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What is Hugo shortcode? A Hugo shortcode is a reusable and customizable template-like feature Shortcodes are written using Hugo’s templating language (based on Go templates). They are placed in the layouts/shortcodes directory of your Hugo project. Create a Shortcode File Save this as layouts/shortcodes/your_shortcode.html my-site/ ├── archetypes/ │ └── default.md ├── assets/ ├── content/ ├── data/ ├── i18n/ ├── layouts/ ├── └── shortcodes/ <-- your_shortcode.html ├── static/ ├── themes/ └── hugo....

Climatic Data in Hydrological Analysis Climatic data such as temperature and precipitation are essential for any hydrological analysis. Usually, several years of data are required to do any type of hydrological analysis. The climatic data is obtained from either satellite-based observation or in situ observation. For hydrological analysis historical precipitation and temperature data is essential. Usually these data can be obtained from Indian Meterolgocal Department (भारतीय मौसम विज्ञान विभाग:IMD) free of cost....

What is PostGIS and why PostGIS is benificial over tredtional analysis approaches. PostGIS is a spatial database extension for PostgreSQL that allows users to store and manipulate geospatial data. PostGIS has several advantages over the traditional approach of geospatial analysis, such as: PostGIS supports a wide range of spatial data types, functions, and operators, enabling complex spatial queries and operations. PostGIS integrates well with other GIS tools and frameworks, such as QGIS, GeoServer, and Leaflet, allowing users to visualize and analyze their data in different ways....

Getting started with gdal GDAL (Geospatial Data Abstraction Library) is a free and open source translator library for raster and vector geospatial data formats. It also comes with a variety of useful command line utilities for data translation and processing. It is used by many GIS software i.e. QGIS, ArcGIS, and GRASS GIS and R. As a core R use, working in climate change and hydrology, I need to process spatia-tempral data daily on daily basis....

HTML slides with Xaringan. Xaringan is an R package for creating slideshows with remark.js through R Markdown. …from https://github.com/yihui/xaringan The package name xaringan comes from Sharingan, a dōjutsu in Naruto with two abilities: the “Eye of Insight” and the “Eye of Hypnotism”. A presentation ninja should have these basic abilities, and I think remark.js may help you acquire these abilities, even if you are not a member of the Uchiha clan....

The main aim of this blog to show, how you can configure R, Python, and SQL in a single R-markdown file. Most of time we have to use data from databases and python code along with R functions, and having a setup that bring goodness of all the tool in one place comes really handy. Setup Python in Rstudio To set up R Python And SQL in the Rstudio you have to first install miniconda....

Course I teach for graduate student and Ph.D. R For Hydrology and Water Resources tools available for Hydrology TauDEM QGIS Grass GIS R (Geo-spatial analysis) Land use/ Land cover classification SWAT basic hydrologic concept and methods guidelines for the stream network analysis Computational Hydrology Hydrology is the study of water across the earth system. I will tell you some interesting phenomenon of hydrology in this blog post(s). The main goal of writing post is to summarize the knowledge of Water resource and Hydrology....

The following packages are required for the random forest if(!require(tidyverse)){install.packages("tidyverse");library(tidyverse)} if(!require(janitor)){install.packages("janitor");library(janitor)} # for rename if(!require(randomForest)){install.packages("randomForest");library(randomForest)} if(!require(caret)){install.packages("caret");library(caret)} # for `confustionMatrix` A Random forest is made of Random Trees Data <- read_csv(file = here::here("content/post/2022-06-26-random-forest", "german_credit.csv")) Exploring the dataset Data <- clean_names(Data) Data$creditability <- as.factor(Data$creditability) glimpse(Data) ## Rows: 1,000 ## Columns: 21 ## $ creditability <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … ## $ account_balance <dbl> 1, 1, 2, 1, 1, 1, 1, 1, 4, 2, 1, 1, … ## $ duration_of_credit_month <dbl> 18, 9, 12, 12, 12, 10, 8, 6, 18, 24,… ## $ payment_status_of_previous_credit <dbl> 4, 4, 2, 4, 4, 4, 4, 4, 4, 2, 4, 4, … ## $ purpose <dbl> 2, 0, 9, 0, 0, 0, 0, 0, 3, 3, 0, 1, … ## $ credit_amount <dbl> 1049, 2799, 841, 2122, 2171, 2241, 3… ## $ value_savings_stocks <dbl> 1, 1, 2, 1, 1, 1, 1, 1, 1, 3, 1, 2, … ## $ length_of_current_employment <dbl> 2, 3, 4, 3, 3, 2, 4, 2, 1, 1, 3, 4, … ## $ instalment_per_cent <dbl> 4, 2, 2, 3, 4, 1, 1, 2, 4, 1, 2, 1, … ## $ sex_marital_status <dbl> 2, 3, 2, 3, 3, 3, 3, 3, 2, 2, 3, 4, … ## $ guarantors <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … ## $ duration_in_current_address <dbl> 4, 2, 4, 2, 4, 3, 4, 4, 4, 4, 2, 4, … ## $ most_valuable_available_asset <dbl> 2, 1, 1, 1, 2, 1, 1, 1, 3, 4, 1, 3, … ## $ age_years <dbl> 21, 36, 23, 39, 38, 48, 39, 40, 65, … ## $ concurrent_credits <dbl> 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, … ## $ type_of_apartment <dbl> 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, … ## $ no_of_credits_at_this_bank <dbl> 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, … ## $ occupation <dbl> 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 3, 3, … ## $ no_of_dependents <dbl> 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, … ## $ telephone <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … ## $ foreign_worker <dbl> 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, … Assess the creditabiliy with the help of other variables # code ------------------------------------------------------------------------- ggplot(data = Data, aes(x = age_years, color = creditability, fill = creditability)) + geom_histogram(binwidth = 5, position = "identity", alpha = 0....