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TabPy Tutorial For Beginners | TabPy Training | Tableau Training

This Edureka tutorial on "TabPy Tutorial For Beginners " is to help you utilize donut charts as a tool, not only for engagement but also comprehension efficiency.

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Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2

#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials

navin prakash

1608789990

The pros of Tableau Software

Although all other tableau features are overshadowed by the ultimate quality visualization of interactive data, the list of the benefits that the software brings to companies is very wide.
Remarkable Visualization Capabilities:
Yes, the unparalleled abilities to visualize data are on the leading of the list of Tableau software advantages. The quality visualization details of the application are primarily what opponents of Tableau software present. Also, standard business intellect vendors’ products, such as Oracle Data Visualization data rendition functions, do not compete with the quality of illustration and model supplied by Tableau.
It transforms unbuilt statistical data into an entire logical result, that is completely optional, interactive and adorable dashboards. They are available in sorts of graphics and are comfortable to utilize in industry affairs.
Ease of Utilize:
The intuitive way Tableau generates graphics and a user-friendly interface enables non-dev users to perform the basic application features to the fullest. In a drag-and-drop way, clients organize raw data into catchy diagrams, which enables the analysis of information and removes the need for the support of an IT team for pattern creation.
With no in-depth training, lay customers can enjoy the capabilities provided by Tableau for stats parsing, such as dashboard creation, and so on. To get into the abilities of the solution, however, a detailed understanding is also a must. Often, if a business wants to extend the functionality of the solution, the close involvement of IT experts is a requirement.
High Performance:
Users rate their overall performance as robust and reliable, in addition to its high visualization capabilities. On even big data, the software still works rapidly, making its strong success an important point in the collection of Tableau advantages.
Multiple Data Source Connections:
The program supports the establishment of connexions with several sources of data, such as HADOOP, SAP and DB Technologies, that increases the performance of data analytics and allows a cohesive, insightful interface to be created.
Thriving Community and Forum:
There is a steady increase in the number of Tableau users who invest their knowledge and abilities in the group. Enterprise customers will strengthen their understanding of data analysis and reporting and get a lot of valuable insight into this community. Forum visitors are also ready to assist in addressing any client concerns and to share their experience.
Mobile-Friend liness:
And there is an effective phone device available for IOS and Android, the last in our collection of core Tableau advantages. This provides Tableau clients with versatility, helps them to keep statistics at their fingertips, and enables the full functionality of the desktop and web models.
Do you desire to learn Tableau Training in Chennai with placement? FITA is the right place to study Tableau Course in Chennai with good knowledge, Tableau Certification will help to get the best career in this domain. We are also having branch Tableau Training in Bangalore.

#tableau training in chennai #tableau course in chennai #tableau course #tableau training #training #course

Willie Beier

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Tutorial: Getting Started with R and RStudio

In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.

If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.

1. Install R
2. Install RStudio
3. First Look at RStudio
4. The Console
5. The Global Environment
6. Install the [tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb26184b) Packages
7. Load the [tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb264c2b) Packages into Memory
8. Identify Loaded Packages
9. Get Help on a Package
10. Get Help on a Function
11. RStudio Projects
12. Save Your “Real” Work. Delete the Rest.
13. R Scripts
14. Run Code
15. Access Built-in Datasets
16. Style
17. Reproducible Reports with R Markdown
18. Use RStudio Cloud
19. Get Your Hands Dirty!
Additional Resources
Bonus: Cheatsheets

#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials

Rowland Gutkowski

1596513720

Tutorial: Loading and Cleaning Data with R and the tidyverse

1. Characteristics of Clean Data and Messy Data

What exactly is clean data? Clean data is accurate, complete, and in a format that is ready to analyze. Characteristics of clean data include data that are:

Free of duplicate rows/values
Error-free (e.g. free of misspellings)
Relevant (e.g. free of special characters)
The appropriate data type for analysis
Free of outliers (or only contain outliers have been identified/understood), and
Follows a “tidy data” structure

Common symptoms of messy data include data that contain:

Special characters (e.g. commas in numeric values)
Numeric values stored as text/character data types
Duplicate rows
Misspellings
Inaccuracies
White space
Missing data
Zeros instead of null values

2. Motivation

In this blog post, we will work with five property-sales datasets that are publicly available on the New York City Department of Finance Rolling Sales Data website. We encourage you to download the datasets and follow along! Each file contains one year of real estate sales data for one of New York City’s five boroughs. We will work with the following Microsoft Excel files:

rollingsales_bronx.xls
rollingsales_brooklyn.xls
rollingsales_manhattan.xls
rollingsales_queens.xls
rollingsales_statenisland.xls

As we work through this blog post, imagine that you are helping a friend launch their home-inspection business in New York City. You offer to help them by analyzing the data to better understand the real-estate market. But you realize that before you can analyze the data in R, you will need to diagnose and clean it first. And before you can diagnose the data, you will need to load it into R!

3. Load Data into R with readxl

Benefits of using tidyverse tools are often evident in the data-loading process. In many cases, the tidyverse package readxl will clean some data for you as Microsoft Excel data is loaded into R. If you are working with CSV data, the tidyverse readr package function read_csv() is the function to use (we’ll cover that later).

Let’s look at an example. Here’s how the Excel file for the Brooklyn borough looks:

The Brooklyn Excel file

Now let’s load the Brooklyn dataset into R from an Excel file. We’ll use the readxlpackage. We specify the function argument skip = 4 because the row that we want to use as the header (i.e. column names) is actually row 5. We can ignore the first four rows entirely and load the data into R beginning at row 5. Here’s the code:

library(readxl) # Load Excel files
brooklyn <- read_excel("rollingsales_brooklyn.xls", skip = 4)

Note we saved this dataset with the variable name brooklyn for future use.

4. View the Data with tidyr::glimpse()

The tidyverse offers a user-friendly way to view this data with the glimpse() function that is part of the tibble package. To use this package, we will need to load it for use in our current session. But rather than loading this package alone, we can load many of the tidyverse packages at one time. If you do not have the tidyverse collection of packages, install it on your machine using the following command in your R or R Studio session:

install.packages("tidyverse")

Once the package is installed, load it to memory:

library(tidyverse)

Now that tidyverse is loaded into memory, take a “glimpse” of the Brooklyn dataset:

glimpse(brooklyn)
## Observations: 20,185
## Variables: 21
## $ BOROUGH <chr> "3", "3", "3", "3", "3", "3", "…
## $ NEIGHBORHOOD <chr> "BATH BEACH", "BATH BEACH", "BA…
## $ `BUILDING CLASS CATEGORY` <chr> "01 ONE FAMILY DWELLINGS", "01 …
## $ `TAX CLASS AT PRESENT` <chr> "1", "1", "1", "1", "1", "1", "…
## $ BLOCK <dbl> 6359, 6360, 6364, 6367, 6371, 6…
## $ LOT <dbl> 70, 48, 74, 24, 19, 32, 65, 20,…
## $ `EASE-MENT` <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `BUILDING CLASS AT PRESENT` <chr> "S1", "A5", "A5", "A9", "A9", "…
## $ ADDRESS <chr> "8684 15TH AVENUE", "14 BAY 10T…
## $ `APARTMENT NUMBER` <chr> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `ZIP CODE` <dbl> 11228, 11228, 11214, 11214, 112…
## $ `RESIDENTIAL UNITS` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ `COMMERCIAL UNITS` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `TOTAL UNITS` <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ `LAND SQUARE FEET` <dbl> 1933, 2513, 2492, 1571, 2320, 3…
## $ `GROSS SQUARE FEET` <dbl> 4080, 1428, 972, 1456, 1566, 22…
## $ `YEAR BUILT` <dbl> 1930, 1930, 1950, 1935, 1930, 1…
## $ `TAX CLASS AT TIME OF SALE` <chr> "1", "1", "1", "1", "1", "1", "…
## $ `BUILDING CLASS AT TIME OF SALE` <chr> "S1", "A5", "A5", "A9", "A9", "…
## $ `SALE PRICE` <dbl> 1300000, 849000, 0, 830000, 0, …
## $ `SALE DATE` <dttm> 2020-04-28, 2020-03-18, 2019-0…

The glimpse() function provides a user-friendly way to view the column names and data types for all columns, or variables, in the data frame. With this function, we are also able to view the first few observations in the data frame. This data frame has 20,185 observations, or property sales records. And there are 21 variables, or columns.

#data science tutorials #beginner #r #r tutorial #r tutorials #rstats #tidyverse #tutorial #tutorials

Edureka Fan

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