The diagnose_outlier() produces outlier information for diagnosing the quality of the numerical data.
diagnose_outlier(.data, ...)
# S3 method for data.frame
diagnose_outlier(.data, ...)
# S3 method for grouped_df
diagnose_outlier(.data, ...)a data.frame or a tbl_df or a grouped_df.
one or more unquoted expressions separated by commas. You can treat variable names like they are positions. Positive values select variables; negative values to drop variables. If the first expression is negative, diagnose_outlier() will automatically start with all variables. These arguments are automatically quoted and evaluated in a context where column names represent column positions. They support unquoting and splicing.
an object of tbl_df.
The scope of the diagnosis is the provide a outlier information. If the number of outliers is small and the difference between the averages including outliers and the averages not including them is large, it is necessary to eliminate or replace the outliers.
The information derived from the numerical data diagnosis is as follows.
variables : variable names
outliers_cnt : number of outliers
outliers_ratio : percent of outliers
outliers_mean : arithmetic average of outliers
with_mean : arithmetic average of with outliers
without_mean : arithmetic average of without outliers
See vignette("diagonosis") for an introduction to these concepts.
# \donttest{
# Diagnosis of numerical variables
diagnose_outlier(heartfailure)
#> variables outliers_cnt outliers_ratio outliers_mean with_mean
#> 1 age 0 0.0000000 NaN 60.82943
#> 2 cpk_enzyme 29 9.6989967 2.905414e+03 581.83946
#> 3 ejection_fraction 2 0.6688963 7.500000e+01 38.08361
#> 4 platelets 21 7.0234114 3.783857e+05 263358.02926
#> 5 creatinine 29 9.6989967 3.793103e+00 1.39388
#> 6 sodium 4 1.3377926 1.185000e+02 136.62542
#> 7 time 0 0.0000000 NaN 130.26087
#> without_mean
#> 1 6.082943e+01
#> 2 3.322704e+02
#> 3 3.783502e+01
#> 4 2.546689e+05
#> 5 1.136185e+00
#> 6 1.368712e+02
#> 7 1.302609e+02
# Select the variable to diagnose
diagnose_outlier(heartfailure, cpk_enzyme, sodium)
#> variables outliers_cnt outliers_ratio outliers_mean with_mean without_mean
#> 1 cpk_enzyme 29 9.698997 2905.414 581.8395 332.2704
#> 2 sodium 4 1.337793 118.500 136.6254 136.8712
diagnose_outlier(heartfailure, -cpk_enzyme, -sodium)
#> variables outliers_cnt outliers_ratio outliers_mean with_mean
#> 1 age 0 0.0000000 NaN 60.82943
#> 2 ejection_fraction 2 0.6688963 7.500000e+01 38.08361
#> 3 platelets 21 7.0234114 3.783857e+05 263358.02926
#> 4 creatinine 29 9.6989967 3.793103e+00 1.39388
#> 5 time 0 0.0000000 NaN 130.26087
#> without_mean
#> 1 6.082943e+01
#> 2 3.783502e+01
#> 3 2.546689e+05
#> 4 1.136185e+00
#> 5 1.302609e+02
diagnose_outlier(heartfailure, "cpk_enzyme", "sodium")
#> variables outliers_cnt outliers_ratio outliers_mean with_mean without_mean
#> 1 cpk_enzyme 29 9.698997 2905.414 581.8395 332.2704
#> 2 sodium 4 1.337793 118.500 136.6254 136.8712
diagnose_outlier(heartfailure, 5)
#> # A tibble: 1 × 6
#> variables outliers_cnt outliers_ratio outliers_mean with_mean without_mean
#> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 ejection_fra… 2 0.669 75 38.1 37.8
# Using pipes ---------------------------------
library(dplyr)
# Diagnosis of all numerical variables
heartfailure %>%
diagnose_outlier()
#> variables outliers_cnt outliers_ratio outliers_mean with_mean
#> 1 age 0 0.0000000 NaN 60.82943
#> 2 cpk_enzyme 29 9.6989967 2.905414e+03 581.83946
#> 3 ejection_fraction 2 0.6688963 7.500000e+01 38.08361
#> 4 platelets 21 7.0234114 3.783857e+05 263358.02926
#> 5 creatinine 29 9.6989967 3.793103e+00 1.39388
#> 6 sodium 4 1.3377926 1.185000e+02 136.62542
#> 7 time 0 0.0000000 NaN 130.26087
#> without_mean
#> 1 6.082943e+01
#> 2 3.322704e+02
#> 3 3.783502e+01
#> 4 2.546689e+05
#> 5 1.136185e+00
#> 6 1.368712e+02
#> 7 1.302609e+02
# Positive values select variables
heartfailure %>%
diagnose_outlier(cpk_enzyme, sodium)
#> variables outliers_cnt outliers_ratio outliers_mean with_mean without_mean
#> 1 cpk_enzyme 29 9.698997 2905.414 581.8395 332.2704
#> 2 sodium 4 1.337793 118.500 136.6254 136.8712
# Negative values to drop variables
heartfailure %>%
diagnose_outlier(-cpk_enzyme, -sodium)
#> variables outliers_cnt outliers_ratio outliers_mean with_mean
#> 1 age 0 0.0000000 NaN 60.82943
#> 2 ejection_fraction 2 0.6688963 7.500000e+01 38.08361
#> 3 platelets 21 7.0234114 3.783857e+05 263358.02926
#> 4 creatinine 29 9.6989967 3.793103e+00 1.39388
#> 5 time 0 0.0000000 NaN 130.26087
#> without_mean
#> 1 6.082943e+01
#> 2 3.783502e+01
#> 3 2.546689e+05
#> 4 1.136185e+00
#> 5 1.302609e+02
# Positions values select variables
heartfailure %>%
diagnose_outlier(5)
#> # A tibble: 1 × 6
#> variables outliers_cnt outliers_ratio outliers_mean with_mean without_mean
#> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 ejection_fra… 2 0.669 75 38.1 37.8
# Negative values to drop variables
heartfailure %>%
diagnose_outlier(-1, -5)
#> variables outliers_cnt outliers_ratio outliers_mean with_mean
#> 1 cpk_enzyme 29 9.698997 2.905414e+03 581.83946
#> 2 platelets 21 7.023411 3.783857e+05 263358.02926
#> 3 creatinine 29 9.698997 3.793103e+00 1.39388
#> 4 sodium 4 1.337793 1.185000e+02 136.62542
#> 5 time 0 0.000000 NaN 130.26087
#> without_mean
#> 1 3.322704e+02
#> 2 2.546689e+05
#> 3 1.136185e+00
#> 4 1.368712e+02
#> 5 1.302609e+02
# Using pipes & dplyr -------------------------
# outlier_ratio is more than 1%
heartfailure %>%
diagnose_outlier() %>%
filter(outliers_ratio > 1)
#> variables outliers_cnt outliers_ratio outliers_mean with_mean
#> 1 cpk_enzyme 29 9.698997 2.905414e+03 581.83946
#> 2 platelets 21 7.023411 3.783857e+05 263358.02926
#> 3 creatinine 29 9.698997 3.793103e+00 1.39388
#> 4 sodium 4 1.337793 1.185000e+02 136.62542
#> without_mean
#> 1 3.322704e+02
#> 2 2.546689e+05
#> 3 1.136185e+00
#> 4 1.368712e+02
# Using group_by ------------------------------
# Calculate the diagnosis of all variables by 'death_event' using group_by()
heartfailure %>%
group_by(death_event) %>%
diagnose_outlier()
#> # A tibble: 14 × 8
#> variables death_event data_cnt outliers_cnt outliers_ratio outliers_mean
#> <chr> <fct> <int> <int> <dbl> <dbl>
#> 1 age No 203 1 0.493 90
#> 2 age Yes 96 0 0 NaN
#> 3 cpk_enzyme No 203 20 9.85 2439.
#> 4 cpk_enzyme Yes 96 9 9.38 3942
#> 5 ejection_frac… No 203 3 1.48 53
#> 6 ejection_frac… Yes 96 7 7.29 62.4
#> 7 platelets No 203 13 6.40 433469.
#> 8 platelets Yes 96 2 2.08 559000
#> 9 creatinine No 203 21 10.3 2.64
#> 10 creatinine Yes 96 8 8.33 5.82
#> 11 sodium No 203 5 2.46 128
#> 12 sodium Yes 96 3 3.12 120.
#> 13 time No 203 0 0 NaN
#> 14 time Yes 96 2 2.08 238
#> # ℹ 2 more variables: with_mean <dbl>, without_mean <dbl>
# }