Lesson 6 start

lesson6/lesson6_student.rmd

@@ -9,7 +9,12 @@ Notes:
 ### Scatterplot Review
 
 ```{r Scatterplot Review}
-
+library(ggplot2)
+data(diamonds)
+ggplot(aes(x = carat, y = price), data = diamonds) + 
+  geom_point() +
+  xlim(0, quantile(diamonds$carat, 0.99)) +
+  ylim(0, quantile(diamonds$price, 0.99))
 ```
 
 ***
@@ -17,15 +22,13 @@ Notes:
 ### Price and Carat Relationship
 Response:
 
-***
+The price increases as the carat increases but it also gains more variability
 
 ### Frances Gerety
 Notes:
 
 #### A diamonds is
-
-
-***
+Forever
 
 ### The Rise of Diamonds
 Notes:
@@ -37,14 +40,14 @@ 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')
+#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)
@@ -55,19 +58,32 @@ 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('.')))
+ggpairs(diamond_samp, 
+        lower = list(continuous = wrap("points", shape = I('.'))),
+        upper = list(combo = wrap("box", outlier.shape = I('.'))))
 ```
 
 What are some things you notice in the ggpairs output?
 Response:
 
-***
+There seems to be some Clarity and Colors that draw a higher price but besides that the size seems to have the largest correlation.
 
 ### The Demand of Diamonds
 Notes:
 
 ```{r The Demand of Diamonds}
+library(gridExtra)
 
+plot1 <- ggplot(aes(x = price), data = diamonds) +
+  geom_histogram() +
+  ggtitle('Price')
+
+plot2 <- ggplot(aes(x = price), data = diamonds) +
+  geom_histogram() +
+  scale_x_log10() +
+  ggtitle('Price (log10)')
+
+grid.arrange(plot1, plot2, ncol = 2)
 ```
 
 ***
@@ -75,7 +91,7 @@ Notes:
 ### Connecting Demand and Price Distributions
 Notes:
 
-***
+There are 2 categories of diamond buyers that are looking for different types
 
 ### Scatterplot Transformation