PROGRAM-3

3.Implement an R function to generate a line graph depicting the trend of a time-series dataset with seperate lines for each group,utilizing ggplot2’s group aesthetic .

Step1:Load the required libraries

library(ggplot2)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ lubridate 1.9.4     ✔ tibble    3.2.1
✔ purrr     1.0.4     ✔ tidyr     1.3.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(tidyr)

Step2:Load the Built-in AirPassangers Dataset

The AirPassengers dataset is a time series object in R.

We first convert it into a dataframe to use it with ggplot2.

• Date: Represents the month and year (from January 1949 to December 1960).

• Passengers: Monthly airline passenger counts.

• Year: Extracted year from the date column, which will be used to group the data.

# Convert time-series data to a dataframe
data <- data.frame(
  Date = seq(as.Date("1949-01-01"), by = "month", length.out = length(AirPassengers)),
  Passengers = as.numeric(AirPassengers),
  Year = as.factor(format(seq(as.Date("1949-01-01"), by = "month", length.out = length(AirPassengers)), "%Y"))
)

# Display first few rows
head(data, n=20)

         Date Passengers Year
1  1949-01-01        112 1949
2  1949-02-01        118 1949
3  1949-03-01        132 1949
4  1949-04-01        129 1949
5  1949-05-01        121 1949
6  1949-06-01        135 1949
7  1949-07-01        148 1949
8  1949-08-01        148 1949
9  1949-09-01        136 1949
10 1949-10-01        119 1949
11 1949-11-01        104 1949
12 1949-12-01        118 1949
13 1950-01-01        115 1950
14 1950-02-01        126 1950
15 1950-03-01        141 1950
16 1950-04-01        135 1950
17 1950-05-01        125 1950
18 1950-06-01        149 1950
19 1950-07-01        170 1950
20 1950-08-01        170 1950

Step 3: Define a Function for Time-Series Line Graph

# Function to plot time-series trend
plot_time_series <- function(data, x_col, y_col, group_col, title="Air Passenger Trends") {
  ggplot(data, aes_string(x = x_col, y = y_col, color = group_col, group = group_col)) +
    geom_line(size = 1.2) +  # Line graph
    geom_point(size = 2) +   # Add points for clarity
    labs(title = title,
         x = "Year",
         y = "Number of Passengers",
         color = "Year") +  # Legend title
    theme_minimal() +
    theme(legend.position = "top")
}

# Call the function
plot_time_series(data, "Date", "Passengers", "Year", "Trend of Airline Passengers Over Time")

Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.