Are sepals larger or smaller than petals in the iris data?
Sometimes you want to melt a “wide” data table which has several distinct pieces of information encoded in each column name. One example is the familiar iris data, which have flower part and measurement dimension encoded in each of four column names:
library(data.table)
data.table(iris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <num> <num> <num> <num> <fctr>
#> 1: 5.1 3.5 1.4 0.2 setosa
#> 2: 4.9 3.0 1.4 0.2 setosa
#> 3: 4.7 3.2 1.3 0.2 setosa
#> 4: 4.6 3.1 1.5 0.2 setosa
#> 5: 5.0 3.6 1.4 0.2 setosa
#> ---
#> 146: 6.7 3.0 5.2 2.3 virginica
#> 147: 6.3 2.5 5.0 1.9 virginica
#> 148: 6.5 3.0 5.2 2.0 virginica
#> 149: 6.2 3.4 5.4 2.3 virginica
#> 150: 5.9 3.0 5.1 1.8 virginicaThe goal in this section will be to convert these data into a format
with a column for each flower part (Sepal and
Petal) so we can easily make a facetted scatterplot to
visually examine whether or not sepals or larger than petals. The
easiest way to perform this conversion is with packages which provide a
function for melting into multiple output columns:
iris.parts <- list(
nc=nc::capture_melt_multiple(
iris,
column=".*?",
"[.]",
dim=".*"),
tidyr=if(requireNamespace("tidyr"))tidyr::pivot_longer(
iris,
cols=1:4,
names_to=c(".value", "dim"),
names_sep="[.]"),
stats=stats::reshape(
iris,
direction="long",
timevar="dim",
varying=1:4,
sep="."),
"data.table::melt"=melt(
data.table(iris),
measure.vars=patterns(
Sepal="^Sepal",
Petal="^Petal")
)[data.table(
variable=factor(1:2), dim=c("Length", "Width")
), on=.(variable)],
if(requireNamespace("cdata"))cdata::rowrecs_to_blocks(
iris,
controlTable=data.frame(
dim=c("Length", "Width"),
Petal=c("Petal.Length", "Petal.Width"),
Sepal=c("Sepal.Length", "Sepal.Width"),
stringsAsFactors=FALSE),
columnsToCopy="Species"))
#> Loading required namespace: cdata
iris.parts$nc
#> Species dim Petal Sepal
#> <fctr> <char> <num> <num>
#> 1: setosa Length 1.4 5.1
#> 2: setosa Length 1.4 4.9
#> 3: setosa Length 1.3 4.7
#> 4: setosa Length 1.5 4.6
#> 5: setosa Length 1.4 5.0
#> ---
#> 296: virginica Width 2.3 3.0
#> 297: virginica Width 1.9 2.5
#> 298: virginica Width 2.0 3.0
#> 299: virginica Width 2.3 3.4
#> 300: virginica Width 1.8 3.0It is clear from the code above that each package is capable of the conversions. However the syntax and level of explicitness varies:
nc::capture_melt_multiplerequires a regular expression: (most implicit, least repetition)- any input column names that match the regex are melted.
- values matched in the
columngroup are used for the output column names.
tidyr::pivot_longerandstats::reshaperequire specification of the input columns to melt along with a separator.stats::reshapeassumes the output columns names occur in the part of the input column name before the separator.tidyr::pivot_longerassumes the output columns occur in the part which corresponds to the.valueelement of thenames_toargument.
data.table::meltrequires a join to recover thedimoutput column.cdata::rowrecs_to_blocksrequires explicit specification of a control table. (most explicit, most repetition)
Any of the results can be visualized via:
if(require(ggplot2)){
ggplot()+
theme_bw()+
theme(panel.spacing=grid::unit(0, "lines"))+
facet_grid(dim ~ Species)+
coord_equal()+
geom_abline(slope=1, intercept=0, color="grey")+
geom_point(aes(
Petal, Sepal),
data=iris.parts$nc)
}
It is clear from the plot above that sepals are larger than petals, for every measured flower.
