OBSERVATIONS 01
—
rows in this dataset
YOUR DATA, IN CONTEXT
A clearer first look
at your data.
Explore the shape, quality, and patterns in a dataset before you begin the deeper analysis.
Private by design. Your file never leaves this device.
START WITH A CSVUP TO 25 MB
Drop your file here
or
CSV UTF-8 or comma-separated
CURRENT DATASET
Example study data
VARIABLES 02
—
columns to explore
MISSING CELLS 03
—
across all variables
COMPLETENESS 04
—
of all data cells present
Live analysis
DATA QUALITY
Missingness by variable
A quick scan of where empty values appear.
No empty cells found. A tidy start.
Missing values 0 total
WHAT TO NOTICE
Quick read
Signals worth a closer look before modeling.
A PEEK INSIDE
First rows
Showing a preview
VARIABLE PROFILE
Every column, at a glance
R-compatible quartiles, sample standard deviation, and transparent IQR outlier counts.
| VARIABLE | TYPE | N | MISSING | UNIQUE | MEAN | MEDIAN | SD | IQR | OUTLIERS |
|---|
No variables match that search.
Numeric summaries use finite values only.
DISTRIBUTION VIEW
Explore a variable
Choose a variable to see its distribution.
Not enough usable values to draw a chart.
SUMMARY STATISTICS
Selected variable
Statistics are calculated in this browser from non-missing values.
How these numbers are calculatedTransparent methods, no black boxes
Empty CSV cells are treated as missing. Numeric columns are recognized when all non-empty values are finite numbers. Quantiles use R's type 7 definition; standard deviation uses the sample formula with n − 1 in the denominator.
QuartilesLinear interpolation at (n − 1)p
IQR outliersx < Q1 − 1.5 × IQR or x > Q3 + 1.5 × IQR
Scaled MADmedian(|x − median(x)|) × 1.4826
Skew & kurtosisStandardized third and fourth moments; kurtosis is excess
The browser app provides exploratory diagnostics, not inferential conclusions. For normality tests, grouped profiles, and reproducible Quarto reports, use the DataSum R package.