r.rst

Emulate R

c()

Agate's :meth:`.Table.select` and :meth:`.Table.exclude` are the equivalent of R's c for selecting columns.

R:

selected <- data[c("last_name", "first_name", "age")]
excluded <- data[!c("last_name", "first_name", "age")]

agate:

selected = table.select(['last_name', 'first_name', 'age'])
excluded = table.exclude(['last_name', 'first_name', 'age'])

subset

Agate's :meth:`.Table.where` is the equivalent of R's subset.

R:

newdata <- subset(data, age >= 20 | age < 10)

agate:

new_table = table.where(lambda row: row['age'] >= 20 or row['age'] < 10)

order

Agate's :meth:`.Table.order_by` is the equivalent of R's order.

R:

newdata <- employees[order(last_name),]

agate:

new_table = employees.order_by('last_name')

merge

Agate's :meth:`.Table.join` is the equivalent of R's merge.

R:

joined <- merge(employees, states, by="usps")

agate:

joined = employees.join(states, 'usps')

rbind

Agate's :meth:`.Table.merge` is the equivalent of R's rbind.

R:

merged <- rbind(first_year, second_year)

agate:

merged = agate.Table.merge(first_year, second_year)

aggregate

Agate's :meth:`.Table.group_by` and :meth:`.TableSet.aggregate` can be used to recreate the functionality of R's aggregate.

R:

aggregates = aggregate(employees$salary, list(job = employees$job), mean)

agate:

jobs = employees.group_by('job')
aggregates = jobs.aggregate([
    ('mean', agate.Mean('salary'))
])

melt

Agate's :meth:`.Table.normalize` is the equivalent of R's melt.

R:

melt(employees, id=c("last_name", "first_name"))

agate:

employees.normalize(['last_name', 'first_name'])

cast

Agate's :meth:`.Table.denormalize` is the equivalent of R's cast.

R:

melted = melt(employees, id=c("name"))
casted = cast(melted, name~variable, mean)

agate:

normalized = employees.normalize(['name'])
denormalized = normalized.denormalize('name')