Initial Commit with Project Code

2018-04-17 19:56:59 -08:00
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+Lesson 6
+========================================================
+
+### Welcome
+Notes:
+
+***
+
+### Scatterplot Review
+
+```{r Scatterplot Review}
+
+```
+
+***
+
+### Price and Carat Relationship
+Response:
+
+***
+
+### Frances Gerety
+Notes:
+
+#### A diamonds is
+
+
+***
+
+### The Rise of Diamonds
+Notes:
+
+***
+
+### ggpairs Function
+Notes:
+
+```{r ggpairs Function}
+# install these if necessary
+install.packages('GGally')
+install.packages('scales')
+install.packages('memisc')
+install.packages('lattice')
+install.packages('MASS')
+install.packages('car')
+install.packages('reshape')
+install.packages('plyr')
+
+# load the ggplot graphics package and the others
+library(ggplot2)
+library(GGally)
+library(scales)
+library(memisc)
+
+# sample 10,000 diamonds from the data set
+set.seed(20022012)
+diamond_samp <- diamonds[sample(1:length(diamonds$price), 10000), ]
+ggpairs(diamond_samp, params = c(shape = I('.'), outlier.shape = I('.')))
+```
+
+What are some things you notice in the ggpairs output?
+Response:
+
+***
+
+### The Demand of Diamonds
+Notes:
+
+```{r The Demand of Diamonds}
+
+```
+
+***
+
+### Connecting Demand and Price Distributions
+Notes:
+
+***
+
+### Scatterplot Transformation
+
+```{r Scatterplot Transformation}
+
+```
+
+
+### Create a new function to transform the carat variable
+
+```{r cuberoot transformation}
+cuberoot_trans = function() trans_new('cuberoot', transform = function(x) x^(1/3),
+                                      inverse = function(x) x^3)
+```
+
+#### Use the cuberoot_trans function
+```{r Use cuberoot_trans}
+ggplot(aes(carat, price), data = diamonds) + 
+  geom_point() + 
+  scale_x_continuous(trans = cuberoot_trans(), limits = c(0.2, 3),
+                     breaks = c(0.2, 0.5, 1, 2, 3)) + 
+  scale_y_continuous(trans = log10_trans(), limits = c(350, 15000),
+                     breaks = c(350, 1000, 5000, 10000, 15000)) +
+  ggtitle('Price (log10) by Cube-Root of Carat')
+```
+
+***
+
+### Overplotting Revisited
+
+```{r Sort and Head Tables}
+
+```
+
+
+```{r Overplotting Revisited}
+ggplot(aes(carat, price), data = diamonds) + 
+  geom_point() + 
+  scale_x_continuous(trans = cuberoot_trans(), limits = c(0.2, 3),
+                     breaks = c(0.2, 0.5, 1, 2, 3)) + 
+  scale_y_continuous(trans = log10_trans(), limits = c(350, 15000),
+                     breaks = c(350, 1000, 5000, 10000, 15000)) +
+  ggtitle('Price (log10) by Cube-Root of Carat')
+```
+
+***
+
+### Other Qualitative Factors
+Notes:
+
+***
+
+### Price vs. Carat and Clarity
+
+Alter the code below.
+```{r Price vs. Carat and Clarity}
+# install and load the RColorBrewer package
+install.packages('RColorBrewer')
+library(RColorBrewer)
+
+ggplot(aes(x = carat, y = price), data = diamonds) + 
+  geom_point(alpha = 0.5, size = 1, position = 'jitter') +
+  scale_color_brewer(type = 'div',
+    guide = guide_legend(title = 'Clarity', reverse = T,
+    override.aes = list(alpha = 1, size = 2))) +  
+  scale_x_continuous(trans = cuberoot_trans(), limits = c(0.2, 3),
+    breaks = c(0.2, 0.5, 1, 2, 3)) + 
+  scale_y_continuous(trans = log10_trans(), limits = c(350, 15000),
+    breaks = c(350, 1000, 5000, 10000, 15000)) +
+  ggtitle('Price (log10) by Cube-Root of Carat and Clarity')
+```
+
+***
+
+### Clarity and Price
+Response:
+
+***
+
+### Price vs. Carat and Cut
+
+Alter the code below.
+```{r Price vs. Carat and Cut}
+ggplot(aes(x = carat, y = price, color = clarity), data = diamonds) + 
+  geom_point(alpha = 0.5, size = 1, position = 'jitter') +
+  scale_color_brewer(type = 'div',
+                     guide = guide_legend(title = 'Clarity', reverse = T,
+                                          override.aes = list(alpha = 1, size = 2))) +  
+  scale_x_continuous(trans = cuberoot_trans(), limits = c(0.2, 3),
+                     breaks = c(0.2, 0.5, 1, 2, 3)) + 
+  scale_y_continuous(trans = log10_trans(), limits = c(350, 15000),
+                     breaks = c(350, 1000, 5000, 10000, 15000)) +
+  ggtitle('Price (log10) by Cube-Root of Carat and Clarity')
+```
+
+***
+
+### Cut and Price
+Response:
+
+***
+
+### Price vs. Carat and Color
+
+Alter the code below.
+```{r Price vs. Carat and Color}
+ggplot(aes(x = carat, y = price, color = cut), data = diamonds) + 
+  geom_point(alpha = 0.5, size = 1, position = 'jitter') +
+  scale_color_brewer(type = 'div',
+                     guide = guide_legend(title = Cut, reverse = T,
+                                          override.aes = list(alpha = 1, size = 2))) +  
+  scale_x_continuous(trans = cuberoot_trans(), limits = c(0.2, 3),
+                     breaks = c(0.2, 0.5, 1, 2, 3)) + 
+  scale_y_continuous(trans = log10_trans(), limits = c(350, 15000),
+                     breaks = c(350, 1000, 5000, 10000, 15000)) +
+  ggtitle('Price (log10) by Cube-Root of Carat and Cut')
+```
+
+***
+
+### Color and Price
+Response:
+
+***
+
+### Linear Models in R
+Notes:
+
+Response:
+
+***
+
+### Building the Linear Model
+Notes:
+
+```{r Building the Linear Model}
+m1 <- lm(I(log(price)) ~ I(carat^(1/3)), data = diamonds)
+m2 <- update(m1, ~ . + carat)
+m3 <- update(m2, ~ . + cut)
+m4 <- update(m3, ~ . + color)
+m5 <- update(m4, ~ . + clarity)
+mtable(m1, m2, m3, m4, m5)
+```
+
+Notice how adding cut to our model does not help explain much of the variance
+in the price of diamonds. This fits with out exploration earlier.
+
+***
+
+### Model Problems
+Video Notes:
+
+Research:
+(Take some time to come up with 2-4 problems for the model)
+(You should 10-20 min on this)
+
+Response:
+
+***
+
+### A Bigger, Better Data Set
+Notes:
+
+```{r A Bigger, Better Data Set}
+install.package('bitops')
+install.packages('RCurl')
+library('bitops')
+library('RCurl')
+
+diamondsurl = getBinaryURL("https://raw.github.com/solomonm/diamonds-data/master/BigDiamonds.Rda")
+load(rawConnection(diamondsurl))
+```
+
+The code used to obtain the data is available here:
+https://github.com/solomonm/diamonds-data
+
+## Building a Model Using the Big Diamonds Data Set
+Notes:
+
+```{r Building a Model Using the Big Diamonds Data Set}
+
+```
+
+
+***
+
+## Predictions
+
+Example Diamond from BlueNile:
+Round 1.00 Very Good I VS1 $5,601
+
+```{r}
+#Be sure you’ve loaded the library memisc and have m5 saved as an object in your workspace.
+thisDiamond = data.frame(carat = 1.00, cut = "V.Good",
+                         color = "I", clarity="VS1")
+modelEstimate = predict(m5, newdata = thisDiamond,
+                        interval="prediction", level = .95)
+```
+
+Evaluate how well the model predicts the BlueNile diamond's price. Think about the fitted point estimate as well as the 95% CI.
+
+***
+
+## Final Thoughts
+Notes:
+
+***
+
+Click **KnitHTML** to see all of your hard work and to have an html
+page of this lesson, your answers, and your notes!
+