Beth  Nabimanya

Beth Nabimanya

1626470520

Localizing Your Chrome Extension: An Easy Tutorial

Localization and Internationalization is a great way to get more users for your extension. Here is a simple tutorial that will help you do that.

This tutorial assumes you already know how to create a chrome extension. If not, you can head to my other tutorial to learn how to do that.

  • Step 1: Changing Manifest.json
  • Step 2: Translation Strings
  • Step 3: Use The Localized Strings
  • Step 4 (optional): Changing the CSS Direction Based on The Locale

#javascript #css #chrome

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Localizing Your Chrome Extension: An Easy Tutorial

Localization - Laravel Localization Example

In this example i will show you localization - laravel localization example.

Laravel’s localization features provide a convenient way to retrieve text in different languages, allowing you to easily support multiple languages within your application. So here i will show you how to create localization or laravel dynamic language.

Read More : Localization - Laravel Localization Example

https://websolutionstuff.com/post/localization-laravel-localization-example


Read Also : How To Integrate Paypal Payment Gateway In Laravel

https://websolutionstuff.com/post/how-to-integrate-paypal-payment-gateway-in-laravel

#localization - laravel localization example #localization tutorial #localization #laravel multi languag #laravel documentation #laravel localization

Tamia  Walter

Tamia Walter

1593255780

Localization in Android - Step by Step Implementation

Localization in Android

The applications that we use in Android devices can be specific to a particular language. That is where Localization comes in the role. Localization is a process that changes the string into various different languages based on user requirements. In this tutorial, we will implement it practically in our application.

Localization can be done for Dates and time as well. Localizing the string is good for the users, but, how about localizing the date and the time? Yes, it can be done too. Luckily, Android SDK includes the classes that format dates and times according to the locale. In Android SDK these date and time are handled using Date class from java.util namespace. To return the current date and time, you can use java.util.Calendar.

You can code it this way to return the Date and the Time:

Date date= Calendar.getInstance().getTime(); // this will get the date and the time
java.text.DateFormat date_format; //This will get the standard format
Date_format = android.text.format.DateFormat.getDateFormat(this);

And for Time:

java.util.Date date_today = Calendar.getInstance().getTime();
java.text.DateFormat time_format;
time_format = android.text.format.DateFormat.getTimeFormat(this);

Language Codes and Folder names:

To implement it in our application we need to first create separate ‘values’ files in the resource folder specifying the language code.

Few of the common language codes that are used in Android are mentioned below:

LanguageCodeFolder NameArabicarvalues-arBengalibnvalues-bnBulgarianbgvalues-bgChinesezhvalues-zhFrenchfrvalues-frGermandevalues-deJapanesejavalues-jaTibetanbovalues-boHindihivalues-hiTelugutevalues-tePunjabipavalues-pa

The above mentioned code and folder names would be mentioned in the strings.xml file, where the string code would be mentioned as:

  • Say for Hindi:
  • res/values-hi / Strings.xml –
<?xml version="1.0" encoding="utf-8"?>
<resources>
   <string name="app_name">स्थानीयकरण उदाहरण</string>
   <string name="hello">नमस्ते दुनिया</string>
   <string name="DataFlair">डाटा फ्लेयर</string>
   <string name="Akshita">अक्षिता ने आपको टेक्स्ट किया</string>
</resources>
  • Say for Japanese:

res/values-ja / Strings.xml –

<resources>
   <string name="DataFlair">データフレア</string>
   <string name="hello">" こんにちは世界"</string>
   <string name="Akshita">明下はあなたにテキストメッセージを送りました</string>
   <string name="app_name" translatable="false">" 私のAndroidローカリゼーション"</string>
</resources>

#android tutorials #android localization #android localization example #android localization tutorial #localization in android

Willie  Beier

Willie Beier

1596728880

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.

Table of Contents

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

Jeromy  Lowe

Jeromy Lowe

1599097440

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

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