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labelr is an R package that supports creation and use of three classes of data.frame labels, the last of which comes in three flavors.
Frame labels - Each data.frame may be given a single “frame label” of 500 characters or fewer, which may describe key general features or characteristics of the data set (e.g., source, date produced or published, high-level contents).
Name labels - Each variable may be given exactly one name label, which is an extended variable name or brief description of the variable. For example, if a variable called “st_b” refers to a survey respondent’s state of birth, then a sensible and useful name label might be “State of Birth”. Or, if a variable called “trust1” consisted of responses to the consumer survey question, “How much do you trust BBC news to give you unbiased information?,” a sensible name label might be “BBC Trust.” As such, name labels are comparable to what Stata and SAS call “variable labels.”
Value labels - labelr offers three kinds of value labels.
One-to-one labels - The canonical value-labeling use case entails mapping distinct values of a variable to distinct labels in a one-to-one fashion, so that each value label uniquely identifies a substantive value. For instance, an administrative data set might assign the integers 1-7 to seven distinct racial/ethnic groups, and value labels would be critical in mapping those numbers to socially substantive racial/ethnic category concepts (e.g., Which number corresponds to the category “Asian American?”).
Many-to-one labels - In an alternative use case, value labels may serve to distill or “bucket” distinct variable values in a way that deliberately “throws away” information for purposes of simplification. For example, one may wish to give the single label “Agree” to the responses “Very Strongly Agree,” “Strongly Agree,” and “Agree.” Or one may wish to differentiate self-identified “White” respondents from “People of Color,” applying the latter value label to all categories other than “White.”
Numerical range labels - Finally, one may wish to carve a numerical variable into an ordinal or qualitative range, such as dichotomizing a variable or dividing it into quantiles. Numerical range labels support one-to-many assignment of a single value label to a range of numerical values for a given variable.
You can install labelr like so:
# install.packages("devtools") # Step 1 to get GitHub version
# devtools::install_github("rhartmano/labelr") #Step 2 to get GitHub version
install.packages("labelr") #CRAN versionAssign labels to your data.frame, its variables, and/or specific variable values. Then use those labels in various ways.
# load the package and assign mtcars to new data.frame mt2
library(labelr)
mt2 <- mtcars
# assign a data.frame "frame" label
mt2 <- add_frame_lab(mt2, frame.lab = "Data extracted from the 1974 Motor
Trend US magazine, comprising fuel consumption and 10 aspects of automobile
design and performance for 32 automobiles (1973–74 models). Source: Henderson
and Velleman (1981), Building multiple regression models interactively.
Biometrics, 37, 391–411.")
get_frame_lab(mt2)
#> data.frame
#> 1 mt2
#> frame.lab
#> 1 Data extracted from the 1974 MotorTrend US magazine, comprising fuel consumption and 10 aspects of automobiledesign and performance for 32 automobiles (1973–74 models). Source: Hendersonand Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411.
# assign variable name labels
mt2 <- add_name_labs(mt2,
name.labs = c(
"mpg" = "Miles/(US) gallon",
"cyl" = "Number of cylinders",
"disp" = "Displacement (cu.in.)",
"hp" = "Gross horsepower",
"drat" = "Rear axle ratio",
"wt" = "Weight (1000 lbs)",
"qsec" = "1/4 mile time",
"vs" = "Engine (0 = V-shaped, 1 = straight)",
"am" = "Transmission (0 = automatic, 1 = manual)",
"gear" = "Number of forward gears",
"carb" = "Number of carburetors"
)
)
get_name_labs(mt2)
#> var lab
#> 1 mpg Miles/(US) gallon
#> 2 cyl Number of cylinders
#> 3 disp Displacement (cu.in.)
#> 4 hp Gross horsepower
#> 5 drat Rear axle ratio
#> 6 wt Weight (1000 lbs)
#> 7 qsec 1/4 mile time
#> 8 vs Engine (0 = V-shaped, 1 = straight)
#> 9 am Transmission (0 = automatic, 1 = manual)
#> 10 gear Number of forward gears
#> 11 carb Number of carburetors
# add 1-to-1 value labels
mt2 <- add_val_labs(
data = mt2,
vars = "am",
vals = c(0, 1),
labs = c("automatic", "manual")
)
# add many-to-1 value labels
mt2 <- add_m1_lab(
data = mt2,
vars = "gear",
vals = 4:5,
lab = "4+"
)
# add quartile-based numerical range value labels
mt2 <- add_quant_labs(
data = mt2,
vars = "disp",
qtiles = 4
)
# add "pretty" cut-based numerical range value labels
(mpg_bins <- pretty(range(mt2$mpg, na.rm = TRUE)))
#> [1] 10 15 20 25 30 35
mt2 <- add_quant_labs(data = mt2, vars = "mpg", vals = mpg_bins)
#> Warning in add_quant_labs(data = mt2, vars = "mpg", vals = mpg_bins):
#>
#> Some of the supplied vals argument values are outside
#> the observed range of var --mpg-- values
# show or use value labels
head(use_val_labs(mt2), 4)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 <=25 6 q050 110 3.90 2.620 16.46 0 manual 4+ 4
#> Mazda RX4 Wag <=25 6 q050 110 3.90 2.875 17.02 0 manual 4+ 4
#> Datsun 710 <=25 4 q025 93 3.85 2.320 18.61 1 manual 4+ 1
#> Hornet 4 Drive <=25 6 q075 110 3.08 3.215 19.44 1 automatic 3 1
# preserve labels and then restore (if lost) or transfer
lab_backup <- get_all_lab_atts(mt2) # back them up
mt2 <- strip_labs(mt2) # strip them away
check_any_lab_atts(mt2) # verify that they have been stripped away
#> [1] FALSE
mt2 <- add_lab_atts(mt2, lab_backup) # now restore them
get_all_lab_atts(mt2) # show that they are back
#> $frame.lab
#> [1] "Data extracted from the 1974 MotorTrend US magazine, comprising fuel consumption and 10 aspects of automobiledesign and performance for 32 automobiles (1973–74 models). Source: Hendersonand Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411."
#>
#> $name.labs
#> mpg cyl disp
#> "Miles/(US) gallon" "Number of cylinders" "Displacement (cu.in.)"
#> hp drat wt
#> "Gross horsepower" "Rear axle ratio" "Weight (1000 lbs)"
#> qsec vs am
#> "1/4 mile time" "Engine (0 = V-shaped, 1 = straight)" "Transmission (0 = automatic, 1 = manual)"
#> gear carb
#> "Number of forward gears" "Number of carburetors"
#>
#> $val.labs.mpg
#> 10 15 20 25 30 35 NA
#> "<=10" "<=15" "<=20" "<=25" "<=30" "<=35" "NA"
#>
#> $val.labs.disp
#> 120.825 196.3 326 472 NA
#> "q025" "q050" "q075" "q100" "NA"
#>
#> $val.labs.am
#> 0 1 NA
#> "automatic" "manual" "NA"
#>
#> $val.labs.gear
#> 3 4 5 NA
#> "3" "4+" "4+" "NA"
# add labels-on columns to the data.frame
mt_plus <- add_lab_cols(mt2)
cols_of_interest <- names(mt_plus)[grepl("am|dis|gear|mpg", names(mt_plus))]
head(mt_plus)[sort(cols_of_interest)]
#> am am_lab disp disp_lab gear gear_lab mpg mpg_lab
#> Mazda RX4 1 manual 160 q050 4 4+ 21.0 <=25
#> Mazda RX4 Wag 1 manual 160 q050 4 4+ 21.0 <=25
#> Datsun 710 1 manual 108 q025 4 4+ 22.8 <=25
#> Hornet 4 Drive 0 automatic 258 q075 3 3 21.4 <=25
#> Hornet Sportabout 0 automatic 360 q100 3 3 18.7 <=20
#> Valiant 0 automatic 225 q075 3 3 18.1 <=20
# show select values with value labels "on"
utils::head(mt2) # head()
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
labelr::headl(mt2) # headl
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 <=25 6 q050 110 3.90 2.620 16.46 0 manual 4+ 4
#> Mazda RX4 Wag <=25 6 q050 110 3.90 2.875 17.02 0 manual 4+ 4
#> Datsun 710 <=25 4 q025 93 3.85 2.320 18.61 1 manual 4+ 1
#> Hornet 4 Drive <=25 6 q075 110 3.08 3.215 19.44 1 automatic 3 1
#> Hornet Sportabout <=20 8 q100 175 3.15 3.440 17.02 0 automatic 3 2
#> Valiant <=20 6 q075 105 2.76 3.460 20.22 1 automatic 3 1
# "flab" - "*F*ilter using value *LAB*els"
flab(mt2, am == "automatic" & mpg %in% c("<=20"))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
# "slab" - "*S*ubset using value *LAB*els"
slab(mt2, am == "automatic" & gear == "4+", am, gear)
#> am gear
#> Merc 240D 0 4
#> Merc 230 0 4
#> Merc 280 0 4
#> Merc 280C 0 4
# "tabl" - Produce label-friendly tables
tabl(mt2, vars = c("am", "gear"), labs.on = TRUE) # labels on, sorted by freq
#> am gear n
#> 1 automatic 3 15
#> 2 manual 4+ 13
#> 3 automatic 4+ 4
#> 4 manual 3 0
tabl(mt2, vars = c("am", "gear"), labs.on = FALSE) # labels off (default)
#> am gear n
#> 1 0 3 15
#> 2 1 4 8
#> 3 1 5 5
#> 4 0 4 4
#> 5 0 5 0
#> 6 1 3 0
# interactively swap in name labels for column names
# (Note: This is a relatively brittle convenience function that will not support
# ... exotic syntax or pointers to objects that exist outside the labeled
# ... data.frame)
with_name_labs(mt2, t.test(mpg ~ am))
#>
#> Welch Two Sample t-test
#>
#> data: Miles/(US) gallon by Transmission (0 = automatic, 1 = manual)
#> t = -3.7671, df = 18.332, p-value = 0.001374
#> alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
#> 95 percent confidence interval:
#> -11.280194 -3.209684
#> sample estimates:
#> mean in group 0 mean in group 1
#> 17.14737 24.39231
# wnl() is a more compact alias for with_name_labs()
wnl(mt2, lm(mpg ~ am * gear))
#>
#> Call:
#> lm(formula = `Miles/(US) gallon` ~ `Transmission (0 = automatic, 1 = manual)` *
#> `Number of forward gears`)
#>
#> Coefficients:
#> (Intercept) `Transmission (0 = automatic, 1 = manual)`
#> 1.277 44.578
#> `Number of forward gears` `Transmission (0 = automatic, 1 = manual)`:`Number of forward gears`
#> 4.943 -9.838
# wnl(mt2, hist(mpg)) #not shown, but works
# wnl(mt2, plot(mpg, carb)) #not shown, but works
# interactively swap in both name and value labels
# ...note that "mpg" and "disp" would not work in these calls unless we
# ...first dropped their value labels, since swapping out labels for values
# ...amounts to coercing these to be character variables
wbl(mt2, t.test(qsec ~ am)) # wbl() is alias for with_both_labs()
#>
#> Welch Two Sample t-test
#>
#> data: 1/4 mile time by Transmission (0 = automatic, 1 = manual)
#> t = 1.2878, df = 25.534, p-value = 0.2093
#> alternative hypothesis: true difference in means between group automatic and group manual is not equal to 0
#> 95 percent confidence interval:
#> -0.4918522 2.1381679
#> sample estimates:
#> mean in group automatic mean in group manual
#> 18.18316 17.36000
wbl(mt2, lm(qsec ~ am + gear + wt * drat))
#>
#> Call:
#> lm(formula = `1/4 mile time` ~ `Transmission (0 = automatic, 1 = manual)` +
#> `Number of forward gears` + `Weight (1000 lbs)` * `Rear axle ratio`)
#>
#> Coefficients:
#> (Intercept) `Transmission (0 = automatic, 1 = manual)`manual `Number of forward gears`4+
#> 7.658 -4.419 3.097
#> `Weight (1000 lbs)` `Rear axle ratio` `Weight (1000 lbs)`:`Rear axle ratio`
#> 4.419 3.904 -1.598These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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