diff --git a/Introduction to Data Visualization with R and ggplot2/IntroDataVizRAndGgplot2.R b/Introduction to Data Visualization with R and ggplot2/IntroDataVizRAndGgplot2.R
deleted file mode 100644
index 4962a15..0000000
--- a/Introduction to Data Visualization with R and ggplot2/IntroDataVizRAndGgplot2.R
+++ /dev/null
@@ -1,174 +0,0 @@
-#
-# Copyright 2017 Data Science Dojo
-#    
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# 
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# 
-
-
-#
-# This R source code file corresponds to the Data Science Dojo webinar 
-# titled "An Introduction to Data Visualization with R and ggplot2" 
-#
-
-#install.packages("ggplot2")
-library(ggplot2)
-
-
-# Load Titanic titanicing data for analysis. Open in spreadsheet view.
-titanic <- read.csv("titanic.csv", stringsAsFactors = FALSE)
-View(titanic)
-
-
-# Set up factors.
-titanic$Pclass <- as.factor(titanic$Pclass)
-titanic$Survived <- as.factor(titanic$Survived)
-titanic$Sex <- as.factor(titanic$Sex)
-titanic$Embarked <- as.factor(titanic$Embarked)
-
-
-#
-# We'll start our visual analysis of the data focusing on questions
-# related to survival rates. Specifically, these questions will use
-# the factor (i.e., categorical) variables in the data. Factor data
-# is very common in the business context and ggplot2 offers many
-# powerful features for visualizing factor data.
-#
-
-
-#
-# First question - What was the survival rate? 
-#
-# As Survived is a factor (i.e., categorical) variable, a bar chart 
-# is a great visualization to use.
-#
-ggplot(titanic, aes(x = Survived)) + 
-  geom_bar()
-
-# If you really want percentages.
-prop.table(table(titanic$Survived))
-
-# Add some customization for labels and theme.
-ggplot(titanic, aes(x = Survived)) + 
-  theme_bw() +
-  geom_bar() +
-  labs(y = "Passenger Count",
-       title = "Titanic Survival Rates")
-
-
-#
-# Second question - What was the survival rate by gender? 
-#
-# We can use color to look at two aspects (i.e., dimensions)
-# of the data simultaneously.
-#
-ggplot(titanic, aes(x = Sex, fill = Survived)) + 
-  theme_bw() +
-  geom_bar() +
-  labs(y = "Passenger Count",
-       title = "Titanic Survival Rates by Sex")
-
-
-#
-# Third question - What was the survival rate by class of ticket? 
-#
-ggplot(titanic, aes(x = Pclass, fill = Survived)) + 
-  theme_bw() +
-  geom_bar() +
-  labs(y = "Passenger Count",
-       title = "Titanic Survival Rates by Pclass")
-
-
-#
-# Fourth question - What was the survival rate by class of ticket
-#                   and gender?
-#
-# We can leverage facets to further segment the data and enable
-# "visual drill-down" into the data.
-#
-ggplot(titanic, aes(x = Sex, fill = Survived)) + 
-  theme_bw() +
-  facet_wrap(~ Pclass) +
-  geom_bar() +
-  labs(y = "Passenger Count",
-       title = "Titanic Survival Rates by Pclass and Sex")
-
-
-
-
-#
-# Next, we'll move on to visualizing continuous (i.e., numeric)
-# data using ggplot2. We'll explore visualizations of single 
-# numeric variables (i.e., columns) and also illustrate how
-# ggplot2 enables visual drill-down on numeric data.
-#
-
-
-#
-# Fifth Question - What is the distribution of passenger ages?
-#
-# The histogram is a staple of visualizing numeric data as it very 
-# powerfully communicates the distrubtion of a variable (i.e., column).
-#
-ggplot(titanic, aes(x = Age)) +
-  theme_bw() +
-  geom_histogram(binwidth = 5) +
-  labs(y = "Passenger Count",
-       x = "Age (binwidth = 5)",
-       title = "Titanic Age Distribtion")
-
-
-#
-# Sixth Question - What are the survival rates by age?
-#
-ggplot(titanic, aes(x = Age, fill = Survived)) +
-  theme_bw() +
-  geom_histogram(binwidth = 5) +
-  labs(y = "Passenger Count",
-       x = "Age (binwidth = 5)",
-       title = "Titanic Survival Rates by Age")
-
-# Another great visualization for this question is the box-and-whisker 
-# plot.
-ggplot(titanic, aes(x = Survived, y = Age)) +
-  theme_bw() +
-  geom_boxplot() +
-  labs(y = "Age",
-       x = "Survived",
-       title = "Titanic Survival Rates by Age")
-
-
-#
-# Seventh Question - What is the survival rates by age when segmented
-#                    by gender and class of ticket?
-#
-# A related visualization to the histogram is a density plot. Think of
-# a density plot as a smoothed version of the histogram. Using ggplot2
-# we can use facets to allow for visual drill-down via density plots.
-#
-ggplot(titanic, aes(x = Age, fill = Survived)) +
-  theme_bw() +
-  facet_wrap(Sex ~ Pclass) +
-  geom_density(alpha = 0.5) +
-  labs(y = "Age",
-       x = "Survived",
-       title = "Titanic Survival Rates by Age, Pclass and Sex")
-
-# If you prefer histograms, no problem!
-ggplot(titanic, aes(x = Age, fill = Survived)) +
-  theme_bw() +
-  facet_wrap(Sex ~ Pclass) +
-  geom_histogram(binwidth = 5) +
-  labs(y = "Age",
-       x = "Survived",
-       title = "Titanic Survival Rates by Age, Pclass and Sex")
-