# آموزش مصورسازی داده با R

Learning R for Data Visualization | UdemyPublisher: UdemyAuthor: Packt PublishingDuration: 01:58:53Link:https://www.udemy.com/learning-r-for-data-visualization/?persist_locale&locale=nl_NLGet to grips with R’s most popular packages and functions to create interactive visualizations for the webR is on the rise and showing itself as a powerful option in many software development domains. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis, creating high-level graphics, and machine learning. R gives aspiring analysts and data scientists the ability to represent complex sets of data in an impressive way.

Introducing Scientific Plotting in R
6 colleges
24:39
This video provides an overview of the entire course.
Introducing Scientific Plotting in R
Preview
05:31
Creating professional looking plots, both static and interactive, may seem hard; however, with R we can create and fully customize plots with a few lines of code.
Preview of R Plotting Functionalities
03:15
Often, beginners fail to properly understand their dataset before analyzing it. However, a good understanding of the origin and structure of the data is of primary importance.
Introducing the Dataset
03:21
It is not always good to import data in R using the default settings. For doing it successfully, several parameters need to be set.
04:41
Importing Excel tables in R may be tricky. However, with the right explanation the proper package can be installed and everything should work out fine.
03:33
Exporting data in R may seem difficult, since we have many options to choose from. However, R has powerful exporting functions that with few options can do the job successfully.
Exporting Data
04:18

Scientific Plotting in ggplot2
6 colleges
21:26
Producing elegant plots in ggplot2 may seem difficult but it is actually quite easy to do. In fact, ggplot2 takes care, by default, of most of the graphical design of the plot, meaning that we can produce beautiful histograms with just a few lines of code.
Creating Histograms
Preview
05:01
Histograms are useful for certain tasks, but for comparing several variables at once they are not the best. Box plots can be used instead, since they allow the comparison of the distribution of multiple variables side by side.
The Importance of Box Plots
03:44
Categorical variables are invariably difficult to visualize in meaningful ways. Bar charts are important for plotting categorical variables and defining their characteristics.
Plotting Bar Charts
02:43
In many cases, we are interested in comparing multiple variables at once and checking their correlation. Scatterplots allow us to do just that and are an important tool in a data analyst's toolbox.
Plotting Multiple Variables – Scatterplots
03:06
In many cases, the variable time is underestimated. However, time-series are extremely useful to determine the temporal pattern of a variable.
Dealing with Time – Time-series Plots
02:38
Many datasets are affected by uncertainty and people not always know how to show this in plots. This video will present ways to solve this and take uncertainty into account.
Handling Uncertainty
04:14

Customizing Plots
6 colleges
22:14
By default, ggplot2 creates plots with a grayish background, and without axes lines and white gridlines. This is not the standard look you normally find in scientific manuscripts.
Changing Theme
Preview
03:06
The default color scale is not always appropriate to spot all the differences in the data we are trying to plot. In many cases, we have to change it so that our plots can become more informative.
Changing Colors
03:19
ggplot2 uses the names of the columns as labels, meaning that if these are not self-explanatory, the plot will not provide a good framework to understand its meaning. By adding some lines of code, we can customize the plot in order to change the labels and make it clearer.
Modifying Axis and Labels
02:40
The default plots created by ggplot2 lack several elements that in many cases are useful to provide additional information to viewers. However, there are simple functions that can be used to add supplementary elements to the plot.
04:08
In many cases, it is crucial to be able to include textual labels on plots to provide viewers with additional information. This can be done in ggplot2 in both static and dynamic ways.
Adding Text Inside and Outside of the Plot
05:02
With the function facet_wrap, it is only possible to create a grid of plots of the same type. However, in some cases, it is necessary to create side-by-side graphs with diverse plots. This can be done in the package gridExtra.
Multi-plots
03:59

Exporting Plots
2 colleges
05:56
We could easily save our plots as images directly from R Studio. This way of saving however, does not provide much flexibility. If we want to customize our images, we need to learn how to export plots from the R code.
Exporting Plots as Images
Preview
03:24
The default size that ggplot2 uses to save plots is ideal for most of our needs, such as embedding plots in Word documents. However, in some cases, we may need to specify a particular page size for our plots, which can be easily done with the option paper.
02:32

Interactive Plots in rCharts
5 colleges
17:36
Static plots are the standard for publishing in traditional media, such as journal papers. However, the world is moving towards an internet-based presentation of results and even scientific journals are quickly adapting it. Many now offer the possibility of including interactive plots. In R, we can create plots for the Web with the rCharts package, which is a bit more difficult to install than ggplot2.
Getting Started with Interactive Plotting
Preview
02:44
rCharts features a syntax more similar to standard plotting in R than what we saw with ggplot2. However, it is easy to pick up by showing simple examples and then including additional details.
Creating Interactive Histograms and Box Plots
04:55
Even though we know nothing about HTML and CSS, we can still obtain beautiful bar-charts using templates created by other users.
Plotting Interactive Bar Charts
03:12
If too many data points are present in our dataset, scatterplot visualization may become very confusing in static plots. However, in interactive plots this limitation no longer applies, since we can select to visualize only some datasets.
Creating Interactive Scatterplots
02:58
Time-series plots are a great way to visualize the temporal pattern of a variable. However, sometimes we cannot fully understand the exact date of each point based only on the values on the x axis. Interactive visualization can solve this problem by adding tooltips in which we can take a look at the raw data.
Developing Interactive Time-series Plots
03:47

Creating a Website with Shiny
6 colleges
27:02
Shiny is a package to build fully featured websites from scratch in R. The way it communicates between the user interface and the server may seem difficult to understand. However, with some explanation, understanding Shiny becomes very easy and intuitive.
Getting Started with Shiny
Preview
04:09
Understanding the structure of a Shiny website is very important. However, presenting it from a website is not enough for the viewers to replicate it. Therefore, in this video, we are going to create a simple website with data and plots we already used, to further help viewers.
Creating a Simple Website
04:52
If we plan to upload our Shiny website on-line, we need to implement a way for users to upload their own data. In this video, we are going to show how to do just that.
File Input
03:09
One of the key components of a successful website is the ability to respond to users’ interactions. This can be achieved with conditional panels, which change the UI based on users’ interactions.
Conditional Panels – UI
03:44
One of the key components of a successful website is the ability to respond to users’ interactions. This can be achieved with conditional panels, which change the UI based on users’ interactions.
Conditional Panels – Servers
05:31
So far, we have looked at ways to create and add elements to a Shiny website. However, sooner or later, this website needs to be deployed on the Internet so that everybody can use it. Here, you will learn how to do it using a free account on shinyapps.io.
Deploying the Site
05:37

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