Key Concepts

Review core concepts you need to learn to master this subject

dplyr package

data frame object

Comma Separated Values (CSV)

Loading and Saving CSVs with R

data frames primary information

pipes

Dplyr’s select()

Excluding Columns with select() in Dplyr

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dplyr package

The dplyr package provides functions that perform data manipulation operations oriented to explore and manipulate datasets. At the most basic level, the package functions refers to data manipulation “verbs” such as select, filter, mutate, arrange, summarize among others that allow to chain multiple steps in a few lines of code. The dplyr package is suitable to work with a single dataset as well as to achieve complex results in large datasets.

Introduction to Data Frames in R
Lesson 1 of 2
  1. 1
    Introduction
    Data lies at the heart of nearly every problem in the business world and society. Having the right tools to manipulate data and organize it in a meaningful way is integral to performing data analys…
    Start
  2. 2
    What is a Data Frame?
    A data frame is an R object that stores tabular data in a table structure made up of rows and columns. You can think of a data frame as a spreadsheet or as a SQL table. While data frames can be cre…
    Start
  3. 3
    CSVs
    When working with data frames, most of the time you will load in data from an existing data set. One of the most common formats for big datasets is the CSV. CSV (comma separated values) is a t…
    Start
  4. 4
    Loading and Saving CSVs
    When you have data in a CSV, you can load it into a data frame in R using readr’s read_csv() function: df <- read_csv(‘my_csv_file.csv’) * In the example above, the read_csv() function is called …
    Start
  5. 5
    Inspecting Data Frames
    When you load a new data frame from a CSV, you want to get an understanding of what the data looks like. If the data frame is small, you can display it by typing its name df. If the data frame is …
    Start
  6. 6
    Piping
    One of the most appealing aspects of dplyr is the ability to easily manipulate data frames. Each of the dplyr functions you will explore takes a data frame as its first argument. The _pipe operato…
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  7. 7
    Selecting Columns
    Suppose you have a data frame called customers, which contains the ages of your business’s customers: |name|age|gender| |-|-|-| |Rebecca Erikson|35|F| |Thomas Roberson|28|M| |Diane Ochoa|42|NA|…
    Start
  8. 8
    Excluding Columns
    Sometimes rather than specify what columns you want to select from a data frame, it’s easier to state what columns you do not want to select. dplyr’s select() function also enables you to do just t…
    Start
  9. 9
    Filtering Rows with Logic I
    In addition to subsetting a data frame by columns, you can also subset a data frame by rows using dplyr’s filter() function and comparison operators! Consider an orders data frame that contains dat…
    Start
  10. 10
    Filtering Rows with Logic II
    The filter() function also allows for more complex filtering with the help of logical operators! Take a look at the same orders data frame from the last exercise: |id|first_name|last_name|email…
    Start
  11. 11
    Arranging Rows
    Sometimes all the data you want is in your data frame, but it’s all unorganized! Step in the handy dandy dplyr function arrange()! arrange() will sort the rows of a data frame in ascending order by…
    Start
  12. 12
    Review
    There you have it! With the power of readr and dplyr in your hands, you can now: * load data from a CSV into a data frame * inspect the data frame with head() and summary() * select() the columns y…
    Start

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