Skip to content

Instantly share code, notes, and snippets.

Avatar
🏠
Working from home

Anirudh Jayaraman anirudhjayaraman

🏠
Working from home
View GitHub Profile
View source_patient_data_india.R
rm(list = ls())
# Load relevant libraries -----------------------------------------------------
library(stringr)
library(data.table)
# =============================================================================
# COVID 19-India API: A volunteer-driven, crowdsourced database
# for COVID-19 stats & patient tracing in India
# =============================================================================
@anirudhjayaraman
anirudhjayaraman / lm_linear_algebra.R
Created Nov 12, 2019
Linear regression implementation using linear algebra in R
View lm_linear_algebra.R
### Linear Regression Using lm() ----------------------------------------
data("swiss")
dat <- swiss
linear_model <- lm(Fertility ~ ., data = dat)
summary(linear_model)
# Call:
# lm(formula = Fertility ~ ., data = dat)
#
View remove_missing_levels.R
remove_missing_levels <- function(fit, test_data) {
library(magrittr)
# https://stackoverflow.com/a/39495480/4185785
# drop empty factor levels in test data
test_data %>%
droplevels() %>%
as.data.frame() -> test_data
View factor_new_levels.R
library(data.table)
train <- fread('train.csv'); test <- fread('test.csv')
# consolidate the 2 data sets after creating a variable indicating train / test
train$flag <- 0; test$flag <- 1
dat <- rbind(train,test)
# change outcome, var_b and var_e into factor var
dat$outcome <- factor(dat$outcome)
View AirPassengers.Rmd
---
title: "ARIMA Modeling in R"
output: html_document
---
Let's start off by loading relevant R libraries!
```{r include = FALSE}
library(tseries)
library(zoo)
library(forecast)
View rice_strucchange.R
library(xlsx)
library(forecast)
library(tseries)
library(strucchange)
## load the data from a CSV or Excel file. This example is done with an Excel sheet.
prod_df <- read.xlsx(file = 'agricultural_productivity.xls', sheetIndex = 'Sheet1', rowIndex = 8:65, colIndex = 2, header = FALSE)
colnames(prod_df) <- c('Rice')
## store rice data as time series objects
rice <- ts(prod_df$Rice, start=c(1951, 1), end=c(2008, 1), frequency=1)
View strucchange_usage.R
# assuming you have a 'ts' object in R
# 1. install package 'strucchange'
# 2. Then write down this code:
library(strucchange)
# store the breakdates
bp_ts <- breakpoints(ts ~ 1)
@anirudhjayaraman
anirudhjayaraman / experimentsWithData.ipynb
Last active Sep 1, 2016
Experiments With Data (Hackathon)
View experimentsWithData.ipynb
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
@anirudhjayaraman
anirudhjayaraman / graphUndirected_output.txt
Created Jul 28, 2016
Output file to graphUndirected.py
View graphUndirected_output.txt
{}
{}
["A:['B', 'C', 'E']", "C:['A', 'B', 'D', 'E']", "B:['A', 'C', 'D']", "E:['A', 'C']", "D:['B', 'C']"]
[[ 0. 1. 1. 0. 1.]
[ 1. 0. 1. 1. 0.]
[ 1. 1. 0. 1. 1.]
@anirudhjayaraman
anirudhjayaraman / graphUndirected.py
Last active Feb 16, 2021
Implementing Undirected Graphs in Python
View graphUndirected.py
class Vertex:
def __init__(self, vertex):
self.name = vertex
self.neighbors = []
def add_neighbor(self, neighbor):
if isinstance(neighbor, Vertex):
if neighbor.name not in self.neighbors:
self.neighbors.append(neighbor.name)
neighbor.neighbors.append(self.name)