Course Content
R Programming for Beginners
You will learn complete R programming in urdu/Hindi
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Introduction to R programming
08:55
Import Data From Excel in R
20:17
Types of Plots in R
07:23
Boxplots in R
18:41
ANOVA and Tukey test in R
13:07
ANOVA and Multiple Mean Comparison in R
28:17
Built-in Datasets in R
09:29
Hashtags of comments in R
03:54
Vectors and Arrays in R
05:53
Sequences and Repeats in R
05:32
Data Visualization in R with ggplot2
The R programming language provides a powerful and flexible framework for visualizing data using the ggplot2 package. With ggplot2, users can quickly and easily create aesthetically pleasing visualizations of their data, including scatter plots, box plots, histograms, and maps. Additionally, ggplot2 provides many options for customizing the look and feel of a visualization, such as adding labels, annotations, and custom themes. With ggplot2, users can quickly and effectively explore the relationships between variables and gain insight into their data.
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ggplot2 in R
18:25
Scatter plots in R
25:04
Violin Plots in R
08:01
Principal Component Analysis (PCA) in R
19:13
Heatmaps in R
10:43
Adding p-values in plots
01:45
Correlation Analysis in R
17:15
Advance R
The R programming language is a powerful and flexible language for data analysis and visualization. It has become one of the most popular languages for data science and is used by many researchers and data scientists to explore, analyze, and visualize data.R is also widely used for creating publication-ready figures. R's easy-to-learn syntax and wide range of packages make it well-suited for producing figures that are both aesthetically pleasing and accurately convey the data. R's ggplot2 package is especially popular for creating publication-ready figures, as it is designed to produce figures that are both beautiful and informative.R also provides a number of advanced features for creating publication-ready figures. R's shiny package allows users to create interactive figures, which can make it easier for readers to explore and interact with the data. The R markdown package allows users to create documents that include embedded figures and code, which can be useful for reproducible research. Finally, R's knitr package allows users to easily create documents that can be exported in a variety of formats, including HTML, PDF, and Word.Overall, R is an excellent choice for creating publication-ready figures. Its easy-to-learn syntax, wide range of packages, and advanced features make it a powerful and flexible tool for data visualization.
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Important Resources to learn R
05:09
Choosing the right statistical Method
41:52
Experimental Designs
06:42
Post-hoc tests in R
09:52
R in VScode (for advance use)
15:48
Publication ready plots
Publication ready plots are data visualisations that are designed to be used in a scientific paper or report. To create publication ready plots, the following steps should be followed:1. Choose the right type of plot for the data: Different types of plots are appropriate for different types of data. For example, bar charts are best for categorical data, while scatter plots are best for continuous data.2. Choose the right plot style: Different plot styles can be used to emphasise different aspects of the data. For example, a heatmap can be used to show the distribution of values in a dataset.3. Choose the right colour palette: Choosing the right colours for the plot can help to emphasise important information. For example, a sequential palette can be used to show the magnitude of values.4. Add labels and annotations: Labels and annotations can be used to add context to the plot and make it easier for the reader to understand.5. Adjust the plot size and position: The size and position of the plot can be adjusted to make the most of the available space.By following these steps, it is possible to create publication ready plots that are easy to read and understand.
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RAW Data to Publication Ready Boxplot
05:51
Make plots automatically
16:25
multi-pannel plots in R
15:03
Adding p-values in all plots
01:45
Barplots with one-way ANOVA and lettering
11:46
Bar plot with Two-way ANOVA and lettering
06:56
Bonous (7-Days Workshop on R with Aammar) 2023
Practice, revise and learn
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Introduction to R programming
11:00
Installation and first line of code (Day-1)
01:27:59
Data Visualization and plotting (Day-2)
01:11:30
Data Transformation and Data Visualization (Day-3)
01:40:42
Data Transformation in R (Day-4)
53:52
Basic Statistics in R (Day-5)
01:46:54
Statistics and plotting in R (Day-6)
01:27:18
t test ANOVA post hoc test in R (Day-7)
03:02:37
R with Aammar
About Lesson

Data can be imported from Excel in R in two ways. The first way is to use the read_excel () function from the readxl package, a powerful R library for reading and writing Excel files. This function allows you to read in an Excel file and convert it into a data frame. The second way is to use the read.xls () function from the xlsx package, which allows you to read in an Excel file directly into a data frame without needing to convert it. In addition to these two functions, you can also use the openxlsx package to read Excel files into R. This package has several functions that can be used to read in an Excel file, including read_xlsx (), read_xlsx2 (), and read_xlsx_chunks (). Once the Excel file is imported into R, you can perform any data manipulation or visualisation that you require. You can also save the data frame as a .csv file for further use. In conclusion, importing data from Excel in R is a straightforward process and there are several packages available that make it quick and easy to do so. By using the read_excel (), read.xls (), and openxlsx packages, you can easily read in Excel files into R and perform any necessary data manipulation or visualisation.

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Omer Reshi 1 year ago
> boxplot(my_data$Crop,my_data$Weight) Error in x[floor(d)] + x[ceiling(d)] : non-numeric argument to binary operator
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Omer Reshi 1 year ago
why this error comes
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