featr stands for “feature” (oh, really?) and pronounced the same way.
There is a bunch of functions to produce not-so-common tasks such as
renaming variables in the data frame or list of data frames,
one-hot-encoding with custom parameters etc. Please see
Examples section
To install featr please use devtools:
devtools::install_github("pavel-filatov/featr")Please note: you may need to install development version of rlang
package:
devtools::install_github("r-lib/rlang")cor_index() fucntion makes a correlation index: it gets each pair of
variables, calculate correlation between them, and turn it all to data
frame. Then the function sort data frame by absolute value of
correlation.
featr::cor_index(mtcars)## # A tibble: 55 x 3
## Var1 Var2 Cor
## <chr> <chr> <dbl>
## 1 cyl disp 0.902
## 2 disp wt 0.888
## 3 mpg wt -0.868
## 4 mpg cyl -0.852
## 5 mpg disp -0.848
## 6 cyl hp 0.832
## 7 cyl vs -0.811
## 8 am gear 0.794
## 9 disp hp 0.791
## 10 cyl wt 0.782
## # ... with 45 more rows
For each pair of variables in input data frame identity_index()
calculates three numbers:
identical- number of observations where first and second variables are equal;non_identical- number of observations where first and second variables aren’t equal;otherall the other cases (e.g. when one of the entries isNA).
identitity_index is computed as ratio
[identity\ index = \frac{identical}{identical + non_identical}]
That’s how the fuction works:
set.seed(42)
df <- tibble(letter1 = sample(c(letters[1:3], NA, NaN), 100, TRUE),
letter2 = sample(c(letters[1:3], NA), 100, TRUE),
letter3 = sample(c(letters[1:3], NA), 100, TRUE),
num1 = sample(1:4, 100, TRUE),
num2 = sample(1:4, 100, TRUE),
num3 = sample(c(1:3, NA), 100, TRUE))
identity_index(df)## # A tibble: 15 x 6
## Var1 Var2 identical non_identical other identity_index
## <chr> <chr> <int> <int> <int> <dbl>
## 1 letter1 letter3 22 40 38 0.355
## 2 letter2 letter3 17 39 44 0.304
## 3 letter1 letter2 15 43 42 0.259
## 4 num1 num2 23 77 0 0.230
## 5 num2 num3 17 58 25 0.227
## 6 num1 num3 11 64 25 0.147
## 7 letter1 num1 0 77 23 0.
## 8 letter1 num2 0 77 23 0.
## 9 letter1 num3 0 57 43 0.
## 10 letter2 num1 0 74 26 0.
## 11 letter2 num2 0 74 26 0.
## 12 letter2 num3 0 59 41 0.
## 13 letter3 num1 0 79 21 0.
## 14 letter3 num2 0 79 21 0.
## 15 letter3 num3 0 55 45 0.
You see that variables letter1 and letter3 have 22 equal, 40
different observations and 38 cases where at least on of them has NA.
.na_include flag lets you consider as identical cases where var1 == NA and var2 == NA. The same way if var1 == NA and var2 != NA it
will count this case as non_identical.
identity_index(df, .na_include = TRUE)## # A tibble: 15 x 6
## Var1 Var2 identical non_identical other identity_index
## <chr> <chr> <int> <int> <int> <dbl>
## 1 letter1 letter3 28 72 0 0.280
## 2 num1 num2 23 77 0 0.230
## 3 letter1 letter2 22 78 0 0.220
## 4 letter2 letter3 20 80 0 0.200
## 5 num2 num3 17 83 0 0.170
## 6 num1 num3 11 89 0 0.110
## 7 letter2 num3 10 90 0 0.100
## 8 letter1 num3 5 95 0 0.0500
## 9 letter3 num3 1 99 0 0.0100
## 10 letter1 num1 0 100 0 0.
## 11 letter1 num2 0 100 0 0.
## 12 letter2 num1 0 100 0 0.
## 13 letter2 num2 0 100 0 0.
## 14 letter3 num1 0 100 0 0.
## 15 letter3 num2 0 100 0 0.
Now letter1 and letter3 have 6 more identical cases.
make_shift() is great option when you need to make a lot of lags (move
down) and leads (move up) for one or more variables. This function
captures variables to apply shifting and vector of window sizes and
returns a list of quoted expressions:
make_shift(mpg, qsec, .n = -2:2)## $mpg_lead2
## dplyr::lead(mpg, 2L)
##
## $mpg_lead1
## dplyr::lead(mpg, 1L)
##
## $mpg_lag1
## dplyr::lag(mpg, 1L)
##
## $mpg_lag2
## dplyr::lag(mpg, 2L)
##
## $qsec_lead2
## dplyr::lead(qsec, 2L)
##
## $qsec_lead1
## dplyr::lead(qsec, 1L)
##
## $qsec_lag1
## dplyr::lag(qsec, 1L)
##
## $qsec_lag2
## dplyr::lag(qsec, 2L)
To make new variables you simply need to unqoute the function output
inside the dplyr::mutate() call using !!! (bang-bang-bang)
opearator:
select(mtcars, mpg, qsec) %>%
mutate(!!!make_shift(mpg, qsec, .n = -1:2))## mpg qsec mpg_lead1 mpg_lag1 mpg_lag2 qsec_lead1 qsec_lag1 qsec_lag2
## 1 21.0 16.46 21.0 NA NA 17.02 NA NA
## 2 21.0 17.02 22.8 21.0 NA 18.61 16.46 NA
## 3 22.8 18.61 21.4 21.0 21.0 19.44 17.02 16.46
## 4 21.4 19.44 18.7 22.8 21.0 17.02 18.61 17.02
## 5 18.7 17.02 18.1 21.4 22.8 20.22 19.44 18.61
## 6 18.1 20.22 14.3 18.7 21.4 15.84 17.02 19.44
## 7 14.3 15.84 24.4 18.1 18.7 20.00 20.22 17.02
## 8 24.4 20.00 22.8 14.3 18.1 22.90 15.84 20.22
## 9 22.8 22.90 19.2 24.4 14.3 18.30 20.00 15.84
## 10 19.2 18.30 17.8 22.8 24.4 18.90 22.90 20.00
## 11 17.8 18.90 16.4 19.2 22.8 17.40 18.30 22.90
## 12 16.4 17.40 17.3 17.8 19.2 17.60 18.90 18.30
## 13 17.3 17.60 15.2 16.4 17.8 18.00 17.40 18.90
## 14 15.2 18.00 10.4 17.3 16.4 17.98 17.60 17.40
## 15 10.4 17.98 10.4 15.2 17.3 17.82 18.00 17.60
## 16 10.4 17.82 14.7 10.4 15.2 17.42 17.98 18.00
## 17 14.7 17.42 32.4 10.4 10.4 19.47 17.82 17.98
## 18 32.4 19.47 30.4 14.7 10.4 18.52 17.42 17.82
## 19 30.4 18.52 33.9 32.4 14.7 19.90 19.47 17.42
## 20 33.9 19.90 21.5 30.4 32.4 20.01 18.52 19.47
## 21 21.5 20.01 15.5 33.9 30.4 16.87 19.90 18.52
## 22 15.5 16.87 15.2 21.5 33.9 17.30 20.01 19.90
## 23 15.2 17.30 13.3 15.5 21.5 15.41 16.87 20.01
## 24 13.3 15.41 19.2 15.2 15.5 17.05 17.30 16.87
## 25 19.2 17.05 27.3 13.3 15.2 18.90 15.41 17.30
## 26 27.3 18.90 26.0 19.2 13.3 16.70 17.05 15.41
## 27 26.0 16.70 30.4 27.3 19.2 16.90 18.90 17.05
## 28 30.4 16.90 15.8 26.0 27.3 14.50 16.70 18.90
## 29 15.8 14.50 19.7 30.4 26.0 15.50 16.90 16.70
## 30 19.7 15.50 15.0 15.8 30.4 14.60 14.50 16.90
## 31 15.0 14.60 21.4 19.7 15.8 18.60 15.50 14.50
## 32 21.4 18.60 NA 15.0 19.7 NA 14.60 15.50