How to convert entire dataframe to numeric while preserving decimals in R

Casting or type conversion is among the data preparation processes that are normally performed in R to prepare the data for analysis or modeling. A use case that is frequently used is the case where one has to convert an entire Data Frame to numeric with decimals. It helps to make all the necessary data straight and clear so that precise calculations can be introduced and important insights extracted accurately using R Programming Language.

Understanding Data Frame Conversion

R allows data to be in the forms of vectors and data frames among others. Such objects can include character, factor, or numeric data, and all of them play different roles. In cases of data analysis by statistics or figures or the creation of graphics, it becomes crucial to refer to numeric data types. Type casting enables the conversion of data into the desired format, to fit the functions or methods expected and to get the correct output from the analysis.

Converting to Numeric

To perform the conversion of a Data Frame to numeric, one needs to check the type of every single column. This includes managing of characters and factors which have to be turned to numeric properly to reduce NA values resulting from coercion.

Preserving Decimals

It is, therefore, crucial to retain the decimal place when converting to numeric. This means that the act of converting the number does not round it to a certain decimal point or even remove the decimal point entirely. This can be done using right functions and if factors and characters are managed properly or correctly.

Example 1: Using type.convert()

The type.convert() function is useful for automatically converting character columns to their appropriate types, including numeric.

# Create a sample data frame with character columns
df <- data.frame(
  A = c("1.1", "2.2", "3.3"),
  B = c("4.4", "5.5", "6.6"),
  C = c("7.7", "8.8", "9.9"),
  stringsAsFactors = FALSE
)

# Convert columns to numeric using type.convert()
df_numeric <- as.data.frame(lapply(df, type.convert, as.is = TRUE))

# Print the converted data frame
print(df_numeric)

# Check the data types of the columns
str(df_numeric)

Output:

    A   B   C
1 1.1 4.4 7.7
2 2.2 5.5 8.8
3 3.3 6.6 9.9

 $ A: num  1.1 2.2 3.3
 $ B: num  4.4 5.5 6.6
 $ C: num  7.7 8.8 9.9

Example 2: Using apply()

The apply() function can be used to apply a function to the margins of an array or matrix, which can also be adapted for data frames.

# Create a sample data frame with character columns
df <- data.frame(
  A = c("1.1", "2.2", "3.3"),
  B = c("4.4", "5.5", "6.6"),
  C = c("7.7", "8.8", "9.9"),
  stringsAsFactors = FALSE
)

# Convert columns to numeric using apply()
df_numeric <- as.data.frame(apply(df, 2, as.numeric))

# Print the converted data frame
print(df_numeric)

# Check the data types of the columns
str(df_numeric)

Output:

    A   B   C
1 1.1 4.4 7.7
2 2.2 5.5 8.8
3 3.3 6.6 9.9

'data.frame':	3 obs. of  3 variables:
 $ A: num  1.1 2.2 3.3
 $ B: num  4.4 5.5 6.6
 $ C: num  7.7 8.8 9.9

Example 3: Using purrr from tidyverse

The purrr package provides a functional programming toolkit for R, which can be used to apply functions over data structures.

# Install and load purrr package if not already installed
# install.packages("purrr")
library(purrr)

# Create a sample data frame with character columns
df <- data.frame(
  A = c("1.1", "2.2", "3.3"),
  B = c("4.4", "5.5", "6.6"),
  C = c("7.7", "8.8", "9.9"),
  stringsAsFactors = FALSE
)

# Convert columns to numeric using map_df
df_numeric <- map_df(df, as.numeric)

# Print the converted data frame
print(df_numeric)

# Check the data types of the columns
str(df_numeric)

Output:

# A tibble: 3 × 3
      A     B     C
  <dbl> <dbl> <dbl>
1   1.1   4.4   7.7
2   2.2   5.5   8.8
3   3.3   6.6   9.9

tibble [3 × 3] (S3: tbl_df/tbl/data.frame)
 $ A: num [1:3] 1.1 2.2 3.3
 $ B: num [1:3] 4.4 5.5 6.6
 $ C: num [1:3] 7.7 8.8 9.9

Example 4: Using data.table::set()

The data.table package allows for fast and memory-efficient data manipulation. The set() function can be used to update columns by reference.

# Install and load data.table package if not already installed
# install.packages("data.table")
library(data.table)

# Create a sample data frame with character columns
df <- data.frame(
  A = c("1.1", "2.2", "3.3"),
  B = c("4.4", "5.5", "6.6"),
  C = c("7.7", "8.8", "9.9"),
  stringsAsFactors = FALSE
)

# Convert data frame to data table
dt <- as.data.table(df)

# Convert all columns to numeric using set()
for (col in names(dt)) {
  set(dt, j = col, value = as.numeric(dt[[col]]))
}

# Print the converted data table
print(dt)

# Check the data types of the columns
str(dt)

Output:

     A   B   C
1: 1.1 4.4 7.7
2: 2.2 5.5 8.8
3: 3.3 6.6 9.9

Classes ‘data.table’ and 'data.frame':	3 obs. of  3 variables:
 $ A: num  1.1 2.2 3.3
 $ B: num  4.4 5.5 6.6
 $ C: num  7.7 8.8 9.9
 - attr(*, ".internal.selfref")=<externalptr> 

Conclusion

Converting an entire data frame to numeric while preserving decimals can be done using various methods in R. The examples provided show how to achieve this using type.convert(), apply(), purrr::map_df, data.table::set(), and custom functions with dplyr::mutate_all(). Each method has its own advantages and can be chosen based on the specific requirements and structure of your data.