This vignette demonstrates how to use fundr for common donor
analytics tasks, using the included fundr_portfolio
dataset.
library(fundr)
fundr_setup()
#> fundr session configured:
#> - Scientific notation disabled (scipen = 999)
#> - Display digits: 7
#> - Fiscal year start month: 7 (July)Donor Status Classification
The donor_status() function classifies donors based on
when they last gave:
- Active: Gave in the current fiscal year
- LYBUNT: Last Year But Unfortunately Not This (gave last FY, not this FY)
- SYBUNT: Some Year But Unfortunately Not This (gave 2+ FYs ago but within threshold)
-
Lapsed: Gave more than
lapsed_yearsago (default: 5) - Never: No giving history
# Classify based on last gift date
status <- donor_status(fundr_portfolio$last_gift_date)
table(status, useNA = "ifany")
#> status
#> Active LYBUNT SYBUNT Lapsed Never
#> 3691 184 680 2446 2999Understanding the Classification
# Sample dates to illustrate classification
sample_dates <- as.Date(c(
"2025-10-15", # Current FY - Active
"2024-09-01", # Last FY - LYBUNT
"2023-12-15", # Two FYs ago - SYBUNT
"2020-06-30", # Older - Lapsed
NA # No gift - Never
))
donor_status(sample_dates)
#> [1] Active LYBUNT SYBUNT Lapsed Never
#> Levels: Active < LYBUNT < SYBUNT < Lapsed < NeverCustomizing the SYBUNT Window
By default, SYBUNT covers gifts from 2 to lapsed_years
(5) fiscal years ago. Use sybunt_years to narrow or widen
this window:
# Default: SYBUNT is 2-5 years ago
donor_status(sample_dates, lapsed_years = 5)
#> [1] Active LYBUNT SYBUNT Lapsed Never
#> Levels: Active < LYBUNT < SYBUNT < Lapsed < Never
# Narrower: SYBUNT is only 2-3 years ago (4+ becomes Lapsed)
donor_status(sample_dates, lapsed_years = 5, sybunt_years = 2)
#> [1] Active LYBUNT SYBUNT Lapsed Never
#> Levels: Active < LYBUNT < SYBUNT < Lapsed < NeverCustom Status Labels
Some organizations prefer different terminology. Use the
labels parameter to customize:
# Custom labels
donor_status(
sample_dates,
labels = c(
"Active" = "Current",
"Never" = "Non-Donor",
"Lapsed" = "Inactive"
)
)
#> [1] Current LYBUNT SYBUNT Inactive Non-Donor
#> Levels: Current < LYBUNT < SYBUNT < Inactive < Non-DonorGiving Analysis
Gift Level Bucketing
Categorize gifts by amount using
bucket_gift_level():
# Sample gift amounts
gifts <- c(25, 100, 500, 1000, 5000, 25000, 100000, 1000000)
# Get giving level labels
bucket_gift_level(gifts, what = "giving_level")
#> [1] $1+ $100+ $500+ $1,000+ $5,000+ $25,000+
#> [7] $100,000+ $1,000,000+
#> 23 Levels: $150,000,000+ < $100,000,000+ < $50,000,000+ < ... < No Amount
# Get broader buckets
bucket_gift_level(gifts, what = "ask_bucket")
#> [1] Less than $100 $100 to $249 $500 to $999 $1K to $2.4K $5K to $9.9K
#> [6] $25K to $49.9K $100K to $249K $1M to $2.49M
#> 23 Levels: $150M+ < $100M to $149M < $50M to $99.9M < ... < No AmountAnalyzing Portfolio Giving
# Bucket the largest gifts
fundr_portfolio$gift_level <- bucket_gift_level(
fundr_portfolio$largest_gift_amount,
what = "giving_level"
)
# Distribution (showing non-NA values)
gift_dist <- table(fundr_portfolio$gift_level)
gift_dist[gift_dist > 0]
#>
#> $1,000,000+ $750,000+ $500,000+ $250,000+ $100,000+ $50,000+
#> 3 1 5 16 52 95
#> $25,000+ $10,000+ $5,000+ $2,500+ $1,000+ $500+
#> 222 474 622 888 1368 1005
#> $250+ $100+ $1+
#> 894 816 540Giving Summary Statistics
# Filter to donors only
donors <- fundr_portfolio[!is.na(fundr_portfolio$first_gift_date), ]
# Summary statistics
cat("Total Donors:", nrow(donors), "\n")
#> Total Donors: 7001
cat("Total Giving: $", format(sum(donors$total_giving, na.rm = TRUE), big.mark = ","), "\n")
#> Total Giving: $ 620,701,426
cat("Average Gift: $", format(mean(donors$largest_gift_amount, na.rm = TRUE), big.mark = ","), "\n")
#> Average Gift: $ 7,700.246
cat("Median Gift: $", format(median(donors$largest_gift_amount, na.rm = TRUE), big.mark = ","), "\n")
#> Median Gift: $ 1,125Prospect Analysis
Research Ratings
The portfolio includes wealth/capacity ratings:
# Rating distribution
rating_dist <- table(fundr_portfolio$research_rating)
rating_dist
#>
#> A - $100M+ B - $50M to $99.9M C - $25M to $49.9M D - $10M to $24.9M
#> 1 3 11 19
#> E - $5M to $9.9M F - $2.5M to $4.9M G - $1M to $2.49M H - $500K to $999K
#> 52 111 187 305
#> I - $250K to $499K J - $100K to $249K K - $50K to $99K L - $25K to $49K
#> 474 849 2045 1805
#> M - $10K to $24K N - Less than $10K U - Unrated
#> 1455 1199 1484Rating Bucketing
Convert ratings to broader categories:
# Get rating buckets
fundr_portfolio$rating_bucket <- bucket_rating_level(
fundr_portfolio$research_rating
)
table(fundr_portfolio$rating_bucket, useNA = "ifany")
#>
#> <NA>
#> 10000Prospect Pipeline
Analyze the prospect pipeline:
# Prospects with assigned fundraisers
assigned <- fundr_portfolio[!is.na(fundr_portfolio$fundraiser), ]
cat("Assigned Prospects:", nrow(assigned), "\n\n")
#> Assigned Prospects: 3636
# Pipeline stages
table(assigned$prospect_status)
#>
#> Identification Qualification Cultivation Solicitation
#> 56 799 1156 667
#> Stewardship Disqualification
#> 647 14Fundraiser Portfolios
# Prospects per fundraiser
table(fundr_portfolio$fundraiser, useNA = "ifany")
#>
#> Amanda Foster Christopher Lee David Rodriguez James Wilson Jennifer Chen
#> 381 401 334 383 387
#> Katherine Brown Marcus Thompson Michael Davis Rachel Williams Sarah Mitchell
#> 320 378 375 337 340
#> <NA>
#> 6364Currency Formatting
Use format_currency() and
format_currency_short() for display:
amounts <- c(1234.56, 50000, 1500000, 25000000)
# Full format
format_currency(amounts)
#> [1] "$1,235" "$50,000" "$1,500,000" "$25,000,000"
# Short format (K/M/B)
format_currency_short(amounts)
#> [1] "$1.2K" "$50K" "$1.5M" "$25M"Percentage Formatting
rates <- c(0.156, 0.0234, 0.789)
format_pct(rates)
#> [1] "15.6%" "2.3%" "78.9%"
format_pct(rates, digits = 1)
#> [1] "15.6%" "2.3%" "78.9%"Constituent Age
Calculate ages from dates of birth:
# Using calc_age() for full dates
sample_dobs <- as.Date(c("1960-05-15", "1975-12-01", "1990-03-22"))
calc_age(sample_dobs)
#> [1] 65 50 35
# Using calc_age_partial() for varied formats (from portfolio)
sample_dobs_varied <- c("05/15/1960", "December 1975", "1990")
calc_age_partial(sample_dobs_varied)
#> [1] NA 50 35Age Distribution in Portfolio
# Calculate ages
ages <- calc_age_partial(fundr_portfolio$dob)
# Summary
summary(ages)
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 21.0 44.0 54.0 54.1 64.0 95.0 6413
# Age buckets
age_buckets <- cut(ages,
breaks = c(0, 30, 40, 50, 60, 70, 80, Inf),
labels = c("Under 30", "30-39", "40-49", "50-59", "60-69", "70-79", "80+"),
right = FALSE
)
table(age_buckets, useNA = "ifany")
#> age_buckets
#> Under 30 30-39 40-49 50-59 60-69 70-79 80+ <NA>
#> 199 408 748 935 767 368 162 6413Giving Tenure
Total Years Giving
# Distribution of giving tenure
table(donors$total_years_giving, useNA = "ifany")
#>
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#> 193 355 375 340 389 332 348 293 267 303 303 268 230 219 224 244 207 234 218 179
#> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
#> 171 168 178 128 127 112 111 92 78 79 73 48 47 28 27 12 1Consecutive Years
# Recent donors with consecutive year streaks
recent_donors <- donors[!is.na(donors$consecutive_years_giving), ]
cat("Donors with active streaks:", nrow(recent_donors), "\n\n")
#> Donors with active streaks: 3875
# Streak distribution
table(recent_donors$consecutive_years_giving)
#>
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#> 903 641 455 361 290 249 189 172 139 95 63 74 65 47 45 29 18 22 11 7Regional Analysis
The portfolio has geographic weighting toward Dallas/Fort Worth:
table(fundr_portfolio$region)
#>
#> Local Texas National International Unknown
#> 4992 2526 1790 499 193Giving by Region
# Average total giving by region
aggregate(total_giving ~ region, data = donors, FUN = mean, na.rm = TRUE)
#> region total_giving
#> 1 Local 90078.73
#> 2 Texas 100897.23
#> 3 National 75724.48
#> 4 International 52271.56
#> 5 Unknown 106824.28Segmentation Example
Combine multiple attributes for donor segmentation:
# Create a high-value active donor segment
high_value_active <- fundr_portfolio[
!is.na(fundr_portfolio$donor_status) &
fundr_portfolio$donor_status == "Active" &
!is.na(fundr_portfolio$largest_gift_amount) &
fundr_portfolio$largest_gift_amount >= 10000,
]
cat("High-Value Active Donors:", nrow(high_value_active), "\n")
#> High-Value Active Donors: 449
cat("Total Giving: $", format(sum(high_value_active$total_giving, na.rm = TRUE), big.mark = ","), "\n")
#> Total Giving: $ 257,339,820Using dplyr (Optional)
If you prefer tidyverse workflows:
library(dplyr)
fundr_portfolio |>
filter(!is.na(first_gift_date)) |>
mutate(
donor_status = donor_status(last_gift_date),
gift_level = bucket_gift_level(largest_gift_amount),
age = calc_age_partial(dob)
) |>
group_by(donor_status) |>
summarize(
n = n(),
total_giving = sum(total_giving, na.rm = TRUE),
avg_age = mean(age, na.rm = TRUE)
)Summary
| Function | Purpose |
|---|---|
donor_status() |
Classify donors by recency |
bucket_gift_level() |
Categorize gift amounts |
bucket_rating_level() |
Categorize wealth ratings |
calc_age() |
Calculate age from DOB |
calc_age_partial() |
Calculate age from partial DOB |
format_currency() |
Format as $1,234.56 |
format_currency_short() |
Format as $1.2M |
format_pct() |
Format as percentage |
years_since() |
Years since a date |
total_giving_years() |
Count distinct giving years |
consecutive_giving_years() |
Current giving streak |