Month: February 2020

Examples, JavaScript, Node.js, Node.js Example

Get Latest CSV file from Folder using Node.js

by Dheeraj Kumar February 21, 2020 No Comments

getLatestFile.js



/***
 * Read lestest file property 
 * 
 */

const fs = require('fs')
async function getLatestFile() {
    const dirPath = '/home/dheeraj/Documents/BlogPost/Downloads';
    let dirCont = fs.readdirSync(dirPath);
    let files = dirCont.filter(function (elm) { return elm.match(/.*\.(csv)/ig); });
    for (let fileP = 0; fileP < files.length; fileP++) {
        let filsStats = fs.statSync(dirPath + "/" + files[fileP]);
        if (new Date(filsStats['birthtime']).getDate() === new Date().getDate() &&
            new Date(filsStats['birthtime']).getMonth() === new Date().getMonth() &&
            new Date(filsStats['birthtime']).getFullYear() === new Date().getFullYear() &&
            new Date(filsStats['birthtime']).getHours() === new Date().getHours() //&& 
        ) {
            return {
                fileName: files[fileP],
                fileLocation: dirPath + "/" + files[fileP]
            };
        }
    }
    return {
        fileName: 'orderFailed.png',
        fileLocation: dirPath + "/" + 'orderFailed.png'
    };
}

Generate test case
getLatestFile.test.js


const {getLatestFile} = require('./getLatestFile')

async function getLatest (){
    let latest = await getLatestFile();
     console.log(latest);
}

getLatest();

OutPut:

Download Code From git Repo

Data Analyst, Data Science, Data Visualization, Examples, R Language

Data visualization Examples in R

by Dheeraj Kumar February 20, 2020 No Comments

In this post, We will explore more examples of Data Visualization using R. For that purpose we are using mtcars as dataset
here is a list of all the features of the observations in mtcars:

  • mpg — Miles/(US) gallon
  • cyl — Number of cylinders
  • disp — Displacement (cu.in.)
  • hp — Gross horsepower
  • drat — Rear axle ratio
  • wt — Weight (lb/1000)
  • qsec — 1/4 mile time
  • vs — V/S engine.
  • am — Transmission (0 = automatic, 1 = manual)
  • gear — Number of forward gears
  • carb — Number of carburetors

Example 1: Plot graph on X and Y axis


# include ggplot2 library
library(ggplot2)

# 1 - Map mpg to x and cyl to y
ggplot(mtcars, aes(x=mpg, y=cyl)) +
  geom_point()

# 2 - Reverse: Map cyl to x and mpg to y
ggplot(mtcars, aes(x=cyl, y=mpg)) +
  geom_point()

OutPut:

Example 2: Change the color, shape, and size of the points


# include ggplot2 library
library(ggplot2)

#chnage color,shape and Size
ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) +
  geom_point(shape=1, size=4)

OutPut:

Example 3: Add alpha and fill


# include ggplot2 library
library(ggplot2)
# Expand to draw points with alpha 0.5 and fill cyl
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) +geom_point(alpha=0.5)

OutPut:

Exercise 4: Change Shape and color


library(ggplot2)
# Change shape and color
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) +geom_point(shape=24,col="yellow")

OutPut:

Exercise 5: Change shape and Size


# include ggplot2 library
library(ggplot2)
# Define a hexadecimal color
change_color <- "#4ABEFF"
# Set the fill aesthetic; color, size and shape attributes
ggplot(mtcars,aes(x=wt,y=mpg,fill=cyl))+ geom_point(size=10,shape=23,col=change_color)

OutPut:

Data Analyst, Data Science, R Language

Explore row data using R

by Dheeraj Kumar February 14, 2020 No Comments

Understanding the structure of your Data

View dimensional

Syntax:


dim()

Example:


# dimensional of mtcars
dim(mtcars)

OutPut:

Looking your DataView dimensional

head()

  • view top of the dataset

Note: By default, it fetches 6 rows but we can also vary a number of rows.
Syntax:


head()

Example:


#head of mtcars
head(mtcars)
# we can vary number of rows
head(mtcars,n=8)

OutPut:

tail()

  • view bottom of the dataset

Example:


#Syntax:tail()
#tail of mtcars
tail(mtcars)

# we can vary number of rows
tail(mtcars,n=8)

Output:
Visualizing your data

hist()

  • view histogram of a single variable

Example:


Syntax:hist()
#histogram
hist(mtcars$mpg)

OutPut:

plot()

  • view plot of two variables

Example:


Synatx: plot()
#plot
plot(mtcars$mpg,mtcars$qsec)

OutPut:

Gather

  • Gather columns into key-value pairs

Syntax:




gather (data, key, value, ...)
/**
 *
data: a data frame
key: bare name of the new key column
value: bare name of the new value column
*/

Spread

  • Opposite of Gather
  • Spread key-value pairs into columns
  • Takes key-value pairs and spread them into multiple columns

Syntax:


spread(data, key, value)
/**
 *
 data: a data frame
 key: bare name of the column containing keys
 value: bare name of the column containing values
*/

Separating columns

  • The separate() function allows you to separate one column into multiple columns.
  • In the case of separate() function, we can also specify sep as an argument for specifying separator.

Syntax:


seperate(data, column_set, c("column1", "column2"))

Uniting column

  • Opposite of separate is unite

Syntax:


unite(data, column-set, c("column1", "column2"))

Note: we can also specify separator between these two columns

Data Visualization, R Language

Introduction to Data Visualization in R

by Dheeraj Kumar February 8, 2020 No Comments

Data Visualization is an essential component of your skillset as a Data Scientist or Data Analyst. Data Visualization is basically a form of Visual communication.

ggplot2 is a plotting package that helps us to create complex plots from data in data frame.

ggplot2 functions built step by step by adding new elements

Install ggplot2 package




# install ggplot2

install.packages(ggplot2)

Load ggplot2 package



# include ggplot2 library

library(ggplot2)

During this discussion, we are going to use mtcars package for the dataset.
Note:
The matcars dataset contains information about 32 cars from 1973 motor trends magazine. The dataset is small but contains a variety of continuous and categorical variables.

Before describing ggplot2 in more detail just have a look mtcars dataset using str() command.



#structure of matcarsbasically
str(mtcars);

OutPut:

Have a look ggplot2 example 

Example:



# include ggplot2 library
library(ggplot2)
ggplot(mtcars , aes(x=wt, y=mpg))+geom_point()

OutPut:

Some points regarding ggplot2ppp

  • VisualizationVisual elements in ggplot2 are called geoms (as in geometric objects bars, points …)
  • The appearance and location of these geoms (size, color) are controlled by aesthetic properties.basicallybasically
  • aesthetic properties are shown by aes()
  • Variable that you want to plot is represented by aes() as shown in the previous example.
Goem layer Description
geom_bar() Create a layer with bars representing different statistical properties.
geom_point() Create a layer with data points.
geom_line() Create a layer with a straight line.
geom_smooth() Create a layer with smoother.
geom_histogram() Create a layer with a histogram.
geom_blogplot() Create a layer with text in it.
geom_text() Create a layer with a text in it.
geom_error_bar() Create a layer with error bars in it.
geom_hline and geom_vline() Create a layer with a user-defined horizontal and vertical line respectively.

How to derive iris.tidy from iris?



library(tidyr)
#Convert iris to iris.tidy using tidy function
iris.tidy <- iris %>%
  gather(key, Value, -Species) %>%
  separate(key, c("Part", "Measure"), "\\.")

print(head(iris.tidy))

How to derive iris.wide from iris?


# Load the tidyr package
library(tidyr)
# Add column with unique ids (don't need to change)
iris$Flower <- 1:nrow(iris)
# Produce the iris.wide dataset
iris.wide <- iris %>%
  gather(key, value, -Species, -Flower) %>%
  separate(key, c("Part", "Measure"), "\\.") %>%
  spread(Measure, value)

OutPut: