#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

# Author: Saurav

## 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

## Error-Series: Just to Plot a Single Graph–How Much Errors you can make

Learning is fun; Sometime its a pain too; So I thought, I will post all the trial and errors done by me in a error series posts. Though, when you finally come up with a plot, It feels good. Once I was trying my best to plot a stack bar for microbiological data. and the… Continue reading Error-Series: Just to Plot a Single Graph–How Much Errors you can make

## 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

## Data Visualization with ggplot2 (#part 1)

ggplot is based on grammer of graphics (gg), which means you can draw every graph with few basic components like: a data seta set of geom or geometry - which represents the data pointsa coordinate system You can marge you data points with aes or aesthetic components to provide a beautiful graphical visualization. For this… Continue reading Data Visualization with ggplot2 (#part 1)

## Correlation Using ggcorrplot

#The hypothetical data frame, #Lets say, >A <- c(2, 4, 6, 8, 12, 5, 7,8) >B <- c(4,5,6,2,9,13,2, 6) >C <- c(3,5,7,2,4,8,3,6) >D <- c(4,6,2,5,7,4,6,9) DD <- data.frame(A,B,C, D) #correlation with package ggcorrplot - installing the package and importing the library install.packages("ggcorrplot")library("ggcorrplot") #doing the correlation test using pearson method and assigning the results to variable… Continue reading Correlation Using ggcorrplot