R Base
In this section, we consider all the methods and functions included in the default packages, i.e., those that are automatically installed along with any R distribution:
#> [1] "base" "compiler" "datasets" "graphics" "grDevices" "grid"
#> [7] "methods" "parallel" "splines" "stats" "stats4" "tcltk"
#> [13] "tools" "utils"Row Subsetting
Quantities objects have all the subsetting methods defined
([, [[, [<-,
[[<-). Therefore they can be used in the same way as
with plain numeric vectors, and in conjunction with which
and other functions to perform subsetting. The subset
function is very handy too and achieves the same result:
iris.q[which(iris.q$Sepal.Length > set_quantities(75, mm)), ]
#> Warning: In '>' : boolean operators not defined for 'errors' objects,
#> uncertainty dropped
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 106 7.6(2) [cm] 3.00(6) [cm] 6.6(1) [cm] 2.10(4) [cm] virginica
#> 118 7.7(2) [cm] 3.80(8) [cm] 6.7(1) [cm] 2.20(4) [cm] virginica
#> 119 7.7(2) [cm] 2.60(5) [cm] 6.9(1) [cm] 2.30(5) [cm] virginica
#> 123 7.7(2) [cm] 2.80(6) [cm] 6.7(1) [cm] 2.00(4) [cm] virginica
#> 132 7.9(2) [cm] 3.80(8) [cm] 6.4(1) [cm] 2.00(4) [cm] virginica
#> 136 7.7(2) [cm] 3.00(6) [cm] 6.1(1) [cm] 2.30(5) [cm] virginica
subset(iris.q, Sepal.Length > set_quantities(75, mm))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 106 7.6(2) [cm] 3.00(6) [cm] 6.6(1) [cm] 2.10(4) [cm] virginica
#> 118 7.7(2) [cm] 3.80(8) [cm] 6.7(1) [cm] 2.20(4) [cm] virginica
#> 119 7.7(2) [cm] 2.60(5) [cm] 6.9(1) [cm] 2.30(5) [cm] virginica
#> 123 7.7(2) [cm] 2.80(6) [cm] 6.7(1) [cm] 2.00(4) [cm] virginica
#> 132 7.9(2) [cm] 3.80(8) [cm] 6.4(1) [cm] 2.00(4) [cm] virginica
#> 136 7.7(2) [cm] 3.00(6) [cm] 6.1(1) [cm] 2.30(5) [cm] virginicaNote that another quantities object is defined for the comparison.
This is needed because different units are incomparable. Also note that
the first line throws a warning telling us that the uncertainty was
dropped for this operation. This kind of warning is thrown once, and
this is why subset succeeds silently.
Row Ordering
The sort function, as its name suggests, sorts
vectors, and it is compatible with quantities:
iris.q$Sepal.Length[1:5]
#> Units: [cm]
#> Errors: 0.102 0.098 0.094 0.092 0.100
#> [1] 5.1 4.9 4.7 4.6 5.0
sort(iris.q$Sepal.Length[1:5])
#> Units: [cm]
#> Errors: 0.092 0.094 0.098 0.100 0.102
#> [1] 4.6 4.7 4.9 5.0 5.1More generally, the order function can be used for data
frame ordering:
head(iris.q[order(iris.q$Sepal.Length), ])
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 14 4.30(9) [cm] 3.00(6) [cm] 1.10(2) [cm] 0.100(2) [cm] setosa
#> 9 4.40(9) [cm] 2.90(6) [cm] 1.40(3) [cm] 0.200(4) [cm] setosa
#> 39 4.40(9) [cm] 3.00(6) [cm] 1.30(3) [cm] 0.200(4) [cm] setosa
#> 43 4.40(9) [cm] 3.20(6) [cm] 1.30(3) [cm] 0.200(4) [cm] setosa
#> 42 4.50(9) [cm] 2.30(5) [cm] 1.30(3) [cm] 0.300(6) [cm] setosa
#> 4 4.60(9) [cm] 3.10(6) [cm] 1.50(3) [cm] 0.200(4) [cm] setosaColumn Transformation
The transform function is able to modify variables in a
data frame or to create new ones. The within function
provides a similar but more flexible approach though. Both are fully
compatible with quantities:
head(within(iris.q, {
Sepal.Area <- Sepal.Length * Sepal.Width
Petal.Area <- Petal.Length * Petal.Width
rm(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)
}))
#> Species Petal.Area Sepal.Area
#> 1 setosa 0.280(8) [cm^2] 17.8(5) [cm^2]
#> 2 setosa 0.280(8) [cm^2] 14.7(4) [cm^2]
#> 3 setosa 0.260(7) [cm^2] 15.0(4) [cm^2]
#> 4 setosa 0.300(8) [cm^2] 14.3(4) [cm^2]
#> 5 setosa 0.280(8) [cm^2] 18.0(5) [cm^2]
#> 6 setosa 0.68(2) [cm^2] 21.1(6) [cm^2]Row Aggregation
Row aggregation is the process of summarising data based on some grouping variable(s). There are several ways of working with data split by factors in R base, and, although they tend to preserve classes, they are generally not very kind to other metadata (i.e., attributes) by default.
In the following example, the average Sepal.Length is
computed per Species, but the metadata gets dropped:
tapply(iris.q$Sepal.Length, iris.q$Species, mean)
#> setosa versicolor virginica
#> 5.006 5.936 6.588Many of these functions include a simplify parameter
which, if set to FALSE, preserves quantities metadata:
(sepal.length.agg <-
tapply(iris.q$Sepal.Length, iris.q$Species, mean, simplify=FALSE))
#> $setosa
#> 5.0(1) [cm]
#>
#> $versicolor
#> 5.9(1) [cm]
#>
#> $virginica
#> 6.6(1) [cm]The only drawback is that the result is a list, and such a list must be unlisted with care, otherwise, metadata gets dropped again:
# drops quantities
unlist(sepal.length.agg)
#> setosa versicolor virginica
#> 5.006 5.936 6.588
# preserves quantities
do.call(c, sepal.length.agg)
#> Units: [cm]
#> Errors: 0.10012 0.11872 0.13176
#> setosa versicolor virginica
#> 5.006 5.936 6.588The by function is an object-oriented wrapper for
tapply applied to data frames which also provides a
simplify parameter. A more convenient way of working with
summary statistics is the aggregate generic, from the
stats namespace. Although there is a
aggregate.data.frame method, there is a more intuitive
interface to it through the aggregate.formula method.
Again, it is necessary to set simplify=FALSE to keep
quantities:
(iris.q.agg <- aggregate(. ~ Species, data = iris.q, mean, simplify=FALSE))
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 setosa 5.006 3.428 1.462 0.246
#> 2 versicolor 5.936 2.77 4.26 1.326
#> 3 virginica 6.588 2.974 5.552 2.026Apparently, the output has no metadata associated, but what really happens is that the resulting columns are lists:
Therefore, as in the tapply/by case, they
must be unlisted with care to still preserve the metadata:
unlist_quantities <- function(x) {
stopifnot(is.list(x) || is.data.frame(x))
unlist <- function(x) {
if (any(class(x[[1]]) %in% c("quantities", "units", "errors")))
do.call(c, x)
else x
}
if (is.data.frame(x))
as.data.frame(lapply(x, unlist), col.names=colnames(x))
else unlist(x)
}
unlist_quantities(iris.q.agg)
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 setosa 5.0(1) [cm] 3.43(7) [cm] 1.46(3) [cm] 0.25(1) [cm]
#> 2 versicolor 5.9(1) [cm] 2.77(6) [cm] 4.26(9) [cm] 1.33(3) [cm]
#> 3 virginica 6.6(1) [cm] 2.97(6) [cm] 5.6(1) [cm] 2.03(4) [cm]And this method works for the tapply/by
case too:
Column Joining
Joining data frames by common columns can done with the
merge generic. Such operations are based on appending
columns, which may be subset or replicated to fit the length of the
merged observations. Therefore, quantities should be preserved in all
cases. In the following example, we generate a data frame with the
height per species and then merge it with the main data set:
height <- data.frame(
Height = set_quantities(c(55, 60, 45), cm, c(45, 30, 35)),
Species = c("setosa", "virginica", "versicolor")
)
head(merge(iris.q, height))
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width Height
#> 1 setosa 5.1(1) [cm] 3.50(7) [cm] 1.40(3) [cm] 0.200(4) [cm] 60(40) [cm]
#> 2 setosa 4.9(1) [cm] 3.00(6) [cm] 1.40(3) [cm] 0.200(4) [cm] 60(40) [cm]
#> 3 setosa 4.70(9) [cm] 3.20(6) [cm] 1.30(3) [cm] 0.200(4) [cm] 60(40) [cm]
#> 4 setosa 4.60(9) [cm] 3.10(6) [cm] 1.50(3) [cm] 0.200(4) [cm] 60(40) [cm]
#> 5 setosa 5.0(1) [cm] 3.60(7) [cm] 1.40(3) [cm] 0.200(4) [cm] 60(40) [cm]
#> 6 setosa 5.4(1) [cm] 3.90(8) [cm] 1.70(3) [cm] 0.400(8) [cm] 60(40) [cm](Un)Pivoting
The reshape function, from the stats
namespace, provides an interface for both pivoting and unpivoting (i.e.,
tidyfying data). In the case of the iris data set,
we would say that it is in the wide format, because each row
has more than one observation.
This function has a quite peculiar nomenclature. First of all, the
unpivoting operation is accessed by providing the argument
direction="long". We need to define the
varying columns (columns to unpivot), as character or
indices, and they are unpivoted based on their names. By default, the
separator sep="." is used, which means that
Sepal.Width will be broken down into Sepal and
Width, and the former will be unpivoted with the latter as
grouping variable. We can specify the name of the grouping variable with
the timevar argument.
Putting everything together, this is how to unpivot the data set by
the dimension (which we will call it dim) of the
petal/sepal:
long.1 <- reshape(iris.q, varying=1:4, timevar="dim", idvar="dim.id", direction="long")
head(long.1)
#> Species dim Sepal Petal dim.id
#> 1.Length setosa Length 5.1(1) [cm] 1.40(3) [cm] 1
#> 2.Length setosa Length 4.9(1) [cm] 1.40(3) [cm] 2
#> 3.Length setosa Length 4.70(9) [cm] 1.30(3) [cm] 3
#> 4.Length setosa Length 4.60(9) [cm] 1.50(3) [cm] 4
#> 5.Length setosa Length 5.0(1) [cm] 1.40(3) [cm] 5
#> 6.Length setosa Length 5.4(1) [cm] 1.70(3) [cm] 6It can be noted that the unpivoting also generates an index to
indentify multiple records from the same group. We have changed the name
of that identifier to dim.id (just id by
default).
We can further unpivot sepal and petal as the part of
the flower. First, we need to prepend a common identifier to columns 3
and 4, which are to be unpivoted:
names(long.1)[3:4] <- paste0("value.", names(long.1)[3:4])
long.2 <- reshape(long.1, varying=3:4, timevar="part", idvar="part.id", direction="long")
head(long.2)
#> Species dim dim.id part value part.id
#> 1.Sepal setosa Length 1 Sepal 5.1(1) [cm] 1
#> 2.Sepal setosa Length 2 Sepal 4.9(1) [cm] 2
#> 3.Sepal setosa Length 3 Sepal 4.70(9) [cm] 3
#> 4.Sepal setosa Length 4 Sepal 4.60(9) [cm] 4
#> 5.Sepal setosa Length 5 Sepal 5.0(1) [cm] 5
#> 6.Sepal setosa Length 6 Sepal 5.4(1) [cm] 6And the final result has one tidy observation per row.
The pivoting operation can be accessed by providing the argument
direction="wide". The process is almost symmetrical, but we
need to specify v.names, as character, instead of
varying columns. First, we can pivot by flower part:
wide.1 <- reshape(long.2, v.names="value", timevar="part", idvar="part.id", direction="wide")
head(wide.1)
#> Species dim dim.id part.id value.Sepal value.Petal
#> 1.Sepal setosa Length 1 1 5.1(1) [cm] 1.40(3) [cm]
#> 2.Sepal setosa Length 2 2 4.9(1) [cm] 1.40(3) [cm]
#> 3.Sepal setosa Length 3 3 4.70(9) [cm] 1.30(3) [cm]
#> 4.Sepal setosa Length 4 4 4.60(9) [cm] 1.50(3) [cm]
#> 5.Sepal setosa Length 5 5 5.0(1) [cm] 1.40(3) [cm]
#> 6.Sepal setosa Length 6 6 5.4(1) [cm] 1.70(3) [cm]Then, we remove "value." from the column names and pivot
by dimension (note that indices are removed to match the initial data
frame):
names(wide.1)[5:6] <- sub("value\\.", "", names(wide.1)[5:6])
wide.2 <- reshape(wide.1, v.names=c("Sepal", "Petal"), timevar="dim", idvar="dim.id", direction="wide")
#> Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
#> varying, : some constant variables (part.id) are really varying
wide.2$dim.id <- NULL
wide.2$part.id <- NULL
head(wide.2)
#> Species Sepal.Length Petal.Length Sepal.Width Petal.Width
#> 1.Sepal setosa 5.1(1) [cm] 1.40(3) [cm] 3.50(7) [cm] 0.200(4) [cm]
#> 2.Sepal setosa 4.9(1) [cm] 1.40(3) [cm] 3.00(6) [cm] 0.200(4) [cm]
#> 3.Sepal setosa 4.70(9) [cm] 1.30(3) [cm] 3.20(6) [cm] 0.200(4) [cm]
#> 4.Sepal setosa 4.60(9) [cm] 1.50(3) [cm] 3.10(6) [cm] 0.200(4) [cm]
#> 5.Sepal setosa 5.0(1) [cm] 1.40(3) [cm] 3.60(7) [cm] 0.200(4) [cm]
#> 6.Sepal setosa 5.4(1) [cm] 1.70(3) [cm] 3.90(8) [cm] 0.400(8) [cm]We have seen that quantities have been correctly preserved through the whole process. Finally, we can check whether both data frames are identical. Given that the order of columns have changed, we can simply check this column name by column name and then put everything together:
Plotting
Quantities support R base scatterplots out of the box: errors are displayed as segments around each point and units are automatically added to the corresponding axis label.
Here is an example of a simple plot of a single quantity, where each value is automatically indexed in the x-axis:
X-Y scatterplots support units and errors in both axes:
# x-y plots
with(iris.q, plot(Sepal.Length, Sepal.Width, col=Species))
# dataframe plots
plot(iris.q[, c("Sepal.Length", "Sepal.Width")], col=iris.q$Species)which are equivalent, and produce the same result, as the formula method:
There is a fundamental limitation in R base for mixed quantities and non-quantities data due to S3 dispatch. It is possible, for instance, to plot quantities in the x-axis and numeric data in the y-axis:
However, when the x-axis has numeric data, quantities methods will not be dispatched for the y-axis:
One way to overcome this limitation is to set unitless and errorless quantities in the x-axis:
