Comparisons with other packages"

In nc: Named Capture to Data Tables

Comparisons with other packages

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

This vignette provides comparisons with other packages that provide similar functionality. This is a work in progress -- for a more detailed / complete / coherent comparison with other packages which provide wide-to-tall data reshaping, see my paper.

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)

The 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"))
iris.parts$nc

It 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_multiple requires a regular expression: (most implicit, least repetition)
• any input column names that match the regex are melted.
• values matched in the column group are used for the output column names.
• tidyr::pivot_longer and stats::reshape require specification of the input columns to melt along with a separator.
• stats::reshape assumes the output columns names occur in the part of the input column name before the separator.
• tidyr::pivot_longer assumes the output columns occur in the part which corresponds to the .value element of the names_to argument.
• data.table::melt requires a join to recover the dim output column.
• cdata::rowrecs_to_blocks requires 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.

Comparing dimensions in iris data

What if we wanted to compare dimensions rather than parts?

iris.dims <- list(
  nc=nc::capture_melt_multiple(
    iris,
    part=".*?",
    "[.]",
    column=".*"),
  stats=stats::reshape(
    structure(iris, names=sub("(.*?)[.](.*)", "\\2.\\1", names(iris))),
    direction="long",
    timevar="part",
    varying=1:4,
    sep="."))
iris.dims$nc

The code above shows that the syntax is mostly the same for this example. The biggest difference is for stats::reshape which assumes that each input column name is composed of (1) the output column name, (2) a delimiter, and (3) some additional information to be stored in the output column given by timevar. Therefore we need to pre-process column names using sub for it to work.

if(require(ggplot2)){
  ggplot()+
    theme_bw()+
    theme(panel.spacing=grid::unit(0, "lines"))+
    facet_grid(part ~ Species)+
    coord_equal()+
    geom_abline(slope=1, intercept=0, color="grey")+
    geom_point(aes(
      Length, Width),
      data=iris.dims$nc)
}

It is clear from the plot above that Length is larger than Width for every measured flower part.

Do columns need to be sorted?

Consider the following wide data set:

TC <- data.table::data.table(
  age.treatment=c(1, 5),
  sex.control=c("M", "M"),
  sex.treatment=c("F", "F"),
  age.control=c(10, 50))

It is clear from the column names how the data should be grouped when they are converted to tall format. However the columns do not appear in regular order (age is before sex for treatment, but age is after sex for control), which causes a problem for stats and data.table:

input.list <- list(
  "nc"=nc::capture_melt_multiple(
    TC,
    column=".*?",
    "[.]",
    group=".*"),
  "cdata"=if(requireNamespace("cdata"))cdata::rowrecs_to_blocks(
    TC,
    controlTable=data.frame(
      group=c("treatment", "control"),
      age=c("age.treatment", "age.control"),
      sex=c("sex.treatment", "sex.control"),
      stringsAsFactors=FALSE)),
  "data.table"=data.table::melt(TC, measure.vars=patterns(
    age="age",
    sex="sex")),
  "stats"=stats::reshape(
    TC,
    varying=1:4,
    direction="long"),
  "tidyr"=if(requireNamespace("tidyr"))tidyr::pivot_longer(
    TC, 1:4,
    names_to=c(".value", "group"),
    names_sep="[.]"))
output.list <- list()
for(pkg in names(input.list)){
  df.or.null <- input.list[[pkg]]
  if(is.data.frame(df.or.null)){
    output.list[[pkg]] <- data.table::data.table(df.or.null)[order(age)]
  }
}
output.list
sapply(output.list, function(DT)identical(DT$sex, c("F", "F", "M", "M")))

In conclusion, when the input column names to melt do not appear in the same order across groups or output columns, then the correct tall data can be computed using one of nc::capture_melt_multiple, tidyr::pivot_longer, cdata::rowrecs_to_blocks.

Melting into a single output column, who data

Another data set where it is useful to do column name pattern matching followed by melting is the World Health Organization data:

if(requireNamespace("tidyr")){
  data(who, package="tidyr")
}else{
  who <- data.frame(id=1, new_sp_m5564=2, newrel_f65=3)
}
names(who)

Each column which starts with new has three distinct pieces of information encoded in its name: diagnosis type (e.g. sp or rel), gender (m or f), and age range (e.g. 5564 or 1524). We would like to use a regex to match these column names, then using the matching columns as measure.vars in a melt, then join the two results. The most convenient way to do that is via:

who.chr.list <- list(
  nc=nc::capture_melt_single(
    who,
    "new_?",
    diagnosis=".*",
    "_",
    gender=".",
    ages=".*"),
  tidyr=if(requireNamespace("tidyr"))tidyr::pivot_longer(
    who,
    new_sp_m014:newrel_f65,
    names_to=c("diagnosis", "gender", "ages"),
    names_pattern="new_?(.*)_(.)(.*)"))

Note the result includes additional column value which contains the melted data. There is also a column for each capture group in the specified pattern. The following example shows how to rename the value column, remove missing values, and use numeric type conversion functions:

who.pattern <- "new_?(.*)_(.)((0|[0-9]{2})([0-9]{0,2}))"
as.numeric.Inf <- function(y)ifelse(y=="", Inf, as.numeric(y))
who.typed.list <- list(
  nc=nc::capture_melt_single(
    who,
    "new_?",
    diagnosis=".*",
    "_",
    gender=".",
    ages=list(
      ymin.num="0|[0-9]{2}", as.numeric,
      ymax.num="[0-9]{0,2}", as.numeric.Inf),
    value.name="count",
    na.rm=TRUE),
  tidyr=if(requireNamespace("tidyr"))try(tidyr::pivot_longer(
    who,
    cols=grep(who.pattern, names(who)),
    names_transform=list(
      ymin.num=as.numeric,
      ymax.num=as.numeric.Inf),
    names_to=c("diagnosis", "gender", "ages", "ymin.num", "ymax.num"),
    names_pattern=who.pattern,
    values_drop_na=TRUE,
    values_to="count")))
str(who.typed.list)

The result above shows that nc::capture_melt_single (1) makes it easier to define complex patterns (2) supports type conversion without a post-processing step, and (3) reduces repetition in user code. There are several sources of repetition in tidyr code:

• Syntax of type conversion:
• capture group name ymin.num appears only once for nc but twice for tidyr.
• capture group name ymax.chr appears only once for nc but three times for tidyr.
• Specification of input data and columns to melt:
• input data table who appears only once for nc but twice for tidyr.
• pattern for input columns to melt appears only once for nc but twice for tidyr.

Other packages for doing this include:

if(requireNamespace("tidyr")){
  gather.result <- tidyr::gather(
    who,
    "variable",
    "count",
    grep(who.pattern, names(who)),
    na.rm=TRUE)
  extract.result <- tidyr::extract(
    gather.result,
    "variable",
    c("diagnosis", "gender", "ages", "ymin.int", "ymax.int"),
    who.pattern,
    convert=TRUE)
  transform.result <- base::transform(
    extract.result,
    ymin.num=as.numeric(ymin.int),
    ymax.num=ifelse(is.na(ymax.int), Inf, as.numeric(ymax.int)))
  str(transform.result)
}

Note that tidyr::gather requires two post-processing steps, which cause the same two types of repetition as tidyr::pivot_longer:

• Syntax of type conversion: base::transform is used for converting age range variables to numeric, since default types are int with convert=TRUE.
• Specification of input data and columns to melt: tidyr::extract is used to convert the melted column into several output columns; this results in repetition in the code because the regex is also used to define the columns to melt/gather.

The reshape2 package suffers from the same two issues:

reshape2.result <- if(requireNamespace("reshape2")){
  reshape2:::melt.data.frame(
    who,
    measure.vars=grep(who.pattern, names(who)),
    na.rm=TRUE,
    value.name="count")
}

Interestingly, data.table::patterns can be used to avoid repeating the data set name, who. However it supports neither type conversion nor regex capture groups.

dt.result <- data.table::melt.data.table(
  data.table(who),
  measure.vars=patterns(who.pattern),
  na.rm=TRUE,
  value.name="count")

Neither cdata nor stats provide an na.rm option:

who.df <- data.frame(who)
is.varying <- grepl(who.pattern, names(who))
names(who.df)[is.varying] <- paste0("count.", names(who)[is.varying])
stats.result <- stats::reshape(
  who.df,
  direction="long",
  timevar="variable",
  varying=is.varying)

if(requireNamespace("cdata")){
  cdata.result <- cdata::rowrecs_to_blocks(
    who, 
    cdata::build_unpivot_control(
      "variable",
      "count",
      grep(who.pattern, names(who), value=TRUE)),
    columnsToCopy=grep(who.pattern, names(who), value=TRUE, invert=TRUE))
}

Melting a wider iris back to original

## Example 1: melting a wider iris data back to original.
library(data.table)
iris.dt <- data.table(
  i=1:nrow(iris),
  iris[,1:4],
  Species=paste(iris$Species))
print(iris.dt)

## what if we had two observations on each row?
set.seed(1)
iris.rand <- iris.dt[sample(.N)]
iris.wide <- cbind(treatment=iris.rand[1:75], control=iris.rand[76:150])
print(iris.wide, topn=2, nrows=10)

## This is the usual data.table syntax for getting the original iris back.
iris.melted <- melt(iris.wide, value.factor=TRUE, measure.vars = patterns(
  i="i$",
  Sepal.Length="Sepal.Length$",
  Sepal.Width="Sepal.Width$",
  Petal.Length="Petal.Length$",
  Petal.Width="Petal.Width$",
  Species="Species$"))
identical(iris.melted[order(i), names(iris.dt), with=FALSE], iris.dt)

## nc can do the same thing -- you must define an R argument named
## column, and another named argument which identifies each group.
(nc.melted <- nc::capture_melt_multiple(
  iris.wide,
  group="[^.]+",
  "[.]",
  column=".*"))
identical(nc.melted[order(i), names(iris.dt), with=FALSE], iris.dt)

## This is how we do it using stats::reshape.
iris.wide.df <- data.frame(iris.wide)
names(iris.wide.df) <- sub("(.*?)[.](.*)", "\\2_\\1", names(iris.wide))
iris.reshaped <- stats::reshape(
  iris.wide.df,
  direction="long",
  timevar="group",
  varying=names(iris.wide.df),
  sep="_")
identical(data.table(iris.reshaped[, names(iris.dt)])[order(i)], iris.dt)

## get the parts columns and groups -- is there any difference
## between groups? of course not!
parts.wide <- nc::capture_melt_multiple(
  iris.wide,
  group=".*?",
  "[.]",
  column=".*?",
  "[.]",
  dim=".*")
if(require("ggplot2")){
  ggplot()+
    theme_bw()+
    theme(panel.spacing=grid::unit(0, "lines"))+
    facet_grid(dim ~ group)+
    coord_equal()+
    geom_abline(slope=1, intercept=0, color="grey")+
    geom_point(aes(
      Petal, Sepal),
      data=parts.wide)
}

Lots of column types

## Example 2. Lots of column types, from example(melt.data.table).
DT <- data.table(
  i_1 = c(1:5, NA),
  i_2 = c(NA,6:10),
  f_1 = factor(sample(c(letters[1:3], NA), 6, TRUE)),
  f_2 = factor(c("z", "a", "x", "c", "x", "x"), ordered=TRUE),
  c_1 = sample(c(letters[1:3], NA), 6, TRUE),
  d_1 = as.Date(c(1:3,NA,4:5), origin="2013-09-01"),
  d_2 = as.Date(6:1, origin="2012-01-01"))
## add a couple of list cols
DT[, l_1 := DT[, list(c=list(rep(i_1, sample(5,1)))), by = i_1]$c]
DT[, l_2 := DT[, list(c=list(rep(c_1, sample(5,1)))), by = i_1]$c]

## original DT syntax is quite repetitive.
melt(DT, measure=patterns(
  i="^i",
  f="^f",
  d="^d",
  l="^l"
))

## nc syntax uses a single regex rather than four.
nc::capture_melt_multiple(
  DT,
  column="^[^c]",
  "_",
  number="[12]")

## id.vars can be specified using original DT syntax.
melt(DT, id=1:2, measure=patterns(
  f="^f",
  l="^l"
))

nc::capture_melt_multiple(
  DT,
  column="^[fl]",
  "_",
  number="[12]")

## reshape does not support list columns.
reshape(
  DT,
  varying=grep("^[fid]", names(DT)),
  sep="_",
  direction="long",
  timevar="number")

## tidyr does, but errors for combining ordered and un-ordered factors.
if(requireNamespace("tidyr")){
  tidyr::pivot_longer(
    DT, grep("[cf]", names(DT), invert=TRUE),
    names_pattern="(.)_(.)",
    names_to=c(".value", "number"))
}

family data

## Example 3, three children, one family per row, from data.table
## vignette.
family.dt <- fread(text="
family_id age_mother dob_child1 dob_child2 dob_child3 gender_child1 gender_child2 gender_child3
1         30 1998-11-26 2000-01-29         NA             1             2            NA
2         27 1996-06-22         NA         NA             2            NA            NA
3         26 2002-07-11 2004-04-05 2007-09-02             2             2             1
4         32 2004-10-10 2009-08-27 2012-07-21             1             1             1
5         29 2000-12-05 2005-02-28         NA             2             1            NA")
(children.melt <- melt(family.dt, measure = patterns(
  dob="^dob", gender="^gender"
), na.rm=TRUE, variable.factor=FALSE))

## nc::field can be used to define group name and pattern at the
## same time, to avoid repetitive code.
(children.nc <- nc::capture_melt_multiple(
  family.dt,
  column="[^_]+",
  "_",
  nc::field("child", "", "[1-3]"),
  na.rm=TRUE))

## reshape works too.
stats::reshape(
  family.dt,
  varying=grep("child", names(family.dt)),
  direction="long",
  sep="_",
  timevar="child.str")

histogram of iris

## Comparison with base R. 1. mfrow means parts on rows, mfcol means
## parts on columns. 2. same number of lines of code. 3. nc/ggplot2
## code has more names and fewer numbers.
imat <- as.matrix(iris[, 1:4])
ylim <- range(table(imat))
xlim <- range(imat)
par(mfcol=c(2,2), mar=c(2,2,1,1))
for(col.i in 1:ncol(imat)){
  hist(
    imat[, col.i],
    breaks=seq(xlim[1], xlim[2], by=0.1),
    ylim=ylim,
    main=colnames(imat)[col.i])
}

single output column simple type conversion

All four packages below can convert the input reshape column name to a numeric output capture column.

pen.peaks.wide <- data.table::data.table(
  data.set=c("foo", "bar"),
  "10.1"=c(5L, 10L),
  "0.3"=c(26L, 39L))
pen.peaks.gather <- if(requireNamespace("tidyr"))tidyr::gather(
  pen.peaks.wide,
  "penalty",
  "peaks",
  -1,
  convert=TRUE)
str(pen.peaks.gather)

pen.peaks.nc <- nc::capture_melt_single(
  pen.peaks.wide,
  penalty="^[0-9.]+", as.numeric,
  value.name="peaks")
str(pen.peaks.nc)

pen.peaks.pivot <- if(requireNamespace("tidyr"))try(tidyr::pivot_longer(
  pen.peaks.wide,
  -1,
  names_to="penalty",
  names_transform=list(penalty=as.numeric),
  values_to="peaks"))
str(pen.peaks.pivot)

varying <- 2:3
pen.peaks.reshape.times <- stats::reshape(
  pen.peaks.wide,
  direction="long",
  varying=varying,
  times=as.numeric(names(pen.peaks.wide)[varying]),
  v.names="peaks",
  timevar="penalty")
str(pen.peaks.reshape.times)

pen.peaks.renamed <- pen.peaks.wide
names(pen.peaks.renamed) <- paste0(ifelse(
  grepl("^[0-9]", names(pen.peaks.wide)),
  "peaks_", ""),
  names(pen.peaks.wide))
pen.peaks.reshape.sep <- stats::reshape(
  pen.peaks.renamed,
  direction="long",
  varying=varying,
  sep="_",
  timevar="penalty")
str(pen.peaks.reshape.sep)

Multiple output columns of different types

peaks.csv <- system.file(
  "extdata", "RD12-0002_PP16HS_5sec_GM_F_1P.csv",
  package="nc", mustWork=TRUE)
peaks.wide <- data.table::fread(peaks.csv)
tidyr.long <- tidyr::pivot_longer(
  peaks.wide,
  grep(" [0-9]", names(peaks.wide)),
  names_pattern = "(.*) ([0-9]+)",
  names_to = c(".value", "peak"),
  names_transform = list(peak=as.integer))
peaks.tall <- nc::capture_melt_multiple(
  peaks.wide,
  column=".*",
  " ",
  peak="[0-9]+", as.integer)

nc documentation built on Sept. 1, 2023, 1:07 a.m.