A common task in biological data analysis is the comparison between several samples of univariate measurements. In this section we’ll explore some possibilities for visualizing and comparing such samples. As an example, we’ll use the intensities of a set of four genes: Fgf4, Gata4, Gata6 and Sox26. On the microarray, they are represented by

The components of ggplot2’s grammar of graphics are one or more datasets, one or more geometric objects that serve as the visual representations of the data, – for instance, points, lines, rectangles, contours, descriptions of how the variables in the data are mapped to visual properties (aesthetics) of the geometric objects, and an associated scale (e. g., linear, logarithmic, rank), one or more coordinate systems, statistical summarization rules, a facet specification, i.e. the use of multiple similar subplots to look at subsets of the same data, optional parameters that affect the layout and rendering, such text size, font and alignment, legend positions.

The ggplot2 library is an extremely popular visualization package that provides an interface for extremely fine control over graphics for plotting. It is used by a number of of other popular packages in their built-in plotting functions. It provides a “grammar of graphics” that is quite useful to know.

There are (at least) two types of data visualization. The first enables a scientist to explore data and make discoveries about the complex processes at work. The other type of visualization provides informative, clear and visually attractive illustrations of her results that she can show to others and eventually include in a publication. Both of these types of visualizations can be made with R. In fact, R offers multiple graphics systems. This is because R is extensible, and because progress in R graphics over the years has proceeded largely not by replacing the old functions, but by adding packages. Each of the different graphics systems has its advantages and limitations. In this chapter we will get to know two of them. First, we have a cursory look at the base R plotting functions1. Subsequently we will switch to ggplot2.

In the last section, you were asked to convert the “smoker” column to logical values. The solution is fairly simple:

The tidyverse is a collection of packages by the creators of RStudio that share an approach to data science. The authors model data science like this:

ggplot2

Plot Basis R

MENGGUNAKAN PROBABILITAS DISKRIT

Introduction to the tidyverse

Menyelesaikan Pemecahan Masalah Pada Konversi Smoker

cara Esport dan Import Data Pada Rstudio

Menginstal Paket Pada RStudio

Penjelasan Tentang Type Data Pada RStudio

Penjelsan Tentang Data Frames Pada Rstudio

Penjelasan Catatan Rstudio dan R

Pengantar R