Previous: variable organization
One of the powerful features of working with Crunch is that you and
all of your collaborators work off of the same data, whether you use the
web client or use R. Circumstances may arise in which you are working in
R and want to add or modify variables in a Crunch dataset, and the
crunch package facilitates that. It provides an idiomatic R
interface to manipulating a Dataset, doing so efficiently, without
copying data off of the server to transform it. In addition, when you
create variables that are derived from other variables, they retain
their functional connection on the server, such that changes to the
input variables automatically propagate to the derived variable.
In our sample dataset, we don’t have an “Age” variable, but we do
have “Birth Year” (birthyr). We can create an age variable
simply by taking the current year and subtracting the “Birth Year”
variable from it–just as you would do if you were working with a
data.frame:
ds$age <- 2015 - ds$birthyrNow, we have an age variable, and it looks like we’d expect it would
relative to birthyr:
summary(ds$age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 22.00 38.00 48.50 49.62 59.75 90.00summary(ds$birthyr)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1925 1955 1966 1965 1977 1993Age is now a derived variable, functionally linked to
birthyr on the server. Note that in order to create
age, we didn’t pull any values off of the server; we just
supplied the derivation expression.
class(2015 - ds$birthyr)## [1] "CrunchExpr"
## attr(,"package")
## [1] "crunch"We can also use these expressions to update values. Suppose, just for
demonstration purposes, that we want to truncate the range of the
birthyr variable and set it to 1945 everywhere where it is
less than 1945. We can once again write the R code we’d write if we were
working with a data.frame, even though we’re working with a
Crunch dataset:
ds$birthyr[ds$birthyr < 1945] <- 1945If we look at the summary again, we’ll see that our data have been updated, and the minimum Birth Year is now 1945.
summary(ds$birthyr)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1945 1955 1966 1966 1977 1993Not only that: since age is a function of
birthyr on the server, its values also update now that
we’ve modified birthyr. Max age is now 70, or 2015 -
1945.
summary(ds$age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 22.00 38.00 48.50 48.98 59.75 70.00Once again, we’ve done this operation by turning idiomatic R expressions into Crunch expression syntax.
class(ds$birthyr < 1945)## [1] "CrunchLogicalExpr"
## attr(,"package")
## [1] "crunch"The work gets done without having to pull data off of the server.