Visualizing weather data is essential for understanding trends, patterns, and anomalies in meteorological information. R, with its robust packages for data manipulation and visualization, offers powerful tools to create insightful weather data visualizations. This article will guide you through the process of visualizing weather data in R, covering data acquisition, preprocessing, and creating various types of visualizations.

Objectives and Goals

The primary objective of this article is to explore weather data visualization techniques and develop a forecasting model using R Programming Language. By exploring a dataset containing historical weather information, we try to demonstrate the power of data visualization in understanding trends and patterns, as well as the effectiveness of forecasting models in predicting future weather conditions.

Dataset Link: Weather History

The weather dataset has various columns such as:

1. Formatted Date
2. Summary
3. Precip Type
4. Temperature (C)
5. Apparent Temperature (C)
6. Humidity
7. Wind Speed (km/h)
8. Wind Bearing (degrees)
9. Visibility (km)
10. Loud Cover
11. Pressure (millibars)
12. Daily Summary

We’re using the weather dataset for our analysis and predictions. By this, we can make sure that our results are reliable and stay the same without adding any extra data from outside sources.

Step 1: Load Required Libraries and Dataset

Loads the necessary libraries ggplot2 and forecast into the R environment. These libraries contain functions and tools that we’ll use for data visualization and forecasting, respectively. Here, we read the weather dataset from the specified file path using the read.csv() function.

# Load necessary libraries
library(readr)     # For reading CSV files
library(dplyr)     # For data manipulation
library(ggplot2)   # For data visualization
library(forecast)  # For forecasting

df<-read.csv('C:\Users\GFG19565\Downloads\daily_weather_2020.csv')

Step 2: Display the Structure of the Dataset

It displays the structure of the weather_data dataset, showing information about its variables, data types, and other properties. It helps us to understand the structure of the dataset we’re working with.

str(df)

Output:

'data.frame':   96453 obs. of  12 variables:
 $ Formatted.Date          : chr  "2006-04-01 00:00:00.000 +0200" "2006-04-01 01:00:00.000 +0200" "
 $ Summary                 : chr  "Partly Cloudy" "Partly Cloudy" "Mostly Cloudy" "Partly Cloudy" ...
 $ Precip.Type             : chr  "rain" "rain" "rain" "rain" ...
 $ Temperature..C.         : num  9.47 9.36 9.38 8.29 8.76 ...
 $ Apparent.Temperature..C.: num  7.39 7.23 9.38 5.94 6.98 ...
 $ Humidity                : num  0.89 0.86 0.89 0.83 0.83 0.85 0.95 0.89 0.82 0.72 ...
 $ Wind.Speed..km.h.       : num  14.12 14.26 3.93 14.1 11.04 ...
 $ Wind.Bearing..degrees.  : num  251 259 204 269 259 258 259 260 259 279 ...
 $ Visibility..km.         : num  15.8 15.8 15 15.8 15.8 ...
 $ Loud.Cover              : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Pressure..millibars.    : num  1015 1016 1016 1016 1017 ...
 $ Daily.Summary           : chr  "Partly cloudy throughout the day." "Partly cloudy throughout the day.

Step 3: Visualization of Weather Data

Now we will plot the visualization of our Weather Data to get some valid pieces of information.

Histogram of Precipitation Intensity

A histogram of precipitation intensity reveals the distribution of precipitation events, highlighting the most common precipitation intensities and indicating the range of values.

ggplot(df, aes(x = precipIntensity)) +
  geom_histogram(binwidth = 0.01, fill = "lightblue", color = "black") +
  labs(title = "Distribution of Precipitation Intensity",
       x = "Precipitation Intensity (mm/hr)",
       y = "Frequency") +
  theme_minimal()

Output:

Weather Data Visualization

Scatter Plot of Wind Speed vs. Temperature

A scatter plot shows the relationship between wind speed and high temperature. This can help identify any correlations between wind conditions and temperature changes.

ggplot(df, aes(x = windSpeed, y = temperatureHigh)) +
  geom_point(alpha = 0.5, color = "purple") +
  labs(title = "Wind Speed vs. High Temperature",
       x = "Wind Speed (km/h)",
       y = "High Temperature (°C)") +
  theme_minimal()

Output:

Weather Data Visualization

Bar Plot of Precipitation Types

A bar plot displays the frequency of different types of precipitation (e.g., rain, snow), providing an overview of the distribution of precipitation types within the dataset.

ggplot(df, aes(x = precipType)) +
  geom_bar(fill = "pink", color = "darkblue") +
  labs(title = "Frequency of Precipitation Types",
       x = "Precipitation Type",
       y = "Frequency") +
  theme_minimal()

Output:

Weather Data Visualization

Histogram with Density Plot

We can plot the distribution of temperatures across the dataset, with the histogram showing the frequency of different temperature ranges.

# Create a histogram with density plot
ggplot(weather_data, aes(x = `Temperature (C)`)) +
  geom_histogram(aes(y = ..density..), bins = 30, fill = "Red", color = "black", 
                 alpha = 0.5) +  # Histogram with filled bars
  geom_density(fill = "green", alpha = 0.5) +  # Density plot with filled area
  labs(title = "Histogram with Density Plot",  # Title of the plot
       x = "Temperature (°C)",  # X-axis label
       y = "Density") +  # Y-axis label
  theme_minimal()  # Using minimal theme for aesthetics

Output:

Weather Data Visualization

The spread of the histogram and density plot illustrates the variability or range of temperatures present in the dataset. A wider spread indicates a greater variability in temperature readings, while a narrower spread suggests more consistency in temperature values.

Conclusion

Visualizing weather data in R allows you to uncover trends, patterns, and anomalies in meteorological data. By leveraging packages like ggplot2, Plotly, and dygraphs, you can create both static and interactive visualizations that provide valuable insights. Whether you’re analyzing temperature trends, humidity levels, or wind speeds, R offers the tools you need to effectively visualize and interpret weather data.