dlookr (version 0.3.9)

describe.tbl_dbi: Compute descriptive statistic

Description

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

Usage

# S3 method for tbl_dbi
describe(.data, ..., in_database = FALSE,
  collect_size = Inf)

Arguments

.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.

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.

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 devation

• se_mean : standrd 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.data.frame, diagnose_numeric.tbl_dbi.

Examples

# NOT RUN {
library(dplyr)

# Generate data for the example
carseats <- ISLR::Carseats
carseats[sample(seq(NROW(carseats)), 20), "Income"] <- NA
carseats[sample(seq(NROW(carseats)), 5), "Urban"] <- NA

# connect DBMS
con_sqlite <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")

# copy carseats to the DBMS with a table named TB_CARSEATS
copy_to(con_sqlite, carseats, name = "TB_CARSEATS", overwrite = TRUE)

# Using pipes ---------------------------------
# Positive values select variables
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  describe(Sales, CompPrice, Income)

# Negative values to drop variables, and In-memory mode and collect size is 200
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  describe(-Sales, -CompPrice, -Income, collect_size = 200)

# Using pipes & dplyr -------------------------
# Find the statistic of all numerical variables by 'ShelveLoc' and 'US',
# and extract only those with 'ShelveLoc' variable level is "Good".
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  group_by(ShelveLoc, US) %>%
  describe() %>%
  filter(ShelveLoc == "Good")

# extract only those with 'Urban' variable level is "Yes",
# and find 'Sales' statistics by 'ShelveLoc' and 'US'
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  filter(Urban == "Yes") %>%
  group_by(ShelveLoc, US) %>%
  describe(Sales)
 
# }