Introduction

Imagine you’re finalizing rosters for Saturday’s matches when you discover that three players share the same name but different email addresses—and six registrations have no signed waiver on file. Suddenly, what should have been a routine check becomes a scramble to manually hunt down missing information. Sound familiar? In grassroots soccer clubs, messy data isn’t just an annoyance—it costs time, risks compliance, and undermines trust in your reports. What if you could run a “data health” check each morning and instantly see only the exceptions you need to fix?

In this post, you’ll learn how a handful of R commands can:

1. Spot duplicates before you overbook a session
2. Flag missing waivers or emergency contacts
3. Detect out-of-range values (like impossible birthdates)
4. Automate alerts so you never miss a dirty record again

No heavy coding, no manual spot-checks—just clear, actionable output.

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Why Data Cleaning Matters

• Decisions Depend on Data
  Under-filled teams, unreturned gear, or lapsed insurance all start with bad data. If your rosters or compliance logs aren’t accurate, any downstream report is suspect.
• Administrative Overhead
  Manually scanning hundreds of rows for edge cases eats into your day—and still misses surprises. Automating quality checks frees you to focus on coaching, communication, and strategic planning.

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Three Real-World Scenarios

1. Duplicate Registrations
   A parent mistakenly fills out your Google Form twice, swapping “Bob” for “Bobby” in the second entry. Without a catch, you risk overestimating headcount and ordering too many jerseys.
2. Missing Critical Fields
   You require waivers and an emergency-contact phone. An oversight in data entry could leave you noncompliant with your league’s insurance requirements.
3. Out-of-Bounds Values
   A typo in the birthdate field can place a U12 player outside the allowed age range—or worse, set their birth year in 2030. Spotting these anomalies quickly is essential for fair play and safety.

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A “Data Health” Script in Action

Below is a concise R script you can adapt to your club’s data. It reads your roster, runs three checks, and outputs the exceptions for you to review.

Click Here to Download the dataset:

Data Health Script

library(dplyr)
library(readr)
library(lubridate)

# Find all records where first_name + last_name appears more than once
dupe_records <- mufc_u14_roster %>%
  group_by(first_name, last_name) %>%
  filter(n() > 1) %>%
  ungroup()

# Now view both rows
View(dupe_records)
#--------------------------------------------------------------------------------


# Check for missing waivers or emergency contacts
missing_info <- mufc_u14_roster %>%
  filter(is.na(waiver_signed) | waiver_signed == FALSE |
           is.na(emergency_phone) | emergency_phone == "")

View(missing_info)

#--------------------------------------------------------------------------------

#  Check for implausible birthdates (e.g., outside U6–U18 range)

library(dplyr)
library(readr)
library(lubridate)

bad_birthdates <- mufc_u14_roster %>%
  filter(birthdate < as.Date("2011-01-01") |
           birthdate > Sys.Date())

View(bad_birthdates)
#-------------------------------------------------------------------------------



# 5. Save these files to your local drive (Write out exception reports)
write_csv (dupes,         "Desktop/Grassroots Soccer Data/dupe_records.csv")
write_csv(missing_info,  "/Desktop/Grassroots Soccer Data/missing_infos.csv")
write_csv(bad_birthdates, "Desktop/Grassroots Soccer Data/bad_birthdates.csv")

Code Output

Result: Implausible birthdates (e.g., outside of 2011)

Result: Missing waivers or emergency contacts

Find all records where first_name + last_name appears more than once.

So far, here’s what we’ve learned—and why even this “basic” data-health script already outshines Excel or Google Sheets:

1. Reproducibility & Consistency
   • One script, one click—every run applies the exact same rules. No more “which filters did I set yesterday?”
2. Powerful Logic in One Place
   • Finding duplicate names despite different emails, flagging missing waivers or phones, and spotting impossible birthdates: all in a single, clear pipeline rather than scattered helper columns and COUNTIFs.
3. Speed & Scalability
   • Processes hundreds or thousands of rows in seconds—with zero lag as your roster grows—whereas Sheets can grind to a halt once you hit a few thousand records.
4. Clean, Ready-to-Use Outputs
   • Save these files to your local drive, they are ready for reporting, archiving, or feeding into downstream workflows—no manual “Save As…” or export steps required.

Even at this introductory stage, R gives you a more reliable, maintainable, and performant way to keep your club’s data clean.