This article represents different ways in which one or more columns in a data frame could be converted to factor when working with R programming language. Please feel free to comment/suggest if I missed mentioning one or more important points. Also, sorry for the typos.
Following are the key points described later in this article:
Following data frame, df, is used in the code sample below:
param_a param_b param_c diagnosis param_d 1 23 0.61 10452 positive y 2 18 0.85 9876 positive n 3 22 0.32 6534 negative y 4 37 0.56 8743 positive y 5 15 0.44 9876 negative n 6 25 0.13 4321 negative n 7 55 0.51 7685 positive y
In above data frame, both diagnosis and param_d are character vectors. One could quickly check classes of all columns using the following command:
sapply(df, class)
Following is demonstrated the code samples along with help text. Pay attention that one could use lapply method to change the single column to factor. However, it does throw warning message.
# Invoke as.factor method on dataframe$columnName df$param_d <- as.factor(df$param_d) # Invoke as.factor method on columns represented array notation df[, 'param_d'] <- as.factor( df[, 'param_d'] ) # Use lapply method; Both of below makes param_d column as factor df[, 'param_d'] <- lapply(df[, 'param_d'], factor) df[, c("param_d")] <- lapply(df[, c("param_d")], factor)
Use lapply method to change columns to factor.
df[, c("param_d", "diagnosis")] <- lapply(df[, c("param_d", "diagnosis")], factor)
In recent years, artificial intelligence (AI) has evolved to include more sophisticated and capable agents,…
Adaptive learning helps in tailoring learning experiences to fit the unique needs of each student.…
With the increasing demand for more powerful machine learning (ML) systems that can handle diverse…
Anxiety is a common mental health condition that affects millions of people around the world.…
In machine learning, confounder features or variables can significantly affect the accuracy and validity of…
Last updated: 26 Sept, 2024 Credit card fraud detection is a major concern for credit…