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Imagine that in the course of your analysis, you regularly require summaries of numerical values. For some applications you want the mean of that quantity, plus/minus a standard deviation; for other applications you want the median, and perhaps an interval around the median based on the interquartile range (IQR). In either case, you may want the summary broken down with respect to groupings in the data. In other words, you want a table of values, something like this:

dist_intervals(iris, "Sepal.Length", "Species") # A tibble: 3 × 7 Species sdlower mean sdupper iqrlower median iqrupper1 setosa 4.653510 5.006 5.358490 4.8000 5.0 5.2000 2 versicolor 5.419829 5.936 6.452171 5.5500 5.9 6.2500 3 virginica 5.952120 6.588 7.223880 6.1625 6.5 6.8375

For a specific data frame, with known column names, such a table is easy to construct using `dplyr::group_by`

and `dplyr::summarize`

. But what if you want a function to calculate this table on an arbitrary data frame, with arbitrary quantity and grouping columns? To write such a function in `dplyr`

can get quite hairy, quite quickly. Try it yourself, and see.

Enter `let`

, from our new package `replyr`

.

`replyr::let`

implements a mapping from the “symbolic” names used in a `dplyr`

expression to the names of the actual columns in a data frame. This allows you to encapsulate complex `dplyr`

expressions without the use of the `lazyeval`

package, which is the currently recommended way to manage `dplyr`

‘s use of non-standard evaluation. Thus, you could write the function to create the table above as:

# to install replyr: # devtools::install_github('WinVector/replyr') library(dplyr) library(replyr) # # calculate mean +/- sd intervals and # median +/- 1/2 IQR intervals # for arbitrary data frame column, with optional grouping # dist_intervals = function(dframe, colname, groupcolname=NULL) { mapping = list(col=colname) if(!is.null(groupcolname)) { dframe %>% group_by_(groupcolname) -> dframe } let(alias=mapping, expr={ dframe %>% summarize(sdlower = mean(col)-sd(col), mean = mean(col), sdupper = mean(col) + sd(col), iqrlower = median(col)-0.5*IQR(col), median = median(col), iqrupper = median(col)+0.5*IQR(col)) })() }

The mapping is specified as a list of assignments *symname*=*colname*, where *symname* is the name used in the `dplyr`

expression, and *colname* is the name (as a string) of the corresponding column in the data frame. We can now call our `dist_intervals`

on the `iris`

dataset:

dist_intervals(iris, "Sepal.Length") sdlower mean sdupper iqrlower median iqrupper 1 5.015267 5.843333 6.671399 5.15 5.8 6.45 dist_intervals(iris, "Sepal.Length", "Species") # A tibble: 3 × 7 Species sdlower mean sdupper iqrlower median iqrupper1 setosa 4.653510 5.006 5.358490 4.8000 5.0 5.2000 2 versicolor 5.419829 5.936 6.452171 5.5500 5.9 6.2500 3 virginica 5.952120 6.588 7.223880 6.1625 6.5 6.8375 dist_intervals(iris, "Petal.Length", "Species") # A tibble: 3 × 7 Species sdlower mean sdupper iqrlower median iqrupper 1 setosa 1.288336 1.462 1.635664 1.4125 1.50 1.5875 2 versicolor 3.790089 4.260 4.729911 4.0500 4.35 4.6500 3 virginica 5.000105 5.552 6.103895 5.1625 5.55 5.9375

The implementation of `let`

is adapted from `gtools::strmacro`

by Gregory R. Warnes. Its primary purpose is for wrapping `dplyr`

, but you can use it to parameterize other functions that take their arguments via non-standard evaluation, like `ggplot2`

functions — in other words, you can use `replyr::let`

instead of `ggplot2::aes_string`

, if you are feeling perverse. Because `let`

creates a macro, you have to avoid variable collisions (for example, remapping `x`

in `ggplot2`

will clobber both sides of `aes(x=x)`

), and you should remember that any side effects of the expression will escape `let`

‘s execution environment.

The `replyr`

package is available on github. Its goal is to supply uniform `dplyr`

-based methods for manipulating data frames and `tbl`

s both locally and on remote (`dplyr`

-supported) back ends. This is a new package, and it is still going through growing pains as we figure out the best ways to implement desired functionality. We welcome suggestions for new functions, and more efficient or more general ways to implement the functionality that we supply.

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