Data Studio: statistics, graphs, Python and R

The studio at a glance

Project → Data lists the datasets; creating or importing one opens the studio: six tabs (Data · Variables · Output · Graphs · Code · Assistant) under a menu bar (File · Data · Transform · Analyze · Graphs · Code). The Analyze menu opens with a search box and a “Common” shortlist in plain language (compare two groups, predict a yes/no outcome…) above the full catalogue.

Data menu — cases

• Select cases… keeps the cases where a condition is true (age >= 18 AND sex = "F"); the others are greyed out and left out of every analysis unless you untick “Active cases only”.
• Split file is an option inside every analysis dialog: the analysis is repeated for each level of the split variable, stacked in one output.
• Sort cases, Remove duplicate cases (by key), Aggregate (one row per group with n, mean, sum, SD…), Restructure wide → long and long → wide, Merge by key and Append cases. Aggregate, restructure, merge and append create a new derived dataset so the original stays intact.

Transform menu — variables

Analyze menu — the statistics engine

Each analysis opens a dialog: pick the variables by role (only suitable ones are offered), set options, watch the live preview, then “Run and save to Output”. Results come as APA-ready tables with statistics, p-values, effect sizes and confidence intervals, a summary sentence you can paste into a manuscript, and charts where they help.

Code — Python and R

The Code tab runs your own script in an isolated sandbox (no network, three-minute limit) against the active cases. Python has pandas, numpy, scipy, statsmodels, lifelines, scikit-learn, seaborn/matplotlib and pingouin; R has survival, ggplot2, lme4, metafor, meta, pROC, car, emmeans, psych, irr, nnet and ordinal.

import pandas as pd
d = pd.read_csv(DATA_PATH + "/data.csv")
hikma.result("n", len(d), label="Cases")
hikma.table("means", d.groupby("arm")["sbp"].mean().reset_index(), "Mean SBP by arm")

d <- hikma$data()
m <- coxph(survival::Surv(time, status) ~ group, data = d)
hikma$table("hr", as.data.frame(summary(m)$conf.int), "Hazard ratios")
hikma$figure("km", function() plot(survival::survfit(survival::Surv(time, status) ~ group, data = d)), "Kaplan–Meier")

Templates in the tab give you a starting script for descriptives, tests, mixed models, Cox models and plots. Every run is saved to Output with its script, so a reviewer can rerun it.

Analysing with AI

Ask your connected agent (project → Your AI) about the dataset in plain language — “does systolic blood pressure differ between arms after adjusting for age?”. Over MCP it describes the dataset and its variable dictionary, runs analyses from the catalogue, writes and runs sandboxed Python or R for what the catalogue cannot express, and reads the results back; every output lands in the Output viewer with the syntax that reproduces it, so its interpretation can be checked number by number.

1. Describe what you want to know, naming the project and the dataset.
2. The steps are planned against the variable dictionary, preferring analyses from the catalogue over hand-written code.
3. Each step runs on your real data in the sandbox; nothing is estimated.
4. The interpretation must come from the returned tables — every number copied, with caveats — because that is all the results contain.
5. The answer, its steps and their tables are saved to Output and listed under Earlier questions.

From Output into the manuscript

Every saved output has “Insert into manuscript”: choose the manuscript, what to include (the summary sentence, the tables, the charts) and where; the tables arrive as real tables, the charts as figures with captions, all as tracked changes attributed to whoever inserted them — “Claude Code” or whatever your connected agent is called — so co-authors can accept or reject them. Meta-analysis results, risk-of-bias and Summary-of-Findings tables insert the same way from their own pages.

Identifier scan and de-identification

Every dataset is scanned when it is created or imported: variable names, labels and a sample of values are checked for direct identifiers (names, e-mail addresses, telephone numbers, postal addresses, national or hospital numbers, dates of birth, device identifiers). A red or amber banner in the studio lists the flagged columns with the reason. Remove or recode them (Variables tab, Transform › Recode), re-scan, or record an attestation that the data are de-identified — the attestation is kept with the dataset's history with your name and the time. Study codes issued by the project are flagged as “possible” only so you can confirm them.

Graphs — from the dataset, saved as outputs

The Graphs tab (and Graphs › New graph) builds a figure from the dataset's own variables: bar (counts or a summary by category, stacked), line, pie, mean ± SD/SE/95% CI, scatter with trend line, box plot, histogram, Kaplan–Meier with log-rank, bubble, forest and funnel plots. Map the variables and the graph draws at once; style it (title, axes, palette, journal size) and press “Save graph” — it is stored in Output as a chart result with its settings, so you can reopen it with “Edit graph”, export it as SVG or 300-DPI PNG, and copy it to project files. The project's Data › Graphs gallery lists every saved graph across datasets; “New graph” always starts from a dataset. Numbers that are not in any dataset can still be graphed from the gallery's “typed values” panel.

Analyses save their own charts too (histograms with normal curve, Q–Q, error bars, ROC, survival curves, forest plots), and the Analysis hub's “Run an analysis” opens the chosen procedure on the chosen dataset in one step.

Output viewer

• Pin what matters; rename outputs; delete drafts.
• Copy any table as tab-separated text for Word or Excel; export any chart as SVG or 300-DPI PNG.
• Analyses carry a Hikma syntax line (ANALYZE …) — the exact specification that produced them.
• The datasets page → Outputs lists results across all datasets of the project.