| Title: | Multivariate Data Handling with S4 Classes and Sparse Matrices |
|---|---|
| Description: | An S4 update of the 'mefa' package using sparse matrices for enhanced efficiency. Sparse array-like objects are supported via lists of sparse matrices. |
| Authors: | Peter Solymos [cre, aut]
|
| Maintainer: | Peter Solymos <[email protected]> |
| License: | GPL-2 |
| Version: | 0.3-11 |
| Built: | 2024-09-24 02:19:21 UTC |
| Source: | https://github.com/psolymos/mefa4 |
An S4 update of the 'mefa' package using sparse matrices for enhanced efficiency.
An S4 update of the 'mefa' package using sparse matrices for enhanced efficiency. Sparse array-like objects are supported via lists of sparse matrices.
Accessor and replacement functions: xtab,
samp, taxa.
Methods: mbind, groupSums,
groupMeans.
Coercion methods and virtual classes defined for
cross compatibility with the mefa package.
S4 object classes are described in Mefa help page.
The vignette vignette("mefa4") gives an overview of the package,
gives a comparison of S3 and S4 object classes, and presents a performance
review.
Peter Solymos
Maintainer: Peter Solymos <[email protected]>
Solymos P. (2008) mefa: an R package for handling and reporting count data. Community Ecology 9, 125–127.
Solymos P. (2009) Processing ecological data in R with the mefa package. Journal of Statistical Software 29(8), 1–28. doi:10.18637/jss.v029.i08
S3 classes: mefa
## Not run: vignette("mefa4") ## End(Not run)## Not run: vignette("mefa4") ## End(Not run)
%notin% is the negation of %in%,
which returns a logical vector indicating if there is a non-match or not
for its left operand.
x %notin% tablex %notin% table
x |
vector or |
table |
vector or |
A logical vector, indicating if a non-match was located for each element of
x: thus the values are TRUE or FALSE and never NA.
Peter Solymos <[email protected]>
All the opposite of what is written for %in%.
1:10 %notin% c(1,3,5,9) sstr <- c("c","ab","B","bba","c",NA,"@","bla","a","Ba","%") sstr[sstr %notin% c(letters, LETTERS)]1:10 %notin% c(1,3,5,9) sstr <- c("c","ab","B","bba","c",NA,"@","bla","a","Ba","%") sstr[sstr %notin% c(letters, LETTERS)]
A data set of bird point counts collected by the Alberta Biodiversity Monitoring Institute (ABMI, https://www.abmi.ca).
data(abmibirds)data(abmibirds)
A data frame with 59341 observations on the following 21 variables.
Rotationa factor. Reference describing when data was collected at a broad level. Code definition: Prototype = 2003–2006, Rotation 1 = 2007–2012
ABMI.Sitea numeric vector. Reference number given to each ABMI data collection site (1–1656).
Yeara numeric vector. Collection year.
Field.Datea factor. Day, month, and year data was collected.
Field.Crew.Membersa factor. Initials for the field technicians collecting the field data.
Identification.Datea factor. Day, month, and year data was analyzed by specialist.
Identification.Analysta factor. Initials for the technicians/specialists identifying the specimens.
Point.Count.Stationa numeric vector. Point count station where recording was made: 9 stations were located around each ABMI site (1–9).
Wind.Conditionsa factor. Estimate of wind conditions on a scale of 0–5. 0 = no wind, 1 = calm, 2 = leaves rustling, 3 small branches moving, 4 = large branches moving, 5 = large branches moving and the tree is swaying
Precipitationa factor. Classification for precipitation conditions in 5 categories. Input value: Drizzle, Fog, Rain, Sleet, Snow, None
Start.of.Point.Counta factor. Time of day recording was started. Input value: 24 hour clock (hh:mm).
End.of.Point.Counta factor. Time of day recording was finished. Input value: 24 hour clock (hh:mm).
Common.Namea factor. Common name of bird species detected during point counts.
Scientific.Namea factor. Scientific name of bird species detected during point count.
Unique.Taxonomic.Identification.Numbera factor. Globally unique identifier of bird species detected during point count. Unique taxonomic identifiers are generally taken from the International Taxonomic Information System (ITIS; https://www.itis.gov/).
Taxonomic.Resolutiona factor. Resolution to which bird species was identified (e.g. Family, Genus, Species etc.).
Time.First.Detecteda factor. Approximate time the bird analyst first detects a bird species from the recording; listed in 10-second intervals.
Interval.1a factor. First time interval of the 10-minute point count (0–200 seconds) when bird species are detected and identified.
Interval.2a factor. Middle time interval of the 10-minute point count (201–400 seconds) when bird species are detected or re-detected.
Interval.3a factor. Last time interval of the 10-minute point count (401–600 seconds) when bird species are detected or re-detected.
Behavioura factor. Classification given to each species detection (if possible).
Breeding birds were measured at nine point count stations. Point count stations were in a grid pattern with point count station no. 1 located at site-centre and the remaining stations located 300 m apart surrounding site centre. We conducted breeding bird surveys from one half hour before sunrise to 10:00 hrs.
We recorded vocalizations of birds for 10 minutes at each point count station using an omni-directional microphone (CZM microphone; River Forks Research Corp.) mounted at ear level on a professional tripod and connected to a mini hard drive recorder. We recorded birds on a Marantz PM D670 or PM D660 Solid State recorder at 320 kbps in .mp3 format. We calibrated the recorder volume to be in the mid ranges.
While conducting the 10 minute bird recordings, we scanned the areas surrounding the point count station for all birds (even those vocalizing), noting species, number of individuals (including flock sizes of birds flying overhead), and distance from the point count station, for all bird observations. We also noted factors that potentially bias bird recordings, such as wind speed and precipitation. Bird recordings were later analyzed by bird identification specialists in a laboratory setting.
If a bird point fell in open water, we established a new point if we were able to get within 100 m of the original point, recording distance and direction from that original point. If it was not possible to get within 100 m of the original point (i.e., <200 m from the last point), we conducted a 10 minute visual point count of the waterbody recording observations into the microphone. We may not have sampled certain points because they were inaccessible (e.g., a stream made access hazardous or impossible).
We analyzed bird recordings in a laboratory setting. We identified the species, time of first detection (within 10 second intervals), behaviour (e.g., singing, calling, or alarm-calling), and the time interval that individual birds were detected. We recognized 3 time intervals: Interval 1 (0–200 seconds), Interval 2 (201–400 seconds), and Interval 3 (401–600 seconds). Individual birds were detected in 1, 2, or 3 of the time intervals. We identified red squirrel (Tamiasciurus hudsonicus) vocalizations in addition to bird vocalizations. Bird recordings are randomly sampled and verified by other experts in bird identification to ensure accuracy.
Throughout ABMI raw data files, the following codes and definitions are applied.
None or 0: None or 0 is applied to any variable that was examined by field crews and found to be absent. None is used for text entries and 0 is used for numerical entries. For example, when field crews examine the canopy and find no Veteran trees in the canopy, this is recorded as None. When there is no slope at the survey site, slope is recorded as 0. The numeral 0 can also be used as a nominal code, for example, wind conditions can be recorded as 0.
VNA Variable Not Applicable: Some ABMI data is collected in a nested manner. For example Tree Species is a parent variable. This variable has a number of child variables that are used to describe the parent variable in more detail (e.g., condition, DBH, decay stage). When the parent variable is recorded as None, child variables are no longer applied and are recorded as VNA. VNA is also used when the protocol calls for a modified sampling procedure based on site conditions (e.g., surface substrate protocol variant for hydric site conditions). The use of VNA implies that users of the data should not expect that any data could be present.
DNC, Did Not Collect: DNC is used to describe variables that should have been collected but were not. There are a number of reasons that data might not have been collected (e.g., staff oversight, equipment failure, safety concerns, environmental conditions, or time constraints). Regardless of the reason data was not collected, if under ideal conditions it should have been, the record in the data entry file reads DNC. The use of DNC implies that users should expect the data to be present, though it is not.
PNA, Protocol Not Available: The ABMI's protocols were, and continue to be, implemented in a staged manner. As a result, the collection of many variables began in years subsequent to the start of the prototype or operational phases or where discontinued after a few years of trial. When a variable was not collected because the protocol had yet to be implemented by the ABMI (or was discontinued by the ABMI), the data entry record reads PNA. This is a global constraint to the data (i.e., a protocol was not implemented until 2006, therefore, previous years cannot have this variable). PNA is to be used to describe the lack of data collection for entire years.
SNI, Species Not Identified: In various fields related to species identification, SNI is used to indicate that the organism was not identified. Some possible reasons that identification was not possible include insufficient or deficient sample collected and lack of field time.
RAW_T26BreedingBirds28621.csv, https://www.abmi.ca
Raw breeding bird data (2004–2006 inclusive) from the Alberta Biodiversity Monitoring Institute was used, in whole or part, to create this product. More information on the Institute can be found at: https://www.abmi.ca
data(abmibirds) str(abmibirds)data(abmibirds) str(abmibirds)
Utility functions for factors and compositional data.
compare_sets(x, y) find_max(x) find_min(x) reclass(x, map, all = FALSE, allow_NA = FALSE) redistribute(x, source, target = NULL)compare_sets(x, y) find_max(x) find_min(x) reclass(x, map, all = FALSE, allow_NA = FALSE) redistribute(x, source, target = NULL)
x, y
|
any type for |
map |
a reclassification matrix with 2 columns (1st: original levels, 2nd: output levels mapped to original levels). |
all |
logical, whether all levels from mapping matrix should be applied on the return object. |
allow_NA |
logical, whether |
source |
numeric or character, single column index for input matrix |
target |
numeric or character, column index or indices for input matrix |
A matrix compare_sets.
A data frame for find_max and find_min.
A reclassified factor for reclass.
A matrix for redistribute where the source column values are
redistributed among the target columns proportionally.
Peter Solymos <[email protected]>
intersect, setdiff,
union, relevel, reorder
## numeric vector compare_sets(1:10, 8:15) ## factor with 'zombie' labels compare_sets(factor(1:10, levels=1:10), factor(8:15, levels=1:15)) (mat <- matrix(rnorm(10*5), 10, 5)) (m <- find_max(mat)) ## column indices as.integer(m$index) find_min(mat) map <- cbind(c("a","b","c","d","e","f","g"), c("A","B","B","C","D","D","E")) #x <- factor(sample(map[1:6,1], 100, replace=TRUE), levels=map[,1]) x <- as.factor(sample(map[1:6,1], 100, replace=TRUE)) x[2] <- NA table(x, reclass(x, map, all = FALSE), useNA="always") table(x, reclass(x, map, all = TRUE), useNA="always") map[c(4, 7), 2] <- NA table(x, reclass(x, map, all = FALSE, allow_NA = TRUE), useNA="always") table(x, reclass(x, map, all = TRUE, allow_NA = TRUE), useNA="always") (mat2 <- exp(mat) / rowSums(exp(mat))) (rmat2 <- redistribute(mat2, source = 1, target = 2:4)) colMeans(mat2) colMeans(rmat2) stopifnot(abs(sum(mat2) - sum(rmat2)) < 10^-6)## numeric vector compare_sets(1:10, 8:15) ## factor with 'zombie' labels compare_sets(factor(1:10, levels=1:10), factor(8:15, levels=1:15)) (mat <- matrix(rnorm(10*5), 10, 5)) (m <- find_max(mat)) ## column indices as.integer(m$index) find_min(mat) map <- cbind(c("a","b","c","d","e","f","g"), c("A","B","B","C","D","D","E")) #x <- factor(sample(map[1:6,1], 100, replace=TRUE), levels=map[,1]) x <- as.factor(sample(map[1:6,1], 100, replace=TRUE)) x[2] <- NA table(x, reclass(x, map, all = FALSE), useNA="always") table(x, reclass(x, map, all = TRUE), useNA="always") map[c(4, 7), 2] <- NA table(x, reclass(x, map, all = FALSE, allow_NA = TRUE), useNA="always") table(x, reclass(x, map, all = TRUE, allow_NA = TRUE), useNA="always") (mat2 <- exp(mat) / rowSums(exp(mat))) (rmat2 <- redistribute(mat2, source = 1, target = 2:4)) colMeans(mat2) colMeans(rmat2) stopifnot(abs(sum(mat2) - sum(rmat2)) < 10^-6)
Compute summary statistics (sums, means) of data subsets.
groupSums(object, ...) ## S4 method for signature 'matrix' groupSums(object, MARGIN, by, na.rm = FALSE, ...) ## S4 method for signature 'sparseMatrix' groupSums(object, MARGIN, by, na.rm = FALSE, ...) ## S4 method for signature 'Mefa' groupSums(object, MARGIN, by, replace, na.rm = FALSE, ...) groupMeans(object, ...) ## S4 method for signature 'matrix' groupMeans(object, MARGIN, by, na.rm = FALSE, ...) ## S4 method for signature 'sparseMatrix' groupMeans(object, MARGIN, by, na.rm = FALSE, ...) ## S4 method for signature 'Mefa' groupMeans(object, MARGIN, by, replace, na.rm = FALSE, ...) sum_by(x, by)groupSums(object, ...) ## S4 method for signature 'matrix' groupSums(object, MARGIN, by, na.rm = FALSE, ...) ## S4 method for signature 'sparseMatrix' groupSums(object, MARGIN, by, na.rm = FALSE, ...) ## S4 method for signature 'Mefa' groupSums(object, MARGIN, by, replace, na.rm = FALSE, ...) groupMeans(object, ...) ## S4 method for signature 'matrix' groupMeans(object, MARGIN, by, na.rm = FALSE, ...) ## S4 method for signature 'sparseMatrix' groupMeans(object, MARGIN, by, na.rm = FALSE, ...) ## S4 method for signature 'Mefa' groupMeans(object, MARGIN, by, replace, na.rm = FALSE, ...) sum_by(x, by)
object |
an object. |
x |
a vector. |
MARGIN |
numeric, |
by |
a vector of grouping elements corresponding to dimensions
of |
replace |
a data frame to be used when applying the method on a
|
na.rm |
logical. Should missing values be removed?
Sum is calculated by zeroing out |
... |
other argument, currently not implemented. |
The method sums/averages cells in a matrix.
The functions behind these methods use sparse matrices,
so calculations can be more efficient compared to using
aggregate.
An object similar to the input one.
Peter Solymos <[email protected]>
rowSums, rowMeans,
colSums, colMeans
Standard aggregate in package stats
aggregate.mefa in package
mefa for S3 "mefa" objects.
x <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), stringsAsFactors = TRUE) samp <- data.frame(samples=levels(x$sample), var1=1:2, stringsAsFactors = TRUE) taxa <- data.frame(specnames=levels(x$species), var2=c("b","a"), stringsAsFactors = TRUE) rownames(samp) <- samp$samples rownames(taxa) <- taxa$specnames x2 <- Xtab(count ~ sample + species, x, cdrop=FALSE,rdrop=TRUE) x5 <- Mefa(x2, samp, taxa, join="inner") groupSums(as.matrix(x2), 1, c(1,1,2)) groupSums(as.matrix(x2), 2, c(1,1,2,2)) groupSums(x2, 1, c(1,1,2)) groupSums(x2, 2, c(1,1,2,2)) groupSums(x5, 1, c(1,1,2)) groupSums(x5, 2, c(1,1,2,2)) groupMeans(as.matrix(x2), 1, c(1,1,2)) groupMeans(as.matrix(x2), 2, c(1,1,2,2)) groupMeans(x2, 1, c(1,1,2)) groupMeans(x2, 2, c(1,1,2,2)) groupMeans(x5, 1, c(1,1,2)) groupMeans(x5, 2, c(1,1,2,2)) sum_by(runif(100, 0, 1), sample(LETTERS[1:4], 100, replace=TRUE))x <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), stringsAsFactors = TRUE) samp <- data.frame(samples=levels(x$sample), var1=1:2, stringsAsFactors = TRUE) taxa <- data.frame(specnames=levels(x$species), var2=c("b","a"), stringsAsFactors = TRUE) rownames(samp) <- samp$samples rownames(taxa) <- taxa$specnames x2 <- Xtab(count ~ sample + species, x, cdrop=FALSE,rdrop=TRUE) x5 <- Mefa(x2, samp, taxa, join="inner") groupSums(as.matrix(x2), 1, c(1,1,2)) groupSums(as.matrix(x2), 2, c(1,1,2,2)) groupSums(x2, 1, c(1,1,2)) groupSums(x2, 2, c(1,1,2,2)) groupSums(x5, 1, c(1,1,2)) groupSums(x5, 2, c(1,1,2,2)) groupMeans(as.matrix(x2), 1, c(1,1,2)) groupMeans(as.matrix(x2), 2, c(1,1,2,2)) groupMeans(x2, 1, c(1,1,2)) groupMeans(x2, 2, c(1,1,2,2)) groupMeans(x5, 1, c(1,1,2)) groupMeans(x5, 2, c(1,1,2,2)) sum_by(runif(100, 0, 1), sample(LETTERS[1:4], 100, replace=TRUE))
Combine R objects by rows and columns.
mbind(x, y, fill, ...) mbind2(x, y, fill, ...)mbind(x, y, fill, ...) mbind2(x, y, fill, ...)
x, y
|
objects to combine, see Details. |
fill |
numeric value or |
... |
other argument, not used. |
x and y are combined in a left join manner,
meaning that all the elements in
x are retained, and only non-overlapping elements in y are used.
Elements of the returning object that are not part of x
and y (outer set) are filled up with fill.
If relational table in x is NULL,
corresponding values from same table
of y are used.
mbind2 combines x and y so that inner set is
calculated as sum of corresponding elements from x and y
(unlike in mbind with a left join manner).
An object similar to the input one.
Peter Solymos <[email protected]>
x=matrix(1:4,2,2) rownames(x) <- c("a","b") colnames(x) <- c("A","B") y=matrix(11:14,2,2) rownames(y) <- c("b","c") colnames(y) <- c("B","C") sampx <- data.frame(x1=1:2, x2=2:1, stringsAsFactors = TRUE) rownames(sampx) <- rownames(x) sampy <- data.frame(x1=3:4, x3=10:11, stringsAsFactors = TRUE) rownames(sampy) <- rownames(y) taxay <- data.frame(x1=1:2, x2=2:1, stringsAsFactors = TRUE) rownames(taxay) <- colnames(y) taxax <- NULL mbind(x,y) mbind(as(x,"sparseMatrix"),as(y,"sparseMatrix")) xy <- mbind(Mefa(x,sampx),Mefa(y,sampy,taxay)) unclass(xy) mbind2(x,y) mbind2(as(x,"sparseMatrix"),as(y,"sparseMatrix")) xtab(xy) <- mbind2(x, y) unclass(xy)x=matrix(1:4,2,2) rownames(x) <- c("a","b") colnames(x) <- c("A","B") y=matrix(11:14,2,2) rownames(y) <- c("b","c") colnames(y) <- c("B","C") sampx <- data.frame(x1=1:2, x2=2:1, stringsAsFactors = TRUE) rownames(sampx) <- rownames(x) sampy <- data.frame(x1=3:4, x3=10:11, stringsAsFactors = TRUE) rownames(sampy) <- rownames(y) taxay <- data.frame(x1=1:2, x2=2:1, stringsAsFactors = TRUE) rownames(taxay) <- colnames(y) taxax <- NULL mbind(x,y) mbind(as(x,"sparseMatrix"),as(y,"sparseMatrix")) xy <- mbind(Mefa(x,sampx),Mefa(y,sampy,taxay)) unclass(xy) mbind2(x,y) mbind2(as(x,"sparseMatrix"),as(y,"sparseMatrix")) xtab(xy) <- mbind2(x, y) unclass(xy)
Creating an object of class "Mefa".
Mefa(xtab, samp, taxa, join = c("left", "inner"), drop = FALSE)Mefa(xtab, samp, taxa, join = c("left", "inner"), drop = FALSE)
xtab |
a matrix or a sparse matrix. |
samp |
a data frame or |
taxa |
a data frame or |
join |
character, |
drop |
logical, if unused levels in the data frames should be dropped. |
samp and taxa tables are matched with
corresponding dimnames in xtab:
rownames with samp, colnames with taxa.
If join = "left", all rows and columns in xtab are retained,
while missing items in the corresponding attribute tables are filled up with NAs.
If join = "inner", only the intersection of corresponding names are retained.
The xtab slot is a sparse matrix (dgCMatrix). The input
should be in class MefaMatrix that is a class union of
matrix and sparseMatrix classes.
The samp and taxa slots take data frame or NULL, which two form
the MefaDataFrame class union.
The virtual classes mefa and stcs are defined for
seamless coercion between S3 and S4 classes.
An S4 object of class "Mefa" with 4 slots: xtab,
samp, taxa, join.
If xtab has no dimnames, matching it up with the attribute tables can be problematic.
Peter Solymos <[email protected]>
Creating crosstabulations: Xtab, xtabs in package stats, xtabs in package stats
"mefa" S3 class: mefa in mefa package.
Accessing and replacing slots: xtab, samp, taxa.
x <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), stringsAsFactors = TRUE) samp <- data.frame(samples=levels(x$sample), var1=1:2, stringsAsFactors = TRUE) taxa <- data.frame(specnames=levels(x$species), var2=c("b","a"), stringsAsFactors = TRUE) rownames(samp) <- samp$samples rownames(taxa) <- taxa$specnames ## Xtab class, counts by repetitions in RHS (x0 <- Xtab(~ sample + species, x)) ## counts by LHS and repetitions in RHS (x1 <- Xtab(count ~ sample + species, x)) ## drop all empty rows (x2 <- Xtab(count ~ sample + species, x, cdrop=FALSE,rdrop=TRUE)) ## drop all empty columns Xtab(count ~ sample + species, x, cdrop=TRUE,rdrop=FALSE) ## drop specific columns by placeholder Xtab(count ~ sample + species, x, cdrop="zero.pseudo") ## Mefa class, standard (x3 <- Mefa(x1, samp, taxa)) unclass(x3) x3@xtab x3@samp x3@taxa x3@join ## effects of left join, NULL taxa slot, xtab is (not sparse) matrix (x4 <- Mefa(as.matrix(x1), samp[1:2,])) unclass(x4) ## effects of inner join (intersect) (x5 <- Mefa(x2, samp, taxa, join="inner")) unclass(x5) unclass(Mefa(x1, samp[1:2,], join="inner")) ## xtab only Mefa (x6 <- Mefa(x1)) ## creating new Mefa object without Mefa() new("Mefa", xtab=x1, samp=samp, taxa=taxa,join="left") ## dim and dimnames dim(x5) dimnames(x5) dn <- list(paste("S", 1:3, sep=""), paste("SPP", 1:4, sep="")) dimnames(x5) <- dn unclass(x5) dimnames(x5)[[1]] <- paste("S", 1:3, sep="_") unclass(x5) dimnames(x5)[[2]] <- paste("SPP", 1:4, sep="_") unclass(x5) ## transpose x5 t(x5) unclass(x5) unclass(t(x5)) ## 0 and 1 row/col Mefa object x3[c(FALSE,FALSE,FALSE,FALSE),c(FALSE,FALSE,FALSE,FALSE)] x3[c(TRUE,FALSE,FALSE,FALSE),c(FALSE,FALSE,FALSE,FALSE)] x3[c(FALSE,FALSE,FALSE,FALSE),c(TRUE,FALSE,FALSE,FALSE)] x3[c(TRUE,FALSE,FALSE,FALSE),c(TRUE,FALSE,FALSE,FALSE)] ## stack stack(x3)x <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), stringsAsFactors = TRUE) samp <- data.frame(samples=levels(x$sample), var1=1:2, stringsAsFactors = TRUE) taxa <- data.frame(specnames=levels(x$species), var2=c("b","a"), stringsAsFactors = TRUE) rownames(samp) <- samp$samples rownames(taxa) <- taxa$specnames ## Xtab class, counts by repetitions in RHS (x0 <- Xtab(~ sample + species, x)) ## counts by LHS and repetitions in RHS (x1 <- Xtab(count ~ sample + species, x)) ## drop all empty rows (x2 <- Xtab(count ~ sample + species, x, cdrop=FALSE,rdrop=TRUE)) ## drop all empty columns Xtab(count ~ sample + species, x, cdrop=TRUE,rdrop=FALSE) ## drop specific columns by placeholder Xtab(count ~ sample + species, x, cdrop="zero.pseudo") ## Mefa class, standard (x3 <- Mefa(x1, samp, taxa)) unclass(x3) x3@xtab x3@samp x3@taxa x3@join ## effects of left join, NULL taxa slot, xtab is (not sparse) matrix (x4 <- Mefa(as.matrix(x1), samp[1:2,])) unclass(x4) ## effects of inner join (intersect) (x5 <- Mefa(x2, samp, taxa, join="inner")) unclass(x5) unclass(Mefa(x1, samp[1:2,], join="inner")) ## xtab only Mefa (x6 <- Mefa(x1)) ## creating new Mefa object without Mefa() new("Mefa", xtab=x1, samp=samp, taxa=taxa,join="left") ## dim and dimnames dim(x5) dimnames(x5) dn <- list(paste("S", 1:3, sep=""), paste("SPP", 1:4, sep="")) dimnames(x5) <- dn unclass(x5) dimnames(x5)[[1]] <- paste("S", 1:3, sep="_") unclass(x5) dimnames(x5)[[2]] <- paste("SPP", 1:4, sep="_") unclass(x5) ## transpose x5 t(x5) unclass(x5) unclass(t(x5)) ## 0 and 1 row/col Mefa object x3[c(FALSE,FALSE,FALSE,FALSE),c(FALSE,FALSE,FALSE,FALSE)] x3[c(TRUE,FALSE,FALSE,FALSE),c(FALSE,FALSE,FALSE,FALSE)] x3[c(FALSE,FALSE,FALSE,FALSE),c(TRUE,FALSE,FALSE,FALSE)] x3[c(TRUE,FALSE,FALSE,FALSE),c(TRUE,FALSE,FALSE,FALSE)] ## stack stack(x3)
The function reverses the side effects of cross tabulation.
Melt(x)Melt(x)
x |
A matrix, or sparse matrix object, a list of sparse matrices with identical dimnames, a 'mefa' or 'Mefa' object. |
A data frame with columns corresponding to rows, cols,
possibly segm (names of the list if x was a list of
sparse matrices), and value. value is numeric, other
columns are factors.
Peter Solymos <[email protected]>
stack in utils,
and melt in mefa package.
xx <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), segment = letters[c(6,13,6,13,6,6)], stringsAsFactors = TRUE) xx xx0 <- Xtab(count ~ sample + species, xx) xx1 <- Xtab(count ~ sample + species + segment, xx) (M1 <- Melt(xx0)) Melt(as.matrix(xx0)) (M2 <- Melt(xx1)) stopifnot(identical(Xtab(value ~ rows + cols, M1), xx0)) stopifnot(identical(Xtab(value ~ rows + cols + segm, M2), xx1))xx <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), segment = letters[c(6,13,6,13,6,6)], stringsAsFactors = TRUE) xx xx0 <- Xtab(count ~ sample + species, xx) xx1 <- Xtab(count ~ sample + species + segment, xx) (M1 <- Melt(xx0)) Melt(as.matrix(xx0)) (M2 <- Melt(xx1)) stopifnot(identical(Xtab(value ~ rows + cols, M1), xx0)) stopifnot(identical(Xtab(value ~ rows + cols + segm, M2), xx1))
Utility functions, mostly for character manipulation.
pasteDate(..., sep = " ", collapse = NULL, sep.date = sep) paste0date(..., collapse = NULL) nameAlnum(x, capitalize=c("asis", "first", "none", "all", "mixed"), collapse=" ") normalizeNames(x, pattern = list(" "), replacement = list("_"), alnum = FALSE, ...)pasteDate(..., sep = " ", collapse = NULL, sep.date = sep) paste0date(..., collapse = NULL) nameAlnum(x, capitalize=c("asis", "first", "none", "all", "mixed"), collapse=" ") normalizeNames(x, pattern = list(" "), replacement = list("_"), alnum = FALSE, ...)
x |
caharacter. |
... |
one or more R objects, to be converted to character vectors.
For |
sep |
a character string to separate the terms. |
collapse |
an optional character string to separate the results.
For |
sep.date |
a character string to separate the terms from the data itself. |
capitalize |
character, which letter of each words should be capitalized.
|
pattern |
a list of character vectors that are replaced.
Must match argument |
replacement |
a list of character vectors that are the replacements for |
alnum |
logical, if |
Character vector with desired changes.
Peter Solymos <[email protected]>
paste0(pasteDate("file", "name", sep="-", sep.date="_"), ".csv") paste0(paste0date("file", "name", sep.date="_"), ".csv") data(abmibirds) x <- data.frame(Name=levels(abmibirds[["Common.Name"]]), NameAlnum=nameAlnum(levels(abmibirds[["Common.Name"]])), stringsAsFactors = TRUE) x[grep("'", x$Name),] data.frame(out=sapply(c("asis", "first", "none", "all", "mixed"), function(z) nameAlnum("Olive-sided Flycatcher", z)), stringsAsFactors = TRUE) z <- data.frame(Name=levels(abmibirds[["Common.Name"]]), NameNormalized=normalizeNames(levels(abmibirds[["Common.Name"]]), pattern=list("'", "-", " "), replacement=list("", "_", "_")), stringsAsFactors = TRUE) z[grepl("'", z$Name) & grepl("-", z$Name),]paste0(pasteDate("file", "name", sep="-", sep.date="_"), ".csv") paste0(paste0date("file", "name", sep.date="_"), ".csv") data(abmibirds) x <- data.frame(Name=levels(abmibirds[["Common.Name"]]), NameAlnum=nameAlnum(levels(abmibirds[["Common.Name"]])), stringsAsFactors = TRUE) x[grep("'", x$Name),] data.frame(out=sapply(c("asis", "first", "none", "all", "mixed"), function(z) nameAlnum("Olive-sided Flycatcher", z)), stringsAsFactors = TRUE) z <- data.frame(Name=levels(abmibirds[["Common.Name"]]), NameNormalized=normalizeNames(levels(abmibirds[["Common.Name"]]), pattern=list("'", "-", " "), replacement=list("", "_", "_")), stringsAsFactors = TRUE) z[grepl("'", z$Name) & grepl("-", z$Name),]
Subset a data frame using non duplicated elements in a vector.
nonDuplicated(x, y, change.rownames = FALSE, na.rm = FALSE)nonDuplicated(x, y, change.rownames = FALSE, na.rm = FALSE)
x |
a data frame. |
y |
a vector. It can be a name of a column in |
change.rownames |
if original rownames of |
na.rm |
logical. If rows should be removed where |
This function is handy to keep only one set of duplicated data that is common in long formatted database files.
A data frame.
Peter Solymos <[email protected]>
data(abmibirds) x <- nonDuplicated(abmibirds, abmibirds$ABMI.Site, TRUE) ## or equivalently #x <- nonDuplicated(abmibirds, ABMI.Site, TRUE) dim(abmibirds) dim(x) length(unique(abmibirds$ABMI.Site))data(abmibirds) x <- nonDuplicated(abmibirds, abmibirds$ABMI.Site, TRUE) ## or equivalently #x <- nonDuplicated(abmibirds, ABMI.Site, TRUE) dim(abmibirds) dim(x) length(unique(abmibirds$ABMI.Site))
Parses an R source file and returns an R markdown document that can be turned into a human readable documentation of what the source file does.
r2rmd(file, out=paste(file, "md", sep=""), header=TRUE, extra)r2rmd(file, out=paste(file, "md", sep=""), header=TRUE, extra)
file |
a file name or connection (see |
out |
an output file name passed to |
header |
logical, if a yaml header (enclosed between triple dashes, |
extra |
character, optional string that is placed into the code chunk openings. |
Leading double hashes ## treated as non-code.
Leading # followed by other than # is code comment.
Leading # after whitespace is code comment.
A newline is code when preceded and followed by code.
The leading double hash ## is trimmed for comment lines.
R markdown chunk start/end stuff is added for code chunks.
The argument extra adds chunk arguments, e.g.
extra=', eval=FALSE' etc. See R markdown website at
https://rmarkdown.rstudio.com/
Returns a character vector invisibly, and writes a file as a side effects
unless out=NULL in which case no file is written.
Peter Solymos <[email protected]>
https://rmarkdown.rstudio.com/
## Not run: (r2rmd(system.file("r2rmd_example.R", package="mefa4"), out=NULL, extra=", eval=FALSE")) ## End(Not run)## Not run: (r2rmd(system.file("r2rmd_example.R", package="mefa4"), out=NULL, extra=", eval=FALSE")) ## End(Not run)
Methods to access and replace parts (elements, slots) of "Mefa" objects.
xtab(x) "xtab<-"(x, value) samp(x) "samp<-"(x, value) taxa(x) "taxa<-"(x, value)xtab(x) "xtab<-"(x, value) samp(x) "samp<-"(x, value) taxa(x) "taxa<-"(x, value)
x |
an object of S4 class |
value |
replacement value. |
The [ method ensures that the xtab sparse matrix part and the
corresponding attribute tables are subsetted correctly.
Validity check is performed when replacing slots of an object.
An object of S4 class "Mefa".
Peter Solymos <[email protected]>
x <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), stringsAsFactors = TRUE) samp <- data.frame(samples=levels(x$sample), var1=1:2, stringsAsFactors = TRUE) taxa <- data.frame(specnames=levels(x$species), var2=c("b","a"), stringsAsFactors = TRUE) rownames(samp) <- samp$samples rownames(taxa) <- taxa$specnames x1 <- Xtab(count ~ sample + species, x) x3 <- Mefa(x1, samp, taxa) ## accessing the xtab slot xtab(x3) ## replacing the slot value x1[3,1] <- 999 xtab(x3) <- x1 xtab(x3) ## accessing and replacing the samp slot samp(x3) samp(x3) <- NULL samp(x3) samp(x3) <- samp[1:3,] samp(x3) ## accessing and replacing the taxa slot taxa(x3) taxa(x3) <- NULL taxa(x3) taxa(x3) <- taxa[1:3,] taxa(x3) ## subsetting unclass(x3[3:2, 1:2]) unclass(x3[3:2,]) unclass(x3[,1:2])x <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), stringsAsFactors = TRUE) samp <- data.frame(samples=levels(x$sample), var1=1:2, stringsAsFactors = TRUE) taxa <- data.frame(specnames=levels(x$species), var2=c("b","a"), stringsAsFactors = TRUE) rownames(samp) <- samp$samples rownames(taxa) <- taxa$specnames x1 <- Xtab(count ~ sample + species, x) x3 <- Mefa(x1, samp, taxa) ## accessing the xtab slot xtab(x3) ## replacing the slot value x1[3,1] <- 999 xtab(x3) <- x1 xtab(x3) ## accessing and replacing the samp slot samp(x3) samp(x3) <- NULL samp(x3) samp(x3) <- samp[1:3,] samp(x3) ## accessing and replacing the taxa slot taxa(x3) taxa(x3) <- NULL taxa(x3) taxa(x3) <- taxa[1:3,] taxa(x3) ## subsetting unclass(x3[3:2, 1:2]) unclass(x3[3:2,]) unclass(x3[,1:2])
Create a contingency table from cross-classifying factors, usually contained in a data frame, using a formula interface.
Xtab(formula = ~., data = parent.frame(), rdrop, cdrop, subset, na.action, exclude = c(NA, NaN), drop.unused.levels = FALSE)Xtab(formula = ~., data = parent.frame(), rdrop, cdrop, subset, na.action, exclude = c(NA, NaN), drop.unused.levels = FALSE)
formula |
a |
data |
an optional matrix or data frame (or similar: see |
rdrop, cdrop
|
logical (should zero marginal rows/columns be removed after cross tabulation), character or numeric (what rows/columns should be removed). |
subset |
an optional vector specifying a subset of observations to be used. |
na.action |
a function which indicates what should happen when the data contain NAs. |
exclude |
a vector of values to be excluded when forming the set of levels of the classifying factors. |
drop.unused.levels |
a logical indicating whether to drop unused levels in the classifying factors. If this is FALSE and there are unused levels, the table will contain zero marginals, and a subsequent chi-squared test for independence of the factors will not work. |
The function creates two- or three-way cross tabulation. Only works for two or three factors.
If a left hand side is given in formula, its entries are simply summed over the cells corresponding to the right hand side; this also works if the left hand side does not give counts.
A sparse numeric matrix inheriting from sparseMatrix, specifically an object of S4 class dgCMatrix.
For three factors, a list of sparse matrices.
This function is a slight modification of the xtabs function in the stats package.
Modified by Peter Solymos <[email protected]>
See also xtabs in stats package.
sum_by for sums over a classes ('one-way table'),
and Melt for the inverse operation of Xtab.
"mefa" S3 class: mefa in mefa package.
x <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), stringsAsFactors = TRUE) x ## Xtab class, counts by repetitions in RHS (x0 <- Xtab(~ sample + species, x)) ## counts by LHS and repetitions in RHS (x1 <- Xtab(count ~ sample + species, x)) ## drop all empty rows (x2 <- Xtab(count ~ sample + species, x, cdrop=FALSE,rdrop=TRUE)) ## drop all empty columns Xtab(count ~ sample + species, x, cdrop=TRUE,rdrop=FALSE) ## drop specific columns by placeholder Xtab(count ~ sample + species, x, cdrop="zero.pseudo") ## 2 and 3 way crosstabs xx <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), segment = letters[c(6,13,6,13,6,6)], stringsAsFactors = TRUE) xx Xtab(count ~ sample + species, xx) Xtab(count ~ sample + species + segment, xx)x <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), stringsAsFactors = TRUE) x ## Xtab class, counts by repetitions in RHS (x0 <- Xtab(~ sample + species, x)) ## counts by LHS and repetitions in RHS (x1 <- Xtab(count ~ sample + species, x)) ## drop all empty rows (x2 <- Xtab(count ~ sample + species, x, cdrop=FALSE,rdrop=TRUE)) ## drop all empty columns Xtab(count ~ sample + species, x, cdrop=TRUE,rdrop=FALSE) ## drop specific columns by placeholder Xtab(count ~ sample + species, x, cdrop="zero.pseudo") ## 2 and 3 way crosstabs xx <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), segment = letters[c(6,13,6,13,6,6)], stringsAsFactors = TRUE) xx Xtab(count ~ sample + species, xx) Xtab(count ~ sample + species + segment, xx)