library(eye)
library(eyedata)eye is dedicated to facilitate ophthalmic research, providing convenient application programming interfaces (API) for common tasks:
eye includes a visual acuity conversion chart.
Pesky visual acuity notations are now a matter of the past. Convert between any of Snellen (meter/ feet/ decimal!), logMAR and ETDRS. The notation will be detected automatically and converted to the desired notation. For some more details see VA conversion. For entries with mixed notation, use va_mixed instead.
You can also decide to simply “clean” your VA vector with cleanVA(x). This will remove all entries that are certainly no VA.
va() (and of course, its wrappers) cleans and converts visual acuity notations (classes) between Snellen (decimal, meter and feet), ETDRS, and logMAR. Each class can be converted from one to another. va() will detect the class automatically based on specific rules detailed below. Calling va() without specifying the “to” argument will simply clean the visual acuity entries - any notations will be accepted, no plausibility checks yet performed. This is then bascially a wrapper around cleanVA.
It takes an (atomic) vector with visual acuity entries as the only required argument. The user can specify the original VA notation, but va will check that and ignore the argument if implausible.
va() basically runs three main steps:
clean_va()which_va()checkVA()convertVA()NA are assigned to missing entries or strings representing such entries (“.”, "“,”{any number of spaces}“,”N/A“,”NA“,”NULL“,”-")Snellen are unfortunately often entered with “+/-”, which is a violation of psychophysical methods designed to assign one (!) unambiguous value to visual acuity, with non-arbitrary thresholds based on psychometric functions. Therefore, transforming “+/-” notation to actual results is in itself problematic and the below suggestion to convert it will remain an approximation to the most likely “true” result. Even more so, as the given conditions should work for charts with 4 or 5 optotypes in a line, and visual acuity is not always tested on such charts. Yet, I believe that the approach is still better than just omitting the letters or (worse) assigning a missing value to those entries.
If the argument smallstep = TRUE, the entries will be converted to logmar values (0.02 logmar for each optotype). This is based on the assumption of 5 optotypes in a row. This argument can be overriden with noplus = TRUE, ignoring the plus minus entries entirely and simply returning the nearest Snellen values.
which_va() based on the following rulesetdrslogmar, snellendec or etdrslogmar or snellendecsnellen (fraction)quali## automatic detection of VA notation and converting to logMAR by default
x <- c(23, 56, 74, 58) ## ETDRS letters
to_logmar(x) # wrapper of va(x, to = "logmar")
#> From etdrs
#> [1] 1.24 0.58 0.22 0.54
## ... or convert to snellen
to_snellen(x) # wrapper of va(x, to = "snellen")
#> From etdrs
#> [1] "20/320" "20/80" "20/32" "20/70"
## eye knows metric as well
to_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 Germany
to_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 choice
x <- 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 NA
to_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 NA
to_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 NA
to_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)
to_snellen(x, noplus = TRUE)
#> From snellen
#> [1] "20/200" "20/20" "20/50" "20/20" "20/32"Makes recoding eye variables very easy.
The following codes are recognized:
If you have different codes, you can change the recognized strings with the eyestrings argument, which needs to be a list. But remember to put the strings for right eyes first, or pass a named list.
You can also more globally change recognized codes with set_eye_strings()
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 codes
recodeye(x, to = c("od", "os", "ou"))
#> [1] "od" "od" "od" "od" "os" "os" "os" "os" "ou" "ou"
## Numeric codes 0:1/ 1:2 are recognized
x <- 1:2
recodeye(x)
#> Eyes coded 1:2. Interpreting r = 1
#> [1] "r" "l"
## with weird missing values
x <- 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" argument
french <- 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 empty
set_eye_strings()eyes offers a very simple tool for counting patients and eyes. It will return a list object which gives you easy access to the data.
An important step in eyes is the guessing of the columns that identify patients and eyes. As for myop and of course blink, a specific column naming is required for a reliable automatic detection of patient and eye column(s) ( see Names and codes)
The arguments id and eye arguments overrule the name guessing for the respective columns.
eyes is looking for names that contain both strings “pat” and “id” (the order doesn’t matter) you can change those codes with set_eye_strings()eyes looks for columns that contain the string either “eye” or “eyes” - you can change those codes with set_eye_strings()For counting eyes, eyes need to be coded in commonly used ways. You can use recodeye for very convenient recoding.
eyes recognizes integer coding 0:1 and 1:2, with right being the lower number.
Or, arguably more appropriate in R, character coding for a categorical variable:
you can change those codes with set_eye_strings()
eyes also include a convenience function to turn the count into a text. This is intended for integration into rmarkdown reports, or for easy copy / pasting. eyes_to_string() parses the output of eyes into text under the hood. Arguments to eyes_to_string are passed via …:
eyestr will create a string which you can paste into a report. The name was chosen because it’s a contraction of “eyes” and “strings” and it’s a tiny bit easier to type than “eyetxt”.
eyestr was designed with the use in rmarkdown in mind, most explicitly for the use inline. You can change the way numbers are converted to english with the english argument. By default, numbers smaller than or equal to 12 will be real English, all other numbers will be … numbers. You can capitalise the first number with the caps argument.
We analyzed `r eyestr(amd2)` gives: We analyzed 3357 eyes of 3357 patients We analyzed `r eyestr(head(amd2, 100))`gives: We analyzed eleven eyes of eleven patients We analyzed `r eyestr(amd2, english = "all")` gives: We analyzed three thousand three hundred and fifty-seven eyes of three thousand three hundred and fifty-seven patients `r eyestr(head(amd2, 100), caps = TRUE)` were analyzed gives: Eleven eyes of eleven patients were analyzed We analyzed `r eyestr(head(amd2, 100), english = "none")` gives: We analyzed 11 eyes of 11 patients
Out of convenience, data is often entered in a “wide” format: In eye research, there will be often two columns for the same variable, one column for each eye.
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. Indeed, in my experience R often needs eyes to be in a single column, with each other variable having their own dedicated column.
Reshaping many such columns can be a daunting task, and myop() makes this easier. It will remove duplicate rows, and pivot the eye variable to one column and generate a single column for each variable, thus shaping the data for specific types of analysis. For example, eight columns that store data of four variables for right and left eyes will be pivoted to 5 columns (one eye column and four further variable columns)). See also Examples.
As with eyes(), myop() requires a specific data format. See names and codes If there is already a column called “eye” or “eyes”, myop will not make any changes - because the data is then already assumed to be in long format.
If there still are variables spread over two columns for right and left eyes, then this is an example of messy data. A solution would be to remove or simply rename the “eye” column and then let myop do the work. However, you need to be very careful in those cases if resulting data frame is plausible.
myop will work reliably if you adhere to the following:
An exception is when there is only one column for each eye. Then the column names can consist of “eye strings” only. In this case, the argument var will be used to name the resulting variable.
If there are only eye columns in your data (should actually not happen), myop will create identifiers by row position.
Please always check the result for plausibility. Depending a lot on how the data was entered, the results could become quite surprising. There is basically a nearly infinite amount of possible combinations of how to enter data, and it is likely that myop will not be able to deal with all of them.
myop() basically runs three main steps:
myop_rename() and sort_substr():
myopizer() and myop_pivot() and itself consists of three steps.
key and value) using tidyr::pivot_longer.key column will be split by position into an eye column and a variable column.variable and value columns will be pivoted wide again with tidyr::pivot_wider.wide1 <- data.frame(id = letters[1:3], r = 11:13 , l = 14:16)
iop_wide <- data.frame(id = letters[1:3], iop_r = 11:13, iop_l = 14:16)
## Mildly messy data frame with several variables spread over two columns:
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
)## the variable has not been exactly named, (but it is probably IOP data),
## you can specify the dimension with the var argument
myop(wide1, var = "iop")
#> # 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
## If the dimension is already part of the column names, this is not necessary.
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
## myop deals with this in a breeze:
myop(wide_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 48Basically the opposite of myop() - a slightly intelligent wrapper around tidyr::pivot_longer() and tidyr::pivot_wider(). Will find the eye column, unify the codes for the eyes (all to “r” and “l”) and pivot the columns wide, that have been specified in “cols”. Again, good names and tidy data always help!
The cols argument takes a tidyselection. Read about tidyselection
myop_df <- myop(wide_df)
hyperop(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
#> # … with 3 more variables: l_iop_postop <chr>, l_va_preop <chr>,
#> # l_va_postop <chr>Although kind of nice, blink is more a nerdy extra and is not likely to be used much. Therefore I decided to stop the work on it. It will be left in the package as such.
See your data in a blink of an eye
blink() is more than just a 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 VA and IOP variable.
This, again, requires a certain format of names and codes - See Names and Codes
fct_level = "x" or any other arbitrary value.myop_rename() and sort_substr()reveal() to all VA and IOP columns.As you can imagine, a lot of those steps rely hugely on reasonable naming of your columns and this is what makes this function unfortunately a bit fragile. However, if you adhere to the naming conventions, blink (and myop) will do a great job for you.
If you are not happy with the automatic column selection, you can manually select the VA and IOP columns with the arguments va_cols or iop_cols. Both accept tidyselection. I personally find starts_with, ends_with, contains() or the more general matches() very useful.
blink(wide_df)
#> The lifecycle of blink() has expired. It will no longer be
#> maintained, but will be kept in the package.
#> From etdrs
#> From etdrs
#>
#> ── blink ───────────────────────────────────────────────────────────────────────
#> ══ Data ════════════════════════════════
#> # A tibble: 8 × 7
#> id eye surgery iop_preop iop_postop va_preop va_postop
#> <chr> <chr> <chr> <chr> <chr> <logmar> <logmar>
#> 1 a right TE 21 11 0.88 0.80
#> 2 a left TE 31 11 0.88 0.80
#> 3 b right TE 22 12 0.86 0.78
#> 4 b left TE 32 12 0.86 0.78
#> 5 c right SLT 23 13 0.84 0.76
#> 6 c left TE 33 13 0.84 0.76
#> 7 d right SLT 24 14 0.82 0.74
#> 8 d left SLT 34 14 0.82 0.74
#>
#> ══ Count of patient and eyes ═══════════
#> ══ Counts ═══════════════
#> id 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 left va_preop 0.8 0 4 0.8 0.9
#> 2 left va_postop 0.8 0 4 0.7 0.8
#> 3 right va_preop 0.8 0 4 0.8 0.9
#> 4 right 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 left iop_preop 32.5 1.3 4 31 34
#> 2 left iop_postop 12.5 1.3 4 11 14
#> 3 right iop_preop 22.5 1.3 4 21 24
#> 4 right iop_postop 12.5 1.3 4 11 14
blink(amd)
#> The lifecycle of blink() has expired. It will no longer be
#> maintained, but will be kept in the package.
#> Warning: Data seems already myopic - no changes made. ?myop for help
#> From etdrs. Could be etdrs, logmar, snellendec
#> Unclear which is the eye column. Counting id only.
#> Specify eye column with "eye_col" argument
#>
#> ── blink ───────────────────────────────────────────────────────────────────────
#> ══ Data ════════════════════════════════
#> # A tibble: 118,255 × 11
#> patid sex age avdays_induc ethnicity loaded time injgiven va regimen
#> <chr> <chr> <fct> <dbl> <chr> <lgl> <int> <lgl> <logmar> <chr>
#> 1 id_1 m 60-69 28 se_asian TRUE 0 TRUE 0.30 ranibi…
#> 2 id_1 m 60-69 28 se_asian TRUE 28 TRUE 0.30 ranibi…
#> 3 id_1 m 60-69 28 se_asian TRUE 56 TRUE 0.30 ranibi…
#> 4 id_1 m 60-69 28 se_asian TRUE 91 TRUE 0.40 ranibi…
#> 5 id_1 m 60-69 28 se_asian TRUE 131 TRUE 0.46 ranibi…
#> 6 id_1 m 60-69 28 se_asian TRUE 173 TRUE 0.40 ranibi…
#> 7 id_1 m 60-69 28 se_asian TRUE 236 TRUE 0.50 ranibi…
#> 8 id_1 m 60-69 28 se_asian TRUE 278 TRUE 0.50 ranibi…
#> 9 id_1 m 60-69 28 se_asian TRUE 411 TRUE 0.60 ranibi…
#> 10 id_1 m 60-69 28 se_asian TRUE 453 TRUE 0.54 ranibi…
#> # … with 118,245 more rows, and 1 more variable: pre2013 <lgl>
#>
#> ══ Count of patient and eyes ═══════════
#> ══ Counts ═══════════════
#> id
#> 7802
#>
#> ══ Visual acuity ═══════════════════════
#>
#> ── $VA_total (all eyes)
#> var mean sd n min max
#> 1 va 0.5 0.4 116530 -0.3 2.3
#>
#> ── $VA_eyes (right and left eyes)
#> NULL
#>
#> ══ Intraocular pressure ════════════════
#>
#> ── $IOP_total (all eyes)
#> NULL
#>
#> ── $IOP_eyes (right and left eyes)
#> NULLeye works smoother with tidy data, and with good names (any package does, really!)
The basic principle of tidy data is: one column for each dimension and one row for each observation.
This chapter explains how you can improve names and codes so that eye will work like a charm.
When I started with R, I found it challenging to rename columns and I found the following methods very helpful:
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"I can rename all names easily:
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:
## if you only want to rename one or a few columns:
names(name_mess)[names(name_mess) %in% c("name", "vision")] <- c("patID", "VA")
names(name_mess)
#> [1] "patID" "oculus" "eyepressure" "VA"For even more methods, I found those two threads on Stackoverflow very helpful:
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 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"reveal() offers a simple API to show common summary statistics for all numeric columns of your data frame. reveal() is basically a slightly complicated wrapper around mean(), sd(), length(), min() and max() (with na.rm = TRUE and length() counting only non-NA values).
It is not really intended to replace other awesome data exploration packages / functions such as skimr::skim, and it will likely remain focussed on summarizing numerical data only.
It uses an S3 generic under the hood with methods for atomic vectors, data frames, and lists of either atomic vectors or data frames. Character vectors will be omitted (and it should give a warning that it has done so).
reveal() takes the grouping argument by and it returns vector for atomic vectors or a data frame for lists.
clean_df <- myop(wide_df)
reveal(clean_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(clean_df, by = "eye")
#> eye var mean sd n min max
#> 1 left iop_preop 32.5 1.3 4 31 34
#> 2 left iop_postop 12.5 1.3 4 11 14
#> 3 left va_preop 42.5 1.3 4 41 44
#> 4 left va_postop 46.5 1.3 4 45 48
#> 5 right iop_preop 22.5 1.3 4 21 24
#> 6 right iop_postop 12.5 1.3 4 11 14
#> 7 right va_preop 42.5 1.3 4 41 44
#> 8 right va_postop 46.5 1.3 4 45 48
reveal(clean_df, by = c("eye", "surgery"))
#> eye surgery var mean sd n min max
#> 1 left SLT iop_preop 34.0 NA 1 34 34
#> 2 left SLT iop_postop 14.0 NA 1 14 14
#> 3 left SLT va_preop 44.0 NA 1 44 44
#> 4 left SLT va_postop 48.0 NA 1 48 48
#> 5 right SLT iop_preop 23.5 0.7 2 23 24
#> 6 right SLT iop_postop 13.5 0.7 2 13 14
#> 7 right SLT va_preop 43.5 0.7 2 43 44
#> 8 right SLT va_postop 47.5 0.7 2 47 48
#> 9 left TE iop_preop 32.0 1.0 3 31 33
#> 10 left TE iop_postop 12.0 1.0 3 11 13
#> 11 left TE va_preop 42.0 1.0 3 41 43
#> 12 left TE va_postop 46.0 1.0 3 45 47
#> 13 right TE iop_preop 21.5 0.7 2 21 22
#> 14 right TE iop_postop 11.5 0.7 2 11 12
#> 15 right TE va_preop 41.5 0.7 2 41 42
#> 16 right TE va_postop 45.5 0.7 2 45 46This is a simple function and should not require much explanation. However, it may be noteworthy to mention the subtle distinction of periods and durations, which are an idiosyncrasy of time measurements and well explained in this thread.
dob <- c("1984-10-16", "2000-01-01")
## If no second date given, the age today
getage(dob)
#> [1] 36.9 21.7
## If the second argument is specified, the age until then
getage(dob, "2000-01-01")
#> [1] 15.2 0.0I 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 as 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 |
getage()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.
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.