This workbook guides you through de-identifying the Apple
Grower Satisfaction Survey
(WorkerSatisfaction_300rows.xlsx: 300 rows, 16
columns).
| Step | Technique | Variables |
|---|---|---|
| 1 | Pseudonymization | worker_id, email_id,
owner_id |
| 2 | Aggregation | age (date of birth → age bands) |
| 3 | Pseudonymization | city, orchard_id |
| 4 | Anonymization | immigration_stat, username_id,
comments |
| Technique | Meaning in this workbook |
|---|---|
| Pseudonymization | Replace identifiers with coded values; a data key can reverse the mapping (Steps 1 and 3) |
| Aggregation | Group a detailed value into broader categories (Step 2: DOB → age band) |
| Anonymization | Irreversibly remove or transform data so individuals cannot be re-identified; no key file (Step 4) |
WorkerSatisfaction_300rows_deidentified.xlsx
— for research (e.g. public access…)data_key_file/WorkerSatisfaction_data_key_DUMMY.xlsx
— for authorized personnel only; store separately from the de-identified
data (dummy example)Do not edit the raw source file
(WorkerSatisfaction_300rows.xlsx).
| Part | Section | What happens |
|---|---|---|
| 1 | Setup | Install packages, set paths |
| 2 | Load and assess | Read raw data; review risks by de-identification step |
| 3 | Transform | Steps 1–4: pseudonymize → aggregate → pseudonymize → anonymize |
| 4 | Verify and deliver | QA checks → export de-identified file → export data key |
| Step | Technique | What to look for (Part 2) | What to do (Part 3) |
|---|---|---|---|
| 1 | Pseudonymization | Names, emails, owner names | Replace with Worker_01, Email_01,
Owner_01, … (reversible via key) |
| 2 | Aggregation | age is date of birth |
Group into 5-year age bands |
| 3 | Pseudonymization | City and orchard names | Replace with City_01, Orchard_01, …
(reversible via key) |
| 4 | Anonymization | Immigration status, handles, free text | Pool small groups; remove username_id and
comments (irreversible) |
R and RStudio (workshop prerequisite)
This workbook assumes you have R and RStudio installed and can open and run.Rmdfiles. If you have not set these up yet, follow the UBC Library Research Commons guide:
Installing R and RStudio
| Package | Purpose |
|---|---|
readxl |
Read the original Excel survey file |
dplyr |
Transform data (native pipe \|>) |
writexl |
Export Excel outputs |
Run Install packages once if a package is missing.
# install.packages("readxl")
# install.packages("dplyr")
# install.packages("writexl")
# install.packages(c("readxl", "dplyr", "writexl"))library(readxl)
library(dplyr)
library(writexl)DATA_FILE <- "WorkerSatisfaction_300rows.xlsx"
OUTPUT_FILE <- "WorkerSatisfaction_300rows_deidentified.xlsx"
KEY_DIR <- "data_key_file"
KEY_FILE <- file.path(KEY_DIR, "WorkerSatisfaction_data_key_DUMMY.xlsx")
K_THRESHOLD <- 5k-anonymity threshold: categories with fewer than 5 records will be pooled before anonymization.
cat("K_THRESHOLD =", K_THRESHOLD, "\n")## K_THRESHOLD = 5cat("Data key file:", KEY_FILE, "\n")## Data key file: data_key_file/WorkerSatisfaction_data_key_DUMMY.xlsxLoad the unmodified survey and review what makes the data identifiable.
raw <- read_excel(DATA_FILE, sheet = "Unmodified data")
cat("Raw data:", nrow(raw), "rows ×", ncol(raw), "columns\n")## Raw data: 300 rows × 16 columnshead(raw, 10)## # A tibble: 10 × 16
## worker_id email_id age immigration_stat city province
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 James Strange james.s… 1998-09-10 00:00:00 Non-immigrant Kelo… B.C.
## 2 Maya Liya maya.li… 1994-08-04 00:00:00 Immigrant Kelo… B.C.
## 3 Amelio Beal amelio.… 1995-01-02 00:00:00 Non-permanent r… West… B.C.
## 4 Cara Sahara cara.sa… 1991-03-22 00:00:00 Non-permanent r… West… B.C.
## 5 Neiv Rieg neivr@g… 1990-07-15 00:00:00 Non-permanent r… Vict… B.C.
## 6 Troy Ahoy tahoy@y… 1988-06-06 00:00:00 Immigrant Vict… B.C.
## 7 Dave Mahew dmahew@… 2000-11-11 00:00:00 Non-immigrant Vern… B.C.
## 8 Jamie Thomas jamiet@… 1992-12-18 00:00:00 Immigrant Vern… B.C.
## 9 Betty Stills bettys5… 1997-09-06 00:00:00 Non-permanent r… Cobb… B.C.
## 10 Enrique Iglasias ei123@h… 1985-10-16 00:00:00 Non-permanent r… Cobb… B.C.
## # ℹ 10 more variables: orchard_id <chr>, owner_id <chr>, username_id <chr>,
## # sns_worked <dbl>, sat_hrs <dbl>, trt_workers <dbl>, trt_manager <dbl>,
## # cmf_manager <dbl>, sat_work_overall <dbl>, comments <chr>These variables point to a specific person. They will be pseudonymized in Step 1 (not dropped) so rows stay linkable within the dataset without exposing real names or contact details. A data key allows authorized staff to reverse the mapping.
| Variable | Why it is risky | Pseudonym prefix |
|---|---|---|
worker_id |
Full name (300 unique values) | Worker_001, … |
email_id |
Email address (300 unique values) | Email_001, … |
owner_id |
Orchard owner name (12 unique values) | Owner_01, … |
The column age stores date of birth,
not a numeric age. Exact DOB is a strong quasi-identifier when combined
with location and employer.
data.frame(
earliest = as.character(min(as.Date(raw$age))),
latest = as.character(max(as.Date(raw$age))),
unique_dates = length(unique(raw$age))
)## earliest latest unique_dates
## 1 1970-02-20 2000-11-11 295Exact DOB will be replaced with 5-year age bands in Step 2 (aggregation).
city has 12 levels; province is always B.C.
Small groups are easier to re-identify. Orchard names link respondents
to a specific workplace. Both will be pseudonymized in Step 3.
raw |> count(city, sort = TRUE)## # A tibble: 12 × 2
## city n
## <chr> <int>
## 1 Victoria 33
## 2 Summerland 32
## 3 Keremeos 31
## 4 Kelowna 29
## 5 West Kelowna 29
## 6 Peachland 27
## 7 Cobble Hill 26
## 8 Vernon 24
## 9 Naramata 18
## 10 Oliver 18
## 11 Osoyoos 17
## 12 Penticton 16Note: If any city had fewer than 5 records, we would pool it into
"Other BC community"before pseudonymizing.
raw |> count(orchard_id, sort = TRUE)## # A tibble: 12 × 2
## orchard_id n
## <chr> <int>
## 1 Billie's Apples 33
## 2 Okanagan Fresh 32
## 3 Valley View Apples 31
## 4 Applejacks 29
## 5 West Kelowna Apples 29
## 6 Lakeside Groves 27
## 7 Country Apples 26
## 8 Bon Appletit 24
## 9 Golden Valley Farms 18
## 10 Ridgeline Orchard 18
## 11 Peak Harvest Co 17
## 12 Sunrise Orchards 16Note: Replace orchard names with non-descriptive codes (
Orchard_01, …).
These variables cannot be safely shared in their original form. Anonymization is irreversible — no data key is created for these fields.
immigration_statSmall category counts increase re-identification risk. Pool any group below the k-anonymity threshold into a broader label.
raw |> count(immigration_stat, sort = TRUE)## # A tibble: 3 × 2
## immigration_stat n
## <chr> <int>
## 1 Non-permanent resident 107
## 2 Non-immigrant 103
## 3 Immigrant 90rare_immigration <- raw |>
count(immigration_stat) |>
filter(n < K_THRESHOLD) |>
pull(immigration_stat)
cat("Rare immigration groups (n <", K_THRESHOLD, "):\n")## Rare immigration groups (n < 5 ):rare_immigration## character(0)Note: In this dataset all immigration groups meet the threshold. The pooling logic in Step 4 still runs as a safeguard.
username_idSocial-media handles are quasi-identifiers (265 unique values; 36 missing). They will be removed in Step 4 — anonymization by deletion.
Apply each step from Part 2 in order. Run chunks sequentially; each step shows 10 rows of the updated dataframe.
step1_ids <- c("worker_id", "email_id", "owner_id")Replace worker_id, email_id, and
owner_id with non-descriptive codes. Store mappings in the
data key (Part 4).
step1 <- raw |>
mutate(
worker_id = paste0("Worker_", sprintf("%03d", as.integer(factor(worker_id)))),
email_id = paste0("Email_", sprintf("%03d", as.integer(factor(email_id)))),
owner_id = paste0("Owner_", sprintf("%02d", as.integer(factor(owner_id))))
)
cat("Step 1:", nrow(step1), "rows ×", ncol(step1), "columns\n")## Step 1: 300 rows × 16 columnshead(step1, 10)## # A tibble: 10 × 16
## worker_id email_id age immigration_stat city province
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 Worker_114 Email_110 1998-09-10 00:00:00 Non-immigrant Kelo… B.C.
## 2 Worker_170 Email_163 1994-08-04 00:00:00 Immigrant Kelo… B.C.
## 3 Worker_010 Email_011 1995-01-02 00:00:00 Non-permanent reside… West… B.C.
## 4 Worker_035 Email_033 1991-03-22 00:00:00 Non-permanent reside… West… B.C.
## 5 Worker_194 Email_193 1990-07-15 00:00:00 Non-permanent reside… Vict… B.C.
## 6 Worker_276 Email_261 1988-06-06 00:00:00 Immigrant Vict… B.C.
## 7 Worker_054 Email_057 2000-11-11 00:00:00 Non-immigrant Vern… B.C.
## 8 Worker_117 Email_114 1992-12-18 00:00:00 Immigrant Vern… B.C.
## 9 Worker_022 Email_021 1997-09-06 00:00:00 Non-permanent reside… Cobb… B.C.
## 10 Worker_064 Email_061 1985-10-16 00:00:00 Non-permanent reside… Cobb… B.C.
## # ℹ 10 more variables: orchard_id <chr>, owner_id <chr>, username_id <chr>,
## # sns_worked <dbl>, sat_hrs <dbl>, trt_workers <dbl>, trt_manager <dbl>,
## # cmf_manager <dbl>, sat_work_overall <dbl>, comments <chr>step1 |>
select(all_of(step1_ids)) |>
slice(1:5)## # A tibble: 5 × 3
## worker_id email_id owner_id
## <chr> <chr> <chr>
## 1 Worker_114 Email_110 Owner_03
## 2 Worker_170 Email_163 Owner_03
## 3 Worker_010 Email_011 Owner_08
## 4 Worker_035 Email_033 Owner_08
## 5 Worker_194 Email_193 Owner_02Replace exact date of birth with 5-year age bands.
to_age_band <- function(dob) {
yrs <- as.integer(difftime(Sys.Date(), as.Date(dob), units = "days") / 365.25)
if (yrs < 25) "18-24"
else if (yrs < 35) "25-34"
else if (yrs < 45) "35-44"
else if (yrs < 55) "45-54"
else "55+"
}
step2 <- step1 |>
mutate(age_band = sapply(age, to_age_band)) |>
select(-age) |>
relocate(age_band, .before = immigration_stat)
cat("Step 2:", nrow(step2), "rows ×", ncol(step2), "columns\n")## Step 2: 300 rows × 16 columnshead(step2, 10)## # A tibble: 10 × 16
## worker_id email_id age_band immigration_stat city province orchard_id
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Worker_114 Email_110 25-34 Non-immigrant Kelo… B.C. Applejacks
## 2 Worker_170 Email_163 25-34 Immigrant Kelo… B.C. Applejacks
## 3 Worker_010 Email_011 25-34 Non-permanent reside… West… B.C. West Kelo…
## 4 Worker_035 Email_033 35-44 Non-permanent reside… West… B.C. West Kelo…
## 5 Worker_194 Email_193 35-44 Non-permanent reside… Vict… B.C. Billie's …
## 6 Worker_276 Email_261 35-44 Immigrant Vict… B.C. Billie's …
## 7 Worker_054 Email_057 25-34 Non-immigrant Vern… B.C. Bon Apple…
## 8 Worker_117 Email_114 25-34 Immigrant Vern… B.C. Bon Apple…
## 9 Worker_022 Email_021 25-34 Non-permanent reside… Cobb… B.C. Country A…
## 10 Worker_064 Email_061 35-44 Non-permanent reside… Cobb… B.C. Country A…
## # ℹ 9 more variables: owner_id <chr>, username_id <chr>, sns_worked <dbl>,
## # sat_hrs <dbl>, trt_workers <dbl>, trt_manager <dbl>, cmf_manager <dbl>,
## # sat_work_overall <dbl>, comments <chr>Pseudonymize city and orchard_id with
non-descriptive codes.
step3 <- step2 |>
mutate(
city = paste0("City_", sprintf("%02d", as.integer(factor(city)))),
orchard_id = paste0("Orchard_", sprintf("%02d", as.integer(factor(orchard_id))))
)
cat("Step 3:", nrow(step3), "rows ×", ncol(step3), "columns\n")## Step 3: 300 rows × 16 columnshead(step3, 10)## # A tibble: 10 × 16
## worker_id email_id age_band immigration_stat city province orchard_id
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Worker_114 Email_110 25-34 Non-immigrant City… B.C. Orchard_01
## 2 Worker_170 Email_163 25-34 Immigrant City… B.C. Orchard_01
## 3 Worker_010 Email_011 25-34 Non-permanent reside… City… B.C. Orchard_12
## 4 Worker_035 Email_033 35-44 Non-permanent reside… City… B.C. Orchard_12
## 5 Worker_194 Email_193 35-44 Non-permanent reside… City… B.C. Orchard_02
## 6 Worker_276 Email_261 35-44 Immigrant City… B.C. Orchard_02
## 7 Worker_054 Email_057 25-34 Non-immigrant City… B.C. Orchard_03
## 8 Worker_117 Email_114 25-34 Immigrant City… B.C. Orchard_03
## 9 Worker_022 Email_021 25-34 Non-permanent reside… City… B.C. Orchard_04
## 10 Worker_064 Email_061 35-44 Non-permanent reside… City… B.C. Orchard_04
## # ℹ 9 more variables: owner_id <chr>, username_id <chr>, sns_worked <dbl>,
## # sat_hrs <dbl>, trt_workers <dbl>, trt_manager <dbl>, cmf_manager <dbl>,
## # sat_work_overall <dbl>, comments <chr>step3 |> count(city, sort = TRUE)## # A tibble: 12 × 2
## city n
## <chr> <int>
## 1 City_11 33
## 2 City_09 32
## 3 City_03 31
## 4 City_02 29
## 5 City_12 29
## 6 City_07 27
## 7 City_01 26
## 8 City_10 24
## 9 City_04 18
## 10 City_05 18
## 11 City_06 17
## 12 City_08 16step3 |> count(orchard_id, sort = TRUE)## # A tibble: 12 × 2
## orchard_id n
## <chr> <int>
## 1 Orchard_02 33
## 2 Orchard_07 32
## 3 Orchard_11 31
## 4 Orchard_01 29
## 5 Orchard_12 29
## 6 Orchard_06 27
## 7 Orchard_04 26
## 8 Orchard_03 24
## 9 Orchard_05 18
## 10 Orchard_09 18
## 11 Orchard_08 17
## 12 Orchard_10 16Anonymization is irreversible — removed columns and pooled labels are not stored in the data key.
step4 <- step3 |>
mutate(
immigration_stat = if_else(
immigration_stat %in% rare_immigration,
"Other immigration status",
immigration_stat
)
) |>
select(-username_id, -comments)
deid <- step4
cat("Step 4 — final:", nrow(deid), "rows ×", ncol(deid), "columns\n")## Step 4 — final: 300 rows × 14 columnshead(deid, 10)## # A tibble: 10 × 14
## worker_id email_id age_band immigration_stat city province orchard_id
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Worker_114 Email_110 25-34 Non-immigrant City… B.C. Orchard_01
## 2 Worker_170 Email_163 25-34 Immigrant City… B.C. Orchard_01
## 3 Worker_010 Email_011 25-34 Non-permanent reside… City… B.C. Orchard_12
## 4 Worker_035 Email_033 35-44 Non-permanent reside… City… B.C. Orchard_12
## 5 Worker_194 Email_193 35-44 Non-permanent reside… City… B.C. Orchard_02
## 6 Worker_276 Email_261 35-44 Immigrant City… B.C. Orchard_02
## 7 Worker_054 Email_057 25-34 Non-immigrant City… B.C. Orchard_03
## 8 Worker_117 Email_114 25-34 Immigrant City… B.C. Orchard_03
## 9 Worker_022 Email_021 25-34 Non-permanent reside… City… B.C. Orchard_04
## 10 Worker_064 Email_061 35-44 Non-permanent reside… City… B.C. Orchard_04
## # ℹ 7 more variables: owner_id <chr>, sns_worked <dbl>, sat_hrs <dbl>,
## # trt_workers <dbl>, trt_manager <dbl>, cmf_manager <dbl>,
## # sat_work_overall <dbl>deid |> count(immigration_stat, sort = TRUE)## # A tibble: 3 × 2
## immigration_stat n
## <chr> <int>
## 1 Non-permanent resident 107
## 2 Non-immigrant 103
## 3 Immigrant 90| Step | Technique | Variables |
|---|---|---|
| 1 | Pseudonymization | worker_id → Worker_001 …;
email_id → Email_001 …; owner_id
→ Owner_01 … |
| 2 | Aggregation | age (DOB) → age_band (5-year groups) |
| 3 | Pseudonymization | city → City_01 …; orchard_id
→ Orchard_01 … |
| 4 | Anonymization | immigration_stat pooled if below threshold;
username_id and comments removed |
Confirm the de-identified data is safe to export. All checks should
show TRUE.
data.frame(
check = c(
"Step 1: direct IDs pseudonymized",
"Step 2: no exact DOB column",
"Step 3: cities pseudonymized",
"Step 3: orchards pseudonymized",
"Step 4: comments removed",
"Step 4: username_id removed",
"Row count unchanged"
),
passed = c(
!any(unique(raw$worker_id) %in% deid$worker_id),
!"age" %in% names(deid),
!any(unique(raw$city) %in% deid$city),
!any(unique(raw$orchard_id) %in% deid$orchard_id),
!"comments" %in% names(deid),
!"username_id" %in% names(deid),
nrow(deid) == nrow(raw)
)
)## check passed
## 1 Step 1: direct IDs pseudonymized TRUE
## 2 Step 2: no exact DOB column TRUE
## 3 Step 3: cities pseudonymized TRUE
## 4 Step 3: orchards pseudonymized TRUE
## 5 Step 4: comments removed TRUE
## 6 Step 4: username_id removed TRUE
## 7 Row count unchanged TRUEdata.frame(
check = c(
"Emails pseudonymized",
"Owners pseudonymized"
),
passed = c(
!any(unique(raw$email_id) %in% deid$email_id),
!any(unique(raw$owner_id) %in% deid$owner_id)
)
)## check passed
## 1 Emails pseudonymized TRUE
## 2 Owners pseudonymized TRUEdata.frame(
check = c(
"No rare immigration groups in output",
"age_band present"
),
passed = c(
all(table(deid$immigration_stat) >= K_THRESHOLD),
"age_band" %in% names(deid)
)
)## check passed
## 1 No rare immigration groups in output TRUE
## 2 age_band present TRUEdata.frame(
rows = nrow(deid),
columns = ncol(deid),
smallest_city = min(table(deid$city)),
smallest_age_band = min(table(deid$age_band)),
smallest_orchard = min(table(deid$orchard_id))
)## rows columns smallest_city smallest_age_band smallest_orchard
## 1 300 14 16 14 16Note: Each group should ideally have at least 5 records, or be pooled into a broader category.
This is the file you can share for analysis. It does not include the data key.
write_xlsx(deid, OUTPUT_FILE)
cat("Exported:", OUTPUT_FILE, "\n")## Exported: WorkerSatisfaction_300rows_deidentified.xlsxcat("Rows:", nrow(deid), "| Columns:", ncol(deid), "\n\n")## Rows: 300 | Columns: 14cat("Preview of exported data:\n")## Preview of exported data:head(deid, 10)## # A tibble: 10 × 14
## worker_id email_id age_band immigration_stat city province orchard_id
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Worker_114 Email_110 25-34 Non-immigrant City… B.C. Orchard_01
## 2 Worker_170 Email_163 25-34 Immigrant City… B.C. Orchard_01
## 3 Worker_010 Email_011 25-34 Non-permanent reside… City… B.C. Orchard_12
## 4 Worker_035 Email_033 35-44 Non-permanent reside… City… B.C. Orchard_12
## 5 Worker_194 Email_193 35-44 Non-permanent reside… City… B.C. Orchard_02
## 6 Worker_276 Email_261 35-44 Immigrant City… B.C. Orchard_02
## 7 Worker_054 Email_057 25-34 Non-immigrant City… B.C. Orchard_03
## 8 Worker_117 Email_114 25-34 Immigrant City… B.C. Orchard_03
## 9 Worker_022 Email_021 25-34 Non-permanent reside… City… B.C. Orchard_04
## 10 Worker_064 Email_061 35-44 Non-permanent reside… City… B.C. Orchard_04
## # ℹ 7 more variables: owner_id <chr>, sns_worked <dbl>, sat_hrs <dbl>,
## # trt_workers <dbl>, trt_manager <dbl>, cmf_manager <dbl>,
## # sat_work_overall <dbl>Keeping the data key file well protected is essential because it is the sole mechanism that can re-identify individuals in otherwise de-identified research data, and its compromise can undermine confidentiality protections and cause harm to participants. In the UBC environment, the key file must be stored separately from research data on UBC-approved secure systems, with access restricted to authorized personnel only and never stored on personal devices or unapproved cloud services.
Here is an example of a data key file for the work described above; this is a dummy file provided for illustrative purposes only, and in real-world research settings, such files must never be shared on public systems.
File path:
data_key_file/WorkerSatisfaction_data_key_DUMMY.xlsx — one
sheet (data_key) with all mappings in a single
table:
| Column | Purpose |
|---|---|
variable |
Which field was pseudonymized (worker_id,
email_id, city, …) |
original_value |
Value in the raw file |
pseudonym_code |
Code used in the de-identified file |
notes |
Optional step label |
To restore a de-identified file: for each
variable, join pseudonym_code in the
de-identified data back to original_value using this table
(never merge the key into the shareable dataset).
The key holds mappings for pseudonymized identifiers
only (Steps 1 and 3). Anonymized fields
(username_id, comments) are not included —
they cannot be reversed.
worker_key <- raw |>
distinct(worker_id) |>
arrange(worker_id) |>
transmute(
original_value = worker_id,
pseudonym_code = paste0("Worker_", sprintf("%03d", row_number()))
)
email_key <- raw |>
distinct(email_id) |>
arrange(email_id) |>
transmute(
original_value = email_id,
pseudonym_code = paste0("Email_", sprintf("%03d", row_number()))
)
owner_key <- raw |>
distinct(owner_id) |>
arrange(owner_id) |>
transmute(
original_value = owner_id,
pseudonym_code = paste0("Owner_", sprintf("%02d", row_number()))
)
orchard_key <- raw |>
distinct(orchard_id) |>
arrange(orchard_id) |>
transmute(
original_value = orchard_id,
pseudonym_code = paste0("Orchard_", sprintf("%02d", row_number()))
)
city_key <- raw |>
distinct(city) |>
arrange(city) |>
transmute(
original_value = city,
pseudonym_code = paste0("City_", sprintf("%02d", row_number()))
)
# One table: stack every mapping for simple lookup and restore
data_key <- bind_rows(
worker_key |> mutate(variable = "worker_id", notes = NA_character_),
email_key |> mutate(variable = "email_id", notes = NA_character_),
owner_key |> mutate(variable = "owner_id", notes = NA_character_),
orchard_key |> mutate(variable = "orchard_id", notes = NA_character_),
city_key |> mutate(variable = "city", notes = NA_character_)
) |>
select(variable, original_value, pseudonym_code, notes) |>
arrange(variable, original_value)
cat("Data key preview (first 10 rows):\n")## Data key preview (first 10 rows):head(data_key, 10)## # A tibble: 10 × 4
## variable original_value pseudonym_code notes
## <chr> <chr> <chr> <chr>
## 1 city Cobble Hill City_01 <NA>
## 2 city Kelowna City_02 <NA>
## 3 city Keremeos City_03 <NA>
## 4 city Naramata City_04 <NA>
## 5 city Oliver City_05 <NA>
## 6 city Osoyoos City_06 <NA>
## 7 city Peachland City_07 <NA>
## 8 city Penticton City_08 <NA>
## 9 city Summerland City_09 <NA>
## 10 city Vernon City_10 <NA>cat("\nRows by variable:\n")##
## Rows by variable:data_key |> count(variable)## # A tibble: 5 × 2
## variable n
## <chr> <int>
## 1 city 12
## 2 email_id 300
## 3 orchard_id 12
## 4 owner_id 12
## 5 worker_id 300dir.create(KEY_DIR, showWarnings = FALSE)
write_xlsx(list(data_key = data_key), KEY_FILE)
cat("Data key exported:", KEY_FILE, "\n")## Data key exported: data_key_file/WorkerSatisfaction_data_key_DUMMY.xlsxcat("Single sheet: data_key (", nrow(data_key), " mapping rows )\n", sep = "")## Single sheet: data_key (636 mapping rows )cat("Store separately from", OUTPUT_FILE, "— never on public systems.\n")## Store separately from WorkerSatisfaction_300rows_deidentified.xlsx — never on public systems.| Step | Technique | What changed |
|---|---|---|
| 1 | Pseudonymization | worker_id, email_id, owner_id
→ pseudonym codes (key file) |
| 2 | Aggregation | age (DOB) → age_band (5-year groups) |
| 3 | Pseudonymization | city → City_01 …; orchard_id
→ Orchard_01 … |
| 4 | Anonymization | immigration_stat pooled if below threshold;
username_id and comments removed |
The de-identified dataset keeps all satisfaction and treatment ratings for analysis while substantially lowering re-identification risk. The data key is highly sensitive — it links pseudonym codes back to real names, emails, and locations for Steps 1 and 3 only. Protect and store it separately.
commentsThe
commentsfield can name people, employers, platforms, and birth years — even after other columns are cleaned.