Higher Applications of Mathematics

Importing data

Loading data sets and checking variables.

RStudio reference Project support Mixed quiz

RStudio reference

Check commands and output meanings.

Project support

Plan reports, conclusions and limitations.

Mixed quiz

Review RStudio and project skills.

Before you start

Know the statistical question you are trying to answer.
Check that variables are named clearly and measured in suitable units.
Be ready to write an interpretation, not just copy RStudio output.

Method chooser

Which RStudio method do I use?

Do you need to load a data file?

Use read.csv() and check the data frame.

Do you need averages or spread?

Use mean(), median(), sd() and summary().

Do you need to compare distributions?

Use boxplot() or hist().

Do you need to check a relationship between two variables?

Use plot() and cor().

Do you need a straight-line model?

Use lm() and interpret the gradient.

Are you estimating a population mean?

Use a confidence interval from t.test().

Are you testing a mean or paired data?

Use t.test(), with paired = TRUE for matched pairs.

Are you testing two proportions?

Use prop.test().

Are you writing the project conclusion?

State evidence, context and limitations clearly.

RStudio lesson

Key idea

This topic focuses on loading data sets and checking variable names before analysis. In Higher Applications, RStudio is used as a practical tool to calculate, graph and test ideas from data.
A strong answer shows what command was used, what output was produced, and what that output means in context. Use careful language such as 'This suggests...' or 'There is evidence to suggest...'.

Key commands and skills

data <- read.csv("data.csv")
names(data)
summary(data)
Use comments in scripts with # to explain your steps.

Technology output practice

Output interpretation preview

Read the simulated output, pick out the key value, then turn it into a written conclusion. This is a learning preview, not a real RStudio environment.

Context

Summary statistics output

A class compares journey times to a sports venue, measured in minutes.

Simulated output

> summary(travel$minutes)
Min.   1st Qu.   Median   Mean   3rd Qu.   Max.
18.0     26.0      30.5    31.4     36.0    48.0

> sd(travel$minutes)
[1] 6.8

Which value would you quote to describe consistency?

Choose an option, then check the feedback.

Worked examples

Walkthrough 1

Run the command

A pupil is using RStudio for loading data sets and checking variable names before analysis with a small school-friendly data set.

Load or identify the data frame.
Check the exact column names with names(data).
Run the key command: data <− read.csv("data.csv")

The output should be checked against the variables and the original question.

Walkthrough 2

Read the output

RStudio has produced numerical or graphical output.

Find the key value, graph feature or p-value.
Check the unit and variable name.
Avoid copying every line of output into the conclusion.

The data frame should contain the rows and variables needed for the project.

Walkthrough 3

Write the interpretation

The result must be used in a project conclusion.

Start with a cautious phrase such as 'This suggests...'.
Refer to the context and variables.
Mention a limitation if the data set is small, biased or observational.

The conclusion should be clear, cautious and linked to evidence.

Watch out

Misspelling a data frame or column name.
Forgetting brackets or quotation marks in a command.
Copying output without explaining what it means.
Claiming causation from correlation.
Using strong language such as 'proves' when the data only suggests evidence.

Next step

Move into practice

Use the learning notes to read output tables carefully, then try varied summary, correlation, regression and test-output interpretation.

RStudio mixed quiz