# Proso Millet

It’s been long, I wanted to write something on the crop I am working on. It is Proso millet. Like all other grass family members, it is from Poaceae family. Genus is Panicum. Scientific name is Panicum miliaceum. It is is mostly cultivated in USA, China, India, Niger, Nepal, Africa, Russia, Ukraine, Belarus, Middle East, … Continue reading Proso Millet

# Odds Ratio

What is odd ratio ? Odds ratio (OR) is statistical quantifier which measures the association between an exposure and an outcome. OR represents the odds of an outcome in present of particular exposure with comparison to odds in the absence of that exposure. In a simple definition you can how likely or probability of occurrence … Continue reading Odds Ratio

# Linear Model

Linear models describes a continuous variable (response/ dependent variable ~y) as a function of one or more predictor or explanatory variables (independent ~x). Linear regression is the statistical method used to create a linear model. It can help you to understand and predict the behavior of a complex system based on the predictor functions. A … Continue reading Linear Model

# Importing Data into R/ R-studio

Importing data is the basic step before starting the analysis, but it can be frustrating sometimes. So, lets see, how we can import our data into R- environment. 1. In R –studio, you can go to environment on the right side of the panel and click on the import dataset and then you can choose … Continue reading Importing Data into R/ R-studio

# Day 2 of 100 days of Code: Bar plot 2: legend and labels in ggplot

#Day 2 of 100days of code #Lets improve the bar plots and look into the other features we can use #I will use the irish database for the plots data("iris") View(iris) head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa … Continue reading Day 2 of 100 days of Code: Bar plot 2: legend and labels in ggplot

# Day 1 of 100days of code: Bar plot

#Blog post Feb 11,2019 #Day 1 of 100 ; 100 days code challenge #I will try to plot some bar plots using dplyr and ggplot package and gapminder dataset #installing the gapminder package for the gapminder dataset install.packages("gapminder") #loading the library and data library(ggplot2) #as I already have installed the packageslibrary(dplyr)library(gapminder)data("gapminder") #to view the headings … Continue reading Day 1 of 100days of code: Bar plot

# Mountain Kanchenjunga and Kanchenjunga Demon

The white clips of Mountain Kanchenjunga (Photo Copyright@SauravDas) Kanchenjunga, the third highest mountain of the world, lying in between Nepal and Sikkim (India). The clips are surrounded with mythical stories. The valley of Kanchenjunga once said to be home to a mountain deity, called Dzo-nga (Kanchenjunga Demon). He is a yeti or big footed snowman. … Continue reading Mountain Kanchenjunga and Kanchenjunga Demon

# Combining Data Frames with join function from”dplyr”

In real life, the data frames you want to work on often comes as separate files and sometime you want to combine them to do your analysis, but often we face lots of trouble in merging the different data sets, lets see how we can use the "dplyr' package to merge the data sets in … Continue reading Combining Data Frames with join function from”dplyr”

# Understanding your data – part 1

To understand the data structure and type, summary statistics and visualization of the distribution of data is much important For basic, A data can be of two types, "Categorical/Qualitative" or "Numerical/Quantative" Categorical can be of two types - a. Ordinal or b. Non-ordinal Numerical data can be of two types - a. Discrete or b. … Continue reading Understanding your data – part 1