dlookr (version 0.5.0)

describe: Compute descriptive statistic

Description

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

Usage

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

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).

Value

An object of the same class as .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

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.

See Also

describe.tbl_dbi, diagnose_numeric.data.frame.

Examples

# NOT RUN {
# 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)

# Select the variable to describe
describe(heartfailure2, sodium, platelets, statistics = c("mean", "sd", "quantiles"))
describe(heartfailure2, -sodium, -platelets)
describe(heartfailure2, 5, statistics = c("mean", "sd", "quantiles"), quantiles = c(0.01, 0.1))

# Using dplyr::grouped_dt
library(dplyr)

gdata <- group_by(heartfailure2, hblood_pressure, death_event)
describe(gdata, "creatinine")

# Using pipes ---------------------------------
# Positive values select variables
heartfailure2 %>%
 describe(platelets, sodium, creatinine)

# Negative values to drop variables
heartfailure2 %>%
 describe(-platelets, -sodium, -creatinine)

# 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")

# 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)
# }