eye

See more with eye

Purpose

eye is dedicated to facilitate very common tasks in ophthalmic research.

Visual acuity conversion for snellen, logMAR and ETDRS
Counting patients and eyes
Recode eye strings
Reshape eye specific variables
Summarizing data with common statistics (mean, sd, n, range)
Calculating age of patients

eye includes amd, a real life data set of people who received intravitreal injections due to age-related macular degeneration in Moorfields Eye Hospital. (Fasler et al. 2019)

Installation

you can install the development version from github with devtools:

# install.packages("devtools")
devtools::install_github("tjebo/eye")

Features

Only eye

va: Conversion of visual acuity notations
eyes: Easy count of patients and eyes
eyestr: return eye count as text for your report
recodeye: recode eye variable
myop: Make your eye data long
hyperop: Make your eye data wide
blink: Perceive your data in a blink of an eye
Visual acuity conversion chart
AMD data: Anonymized real life data from a large cohort of patients with treatment-naive neovascular age-related macular degeneration (AMD) who received intravitreal anti-VEGF therapy in Moorfields Eye Hospital, London, UK. Kindly reference this data by citing the corresponding publication. (Fasler et al. 2019)

Details and examples

va

Easy conversion from visual acuity notations in a single call to va(): Automatic detection of VA notation and convert to logMAR by default (but you can convert to snellen or ETDRS as well). For some more details see VA conversion

## automatic detection of VA notation and converting to logMAR by default
x <- c(23, 56, 74, 58) ## ETDRS letters
va(x)
#> x: from etdrs
#> [1] 1.24 0.58 0.22 0.54

va(x, to = "snellen") ## ... or convert to snellen
#> x: from etdrs
#> [1] "20/320" "20/80"  "20/32"  "20/70"

## A mix of notations
x <- c("NLP", "0.8", "34", "3/60", "2/200", "20/50")
va(x)
#> Mixed object (x) - converting one by one
#> [1] 3.00 0.80 1.02 1.30 2.00 0.40

## "plus/minus" entries are converted to the most probable threshold (any spaces allowed)
x <- c("20/200", "20/200 - 1", "6/6", "6/6-2", "20/50 + 3", "20/50 -2")
va(x)
#> x: from snellen
#> [1] 1.00 1.00 0.00 0.10 0.40 0.48

## or evaluating them as logmar values 
va(x, logmarstep = TRUE)
#> x: from snellen
#> [1]  1.00  0.98  0.00 -0.04  0.46  0.36

## on the inbuilt data set:
head(va(amd$VA_ETDRS_Letters), 10) 
#> Warning: amd$VA_ETDRS_Letters (from etdrs): NA introduced - implausible values
#>  [1] 0.82 0.08 0.70 0.90 1.06 1.02 0.96 1.06 0.40 0.46

## and indeed, there are unplausible ETDRS values in this data set:
range(amd$VA_ETDRS_Letters)
#> [1]   0 105

eyes

Count patient and eyes (eyes or eyestr)

eyes(amd)
#> Eyes coded 0:1. Interpreting r = 0
#> patients     eyes    right     left 
#>     3357     3357     1681     1676

eyestr

Same as eyes, but as text for reports

eyestr(amd)
#> [1] "3357 eyes of 3357 patients"
 
 ## Numbers smaller than or equal to 12 will be real English
eyestr(head(amd, 100))
#> [1] "Eleven eyes of eleven patients"

recodeye

Makes recoding eye variables very easy. It deals with weird missing entries like "." and "", or "N/A"

x <- c("r", "re", "od", "right", "l", "le", "os", "left")
recodeye(x)
#> [1] "r" "r" "r" "r" "l" "l" "l" "l"

## Numeric codes 0:1/ 1:2 are recognized 
x <- 1:2
recodeye(x)
#> Eyes coded 1:2. Interpreting r = 1
#> [1] "r" "l"

## chose the resulting codes
recodeye(x, to = c("right", "left"))
#> Eyes coded 1:2. Interpreting r = 1
#> [1] "right" "left"

## or, if right is coded with 2)
recodeye(x, numcode = 2:1)
#> Eyes coded 2:1 with r = 2
#> [1] "l" "r"

## with weird missing values
x <- c(1:2, ".", NA, "", "    ")
recodeye(x)
#> Eyes coded 1:2. Interpreting r = 1
#> [1] "r" "l" NA  NA  NA  NA

## Or if you have weird codes for eyes
x <- c("alright", "righton", "lefty","leftover")

recodeye(x, eyecodes = list(c("alright","righton"), c("lefty","leftover")))
#> [1] "r" "r" "l" "l"

myop - Make your data long

Often enough, there are right eye / left eye columns for more than one variable, e.g., for both IOP and VA. This may be a necessary data formal for specific questions. However, “eye” is also variable (a dimension of your observation), and it can also be stored in a separate column. The data 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) in their own dedicated column.

myop provides an easy to use API for an automatic reshape of your data to a “myop” format.

## Simple data frame with one column for right eye and left eye.
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 x 3
#>   id    eye   iop  
#>   <chr> <chr> <chr>
#> 1 a     r     11   
#> 2 a     l     14   
#> 3 b     r     12   
#> 4 b     l     15   
#> 5 c     r     13   
#> 6 c     l     16

Or another example with many more variables:

wide_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 x 7
#>   id    eye   surgery iop_preop iop_postop va_preop va_postop
#>   <chr> <chr> <chr>   <chr>     <chr>      <chr>    <chr>    
#> 1 a     r     TE      21        11         41       45       
#> 2 a     l     TE      31        11         41       45       
#> 3 b     r     TE      22        12         42       46       
#> 4 b     l     TE      32        12         42       46       
#> 5 c     r     SLT     23        13         43       47       
#> 6 c     l     TE      33        13         43       47       
#> 7 d     r     SLT     24        14         44       48       
#> 8 d     l     SLT     34        14         44       48

hyperop

If you actually need certain eye-related variables spread over two columns, hyperop() is your friend:

hyperop(myop(iop_wide), iop)
#> # A tibble: 3 x 3
#>   id    r_iop l_iop
#>   <chr> <chr> <chr>
#> 1 a     11    14   
#> 2 b     12    15   
#> 3 c     13    16

hyperop(myop_df, cols = matches("va|iop"))
#> # A tibble: 5 x 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         
#> # … with 3 more variables: l_iop_postop <chr>, l_va_preop <chr>,
#> #   l_va_postop <chr>

blink

See your data in a blink of an eye - wrapper around myop, eyes, va and reveal. It will look for VA and for IOP columns and provide the summary stats for the entire cohort and for right and left eyes for each variable.

This requires a certain format of your names and codes

blink(wide_df)
#> va_preop: from etdrs
#> va_postop: from etdrs
#> 
#> ── blink ─────────────────────────────────────────────────
#> ══ Data ════════════════════════════════
#> # A tibble: 8 x 7
#>   id    eye   surgery iop_preop iop_postop va_preop va_postop
#>   <chr> <chr> <chr>   <chr>     <chr>      <logmar> <logmar> 
#> 1 a     r     TE      21        11         0.88     0.80     
#> 2 a     l     TE      31        11         0.88     0.80     
#> 3 b     r     TE      22        12         0.86     0.78     
#> 4 b     l     TE      32        12         0.86     0.78     
#> 5 c     r     SLT     23        13         0.84     0.76     
#> 6 c     l     TE      33        13         0.84     0.76     
#> 7 d     r     SLT     24        14         0.82     0.74     
#> 8 d     l     SLT     34        14         0.82     0.74     
#> 
#> ══ Count of patient and eyes ═══════════
#> patients     eyes    right     left 
#>        4        8        4        4 
#> 
#> ══ Visual acuity ═══════════════════════
#> 
#> ── $VA_total (all eyes)
#>         var mean sd n min max
#> 1  va_preop  0.8  0 8 0.8 0.9
#> 2 va_postop  0.8  0 8 0.7 0.8
#> 
#> ── $VA_eyes (right and left eyes)
#>   eye       var mean sd n min max
#> 1   l  va_preop  0.8  0 4 0.8 0.9
#> 2   l va_postop  0.8  0 4 0.7 0.8
#> 3   r  va_preop  0.8  0 4 0.8 0.9
#> 4   r va_postop  0.8  0 4 0.7 0.8
#> 
#> ══ Intraocular pressure ════════════════
#> 
#> ── $IOP_total (all eyes)
#>          var mean  sd n min max
#> 1  iop_preop 27.5 5.5 8  21  34
#> 2 iop_postop 12.5 1.2 8  11  14
#> 
#> ── $IOP_eyes (right and left eyes)
#>   eye        var mean  sd n min max
#> 1   l  iop_preop 32.5 1.3 4  31  34
#> 2   l iop_postop 12.5 1.3 4  11  14
#> 3   r  iop_preop 22.5 1.3 4  21  24
#> 4   r iop_postop 12.5 1.3 4  11  14

reveal

Show common statistics for all numeric columns, for the entire cohort or aggregated by group(s):

reveal(myop_df)
#>          var mean  sd n min max
#> 1  iop_preop 27.5 5.5 8  21  34
#> 2 iop_postop 12.5 1.2 8  11  14
#> 3   va_preop 42.5 1.2 8  41  44
#> 4  va_postop 46.5 1.2 8  45  48

reveal(myop_df, by = "eye")
#>   eye        var mean  sd n min max
#> 1   l  iop_preop 32.5 1.3 4  31  34
#> 2   l iop_postop 12.5 1.3 4  11  14
#> 3   l   va_preop 42.5 1.3 4  41  44
#> 4   l  va_postop 46.5 1.3 4  45  48
#> 5   r  iop_preop 22.5 1.3 4  21  24
#> 6   r iop_postop 12.5 1.3 4  11  14
#> 7   r   va_preop 42.5 1.3 4  41  44
#> 8   r  va_postop 46.5 1.3 4  45  48

reveal(myop_df, by = c("eye", "surgery"))
#>    eye surgery        var mean  sd n min max
#> 1    l     SLT  iop_preop 34.0  NA 1  34  34
#> 2    l     SLT iop_postop 14.0  NA 1  14  14
#> 3    l     SLT   va_preop 44.0  NA 1  44  44
#> 4    l     SLT  va_postop 48.0  NA 1  48  48
#> 5    r     SLT  iop_preop 23.5 0.7 2  23  24
#> 6    r     SLT iop_postop 13.5 0.7 2  13  14
#> 7    r     SLT   va_preop 43.5 0.7 2  43  44
#> 8    r     SLT  va_postop 47.5 0.7 2  47  48
#> 9    l      TE  iop_preop 32.0 1.0 3  31  33
#> 10   l      TE iop_postop 12.0 1.0 3  11  13
#> 11   l      TE   va_preop 42.0 1.0 3  41  43
#> 12   l      TE  va_postop 46.0 1.0 3  45  47
#> 13   r      TE  iop_preop 21.5 0.7 2  21  22
#> 14   r      TE iop_postop 11.5 0.7 2  11  12
#> 15   r      TE   va_preop 41.5 0.7 2  41  42
#> 16   r      TE  va_postop 45.5 0.7 2  45  46

age

Calculate age in years, as periods or durations

dob <- c("1984-10-16", "2000-01-01")

## If no second date given, the age today
age(dob)
#> [1] 35.7 20.5
age(dob, "2000-01-01")                                                    
#> [1] 15.2  0.0

Names and codes

eye works smoother with tidy data (any package does, really!)

An important part of tidy data are good names. Learn more about tidy data.

Tips and rules for naming:

Don’t be too creative with your names!
Use common coding:

eyes: “r”, “re”, “od”, “right” - or numeric coding r:l = 0:1 or 1:2
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 section divided by an underscore as a relevant characteristic of your variable. E.g., “preop” instead of “pre_op”, or simply “VA” instead of “VA_ETDRS_Letters”

Name examples

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"
names(amd) 
#> [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 columns
c("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 names
c("person", "goldmann", "vision")
#> [1] "person"   "goldmann" "vision"

## Not even clear to humans
c("var1", "var2", "var3")
#> [1] "var1" "var2" "var3"

How do I rename columns in R?

When I started with R, I found it challenging to rename columns and I found 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 names
names(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"

Important notes

I do not assume responsability for your data or analysis. Please always keep a critical mind when working with data - if you do get results that seem implausible, there may be a chance that the data is in an unfortunate shape for which eye may not be suitable.

VA conversion

VA conversion between Snellen, ETDRS and logMAR is based on charts and formulas in (Holladay 2004), (Beck et al. 2003) and (Gregori, Feuer, and Rosenfeld 2010)
Categories counting fingers and hand movements are converted following (Schulze-Bonsel et al. 2006)
Categories (no) light perception are converted following the suggestions by Michael Bach

VA conversion chart

This chart is included in the package (va_chart)

Snellen feet	Snellen meter	Snellen decimal	logMAR	ETDRS	Categories
20/20000	6/6000	0.001	3	0	NLP
20/10000	6/3000	0.002	2.7	0	LP
20/4000	6/1200	0.005	2.3	0	HM
20/2000	6/600	0.01	1.9	2	CF
20/800	6/240	0.025	1.6	5	NA
20/630	6/190	0.032	1.5	10	NA
20/500	6/150	0.04	1.4	15	NA
20/400	6/120	0.05	1.3	20	NA
20/320	6/96	0.062	1.2	25	NA
20/300	6/90	0.067	1.18	26	NA
20/250	6/75	0.08	1.1	30	NA
20/200	6/60	0.1	1.0	35	NA
20/160	6/48	0.125	0.9	40	NA
20/125	6/38	0.16	0.8	45	NA
20/120	6/36	0.167	0.78	46	NA
20/100	6/30	0.2	0.7	50	NA
20/80	6/24	0.25	0.6	55	NA
20/70	6/21	0.29	0.54	58	NA
20/63	6/19	0.32	0.5	60	NA
20/60	6/18	0.33	0.48	61	NA
20/50	6/15	0.4	0.4	65	NA
20/40	6/12	0.5	0.3	70	NA
20/32	6/9.6	0.625	0.2	75	NA
20/30	6/9	0.66	0.18	76	NA
20/25	6/7.5	0.8	0.1	80	NA
20/20	6/6	1.0	0.0	85	NA
20/16	6/5	1.25	-0.1	90	NA
20/15	6/4.5	1.33	-0.12	91	NA
20/13	6/4	1.5	-0.2	95	NA
20/10	6/3	2.0	-0.3	100	NA

Acknowledgements

Thanks to Alasdair Warwick, Aaron Lee, Tim Yap, Siegfried Wagner and Abraham Olvera for great suggestions, testing and code review.
Thanks to Antoine Fabri for his contribution to age()
Thanks to Hadley Wickham and all developers of the tidyverse packages and the packages roxygen2, usethis, testthis and devtools, all on which eye heavily relies.

Resources

References

Beck, Roy W, Pamela S Moke, Andrew H Turpin, Frederick L Ferris, John Paul SanGiovanni, Chris A Johnson, Eileen E Birch, et al. 2003. “A Computerized Method of Visual Acuity Testing.” American Journal of Ophthalmology 135 (2). Elsevier BV: 194–205. https://doi.org/10.1016/s0002-9394(02)01825-1.

Fasler, Katrin, Gabriella Moraes, Siegfried Wagner, Karsten U Kortuem, Reena Chopra, Livia Faes, Gabriella Preston, et al. 2019. “One- and Two-Year Visual Outcomes from the Moorfields Age-Related Macular Degeneration Database: A Retrospective Cohort Study and an Open Science Resource.” BMJ Open 9 (6). British Medical Journal Publishing Group. https://doi.org/10.1136/bmjopen-2018-027441.

Gregori, Ninel Z, William Feuer, and Philip J Rosenfeld. 2010. “Novel Method for Analyzing Snellen Visual Acuity Measurements.” Retina 30 (7). Ovid Technologies (Wolters Kluwer Health): 1046–50. https://doi.org/10.1097/iae.0b013e3181d87e04.

Holladay, Jack T. 2004. “Visual Acuity Measurements.” Journal of Cataract and Refractive Surgery 30 (2): 287–90. https://doi.org/10.1016/j.jcrs.2004.01.014.

Schulze-Bonsel, Kilian, Nicolas Feltgen, Hermann Burau, Lutz Hansen, and Michael Bach. 2006. “Visual Acuities ‘Hand Motion’ and ‘Counting Fingers’ Can Be Quantified with the Freiburg Visual Acuity Test.” Investigative Ophthalmology & Visual Science 47 (3): 1236–40. https://doi.org/10.1167/iovs.05-0981.

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