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 setosaContinue 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 headingsContinue reading “Day 1 of 100days of code: Bar plot”

Mountain Kanchenjunga and Kanchenjunga Demon

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. In, 1925. Tombazi, a Greek photographer who wasContinue 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 theContinue 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 inContinue 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”