
CLOUD . R-PROJECT . ORG {
}
Title:
Efficient reshaping using data.tables
Description:
No description found...
Website Age:
25 years and 8 months (reg. 1999-10-27).
Matching Content Categories {📚}
- Non-Profit & Charity
- Family & Parenting
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Website use litedown 0.7.
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🚄 Respectable Traffic: 10k - 20k visitors per month
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Keywords {🔍}
dobchild, int, child, columns, familyid, agemother, gender, idat, melt, dob, num, dtm, dcast, measurevars, column, datatables, data, variable, measure, char, multiple, format, setosa, virginica, functionality, idate, fctr, wide, long, datatable, sepal, length, width, petal, default, straightforward, variables, idvars, meltdt, result, operation, valuevar, number, function, genderchild, part, groups, melting, simultaneously, attr,
Topics {✒️}
perl-compatible regular expression previous melt/dcast approaches automatically named variable child string values gender type columns identify single observation m3 = melt na na provide multiple functions computationally involved operation multiple columns simultaneously fast radix ordering provide regular expressions age_mother data sets directly width petal reshaping functions melt length petal length sepal column names split child observations ymax=function numeric output columns gender columns performing operations efficiently m2 = melt family_id family_id ~ regular expression data set dt provide multiple dob columns integer argument means multiple functions variable column set variable preserve attributes dob_child3 vars simultaneously regular names regular structure original data multiple columns separate observation previous section values defined flower part automatically considered type factor output columns
Questions {❓}
- Check ?
- Check the examples in ?
- M1, how can we get the number of children in each family?
- M2 as shown above, how can we get back to the same format as the original data we started with?
- Vars are specified, as mentioned under ?
External Links {🔗}(2)
Libraries {📚}
- Prism.js
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CDN Services {📦}
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