Datasets in R: Joining & Visualizing Data

R Programming 4.0+    |    Intermediate
  • 6 videos | 46m 42s
  • Includes Assessment
  • Earns a Badge
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Data for the same entity is often stored in multiple locations. Your analysis may require bringing this data together in a single location. Doing this forms a core part of data preprocessing. Another core task is recognizing the relationships in your data. In this course, you'll practice methods to merge data to prepare for statistical and predictive modeling and identify relationships in your data using charts and graphs. You'll combine data in different data frames (or tibbles) based on the values in common columns. You'll use the merge() function to perform join operations and implement joins using functions from the tidyverse. You'll also examine the plotting systems available in R and use the plot() functionality and the ggplot2 package to visualize and explore your data. Upon completion of this course, you'll be able to combine your data in a meaningful way and uncover data relationships.


  • discover the key concepts covered in this course
    perform joins on data frames using the merge() function
    use the dplyr inner_join() function and perform filtering joins
  • create histograms and KDE curves using plot() and ggplot2
    visualize data using scatter plots, box plots, and line charts
    summarize the key concepts covered in this course


  • 1m 57s
    In this video, you’ll learn more about the course and the instructor. In this course, you’ll learn to combine data in data frames or tibbles based on the values in columns. You’ll also learn the merge function in R to perform join operations, inner join, left outer join, right outer join, and the full outer join. You'll also implement joins using functions from the tidyverse. Then, you’ll learn the plotting systems available in R. FREE ACCESS
  • 11m 1s
    In this video, you’ll watch a demo.  In this demo, you’ll see how you can perform joining operations in R in this demo. You’ll first invoke the rm function and pass in list = ls to get empty the memory. Next, you’ll work with data spread across two different files. The first part of the data is in the file life_exp.csv. FREE ACCESS
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    3.  Joining Tibbles Using Joins and Filtering Joins
    8m 54s
    In this video, you’ll watch a demo.  In this demo, you’ll learn you can also join data frames together using functionality that’s part of the dplyr package. The dplyr package is a part of tidyverse. You’ve already loaded dplyr into your current R program. Now, you’ll read the original CSV files back in, in the tibble format. FREE ACCESS
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    4.  Creating Histograms and Density Curves
    11m 33s
    In this video, you’ll watch a demo.  In this demo, you’ll learn data exploration and analysis aren't complete until you visualize your data. Visualizations allow you to view the shape of your data and relationships that exist in your data. You’ll learn how to visualize your data in R. First, you'll invoke the rm function passing in list is equals ls to get rid of objects in the current R memory. FREE ACCESS
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    5.  Using Plots and Charts to Visualize Data
    10m 54s
    In this video, you’ll watch a demo.  In this demo, you’ll explore more visualizations in R and plot data using both ggplot and the base graphics libraries. You’ll see a special function in R called attach that attaches a set of R objects to the R search path. In the data in bank loan data frame, you’ll attach the search path. Now you can reference variables in this data frame without specifying the data frame. FREE ACCESS
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    6.  Course Summary
    2m 23s
    In this video, you’ll summarize what you’ve learned in the course. In this course, you’ve learned how to merge data for the same entity located across multiple data frames. You also learned about relationships that exist in our data using different types of visualizations. You also performed join operations on tibbles and data frames. You used the merge function, which is part of the base R package. FREE ACCESS


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