A guided data-quality workflow • cataScience

library(cataScience)
library(readxl)
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(DSIR)

This 20–30 minute lab follows Import → Cleaning → Visualize. The aim is to explain a cleaning decision and its effect on a conclusion. Use the bundled cat dataset for the first exercise.

The app activities are the main learning route. The R chunks below are optional, reproducible companion examples. They read the same workbook; they do not modify a running app session.

1. Import and inspect

In the app, open Import and click Or use the example data under Dataset 1. Inspect the preview before changing anything. Ask:

What does one row represent?
Which column identifies a cat?
Which columns are numeric, text or logical?
Where are values missing or labels inconsistent?

Read the same workbook in R:

example_file <- system.file(
  "app", "data", "cat-dirty-data.xlsx", package = "cataScience"
)
cat_data <- read_excel(example_file)
head(cat_data)
#> # A tibble: 6 × 8
#>   id    name     gender length_cm height_cm weight_kg age_years neutered
#>   <chr> <chr>    <chr>      <dbl>     <dbl>     <dbl>     <dbl> <lgl>   
#> 1 a01   Dazhuang Male        72.3      38.5       9.2         3 FALSE   
#> 2 a02   Maomao   Male        54.1      32.3       3.1         8 TRUE    
#> 3 a03   Mimi     F           42.4      28.4       5.5         1 TRUE    
#> 4 a04   Shane    Male        47.1      29.8       7.9         1 FALSE   
#> 5 a05   Joshua   Female      45.4      32.6      NA           8 TRUE    
#> 6 a06   Mark     Male        36.9      27         4.1         7 FALSE

missing_summary <- tibble(
  variable = names(cat_data),
  n_missing = vapply(cat_data, function(x) sum(is.na(x)), integer(1))
)
missing_summary
#> # A tibble: 8 × 2
#>   variable  n_missing
#>   <chr>         <int>
#> 1 id                0
#> 2 name              0
#> 3 gender            0
#> 4 length_cm         0
#> 5 height_cm         0
#> 6 weight_kg         3
#> 7 age_years         0
#> 8 neutered          0

Count repeated identifiers separately. A duplicate can be a repeated entry, or it can reflect a genuine second observation; check the source before removing it.

duplicate_ids <- cat_data |>
  count(id, name = "n_rows") |>
  filter(n_rows > 1)
duplicate_ids
#> # A tibble: 0 × 2
#> # ℹ 2 variables: id <chr>, n_rows <int>

2. Compare missing-data choices

Open Cleaning → Missing data. The Deal tab offers row removal, filling values down or up, mean replacement, median replacement and kNN imputation. Select the variables you intend to change and inspect the result before clicking Apply cleaning.

Compare row removal and median replacement for weight_kg. Use Reset to original data between alternatives. Ask how many cats remain and whether the weight distribution changes.

The following R example deliberately changes only one variable and keeps the original dataset:

complete_weights <- cat_data |>
  filter(!is.na(weight_kg))

median_filled <- cat_data |>
  mutate(weight_kg = replace_na(weight_kg, median(weight_kg, na.rm = TRUE)))

comparison_data <- bind_rows(
  "Observed weights" = complete_weights,
  "Median replacement" = median_filled,
  .id = "method"
)

method_summary <- comparison_data |>
  summarise(
    n_cats = n(),
    mean_weight_kg = mean(weight_kg),
    median_weight_kg = median(weight_kg),
    .by = method
  )
method_summary
#> # A tibble: 2 × 4
#>   method             n_cats mean_weight_kg median_weight_kg
#>   <chr>               <int>          <dbl>            <dbl>
#> 1 Observed weights       28           6.55             4.45
#> 2 Median replacement     31           6.35             4.45

Mean and median replacement are useful teaching comparisons, but each fills a gap with a single value and understates uncertainty. For analytical work, consider multiple imputation with mice::mice() when its assumptions are appropriate, and pool the analysis across imputations. Do not interpret the filled values as newly observed data.

Filling down or up assumes adjacent rows belong together and that their order is meaningful. kNN depends on comparable variables and suitable scaling. None of these choices establishes why the values were missing.

3. Investigate outliers

Open Cleaning → Outliers. Inspect the boxplot and the flagged variables. The app uses the interquartile range (IQR) rule:

$\text{lower fence} = Q_1 - 1.5 \times \mathrm{IQR},\qquad \text{upper fence} = Q_3 + 1.5 \times \mathrm{IQR}.$

A flag identifies an unusual value; it does not prove that the value is wrong. Check units, source records and the group to which the cat belongs.

weight_quartiles <- quantile(cat_data$weight_kg, c(0.25, 0.75), na.rm = TRUE)
weight_iqr <- diff(weight_quartiles)
lower_fence <- unname(weight_quartiles[1] - 1.5 * weight_iqr)
upper_fence <- unname(weight_quartiles[2] + 1.5 * weight_iqr)

flagged_weights <- cat_data |>
  filter(!is.na(weight_kg), weight_kg < lower_fence | weight_kg > upper_fence)
flagged_weights |>
  select(id, name, weight_kg)
#> # A tibble: 4 × 3
#>   id    name     weight_kg
#>   <chr> <chr>        <dbl>
#> 1 a01   Dazhuang       9.2
#> 2 a04   Shane          7.9
#> 3 a08   Stanley       56  
#> 4 a28   Kathryn        1.8

Compare Remove extreme values with Winsorize extreme values in the app. Removal changes the sample. Winsorizing keeps the row but caps a value at a fence. Keep a record of the rule and the affected observations.

4. Standardize text labels

Open Cleaning → Text, choose a character variable and compare the case options. Changing case alone does not make F and Female the same label. Inspect the actual categories and agree on a mapping.

sort(unique(cat_data$gender))
#> [1] "F"      "Female" "M"      "Male"

standardized_data <- cat_data |>
  mutate(
    gender_key = str_to_lower(str_trim(gender)),
    gender = case_when(
      gender_key %in% c("f", "female") ~ "Female",
      gender_key %in% c("m", "male") ~ "Male",
      TRUE ~ gender
    )
  ) |>
  select(-gender_key)

standardized_data |>
  count(gender, sort = TRUE)
#> # A tibble: 2 × 2
#>   gender     n
#>   <chr>  <int>
#> 1 Female    16
#> 2 Male      15

This explicit recoding is an R companion exercise. The app’s Text page provides case conversion; it does not apply this custom mapping for you.

5. Check keys before merging

Load Dataset 2 when you are ready to practise joins. Open Cleaning → Merge, select corresponding keys, choose a join and inspect the result. The app offers left, right, inner and full joins.

Check that the key uniquely identifies rows at the intended level. A join can multiply rows when a key appears more than once on both sides. Count rows before and after, and inspect unmatched keys. Do not treat a larger joined table as automatically more complete.

6. Visualize a decision

Open Visualize and compare the original and modified dataset after resetting and applying the chosen cleaning method. Use a boxplot or histogram for the weight distribution; a scatter plot can address a relationship between two numeric variables.

This companion figure compares observed weights with median replacement:

ggplot(comparison_data, aes(x = method, y = weight_kg, fill = method)) +
  geom_boxplot(width = 0.45, outlier.shape = NA, alpha = 0.6) +
  geom_jitter(width = 0.08, height = 0, alpha = 0.5, size = 1.5) +
  scale_fill_brewer(palette = "Set2", guide = "none") +
  theme_dsi() +
  labs(
    title = "A cleaning choice changes the data we see",
    subtitle = "Bundled cat teaching data; median replacement is an illustration",
    x = NULL, y = "Weight (kg)"
  )

Boxplots of observed cat weights and weights after median replacement, with individual values shown.

Explain the denominator, the changed observations and the limitations before describing a difference in the chart. A clean-looking chart can still support a weak claim.

7. Debrief and test understanding

Ask each learner to complete three sentences:

“I changed … because …”
“This changed the sample or distribution by …”
“Before using this result, I still need to check …”

In Quiz, select the topics covered in the lab and click Apply topics and restart. Use the answer explanations to revisit a decision, rather than treating the score as the only learning outcome.

For a longer session, continue to Statistics or the AI-assisted analysis pages. See Planning a data science and AI training for agendas and a critique activity.