Install and load package (DataExplorer):

#install.packages("DataExplorer")
# Load library
library(DataExplorer) # load DataExplorer
library(datasets)
library(ggplot2)

Download used data LAI_factors.csv and Countries_LAI_and_LST.csv

Read in dataset

dt1<-read.csv(file.path('D:', 'R4Researchers', 'Countries_LAI_and_LST.csv'))
dt2<-read.csv(file.path('D:', 'R4Researchers', 'LAI_factors.csv'))

introduce(airquality) # to describe basic information
introduce(dt1)

plot_bar(mtcars)
plot_boxplot(iris, by = "Species", ncol = 2L)
plot_correlation(iris)
plot_histogram(iris, ncol = 2L)
plot_prcomp(na.omit(airquality), nrow = 2L, ncol = 2L) # Visualize principal component analysis
plot_qq(iris) # plot quantile-quantile for each continuous feature
plot_scatterplot(iris, by = "Species") # create scatterplot for all features
plot_str(iris) # visualize data structure

Create a report

create_report(iris)
create_report(airquality, y = "Ozone")

create_report(dt1)
plot_histogram(dt1)

create_report(dt2)
plot_histogram(dt2)

Create customized report

create_report(
  data = dt2,
  output_format = html_document(toc = TRUE, toc_depth = 6, theme = "flatly"),
  output_file = "report_LAI_factors.html",
  output_dir = getwd(),
  y = "Year",
  config = configure_report(
    add_plot_prcomp = TRUE,
    plot_qq_args = list("by" = "Year", sampled_rows = 1000L),
    plot_bar_args = list("with" = "LAI_India"),
    plot_correlation_args = list("cor_args" = list("use" = "pairwise.complete.obs")),
    plot_boxplot_args = list("by" = "LST_India"),
    global_ggtheme = quote(theme_light())
  )
)

The output will be saved in the directory and opened as html file.

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