Transforming Variables in R

These flashcards cover key concepts related to the transformation of data variables in R, focusing on practical applications, definitions of terms, and processes in data analysis.

Library

A collection of pre-written code that you can use to perform specific tasks in R.

descr package

An R package used for descriptive statistics.

Hmisc package

An R package that includes functions for a variety of statistical tools and data analysis.

cut2 function

A function from the Hmisc package that collapses numeric variables into several binned categories.

Data transformation

The process of modifying a data set to make it more manageable or interpretable.

Variable name

A label assigned to a data variable for reference.

Attribute

Characteristics of a variable that provide metadata about the variable.

Factor variable

A categorical variable that can take on a limited, fixed number of values.

Ordered factor

A type of factor where the categories have a natural order.

Levels function

An R function used to access and modify the levels of a factor variable.

Histograms

Graphical representations of the distribution of data.

Data manipulation

The process of adjusting data to improve its usefulness for analysis.

Dichotomous variable

A variable that is categorical with two possible values.

Numeric variable

A variable that can be measured numerically and used in calculations.

Barplot

A chart that presents categorical data with rectangular bars with lengths proportional to the values they represent.

Script file

A file containing a sequence of R commands that can be executed together.

Frequency table

A table that displays the frequency of various outcomes in a sample.

Value labels

Descriptive labels for the categories of a variable.

Data frame

A two-dimensional structure in R where data is stored in a table format.

Binned categories

Groups that represent ranges of numeric values.

Re-labeling

Changing the names of variable categories to make them more interpretable.

Reporting transformations

Documenting changes made to data for transparency and reproducibility.

Data visualization

The graphical representation of information and data.

Research design

The framework for collecting and analyzing data to answer specific research questions.

Polarity in datasets

Indications of the direction of attitudes; positive or negative.

Index creation

The process of combining multiple variables into a single composite measure.

Scripting in R

The practice of writing code in R to automate data analysis tasks.

Transformation process

Steps taken to change the structure or labeling of data variables.

Data integrity

The accuracy and consistency of data over its lifecycle.

Command execution

Running R commands in the R environment.

Research accountability

The concept of keeping track of changes made to data for reproducibility.

Semantic understanding

Grasping the meaning of data variables and their categories.

Variable recoding

The process of changing the values or categories of a variable.

Central tendency

A measure that represents the center point or typical value of a dataset.

Dispersion

The extent to which a distribution is stretched or squeezed.

Ordinal variable

A variable that has a clear ordering of its categories.

Aggregating data

Combining data from multiple variables or observations.

Research outcomes

Results derived from data analysis relevant to research questions.

Statistical techniques

Methods applied to manipulate and analyze data.

Qualitative variable

A variable that describes categories rather than numerical values.

Weighting variables

Adjusting variables to account for their importance or prevalence in analysis.

Categorical data

Data that can be sorted into groups or categories.

Comparative analysis

Examining similarities and differences across datasets.

Sample size

The number of observations or data points collected in a dataset.

Hypothesis testing

A method for testing a claim or hypothesis about a parameter in a population.

Variable prevalence

The extent to which a variable appears within a dataset.

Data consistency

The degree to which data values are reliable and variations are explained.

Permutations

Different arrangements of a dataset or variable values.

Longitudinal data

Data collected over time, tracking changes in the same subjects.