's age based on their favorite sword:

```{r}
pirateplot(  age ~ sword.type, 
           data = pirates,
           main = "Pirateplot of ages by favorite sword")
```

Now let's make another pirateplot showing the relationship between sex and height using a different plotting theme and the `"pony"` color palette:

```{r}
pirateplot( height ~ sex,               # Plot weight as a function of sex
           data = pirates,                       
           main = "Pirateplot of height by sex",
           pal = "pony",                         # Use the info color palette
           theme = 3)                            # Use theme 3
```

The `"pony"` palette is contained in the `piratepal()` function. Let's see where the `"pony"` palette comes from...

```{r}
# Show me the pony palette!
piratepal(palette = "pony",
          plot.result = TRUE,   # Plot the result
          trans = .1)           # Slightly transparent
```


## Hypothesis tests

Now, let's do some basic hypothesis tests. First, let's conduct a two-sample t-test to see if there is a significant difference between the ages of pirates who do wear a headband, and those who do not:

```{r}
# Age by headband t-test
t.test(age ~ headband,
       data = pirates,
       alternative = 'two.sided')
```
#With a p-value of 0.7259, we don't have sufficient evidence to say there is a difference in the mean age of pirates who wear headbands and those who do not.

#Next, let's test if there is a significant correlation between a pirate's height and weight using the `cor.test()` function:

```{r}
cor.test(formula = ~ height + weight,
         data = pirates)
```

We got a p-value of `p < 2.2e-16`, that's scientific notation for `p < .00000000000000016` -- which is pretty much 0. Thus, we'd conclude that there is a significant (positive) relationship between a pirate's height and weight.

Now, let's do an ANOVA testing if there is a difference between the number of tattoos pirates have based on their favorite sword

```{r}
# Create tattoos model
tat.sword.lm <- lm(formula = tattoos ~ sword.type,
                   data = pirates)

# Get ANOVA table
anova(tat.sword.lm)
```

Sure enough, we see another very small p-value of `p < 2.2e-16`, suggesting that the number of tattoos pirates have are different based on their favorite sword.

## Regression analysis

Finally, let's run a regression analysis to see if a pirate's age, weight, and number of tattoos (s)he has predicts how many treasure chests he/she's found:

```{r}
# Create a linear regression model: DV = tchests, IV = age, weight, tattoos
tchests.model <- lm(formula = tchests ~ age + weight + tattoos,
                    data = pirates)

# Show summary statistics
summary(tchests.model)
```

It looks like the only significant predictor of the number of treasure chests that a pirate has found is his/her age. There does not seem to be significant effect of weight or tattoos.

## Bayesian Statistics

Now, let's repeat some of our previous analyses with Bayesian versions. First we'll install and load the \texttt{BayesFactor} package which contains the Bayesian statistics functions we'll use:

```{r eval = FALSE}
# Install and load the BayesFactor package
install.packages('BayesFactor')
library(BayesFactor)
```

Now that the package is installed and loaded, we're good to go! Let's do a Bayesian version of our earlier t-test asking if pirates who wear a headband are older or younger than those who do not.

```{r}
# Bayesian t-test comparing the age of pirates with and without headbands
ttestBF(formula = age ~ headband,
        data = pirates)
```

It looks like we got a Bayes factor of 0.12 which is strong evidence *for* the null hypothesis (that the mean age does not differ between pirates with and without headbands)