FAD1015 L19 — Input Data & Descriptive Statistics in R

Lecture 19 introducing R programming for data input, descriptive statistics, and data visualization. Source file: (L19) FAD1015 w11 input data R.pdf

Summary

Introduction to R for statistical computing: computing simple statistics from vectors, importing data into RStudio, exploring built-in datasets, and basic visualization with the Nile dataset example.

Key Concepts

• R Programming — statistical computing environment
• Data Input — importing via RStudio interface, built-in datasets
• Descriptive Statistics — mean, median, sd, var, range, sum, sort
• Data Visualization — plot, hist, boxplot
• Built-in Datasets — data(), help()
• Time Series — Nile dataset example

Lecture Coverage

1. Simple Statistics

Mathematical and statistical summaries computed from a vector:

1:4          # Sequence 1 to 4
sum(1:4)     # 10
max(1:10)    # 10
min(1:10)    # 1
range(1:10)  # Returns min and max: 1  10

Descriptive statistics on random data:

X <- rnorm(10)
mean(X)      # Arithmetic mean
sd(X)        # Standard deviation
median(X)    # Median
var(X)       # Variance
sort(X)      # Sorted values

2. Importing Data into RStudio

Via the Environment Pane:

• Use the Import Dataset dropdown in the Environment pane.
• Supports: Text (base), Text (readr), Excel, SPSS, SAS, Stata.

Data Preparation Notes:

• Avoid spaces and special characters in column names (use underscores or periods).
• Handle missing data appropriately before import.

3. Built-in Datasets

List and load available datasets:

data()          # View all built-in datasets
data("Nile")    # Load the Nile dataset
help(Nile)      # View dataset description

4. Working with the Nile Dataset

The Nile dataset contains annual flow of the river Nile at Aswan from 1871–1970.

Descriptive Statistics:

mean(Nile)
sd(Nile)
summary(Nile)   # Min, 1st Qu, Median, Mean, 3rd Qu, Max

Visualization:

plot(Nile)      # Time series plot
hist(Nile)      # Histogram

5. Practice Activities

Activity 1 — Dahlia.csv: Load Dahlia.csv and perform:

1. Summarize the dataset (summary())
2. Display rows and columns (dim())
3. Display column names (names())
4. Create a histogram of sepal length with labeled axes
5. Create a scatter plot of sepal length vs sepal width
6. Create a boxplot of sepal length by species
7. Extract sepal.length and calculate its standard deviation

Activity 2: Using any available dataset in R, calculate summary statistics and plot histograms/scatterplots.

Advanced Visualization Reference: Top 50 Ggplot2 Visualizations

Related Topics

• FAD1015 L17-L18 — Uniform & Exponential Distributions + R Intro — prerequisite R concepts
• FAD1015 L25-L26 — Hypothesis Testing in R — extends R programming
• FAD1015 Tutorial 12 — Hypothesis Testing in R — practice problems

Related Course Page

• FAD1015 - Mathematics III