The Tool Desk
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What the Titanic files contain
Kaggle describes Titanic as a beginner “Getting Started” prediction competition. The training data include the Survived outcome used for exploration and model development; outcomes for the 418 test passengers are withheld, and a submission contains predictions for those rows. Keep the partitions distinct: a test file is not a second labeled training sample.
| File | Rows stated by Kaggle | Role | Outcome field |
|---|---|---|---|
train.csv |
891 passengers | Labeled exploration and model development | Survived is present |
test.csv |
418 passengers | Prediction input | Survival is withheld |
The data dictionary defines Pclass as a proxy for socioeconomic status; Age is measured in years, can be fractional for infants, and uses a .5 convention for estimated ages. SibSp and Parch use Kaggle’s specific definitions for relatives aboard. Embarkation codes are C (Cherbourg), Q (Queenstown), and S (Southampton). Use those definitions when interpreting a chart rather than treating a column name as self-explanatory.
Install the R packages and load both partitions
naniar is designed to summarize, handle, and visualize missing values. Its UpSet helper uses the UpSetR plotting system to display co-occurring missingness.
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install.packages(c("naniar", "UpSetR", "tidyverse"))
library(tidyverse)
library(naniar)
train <- read_csv("train.csv", show_col_types = FALSE)
test <- read_csv("test.csv", show_col_types = FALSE)
glimpse(train)
glimpse(test)
names(train)
names(test)
dim(train)
dim(test)Run the inspection commands before plotting. They reveal whether your files contain the expected columns, whether a path points to the intended download, and how R parsed each field.
Find missing values before drawing a chart
Count missing cells by variable
Calculate counts separately for training and test data. This produces an analysis of your downloaded CSVs, not an official Kaggle statistic.
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missing_by_variable <- function(data) {
tibble(
variable = names(data),
missing = map_int(data, ~ sum(is.na(.x))),
rows = nrow(data),
percent_missing = 100 * missing / rows
) |>
arrange(desc(missing))
}
missing_by_variable(train)
missing_by_variable(test)For character columns, an empty string is not automatically the same as NA. If your copy represents blank cells as text, inspect and standardize that representation deliberately before counting.
Count missing fields by row
missing_by_case <- function(data) {
data |>
mutate(
missing_fields = rowSums(is.na(across(everything())))
) |>
count(missing_fields, name = "rows") |>
arrange(missing_fields)
}
missing_by_case(train)
missing_by_case(test)A variable summary answers “which columns have gaps?” A case summary answers “how many fields are incomplete in each passenger record?” Neither establishes why a value is absent.
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Use an overall missingness overview
Start with a broad visual view, then move to intersections. For example:
gg_miss_var(train, show_pct = TRUE) +
labs(title = "Missing values by variable: Titanic training data")
gg_miss_case(train) +
labs(title = "Missing values by passenger: Titanic training data")These plots emphasize amount and distribution. Repeat them for test when the production input matters, because the two files can have different columns and different missingness patterns.
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Show combinations with gg_miss_upset()
An UpSet plot treats each row as a case and each variable with a missing value as a set. Its intersections therefore represent combinations of missing fields across rows. It is useful for questions such as “which variables are commonly missing together?”
gg_miss_upset(train)The naniar reference documents defaults of up to five sets and 40 intersections, with intersections ordered by frequency by default. Those limits make a plot readable, but they also mean the default view is selected rather than a complete list of every possible pattern.
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Choose the variables and intersections intentionally
# Focus on selected fields and show more intersections
train |>
select(Age, Cabin, Embarked, Fare) |>
gg_miss_upset(nsets = 4, nintersects = 20)Use nsets when you want to restrict how many variables are displayed and nintersects when you want more or fewer combinations. A smaller view is easier to read; a larger view can expose less frequent patterns. Record those choices with your result so another reader knows what the chart omitted.
Read the plot correctly
- The set-size bars indicate how often each selected variable is missing.
- The intersection bars indicate rows sharing a particular combination of missing variables.
- A combination is a pattern in the observed file, not a diagnosis of the collection process.
- Variables excluded by
select(), and intersections beyond the display limit, are not represented in that view.
Overview plot or UpSet plot?
| View | Main question | What it shows | What can be omitted |
|---|---|---|---|
| Variable- or case-level summary | How much missingness is present, and where? | Counts or percentages by column or row | Joint combinations are not prominent |
| UpSet intersection view | Which missing fields occur together? | Selected sets and their intersections | Variables and low-frequency intersections outside the limits |
Use both when your goal is a defensible description: first establish the amount and distribution, then inspect combinations that may guide data-cleaning questions.
What the chart cannot tell you
- Missingness intersections do not reveal the cause of a blank value.
- They do not prove that data are missing completely at random, missing at random, or missing not at random.
- They do not select an imputation method. Any imputation decision requires substantive knowledge, diagnostics, and validation.
- They do not predict survival. Titanic survival prediction is a separate supervised-learning task using the labeled training outcomes.
The naniar visualization guide demonstrates the same approach with airquality and riskfactors; those examples are not Titanic results. For Titanic, report values generated from the exact files and code you used, naming the partition and any filters.
A reproducible exploration checklist
- Download Kaggle’s competition files and confirm that
train.csvandtest.csvare the files being read. - Inspect dimensions, names, and data types with
dim(),names(), andglimpse(). - Compute variable-level and case-level missingness separately for training and test data.
- Review an overview plot before investigating combinations.
- Run
gg_miss_upset()and setnsets,nintersects, and selected variables to match the question. - Save the code, package versions, file partition, and display limits alongside any exported figure.
- Only after exploration, decide whether a modeling workflow needs transformations, exclusions, or imputation; validate those choices on the training workflow without treating test outcomes as known.
Frequently Asked Questions
How do I find missing values in the Titanic dataset in R?
Read the CSV with read_csv(), then count is.na() values by column and row. The missing_by_variable() and missing_by_case() functions above provide reproducible summaries for each partition.
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Load naniar and call gg_miss_upset(train). Adjust nsets and nintersects, or select a subset of variables first, to control the intersections displayed.
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