Number manipulation is a fundamental part of data preprocessing and analysis in R, especially in the field of personal finance analytics. R offers several powerful packages designed specifically for number manipulation tasks, enabling users to clean, transform, and analyze numerical data efficiently.
In this blog post, we will explore popular R packages for number manipulation, their important functions, common use cases, code examples, and how they are utilized in the personal finance analytics workflow.
Popular R Packages
Popular packages for number manipulation and processing in R are as followings :
dplyr
base R
lubridate
zoo
Important Number Manipulation Functions
Once you are able to extract numbers from its original data type e.g. from a text, you can manipulate the numbers in many way. The most obvious one is that you can perform arithmetic operations on the number. Another way is to rearrange the number into datetime format and then convert it into temporal.
The functions in the following R packages will allow you to manipulate numbers to fit into your data processing pipeline.
1. dplyr
mutate(): Add new variables or transform existing variables.
filter(): Select rows based on conditions.
summarise(): Summarize data into single values.
arrange(): Reorder rows based on variable values.
2. base R
sum(): Calculate the sum of a vector.
mean(): Calculate the mean of a vector.
median(): Calculate the median of a vector.
sd(): Calculate the standard deviation of a vector.
3. lubridate
ymd(): Parse dates in "year-month-day" format.
ymd_hms(): Parse dates in "year-month-day hour:minute:second" format.
years(), months(), days(): Extract years, months, and days from date objects.
4. zoo
rollmean(): Calculate rolling mean.
rollapply(): Apply a function over a rolling window.
Common Use of the Packages :
Data Aggregation: Summarizing and calculating statistics on financial data (e.g., total expenses, average monthly income).
Date and Time Manipulation: Parsing and formatting date and time data for time-series analysis.
Filtering and Sorting: Selecting and ordering financial data based on specific conditions.
Calculating Rolling Statistics: Analyzing trends and patterns in financial data using rolling averages and other rolling statistics.
Currency Conversion and Exchange Rate Calculation: Converting between different currencies and calculating exchange rates.
String to Number Conversion: Converting string data to numerical data for mathematical operations.
Code Example
Here's a simple example using the dplyr package to calculate the total expenses and average monthly income, and also convert string to number.
# Load the dplyr package
library(dplyr)
# Sample financial data in a data frame
financial_data <- data.frame(
month = c("January", "February", "March"),
expenses = c("$1000", "$1200", "$1100"),
income = c("$3000", "$3100", "$3200")
)
# Convert string to number
financial_data$expenses <- as.numeric(gsub("\\\\$", "", financial_data$expenses))
financial_data$income <- as.numeric(gsub("\\\\$", "", financial_data$income))
# Calculate total expenses and average monthly income
financial_summary <- financial_data %>%
summarise(
total_expenses = sum(expenses),
avg_monthly_income = mean(income)
)
# Print the financial summary
print(financial_summary)Mastering number manipulation in R is essential for effective data preprocessing and analysis in personal finance analytics. With the powerful packages like dplyr, base R, lubridate, and zoo, you can efficiently clean, transform, and analyze numerical data to derive valuable insights from your personal finance datasets. Explore the documentation and experiment with these packages to enhance your number manipulation skills in R.