Data Visualization

Easy ggplot2 Theme customization with {ggeasy}

In this post, We’ll learn about {ggeasy} an R package by Jonathan Carroll. The goal of {ggeasy} is to help R programmers make ggplot2 theme customizations with simple-easy English functions. (much easier than playing with theme()) We use dataset generated by {fakir} for this tutorial. Youtube: https://youtu.be/iAH1GJoBZmI Video Tutorial Code library(fakir) library(tidyverse) library(ggeasy) # generate dataset clients <- fakir::fake_ticket_client(100) # rotate x axis labels clients %>% count(state) %>% ggplot() + geom_col(aes(state,n)) + easy_rotate_x_labels() # color the text and increase text size clients %>% count(state) %>% ggplot() + geom_col(aes(n,state), fill = "orange") + easy_text_color("orange") + easy_text_size(25, teach = TRUE) # move legend position clients %>% count(state, source_call) %>%# View() ggplot() + geom_col(aes(n,state, fill = source_call)) + #easy_move_legend("bottom", teach = TRUE) theme(legend.

How to create Bar Race Animation Charts in R

Bar Race Animation Charts have started going Viral on Social Media leaving a lot of Data Enthusiasts wondering how are these Bar Race Animation Charts made. The objective of this post is to explain how to build such Bar Race Animation Charts using R — R with the power of versatile packages. Packages The packages that are required to build animated plots in R are: ggplot2 gganimate While those above two are the essential packages, We have also used the entire tidyverse, janitor and scales in this project for Data Manipulation, Cleaning and Formatting.

Hindi and Other Languages in India based on 2001 census

India is the world’s largest Democracy and as it goes, also a highly diverse place. This is my attempt to see how “Hindi” and other languages are spoken in India. In this post, we’ll see how to collect data for this relevant puzzle - directly from Wikipedia and How we’re going to visualize it - highlighting the insight. Data Wikipedia is a great source for data like this - Languages spoken in India and also because Wikipedia lists these tables as html <table> it becomes quite easier for us to use rvest::html_table() to extract the table as dataframe without much hassle.

One-line Code using viridis for How to change the color scale in ggplot plots

This is a small code snippet to explain how to change the color scale of a ggplot. Continuous Scale Package: viridis Function: scale_fill_viridis_c() (since it’s a continuous scaled value) library(dplyr) library(ggplot2) library(viridis) mtcars %>% tibble::rownames_to_column('Car') %>% tidyr::separate('Car',c('Brand','Model'), remove = F) %>% group_by(Brand) %>% summarize(avg_mpg = mean(mpg)) %>% ggplot() + geom_bar(aes(reorder(Brand,avg_mpg),avg_mpg, fill = avg_mpg), stat = 'identity') + scale_fill_viridis_c() + theme_minimal() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs( title = 'How to arrange Ggplot Bar plot', x = 'mpg') Discrete Scale Package: viridis Function: scale_fill_viridis_d() (since it’s a discrete scaled value)

How to arrange ggplot (barplot) bars in ascending or descending order

One of the reasons you’d see a bar plot made with ggplot2 with no ascending / descending order - ordering / arranged is because, By default, ggplot arranges bars in a bar plot alphabetically. But most of the times, it would make more sense to arrange it based on the y-axis it represents (rather than alphabetically). It could be your month-wise time series or high-medium-low bars - these are some examples where an alphabetically-sorted bar chart wouldn’t make sense in fact would hinder the process of data communication.

How to Automate EDA with DataExplorer in R

EDA (Exploratory Data Analysis) is one of the key steps in any Data Science Project. The better the EDA is the better the Feature Engineering could be done. From Modelling to Communication, EDA has got much more hidden benefits that aren’t often emphasised while beginners start while teaching Data Science for beginners. The Problem That said, EDA is also one of the areas of the Data Science Pipeline where a lot of manual code is written for different types of plots and different types for inference.

How to make Square (Pie) Charts for Infographics in R

Are you looking for some unique way of visualizing your numbers instead of simply using bar charts - which sometimes could be boring the audience - if used, slide after slide? Here’s Square Pie / Waffle Chart for you. Waffle Chart or as it goes technically, Square Pie Chart is just is just a pie chart that use squares instead of circles to represent percentages. So, it’s good to keep in mind that this is applicable better for Percentages.

How to create unigrams, bigrams and n-grams of App Reviews

This is one of the frequent questions I’ve heard from the first timer NLP / Text Analytics - programmers (or as the world likes it to be called “Data Scientists”). Prerequisite For simplicity, this post assumes that you already know how to install a package and so you’ve got tidytext installed on your R machine. install.packages("tidytext") Loading the Library Let’s start with loading the tidytext library. library(tidytext) Extracting App Reviews We’ll use the R-package itunesr for downloading iOS App Reviews on which we’ll perform Simple Text Analysis (unigrams, bigrams, n-grams).

Interactive Visualization in R with apexcharter

Interactive Visualizations are powerful these days because those are all made for web. Web - simply a combination of html,css and javascript which build interactive visualizations. Thus, paving way for a lot of javascript charting libraries like highcharts.js, apexcharts.js. Thanks to htmlwidgets of R, many R developers have started porting those javascript charting libraries to R and dreamRs is one of such leading Developer groups working on the intersection R + Web.