Data Visualization With R: Learn To Modify Font Of Graphical Parameters
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The document provides instructions on modifying font and font size in R graphical parameters using the plot() function. It explains how to customize the fonts for titles, subtitles, axes, and labels, as well as the corresponding arguments (font.main, font.sub, font.axis, font.lab, cex.main, cex.sub, cex.axis, cex.lab) and their value ranges. The document also includes example code for each modification along with visual representations.
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Explore Data using dplyrRsquared Academy The document outlines data manipulation techniques using R's dplyr and readr libraries, demonstrating methods to extract, rename, and sample data from a tibble containing website visitor metrics. It includes information about distinct traffic sources, device types, and offers examples of tabulating and counting observations related to referrers and purchases. Additionally, it provides instructions on data extraction, slicing, and conditional mutation for analysis.
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Data Wrangling with dplyrRsquared Academy The document discusses data manipulation techniques using the dplyr library, emphasizing the importance of efficient data cleaning for data scientists. It provides various examples for filtering, selecting, and summarizing data, including case studies analyzing average order values and user behavior by device type. The document concludes with practical applications of grouping and arranging data to derive insights from an example dataset.
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Introduction to tibblesRsquared Academy The document provides an overview of tibbles, a modern version of data frames in R that offer advantages such as not altering variable names or types and enhanced printing for complex data sets. It details how to create and manipulate tibbles, highlighting their unique features like handling of row names, and provides examples of operations like adding and removing rows and columns. Tibbles encourage cleaner and more expressive code by forcing users to directly address data issues.
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Read data from Excel spreadsheets into RRsquared Academy The document provides a comprehensive guide on how to read data from various file formats such as Excel, SAS, SPSS, and Stata using R programming. It includes specific commands and examples for reading sheets, cells, columns, and rows from Excel files and integrating data from other statistical software formats. Key functionalities and illustrative data outputs are showcased for clarity.
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Read/Import data from flat/delimited files into RRsquared Academy This document provides examples of using the readr package in R to read data from CSV files. It demonstrates how to handle column names, skip text lines, specify column types as numeric, integer or factor, and read specific columns. Functions used include read_csv(), read_delim(), spec_csv(), and arguments like col_names, skip, col_types, and cols_only to control reading of columns.
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R Programming: Introduction to MatricesRsquared Academy The document is a course material for an R programming class focusing on matrices, including creation, operations, and manipulation methods. Key topics covered include matrix creation syntax, operations such as addition and multiplication, and methods to index and subset matrices. It also introduces concepts like lists and data coercion, providing a comprehensive guide for handling matrices in R.
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R Programming: Introduction to VectorsRsquared Academy El curso de R Academy se centra en la comprensión y creación de vectores, un tipo de estructura de datos fundamental en R. Se abordan temas como la coerción de tipos de datos, operaciones básicas en vectores, manejo de datos faltantes y técnicas de indexación. Además, se proporcionan recursos adicionales en línea para apoyar el aprendizaje y la práctica.
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R Programming: Mathematical Functions In RRsquared Academy The document outlines a course on R programming, covering fundamental data analysis skills including programming basics, working with data, and various mathematical functions. It includes specific examples of arithmetic, cumulative, and set operations, along with detailed explanations of commands and functions used within R. The course also offers insights into the next topics, such as selection statements and connecting with additional resources for further learning.
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NVIDIA Triton Inference Server, a game-changing platform for deploying AI mod...Tamanna36 NVIDIA Triton Inference Server! 🌟
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All the DataOps, all the paradigms .
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Measurecamp Copenhagen - Consent Context
Measurecamp Copenhagen - Consent ContextHuman37 Measurecamp Copenhagen 2025 presentation by Glenn Vanderlinden, Human37
Data Visualization With R: Learn To Modify Font Of Graphical Parameters
- 1. www.r-squared.in/git-hub dataCrunch Data Visualization With R Learn To Modify Font Of Graphical Parameters
- 3. dataCrunchFont Slide 3 The font argument can be used along with main, sub, axis and lab arguments to specify the font of the title, subtitle, axes and the labels. Feature Argument Title font.main Subtitle font.sub Axis font.axis Labels font.lab The font argument takes values from 1 - 5. The font type represented by each value is shown in the above table. Value Font Type 1 Plain 2 Bold 3 Italic 4 Bold Italic 5 Symbol
- 4. dataCrunchFont: Title Slide 4 # modify the font of the title plot(mtcars$disp, mtcars$mpg, main = "Displacement vs Miles Per Gallon", font.main = 1) Description The font of the title can be modified using the font. main argument in the plot() function. Code
- 5. dataCrunchFont: Title Slide 5 The below plot depicts the appearance of the title when different options for font type are applied:
- 6. dataCrunchFont: Subtitle Slide 6 # modify the font of the subtitle plot(mtcars$disp, mtcars$mpg, sub= "Displacement vs Miles Per Gallon", font.sub = 3) Description The font of the subtitle can be modified using the font.sub argument in the plot() function. Code
- 7. dataCrunchFont: Subtitle Slide 7 The below plot depicts the appearance of the subtitle when different options for font type are applied:
- 8. dataCrunchFont: Axis Slide 8 # modify the font of the axis plot(mtcars$disp, mtcars$mpg, font.axis = 3) Description The font of the axis can be modified using the font.axis argument in the plot() function. Code
- 9. dataCrunchFont: Axis Slide 9 The below plot depicts the appearance of the axis when different options for font type are applied:
- 10. dataCrunchFont: Labels Slide 10 # modify the font of the labels plot(mtcars$disp, mtcars$mpg, xlab = "Displacement", ylab = "Miles Per Gallon", font.lab = 3) Description The font of the labels can be modified using the font.lab argument in the plot() function. Code
- 11. dataCrunchFont: Labels Slide 11 The below plot depicts the appearance of the labels when different options for font type are applied:
- 13. dataCrunchFont Size Slide 13 The cex argument can be used along with main, sub, axis and lab arguments to specify the font size of the title, subtitle, axes and the labels. Feature Argument Title cex.main Subtitle cex.sub Axis cex.axis Labels cex.lab The values taken by the cex argument are relative to 1 i.e the default size is represented by 1 and if the supplied value is less than 1, the size of the font will be relatively smaller, and if the supplied value is greater than 1, the size of the font will be relatively bigger.
- 14. dataCrunchFont Size: Title Slide 14 # modify the font size of the title plot(mtcars$disp, mtcars$mpg, main = "Scatter Plot", cex.main = 1.5) Description The font size of the title can be modified using the cex.main argument in the plot() function. Code
- 15. dataCrunchFont Size: Title Slide 15 The below plot depicts the appearance of the title when different options for font size are applied:
- 16. dataCrunchFont Size: Subtitle Slide 16 # modify the font size of the subtitle plot(mtcars$disp, mtcars$mpg, sub= "Scatter Plot", cex.sub = 1.5) Description The font size of the subtitle can be modified using the cex.sub argument in the plot() function. Code
- 17. dataCrunchFont: Subtitle Slide 17 The below plot depicts the appearance of the subtitle when different options for font size are applied:
- 18. dataCrunchFont Size: Axis Slide 18 # modify the font size of the axis plot(mtcars$disp, mtcars$mpg, cex.axis = 1.5) Description The font size of the axis can be modified using the cex.axis argument in the plot() function. Code
- 19. dataCrunchFont Size: Axis Slide 19 The below plot depicts the appearance of the axis when different options for font size are applied:
- 20. dataCrunchFont Size: Labels Slide 20 # modify the font size of the labels plot(mtcars$disp, mtcars$mpg, xlab = "Displacement", ylab = "Miles Per Gallon", cex.lab = 1.5) Description The font size of the labels can be modified using the cex.lab argument in the plot() function. Code
- 21. dataCrunchFont Size: Labels Slide 21 The below plot depicts the appearance of the subtitle when different options for font size are applied:
- 22. dataCrunch Slide 22 Visit dataCrunch for tutorials on: → R Programming → Business Analytics → Data Visualization → Web Applications → Package Development → Git & GitHub