LFQ import
There are three main ways in which you can arrange your data in a
spreadsheet or text file and easily import it into R an convert it to a
LFQ list (Fig. 1-3). Each of these 3 methods works evenly well with a
‘csv’, ‘xls’, or ‘txt’ file. For simplification, I will present all
three methods with ‘csv’ files and consequently demonstrate how to use a
‘xlsx’ and a ‘txt’ file. This vignette requires to load the TropFishR
package, require(TropFishR).
Data arrangement 1
The first data arrangement assumes that each measurement was inserted into a specific cell in the spreadsheet, together with associated date when this measurement was done.
The data in this arrangement can be loaded into R by following code.
lfq1 <- read.csv2("lfq1.csv")
lfq1$date <- as.Date(lfq1$date, format = "%d.%m.%Y")
lfq1new <- lfqCreate(data = lfq1, Lname = "length", Dname = "date")
plot(lfq1new, Fname = "catch")It is awlays important to convert the date information into the R
class ‘Date’ which can be done by the function as.Date().
The second argument in that function displays the format of the date in
your data, ‘%d’ stand for the day, ‘%m’ for the month, and ‘%Y’ for the
year with the century, e.g. 2014, without the century it would be ‘%y’,
e.g. 14. The separator, here ‘.’, should be the same as in your data
(see other examples below). The function lfqCreate() is a
handy function, which allows you to convert the dataframe into a list in
the right format. The arguments ‘Lname’ and ‘Dname’ define the names of
the columns in your dataframe with the length measurements and the
dates, respectively. They should correspond to
colnames(lfq1). Besides the arguments above it allows for
many other adjustments, such as setting the bin size or aggregating the
data monthly, see ?lfqCreate for more information.
Data arrangement 2
The second data arrangement represents the case with an additional column which contains the number of times the length was measured at respective date.
Data of this arrangement can be loaded into R by following code.
lfq2 <- read.csv2("lfq2.csv")
lfq2$date <- as.Date(lfq2$date, format = "%d/%m/%y")
lfq2new <- lfqCreate(data = lfq2, Lname = "Length_CM", Dname = "DATE", Fname = "Frequency")
plot(lfq2new, Fname = "catch")Be aware of the different date format, the different column headers
or names and the additional argument in the function
lfqCreate() called ‘Fname’ displaying the name of the
column with the frequency information.
Data arrangement 3
The last data arragement presented here, assumes that the data was already sorted into length classes within your spreadsheet. The first column does now represent the length classes and the number of times each length class was observed at repsective sampling times is in the subsequent columns, where each column represent one sampling time (here aggregated per month). In this example, the information about the date is contained within the header of the not-length-class columns (cp Fig. 3). This is however not necessary and the date information can also be added manually in R.
Data in this arrangement have to be treated a little differently:
lfq3 <- read.csv2("lfq3.csv")
dates <- colnames(lfq3)[-1]
dates <- strsplit(dates, "X")
dates <- unlist(lapply(dates, function(x) x[2]))
dates <- as.Date(dates, "%Y-%d-%m")
lfq3new <- list(dates = dates,
midLengths = lfq3$lengthClass,
catch = as.matrix(lfq3[,-1]))
class(lfq3new) <- "lfq"
lfq3new$catch[is.na(lfq3new$catch)] <- 0
plot(lfq3new, Fname = "catch")In this case, the function lfqCreate() can not be used,
instead the data is arranged into a list manually. Extracting the dates
involves extra steps to separate the actual dates from the ‘X’. Next,
the dates, mid lengths and the catch matrix can be directly arranged
together with the function list(). It is of crucial
importance to use the exact names and spelling (incl. case sensitivity)
in the list (‘dates’, ‘midLengths’, ‘catch’). with the function
class() the TropFishR internal class ‘lfq’ can be assigned
to the created list. Missing values in the catch matrix
(NA, NaN) should be put to 0.
Other file formats
Of course, your data has not to be in the csv format, it can just as
well be ‘odt’, ‘xls’, ‘xlsx’, ‘txt’ or any text file. Other formats
require different functions to load the data into R (beside
read.csv2()). The following code demonstrats this for a
‘xlsx’ and ‘txt’ file.
lfq2a <- read.table("lfq2.txt", sep="\t", dec=',', fileEncoding="latin1", skipNul=TRUE)
install.packages("openxlsx")
require(openxlsx)
lfq3a <- read.xlsx("lfq3.xlsx", sheet = 1)The function read.table() allows to read and load many
different file formats into R, The arguments ‘sep’ and ‘dec’ control the
separator between columns (here tabstop) and the decimal separator (here
comma), respectively. Additional arguments like ‘fileEncoding’ and
‘skipNul’ might be necessary to use for controlling the encoding of the
file and if nuls should be skipped. More information about the arguments
can be viewed with ?read.table. There are many different R
packages available to read ‘xls’ and ‘xlsx’ files into R, one of the
fastest is openxlsx, but it does not allow to read in ‘xls’ files. For
this you might consider using the package ‘xlsx’. These functions allow
you to read in any sheet of your ‘xlsx’ file by using the ‘sheet’
argument.