• all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences.

• all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences.

How do data scientists mine out insights?

• It starts with data exploration.
• When given a challenging question, data scientists become detectives and investigate leads and try to understand patterns within the data.
• Data scientists may apply quantitative technique in order to get a level deeper → e.g. inferential models, segmentation analysis, time series forecasting, synthetic control experiments, etc.
• The intent is to scientifically piece together a forensic view of what the data is really saying.
• THATS WHY IT IS SO EXCITING!

R is a programming language

"R is a system for statistical computation and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories by John Chambers and colleagues. R can be considered as a different implementation of S…..R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS." (from http://r-project.org/)

Directly from the website https://cran.r-project.org

• A software program, which provides a GUI for R (trademark of RStudio, Inc.)
• Helps writing and executing R code and analyzing data with R.
• Integrated text editor and data and package manager.
• Provides version control, LaTeX integration, keyboard shortcuts, and debugging tools.
• Has become standard amongst R users.
• Open source and commercial editions available: www.rstudio.com
• Runs on desktops (Windows, Mac, and Linux) or in a web browser connected to RStudio Server → We will use R Studio Server during the course!

• Transparency and reproducibility - Not only the results but each step of the analysis are visible.
• Flexibility - Some analyses need only a few code tweaks of an existing R script.
• Exchange - In theory every R user should understand your script to allow easy sharing of code.

• Open a new script (File ➔ New File ➔ R Script)
• Write code into the empty script (in the editor pane)
• Send the code to the R console:
  • mark the code chunk and copy and paste it into the console (NOT recommended!)
  • press ctrl + enter ➔ code of current line (where the cursor is) is executed; cursor jumps then automatically to the next line of code
  • mark the code chunk ➔ ctrl + enter ➔ entire code chunk is executed

• Use a style guide and stick to it.
• Every script should be as small as possible and as complex as needed.
• Every script should be run by the console from start to end without any error messages.
• Use "#" to comment your code.
• Comment why you do something, not what you do.
• Never use attach().
• Assignment operator: <- (do not use "=")

• Variable names should be nouns and function names should be verbs.
• Object names cannot begin with numbers.
• Don't uses special characters (e.g. !,/,%)
• Wise to avoid names already in use (for functions).
• Use lowercase
• Do not use empty spaces in names, instead combine with underscore.
• Ok to use:
  • a, x, my_list, my_dat, dat1
• Not recommended:
  • 1_a, c, list, _mydat, $dat

Put a space

• before and after all infix operators (=, +, -, <-, etc.)
• when naming arguments in function calls
• after a comma, but never before

In its most basic form, R can be used as a simple calculator.

Consider the following arithmetic operators:

Consider the following arithmetic operators:

5 + 5  
5 - 5  
3 * 5 + 2 
(5 + 5) / 2 

Consider the following arithmetic operators:

5 + 5  

## [1] 10

5 - 5  

## [1] 0

3 * 5 + 2 # multipl. then add.

## [1] 17

(5 + 5) / 2 # add. then div.

## [1] 5

It also has functions that let you do more sophisticated manipulations, which you can combine by using parentheses:

a <- c(1,2,3,4)
c <- (a + sqrt(a))/(exp(2)+1)

It also has functions that let you do more sophisticated manipulations, which you can combine by using parentheses:

a <- c(1,2,3,4)
c <- (a + sqrt(a))/(exp(2)+1)

If your prompt turns into a "+", R thinks you haven't finished your previous command. Either finish the command, or press escape.

• Functions are the heart and soul of R.
• A function is a block of code that gives instructions to R to carry out.
• Some functions come in R's base package others in additional packages.
• They work similar to functions in other packages:

• Functions are the heart and soul of R.
• A function is a block of code that gives instructions to R to carry out.
• Some functions come in R's base package others in additional packages.
• They work similar to functions in other packages:
  • in Excel, to sum up over cells A1 to A10 you write sum(A1:A23).
  • in R there is an equivalent function named sum(), which takes the elements it should sum over as argument:

Getting help for a specific function:

help("mean")
?mean

Search the help pages:

??mean
help.search("mean")

List all functions, which contain "mean":

apropos("mean")