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Easy plotting with R

A reference guide with examples for plotting in R. Introduction to R and Rstudio. Basic R abilities, packages, paths, data. Learn about the power and flexibility of R. Make beautiful plots effortlessly.

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Easy plotting with R

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  1. Easy plotting with R Yoo! R so beautiful Talia Kustin reference

  2. Examples reference

  3. Agenda • Introduction to R and Rstudio • Basic R abilities • Packages, paths, data • Plotting in R: • Introduction • First plot together • Second plot – on your own!

  4. What is R? • Open source programming language • Great for statistical computing, graphics and data analysis

  5. Why use R? • R is free • R is popular • R is powerful • R is flexible • R is supported

  6. How does R work? • Functions: • Print • Plot graph • Open file • Variables: • Data and tables • Text • Numbers my_name<- “Talia Kustin” Function- print(my_name) Result - “Talia Kustin”

  7. Keyboard shortcuts • Control + enter  run current line / selection in console • Alt + -  Insert assignment operator (<-) • Control + 1  Move to script editor • Control + 2  Move to console More shortcuts here

  8. Script viewer • Write scripts and save them as files • Run lines in the console (Control + enter)

  9. Data viewer • View data • Filter data

  10. Console • Write command and run them • Run commands from the script viewer (Control + enter) • Saves history of commands run (up and down with the arrow keys) • Working path

  11. Upper panel • Environment • Data – all the tables that are used in this session • Values – strings, numbers and more • History – all commands typed

  12. Lower panel • Files – list of all files in the working directory • Plots – all the plots that we drew • Packages – all packages installed • (see specific slide) • Help – see next slide

  13. Where can I get help? • R studio help • Ask the professionals

  14. Useful packages in R • ggplot2 – plotting • ggsignif– statistical test for plots • plyr–summaries data and more • reshape2 - restructure and aggregate data

  15. Packages in R • There are lot’s of packages in R • Install • Load

  16. Installing packages in R • ggplot2 • ggsignif • plyr • reshape

  17. Paths • Every directory and file has a path • Desktop - c:/Users/(username)/Desktop • File in desktop - c:/Users/(username)/Desktop/file.docx • When we work with files and directories we have to specify it to the program

  18. Set directory • Specify a working directory: • setwd(“c:/Users/(username)/Desktop”) • Get the working directory: • getwd()

  19. Data files • CSV = comma-separated values • R can work with excel files as well =

  20. Data import • experiment<- read.csv(“c:/Users/(username)/Desktop/experiment.csv”) Or • setwd(“c:/Users/(username)/Desktop”) • experiment<- read.csv(”experiment.csv”) Or • file <- file.choose() #chose file from file explorer • experiment<- read.csv(file)

  21. Data in R • Get information on data: • class(experiment ) • str(experiment ) • colnames(experiment ) • dim(experiment ) • experiment reference

  22. Wide vs long formats • Usually we use wide format • For plotting in R we need long format melt dcast

  23. experiment: Melt command(wide  long) experiment_long <- melt(experiment, id.vars=c("subject", "sex"), variable.name="condition", value.name="measurement" ) הפקודה משתנים קטגוריאליים ה-data.frame הישן ה-data.frame החדש שם המשתנה החדש שם הערך

  24. Wide vs long formats experiment_long experiment

  25. experiment_long: Data in R • Get information on data #2 • max(experiment_long$measurement) – 13.8 • min(experiment_long$measurement) – 1.3 • mean(experiment_long$measurement) - 9.116667 • unique(experiment_long$condition) – control cond1 cond2 • length(experiment_long$sex[which(experiment_long$sex=="Female")]) - 6

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