Install and load packages (DataExplorer, datasets, ggplot2):
#install.packages("DataExplorer")
# Load library
library(DataExplorer) # load DataExplorer
library(datasets)
library(ggplot2)
Download the data files LAI_factors.csv and Countries_LAI_and_LST.csv.
Read in datasets:
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 specified directory as an HTML file.
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