README.md
In paulhendricks/profilr: Quickly Profile Data in R
profilr
profilr quickly and easily profiles data using common descriptive statistics.
Installation
You can install the latest development version from CRAN:
install.packages("profilr")
Or from GitHub with:
if (packageVersion("devtools") < 1.6) {
install.packages("devtools")
}
devtools::install_github("paulhendricks/profilr")
If you encounter a clear bug, please file a minimal reproducible example on GitHub.
API
library(dplyr, warn.conflicts = FALSE)
library(profilr)
#>
#> Attaching package: 'profilr'
#> The following object is masked from 'package:stats':
#>
#> profile
mtcars %>%
profile
#> .column_name .column_class .column_type .count_elements .count_uniques
#> 1 mpg numeric double 32 25
#> 2 cyl numeric double 32 3
#> 3 disp numeric double 32 27
#> 4 hp numeric double 32 22
#> 5 drat numeric double 32 22
#> 6 wt numeric double 32 29
#> 7 qsec numeric double 32 30
#> 8 vs numeric double 32 2
#> 9 am numeric double 32 2
#> 10 gear numeric double 32 3
#> 11 carb numeric double 32 6
#> .percent_uniques .count_NULLs .percent_NULLs .count_NAs .percent_NAs
#> 1 0.78125 0 0 0 0
#> 2 0.09375 0 0 0 0
#> 3 0.84375 0 0 0 0
#> 4 0.68750 0 0 0 0
#> 5 0.68750 0 0 0 0
#> 6 0.90625 0 0 0 0
#> 7 0.93750 0 0 0 0
#> 8 0.06250 0 0 0 0
#> 9 0.06250 0 0 0 0
#> 10 0.09375 0 0 0 0
#> 11 0.18750 0 0 0 0
#> .count_zeroes .percent_zeros .mean_value .sd_value .q0_value
#> 1 0 0.00000 20.090625 6.0269481 10.400
#> 2 0 0.00000 6.187500 1.7859216 4.000
#> 3 0 0.00000 230.721875 123.9386938 71.100
#> 4 0 0.00000 146.687500 68.5628685 52.000
#> 5 0 0.00000 3.596563 0.5346787 2.760
#> 6 0 0.00000 3.217250 0.9784574 1.513
#> 7 0 0.00000 17.848750 1.7869432 14.500
#> 8 18 0.56250 0.437500 0.5040161 0.000
#> 9 19 0.59375 0.406250 0.4989909 0.000
#> 10 0 0.00000 3.687500 0.7378041 3.000
#> 11 0 0.00000 2.812500 1.6152000 1.000
#> .q25_value .q50_value .q75_value .q100_value
#> 1 15.42500 19.200 22.80 33.900
#> 2 4.00000 6.000 8.00 8.000
#> 3 120.82500 196.300 326.00 472.000
#> 4 96.50000 123.000 180.00 335.000
#> 5 3.08000 3.695 3.92 4.930
#> 6 2.58125 3.325 3.61 5.424
#> 7 16.89250 17.710 18.90 22.900
#> 8 0.00000 0.000 1.00 1.000
#> 9 0.00000 0.000 1.00 1.000
#> 10 3.00000 4.000 4.00 5.000
#> 11 2.00000 2.000 4.00 8.000
#> .top_5_values
#> 1 (1) 10.4 [2]\n (1) 15.2 [2]\n (1) 19.2 [2]\n (1) 21 [2]\n (1) 21.4 [2]\n
#> 2 (1) 8 [14]\n (2) 4 [11]\n (3) 6 [7]\n
#> 3 (1) 275.8 [3]\n (2) 160 [2]\n (2) 167.6 [2]\n (2) 360 [2]\n (5) 71.1 [1]\n
#> 4 (1) 110 [3]\n (1) 175 [3]\n (1) 180 [3]\n (4) 66 [2]\n (4) 123 [2]\n
#> 5 (1) 3.07 [3]\n (1) 3.92 [3]\n (3) 2.76 [2]\n (3) 3.08 [2]\n (3) 3.15 [2]\n
#> 6 (1) 3.44 [3]\n (2) 3.57 [2]\n (3) 1.513 [1]\n (3) 1.615 [1]\n (3) 1.835 [1]\n
#> 7 (1) 17.02 [2]\n (1) 18.9 [2]\n (3) 14.5 [1]\n (3) 14.6 [1]\n (3) 15.41 [1]\n
#> 8 (1) 0 [18]\n (2) 1 [14]\n
#> 9 (1) 0 [19]\n (2) 1 [13]\n
#> 10 (1) 3 [15]\n (2) 4 [12]\n (3) 5 [5]\n
#> 11 (1) 2 [10]\n (1) 4 [10]\n (3) 1 [7]\n (4) 3 [3]\n (5) 6 [1]\n
#> .bottom_5_values
#> 1 (8) 33.9 [1]\n (8) 32.4 [1]\n (8) 27.3 [1]\n (8) 26 [1]\n (8) 24.4 [1]\n
#> 2 (3) 6 [7]\n (2) 4 [11]\n (1) 8 [14]\n
#> 3 (5) 472 [1]\n (5) 460 [1]\n (5) 440 [1]\n (5) 400 [1]\n (5) 351 [1]\n
#> 4 (8) 335 [1]\n (8) 264 [1]\n (8) 230 [1]\n (8) 215 [1]\n (8) 205 [1]\n
#> 5 (9) 4.93 [1]\n (9) 4.43 [1]\n (9) 4.11 [1]\n (9) 3.85 [1]\n (9) 3.77 [1]\n
#> 6 (3) 5.424 [1]\n (3) 5.345 [1]\n (3) 5.25 [1]\n (3) 4.07 [1]\n (3) 3.845 [1]\n
#> 7 (3) 22.9 [1]\n (3) 20.22 [1]\n (3) 20.01 [1]\n (3) 20 [1]\n (3) 19.9 [1]\n
#> 8 (2) 1 [14]\n (1) 0 [18]\n
#> 9 (2) 1 [13]\n (1) 0 [19]\n
#> 10 (3) 5 [5]\n (2) 4 [12]\n (1) 3 [15]\n
#> 11 (5) 8 [1]\n (5) 6 [1]\n (4) 3 [3]\n (3) 1 [7]\n (1) 4 [10]\n
mtcars %>%
group_by(cyl) %>%
do(profile(.))
#> # A tibble: 33 x 22
#> # Groups: cyl [3]
#> cyl .column_name .column_class .column_type .count_elements
#> <dbl> <chr> <chr> <chr> <int>
#> 1 4 mpg numeric double 11
#> 2 4 cyl numeric double 11
#> 3 4 disp numeric double 11
#> 4 4 hp numeric double 11
#> 5 4 drat numeric double 11
#> 6 4 wt numeric double 11
#> 7 4 qsec numeric double 11
#> 8 4 vs numeric double 11
#> 9 4 am numeric double 11
#> 10 4 gear numeric double 11
#> # ... with 23 more rows, and 17 more variables: .count_uniques <int>,
#> # .percent_uniques <dbl>, .count_NULLs <int>, .percent_NULLs <dbl>,
#> # .count_NAs <int>, .percent_NAs <dbl>, .count_zeroes <int>,
#> # .percent_zeros <dbl>, .mean_value <dbl>, .sd_value <dbl>,
#> # .q0_value <dbl>, .q25_value <dbl>, .q50_value <dbl>, .q75_value <dbl>,
#> # .q100_value <dbl>, .top_5_values <chr>, .bottom_5_values <chr>
Citation
To cite package ‘profilr’ in publications use:
Paul Hendricks (2015). profilr: Quickly Profile Data in R. R package version 0.1.0. https://CRAN.R-project.org/package=profilr
A BibTeX entry for LaTeX users is
@Manual{,
title = {profilr: Quickly Profile Data in R},
author = {Paul Hendricks},
year = {2015},
note = {R package version 0.1.0},
url = {https://CRAN.R-project.org/package=profilr},
}
paulhendricks/profilr documentation built on May 24, 2019, 8:43 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
profilr
profilr quickly and easily profiles data using common descriptive statistics.
Installation
You can install the latest development version from CRAN:
install.packages("profilr")
Or from GitHub with:
if (packageVersion("devtools") < 1.6) {
install.packages("devtools")
}
devtools::install_github("paulhendricks/profilr")
If you encounter a clear bug, please file a minimal reproducible example on GitHub.
API
library(dplyr, warn.conflicts = FALSE)
library(profilr)
#>
#> Attaching package: 'profilr'
#> The following object is masked from 'package:stats':
#>
#> profile
mtcars %>%
profile
#> .column_name .column_class .column_type .count_elements .count_uniques
#> 1 mpg numeric double 32 25
#> 2 cyl numeric double 32 3
#> 3 disp numeric double 32 27
#> 4 hp numeric double 32 22
#> 5 drat numeric double 32 22
#> 6 wt numeric double 32 29
#> 7 qsec numeric double 32 30
#> 8 vs numeric double 32 2
#> 9 am numeric double 32 2
#> 10 gear numeric double 32 3
#> 11 carb numeric double 32 6
#> .percent_uniques .count_NULLs .percent_NULLs .count_NAs .percent_NAs
#> 1 0.78125 0 0 0 0
#> 2 0.09375 0 0 0 0
#> 3 0.84375 0 0 0 0
#> 4 0.68750 0 0 0 0
#> 5 0.68750 0 0 0 0
#> 6 0.90625 0 0 0 0
#> 7 0.93750 0 0 0 0
#> 8 0.06250 0 0 0 0
#> 9 0.06250 0 0 0 0
#> 10 0.09375 0 0 0 0
#> 11 0.18750 0 0 0 0
#> .count_zeroes .percent_zeros .mean_value .sd_value .q0_value
#> 1 0 0.00000 20.090625 6.0269481 10.400
#> 2 0 0.00000 6.187500 1.7859216 4.000
#> 3 0 0.00000 230.721875 123.9386938 71.100
#> 4 0 0.00000 146.687500 68.5628685 52.000
#> 5 0 0.00000 3.596563 0.5346787 2.760
#> 6 0 0.00000 3.217250 0.9784574 1.513
#> 7 0 0.00000 17.848750 1.7869432 14.500
#> 8 18 0.56250 0.437500 0.5040161 0.000
#> 9 19 0.59375 0.406250 0.4989909 0.000
#> 10 0 0.00000 3.687500 0.7378041 3.000
#> 11 0 0.00000 2.812500 1.6152000 1.000
#> .q25_value .q50_value .q75_value .q100_value
#> 1 15.42500 19.200 22.80 33.900
#> 2 4.00000 6.000 8.00 8.000
#> 3 120.82500 196.300 326.00 472.000
#> 4 96.50000 123.000 180.00 335.000
#> 5 3.08000 3.695 3.92 4.930
#> 6 2.58125 3.325 3.61 5.424
#> 7 16.89250 17.710 18.90 22.900
#> 8 0.00000 0.000 1.00 1.000
#> 9 0.00000 0.000 1.00 1.000
#> 10 3.00000 4.000 4.00 5.000
#> 11 2.00000 2.000 4.00 8.000
#> .top_5_values
#> 1 (1) 10.4 [2]\n (1) 15.2 [2]\n (1) 19.2 [2]\n (1) 21 [2]\n (1) 21.4 [2]\n
#> 2 (1) 8 [14]\n (2) 4 [11]\n (3) 6 [7]\n
#> 3 (1) 275.8 [3]\n (2) 160 [2]\n (2) 167.6 [2]\n (2) 360 [2]\n (5) 71.1 [1]\n
#> 4 (1) 110 [3]\n (1) 175 [3]\n (1) 180 [3]\n (4) 66 [2]\n (4) 123 [2]\n
#> 5 (1) 3.07 [3]\n (1) 3.92 [3]\n (3) 2.76 [2]\n (3) 3.08 [2]\n (3) 3.15 [2]\n
#> 6 (1) 3.44 [3]\n (2) 3.57 [2]\n (3) 1.513 [1]\n (3) 1.615 [1]\n (3) 1.835 [1]\n
#> 7 (1) 17.02 [2]\n (1) 18.9 [2]\n (3) 14.5 [1]\n (3) 14.6 [1]\n (3) 15.41 [1]\n
#> 8 (1) 0 [18]\n (2) 1 [14]\n
#> 9 (1) 0 [19]\n (2) 1 [13]\n
#> 10 (1) 3 [15]\n (2) 4 [12]\n (3) 5 [5]\n
#> 11 (1) 2 [10]\n (1) 4 [10]\n (3) 1 [7]\n (4) 3 [3]\n (5) 6 [1]\n
#> .bottom_5_values
#> 1 (8) 33.9 [1]\n (8) 32.4 [1]\n (8) 27.3 [1]\n (8) 26 [1]\n (8) 24.4 [1]\n
#> 2 (3) 6 [7]\n (2) 4 [11]\n (1) 8 [14]\n
#> 3 (5) 472 [1]\n (5) 460 [1]\n (5) 440 [1]\n (5) 400 [1]\n (5) 351 [1]\n
#> 4 (8) 335 [1]\n (8) 264 [1]\n (8) 230 [1]\n (8) 215 [1]\n (8) 205 [1]\n
#> 5 (9) 4.93 [1]\n (9) 4.43 [1]\n (9) 4.11 [1]\n (9) 3.85 [1]\n (9) 3.77 [1]\n
#> 6 (3) 5.424 [1]\n (3) 5.345 [1]\n (3) 5.25 [1]\n (3) 4.07 [1]\n (3) 3.845 [1]\n
#> 7 (3) 22.9 [1]\n (3) 20.22 [1]\n (3) 20.01 [1]\n (3) 20 [1]\n (3) 19.9 [1]\n
#> 8 (2) 1 [14]\n (1) 0 [18]\n
#> 9 (2) 1 [13]\n (1) 0 [19]\n
#> 10 (3) 5 [5]\n (2) 4 [12]\n (1) 3 [15]\n
#> 11 (5) 8 [1]\n (5) 6 [1]\n (4) 3 [3]\n (3) 1 [7]\n (1) 4 [10]\n
mtcars %>%
group_by(cyl) %>%
do(profile(.))
#> # A tibble: 33 x 22
#> # Groups: cyl [3]
#> cyl .column_name .column_class .column_type .count_elements
#> <dbl> <chr> <chr> <chr> <int>
#> 1 4 mpg numeric double 11
#> 2 4 cyl numeric double 11
#> 3 4 disp numeric double 11
#> 4 4 hp numeric double 11
#> 5 4 drat numeric double 11
#> 6 4 wt numeric double 11
#> 7 4 qsec numeric double 11
#> 8 4 vs numeric double 11
#> 9 4 am numeric double 11
#> 10 4 gear numeric double 11
#> # ... with 23 more rows, and 17 more variables: .count_uniques <int>,
#> # .percent_uniques <dbl>, .count_NULLs <int>, .percent_NULLs <dbl>,
#> # .count_NAs <int>, .percent_NAs <dbl>, .count_zeroes <int>,
#> # .percent_zeros <dbl>, .mean_value <dbl>, .sd_value <dbl>,
#> # .q0_value <dbl>, .q25_value <dbl>, .q50_value <dbl>, .q75_value <dbl>,
#> # .q100_value <dbl>, .top_5_values <chr>, .bottom_5_values <chr>
Citation
To cite package ‘profilr’ in publications use:
Paul Hendricks (2015). profilr: Quickly Profile Data in R. R package version 0.1.0. https://CRAN.R-project.org/package=profilr
A BibTeX entry for LaTeX users is
@Manual{,
title = {profilr: Quickly Profile Data in R},
author = {Paul Hendricks},
year = {2015},
note = {R package version 0.1.0},
url = {https://CRAN.R-project.org/package=profilr},
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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