Data Modeling with R, outlier detection, missing values
- 1. Data Modeling with R DR JOE ARUN RAJA PRESIDENCY UNIVERSITY, BENGALURU
- 2. What is R? R is a scripting or programming language which provides an environment for statistical computing, data science , and graphics.
- 4. Installing R
- 6. R studio
- 7. Package Installation 1. Installing packages from CRAN (R package Repository) 2. Installing Packages from GitHub( Special R Package /RAW files) install.packages(“ggplot2”) install.packages("remotes") github_pkgs <- c("rkabacoff/ggpie", "hrbrmstr/waffle“) remotes::install_github(github_pkgs, dependencies = TRUE)
- 8. R commands installed.packages() remove.packages() packageDescription() help(package = “package”) find.package() library()
- 9. Data types supported by R 1. Logical 2. Numeric- Integer 3. Character 4. Double 5. Complex 6. Raw Class() and typeof() function can be reveal the data type
- 10. Numeric
- 11. Double
- 12. Coercion
- 14. R objects 1. Vector 2. List 3. Matrix 4. Array 5. Factor 6. Data Frames
- 15. Expressions Operation Operator Addition X+Y Subtraction X-Y Multiplication X* Y Division x/y Exponentiation X^Y Modulus X %% Y Integer division X%/%Y
- 16. Importing Data library(readr) # import data from a comma delimited file Salaries <- read_csv("salaries.csv") # import data from a tab delimited file Salaries <- read_tsv("salaries.txt")
- 17. Read data from Excel library(readxl) # import data from an Excel workbook Salaries <- read_excel("salaries.xlsx", sheet=1)
- 18. Cleaning Data
- 19. Data Analytics Univariate analysis Bivariate analysis Multivariate analysis
- 20. “It’s one small step for man, one giant leap for mankind.” - NEIL ARMSTRONG