Building a Classification Model for Assessing Environmental Effects of Chemicals using R

May 22, 2023

Andrew Jameson

Canada

R Programming

PhD in Environmental Science from MIT, is a renowned Environmental Data Scientist with over 15 years of experience. An authority in using R for environmental studies.

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First, let's talk about the advantages of R programming:

R is a strong language for statistical analysis and data visualization, making it especially well-suited to study and model how various chemicals affect the environment. Here are a few major advantages of using R in this situation.

Data Handling:

A powerful environment for managing data is provided by R. You can easily manipulate, process, and transform data as necessary thanks to its ability to handle both small and large datasets.

Statistical Analysis:

R is a fantastic choice for creating complex statistical models, such as the classification model covered in this blog post, because it was specifically created for statistical computation.

Machine Learning:

In R, machine learning algorithms are supported in great detail. There are numerous packages that offer functions for creating and analyzing machine learning models, including classification, regression, clustering, and other techniques (such as "randomForest," "e1071,," and "caret").

Visualization:

One of R's most well-liked features is its graphic capabilities. 'ggplot2' and similar packages offer sophisticated functions for making excellent plots and charts. When exploring data and presenting the findings of your analysis, this can be incredibly helpful.

Reproducibility:

R is a fantastic tool for reproducible research. You can create a script that details every step of your analysis, from importing the data to visualizing the results, so that it is simple for others to duplicate and verify it.

Open-Source and Community:

R is open-source, which means that the community is constantly updating and improving it. It is also free to use. There are a ton of tools, guides, and fixes for typical issues that R users all over the world share.

Let us dive to our Topic:

Analyzing the environmental effect:

It can be difficult and multifaceted to analyze how different chemicals affect the environment. It necessitates knowledge of various chemical properties, environmental elements, and the capacity to decipher how these elements interact with one another.

Today, we'll look at how to create a classification model for this purpose using R, one of the most robust and flexible languages for machine learning and data analysis.

Classification Models and Environmental Studies:

Its Importance In environmental studies, classification models play a crucial role in assisting researchers in outcome prediction and decision-making. Based on their characteristics, these models are able to classify the potential effects of various chemical substances.

For instance, based on a substance's physical and chemical characteristics, we can determine whether it is likely to harm the environment. This type of prediction, which is frequently based on machine learning algorithms, can assist in the planning of safer industrial practices as well as potential ecological disasters.


# Setting the seed for reproducibility
set.seed(123)
# Creating the dataset
data <- data.frame(
Solubility = runif(100, 0, 100),
pH = runif(100, 0, 14),
Production_Volume = rnorm(100, 5000, 2000),
Usage = sample(c("Industrial", "Commercial", "Household"), 100, replace = TRUE),
Environmental_Effect = sample(c("Harmful", "Not harmful"), 100, replace = TRUE)
# Checking the dataset
head(data)

While rnorm produces numbers from a normal distribution, runif generates uniform random numbers within a given range. For the purpose of producing categorical variables at random, use the sample function.

Preprocessing of Data:

We must preprocess the data before training the model. Data cleaning (handling missing values), feature scaling (bringing all features to the same scale), and encoding for categorical variables are frequently involved in this step.

We only need to encode the categorical variables and scale the numerical ones because our synthetic dataset is clear and devoid of any missing values.


# Encoding the categorical variables
data$Usage <- as.numeric(factor(data$Usage))
data$Environmental_Effect <- as.numeric(factor(data$Environmental_Effect))
# Scaling the numerical variables
data$Solubility <- scale(data$Solubility)
data$pH <- scale(data$pH)
data$Production_Volume <- scale(data$Production_Volume)
head(data)

Categorical variables are transformed into factors using the factor function, and these factors are then encoded as numerical values using as.numeric. Scaling of features is done using the scale function.

Divvying Up the Dataset:

Our dataset must then be divided into a training set and a test set. The model is trained using the training set, and its performance is assessed using the test set. The split will be 70/30, meaning that 70% of the data will be used for training and 30% for testing.


#Set the seed for reproducibility with set.seed(123).
# Divvying up the dataset
sample for train_index(Data:1:nrow, Data:0.7*nrow))
data from train_set[train_index,]
data from test_set[-train_index,]
# Examining the practice and exam sets
head(train_set)
head(test_set)
For the training set, 70% of the rows are randomly chosen, and the remaining 30% are used for the test set.

For the training set, 70% of the rows are randomly chosen, and the remaining 30% are used for the test set.

Construction of the Classification Model:

We will employ a Random Forest classifier for our task, which is a strong and adaptable machine learning algorithm that can handle both categorical and numerical variables. During training, Random Forest builds a large number of decision trees and outputs the mode of the classes (classification) of the individual trees.

To train our model, we'll use the randomForest package in R. You can install it if you haven't already by running the following command:


# Installing the randomForest package
install.packages("randomForest")
#Then, load the library:
# Loading the randomForest library
library(randomForest)
#Let's construct the model now:
# Setting the seed for reproducibility
set.seed(123)
# Building the Random Forest model
model <- randomForest(Environmental_Effect ~ ., data = train_set, ntree = 500)
# Checking the model
print(model)

For model training, the randomForest function is utilized. We want to predict the Environmental_Effect based on all other variables in the dataset, as shown by the formula Environmental_Effect . The number of trees to be grown in the forest is specified by the ntree parameter.

Evaluating the Model:

We must assess the model's performance after training. On the test set, predictions are made, and these predictions are compared to the actual values.


# Making predictions
predictions <- predict(model, newdata = test_set)
# Checking the predictions
print(predictions)
#Then, we can calculate the accuracy of the model:
# Calculating the accuracy
accuracy <- sum(predictions == test_set$Environmental_Effect) / nrow(test_set)
# Printing the accuracy
print(paste("Accuracy:", round(accuracy, 2)))

Let's dissect the R code that was used to write this blog post.

Getting the Dataset Ready:


# Setting the seed for reproducibility
set.seed(123)
The random number generation seed in R is set in this line. When your work needs to be reproducible, setting the seed ensures that the random number sequence is repeatable.
# Creating the dataset
data <- data.frame(
Solubility = runif(100, 0, 100),
pH = runif(100, 0, 14),
Production_Volume = rnorm(100, 5000, 2000),
Usage = sample(c("Industrial", "Commercial", "Household"), 100, replace = TRUE),
Environmental_Effect = sample(c("Harmful", "Not harmful"), 100, replace = TRUE)
)

Here, 100 instances of each of the five variables solubility, pH, production_volume, usage, and environmental_effect are combined to create a synthetic dataset.

Preprocessing Data:


# Encoding the categorical variables
data$Usage <- as.numeric(factor(data$Usage))
data$Environmental_Effect <- as.numeric(factor(data$Environmental_Effect))
The categorical variables are transformed into factor levels by the factor function, and those factor levels are then given numerical values by as.numeric.
# Scaling the numerical variables
data$Solubility <- scale(data$Solubility)
data$pH <- scale(data$pH)
data$Production_Volume <- scale(data$Production_Volume)

The numerical variables are standardized using the scale function. It divides by the standard deviation and subtracts the mean to produce variables with a mean of 0 and a standard deviation of 1.

Splitting Dataset


# Setting the seed for reproducibility
set.seed(123)
# Splitting the dataset
train_index <- sample(1:nrow(data), 0.7*nrow(data))
train_set <- data[train_index, ]
test_set <- data[-train_index, ]

A training set contains seventy percent of the data, and a test set contains the remaining thirty percent. For the training set, a random sample of row indices is created using the sample function.

Construction of the Classification Model:


# Setting the seed for reproducibility
set.seed(123)
# Building the Random Forest model
model <- randomForest(Environmental_Effect ~ ., data = train_set, ntree = 500)

Environmental_Effect is used as the dependent variable and all other variables are used as predictors when training a Random Forest classifier using the randomForest function. The number of decision trees that will be grown in the forest is determined by the ntree argument.


# Making predictions
predictions <- predict(model, newdata = test_set)
Using the trained model on the test set, the predict function generates predictions.
# Calculating the accuracy
accuracy <- sum(predictions == test_set$Environmental_Effect) / nrow(test_set)

Finally, the ratio of correct predictions to the total number of instances in the test set is used to determine the model's accuracy. The predicted and actual values are compared using the == operator, producing a vector of TRUE (for matches) and FALSE (for mismatches) as a result. The number of TRUE values (i.e., accurate predictions) is then counted by the sum function.

enarios, it is crucial to test out different algorithms and hyperparameters.

Conclusion:

Machine learning, in particular classification models, can significantly help in comprehending and anticipating the environmental effects of various chemicals. In this article, we have shown how to prepare the data, preprocess it, create a Random Forest classifier using R, and assess the performance of the classifier. Using this methodology, we can predict a chemical's ecological impact based on its usage and properties, enabling us to make more informed choices about the production, use, and disposal of chemicals.

We must always keep in mind that while this model offers insightful information, the accuracy and completeness of the input data are crucial to its predictions. Since you want to analyze real-world scenarios, it is imperative to make sure the data is well-curated and representative of those scenarios. Additionally, it's crucial to test out various models because different scenarios might call for various machine learning techniques in order to achieve the best outcomes.

The use of data science in this area can not only increase understanding of chemical effects but also help shape ethical industrial practices, as the connection between chemicals and the environment continues to be a major concern. We can anticipate more sophisticated models that will help maintain a healthy balance between industrial progress and environmental sustainability thanks to ongoing developments in data science and machine learning.