The describe() compute descriptive statistic of numeric variable for exploratory data analysis.

describe(.data, ...)

# S3 method for data.frame
describe(.data, ..., statistics = NULL, quantiles = NULL)

# S3 method for grouped_df
describe(
.data,
...,
statistics = NULL,
quantiles = NULL,
all.combinations = FALSE
)

## Arguments

.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, describe() 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.

See vignette("EDA") for an introduction to these concepts.

statistics

character. the name of the descriptive statistic to calculate. The defaults is c("mean", "sd", "se_mean", "IQR", "skewness", "kurtosis", "quantiles")

quantiles

numeric. list of quantiles to calculate. The values of elements must be between 0 and 1. and to calculate quantiles, you must include "quantiles" in the statistics argument value. The default is c(0, .01, .05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.9, 0.95, 0.99, 1).

all.combinations

logical. When used with group_by(), this argument expresses all combinations of group combinations. If the argument value is TRUE, cases that do not exist as actual data are also included in the output.

## Value

An object of the same class as .data.

## Details

This function is useful when used with the group_by function of the dplyr package. If you want to calculate the statistic by level of the categorical data you are interested in, rather than the whole statistic, you can use grouped_df as the group_by() function.

From version 0.5.5, the 'variable' column in the "descriptive statistic information" tibble object has been changed to 'described_variables'. This is because there are cases where 'variable' is included in the variable name of the data. There is probably no case where 'described_variables' is included in the variable name of the data.

## Descriptive statistic information

The information derived from the numerical data describe is as follows.

• n : number of observations excluding missing values

• na : number of missing values

• mean : arithmetic average

• sd : standard deviation

• se_mean : standard error mean. sd/sqrt(n)

• IQR : interquartile range (Q3-Q1)

• skewness : skewness

• kurtosis : kurtosis

• p25 : Q1. 25% percentile

• p50 : median. 50% percentile

• p75 : Q3. 75% percentile

• p01, p05, p10, p20, p30 : 1%, 5%, 20%, 30% percentiles

• p40, p60, p70, p80 : 40%, 60%, 70%, 80% percentiles

• p90, p95, p99, p100 : 90%, 95%, 99%, 100% percentiles

describe.tbl_dbi, diagnose_numeric.data.frame.

## Examples

# \donttest{
# Generate data for the example
heartfailure2 <- heartfailure
heartfailure2[sample(seq(NROW(heartfailure2)), 20), "sodium"] <- NA
heartfailure2[sample(seq(NROW(heartfailure2)), 5), "smoking"] <- NA

# Describe descriptive statistics of numerical variables
describe(heartfailure2)
#> # A tibble: 7 × 26
#>   described_variables     n    na      mean       sd   se_mean     IQR skewness
#>   <chr>               <int> <int>     <dbl>    <dbl>     <dbl>   <dbl>    <dbl>
#> 1 age                   299     0     60.8     11.9     0.688     19      0.424
#> 2 cpk_enzyme            299     0    582.     970.     56.1      466.     4.46
#> 3 ejection_fraction     299     0     38.1     11.8     0.684     15      0.555
#> 4 platelets             299     0 263358.   97804.   5656.     91000      1.46
#> 5 creatinine            299     0      1.39     1.03    0.0598     0.5    4.46
#> 6 sodium                279    20    137.       4.46    0.267      5.5   -1.06
#> 7 time                  299     0    130.      77.6     4.49     130      0.128
#> # ℹ 18 more variables: kurtosis <dbl>, p00 <dbl>, p01 <dbl>, p05 <dbl>,
#> #   p10 <dbl>, p20 <dbl>, p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>,
#> #   p60 <dbl>, p70 <dbl>, p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>,
#> #   p99 <dbl>, p100 <dbl>

# Select the variable to describe
describe(heartfailure2, sodium, platelets, statistics = c("mean", "sd", "quantiles"))
#> # A tibble: 2 × 22
#>   described_variables     n    na    mean       sd   p00    p01    p05    p10
#>   <chr>               <int> <int>   <dbl>    <dbl> <dbl>  <dbl>  <dbl>  <dbl>
#> 1 sodium                279    20    137.     4.46   113   123.    130    132
#> 2 platelets             299     0 263358. 97804.   25100 61780  131800 153000
#> # ℹ 13 more variables: p20 <dbl>, p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>,
#> #   p60 <dbl>, p70 <dbl>, p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>,
#> #   p99 <dbl>, p100 <dbl>
describe(heartfailure2, -sodium, -platelets)
#> # A tibble: 5 × 26
#>   described_variables     n    na   mean     sd se_mean   IQR skewness kurtosis
#>   <chr>               <int> <int>  <dbl>  <dbl>   <dbl> <dbl>    <dbl>    <dbl>
#> 1 age                   299     0  60.8   11.9   0.688   19      0.424  -0.184
#> 2 cpk_enzyme            299     0 582.   970.   56.1    466.     4.46   25.1
#> 3 ejection_fraction     299     0  38.1   11.8   0.684   15      0.555   0.0414
#> 4 creatinine            299     0   1.39   1.03  0.0598   0.5    4.46   25.8
#> 5 time                  299     0 130.    77.6   4.49   130      0.128  -1.21
#> # ℹ 17 more variables: p00 <dbl>, p01 <dbl>, p05 <dbl>, p10 <dbl>, p20 <dbl>,
#> #   p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,
#> #   p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl>
describe(heartfailure2, 5, statistics = c("mean", "sd", "quantiles"), quantiles = c(0.01, 0.1))
#> # A tibble: 1 × 7
#>   described_variables     n    na  mean    sd   p01   p10
#>   <chr>               <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 ejection_fraction     299     0  38.1  11.8  17.0    25

# Using dplyr::grouped_dt
library(dplyr)

gdata <- group_by(heartfailure2, hblood_pressure, death_event)
describe(gdata, "creatinine")
#> # A tibble: 4 × 28
#>   described_variables hblood_pressure death_event     n    na  mean    sd
#>   <chr>               <fct>           <fct>       <int> <int> <dbl> <dbl>
#> 1 creatinine          No              No            137     0  1.25 0.735
#> 2 creatinine          No              Yes            57     0  1.76 1.12
#> 3 creatinine          Yes             No             66     0  1.05 0.415
#> 4 creatinine          Yes             Yes            39     0  1.95 1.88
#> # ℹ 21 more variables: se_mean <dbl>, IQR <dbl>, skewness <dbl>,
#> #   kurtosis <dbl>, p00 <dbl>, p01 <dbl>, p05 <dbl>, p10 <dbl>, p20 <dbl>,
#> #   p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,
#> #   p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl>

# Using pipes ---------------------------------
# Positive values select variables
heartfailure2 %>%
describe(platelets, sodium, creatinine)
#> # A tibble: 3 × 26
#>   described_variables     n    na   mean     sd se_mean    IQR skewness kurtosis
#>   <chr>               <int> <int>  <dbl>  <dbl>   <dbl>  <dbl>    <dbl>    <dbl>
#> 1 platelets             299     0 2.63e5 9.78e4 5.66e+3 9.1e+4     1.46     6.21
#> 2 sodium                279    20 1.37e2 4.46e0 2.67e-1 5.5e+0    -1.06     4.09
#> 3 creatinine            299     0 1.39e0 1.03e0 5.98e-2 5  e-1     4.46    25.8
#> # ℹ 17 more variables: p00 <dbl>, p01 <dbl>, p05 <dbl>, p10 <dbl>, p20 <dbl>,
#> #   p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,
#> #   p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl>

# Negative values to drop variables
heartfailure2 %>%
describe(-platelets, -sodium, -creatinine)
#> # A tibble: 4 × 26
#>   described_variables     n    na  mean    sd se_mean   IQR skewness kurtosis
#>   <chr>               <int> <int> <dbl> <dbl>   <dbl> <dbl>    <dbl>    <dbl>
#> 1 age                   299     0  60.8  11.9   0.688   19     0.424  -0.184
#> 2 cpk_enzyme            299     0 582.  970.   56.1    466.    4.46   25.1
#> 3 ejection_fraction     299     0  38.1  11.8   0.684   15     0.555   0.0414
#> 4 time                  299     0 130.   77.6   4.49   130     0.128  -1.21
#> # ℹ 17 more variables: p00 <dbl>, p01 <dbl>, p05 <dbl>, p10 <dbl>, p20 <dbl>,
#> #   p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,
#> #   p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl>

# Using pipes & dplyr -------------------------
# Find the statistic of all numerical variables by 'hblood_pressure' and 'death_event',
# and extract only those with 'hblood_pressure' variable level is "Yes".
heartfailure2 %>%
group_by(hblood_pressure, death_event) %>%
describe() %>%
filter(hblood_pressure == "Yes")
#> # A tibble: 14 × 28
#>    described_variables hblood_pressure death_event     n    na      mean      sd
#>    <chr>               <fct>           <fct>       <int> <int>     <dbl>   <dbl>
#>  1 age                 Yes             No             66     0     60.1  9.42e+0
#>  2 age                 Yes             Yes            39     0     66.1  1.37e+1
#>  3 cpk_enzyme          Yes             No             66     0    445.   5.89e+2
#>  4 cpk_enzyme          Yes             Yes            39     0    563.   1.21e+3
#>  5 creatinine          Yes             No             66     0      1.05 4.15e-1
#>  6 creatinine          Yes             Yes            39     0      1.95 1.88e+0
#>  7 ejection_fraction   Yes             No             66     0     41.7  1.20e+1
#>  8 ejection_fraction   Yes             Yes            39     0     33.1  1.14e+1
#>  9 platelets           Yes             No             66     0 267512.   7.77e+4
#> 10 platelets           Yes             Yes            39     0 274181.   9.61e+4
#> 11 sodium              Yes             No             60     6    137.   4.11e+0
#> 12 sodium              Yes             Yes            38     1    136.   4.08e+0
#> 13 time                Yes             No             66     0    141.   6.17e+1
#> 14 time                Yes             Yes            39     0     57.1  5.31e+1
#> # ℹ 21 more variables: se_mean <dbl>, IQR <dbl>, skewness <dbl>,
#> #   kurtosis <dbl>, p00 <dbl>, p01 <dbl>, p05 <dbl>, p10 <dbl>, p20 <dbl>,
#> #   p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,
#> #   p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl>

# Using all.combinations = TRUE
heartfailure2 %>%
filter(!hblood_pressure %in% "Yes" | !death_event %in% "Yes") %>%
group_by(hblood_pressure, death_event) %>%
describe(all.combinations = TRUE)
#> # A tibble: 28 × 28
#>    described_variables hblood_pressure death_event     n    na   mean       sd
#>    <chr>               <fct>           <fct>       <dbl> <int>  <dbl>    <dbl>
#>  1 age                 No              No            137     0  58.1    11.2
#>  2 age                 No              Yes            57     0  64.6    12.9
#>  3 age                 Yes             No             66     0  60.1     9.42
#>  4 age                 Yes             Yes             0    NA  NA      NA
#>  5 cpk_enzyme          No              No            137     0 586.    820.
#>  6 cpk_enzyme          No              Yes            57     0 743.   1390.
#>  7 cpk_enzyme          Yes             No             66     0 445.    589.
#>  8 cpk_enzyme          Yes             Yes             0    NA  NA      NA
#>  9 creatinine          No              No            137     0   1.25    0.735
#> 10 creatinine          No              Yes            57     0   1.76    1.12
#> # ℹ 18 more rows
#> # ℹ 21 more variables: se_mean <dbl>, IQR <dbl>, skewness <dbl>,
#> #   kurtosis <dbl>, p00 <dbl>, p01 <dbl>, p05 <dbl>, p10 <dbl>, p20 <dbl>,
#> #   p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,
#> #   p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl>

# extract only those with 'smoking' variable level is "Yes",
# and find 'creatinine' statistics by 'hblood_pressure' and 'death_event'
heartfailure2 %>%
filter(smoking == "Yes") %>%
group_by(hblood_pressure, death_event) %>%
describe(creatinine)
#> # A tibble: 4 × 28
#>   described_variables hblood_pressure death_event     n    na  mean    sd
#>   <chr>               <fct>           <fct>       <int> <int> <dbl> <dbl>
#> 1 creatinine          No              No             50     0  1.18 0.557
#> 2 creatinine          No              Yes            15     0  1.44 0.584
#> 3 creatinine          Yes             No             16     0  1.16 0.683
#> 4 creatinine          Yes             Yes            14     0  2.09 2.26
#> # ℹ 21 more variables: se_mean <dbl>, IQR <dbl>, skewness <dbl>,
#> #   kurtosis <dbl>, p00 <dbl>, p01 <dbl>, p05 <dbl>, p10 <dbl>, p20 <dbl>,
#> #   p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>,
#> #   p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl>
# }