Bhargav-Rao

NO,SECTION,Best Practice,State,Year of Launch ,Sector,Agency
I,AADHAAR ENABLED SERVICES,Aadhaar Enabled Fertilizer Distribution System (AeFDS) – Krishna District,Andhra Pradesh,2014,Fertilizer,"District Administration, Government of Andhra Pradesh "
II,AADHAAR ENABLED SERVICES,Aadhaar Enabled Public Distribution System through ePOS Devices,Andhra Pradesh,2015,Food and Public Distribution System,"District Administration, Government of Andhra Pradesh "
I,AGRICULTURE AND ALLIED SECTORS ,Community-based Groundwater Management,Andhra Pradesh,2004,Agriculture,Bharathi Integrated Rural Development Society
II,AGRICULTURE AND ALLIED SECTORS ,Direct BenefitTransfer in Seed Subsidy,Uttar Pradesh,2015,Agriculture,"Agriculture Department, UP"
III,AGRICULTURE AND ALLIED SECTORS ,Integrating Farmers with APMC Mandis through E-Platform,Karnataka,2014,Agriculture,Rashtriya e-Market Services
IV,AGRICULTURE AND ALLIED SECTORS ,Micro-Irrigation Systems for Water and Cost Economy and HigherYield,Gujarat,2005,Agriculture (Irrigati

Bhargav-Rao

library(maptools)
library(plot3D)
ShapeFile <- readShapeSpatial('Test.shp')
Polygons <- slot(ShapeFile,"polygons")
coordinates <- list( Latitude  = numeric(0),
                     Longitude = numeric(0),
                     PolygonID = numeric(0) ) 
for(i in 1:length(Polygons)){
  Polygon <- Polygons[[i]]
  ID <- slot(Polygon, "ID")

Bhargav-Rao

What this room is for

This room is only for the weekly rep gaining events, discussion(s) about gaining rep. 
It is just a personal goal to motivate people to contribute more to the site. 

What this room is not for

This is not an invitation to vote on each other's posts. 
This is not a race to see who will beat others. 

Bhargav-Rao

 "1. INTRODUCTION. The field of Education Data mining has depended to a large extent on the model that was developed by Corbett and Anderson [2] and enhanced by a number of authors for predicting student performance. Over the years many new models have been built to improve upon the prediction accuracy of KT. Wang and Heffernan have also shown that better predictions are achieved with the inclusion of additional parameters relating to the skills and groups to which a student belongs. [4] Standard KT makes a number of assumptions, including the fact that the rate of learning is constant and that the transition from one knowledge state to the other is not dependent of previous performance. [2] Other researchers have introduced different models that seem to deal with this anomaly with the KT model [7], whiles some have compared these different models to determine which best predicts student performances considering prior performance. [6] Ohlsson theorized that humans in general are able to learn from their previ

Bhargav-Rao

library("nycflights13")
library("dplyr")
reduced_data <- select(flights, carrier, hour, month)
grps <- group_by(filter(reduced_data, hour>10 & month==4),carrier,add=TRUE)
summarize(grps,count=n())

Bhargav-Rao

library("nycflights13")
library("dplyr")
vect <- select(flights, distance, air_time)
avrg <- mutate(vect, average_speed = distance / air_time * 60)
sorted_avrg <- na.omit(avrg[order(avrg$average_speed, decreasing = TRUE),])

Bhargav-Rao

fname <- "Data.txt"
if (file.exists(fname))
{
    file.remove(fname)
}
for (i in 1:1000)
{
    toss <- sample(0:1,3,replace = TRUE)
    len <- length(toss[toss[]==1])
    write(len,file = fname,append = TRUE)