See more with eye
eye is dedicated to facilitate very common tasks in ophthalmic research.
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)
you can install the development version from github with devtools:
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 105Count patient and eyes (eyes or 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"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"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 16Or another example with many more variables:
Click to unfold code to create
wide_df
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 48If 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>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 14Show 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 46dob <- 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 0.0eye works smoother with tidy data (any package does, really!)
An important part of tidy data are good names. Learn more about tidy data.
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"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"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.
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 |
age()tidyverse packages and the packages roxygen2, usethis, testthis and devtools, all on which eye heavily relies.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.