R Programming: Advanced Analytics In R For Data Science
Take Your R & R Studio Skills To The Next Level. Data Analytics, Data Science, Statistical Analysis in Business, GGPlot2
- Perform Data Preparation in R
- Identify missing records in dataframes
- Locate missing data in your dataframes
- Apply the Median Imputation method to replace missing records
- Apply the Factual Analysis method to replace missing records
- Understand how to use the which() function
- Know how to reset the dataframe index
- Work with the gsub() and sub() functions for replacing strings
- Explain why NA is a third type of logical constant
- Deal with date-times in R
- Convert date-times into POSIXct time format
- Create, use, append, modify, rename, access and subset Lists in R
- Understand when to use  and when to use [] or the $ sign when working with Lists
- Create a timeseries plot in R
- Understand how the Apply family of functions works
- Recreate an apply statement with a for() loop
- Use apply() when working with matrices
- Use lapply() and sapply() when working with lists and vectors
- Add your own functions into apply statements
- Nest apply(), lapply() and sapply() functions within each other
- Use the which.max() and which.min() functions
- Basic knowledge of R
- Knowledge of the GGPlot2 package is recommended
- Knowledge of dataframes
- Knowledge of vectors and vectorized operations
Ready to take your R Programming skills to the next level?
Want to truly become proficient at Data Science and Analytics with R?
This course is for you!
Professional R Video training, unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for Analytics of the REAL WORLD.
In this course you will learn:
- How to prepare data for analysis in R
- How to perform the median imputation method in R
- How to work with date-times in R
- What Lists are and how to use them
- What the Apply family of functions is
- How to use apply(), lapply() and sapply() instead of loops
- How to nest your own functions within apply-type functions
- How to nest apply(), lapply() and sapply() functions within each other
- And much, much more!
The more you learn the better you will get. After every module you will already have a strong set of skills to take with you into your Data Science career.
- Anybody who has basic R knowledge and would like to take their skills to the next level
- Anybody who has already completed the R Programming A-Z course
- This course is NOT for complete beginners in R
Created by Kirill Eremenko, SuperDataScience Team
Last updated 3/2020
English, French [Auto-generated]
Size: 1.59 GB
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