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Introduction to fundr

Source: vignettes/fundr.Rmd
fundr.Rmd

fundr is an R package providing lightweight, tidyverse-compatible utilities for fundraising and advancement analytics. It’s designed with minimal dependencies while offering powerful tools for common nonprofit data tasks. ## Installation

# Install from GitHub
# install.packages("pak")
pak::pak("mattfarrow1/fundr")

Quick Start

Load the package and set up your session:

library(fundr)

# Configure session for fundraising work
fundr_setup()
#> fundr session configured:
#> - Scientific notation disabled (scipen = 999)
#> - Display digits: 7
#> - Fiscal year start month: 7 (July)

fundr_setup() disables scientific notation so large dollar amounts display properly (e.g., $1,000,000 instead of 1e+06).

Sample Data

fundr includes fundr_portfolio, a synthetic dataset of 10,000 constituent records for learning and testing:

dim(fundr_portfolio)
#> [1] 10000    47
names(fundr_portfolio)[1:15]
#>  [1] "constituent_id"      "household_id"        "household_position" 
#>  [4] "record_type"         "title"               "first_name"         
#>  [7] "middle_name"         "last_name"           "suffix"             
#> [10] "nickname"            "company_name"        "gender"             
#> [13] "dob"                 "relationship_status" "address_1"

The dataset includes individuals (singles and married couples) and organizations with realistic data quality variations:

table(fundr_portfolio$record_type)
#> 
#>   Individual Organization 
#>         9200          800

Package Overview

Data Cleaning

Phone normalization - Clean messy phone numbers:

messy_phones <- c("(214) 555-1234", "972.555.4567", "1-817-555-8901")
normalize_phone(messy_phones)
#> [1] "214-555-1234" "972-555-4567" "817-555-8901"

ZIP code normalization - Standardize postal codes:

messy_zips <- c("75201-1234", "75201 5678", " 75201 ")
normalize_zip(messy_zips)
#> [1] "75201" "75201" "75201"

Case conversion - Convert between naming conventions:

to_snake_case("FirstName")
#> [1] "first_name"
to_title_case("first_name")
#> [1] "First Name"

Fiscal Year Functions

Work with fiscal years (default July start):

dates <- as.Date(c("2024-06-30", "2024-07-01", "2025-01-15"))

fy_year(dates)
#> [1] 2024 2025 2025
fy_label(dates)
#> [1] "FY24" "FY25" "FY25"
fy_quarter(dates)
#> [1] 4 1 3

Donor Analytics

Classify donors by recency:

last_gifts <- as.Date(c("2025-10-15", "2024-08-01", "2023-05-20", "2020-01-01", NA))
donor_status(last_gifts)
#> [1] Active LYBUNT SYBUNT Lapsed Never 
#> Levels: Active < LYBUNT < SYBUNT < Lapsed < Never

Calculate constituent age:

calc_age(as.Date("1985-03-15"))
#> [1] 41

Currency Formatting

Format numbers as currency:

amounts <- c(1234.56, 1500000, 25000000)

format_currency(amounts)
#> [1] "$1,235"      "$1,500,000"  "$25,000,000"
format_currency_short(amounts)
#> [1] "$1.2K" "$1.5M" "$25M"

Bucketing

Categorize gifts and ratings:

gift_amounts <- c(500, 50000, 1000000)
bucket_gift_level(gift_amounts, what = "giving_level")
#> [1] $500+       $50,000+    $1,000,000+
#> 23 Levels: $150,000,000+ < $100,000,000+ < $50,000,000+ < ... < No Amount
bucket_gift_level(gift_amounts, what = "ask_bucket")
#> [1] $500 to $999   $50K to $99.9K $1M to $2.49M 
#> 23 Levels: $150M+ < $100M to $149M < $50M to $99.9M < ... < No Amount

Visualization (requires ggplot2)

fundr provides a custom theme, color palettes, and currency scales:

library(ggplot2)

ggplot(data, aes(x = year, y = amount)) +
geom_col(fill = fundr_colors("teal")) +
  scale_y_currency(short = TRUE) +
  theme_fundr()

Learning More

Explore the other vignettes for deeper dives:

Convenience Operators

fundr includes helpful operators:

# "not in" operator (opposite of %in%)
c("a", "b", "c") %notin% c("b", "d")
#> [1]  TRUE FALSE  TRUE

And comment formatting helpers for scripts:

comment_header("Data Import")
#> [1] "# Data Import ------------------------------------------------------------------"
comment_divider()
#> [1] "# =============================================================================="