Subsetting (Sort/Select) Data in R with Square Brackets | R Tutorial 1.9| MarinStatsLectures

MarinStatsLectures-R Programming & Statistics
8 Aug 201304:38
EducationalLearning
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TLDRIn this video, Mike Marin demonstrates how to subset data in R using square brackets. He starts with basic commands to understand data dimensions and length, then moves on to subsetting data based on specific criteria, such as gender. He creates subsets for females and males and calculates mean age for each group. Additionally, he extracts a subset for males over 15 years old. The tutorial emphasizes practical steps and commands, making it easy for viewers to follow along and apply subsetting techniques to their own datasets.

Takeaways
  • 📊 The video focuses on subsetting data using square brackets in R.
  • 📁 LungCapData has been imported and attached, with 725 rows and 6 columns.
  • 🔢 The 'dim' command shows data dimensions, and 'length' shows the number of observations in a vector.
  • 🔍 Square brackets can subset data by rows and columns, demonstrated with rows 11 to 14.
  • 👩‍🔬 Subsetting can be based on values of other variables, such as calculating mean Age for females.
  • ✏️ A double equal sign (==) checks for equality in R, while a single equal sign (=) assigns values.
  • 📊 Character strings or factors, like 'female' and 'male', need to be in quotations for subsetting.
  • 👩‍👧‍👦 Subsetting data for only 'females' or 'males' creates new objects FemData and MaleData.
  • ✅ FemData and MaleData dimensions can be checked, showing 358 females and 367 males respectively.
  • 🧒 Subsetting males over 15 years old creates the MaleOver15 object, verified with 89 rows.
Q & A

  • What is the purpose of the 'dim' command in R?

    -The 'dim' command in R is used to find out the dimensions of a data frame or matrix. It returns the number of rows and columns in the dataset.

  • How can you determine the number of observations in a vector or variable in R?

    -You can determine the number of observations in a vector or variable using the 'length' command in R.

  • What does the double equal sign (==) signify in R?

    -In R, the double equal sign (==) is used to represent equality in a mathematical sense. It is used to compare values.

  • How can you subset data based on specific values of a variable in R?

    -You can subset data based on specific values of a variable using square brackets and specifying the condition. For example, to subset rows where Gender is 'female', you would use `LungCapData[Gender == 'female', ]`.

  • How do you create a subset of data containing only females in R?

    -To create a subset of data containing only females, you can use the following command: `FemData <- LungCapData[Gender == 'female', ]`.

  • What is the command to check the dimensions of a subset in R?

    -To check the dimensions of a subset in R, you can use the 'dim' command. For example, `dim(FemData)` will return the dimensions of the FemData subset.

  • How can you create a subset of data for males over 15 years old in R?

    -You can create a subset of data for males over 15 years old using the following command: `MaleOver15 <- LungCapData[Gender == 'male' & Age > 15, ]`.

  • What does the 'summary' function do in R?

    -The 'summary' function in R provides a summary of the contents of a variable or dataset, including statistics like mean, median, min, max, and quartiles for numerical data, and counts for factor levels.

  • Why is the word 'female' placed in quotations when subsetting data based on Gender?

    -The word 'female' is placed in quotations when subsetting data because it is a character string or a factor level in the dataset.

  • How can you verify that the subsets FemData and MaleData have the correct number of rows?

    -You can verify that the subsets FemData and MaleData have the correct number of rows by checking their dimensions using the 'dim' command or by summarizing the Gender variable to ensure the counts match the expected values.

Outlines
00:00
📊 Data Subsetting in R with Square Brackets

In this video, Mike Marin discusses advanced techniques for subsetting data in R using square brackets. He begins by reviewing the 'dim' and 'length' commands to understand the dimensions and the number of observations in the LungCapData dataset. Mike then demonstrates how to subset data for specific observations and variables, such as extracting ages for a range of observation numbers. He also explains how to subset data based on conditions, like calculating the mean age for females using a double equal sign for equality checks and character strings for categorical data. The video proceeds to show how to create subsets for different gender groups, saving them as FemData and MaleData, and verifying the subset operations by checking dimensions and summaries. Finally, Mike illustrates how to extract a subset of males over 15 years old into a new object, MaleOver15, and confirms the subset by examining its dimensions and a preview of its rows.

Mindmap
Keywords
💡Subsetting
Subsetting in the context of data analysis refers to the process of selecting a subset of data from a larger dataset based on specific criteria. In the video, Mike Marin demonstrates how to subset data using square brackets in R to extract specific rows or columns from a dataset. For example, he shows how to subset the 'Age' variable for observations 11 to 14, and how to subset data for only females or males in the 'LungCapData' dataset.
💡Square Brackets
Square brackets in R are used for subsetting data. They allow the user to specify the rows and columns to include in the subset. In the video, Mike Marin uses square brackets to demonstrate how to select specific observations, such as ages 11 to 14, and to subset the data based on the 'Gender' variable, selecting only females or males.
💡LungCapData
LungCapData is the dataset used in the video for demonstrating subsetting techniques in R. It is a dataset that was introduced in earlier videos of the series and contains variables such as 'Age' and 'Gender'. Mike Marin uses this dataset to show how to subset data based on the values of these variables.
💡Dimensions
The term 'dimensions' in data analysis refers to the number of rows and columns in a dataset. In the video, Mike Marin uses the 'dim' command in R to show the dimensions of the 'LungCapData', which has 725 rows and 6 columns, indicating the size of the dataset.
💡Length Command
The 'length' command in R is used to determine the number of elements in a vector or the number of observations in a variable. In the video, Mike Marin uses this command to show that the 'Age' variable in the 'LungCapData' consists of 725 observations.
💡Gender Variable
The 'Gender' variable in the 'LungCapData' dataset is a categorical variable that classifies individuals as either 'female' or 'male'. In the video, Mike Marin uses this variable to demonstrate how to subset the data for specific genders, such as calculating the mean 'Age' for females or creating subsets for 'FemData' and 'MaleData'.
💡Mean Age
The 'mean age' refers to the average age of a group of individuals. In the video, Mike Marin calculates the mean 'Age' for subsets of the 'LungCapData' based on gender, such as calculating the mean 'Age' for only females or males, to demonstrate how to perform calculations on subsetted data.
💡Character String
A character string in R is a sequence of characters used to represent text. In the video, Mike Marin mentions that the word 'female' is placed in quotations to indicate that it is a character string, which is important when subsetting the data based on the 'Gender' variable.
💡Factor
In R, a factor is a special type of categorical variable that can only take on a limited, and usually fixed, set of possible values. In the video, Mike Marin refers to 'Gender' as a factor with levels 'female' and 'male', which is important for understanding how subsetting based on categorical variables works.
💡Equality Operator
The double equal sign '==' in R is used to test for equality in a condition. In the video, Mike Marin explains that while a single equal sign '=' is used to assign values to objects, the double equal sign is used within square brackets to subset data where a condition, such as 'Gender == "female"', is true.
💡Summary
The 'summary' function in R provides a summary of the data, including the number of observations, mean, median, and other statistics. In the video, Mike Marin uses the 'summary' function to confirm the gender distribution in the 'FemData' and 'MaleData' subsets, showing that there are 358 females and 367 males as expected.
Highlights

Introduction to subsetting data using square brackets in R.

Importing and attaching the LungCapData dataset for demonstration.

Using 'dim' command to check the dimensions of the dataset.

Utilizing 'length' command to find the number of observations in a variable.

Subsetting a single variable or vector to view specific observations.

Examining the use of square brackets on a matrix or data frame for subsetting.

Calculating the mean Age for females using conditional subsetting.

Understanding the difference between single and double equal signs in R.

Creating subsets for different genders and checking their dimensions.

Using summary functions to confirm the gender distribution in subsets.

Viewing the first few rows of a subset to ensure correct subsetting.

Subsetting data for males over 15 years old and checking the dimensions.

Creating a new object 'MaleOver15' for males over 15 and examining its content.

Introduction to the next video's content on logic commands and random commands in R.

Encouragement to watch more instructional videos for further learning.

Transcripts

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Related Tags
Data SubsettingR ProgrammingGender AnalysisAge GroupsStatistical MethodsData AnalysisLungCapDataMean CalculationSubset CreationR Tutorial