Summary  
This chapter introduces how to use pipeline functions to filter rows based on conditions and arrange them by one or more columns, enabling efficient and expressive data queries.  

General domain of usage  
Data wrangling

Filtering and arranging data are essential tasks in data wrangling, allowing you to focus your analysis on relevant subsets and to organize your data for easier interpretation. In the Tidyverse, the `dplyr` package provides two powerful functions for these purposes: **`filter`** and **`arrange`**. 

The `filter` function lets you select rows from a data frame that meet specific logical conditions.

Definition

 For example, you might want to extract only those observations where a variable exceeds a certain value or matches a particular category. 

The `arrange` function reorders the rows of your data frame based on the values of one or more columns, either in ascending or descending order.

 This is useful for ranking, prioritizing, or simply making large datasets easier to read.

library(dplyr)

# Sample data frame
df <- data.frame(
  name = c("Alice", "Bob", "Charlie", "Diana"),
  age = c(25, 30, 22, 28),
  score = c(88, 95, 78, 90)
)

# Filtering rows where age is greater than 24 and arrange by score descending, then by name ascending
filtered_arranged <- df %>%
  filter(age > 24) %>%
  arrange(desc(score), name)

print(filtered_arranged)

You can combine `filter` and `arrange` in a single pipeline to create complex data queries. This approach allows you to first narrow down your dataset to the rows that matter most, and then sort the results to highlight patterns or outliers. For instance, you might filter for all records that meet a certain threshold and then arrange them by multiple columns to see the highest or lowest values grouped by categories. Using these functions together makes your data wrangling both **efficient** and **expressive**, helping you extract meaningful insights from your data.

**Which statement best describes the difference between `filter` and `arrange` when using dplyr for data manipulation?**

library(testthat)
library(dplyr)

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

# Функція для чистого виводу результатів
check_logic <- function(test_name, condition, error_msg) {
  test_that(test_name, {
    if (isTRUE(condition)) {
      expect_true(TRUE) # Лише один сигнал успіху на блок
    } else {
      fail(error_msg)
    }
  })
}

# --- ПЕРЕВІРКИ ---

# 1. Перевірка існування та структури
ok1 <- exists("filtered_sorted_cities") && is.data.frame(filtered_sorted_cities)

check_logic(
  "1. Object filtered_sorted_cities exists and is a data frame",
  ok1,
  "The variable 'filtered_sorted_cities' is missing or is not a data frame."
)

# 2. Перевірка фільтрації (чи залишилися правильні міста)
expected_cities <- c("New York", "Los Angeles", "Chicago", "Houston")
ok2 <- exists("filtered_sorted_cities") && 
       setequal(filtered_sorted_cities$city, expected_cities) && 
       !("Phoenix" %in% filtered_sorted_cities$city)

check_logic(
  "2. Data is correctly filtered",
  ok2,
  "The filtered data contains incorrect cities or is missing required ones (e.g., Phoenix should be removed)."
)

# 3. Перевірка сортування (за спаданням avg_temp)
ok3 <- FALSE
if (exists("filtered_sorted_cities") && "avg_temp" %in% colnames(filtered_sorted_cities)) {
  temps <- filtered_sorted_cities$avg_temp
  # Перевіряємо, що різниця між сусідніми елементами завжди <= 0 (спадання)
  ok3 <- all(diff(temps) <= 0)
}

check_logic(
  "3. Data is arranged by avg_temp in descending order",
  ok3,
  "The rows are not sorted correctly. Ensure you used arrange(desc(avg_temp))."
)

test_main.R

Gain hands-on skills in importing, cleaning, transforming, and preparing data for analysis and visualization in R. This focused course uses the Tidyverse suite to teach practical workflows for real-world data preparation, including handling missing values, data transformation, and building tidy datasets.

Filtering and Arranging Data

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