How to create a dotplot using lattice package in R?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

How to create a dotplot using lattice package in R?

How to create a dotplot using lattice package in R?

This recipe helps you create a dotplot using lattice package in R

Recipe Objective

How to create a dotplot using lattice package in R? A dot plot or dot chart is a graphical representation of data points on the graph as circles (dots). A dot plot is similar to a bar plot with the exception of plotting dots instead of bars. Dot plots are used for quantitative/ categorical type of data. Lattice is a data visualization and graphics package in R. This recipe demonstrates an example of dot plots.

Step 1 - Install necessary package and library

install.packages("lattice") library(lattice)

Step 2 - Create a dataframe

data <- data.frame(values = c(10,20,30,40,50,60,70,80,90,100), count = c(5,5,5,5,6,6,6,7,7,7)) print(data)
 "Input data is : " 
   values count
1      10     5
2      20     5
3      30     5
4      40     5
5      50     6
6      60     6
7      70     6
8      80     7
9      90     7
10    100     7

Step 3 - Plot a dot plot

Syntax - dotplot(y ~ x , data , main , xlab, ylab) where, x , y - input variables data - input dataframe main -title of the plot xlab - title of the x axis ylab - title of the y axis

dotplot(values ~ count , data = data,main = "dot plot", xlab="x_data", ylab="y_data")
 " Output of the code is :"

Relevant Projects

Forecasting Business KPI's with Tensorflow and Python
In this machine learning project, you will use the video clip of an IPL match played between CSK and RCB to forecast key performance indicators like the number of appearances of a brand logo, the frames, and the shortest and longest area percentage in the video.

Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Build a Face Recognition System in Python using FaceNet
In this deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.

Avocado Machine Learning Project Python for Price Prediction
In this ML Project, you will use the Avocado dataset to build a machine learning model to predict the average price of avocado which is continuous in nature based on region and varieties of avocado.

Abstractive Text Summarization using Transformers-BART Model
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.

RASA NLU chatbot creation
The project will use rasa NLU for the Intent classifier, spacy for entity tagging, and mongo dB as the DB. The project will incorporate slot filling and context management and will be supporting the following intent and entities. Intents : product_info | ask_price|cancel_order Entities : product_name|location|order id The project will demonstrate how to generate data on the fly, annotate using framework and how to process those for different pieces of training as discussed above .

Census Income Data Set Project - Predict Adult Census Income
Use the Adult Income dataset to predict whether income exceeds 50K yr based on census data.

Churn Prediction in Telecom using Machine Learning in R
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.

Build a Collaborative Filtering Recommender System in Python
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.