README.md
In pavel-filatov/featr: Easy-to-use feature preprocessing tools
featr package
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
Installation
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")
Examples
cor_index()
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
identity_index()
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;other all the other cases (e.g. when one of the entries is
NA).
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()
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
pavel-filatov/featr documentation built on May 12, 2019, 1:29 a.m.
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
featr package
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
Installation
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")
Examples
cor_index()
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
identity_index()
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()
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
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