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To extract a table from a website using the R programming language, you can follow these steps:

1. Install and load the required R libraries, such as rvest, tidyr, and dplyr.

2. Identify the URL where the table is located.

3. Use the read_html() function from the rvest library to read the HTML code of the webpage.

4. Use the html_nodes() function from the rvest library to select the table from the HTML code. You can identify the table by its HTML tag, class, or ID.

5. Use the html_table() function from the rvest library to convert the selected table into a data frame.

6. Clean and format the data frame using the tidyr and dplyr libraries. You can remove unnecessary columns, rename columns, and convert data types.

7. Save the extracted and cleaned data as a CSV, Excel, or other file format.

Here's an example code snippet that shows how to extract a table from a website:

library(rvest)
library(tidyr)
library(dplyr)

# Specify the URL of the webpage
url <- "https://example.com/table.html"

# Read the HTML code of the webpage
html <- read_html(url)

# Select the table from the HTML code
table <- html %>%
  html_nodes("table.class") %>%
  html_table()

# Clean and format the data frame
table_df <- table %>% 
  select(-1) %>% # remove first column
  rename(newname = oldname) %>% # rename a column
  mutate(newcol = as.numeric(oldcol)) %>% # convert data type
  filter(!is.na(newcol)) %>% # remove rows with missing data
  group_by(groupvar) %>% # group data by variable
  summarise(meanval = mean(newcol)) # calculate summary statistics

# Save the extracted and cleaned data as a CSV file
write.csv(table_df, "table_data.csv", row.names = FALSE)