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See more witheye
eye is dedicated to facilitate very common tasks in ophthalmicresearch.
- Handling of visual acuity notations
- Super easy count of subjects and eyes,with smooth integration in your rmarkdown report
- Recode your eye variable
- Reshape your eye data -long orwide
- Quick summary of your eye data
- Get common summary statistics
- Calculate age
- Clean NA equivalent entries
You can install eyefrom CRANusinginstall.packages("eye")
Or you can install the development version from github:
# install.packages("devtools")devtools::install_github("tjebo/eye")I recommend to also geteyedata, apackage collating open source ophthalmic data sets.
Pesky visual acuity notations are now a matter of the past. Convertbetween any of Snellen (meter/ feet/ decimal!), logMAR and ETDRS. Thenotation will be detected automatically and converted to the desirednotation. For some more details seeVA conversion. Forentries with mixed notation, useva_mixed instead.
You can also decide to simply “clean” your VA vector withva(x). Thiswill remove all entries that are certainly no VA.
x<- c(23,56,74,58)## ETDRS lettersto_logmar(x)# wrapper of va(x, to = "logmar")#> From etdrs#> [1] 1.24 0.58 0.22 0.54## ... or convert to snellento_snellen(x)#> From etdrs#> [1] "20/320" "20/80" "20/32" "20/70"## eye knows metric as wellto_snellen(x,type="m")#> From etdrs#> [1] "6/96" "6/24" "6/9.6" "6/21"## And the decimal snellen notation, so much loved in Germanyto_snellen(x,type="dec")#> From etdrs#> [1] "0.062" "0.25" "0.625" "0.3"## Remove weird entries and implausible entries depending on the VA choicex<- c("NLP","0.8","34","3/60","2/200","20/50","",".","-","NULL")to_snellen(x)#> From snellen. Could be snellen, logmar, snellendec, etdrs#> 6x NA introduced for: 0.8, 34, , ., -, NULL#> [1] "20/20000" NA NA "20/400" "20/2000" "20/50"#> [7] NA NA NA NAto_snellen(x,from="snellendec")#> 8x NA introduced for: 34, 3/60, 2/200, 20/50, , ., -, NULL#> [1] "20/20000" "20/25" NA NA NA NA#> [7] NA NA NA NAto_snellen(x,from="etdrs")#> 8x NA introduced for: 0.8, 3/60, 2/200, 20/50, , ., -, NULL#> [1] "20/20000" NA "20/200" NA NA NA#> [7] NA NA NA NAto_snellen(x,from="logmar")#> 8x NA introduced for: 34, 3/60, 2/200, 20/50, , ., -, NULL#> [1] "20/20000" "20/125" NA NA NA NA#> [7] NA NA NA NA## "plus/minus" entries are converted to the most probable threshold (any spaces allowed)x<- c("20/200 - 1","6/6-2","20/50 + 3","6/6-4","20/33 + 4")to_logmar(x)#> From snellen#> [1] 1.0 0.0 0.3 0.1 0.1## or evaluating them as logmar values (each optotype equals 0.02 logmar)to_logmar(x,smallstep=TRUE)#> From snellen#> [1] 1.02 0.04 0.34 0.08 0.14## or you can also decide to completely ignore them (converting them to the nearest snellen value in the VA chart, or if you convert to logMAR, rounding to the first digit)to_snellen(x,noplus=TRUE)#> From snellen#> [1] "20/200" "20/20" "20/50" "20/20" "20/32"# terribly mixed notationsx<- c(NA,"nlp",1:2,1.1,-1,"20/40","4/6","6/1000",34)va_mixed(x,to="snellen")#> [1] NA "20/20000" "20/2000" "20/2000" "20/250" NA#> [7] "20/40" "20/32" "20/4000" "20/200"# "I only have snellen and snellen decimal notation in my data"va_mixed(x,to="snellen",possible= c("snellen","snellendec"))#> [1] NA "20/20000" "20/20" "20/10" "20/20" NA#> [7] "20/40" "20/32" "20/4000" NA# "I have snellen, logmar and etdrs in my data, and there is no etdrs value less than 4"va_mixed(x,to="snellen",possible= c("snellen","logmar","etdrs"))#> [1] NA "20/20000" "20/200" "20/2000" "20/250" NA#> [7] "20/40" "20/32" "20/4000" "20/200"## just clean your entries without conversionva(x)#> [1] NA "nlp" "1" "2" "1.1" "-1" "20/40" "4/6"#> [9] "6/1000" "34"## recognises various ways to write qualitative values such as "hand motion"y<- c(23,"20/50","hand motion","hm","count fingers","no light perception","nlp","nonsense")va(y)#> 1x NA introduced for: nonsense#> [1] "23" "20/50" "hm" "hm" "cf" "nlp" "nlp" NA## you can set custom strings that are recognised as valuesset_eye_strings(nlp= c("nonsense","nlp","no light perception"))va(y)#> [1] "23" "20/50" "hm" "hm" "cf" "nlp" "nlp" "nlp"## reset to default with an empty call to set_eye_stringsset_eye_strings()va(y)#> 1x NA introduced for: nonsense#> [1] "23" "20/50" "hm" "hm" "cf" "nlp" "nlp" NA## use your own custom values for qualitative entriesto_logmar(y)#> From snellen. Could be snellen, logmar, snellendec, etdrs#> 2x NA introduced for: 23, nonsense#> [1] NA 0.4 2.3 2.3 2.0 3.0 3.0 NAto_logmar(y,quali_values=list(cf=2,hm=3,lp=4,nlp=6 ))#> From snellen. Could be snellen, logmar, snellendec, etdrs#> 2x NA introduced for: 23, nonsense#> [1] NA 0.4 3.0 3.0 2.0 6.0 6.0 NA
This is a massive convenience function to count subjects and eyes.Because this essentially returns a list, the stored data can easily beaccessed by subsetting (e.g., with$). You can get the subject IDs foreach subset withdetails = TRUE.
library(eyedata)eyes(amd2)#> ══ Counts ═══════════════#> id eyes right left#> 3357 3357 1681 1676eyes(amd2)$right#> [1] 1681eyes(amd2,details=TRUE)#> ══ $counts ══════════════════════════════════#> id eyes right left both#> 3357 3357 1681 1676 0#>#> ══ $id ══════════════════════════════════════#> $right#> [1] "id_100" "id_1001" "id_1002" "id_1003" "id_1007" "id_1009"#> # … with 1675 more subjects#>#> $left#> [1] "id_1" "id_10" "id_1000" "id_1004" "id_1005" "id_1006"#> # … with 1670 more subjects#>#> $both#> character(0)head(eyes(amd2,details=TRUE)$id$right)#> [1] "id_100" "id_1001" "id_1002" "id_1003" "id_1007" "id_1009"
eyestr was designed with the use in rmarkdown in mind, most explicitlyfor the use inline. You can change the way numbers are converted toenglish with theenglish argument. By default, numbers smaller than orequal to 12 will be real English, all other numbers will be … numbers.You can capitalise the first number with thecaps argument.
| rmarkdown code | results in |
|---|---|
We analyzed`r eyestr(amd2)` | We analyzed 3357 eyes of 3357 patients |
We analyzed`r eyestr(head(amd2, 100))` | We analyzed eleven eyes of eleven patients |
We analyzed`r eyestr(amd2, english = "all")` | We analyzed three thousand three hundred fifty-seven eyes of three thousand three hundred fifty-seven patients |
`r eyestr(head(amd2, 100), caps = TRUE)` were analyzed | Eleven eyes of eleven patients were analyzed |
We analyzed`r eyestr(head(amd2, 100), english = "none")` | We analyzed 11 eyes of 11 patients |
Makes recoding eye variables very easy. It deals with weird missingentries like"." and"", or"N/A"
x<- c("r","re","od","right","l","le","os","left","both","ou")recodeye(x)#> [1] "r" "r" "r" "r" "l" "l" "l" "l" "b" "b"## chose the resulting codesrecodeye(x,to= c("od","os","ou"))#> [1] "od" "od" "od" "od" "os" "os" "os" "os" "ou" "ou"## Numeric codes 0:1/ 1:2 are recognizedx<-1:2recodeye(x)#> Eyes coded 1:2. Interpreting r = 1#> [1] "r" "l"## with weird missing valuesx<- c(1:2,".",NA,"","")recodeye(x)#> Missing values and/or meaningless strings contained#> Eyes coded 1:2. Interpreting r = 1#> [1] "r" "l" NA NA NA NA## If you are using different strings to code for eyes, e.g., you are using a different language, you can change this either with the "eyestrings" argumentfrench<- c("OD","droit","gauche","OG")recodeye(french,eyestrings=list(r= c("droit","od"),l= c("gauche","og")))#> [1] "r" "r" "l" "l"## or change it more globally with `set_eye_strings`set_eye_strings(right= c("droit","od"),left= c("gauche","og"))recodeye(french)#> [1] "r" "r" "l" "l"# to restore the default, call set_eye_strings emptyset_eye_strings()
x<- c("a","",".","-","NULL")tidyNA(x)#> [1] "a" NA NA NA NA# in addition to the default strings, a new string can be addedtidyNA(x,string="a")#> [1] NA NA NA NA NA# or just remove the strings you wanttidyNA(x,string="a",defaultstrings=FALSE)#> [1] NA " " "." "-" "NULL"
Show common statistics for all numeric columns, for the entire cohort oraggregated by group(s):
reveal(iris)#> var mean sd n min max#> 1 Sepal.Length 5.8 0.8 150 4.3 7.9#> 2 Sepal.Width 3.1 0.4 150 2.0 4.4#> 3 Petal.Length 3.8 1.8 150 1.0 6.9#> 4 Petal.Width 1.2 0.8 150 0.1 2.5#> 5 Species 2.0 0.8 150 1.0 3.0reveal(iris,by="Species")#can be several groups#> var1 var2 var3 var4 var5 var6 var7 var8#> 1 setosa setosa setosa setosa versicolor versicolor versicolor versicolor#> 2 setosa setosa setosa setosa versicolor versicolor versicolor versicolor#> 3 setosa setosa setosa setosa versicolor versicolor versicolor versicolor#> 4 setosa setosa setosa setosa versicolor versicolor versicolor versicolor#> 5 setosa setosa setosa setosa versicolor versicolor versicolor versicolor#> 6 setosa setosa setosa setosa versicolor versicolor versicolor versicolor#> 7 setosa setosa setosa setosa versicolor versicolor versicolor versicolor#> 8 setosa setosa setosa setosa versicolor versicolor versicolor versicolor#> 9 setosa setosa setosa setosa versicolor versicolor versicolor versicolor#> 10 setosa setosa setosa setosa versicolor versicolor versicolor versicolor#> 11 setosa setosa setosa setosa versicolor versicolor versicolor versicolor#> 12 setosa setosa setosa setosa versicolor versicolor versicolor versicolor#> var9 var10 var11 var12 var mean sd n min max#> 1 virginica virginica virginica virginica Sepal.Length 5.0 0.4 50 4.3 5.8#> 2 virginica virginica virginica virginica Sepal.Width 3.4 0.4 50 2.3 4.4#> 3 virginica virginica virginica virginica Petal.Length 1.5 0.2 50 1.0 1.9#> 4 virginica virginica virginica virginica Petal.Width 0.2 0.1 50 0.1 0.6#> 5 virginica virginica virginica virginica Sepal.Length 5.9 0.5 50 4.9 7.0#> 6 virginica virginica virginica virginica Sepal.Width 2.8 0.3 50 2.0 3.4#> 7 virginica virginica virginica virginica Petal.Length 4.3 0.5 50 3.0 5.1#> 8 virginica virginica virginica virginica Petal.Width 1.3 0.2 50 1.0 1.8#> 9 virginica virginica virginica virginica Sepal.Length 6.6 0.6 50 4.9 7.9#> 10 virginica virginica virginica virginica Sepal.Width 3.0 0.3 50 2.2 3.8#> 11 virginica virginica virginica virginica Petal.Length 5.6 0.6 50 4.5 6.9#> 12 virginica virginica virginica virginica Petal.Width 2.0 0.3 50 1.4 2.5
- Calculate age in years, asperiods ordurations
dob<- c("1984-10-16","2000-01-01")## If no second date given, the age todaygetage(dob)#> [1] 40.9 25.7getage(dob,"2000-01-01")#> [1] 15.2 0.0
Often enough, there are right eye / left eye columns for more than onevariable, e.g., for both IOP and VA. This may be a necessary data formalfor specific questions. However, “eye” is also variable (a dimension ofyour observation), and it can also be stored in a separate column. Thedata would be “longer”.
Indeed, R requires exactly this data shape for many tasks: “eye[r/l]”as a separate column, and each eye-related variable (e.g., IOP or VA) intheir own dedicated column.
myop provides an easy to use API for an automatic reshape of your datato a “myop” format.
## Simple data frame with one column for right eye and left eye.iop_wide<-data.frame(id=letters[1:3],iop_r=11:13,iop_l=14:16)iop_wide#> id iop_r iop_l#> 1 a 11 14#> 2 b 12 15#> 3 c 13 16
myop(iop_wide)#> # A tibble: 6 × 3#> id eye iop#> <chr> <chr> <chr>#> 1 a right 11#> 2 a left 14#> 3 b right 12#> 4 b left 15#> 5 c right 13#> 6 c left 16
Or another example with many more variables:
Click to unfold code to createwide_df
wide_dfwide_df<-data.frame(id=letters[1:4],surgery_right= c("TE","TE","SLT","SLT"),surgery_left= c("TE","TE","TE","SLT"),iop_r_preop=21:24,iop_r_postop=11:14,iop_l_preop=31:34,iop_l_postop=11:14,va_r_preop=41:44,va_r_postop=45:48,va_l_preop=41:44,va_l_postop=45:48)
myop_df<- myop(wide_df)myop_df#> # A tibble: 8 × 7#> id eye surgery iop_preop iop_postop va_preop va_postop#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>#> 1 a right TE 21 11 41 45#> 2 a left TE 31 11 41 45#> 3 b right TE 22 12 42 46#> 4 b left TE 32 12 42 46#> 5 c right SLT 23 13 43 47#> 6 c left TE 33 13 43 47#> 7 d right SLT 24 14 44 48#> 8 d left SLT 34 14 44 48
If you actually need certain eye-related variables spread over twocolumns,hyperop() is your friend:
hyperop(myop(iop_wide),iop)#> # A tibble: 3 × 3#> id r_iop l_iop#> <chr> <chr> <chr>#> 1 a 11 14#> 2 b 12 15#> 3 c 13 16hyperop(myop_df,cols= matches("va|iop"))#> # A tibble: 5 × 10#> id surgery r_iop_preop r_iop_postop r_va_preop r_va_postop l_iop_preop#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>#> 1 a TE 21 11 41 45 31#> 2 b TE 22 12 42 46 32#> 3 c SLT 23 13 43 47 <NA>#> 4 c TE <NA> <NA> <NA> <NA> 33#> 5 d SLT 24 14 44 48 34#> # ℹ 3 more variables: l_iop_postop <chr>, l_va_preop <chr>, l_va_postop <chr>
See your data in a blink of an eye - wrapper aroundmyop,eyes,va andreveal. It will look forVA and for IOP columns and provide the summary stats for the entirecohort and for right and left eyes for each variable.
This requires a certain format of your names andcodes
blink(wide_df)eye works smoother with tidy data (any package does, really!)
An important part of tidy data are good names.Learn more about tidydata.
- Don’t be too creative with your names!
- Use common coding:
- eyes: “r”, “re”, “od”, “right” - or numeric coding r:l = 0:1 or1:2
- Visual acuity: “VA”, “BCVA”, “Acuity”
- Intraocular pressure: “IOP”, “GAT”, “NCT”, “pressure”
- Patient identifier: “pat”, “patient”, “ID” (ideally both:“patientID” or “patID”)
- Column names:
- No spaces!
- Do not use numeric coding for eyes in column names
- Separate eye and VA and IOP codes with underscores (“bcva_l_preop”,“VA_r”, “left_va”, “IOP_re”)
- Keep names short
- Don’t use underscores when you don’t need to: Consider each sectiondivided by an underscore as a relevant characteristic of yourvariable. E.g., “preop” instead of “pre_op”, or simply “VA” instead of“VA_ETDRS_Letters”
Good names (eye will work nicely)
## right and left eyes have common codes## information on the tested dimension is included ("iop")## VA and eye strings are separated by underscores## No unnecessary underscores.names(wide_df)#> [1] "id" "surgery_right" "surgery_left" "iop_r_preop"#> [5] "iop_r_postop" "iop_l_preop" "iop_l_postop" "va_r_preop"#> [9] "va_r_postop" "va_l_preop" "va_l_postop"names(iop_wide)#> [1] "id" "iop_r" "iop_l"
OK names (eye will work)
## Id and Eye are common names, there are no spaces## VA is separated from the rest with an underscore## BUT:## The names are quite long## There is an unnecessary underscore (etdrs are always letters). Better just "VA"c("Id","Eye","FollowupDays","BaselineAge","Gender","VA_ETDRS_Letters","InjectionNumber")#> [1] "Id" "Eye" "FollowupDays" "BaselineAge"#> [5] "Gender" "VA_ETDRS_Letters" "InjectionNumber"## All names are commonly used (good!)## But which dimension of "r"/"l" are we exactly looking at?c("id","r","l")#> [1] "id" "r" "l"
Bad names (eye will fail)
## VA/IOP not separated with underscore## `eye` won't be able to recognize IOP and VA columnsc("id","iopr","iopl","VAr","VAl")#> [1] "id" "iopr" "iopl" "VAr" "VAl"## A human may think this is clear## But `eye` will fail to understand those variable namesc("person","goldmann","vision")#> [1] "person" "goldmann" "vision"## Not even clear to humansc("var1","var2","var3")#> [1] "var1" "var2" "var3"
When I started with R, I found it challenging to rename columns and Ifound the following threads on stackoverflow very helpful:
I find the two following methods straight forward:
# I've got a data frame with unfortunate names:name_mess<-data.frame(name="a",oculus="r",eyepressure=14,vision=0.2)names(name_mess)#> [1] "name" "oculus" "eyepressure" "vision"## rename all namesnames(name_mess)<- c("patID","eye","IOP","VA")names(name_mess)#> [1] "patID" "eye" "IOP" "VA"
## To rename only specific columns, even if you are not sure about their exact position:names(name_mess)[names(name_mess)%in% c("name","vision")]<- c("patID","VA")names(name_mess)#> [1] "patID" "oculus" "eyepressure" "VA"
I do not assume responsability for your data or analysis. Pleasealways keep a critical mind when working with data - if you do getresults that seem implausible, there may be a chance that the data is inan unfortunate shape for whicheye may not be suitable.
- VA conversion between Snellen, ETDRS and logMAR is based on charts andformulas in (Holladay 2004), (Beck et al.2003) and (Gregori, Feuer, and Rosenfeld2010)
- Categoriescounting fingers andhand movements are convertedfollowing (Schulze-Bonsel et al. 2006)
- Categories(no) light perception are converted following thesuggestions by Michael Bach
- Thanks toAlasdair Warwick,Aaron Lee,Tim Yap,Siegfried Wagner andAbraham Olvera for great suggestions,testing and code review.
- Pearse Keane,Dun Jack Fu,Katrin Fasler andChristophKern for their contribution of open source data
- Thanks toAntoine Fabri for hiscontribution to
getage() - Thanks to Hadley Wickham and all developers of the
tidyversepackages and the packagesroxygen2,usethis,testthisanddevtools, all on whicheyeheavily relies.
Beck, Roy W, Pamela S Moke, Andrew H Turpin, Frederick L Ferris, JohnPaul SanGiovanni, Chris A Johnson, Eileen E Birch, et al. 2003. “AComputerized Method of Visual Acuity Testing.”American Journal ofOphthalmology 135 (2): 194–205.https://doi.org/10.1016/s0002-9394(02)01825-1.
Gregori, Ninel Z, William Feuer, and Philip J Rosenfeld. 2010. “NovelMethod for Analyzing Snellen Visual Acuity Measurements.”Retina 30(7): 1046–50.https://doi.org/10.1097/iae.0b013e3181d87e04.
Holladay, Jack T. 2004. “Visual Acuity Measurements.”Journal ofCataract and Refractive Surgery 30 (February): 287–90.https://doi.org/10.1016/j.jcrs.2004.01.014.
Schulze-Bonsel, Kilian, Nicolas Feltgen, Hermann Burau, Lutz Hansen, andMichael Bach. 2006. “Visual Acuities ‘Hand Motion’and ‘Counting Fingers’ Can Be Quantified with the Freiburg Visual AcuityTest.”Investigative Ophthalmology & Visual Science 47 (3):1236–40.https://doi.org/10.1167/iovs.05-0981.
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