# Visual field dataset The main object of the visualFields package is a table with a specific format and fields that are mandatory for their management and processing (mainly statistical analysis). Each record (row) in the table contains data for a single visual field test. The mandatory fields specify subject (by its ID code), eye, and test date and time. There are required fields statistical and reliability analyses (e.g., age for the determination of total-deviation and pattern-deviation values, and for global indices and fpr, fnr, fl for the proportion of false positives, false negative, and fixation losses). The rest of mandatory fields are sensitivity or deviation data for each visual field test location. (The number of fields for tested locations varies with the location map, 54 for the 24-2, 76 for the 30-2, 68 for the 10-2, etc.). Check section `Structure of visual fields data` below for details about the required structure of the table contatining the visual fields datasets. The following functions carry out analysis on visual fields data: * `vfdesc` descriptive summary of a visual field dataset * `vfsort` sort visual field data * `vfisvalid` check if a table with visual field data is properly formatted and valid for analysis * `vfread` read a csv file with visual field data * `vfwrite` write a csv file with visual field data * `vfjoin` joins two visual field datasets * `vffilter` filters elements from a visual field dataset with matching conditions. This function is just a wrapper for `dplyr`'s function `filter` * `vfselect` select visual field data by index or the first or last `n` visits per subject and eye * `gettd` computes total-deviation (TD) values and probability values * `gettdp` computes total-deviation (TD) probability values * `getpd` computes pattern-deviation (PD) values * `getpdp` computes pattern-deviation (PD) probability values * `getgh` computes the general height (GH) from the TD tables * `getgl` computes visual fields global indices * `getglp` computes computes visual fields global indices probability values ```r vfdesc(vf) vfsort(vf, decreasing = FALSE) vfisvalid(vf) vfread(file, dateformat = "%Y-%m-%d", eyecodes = c("OD", "OS", "OU"), ...) vfwrite( vf, file, dateformat = "%Y-%m-%d", eyecodes = c("OD", "OS", "OU"), ... ) vfjoin(vf1, vf2) vffilter(vf, ...) vfselect(vf, sel = "last", n = 1) gettd(vf) gettdp(td) getpd(td) getpdp(pd) getgh(td) getgl(vf) getglp(g) ``` ## Arguments - `vf`: visual field data - `decreasing`: sort decreasing or increasing? Default is increasing, that is `decreasing = FALSE` - `file`: the name of the csv file where to write the data - `dateformat`: format to be used for date. Its default value is `%Y-%m-%d` - `eyecodes`: codification for right and left eye, respectively. By default in visualField uses ``OD`` and ``OS`` for right and left eye respectively, but it is common to receive csv files with the codes ``R`` and ``L``. The code ``OU`` for both eyes is also allowed `eyecodes` should be equal to ``c("OD", "OS")`` or ``c("R", "L")``. By default it is ``eyecodes = c("OD", "OS", "OU")`` - `...`: arguments to be passed to or from methods - `vf1, vf2`: the two visual field data objects to join or merge - `sel`: it can be two things, an array of indices to select from visual field data or a string with the values ``first`` or ``last`` indicating that only the first few n visits per subject ``id`` and ``eye`` are to be selected. Default is ``last``. - `n`: number of visits to select. Default value is 1, but it is ignored if `sel` is an index array - `td`: total-deviation (TD) values - `pd`: pattern-deviation (PD) values - `g`: global indices ## Returns `vfdesc` returns descriptive statistics of a visual field dataset `vfsort` returns a sorted visual field dataset `vfisvalid` returns `TRUE` or `FALSE` `vfread` returns a visual field dataset `vfwrite` No return value `vfjoin` returns a visual field dataset `vffilter` returns a visual field dataset `vfselect` returns a visual field dataset `gettd` returns a visual field dataset with total deviation values `gettdp` returns a visual field dataset with total deviation probability values `getpd` returns a visual field dataset with pattern deviation values `getpdp` returns a visual field dataset with pattern deviation probability values `getgh` returns the general height of visual fields tests `getgl` returns visual fields global indices `getglp` returns probability values of visual fields global indices ## Details * `vfselect` when selecting the last or first few visual fields per subject and eye, if that subject and eye has fewer than `n` visits, then all visits are returned ## Structure of visual fields data Visual fields data is the central object used in visualFields. It is a table of visual field data collected with the same perimeter, background and stimulus paradigm (e.g., static automated perimetry or frequency-doubling perimetry), stimulus size (e.g., Goldmann size III), grid of visual field test locations (e.g., 24-2), and psychophysical testing strategy (e.g., SITA standard). Normative values can be obtained from appropriate datasets with data for healthy eyes and these normative values can then be used to generate statistical analyses and visualizations of data for patients with retinal or visual anomalies. Each record correspond to a specific test for an eye of a subject taken on a specific date at a specific time. Visual field data must have the following columns * `id` an id uniquely identifying a subject. This field is mandatory * `eye` should be "OD" for right eye or "OS" for left eye. This field is mandatory * `date` test date. This field is mandatory * `time` test time. This field is mandatory * `age` age of the patient on the test date. This field is required to obtain total-deviation, pattern-deviation values, and other age-dependent local and global indices * `type` type of subject, Could be a healthy subject (ctr for control) or a patient with glaucoma (pwg) or a patient with idiopatic intraocular hypertension (iih) or other. This field is no required for management or statistical analysis. * `fpr` false positive rate. This field is no required for management or statistical analysis. * `fnr` false negative rate. This field is no required for management or statistical analysis. * `fl` fixation losses. This field is no required for management or statistical analysis. * `l1..ln` sensitivity, total-deviation, or pattern-deviation values for each location. For analysis with visualFields there should be as many columns as coordinates in the location map set in the visualFields environment. These fields are mandatory. ## Examples ```r # get dataset description from visual field table vfdesc(vfctrSunyiu24d2) # sort dataset vfsort(vfctrSunyiu24d2[c(5, 4, 10, 50, 30),]) # check if a visualField is valid vf <- vfctrSunyiu24d2 vfisvalid(vf) # valid visual field data vf$id[5] <- NA vfisvalid(vf) # invalid visual field data # write and read visual field data vf <- vfctrSunyiu24d2 tf <- tempfile("vf") vfwrite(vf, file = tf) # save current locmap in a temp file head(vfread(tf)) # read the temp file # join visual fields datasets vfjoin(vfctrSunyiu24d2, vfpwgRetest24d2) # visual field subselection vffilter(vf, id == 1) # fields corresponding to a single subject vffilter(vf, id == 1 & eye == "OD") # fields for a single subject's right eye unique(vffilter(vf, eye == "OS")$eye) # only left eyes vffilter(vfjoin(vfctrSunyiu24d2, vfpwgRetest24d2), type == "ctr") # get only controls vffilter(vfjoin(vfctrSunyiu24d2, vfpwgRetest24d2), type == "pwg") # get only patients # select visual fields by index vfselect(vfctrSunyiu24d2, sel = c(1:4, 150)) # select last few visual fields per subject and eye vfselect(vfpwgRetest24d2, sel = "last") # select first few visual fields per subject and eye vfselect(vfpwgRetest24d2, sel = "first") vfselect(vfpwgRetest24d2, sel = "first", n = 5) # get the last 5 visits # compute visual field statistics vf <- vfpwgSunyiu24d2 td <- gettd(vf) # get TD values tdp <- gettdp(td) # get TD probability values pd <- getpd(td) # get PD values pdp <- getpdp(pd) # get PD probability values gh <- getgh(td) # get the general height g <- getgl(vf) # get global indices gp <- getglp(g) # get global indices probability values ```

vf function