Interactive Course

PHP 1560/2560 - Statistical Programming in R

  • 0 hours
  • 76 Videos
  • 137 Exercises
  • 193 Participants
  • 7,325 XP

Loved by learners at thousands of top companies:

deloitte-grey.svg
roche-grey.svg
paypal-grey.svg
dell-grey.svg
forrester-grey.svg
ea-grey.svg

Course Description

This is the course page for PHP 1560/2560. This is not the typical Datacamp course but is the online version of PHP 2560 at Brown University School of Public Health. With that being said this represents a video textbook for a class that meets in person to explore Statistical Computing further.

  1. 1

    Basics of R Programming

    In this Section we will begin to explore R and learn how it handles data. We will focus on major data types that we use frequently in statistics and the basics of how we use them. As we move forward these data types will be key to analyzing statistics.

  2. 3

    Flow Control and Initial Functions in R

    In this chapter we will work on flow control as well as the start of some basic functions in R.

  3. 5

    Simulations

    In this chapter we will cover statistical simulations.

  4. 7

    Textmining in R

    In this Section we will focus on working with character and string data in R.

  5. 9

    Webscraping in R

    We have spent a lot of time working with data and learning how to use Functions and evern write simulations. Now we will focus on bringing data into R.

  6. 2

    Data Wrangling in R with plyr and dplyr

    In this chapter we will begin to explore large data in R. We will learn data wrangling techniques such as split, apply and combine with plyr and using tibbles and dplyr on large data.

  7. 4

    Functions in R

    In this chapter we will explore creating and debugging functions in R.

  8. 6

    Working with Databases in R

    Once we know how to bring data into R it is time we learn how to deal with big data in R.

  9. 8

    ggplot

    In this chapter we will explore how to create data visualizations in ggplot.

  1. 1

    Basics of R Programming

    In this Section we will begin to explore R and learn how it handles data. We will focus on major data types that we use frequently in statistics and the basics of how we use them. As we move forward these data types will be key to analyzing statistics.

  2. 2

    Data Wrangling in R with plyr and dplyr

    In this chapter we will begin to explore large data in R. We will learn data wrangling techniques such as split, apply and combine with plyr and using tibbles and dplyr on large data.

  3. 3

    Flow Control and Initial Functions in R

    In this chapter we will work on flow control as well as the start of some basic functions in R.

  4. 4

    Functions in R

    In this chapter we will explore creating and debugging functions in R.

  5. 5

    Simulations

    In this chapter we will cover statistical simulations.

  6. 6

    Working with Databases in R

    Once we know how to bring data into R it is time we learn how to deal with big data in R.

  7. 7

    Textmining in R

    In this Section we will focus on working with character and string data in R.

  8. 8

    ggplot

    In this chapter we will explore how to create data visualizations in ggplot.

  9. 9

    Webscraping in R

    We have spent a lot of time working with data and learning how to use Functions and evern write simulations. Now we will focus on bringing data into R.

What do other learners have to say?

Devon

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

Louis

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

Ronbowers

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Icon Icon Icon professional info