Updating sda page

src/party/sda.md

@@ -4,76 +4,78 @@ index: true
 
 # Simple data analysis
 
-[Simple data analysis](https://nshiab.github.io/simple-data-analysis/), a library maintained by computational journalist Nael Shiab, purports to be “a high-performance and convenient solution in JavaScript for data analysis. It's based on DuckDB and inspired by Pandas (Python) and the Tidyverse (R).”
+[Simple data analysis](https://github.com/nshiab/simple-data-analysis), a library maintained by computational journalist Nael Shiab, purports to be “a high-performance and convenient solution in JavaScript for data analysis. It’s based on DuckDB and inspired by Pandas (Python) and the Tidyverse (R).”
 
 ```js echo
-import {SimpleDB} from "npm:simple-data-analysis@2";
+import { SimpleWebDB } from "https://esm.sh/jsr/@nshiab/simple-data-analysis@3.15.3/web";
 ```
 
-_Note: the API of this library has changed in version 3. This page would need a refresher._
-
 ```js echo
-// We start a new instance of SimpleDB
-const sdb = new SimpleDB();
-
-// We load daily temperatures for three cities.
-// We put the data in the table dailyTemperatures.
-await sdb.loadData(
-  "dailyTemperatures",
-  "https://raw.githubusercontent.com/nshiab/simple-data-analysis/main/test/data/files/dailyTemperatures.csv"
+// We start a new instance of SimpleWebDB
+const sdb = new SimpleWebDB();
+
+// We create a new table.
+const tableTemperature = sdb.newTable("temperature");
+
+// We fetch daily temperatures for three cities.
+await tableTemperature.fetchData(
+  "https://raw.githubusercontent.com/nshiab/simple-data-analysis/main/test/data/files/dailyTemperatures.csv",
 );
 
 // We compute the decade from each date
 // and put the result in the decade column.
-await sdb.addColumn(
-  "dailyTemperatures",
+await tableTemperature.addColumn(
   "decade",
   "integer",
-  "FLOOR(YEAR(time)/10)*10" // This is SQL
+  "FLOOR(YEAR(time)/10)*10", // This is SQL
 );
 
 // We summarize the data by computing
-// the average dailyTemperature
+// the average temperature
 // per decade and per city.
-await sdb.summarize("dailyTemperatures", {
+await tableTemperature.summarize({
   values: "t",
   categories: ["decade", "id"],
-  summaries: "mean"
+  summaries: "mean",
 });
 
 // We run linear regressions
 // to check for trends.
-await sdb.linearRegressions("dailyTemperatures", {
+await tableTemperature.linearRegressions({
   x: "decade",
   y: "mean",
   categories: "id",
-  decimals: 4
+  decimals: 4,
 });
 
-// The dailyTemperature table does not have
+// The tableTemperature does not have
 // the name of the cities, just the ids.
 // We load another file with the names
-// in the table cities.
-await sdb.loadData(
-  "cities",
-  "https://raw.githubusercontent.com/nshiab/simple-data-analysis/main/test/data/files/cities.csv"
+// in a new table.
+
+// We create a new table.
+const tableCities = sdb.newTable("cities");
+
+// We load another file with
+// the cities ids and names.
+await tableCities.fetchData(
+  "https://raw.githubusercontent.com/nshiab/simple-data-analysis/main/test/data/files/cities.csv",
 );
 
-// We join the two tables. By default,
-// join searches for a common column
-// and does a left join. The result is stored in
-// the left table (dailyTemperatures here).
-await sdb.join("dailyTemperatures", "cities");
+// We join the two tables based on the ids.
+// By default, join will automatically look
+// for a common column, do a left join, and
+// put the result in the left table.
+await tableCities.join(tableTemperature);
 
-// We select the columns of interest
-// after the join operation.
-await sdb.selectColumns("dailyTemperatures", ["city", "slope", "yIntercept", "r2"]);
+// We select the columns of interest.
+await tableCities.selectColumns(["city", "slope", "yIntercept", "r2"]);
 
 // We log the results table.
-await sdb.logTable("dailyTemperatures");
+await tableCities.logTable();
 
 // We store the data in a variable.
-const results = await sdb.getData("dailyTemperatures");
+const results = await tableCities.getData();
 
 display(Inputs.table(results));
 ```