Workload.Rmd

---
title: "Analysis of Weekly Workload of Platform Workers"
output:
  html_notebook:
    toc: yes
    toc_float: yes
    theme: united
---

### Introduction

This notebook analyzes the habitual weekly workload of platform workers based on the `trabalhadoresAPP_2020` dataset. A histogram will be generated to visualize the distribution of work hours.

### 1. Load Libraries

First, we load the necessary packages for data analysis and visualization.

```{r setup, message=FALSE, warning=FALSE}
library(tidyverse) # Contains ggplot2 for charts and dplyr for manipulation
```

### 2. Data Preparation

In this step, the `trabalhadoresAPP_2020` dataframe is prepared. We will use the `Carga horária semanal habitual` column, ensuring it is numeric and filtering out any missing values. Then, we'll calculate the average weekly hours.

```{r data_preparation_workload}
# This code assumes that 'trabalhadoresAPP_2020' already exists in the environment.

# Check if the column exists
if (!"Carga horária semanal habitual" %in% names(trabalhadoresAPP_2020)) {
  stop("ERROR: The 'Carga horária semanal habitual' column was not found in the dataframe.")
}

# Process the workload data
workload_data <- trabalhadoresAPP_2020 %>%
  # Rename for easier use and ensure it's numeric, coercing errors to NA
  mutate(Work_Hours = as.numeric(`Carga horária semanal habitual`)) %>%
  # Remove missing values and filter for a reasonable range of hours (e.g., up to 100)
  filter(!is.na(Work_Hours) & Work_Hours <= 100)

# Calculate the average weekly workload
average_hours <- mean(workload_data$Work_Hours, na.rm = TRUE)

# Display the prepared data summary
print(paste("The average weekly workload is:", round(average_hours, 2), "hours."))
print("Summary of workload data:")
summary(workload_data$Work_Hours)
```

### 3. Chart: Histogram of Weekly Workload

The histogram below shows the distribution of habitual weekly work hours. A vertical dashed line marks the average, and a highlighted band indicates the standard 40-44 hour work week for comparison.

```{r histogram_plot_workload, fig.width=10, fig.height=7}
workload_chart <- ggplot(workload_data, aes(x = Work_Hours)) +
  # Add a rectangle to highlight the standard 40-44 hour work week
  geom_rect(
    aes(xmin = 40, xmax = 60, ymin = -Inf, ymax = Inf),
    fill = "#fde29b", alpha = 0.02
  ) +
  geom_histogram(binwidth = 2, fill = "#1380A1", color = "white", alpha = 0.9) +
  geom_vline(
    aes(xintercept = average_hours),
    color = "#e76f51",
    linetype = "dashed",
    size = 1
  ) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  # Annotation for the average
  annotate(
    "text",
    x = average_hours + 15,
    y = 3000,
    label = paste("Average:", round(average_hours, 1), "hours/week"),
    color = "#e76f51",
    size = 4.5,
    fontface = "bold"
  ) +
  # Annotation for the standard work week
  annotate(
    "text",
    x = 81,
    y = 3800,
    label = "Workload of around 40 or 60 hours per week",
    color = "#c7a759",
    size = 4.5,
    fontface = "bold"
  ) +
  labs(
    title = "Workload Distribution of Platform Workers",
    subtitle = "Most workers report a workload of around 40 or 60 hours per week.",
    x = "Habitual Weekly Work Hours",
    y = "Number of Workers",
    caption = "Source: PNAD-COVID19 Data (IBGE), 2020."
  ) +
  scale_x_continuous(breaks = seq(0, 100, by = 5)) +
  # Theme inspired by the BBC R Cookbook
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0, size = 20, face = "bold", color = "#222222"),
    plot.subtitle = element_text(hjust = 0, size = 16, margin = margin(t = 5, b = 20), color = "grey40"),
    plot.caption = element_text(hjust = 1, size = 12, color = "grey40", face = "italic"),
    panel.background = element_blank(),
    panel.grid.major.y = element_line(color = "#cbcbcb", linetype = "dashed"),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    axis.ticks = element_blank(),
    axis.text = element_text(size = 12, color = "#333333"),
    axis.title = element_text(size = 14, face = "bold"),
    axis.title.x = element_text(margin = margin(t = 10))
  )

# Display the chart
print(workload_chart)
```