EASC 201 exam 2 part 3

What are the common uses of R in environmental science?

Data analysis, graphing, statistics, mapping, modeling, organizing datasets, and reproducible research.

Why is R useful in environmental science?

It helps scientists efficiently analyze and visualize large datasets and perform statistical analysis.

What is R?

A programming language used for statistical computing, data analysis, graphing, and scientific research.

What is RStudio?

An integrated development environment (IDE) for R that simplifies coding, plotting, file management, and workflow.

What is the difference between R and RStudio?

R is the programming language itself, while RStudio is the user-friendly interface used to work with R.

What are the advantages of RStudio over R?

It provides a cleaner interface, script editor, environment pane, plotting pane, file management, package tools, and easier workflow.

What are the benefits of learning R?

It improves data analysis skills, supports scientific research, increases reproducibility, and is widely used in many sciences.

Why is R important for future careers?

It is a valuable technical skill used in research, government, consulting, nonprofits, and many STEM careers.

What is reproducibility in science?

The ability for others to repeat an analysis and get the same results using the same data and methods.

Why does R improve reproducibility?

Code clearly documents the steps used in analysis, reducing guesswork and making results easier to verify.

What is a Working Directory in R?

The folder where R looks for files to import and where it saves outputs unless another path is specified.

Why is choosing the Working Directory important?

It helps R find files correctly and keeps projects organized and easier to reproduce.

What can happen if the Working Directory is set incorrectly?

R may fail to import data, save outputs in the wrong place, or produce file path errors.

What is an R script?

A saved text file containing R code that can be run later to repeat analyses.

Why are R scripts useful?

They save work, improve organization, make analyses reproducible, and allow users to rerun or edit code.

What is the command to install a package in R?

install.packages("package_name")

What is the command to load a package in R?

library(package_name)

What is a package in R?

A collection of functions, tools, and datasets created to extend the capabilities of R.

Why are packages important in R?

They allow users to perform specialized tasks efficiently without writing code from scratch.

What are the benefits of packages developed by multiple users?

They allow for collaboration, innovation, peer improvement, specialized tools, and shared scientific workflows.

What is the command to import data in R?

read.csv("filename.csv") or readr::read_csv("filename.csv")

What is the command to view data in R?

head(dataframe) or View(dataframe)

What is the command to plot data in R?

plot(x, y) or ggplot(dataframe, aes(x, y)) + geom_point()

What is the command to run a regression in R?

lm(y ~ x, data = dataframe)

What does regression analysis do?

It analyzes the relationship between variables and can be used to predict one variable from another.

What is the command for correlation in R?

cor(x, y)

What does correlation measure?

It measures the strength and direction of a relationship between two variables.

What does a positive correlation indicate?

As one variable increases, the other tends to increase.

What does a negative correlation indicate?

As one variable increases, the other tends to decrease.

What does no correlation indicate?

There is little or no consistent relationship between two variables.

Why are plotting and graphing important in R?

They help visualize trends, patterns, outliers, and relationships in data.

Why is coding useful compared to manual analysis?

Coding is faster, more accurate, easier to reproduce, and better for handling large datasets.