Scoped helper verbs included in this R Documentation file allow for targeted commands on specified columns. They also rename the ensuing output to conform to my preferred style. The commands here are multiple and explained in the details section below.
Usage
center_at(data, x, prefix = "c", na = TRUE, .by = NULL)
diff_at(data, x, o = 1, prefix = "d", .by = NULL)
group_mean_center_at(
data,
x,
mean_prefix = "mean",
prefix = "b",
na = TRUE,
.by
)
lag_at(data, x, prefix = "l", o = 1, .by = NULL)
log_at(data, x, prefix = "ln", plus_1 = FALSE)
mean_at(data, x, prefix = "mean", na = TRUE, .by = NULL)
r1sd_at(data, x, prefix = "s", na = TRUE, .by = NULL)
r2sd_at(data, x, prefix = "z", na = TRUE, .by = NULL)Arguments
- data
a data frame
- x
a vector, likely in your data frame
- prefix
Allows the user to rename the prefix of the new variables. Each function has defaults (see details section).
- na
a logical about whether missing values should be ignored in the creation of means and re-scaled variables. Defaults to TRUE (i.e. pass over/remove missing observations). Not applicable to
diff_at,lag_at, andlog_at.- .by
a selection of columns by which to group the operation. Defaults to NULL. This will eventually become a standard feature of the functions as this operator moves beyond the experimental in dplyr. The argument is not applicable to
log_at(why would it be) and is optional for all functions exceptgroup_mean_center_at.group_mean_center_atmust have something specified for grouped mean-centering.- o
The order of lags for calculating differences or lags in
diff_atorlag_at. Applicable only to these functions.- mean_prefix
Applicable only to
group_mean_center_at. Specifies the prefix of the (assumed) total population mean variables. Default is "mean", though the user can change this as they see fit.- plus_1
Applicable only to
log_at. If TRUE, adds 1 to the variables prior to log transformation. If FALSE, performs logarithmic transformation on variables no matter whether 0 occurs (i.e. 0s will come back as -Inf). Defaults to FALSE.
Value
The function returns a set of new vectors in a data frame after performing relevant functions. The new vectors have distinct prefixes corresponding with the action performed on them.
Details
center_at is a wrapper for mutate_at and rename_at from
dplyr. It takes supplied vectors and effectively centers them from the
mean. It then renames these new variables to have a prefix of c_. The
default prefix ("c") can be changed by way of an argument in the function.
diff_at is a wrapper for mutate and across from
dplyr. It takes supplied vectors and creates differences from the
previous value recorded above it. It then renames these new variables to have
a prefix of d_ (in the case of a first difference), or something like
d2_ in the case of second differences, or d3_ in the case of
third differences (and so on). The exact prefix depends on the o
argument, which communicates the order of lags you want. It defaults to 1. The
default prefix ("d") can be changed by way of an argument in the function,
though the naming convention will omit a numerical prefix for first
differences.
group_mean_center_at is a wrapper for mutate and across
in dplyr. It takes supplied vectors and centers an (assumed) group mean
of the variables from an (assumed) total population mean of the variables
provided to it. It then returns the new variables with a prefix, whose default
is b_. This prefix communicates, if you will, a kind of "between"
variable in the panel model context, in juxtaposition to "within" variables
in the panel model context.
lag_at is a wrapper for mutate and across from
dplyr. It takes supplied vector(s) and creates lag variables from them.
These new variables have a prefix of l[o]_ where o corresponds
to the order of the lag (specified by an argument in the function, which
defaults to 1). This default prefix ("l") can be changed by way of an
another argument in the function.
log_at is a wrapper for mutate and across from
dplyr. It takes supplied vectors and creates a variable that takes
a natural logarithmic transformation of them. It then renames these new
variables to have a prefix of ln_. This default prefix ("ln") can be
changed by way of an argument in the function. Users can optionally specify
that they want to add 1 to the vector before taking its natural logarithm,
which is a popular thing to do when positive reals have naturally occurring
zeroes.
mean_at is a wrapper for mutate and across from
dplyr. It takes supplied vectors and creates a variable communicating
the mean of the variable. It then renames these new variables to have a
prefix of mean_. This default prefix ("mean") can be changed by way of
an argument in the function.
r1sd_at is a wrapper for mutate and across from
dplyr. It both rescales the supplied vectors to new vectors and renames
the vectors to each have a prefix of s_. Note the rescaling here is
just by one standard deviation and not two. The default prefix ("s") can be
changed by way of an argument in the function.
r2sd_at is a wrapper for mutate and across from
dplyr. It both rescales the supplied vectors to new vectors and renames
the vectors to each have a prefix of z_. Note the rescaling here is by
two standard deviations and not one. The default prefix ("z") can be
changed by way of an argument in the function.
All functions, except for lag_at, will fail in the absence of a
character vector of a length of one. They are intended to work across multiple
columns instead of just one. If you are wanting to create one new variable,
you should think about using some other dplyr verb on its own.
Examples
set.seed(8675309)
Example <- data.frame(category = c(rep("A", 5),
rep("B", 5),
rep("C", 5)),
x = runif(15), y = runif(15),
z = sample(1:20, 15, replace=TRUE))
my_vars <- c("x", "y", "z")
center_at(Example, my_vars)
#> category x y z c_x c_y c_z
#> 1 A 0.1594836 0.91822046 9 -0.45270578 0.38488743 -4
#> 2 A 0.4781883 0.71636154 19 -0.13400109 0.18302851 6
#> 3 A 0.7647987 0.20624914 15 0.15260928 -0.32708389 2
#> 4 A 0.7696877 0.81691683 20 0.15749826 0.28358381 7
#> 5 A 0.2685485 0.71585943 14 -0.34364092 0.18252640 1
#> 6 B 0.6730459 0.06062449 14 0.06085649 -0.47270853 1
#> 7 B 0.9787908 0.84710058 17 0.36660137 0.31376756 4
#> 8 B 0.8463270 0.84676044 8 0.23413755 0.31342741 -5
#> 9 B 0.8566562 0.33261085 16 0.24446673 -0.20072218 3
#> 10 B 0.4451601 0.55965050 16 -0.16702927 0.02631747 3
#> 11 C 0.8382325 0.66946933 12 0.22604312 0.13613631 -1
#> 12 C 0.5833169 0.25463848 18 -0.02887250 -0.27869455 5
#> 13 C 0.5109512 0.07917477 5 -0.10123826 -0.45415826 -8
#> 14 C 0.2601681 0.15996809 6 -0.35202128 -0.37336494 -7
#> 15 C 0.7494857 0.81639049 6 0.13729632 0.28305746 -7
diff_at(Example, my_vars)
#> category x y z d_x d_y d_z
#> 1 A 0.1594836 0.91822046 9 NA NA NA
#> 2 A 0.4781883 0.71636154 19 0.318704692 -0.2018589161 10
#> 3 A 0.7647987 0.20624914 15 0.286610370 -0.5101124048 -4
#> 4 A 0.7696877 0.81691683 20 0.004888979 0.6106676969 5
#> 5 A 0.2685485 0.71585943 14 -0.501139179 -0.1010574063 -6
#> 6 B 0.6730459 0.06062449 14 0.404497413 -0.6552349348 0
#> 7 B 0.9787908 0.84710058 17 0.305744875 0.7864760908 3
#> 8 B 0.8463270 0.84676044 8 -0.132463813 -0.0003401469 -9
#> 9 B 0.8566562 0.33261085 16 0.010329181 -0.5141495881 8
#> 10 B 0.4451601 0.55965050 16 -0.411496004 0.2270396501 0
#> 11 C 0.8382325 0.66946933 12 0.393072386 0.1098188350 -4
#> 12 C 0.5833169 0.25463848 18 -0.254915616 -0.4148308551 6
#> 13 C 0.5109512 0.07917477 5 -0.072365765 -0.1754637090 -13
#> 14 C 0.2601681 0.15996809 6 -0.250783015 0.0807933176 1
#> 15 C 0.7494857 0.81639049 6 0.489317597 0.6564223976 0
diff_at(Example, my_vars, o=3)
#> category x y z d3_x d3_y d3_z
#> 1 A 0.1594836 0.91822046 9 NA NA NA
#> 2 A 0.4781883 0.71636154 19 NA NA NA
#> 3 A 0.7647987 0.20624914 15 NA NA NA
#> 4 A 0.7696877 0.81691683 20 0.610204041 -0.1013036240 11
#> 5 A 0.2685485 0.71585943 14 -0.209639830 -0.0005021142 -5
#> 6 B 0.6730459 0.06062449 14 -0.091752786 -0.1456246441 -1
#> 7 B 0.9787908 0.84710058 17 0.209103110 0.0301837497 -3
#> 8 B 0.8463270 0.84676044 8 0.577778475 0.1309010091 -6
#> 9 B 0.8566562 0.33261085 16 0.183610243 0.2719863558 2
#> 10 B 0.4451601 0.55965050 16 -0.533630636 -0.2874500849 -1
#> 11 C 0.8382325 0.66946933 12 -0.008094437 -0.1772911029 4
#> 12 C 0.5833169 0.25463848 18 -0.273339234 -0.0779723700 2
#> 13 C 0.5109512 0.07917477 5 0.065791005 -0.4804757291 -11
#> 14 C 0.2601681 0.15996809 6 -0.578064396 -0.5095012465 -6
#> 15 C 0.7494857 0.81639049 6 0.166168816 0.5617520062 -12
lag_at(Example, my_vars)
#> category x y z l1_x l1_y l1_z
#> 1 A 0.1594836 0.91822046 9 NA NA NA
#> 2 A 0.4781883 0.71636154 19 0.1594836 0.91822046 9
#> 3 A 0.7647987 0.20624914 15 0.4781883 0.71636154 19
#> 4 A 0.7696877 0.81691683 20 0.7647987 0.20624914 15
#> 5 A 0.2685485 0.71585943 14 0.7696877 0.81691683 20
#> 6 B 0.6730459 0.06062449 14 0.2685485 0.71585943 14
#> 7 B 0.9787908 0.84710058 17 0.6730459 0.06062449 14
#> 8 B 0.8463270 0.84676044 8 0.9787908 0.84710058 17
#> 9 B 0.8566562 0.33261085 16 0.8463270 0.84676044 8
#> 10 B 0.4451601 0.55965050 16 0.8566562 0.33261085 16
#> 11 C 0.8382325 0.66946933 12 0.4451601 0.55965050 16
#> 12 C 0.5833169 0.25463848 18 0.8382325 0.66946933 12
#> 13 C 0.5109512 0.07917477 5 0.5833169 0.25463848 18
#> 14 C 0.2601681 0.15996809 6 0.5109512 0.07917477 5
#> 15 C 0.7494857 0.81639049 6 0.2601681 0.15996809 6
lag_at(Example, my_vars, o=3)
#> category x y z l1_x l2_x l3_x l1_y
#> 1 A 0.1594836 0.91822046 9 NA NA NA NA
#> 2 A 0.4781883 0.71636154 19 0.1594836 NA NA 0.91822046
#> 3 A 0.7647987 0.20624914 15 0.4781883 0.1594836 NA 0.71636154
#> 4 A 0.7696877 0.81691683 20 0.7647987 0.4781883 0.1594836 0.20624914
#> 5 A 0.2685485 0.71585943 14 0.7696877 0.7647987 0.4781883 0.81691683
#> 6 B 0.6730459 0.06062449 14 0.2685485 0.7696877 0.7647987 0.71585943
#> 7 B 0.9787908 0.84710058 17 0.6730459 0.2685485 0.7696877 0.06062449
#> 8 B 0.8463270 0.84676044 8 0.9787908 0.6730459 0.2685485 0.84710058
#> 9 B 0.8566562 0.33261085 16 0.8463270 0.9787908 0.6730459 0.84676044
#> 10 B 0.4451601 0.55965050 16 0.8566562 0.8463270 0.9787908 0.33261085
#> 11 C 0.8382325 0.66946933 12 0.4451601 0.8566562 0.8463270 0.55965050
#> 12 C 0.5833169 0.25463848 18 0.8382325 0.4451601 0.8566562 0.66946933
#> 13 C 0.5109512 0.07917477 5 0.5833169 0.8382325 0.4451601 0.25463848
#> 14 C 0.2601681 0.15996809 6 0.5109512 0.5833169 0.8382325 0.07917477
#> 15 C 0.7494857 0.81639049 6 0.2601681 0.5109512 0.5833169 0.15996809
#> l2_y l3_y l1_z l2_z l3_z
#> 1 NA NA NA NA NA
#> 2 NA NA 9 NA NA
#> 3 0.91822046 NA 19 9 NA
#> 4 0.71636154 0.91822046 15 19 9
#> 5 0.20624914 0.71636154 20 15 19
#> 6 0.81691683 0.20624914 14 20 15
#> 7 0.71585943 0.81691683 14 14 20
#> 8 0.06062449 0.71585943 17 14 14
#> 9 0.84710058 0.06062449 8 17 14
#> 10 0.84676044 0.84710058 16 8 17
#> 11 0.33261085 0.84676044 16 16 8
#> 12 0.55965050 0.33261085 12 16 16
#> 13 0.66946933 0.55965050 18 12 16
#> 14 0.25463848 0.66946933 5 18 12
#> 15 0.07917477 0.25463848 6 5 18
log_at(Example, my_vars)
#> category x y z ln_x ln_y ln_z
#> 1 A 0.1594836 0.91822046 9 -1.83581396 -0.08531777 2.197225
#> 2 A 0.4781883 0.71636154 19 -0.73775063 -0.33357029 2.944439
#> 3 A 0.7647987 0.20624914 15 -0.26814262 -1.57867043 2.708050
#> 4 A 0.7696877 0.81691683 20 -0.26177046 -0.20221798 2.995732
#> 5 A 0.2685485 0.71585943 14 -1.31472376 -0.33427146 2.639057
#> 6 B 0.6730459 0.06062449 14 -0.39594173 -2.80305628 2.639057
#> 7 B 0.9787908 0.84710058 17 -0.02143736 -0.16593584 2.833213
#> 8 B 0.8463270 0.84676044 8 -0.16684950 -0.16633746 2.079442
#> 9 B 0.8566562 0.33261085 16 -0.15471866 -1.10078209 2.772589
#> 10 B 0.4451601 0.55965050 16 -0.80932117 -0.58044280 2.772589
#> 11 C 0.8382325 0.66946933 12 -0.17645973 -0.40126992 2.484907
#> 12 C 0.5833169 0.25463848 18 -0.53902464 -1.36791047 2.890372
#> 13 C 0.5109512 0.07917477 5 -0.67148128 -2.53609759 1.609438
#> 14 C 0.2601681 0.15996809 6 -1.34642717 -1.83278093 1.791759
#> 15 C 0.7494857 0.81639049 6 -0.28836799 -0.20286250 1.791759
log_at(Example, my_vars, plus_1 = TRUE)
#> category x y z ln_x ln_y ln_z
#> 1 A 0.1594836 0.91822046 9 0.1479748 0.65139791 2.302585
#> 2 A 0.4781883 0.71636154 19 0.3908172 0.54020667 2.995732
#> 3 A 0.7647987 0.20624914 15 0.5680366 0.18751566 2.772589
#> 4 A 0.7696877 0.81691683 20 0.5708031 0.59714102 3.044522
#> 5 A 0.2685485 0.71585943 14 0.2378733 0.53991408 2.708050
#> 6 B 0.6730459 0.06062449 14 0.5146459 0.05885788 2.708050
#> 7 B 0.9787908 0.84710058 17 0.6824859 0.61361716 2.890372
#> 8 B 0.8463270 0.84676044 8 0.6131982 0.61343299 2.197225
#> 9 B 0.8566562 0.33261085 16 0.6187771 0.28714006 2.833213
#> 10 B 0.4451601 0.55965050 16 0.3682201 0.44446176 2.833213
#> 11 C 0.8382325 0.66946933 12 0.6088045 0.51250581 2.564949
#> 12 C 0.5833169 0.25463848 18 0.4595220 0.22684747 2.944439
#> 13 C 0.5109512 0.07917477 5 0.4127394 0.07619665 1.791759
#> 14 C 0.2601681 0.15996809 6 0.2312452 0.14839249 1.945910
#> 15 C 0.7494857 0.81639049 6 0.5593219 0.59685128 1.945910
mean_at(Example, my_vars)
#> category x y z mean_x mean_y mean_z
#> 1 A 0.1594836 0.91822046 9 0.6121894 0.533333 13
#> 2 A 0.4781883 0.71636154 19 0.6121894 0.533333 13
#> 3 A 0.7647987 0.20624914 15 0.6121894 0.533333 13
#> 4 A 0.7696877 0.81691683 20 0.6121894 0.533333 13
#> 5 A 0.2685485 0.71585943 14 0.6121894 0.533333 13
#> 6 B 0.6730459 0.06062449 14 0.6121894 0.533333 13
#> 7 B 0.9787908 0.84710058 17 0.6121894 0.533333 13
#> 8 B 0.8463270 0.84676044 8 0.6121894 0.533333 13
#> 9 B 0.8566562 0.33261085 16 0.6121894 0.533333 13
#> 10 B 0.4451601 0.55965050 16 0.6121894 0.533333 13
#> 11 C 0.8382325 0.66946933 12 0.6121894 0.533333 13
#> 12 C 0.5833169 0.25463848 18 0.6121894 0.533333 13
#> 13 C 0.5109512 0.07917477 5 0.6121894 0.533333 13
#> 14 C 0.2601681 0.15996809 6 0.6121894 0.533333 13
#> 15 C 0.7494857 0.81639049 6 0.6121894 0.533333 13
r1sd_at(Example, my_vars)
#> category x y z s_x s_y s_z
#> 1 A 0.1594836 0.91822046 9 -1.8112227 1.22348833 -0.7954674
#> 2 A 0.4781883 0.71636154 19 -0.5361226 0.58181493 1.1932011
#> 3 A 0.7647987 0.20624914 15 0.6105718 -1.03974121 0.3977337
#> 4 A 0.7696877 0.81691683 20 0.6301320 0.90146222 1.3920679
#> 5 A 0.2685485 0.71585943 14 -1.3748670 0.58021879 0.1988668
#> 6 B 0.6730459 0.06062449 14 0.2434797 -1.50265592 0.1988668
#> 7 B 0.9787908 0.84710058 17 1.4667290 0.99741097 0.7954674
#> 8 B 0.8463270 0.84676044 8 0.9367569 0.99632970 -0.9943342
#> 9 B 0.8566562 0.33261085 16 0.9780827 -0.63805992 0.5966005
#> 10 B 0.4451601 0.55965050 16 -0.6682645 0.08365854 0.5966005
#> 11 C 0.8382325 0.66946933 12 0.9043720 0.43275298 -0.1988668
#> 12 C 0.5833169 0.25463848 18 -0.1155155 -0.88592015 0.9943342
#> 13 C 0.5109512 0.07917477 5 -0.4050424 -1.44368791 -1.5909348
#> 14 C 0.2601681 0.15996809 6 -1.4083958 -1.18686040 -1.3920679
#> 15 C 0.7494857 0.81639049 6 0.5493064 0.89978905 -1.3920679
r2sd_at(Example, my_vars)
#> category x y z z_x z_y z_z
#> 1 A 0.1594836 0.91822046 9 -0.90561135 0.61174417 -0.39773369
#> 2 A 0.4781883 0.71636154 19 -0.26806132 0.29090746 0.59660054
#> 3 A 0.7647987 0.20624914 15 0.30528590 -0.51987061 0.19886685
#> 4 A 0.7696877 0.81691683 20 0.31506602 0.45073111 0.69603396
#> 5 A 0.2685485 0.71585943 14 -0.68743350 0.29010940 0.09943342
#> 6 B 0.6730459 0.06062449 14 0.12173984 -0.75132796 0.09943342
#> 7 B 0.9787908 0.84710058 17 0.73336452 0.49870548 0.39773369
#> 8 B 0.8463270 0.84676044 8 0.46837843 0.49816485 -0.49716712
#> 9 B 0.8566562 0.33261085 16 0.48904135 -0.31902996 0.29830027
#> 10 B 0.4451601 0.55965050 16 -0.33413225 0.04182927 0.29830027
#> 11 C 0.8382325 0.66946933 12 0.45218599 0.21637649 -0.09943342
#> 12 C 0.5833169 0.25463848 18 -0.05775774 -0.44296007 0.49716712
#> 13 C 0.5109512 0.07917477 5 -0.20252121 -0.72184395 -0.79546739
#> 14 C 0.2601681 0.15996809 6 -0.70419791 -0.59343020 -0.69603396
#> 15 C 0.7494857 0.81639049 6 0.27465322 0.44989453 -0.69603396