Summary  
This chapter shows how to implement code that generates line plots to visualize sequential data and how to overlay trend lines using smoothing methods like loess or linear regression.

General domain of usage  
Time series data visualization

Line plots are essential for visualizing time series data, where the order of observations is important and typically mapped to the x-axis. You use line plots to show how a variable changes over time, such as tracking sales by month or monitoring temperature by day. In these plots, points are connected in the order of the x-variable, making it easy to see patterns, trends, and fluctuations.

library(ggplot2)

# Example time series data: monthly sales
sales_data <- data.frame(
  month = 1:12,
  sales = c(120, 135, 150, 160, 170, 180, 175, 165, 155, 145, 140, 130)
)

# Basic line plot
ggplot(sales_data, aes(x = month, y = sales)) +
  geom_line(color = "blue", linewidth = 1) +
  labs(title = "Monthly Sales Over a Year", x = "Month", y = "Sales")

library(ggplot2)

sales_data <- data.frame(
  month = 1:12,
  sales = c(120, 135, 150, 160, 170, 180, 175, 165, 155, 145, 140, 130)
)
# Add a trend line using geom_smooth()
ggplot(sales_data, aes(x = month, y = sales)) +
  geom_line(color = "blue", linewidth = 1) +
  geom_smooth(method = "loess", se = FALSE, color = "red", linetype = "dashed") +
  labs(title = "Monthly Sales with Trend Line", x = "Month", y = "Sales")

When you interpret line plots, look for patterns such as upward or downward trends, cycles, or sudden changes. Adding a trend line with `geom_smooth()` helps you see the underlying direction of the data, filtering out short-term fluctuations. The `method` argument in `geom_smooth()` lets you choose the smoothing technique, such as `"loess"` for flexible local regression or `"lm"` for a straight linear fit. Understanding the shape and direction of the trend line can guide your analysis and help you make informed decisions based on time series data.

The parameters in `geom_smooth` control the appearance and calculation of the trend line:

- `method = "loess"`: chooses the **local regression** smoothing technique, which fits a smooth curve that adapts to local patterns in the data;
- `se = FALSE`: hides the **shaded confidence interval** around the trend line, making the plot less cluttered;
- `color = "red"`: sets the **trend line color** to red for clear visual distinction;
- `linetype = "dashed"`: draws the trend line as a **dashed line** instead of a solid one, helping it stand out from the main data line.

Note

library(testthat)
library(ggplot2)

# Завантажуємо код користувача
source("user_code.R")

# Наш стандартний помічник для чистого виводу
check_logic <- function(test_name, condition, error_msg) {
  test_that(test_name, {
    if (isTRUE(condition)) {
      expect_true(TRUE)
    } else {
      fail(error_msg)
    }
  })
}

# Допоміжна функція для перевірки тексту мапінгу
get_mapping_text <- function(mapping) {
  if (is.null(mapping)) return("")
  txt <- as.character(rlang::quo_get_expr(rlang::enquo(mapping)))
  gsub("~", "", txt[length(txt)])
}

# --- АНАЛІЗ ОБ'ЄКТА ---
plt <- if (exists("temperature_plot")) temperature_plot else last_plot()

# --- ТЕСТИ ---

# 1. Наявність об'єкта та базові мапінги
ok1 <- !is.null(plt) && inherits(plt, "ggplot") &&
       get_mapping_text(plt$mapping$x) == "day" &&
       get_mapping_text(plt$mapping$y) == "temperature"

check_logic(
  "1. temperature_plot is a ggplot object with correct x and y mappings",
  ok1,
  "Ensure 'temperature_plot' exists and maps x='day' and y='temperature'."
)

# 2. Перевірка синьої лінії (geom_line)
ok2 <- FALSE
if (!is.null(plt)) {
  ok2 <- any(sapply(plt$layers, function(layer) {
    is_line <- inherits(layer$geom, "GeomLine")
    # Перевіряємо колір у параметрах (не в aes, бо він фіксований)
    is_blue <- identical(layer$aes_params$colour, "blue") || 
               identical(layer$geom_params$colour, "blue")
    return(is_line && is_blue)
  }))
}

check_logic(
  "2. temperature_plot contains a blue geom_line",
  ok2,
  "You must add a blue line using geom_line(color = 'blue')."
)

# 3. Перевірка трендової лінії (geom_smooth)
ok3 <- FALSE
if (!is.null(plt)) {
  # Шукаємо шар geom_smooth
  smooth_layers <- Filter(function(layer) inherits(layer$geom, "GeomSmooth"), plt$layers)
  
  if (length(smooth_layers) > 0) {
    for (layer in smooth_layers) {
      # Перевірка всіх вимог: loess, se=FALSE, darkgreen, dotdash
      is_loess    <- identical(layer$stat_params$method, "loess")
      is_no_se    <- identical(layer$stat_params$se, FALSE)
      is_green    <- identical(layer$aes_params$colour, "darkgreen")
      is_dotdash  <- identical(layer$aes_params$linetype, "dotdash")
      
      if (is_loess && is_no_se && is_green && is_dotdash) {
        ok3 <- TRUE
        break
      }
    }
  }
}

check_logic(
  "3. Trend line uses method='loess', se=FALSE, color='darkgreen', linetype='dotdash'",
  ok3,
  "Check geom_smooth() parameters: method='loess', se=FALSE, color='darkgreen', linetype='dotdash'."
)

test_main.R

This beginner-friendly course introduces learners to the fundamentals of data visualization using the ggplot2 package in R. Through practical exercises, students will learn how to create a variety of essential visualizations, including scatter plots, bar charts, histograms, and boxplots. The course emphasizes hands-on experience, guiding learners step-by-step through the process of building, customizing, and interpreting visualizations to effectively communicate data insights.

Master all essential aspects of data visualization in R using ggplot2, from basic plots to advanced customization and exporting graphics.

Line Plots and Trend Lines

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