diagnose_report.tbl_dbi: Reporting the information of data diagnosis for table of the...

In dlookr: Tools for Data Diagnosis, Exploration, Transformation

Reporting the information of data diagnosis for table of the DBMS

Description

The diagnose_report() report the information for diagnosing the quality of the DBMS table through tbl_dbi

Usage

## S3 method for class 'tbl_dbi'
diagnose_report(
  .data,
  output_format = c("pdf", "html"),
  output_file = NULL,
  output_dir = tempdir(),
  font_family = NULL,
  in_database = FALSE,
  collect_size = Inf,
  ...
)

Arguments

.data	a tbl_dbi.
output_format	report output type. Choose either "pdf" and "html". "pdf" create pdf file by knitr::knit(). "html" create html file by rmarkdown::render().
output_file	name of generated file. default is NULL.
output_dir	name of directory to generate report file. default is tempdir().
font_family	character. font family name for figure in pdf.
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.
...	arguments to be passed to methods.

Details

Generate generalized data diagnostic reports automatically. You can choose to output to pdf and html files. This is useful for diagnosing a data frame with a large number of variables than data with a small number of variables. For pdf output, Korean Gothic font must be installed in Korean operating system.

Value

No return value. This function only generates a report.

Reported information

Reported from the data diagnosis is as follows.

• Diagnose Data

  • Overview of Diagnosis

    • List of all variables quality

    • Diagnosis of missing data

    • Diagnosis of unique data(Text and Category)

    • Diagnosis of unique data(Numerical)

  • Detailed data diagnosis

    • Diagnosis of categorical variables

    • Diagnosis of numerical variables

    • List of numerical diagnosis (zero)

    • List of numerical diagnosis (minus)

• Diagnose Outliers

  • Overview of Diagnosis

    • Diagnosis of numerical variable outliers

    • Detailed outliers diagnosis

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

See Also

diagnose_report.data.frame.

Examples

# If you have the 'DBI' and 'RSQLite' packages installed, perform the code block:
if (FALSE) {
library(dplyr)

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

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

# copy heartfailure2 to the DBMS with a table named TB_HEARTFAILURE
copy_to(con_sqlite, heartfailure2, name = "TB_HEARTFAILURE", overwrite = TRUE)

# reporting the diagnosis information -------------------------
# create pdf file. file name is DataDiagnosis_Report.pdf
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  diagnose_report()
  
# create pdf file. file name is Diagn.pdf, and collect size is 350
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  diagnose_report(collect_size = 350, output_file = "Diagn.pdf")

# create html file. file name is Diagnosis_Report.html
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  diagnose_report(output_format = "html")

# create html file. file name is Diagn.html
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  diagnose_report(output_format = "html", output_file = "Diagn.html")
  
# Disconnect DBMS   
DBI::dbDisconnect(con_sqlite)
}

dlookr documentation built on May 29, 2024, 2 a.m.