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\nR programming in one hour - A crash course for beginners\n

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\n\nPublisher:\n\nR Programming 101\n\n\n
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\nR programming is easy. In this video, I'll walk you though how to clean your data; how to manipulate (or wrangle) your data; how to summarize your data; how to create tables and describe your data; how to visualize your data using ggplot...\nR programming is easy. In this video, I'll walk you though how to clean your data; how to manipulate (or wrangle) your data; how to summarize your data; how to create tables and describe your data; how to visualize your data using ggplot (a powerful data visualization package that I use to create bar charts, boxplots, scatterplots, histograms, line graphs and more) . And of course how to analyze your data. The data analysis includes using hypothesis testing and p-values to make inference about a population using sample data. To do this, I'll teach you how to do a t-test, ANOVA, chi-squared test and linear regression models. So if you're a data scientists or researcher doing statistical analysis or some kind of quantitative analysis of data, this video is for you. I cover all of this in one hour.\n
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Resource Details

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Curator Rating
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Educator Rating
Not yet Rated
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Media Length
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58:27
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Grade
Higher Ed
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Subjects
Math
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Media Type
Instructional Videos
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Source:
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\nR Programming 101\n\n
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Date
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2022
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Language
English
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Audiences
For Teacher Use
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Usage Permissions
Fine Print: Educational Use
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Concepts

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data, box plot, star wars, life expectancy, plot, data science, statistics
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Additional Tags

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r programming, programming, boxplot, ggplot, visualisation, visualization, numeric, quantitative, plots, analysis, descriptive, machine learning, ai, statistical software, stata, spss, linear model, density plot, data set, etcetera, lemon squeezy, hypothesis testing, square brackets, closed brackets, complete cases, steel blue, confidence interval, statistically significant, null hypothesis, sepal length, data structure, recode data, data frame, data visualization, bar plot, geom point, gg plot, missing data, column headings, existing variable, variable headings, numeric variable, hair color
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Classroom Considerations

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Best For: Explaining a topic
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