The eda_web_report() report the information of exploratory data analysis for the DBMS table through tbl_dbi

# S3 method for tbl_dbi
  target = NULL,
  output_file = NULL,
  output_dir = tempdir(),
  browse = TRUE,
  title = "EDA",
  subtitle = deparse(substitute(.data)),
  author = "dlookr",
  title_color = "gray",
  logo_img = NULL,
  create_date = Sys.time(),
  theme = c("orange", "blue")[1],
  sample_percent = 100,
  in_database = FALSE,
  collect_size = Inf,
  as_factor = TRUE,



a tbl_dbi.


character. target variable.


name of generated file. default is NULL.


name of directory to generate report file. default is tempdir().


logical. choose whether to output the report results to the browser.


character. title of report. default is "EDA Report".


character. subtitle of report. default is name of data.


character. author of report. default is "dlookr".


character. color of title. default is "gray".


character. name of logo image on top right.


Date or POSIXct, character. The date on which the report is generated. The default value is the result of Sys.time().


character. name of theme for report. support "orange" and "blue". default is "orange".


numeric. Sample percent of data for performing EDA. It has a value between (0, 100]. 100 means all data, and 5 means 5% of sample data. This is useful for data with a large number of observations.


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.


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


logical. whether to convert to factor when importing a character type variable from DBMS table into R.


arguments to be passed to methods.


Generate generalized EDA report automatically. This feature is useful for EDA of data with many variables, rather than data with fewer variables.

Reported information

Reported from the EDA is as follows.

  • Overview

    • Data Structures

    • Data Types

    • Job Informations

  • Univariate Analysis

    • Descriptive Statistics

    • Normality Test

  • Bivariate Analysis

    • Compare Numerical Variables

    • Compare Categorical Variables

  • Multivariate Analysis

    • Correlation Analysis

      • Correlation Matrix

      • Correlation Plot

  • Target based Analysis

    • Grouped Numerical Variables

    • Grouped Categorical Variables

    • Grouped Correlation

See also


# \donttest{ 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 EDA_Report.html con_sqlite %>% tbl("TB_HEARTFAILURE") %>% eda_web_report(target = "death_event") # create pdf file. file name is EDA.html, and collect size is 250 con_sqlite %>% tbl("TB_HEARTFAILURE") %>% eda_web_report(collect_size = 250, output_file = "EDA.html") # Disconnect DBMS DBI::dbDisconnect(con_sqlite) } # }