The describe() compute descriptive statistic of numerical(INTEGER, NUMBER, etc.) column of the DBMS table through tbl_dbi for exploratory data analysis.

```
# S3 method for tbl_dbi
describe(
.data,
...,
statistics = NULL,
quantiles = NULL,
all.combinations = FALSE,
in_database = FALSE,
collect_size = Inf
)
```

- .data
a tbl_dbi.

- ...
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.

- 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.

- in_database
Specifies whether to perform in-database operations. If TRUE, most operations are performed in the DBMS. if FALSE, table data is taken in R and operated in-memory. Not yet supported in_database = TRUE.

- collect_size
a integer. The number of data samples from the DBMS to R. Applies only if in_database = FALSE.

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

An object of the same class as .data.

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.

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

```
# If you have the 'DBI' and 'RSQLite' packages installed, perform the code block:
if (FALSE) {
library(dplyr)
# connect DBMS
con_sqlite <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
# copy heartfailure to the DBMS with a table named TB_HEARTFAILURE
copy_to(con_sqlite, heartfailure, name = "TB_HEARTFAILURE", overwrite = TRUE)
# Using pipes ---------------------------------
# Positive values select variables
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
describe(platelets, creatinine, sodium)
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
describe(platelets, creatinine, sodium,
statistics = c("mean", "sd", "quantiles"), quantiles = 0.1)
# Negative values to drop variables, and In-memory mode and collect size is 200
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
describe(-platelets, -creatinine, -sodium, collect_size = 200)
# Using pipes & dplyr -------------------------
# Find the statistic of all numerical variables by 'smoking' and 'death_event',
# and extract only those with 'smoking' variable level is "Yes".
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
group_by(smoking, death_event) %>%
describe() %>%
filter(smoking == "Yes")
# Using all.combinations = TRUE
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
filter(!smoking %in% "Yes" | !death_event %in% "Yes") %>%
group_by(smoking, death_event) %>%
describe(all.combinations = TRUE) %>%
filter(smoking == "Yes")
# extract only those with 'sex' variable level is "Male",
# and find 'sodium' statistics by 'smoking' and 'death_event'
con_sqlite %>%
tbl("TB_HEARTFAILURE") %>%
filter(sex == "Male") %>%
group_by(smoking, death_event) %>%
describe(sodium)
# Disconnect DBMS
DBI::dbDisconnect(con_sqlite)
}
```