Initial Commit with Project Code

lesson2/What_is_a_RMD_file.Rmd

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+Title
+========================================================
+
+This is an R Markdown document or RMD. Markdown is a simple formatting syntax for authoring web pages (click the **Help** toolbar button for more details on using R Markdown).
+
+
+
+
+
+
+
+When you click the **Knit HTML** button a web page will be generated that includes both content as well as the output of any embedded R code chunks within the document.

lesson2/demystifying.R

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+# The goal of this file is to introduce you to the
+# R programming language. Let's start with by unraveling a
+# little mystery!
+
+# 1. Run the code below to create the vector 'udacious'.
+# You need to highlight all of the lines of the code and then
+# run it. You should see "udacious" appear in the workspace.
+
+udacious <- c("Chris Saden", "Lauren Castellano",
+              "Sarah Spikes","Dean Eckles",
+              "Andy Brown", "Moira Burke",
+              "Kunal Chawla")
+
+# You should see something like "chr[1:7]" in the 'Environment'
+# or 'Workspace' tab. This is because you created a 'vector' with
+# 7 names that have a 'type' of character. The arrow-like
+# '<-' symbol is the assignment operator in R, similar to the
+# equal sign '=' in other programming languages. The c() is a
+# generic function that combines arguments, in this case the
+# names of people, to form a vector.
+
+# A 'vector' is one of the data types in R. Vectors must contain
+# the same type of data, that is the entries must all be of the
+# same type: character (most programmers call these strings),
+# logical (TRUE or FALSE), or numeric.
+
+# Print out the vector udacious by running this next line of code.
+
+udacious
+
+# Notice how there are numbers next to the output.
+# Each number corresponds to the index of the entry in the vector.
+# Chris Saden is the first entry so [1]
+# Dean Eckles is the fourth entry so [4]
+# Kunal Chawla is the seventh entry so [7]
+
+# Depending on the size of you window you may see different numbers
+# in the output.
+
+# ANOTHER HELPFUL TIP: You can add values to a vector.
+# Run each line of code one at a time below to see what is happening.
+
+numbers <- c(1:10)
+
+numbers
+
+numbers <- c(numbers, 11:20)
+
+numbers
+
+
+# 2. Replace YOUR_NAME with your actual name in the vector
+# 'udacious' and run the code. Be sure to use quotes around it.
+
+udacious <- c("Chris Saden", "Lauren Castellano",
+              "Sarah Spikes","Dean Eckles",
+              "Andy Brown", "Moira Burke",
+              "Kunal Chawla", YOUR_NAME)
+
+# Notice how R updates 'udacious' in the workspace.
+# It should now say something like 'chr[1:8]'.
+
+# 3. Run the following two lines of code. You can highlight both lines
+# of code and run them.
+
+mystery = nchar(udacious)
+mystery
+
+# You just created a new vector called mystery. What do you
+# think is in this vector? (scroll down for the answer)
+
+
+
+
+
+
+
+
+# Mystery is a vector that contains the number of characters
+# for each of the names in udacious, including your name.
+
+# 4. Run this next line of code.
+
+mystery == 11
+
+# Here we get a logical (or boolean) vector that tells us
+# which locations or indices in the vector contain a name
+# that has exactly 11 characters.
+
+# 5. Let's use this boolean vector, mystery, to subset our
+# udacious vector. What do you think the result will be when
+# running the line of code below?
+
+# Think about the output before you run this next line of code.
+# Notice how there are brackets in the code. Brackets are often
+# used in R for subsetting.
+
+udacious[mystery == 11]
+
+
+# Scroll down for the answer
+
+
+
+
+
+
+
+
+
+# It's your Udacious Instructors for the course!
+# (and you may be in the output if you're lucky enough
+# to have 11 characters in YOUR_NAME) Either way, we
+# think you're pretty udacious for taking this course.
+
+
+
+
+
+# 6. Alright, all mystery aside...let's dive into some data!
+# The R installation has a few datasets already built into it
+# that you can play with. Right now, you'll load one of these,
+# which is named mtcars.
+
+# Run this next command to load the mtcars data.
+
+data(mtcars)
+
+
+# You should see mtcars appear in the 'Environment' tab with
+# <Promise> listed next to it. 
+
+# The object (mtcars) appears as a 'Promise' object in the
+# workspace until we run some code that uses the object.
+
+# R has stored the mtcars data into a spreadsheet-like object
+# called a data frame. Run the next command to see what variables
+# are in the data set and to fully load the data set as an
+# object in R. You should see <Promise> disappear when you
+# run the next line of code.
+
+# Visit http://cran.r-project.org/doc/manuals/r-release/R-lang.html#Promise-objects
+# if you want the expert insight on Promise objects. You won't
+# need to the info on Promise objects to be successful in this course.
+
+names(mtcars)
+
+# names(mtcars) should output all the variable
+# names in the data set. You might notice that the car names
+# are not a variable in the data set. The car names have been saved
+# as row names. More on this later.
+
+# You should also see how many observations (obs.) are in the
+# the data frame and the number of variables on each observation.
+
+# 7. To get more information on the data set and the variables
+# run the this next line of code.
+
+?mtcars
+
+# You can type a '?' before any command or a data set to learn
+# more about it. The details and documentation will appear in
+# the 'Help' tab.
+
+
+# 8. To print out the data, run this next line as code.
+
+mtcars
+
+# Scroll up and down in the console to check out the data.
+# This is the entire data frame printed out.
+
+# 9. Run these next two functions, one at a time,
+# and see if you can figure out what they do.
+
+str(mtcars)
+
+dim(mtcars)
+
+# Scroll down for the answer.
+
+
+
+
+
+
+
+
+
+# The first command, str(mtcars), gives us the structure of the
+# data frame. It lists the variable names, the type of each variable
+# (all of these variables are numerics) and some values for each
+# variable.
+
+
+# The second command, dim(mtcars), should output '[1] 32 11'
+# to the console. The [1] indicates that 32 is the first value
+# in the output.
+
+# R uses 1 to start indexing (AND NOT ZERO BASED INDEXING as is true
+# of many other programming languages.)
+
+# 10. Read the documentation for row.names if you're want to know more.
+?row.names
+
+# Run this code to see the current row names in the data frame.
+row.names(mtcars)
+
+# Run this code to change the row names of the cars to numbers.
+row.names(mtcars) <- c(1:32)
+
+# Now print out the data frame by running the code below.
+mtcars
+
+# It's tedious to relabel our data frame with the right car names
+# so let's reload the data set and print out the first ten rows.
+
+data(mtcars)
+head(mtcars, 10)
+
+# The head() function prints out the first six rows of a data frame
+# by default. Run the code below to see.
+head(mtcars)
+
+# I think you'll know what this does.
+tail(mtcars, 3)
+
+
+# 11. We've run nine commands so far:
+#      c, nchar, data, str, dim, names, row.names, head, and tail.
+
+# All of these commands took some inputs or arguments.
+# To determine if a command takes more arguments or to learn
+# about any default settings, you can look up the documentation
+# using '?' before the command, much like you did to learn about
+# the mtcars data set and the row.names
+
+
+
+# 12. Let's examine our car data more closely. We can access an
+# an individual variable (or column) from the data frame using
+# the '$' sign. Run the code below to print out the variable
+# miles per gallon. This is the mpg column in the data frame.
+
+mtcars$mpg
+
+# Print out any two other variables to the console.
+
+
+
+# This is a vector containing the mpg (miles per gallon) of
+# the 32 cars. Run this next line of code to get the average mpg for
+# for all the cars. What is it?
+
+# Enter this number for the quiz on the Udacity website.
+# https://www.udacity.com/course/viewer#!/c-ud651/l-729069797/e-804129314/m-830829287
+
+mean(mtcars$mpg)
+
+
+

lesson2/demystifyingR2.Rmd

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+Demystifying R Part 2
+========================================================
+
+You might see a warning message just above this file. Something like...
+"R Markdown requires the knitr package (version 1.2 or higher)"
+Don't worry about this for now. We'll address it at the end of this file.
+
+1. Run the following command to see what it does.
+```{r}
+summary(mtcars)
+```
+
+If you know about quantiles, then the output should look familiar.
+If not, you probably recognize the min (minimum), median, mean, and max (maximum).
+We'll go over quantiles in Lesson 3 so don't worry if the output seems overwhelming.
+
+The str() and summary() functions are helpful commands when working with a new data set.
+The str() function gives us the variable names and their types.
+The summary() function gives us an idea of the values a variable can take on.
+
+2. In 2013, the average mpg (miles per gallon) for a car was 23 mpg.
+The car models in the mtcars data set come from the year 1973-1974.
+Subset the data so that you create a new data frame that contains
+cars that get 23 or more mpg (miles per gallon). Save it to a new data
+frame called efficient.
+```{r}
+
+```
+
+3. How many cars get more than 23 mpg? Use one of the commands you
+learned in the demystifying.R to answer this question.
+```{r}
+
+```
+
+4. We can also use logical operators to find out which car(s) get greater
+than 30 miles per gallon (mpg) and have more than 100 raw horsepower.
+```{r}
+subset(mtcars, mpg > 30 & hp > 100)
+```
+
+There's only one car that gets more than 30 mpg and 100 hp.
+
+5. What do you think this code does? Scroll down for the answer.
+```{r}
+subset(mtcars, mpg < 14 | disp > 390)
+```
+
+Note: You may be familiar with the || operator in Java. R uses one single & for the logical
+operator AND. It also uses one | for the logical operator OR.
+
+
+
+
+
+
+
+
+The command above creates a data frame of cars that have mpg less than 14
+OR a displacement of more than 390. Only one of the conditions for a car
+needs to be satisfied so that the car makes it into the subset. Any of the
+cars that fit the criteria are printed to the console.
+
+Now you try some.
+
+6. Print the cars that have a 1/4 mile time (qsec) less than or equal to
+16.90 seconds to the console.
+```{r}
+
+```
+
+7. Save the subset of cars that weigh under 2000 pounds (weight is measured in lb/1000)
+to a variable called lightCars. Print the numbers of cars and the subset to the console.
+```{r}
+
+```
+
+8. You can also create new variables in a data frame. Let's say you wanted
+to have the year of each car's model. We can create the variable
+mtcars$year. Here we'll assume that all of the models were from 1974.
+Run the code below.
+```{r}
+mtcars$year <- 1974
+```
+
+Notice how the number of variables changed in the work space. You can
+also see the result by double clicking on mtcars in the workspace and
+examining the data in a table.
+
+To drop a variable, subset the data frame and select the variable you
+want to drop with a negative sign in front of it.
+```{r}
+mtcars <- subset(mtcars, select = -year)
+```
+
+Notice, we are back to 11 variables in the data frame.
+
+9. What do you think this code does? Run it to find out.
+```{r}
+mtcars$year <- c(1973, 1974)
+```
+
+Open the table of values to see what values year takes on.
+
+Drop the year variable from the data set.
+```{r}
+
+```
+
+
+10. Now you are going to get a preview of ifelse(). For those new
+to programming this example may be confusing. See if you can understand
+the code by running the commands one line at a time. Read the output and
+make sense of what the code is doing at each step.
+
+If you are having trouble don't worry, we will review the ifelse statement
+at the end of Lesson 3. You won't be quizzed on it, and it's not essential
+to keep going in this course. We just want you to try to get familiar with
+more code.
+```{r}
+mtcars$wt
+cond <- mtcars$wt < 3
+cond
+mtcars$weight_class <- ifelse(cond, 'light', 'average')
+mtcars$weight_class
+cond <- mtcars$wt > 3.5
+mtcars$weight_class <- ifelse(cond, 'heavy', mtcars$weight_class)
+mtcars$weight_class
+```
+
+You have some variables in your workspace or environment like 'cond' and
+efficient. You want to be careful that you don't bring in too much data
+into R at once since R will hold all the data in working memory. We have
+nothing to worry about here, but let's delete those variables from the
+work space.
+
+```{r}
+rm(cond)
+rm(efficient)
+```
+
+Save this file if you haven't done so yet.
+
+
+You'll have the opportunity to create one Rmd file for the final project in
+this class and submit the Rmd file and knitted output (or HTML file). You'll
+need the knitr package to do that so let's install that now. **Uncomment** the
+following two lines of code and run them.
+
+```{r}
+# install.packages('knitr', dependencies = T)
+# library(knitr)
+```
+
+Once you've installed knitr, **comment** out the two lines of code above.
+When you click the **Knit HTML** button a web page will be generated that
+includes both content (text and text formatting from Markdown) as well as
+the output of any embedded R code chunks within the document.
+
+
+You've reached the end of the file so now it's time to write some code to
+answer a question to continue on in Lesson 2.
+
+Which car(s) have an mpg (miles per gallon) greater than or equal to 30
+OR hp (horsepower) less than 60? Create an R chunk of code to answer the question.
+
+
+
+Once you have the answer, go the [Udacity website](https://www.udacity.com/course/viewer#!/c-ud651/l-729069797/e-804129319/m-811719066) to continue with Lesson 2.
+
+Note: You use brackets around text followed by two parentheses to create a link.
+There must be no spaces between the brackets and the parentheses. Paste or type
+the link into the parentheses. This also works on the discussions!
+
+And if you want to see all of your HARD WORK from this file, click
+the **KNIT HTML** button now. (You may or may not need to restart R).
+
+# CONGRATULATIONS
+#### You'll be exploring data soon with your new knowledge of R.

lesson2/stateData.csv

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+"","state.abb","state.area","state.region","population","income","illiteracy","life.exp","murder","highSchoolGrad","frost","area"
+"Alabama","AL","51609","2","3615","3624","2.1","69.05","15.1","41.3","20","50708"
+"Alaska","AK","589757","4","365","6315","1.5","69.31","11.3","66.7","152","566432"
+"Arizona","AZ","113909","4","2212","4530","1.8","70.55","7.8","58.1","15","113417"
+"Arkansas","AR","53104","2","2110","3378","1.9","70.66","10.1","39.9","65","51945"
+"California","CA","158693","4","21198","5114","1.1","71.71","10.3","62.6","20","156361"
+"Colorado","CO","104247","4","2541","4884","0.7","72.06","6.8","63.9","166","103766"
+"Connecticut","CT","5009","1","3100","5348","1.1","72.48","3.1","56","139","4862"
+"Delaware","DE","2057","2","579","4809","0.9","70.06","6.2","54.6","103","1982"
+"Florida","FL","58560","2","8277","4815","1.3","70.66","10.7","52.6","11","54090"
+"Georgia","GA","58876","2","4931","4091","2","68.54","13.9","40.6","60","58073"
+"Hawaii","HI","6450","4","868","4963","1.9","73.6","6.2","61.9","0","6425"
+"Idaho","ID","83557","4","813","4119","0.6","71.87","5.3","59.5","126","82677"
+"Illinois","IL","56400","3","11197","5107","0.9","70.14","10.3","52.6","127","55748"
+"Indiana","IN","36291","3","5313","4458","0.7","70.88","7.1","52.9","122","36097"
+"Iowa","IA","56290","3","2861","4628","0.5","72.56","2.3","59","140","55941"
+"Kansas","KS","82264","3","2280","4669","0.6","72.58","4.5","59.9","114","81787"
+"Kentucky","KY","40395","2","3387","3712","1.6","70.1","10.6","38.5","95","39650"
+"Louisiana","LA","48523","2","3806","3545","2.8","68.76","13.2","42.2","12","44930"
+"Maine","ME","33215","1","1058","3694","0.7","70.39","2.7","54.7","161","30920"
+"Maryland","MD","10577","2","4122","5299","0.9","70.22","8.5","52.3","101","9891"
+"Massachusetts","MA","8257","1","5814","4755","1.1","71.83","3.3","58.5","103","7826"
+"Michigan","MI","58216","3","9111","4751","0.9","70.63","11.1","52.8","125","56817"
+"Minnesota","MN","84068","3","3921","4675","0.6","72.96","2.3","57.6","160","79289"
+"Mississippi","MS","47716","2","2341","3098","2.4","68.09","12.5","41","50","47296"
+"Missouri","MO","69686","3","4767","4254","0.8","70.69","9.3","48.8","108","68995"
+"Montana","MT","147138","4","746","4347","0.6","70.56","5","59.2","155","145587"
+"Nebraska","NE","77227","3","1544","4508","0.6","72.6","2.9","59.3","139","76483"
+"Nevada","NV","110540","4","590","5149","0.5","69.03","11.5","65.2","188","109889"
+"New Hampshire","NH","9304","1","812","4281","0.7","71.23","3.3","57.6","174","9027"
+"New Jersey","NJ","7836","1","7333","5237","1.1","70.93","5.2","52.5","115","7521"
+"New Mexico","NM","121666","4","1144","3601","2.2","70.32","9.7","55.2","120","121412"
+"New York","NY","49576","1","18076","4903","1.4","70.55","10.9","52.7","82","47831"
+"North Carolina","NC","52586","2","5441","3875","1.8","69.21","11.1","38.5","80","48798"
+"North Dakota","ND","70665","3","637","5087","0.8","72.78","1.4","50.3","186","69273"
+"Ohio","OH","41222","3","10735","4561","0.8","70.82","7.4","53.2","124","40975"
+"Oklahoma","OK","69919","2","2715","3983","1.1","71.42","6.4","51.6","82","68782"
+"Oregon","OR","96981","4","2284","4660","0.6","72.13","4.2","60","44","96184"
+"Pennsylvania","PA","45333","1","11860","4449","1","70.43","6.1","50.2","126","44966"
+"Rhode Island","RI","1214","1","931","4558","1.3","71.9","2.4","46.4","127","1049"
+"South Carolina","SC","31055","2","2816","3635","2.3","67.96","11.6","37.8","65","30225"
+"South Dakota","SD","77047","3","681","4167","0.5","72.08","1.7","53.3","172","75955"
+"Tennessee","TN","42244","2","4173","3821","1.7","70.11","11","41.8","70","41328"
+"Texas","TX","267339","2","12237","4188","2.2","70.9","12.2","47.4","35","262134"
+"Utah","UT","84916","4","1203","4022","0.6","72.9","4.5","67.3","137","82096"
+"Vermont","VT","9609","1","472","3907","0.6","71.64","5.5","57.1","168","9267"
+"Virginia","VA","40815","2","4981","4701","1.4","70.08","9.5","47.8","85","39780"
+"Washington","WA","68192","4","3559","4864","0.6","71.72","4.3","63.5","32","66570"
+"West Virginia","WV","24181","2","1799","3617","1.4","69.48","6.7","41.6","100","24070"
+"Wisconsin","WI","56154","3","4589","4468","0.7","72.48","3","54.5","149","54464"
+"Wyoming","WY","97914","4","376","4566","0.6","70.29","6.9","62.9","173","97203"