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Extract Untranslated Strings from Laravel Projects

Localizator is a small tool for Laravel that gives you the ability to extract untranslated strings from project files. It works using the artisan command line and the provided localize command:

php artisan localize de,fr

#laravel #php #web-development #developer

What Is R Programming Language? introduction & Basics

In this R article, we will learn about What Is R Programming Language? introduction & Basics. R is a programming language developed by Ross Ihaka and Robert Gentleman in 1993. R possesses an extensive catalog of statistical and graphical methods. It includes machine learning algorithms, linear regression, time series, statistical inference to name a few. Most of the R libraries are written in R, but for heavy computational tasks, C, C++, and Fortran codes are preferred.

Data analysis with R is done in a series of steps; programming, transforming, discovering, modeling and communicating the results

Program: R is a clear and accessible programming tool
Transform: R is made up of a collection of libraries designed specifically for data science
Discover: Investigate the data, refine your hypothesis and analyze them
Model: R provides a wide array of tools to capture the right model for your data
Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world.

What is R used for?

Statistical inference
Data analysis
Machine learning algorithm

As conclusion, R is the world’s most widely used statistics programming language. It’s the 1st choice of data scientists and supported by a vibrant and talented community of contributors. R is taught in universities and deployed in mission-critical business applications.

R-environment setup

Windows Installation – We can download the Windows installer version of R from R-3.2.2 for windows (32/64)

As it is a Windows installer (.exe) with the name “R-version-win.exe”. You can just double click and run the installer accepting the default settings. If your Windows is a 32-bit version, it installs the 32-bit version. But if your windows are 64-bit, then it installs both the 32-bit and 64-bit versions.

After installation, you can locate the icon to run the program in a directory structure “R\R3.2.2\bin\i386\Rgui.exe” under the Windows Program Files. Clicking this icon brings up the R-GUI which is the R console to do R Programming.

R basic Syntax

R Programming is a very popular programming language that is broadly used in data analysis. The way in which we define its code is quite simple. The “Hello World!” is the basic program for all the languages, and now we will understand the syntax of R programming with the “Hello world” program. We can write our code either in the command prompt, or we can use an R script file.

R command prompt

Once you have R environment setup, then it’s easy to start your R command prompt by just typing the following command at your command prompt −
$R
This will launch R interpreter and you will get a prompt > where you can start typing your program as follows −

>myString <- "Hello, World"
>print (myString)
[1] "Hello, World!"

Here the first statement defines a string variable myString, where we assign a string “Hello, World!” and then the next statement print() is being used to print the value stored in myString variable.

R data-types

While doing programming in any programming language, you need to use various variables to store various information. Variables are nothing but reserved memory locations to store values. This means that when you create a variable you reserve some space in memory.

In contrast to other programming languages like C and java in R, the variables are not declared as some data type. The variables are assigned with R-Objects and the data type of the R-object becomes the data type of the variable. There are many types of R-objects. The frequently used ones are −

Vectors
Lists
Matrices
Arrays
Factors
Data Frames

Vectors

#create a vector and find the elements which are >5
v<-c(1,2,3,4,5,6,5,8)
v[v>5]

#subset
subset(v,v>5)

#position in the vector created in which square of the numbers of v is >10 holds good
which(v*v>10)

#to know the values 
v[v*v>10]

Output: [1] 6 8 Output: [1] 6 8 Output: [1] 4 5 6 7 8 Output: [1] 4 5 6 5 8

Matrices

A matrix is a two-dimensional rectangular data set. It can be created using a vector input to the matrix function.

#matrices: a vector with two dimensional attributes
mat<-matrix(c(1,2,3,4))
 
mat1<-matrix(c(1,2,3,4),nrow=2)
mat1

Output: [,1] [,2] [1,] 1 3 [2,] 2 4

mat2<-matrix(c(1,2,3,4),ncol=2,byrow=T)
mat2

Output: [,1] [,2] [1,] 1 2 [2,] 3 4

mat3<-matrix(c(1,2,3,4),byrow=T)
mat3

#transpose of matrix
mattrans<-t(mat)
mattrans

#create a character matrix called fruits with elements apple, orange, pear, grapes
fruits<-matrix(c("apple","orange","pear","grapes"),2)
#create 3×4 matrix of marks obtained in each quarterly exams for 4 different subjects 
X<-matrix(c(50,70,40,90,60, 80,50, 90,100, 50,30, 70),nrow=3)
X

#give row names and column names
rownames(X)<-paste(prefix="Test.",1:3)
subs<-c("Maths", "English", "Science", "History")
colnames(X)<-subs
X

Output: [,1] [1,] 1 [2,] 2 [3,] 3 [4,] 4 Output: [,1] [,2] [,3] [,4] [1,] 1 2 3 4 Output: [,1] [,2] [,3] [,4] [1,] 50 90 50 50 [2,] 70 60 90 30 [3,] 40 80 100 70 Output: Maths English Science History Test. 1 50 90 50 50 Test. 2 70 60 90 30 Test. 3 40 80 100 70

Arrays

While matrices are confined to two dimensions, arrays can be of any number of dimensions. The array function takes a dim attribute which creates the required number of dimensions. In the below example we create an array with two elements which are 3×3 matrices each.

#Arrays
arr<-array(1:24,dim=c(3,4,2))
arr

#create an array using alphabets with dimensions 3 rows, 2 columns and 3 arrays
arr1<-array(letters[1:18],dim=c(3,2,3))

#select only 1st two matrix of an array
arr1[,,c(1:2)]

#LIST
X<-list(u=2, n='abc')
X
X$u

 [,1] [,2] [,3] [,4]

 [,1] [,2] [,3] [,4]

 [,1] [,2]

 [,1] [,2]

Dataframes

Data frames are tabular data objects. Unlike a matrix in a data frame, each column can contain different modes of data. The first column can be numeric while the second column can be character and the third column can be logical. It is a list of vectors of equal length.

#Dataframes
students<-c("J","L","M","K","I","F","R","S")
Subjects<-rep(c("science","maths"),each=2)
marks<-c(55,70,66,85,88,90,56,78)
data<-data.frame(students,Subjects,marks)
#Accessing dataframes
data[[1]]

data$Subjects
data[,1]

Output: [1] J L M K I F R S Levels: F I J K L M R S Output: data$Subjects [1] science science maths maths science science maths maths Levels: maths science

Factors

Factors are the r-objects which are created using a vector. It stores the vector along with the distinct values of the elements in the vector as labels. The labels are always character irrespective of whether it is numeric or character or Boolean etc. in the input vector. They are useful in statistical modeling.

Factors are created using the factor() function. The nlevels function gives the count of levels.

#Factors
x<-c(1,2,3)
factor(x)

#apply function
data1<-data.frame(age=c(55,34,42,66,77),bmi=c(26,25,21,30,22))
d<-apply(data1,2,mean)
d

#create two vectors age and gender and find mean age with respect to gender
age<-c(33,34,55,54)
gender<-factor(c("m","f","m","f"))
tapply(age,gender,mean)

Output: [1] 1 2 3 Levels: 1 2 3 Output: age bmi 54.8 24.8 Output: f m 44 44

R Variables

A variable provides us with named storage that our programs can manipulate. A variable in R can store an atomic vector, a group of atomic vectors, or a combination of many R objects. A valid variable name consists of letters, numbers, and the dot or underlines characters.

Rules for writing Identifiers in R

Identifiers can be a combination of letters, digits, period (.), and underscore (_).
It must start with a letter or a period. If it starts with a period, it cannot be followed by a digit.
Reserved words in R cannot be used as identifiers.

Valid identifiers in R

total, sum, .fine.with.dot, this_is_acceptable, Number5

Invalid identifiers in R

tot@l, 5um, _fine, TRUE, .0ne

Best Practices

Earlier versions of R used underscore (_) as an assignment operator. So, the period (.) was used extensively in variable names having multiple words. Current versions of R support underscore as a valid identifier but it is good practice to use a period as word separators.
For example, a.variable.name is preferred over a_variable_name or alternatively we could use camel case as aVariableName.

Constants in R

Constants, as the name suggests, are entities whose value cannot be altered. Basic types of constant are numeric constants and character constants.

Numeric Constants

All numbers fall under this category. They can be of type integer, double or complex. It can be checked with the typeof() function.
Numeric Constants followed by L are regarded as integers and those followed by i are regarded as complex.

> typeof(5)
> typeof(5L)
> typeof(5L)

[1] “double” [1] “double” [[1] “double”

Character Constants

Character constants can be represented using either single quotes (‘) or double quotes (“) as delimiters.

> 'example'
> typeof("5")

[1] "example" [1] "character"

R Operators

Operators – Arithmetic, Relational, Logical, Assignment, and some of the Miscellaneous Operators that R programming language provides.

There are four main categories of Operators in the R programming language.

Arithmetic Operators
Relational Operators
Logical Operators
Assignment Operators
Mixed Operators

x <- 35
y<-10

x+y > x-y > x*y > x/y > x%/%y > x%%y > x^y [1] 45 [1] 25 [1] 350 [1] 3.5 [1] 3 [1] 5 [1]2.75e+15

Logical Operators

The below table shows the logical operators in R. Operators & and | perform element-wise operation producing result having a length of the longer operand. But && and || examines only the first element of the operands resulting in a single length logical vector.

a <- c(TRUE,TRUE,FALSE,0,6,7)
b <- c(FALSE,TRUE,FALSE,TRUE,TRUE,TRUE)
a&b 
[1] FALSE TRUE FALSE FALSE TRUE TRUE
a&&b
[1] FALSE
> a|b
[1] TRUE TRUE FALSE TRUE TRUE TRUE
> a||b
[1] TRUE
> !a
[1] FALSE FALSE TRUE TRUE FALSE FALSE
> !b
[1] TRUE FALSE TRUE FALSE FALSE FALSE

R functions

Functions are defined using the function() directive and are stored as R objects just like anything else. In particular, they are R objects of class “function”. Here’s a simple function that takes no arguments simply prints ‘Hi statistics’.

#define the function
f <- function() {
print("Hi statistics!!!")
}
#Call the function
f()

Output: [1] "Hi statistics!!!"

Now let’s define a function called standardize, and the function has a single argument x which is used in the body of a function.

#Define the function that will calculate standardized score.
standardize = function(x) {
m = mean(x)
sd = sd(x)
result = (x – m) / sd
result
}
input<- c(40:50) #Take input for what we want to calculate a standardized score.
standardize(input) #Call the function

Output: standardize(input) #Call the function [1] -1.5075567 -1.2060454 -0.9045340 -0.6030227 -0.3015113 0.0000000 0.3015113 0.6030227 0.9045340 1.2060454 1.5075567

Loop Functions

R has some very useful functions which implement looping in a compact form to make life easier. The very rich and powerful family of applied functions is made of intrinsically vectorized functions. These functions in R allow you to apply some function to a series of objects (eg. vectors, matrices, data frames, or files). They include:

lapply(): Loop over a list and evaluate a function on each element
sapply(): Same as lapply but try to simplify the result
apply(): Apply a function over the margins of an array
tapply(): Apply a function over subsets of a vector
mapply(): Multivariate version of lapply

There is another function called split() which is also useful, particularly in conjunction with lapply.

R Vectors

A vector is a sequence of data elements of the same basic type. Members in a vector are officially called components. Vectors are the most basic R data objects and there are six types of atomic vectors. They are logical, integer, double, complex, character, and raw.

The c() function can be used to create vectors of objects by concatenating things together. 
x <- c(1,2,3,4,5) #double
x #If you use only x auto-printing occurs
l <- c(TRUE, FALSE) #logical
l <- c(T, F) ## logical
c <- c("a", "b", "c", "d") ## character
i <- 1:20 ## integer
cm <- c(2+2i, 3+3i) ## complex
print(l)
print(c)
print(i)
print(cm)

You can see the type of each vector using typeof() function in R.
typeof(x)
typeof(l)
typeof(c)
typeof(i)
typeof(cm)

Output: print(l) [1] TRUE FALSE print(c) [1] "a" "b" "c" "d" print(i) [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 print(cm) [1] 2+2i 3+3i Output: typeof(x) [1] "double" typeof(l) [1] "logical" typeof(c) [1] "character" typeof(i) [1] "integer" typeof(cm) [1] "complex"

Creating a vector using seq() function:

We can use the seq() function to create a vector within an interval by specifying step size or specifying the length of the vector.

seq(1:10) #By default it will be incremented by 1
seq(1, 20, length.out=5) # specify length of the vector
seq(1, 20, by=2) # specify step size

Output: > seq(1:10) #By default it will be incremented by 1 [1] 1 2 3 4 5 6 7 8 9 10 > seq(1, 20, length.out=5) # specify length of the vector [1] 1.00 5.75 10.50 15.25 20.00 > seq(1, 20, by=2) # specify step size [1] 1 3 5 7 9 11 13 15 17 19

Extract Elements from a Vector:

Elements of a vector can be accessed using indexing. The vector indexing can be logical, integer, or character. The [ ] brackets are used for indexing. Indexing starts with position 1, unlike most programming languages where indexing starts from 0.

Extract Using Integer as Index:

We can use integers as an index to access specific elements. We can also use negative integers to return all elements except that specific element.

x<- 101:110
x[1]   #access the first element
x[c(2,3,4,5)] #Extract 2nd, 3rd, 4th, and 5th elements
x[5:10]        #Extract all elements from 5th to 10th
x[c(-5,-10)] #Extract all elements except 5th and 10th
x[-c(5:10)] #Extract all elements except from 5th to 10th

Output: x[1] #Extract the first element [1] 101 x[c(2,3,4,5)] #Extract 2nd, 3rd, 4th, and 5th elements [1] 102 103 104 105 x[5:10] #Extract all elements from 5th to 10th [1] 105 106 107 108 109 110 x[c(-5,-10)] #Extract all elements except 5th and 10th [1] 101 102 103 104 106 107 108 109 x[-c(5:10)] #Extract all elements except from 5th to 10th [1] 101 102 103 104

Extract Using Logical Vector as Index:

If you use a logical vector for indexing, the position where the logical vector is TRUE will be returned.

x[x < 105]
x[x>=104]

Output: x[x < 105] [1] 101 102 103 104 x[x>=104] [1] 104 105 106 107 108 109 110

Modify a Vector in R:

We can modify a vector and assign a new value to it. You can truncate a vector by using reassignments. Check the below example.

x<- 10:12
x[1]<- 101 #Modify the first element
x
x[2]<-102 #Modify the 2nd element
x
x<- x[1:2] #Truncate the last element
x

Output: x [1] 101 11 12 x[2]<-102 #Modify the 2nd element x [1] 101 102 12 x<- x[1:2] #Truncate the last element x [1] 101 102

Arithmetic Operations on Vectors:

We can use arithmetic operations on two vectors of the same length. They can be added, subtracted, multiplied, or divided. Check the output of the below code.

# Create two vectors.
v1 <- c(1:10)
v2 <- c(101:110)

# Vector addition.
add.result <- v1+v2
print(add.result)
# Vector subtraction.
sub.result <- v2-v1
print(sub.result)
# Vector multiplication.
multi.result <- v1*v2
print(multi.result)
# Vector division.
divi.result <- v2/v1
print(divi.result)

Output: print(add.result) [1] 102 104 106 108 110 112 114 116 118 120 print(sub.result) [1] 100 100 100 100 100 100 100 100 100 100 print(multi.result) [1] 101 204 309 416 525 636 749 864 981 1100 print(divi.result) [1] 101.00000 51.00000 34.33333 26.00000 21.00000 17.66667 15.28571 13.50000 12.11111 11.00000

Find Minimum and Maximum in a Vector:

The minimum and the maximum of a vector can be found using the min() or the max() function. range() is also available which returns the minimum and maximum in a vector.

x<- 1001:1010
max(x) # Find the maximum
min(x) # Find the minimum
range(x) #Find the range

Output: max(x) # Find the maximum [1] 1010 min(x) # Find the minimum [1] 1001 range(x) #Find the range [1] 1001 1010

R Lists

The list is a data structure having elements of mixed data types. A vector having all elements of the same type is called an atomic vector but a vector having elements of a different type is called list.
We can check the type with typeof() or class() function and find the length using length()function.

x <- list("stat",5.1, TRUE, 1 + 4i)
x
class(x)
typeof(x)
length(x)

Output: x [[1]] [1] "stat" [[2]] [1] 5.1 [[3]] [1] TRUE [[4]] [1] 1+4i class(x) [1] “list” typeof(x) [1] “list” length(x) [1] 4

You can create an empty list of a prespecified length with the vector() function.

x <- vector("list", length = 10)
x

Output: x [[1]] NULL [[2]] NULL [[3]] NULL [[4]] NULL [[5]] NULL [[6]] NULL [[7]] NULL [[8]] NULL [[9]] NULL [[10]] NULL

How to extract elements from a list?

Lists can be subset using two syntaxes, the $ operator, and square brackets []. The $ operator returns a named element of a list. The [] syntax returns a list, while the [[]] returns an element of a list.

# subsetting
l$e
l["e"]
l[1:2]
l[c(1:2)] #index using integer vector
l[-c(3:length(l))] #negative index to exclude elements from 3rd up to last.
l[c(T,F,F,F,F)] # logical index to access elements

Output: > l$e [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 1 0 0 0 0 0 0 0 0 0 [2,] 0 1 0 0 0 0 0 0 0 0 [3,] 0 0 1 0 0 0 0 0 0 0 [4,] 0 0 0 1 0 0 0 0 0 0 [5,] 0 0 0 0 1 0 0 0 0 0 [6,] 0 0 0 0 0 1 0 0 0 0 [7,] 0 0 0 0 0 0 1 0 0 0 [8,] 0 0 0 0 0 0 0 1 0 0 [9,] 0 0 0 0 0 0 0 0 1 0 [10,] 0 0 0 0 0 0 0 0 0 1 > l["e"] $e [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 1 0 0 0 0 0 0 0 0 0 [2,] 0 1 0 0 0 0 0 0 0 0 [3,] 0 0 1 0 0 0 0 0 0 0 [4,] 0 0 0 1 0 0 0 0 0 0 [5,] 0 0 0 0 1 0 0 0 0 0 [6,] 0 0 0 0 0 1 0 0 0 0 [7,] 0 0 0 0 0 0 1 0 0 0 [8,] 0 0 0 0 0 0 0 1 0 0 [9,] 0 0 0 0 0 0 0 0 1 0 [10,] 0 0 0 0 0 0 0 0 0 1 > l[1:2] [[1]] [1] 1 2 3 4 [[2]] [1] FALSE > l[c(1:2)] #index using integer vector [[1]] [1] 1 2 3 4 [[2]] [1] FALSE > l[-c(3:length(l))] #negative index to exclude elements from 3rd up to last. [[1]] [1] 1 2 3 4 [[2]] [1] FALSE l[c(T,F,F,F,F)] [[1]] [1] 1 2 3 4

Modifying a List in R:

We can change components of a list through reassignment.

l[["name"]] <- "Kalyan Nandi"
l

Output: [[1]] [1] 1 2 3 4 [[2]] [1] FALSE [[3]] [1] “Hello Statistics!” $d function (arg = 42) { print(“Hello World!”) } $name [1] “Kalyan Nandi”

R Matrices

In R Programming Matrix is a two-dimensional data structure. They contain elements of the same atomic types. A Matrix can be created using the matrix() function. R can also be used for matrix calculations. Matrices have rows and columns containing a single data type. In a matrix, the order of rows and columns is important. Dimension can be checked directly with the dim() function and all attributes of an object can be checked with the attributes() function. Check the below example.

Creating a matrix in R

m <- matrix(nrow = 2, ncol = 3)
dim(m)
attributes(m)
m <- matrix(1:20, nrow = 4, ncol = 5)
m

Output: dim(m) [1] 2 3 attributes(m) $dim [1] 2 3 m <- matrix(1:20, nrow = 4, ncol = 5) m [,1] [,2] [,3] [,4] [,5] [1,] 1 5 9 13 17 [2,] 2 6 10 14 18 [3,] 3 7 11 15 19 [4,] 4 8 12 16 20

Matrices can be created by column-binding or row-binding with the cbind() and rbind() functions.

x<-1:3
y<-10:12
z<-30:32
cbind(x,y,z)
rbind(x,y,z)

Output: cbind(x,y,z) x y z [1,] 1 10 30 [2,] 2 11 31 [3,] 3 12 32 rbind(x,y,z) [,1] [,2] [,3] x 1 2 3 y 10 11 12 z 30 31 32

By default, the matrix function reorders a vector into columns, but we can also tell R to use rows instead.

x <-1:9
matrix(x, nrow = 3, ncol = 3)
matrix(x, nrow = 3, ncol = 3, byrow = TRUE)

Output cbind(x,y,z) x y z [1,] 1 10 30 [2,] 2 11 31 [3,] 3 12 32 rbind(x,y,z) [,1] [,2] [,3] x 1 2 3 y 10 11 12 z 30 31 32

R Arrays

In R, Arrays are the data types that can store data in more than two dimensions. An array can be created using the array() function. It takes vectors as input and uses the values in the dim parameter to create an array. If you create an array of dimensions (2, 3, 4) then it creates 4 rectangular matrices each with 2 rows and 3 columns. Arrays can store only data type.

Give a Name to Columns and Rows:

We can give names to the rows, columns, and matrices in the array by setting the dimnames parameter.

v1 <- c(1,2,3)
v2 <- 100:110
col.names <- c("Col1","Col2","Col3","Col4","Col5","Col6","Col7")
row.names <- c("Row1","Row2")
matrix.names <- c("Matrix1","Matrix2")
arr4 <- array(c(v1,v2), dim=c(2,7,2), dimnames = list(row.names,col.names, matrix.names))
arr4

Output: , , Matrix1 Col1 Col2 Col3 Col4 Col5 Col6 Col7 Row1 1 3 101 103 105 107 109 Row2 2 100 102 104 106 108 110 , , Matrix2 Col1 Col2 Col3 Col4 Col5 Col6 Col7 Row1 1 3 101 103 105 107 109 Row2 2 100 102 104 106 108 110

Accessing/Extracting Array Elements:

# Print the 2nd row of the 1st matrix of the array.
print(arr4[2,,1])
# Print the element in the 2nd row and 4th column of the 2nd matrix.
print(arr4[2,4,2])
# Print the 2nd Matrix.
print(arr4[,,2])

Output: > print(arr4[2,,1]) Col1 Col2 Col3 Col4 Col5 Col6 Col7 2 100 102 104 106 108 110 > > # Print the element in the 2nd row and 4th column of the 2nd matrix. > print(arr4[2,4,2]) [1] 104 > > # Print the 2nd Matrix. > print(arr4[,,2]) Col1 Col2 Col3 Col4 Col5 Col6 Col7 Row1 1 3 101 103 105 107 109 Row2 2 100 102 104 106 108 110

R Factors

Factors are used to represent categorical data and can be unordered or ordered. An example might be “Male” and “Female” if we consider gender. Factor objects can be created with the factor() function.

x <- factor(c("male", "female", "male", "male", "female"))
x
table(x)

Output: x [1] male female male male female Levels: female male table(x) x female male 2 3

By default, Levels are put in alphabetical order. If you print the above code you will get levels as female and male. But if you want to get your levels in a particular order then set levels parameter like this.

x <- factor(c("male", "female", "male", "male", "female"), levels=c("male", "female"))
x
table(x)

Output: x [1] male female male male female Levels: male female table(x) x male female 3 2

R Dataframes

Data frames are used to store tabular data in R. They are an important type of object in R and are used in a variety of statistical modeling applications. Data frames are represented as a special type of list where every element of the list has to have the same length. Each element of the list can be thought of as a column and the length of each element of the list is the number of rows. Unlike matrices, data frames can store different classes of objects in each column. Matrices must have every element be the same class (e.g. all integers or all numeric).

Creating a Data Frame:

Data frames can be created explicitly with the data.frame() function.

employee <- c('Ram','Sham','Jadu')
salary <- c(21000, 23400, 26800)
startdate <- as.Date(c('2016-11-1','2015-3-25','2017-3-14'))
employ_data <- data.frame(employee, salary, startdate)
employ_data
View(employ_data)

Output: employ_data employee salary startdate 1 Ram 21000 2016-11-01 2 Sham 23400 2015-03-25 3 Jadu 26800 2017-03-14 View(employ_data)

Get the Structure of the Data Frame:

If you look at the structure of the data frame now, you see that the variable employee is a character vector, as shown in the following output:

str(employ_data)

Output: > str(employ_data) 'data.frame': 3 obs. of 3 variables: $ employee : Factor w/ 3 levels "Jadu","Ram","Sham": 2 3 1 $ salary : num 21000 23400 26800 $ startdate: Date, format: "2016-11-01" "2015-03-25" "2017-03-14"

Note that the first column, employee, is of type factor, instead of a character vector. By default, data.frame() function converts character vector into factor. To suppress this behavior, we can pass the argument stringsAsFactors=FALSE.

employ_data <- data.frame(employee, salary, startdate, stringsAsFactors = FALSE)
str(employ_data)

Output: 'data.frame': 3 obs. of 3 variables: $ employee : chr "Ram" "Sham" "Jadu" $ salary : num 21000 23400 26800 $ startdate: Date, format: "2016-11-01" "2015-03-25" "2017-03-14"

R Packages

The primary location for obtaining R packages is CRAN.

You can obtain information about the available packages on CRAN with the available.packages() function.
a <- available.packages()

head(rownames(a), 30) # Show the names of the first 30 packages
Packages can be installed with the install.packages() function in R. To install a single package, pass the name of the lecture to the install.packages() function as the first argument.
The following code installs the ggplot2 package from CRAN.
install.packages(“ggplot2”)
You can install multiple R packages at once with a single call to install.packages(). Place the names of the R packages in a character vector.
install.packages(c(“caret”, “ggplot2”, “dplyr”))

Loading packages
Installing a package does not make it immediately available to you in R; you must load the package. The library() function is used to load packages into R. The following code is used to load the ggplot2 package into R. Do not put the package name in quotes.
library(ggplot2)
If you have Installed your packages without root access using the command install.packages(“ggplot2″, lib=”/data/Rpackages/”). Then to load use the below command.
library(ggplot2, lib.loc=”/data/Rpackages/”)
After loading a package, the functions exported by that package will be attached to the top of the search() list (after the workspace).
library(ggplot2)

search()

R – CSV() files

In R, we can read data from files stored outside the R environment. We can also write data into files that will be stored and accessed by the operating system. R can read and write into various file formats like CSV, Excel, XML, etc.

Getting and Setting the Working Directory

We can check which directory the R workspace is pointing to using the getwd() function. You can also set a new working directory using setwd()function.

# Get and print current working directory.
print(getwd())

# Set current working directory.
setwd("/web/com")

# Get and print current working directory.
print(getwd())

Output: [1] "/web/com/1441086124_2016" [1] "/web/com"

Input as CSV File

The CSV file is a text file in which the values in the columns are separated by a comma. Let’s consider the following data present in the file named input.csv.

You can create this file using windows notepad by copying and pasting this data. Save the file as input.csv using the save As All files(*.*) option in notepad.

Reading a CSV File

Following is a simple example of read.csv() function to read a CSV file available in your current working directory −

data <- read.csv("input.csv")
print(data)

  id,   name,    salary,   start_date,     dept

R- Charts and Graphs

R- Pie Charts

Pie charts are created with the function pie(x, labels=) where x is a non-negative numeric vector indicating the area of each slice and labels= notes a character vector of names for the slices.

Syntax

The basic syntax for creating a pie-chart using the R is −

pie(x, labels, radius, main, col, clockwise)

Following is the description of the parameters used −

x is a vector containing the numeric values used in the pie chart.
labels are used to give a description of the slices.
radius indicates the radius of the circle of the pie chart. (value between −1 and +1).
main indicates the title of the chart.
col indicates the color palette.
clockwise is a logical value indicating if the slices are drawn clockwise or anti-clockwise.

Simple Pie chart

# Simple Pie Chart
slices <- c(10, 12,4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie(slices, labels = lbls, main="Pie Chart of Countries")

3-D pie chart

The pie3D( ) function in the plotrix package provides 3D exploded pie charts.

# 3D Exploded Pie Chart
library(plotrix)
slices <- c(10, 12, 4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie3D(slices,labels=lbls,explode=0.1,
   main="Pie Chart of Countries ")

R -Bar Charts

A bar chart represents data in rectangular bars with a length of the bar proportional to the value of the variable. R uses the function barplot() to create bar charts. R can draw both vertical and Horizontal bars in the bar chart. In the bar chart, each of the bars can be given different colors.

Let us suppose, we have a vector of maximum temperatures (in degree Celsius) for seven days as follows.

max.temp <- c(22, 27, 26, 24, 23, 26, 28)
barplot(max.temp)

Some of the frequently used ones are, “main” to give the title, “xlab” and “ylab” to provide labels for the axes, names.arg for naming each bar, “col” to define color, etc.

We can also plot bars horizontally by providing the argument horiz=TRUE.

# barchart with added parameters
barplot(max.temp,
main = "Maximum Temperatures in a Week",
xlab = "Degree Celsius",
ylab = "Day",
names.arg = c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"),
col = "darkred",
horiz = TRUE)

Simply doing barplot(age) will not give us the required plot. It will plot 10 bars with height equal to the student’s age. But we want to know the number of students in each age category.

This count can be quickly found using the table() function, as shown below.

> table(age)
age
16 17 18 19 
1  2  6  1

Now plotting this data will give our required bar plot. Note below, that we define the argument “density” to shade the bars.

barplot(table(age),
main="Age Count of 10 Students",
xlab="Age",
ylab="Count",
border="red",
col="blue",
density=10
)

A histogram represents the frequencies of values of a variable bucketed into ranges. Histogram is similar to bar chat but the difference is it groups the values into continuous ranges. Each bar in histogram represents the height of the number of values present in that range.

R creates histogram using hist() function. This function takes a vector as an input and uses some more parameters to plot histograms.

Syntax

The basic syntax for creating a histogram using R is −

hist(v,main,xlab,xlim,ylim,breaks,col,border)

Following is the description of the parameters used −

v is a vector containing numeric values used in the histogram.
main indicates the title of the chart.
col is used to set the color of the bars.
border is used to set the border color of each bar.
xlab is used to give a description of the x-axis.
xlim is used to specify the range of values on the x-axis.
ylim is used to specify the range of values on the y-axis.
breaks are used to mention the width of each bar.

Example

A simple histogram is created using input vector, label, col, and border parameters.

The script given below will create and save the histogram in the current R working directory.

# Create data for the graph.
v <-  c(9,13,21,8,36,22,12,41,31,33,19)

# Give the chart file a name.
png(file = "histogram.png")

# Create the histogram.
hist(v,xlab = "Weight",col = "yellow",border = "blue")

# Save the file.
dev.off()

Range of X and Y values

To specify the range of values allowed in X axis and Y axis, we can use the xlim and ylim parameters.

The width of each bar can be decided by using breaks.

# Create data for the graph.
v <- c(9,13,21,8,36,22,12,41,31,33,19)

# Give the chart file a name.
png(file = "histogram_lim_breaks.png")

# Create the histogram.
hist(v,xlab = "Weight",col = "green",border = "red", xlim = c(0,40), ylim = c(0,5),
   breaks = 5)

# Save the file.
dev.off()

R vs SAS – Which Tool is Better?

The debate around data analytics tools has been going on forever. Each time a new one comes out, comparisons transpire. Although many aspects of the tool remain subjective, beginners want to know which tool is better to start with.
The most popular and widely used tools for data analytics are R and SAS. Both of them have been around for a long time and are often pitted against each other. So, let’s compare them based on the most relevant factors.

Availability and Cost: SAS is widely used in most private organizations as it is a commercial software. It is more expensive than any other data analytics tool available. It might thus be a bit difficult buying the software if you are an individual professional or a student starting out. On the other hand, R is an open source software and is completely free to use. Anyone can begin using it right away without having to spend a penny. So, regarding availability and cost, R is hands down the better tool.
Ease of learning: Since SAS is a commercial software, it has a whole lot of online resources available. Also, those who already know SQL might find it easier to adapt to SAS as it comes with PROC SQL option. The tool has a user-friendly GUI. It comes with an extensive documentation and tutorial base which can help early learners get started seamlessly. Whereas, the learning curve for R is quite steep. You need to learn to code at the root level and carrying out simple tasks demand a lot of time and effort with R. However, several forums and online communities post religiously about its usage.
Data Handling Capabilities: When it comes to data handling, both SAS and R perform well, but there are some caveats for the latter. While SAS can even churn through terabytes of data with ease, R might be constrained as it makes use of the available RAM in the machine. This can be a hassle for 32-bit systems with low RAM capacity. Due to this, R can at times become unresponsive or give an ‘out of memory’ error. Both of them can run parallel computations, support integrations for Hadoop, Spark, Cloudera and Apache Pig among others. Also, the availability of devices with better RAM capacity might negate the disadvantages of R.
Graphical Capabilities: Graphical capabilities or data visualization is the strongest forte of R. This is where SAS lacks behind in a major way. R has access to packages like GGPlot, RGIS, Lattice, and GGVIS among others which provide superior graphical competency. In comparison, Base SAS is struggling hard to catch up with the advancements in graphics and visualization in data analytics. Even the graphics packages available in SAS are poorly documented which makes them difficult to use.
Advancements in Tool: Advancements in the industry give way to advancements in tools, and both SAS and R hold up pretty well in this regard. SAS, being a corporate software, rolls out new features and technologies frequently with new versions of its software. However, the updates are not as fast as R since it is open source software and has many contributors throughout the world. Alternatively, the latest updates in SAS are pushed out after thorough testing, making them much more stable, and reliable than R. Both the tools come with a fair share of pros & cons.
Job Scenario: Currently, large corporations insist on using SAS, but SMEs and start-ups are increasingly opting for R, given that it’s free. The current job trend seems to show that while SAS is losing its momentum, R is gaining potential. The job scenario is on the cusp of change, and both the tools seem strong, but since R is on an uphill path, it can probably witness more jobs in the future, albeit not in huge corporates.
Deep Learning Support: While SAS has just begun work on adding deep learning support, R has added support for a few packages which enable deep learning capabilities in the tool. You can use KerasR and keras package in R which are mere interfaces for the original Keras package built on Python. Although none of the tools are excellent facilitators of deep learning, R has seen some recent active developments on this front.
Customer Service Support and Community: As one would expect from full-fledged commercial software, SAS offers excellent customer service support as well as the backing of a helpful community. Since R is free open-source software, expecting customer support will be hard to justify. However, it has a vast online community that can help you with almost everything. On the other hand, no matter what problem you face with SAS, you can immediately reach out to their customer support and get it solved without any hassles.

Final Verdict
As per estimations by the Economic Times, the analytics industry will grow to $16 billion till 2025 in India. If you wish to venture into this domain, there can’t be a better time. Just start learning the tool you think is better based on the comparison points above.

Original article source at: https://www.mygreatlearning.com

#r #programming

Autumn Blick

1593867420

Top Android Projects with Source Code

Android Projects with Source Code – Your entry pass into the world of Android

Hello Everyone, welcome to this article, which is going to be really important to all those who’re in dilemma for their projects and the project submissions. This article is also going to help you if you’re an enthusiast looking forward to explore and enhance your Android skills. The reason is that we’re here to provide you the best ideas of Android Project with source code that you can choose as per your choice.

These project ideas are simple suggestions to help you deal with the difficulty of choosing the correct projects. In this article, we’ll see the project ideas from beginners level and later we’ll move on to intermediate to advance.

Android Projects with Source Code

Before working on real-time projects, it is recommended to create a sample hello world project in android studio and get a flavor of project creation as well as execution: Create your first android project

Android Projects for beginners

1. Calculator

Android Project: A calculator will be an easy application if you have just learned Android and coding for Java. This Application will simply take the input values and the operation to be performed from the users. After taking the input it’ll return the results to them on the screen. This is a really easy application and doesn’t need use of any particular package.

To make a calculator you’d need Android IDE, Kotlin/Java for coding, and for layout of your application, you’d need XML or JSON. For this, coding would be the same as that in any language, but in the form of an application. Not to forget creating a calculator initially will increase your logical thinking.

Once the user installs the calculator, they’re ready to use it even without the internet. They’ll enter the values, and the application will show them the value after performing the given operations on the entered operands.

Source Code: Simple Calculator Project

2. A Reminder App

Android Project: This is a good project for beginners. A Reminder App can help you set reminders for different events that you have throughout the day. It’ll help you stay updated with all your tasks for the day. It can be useful for all those who are not so good at organizing their plans and forget easily. This would be a simple application just whose task would be just to remind you of something at a particular time.

To make a Reminder App you need to code in Kotlin/Java and design the layout using XML or JSON. For the functionality of the app, you’d need to make use of AlarmManager Class and Notifications in Android.

In this, the user would be able to set reminders and time in the application. Users can schedule reminders that would remind them to drink water again and again throughout the day. Or to remind them of their medications.

3. Quiz Application

Android Project: Another beginner’s level project Idea can be a Quiz Application in android. Here you can provide the users with Quiz on various general knowledge topics. These practices will ensure that you’re able to set the layouts properly and slowly increase your pace of learning the Android application development. In this you’ll learn to use various Layout components at the same time understanding them better.

To make a quiz application you’ll need to code in Java and set layouts using xml or java whichever you prefer. You can also use JSON for the layouts whichever preferable.

In the app, questions would be asked and answers would be shown as multiple choices. The user selects the answer and gets shown on the screen if the answers are correct. In the end the final marks would be shown to the users.

4. Simple Tic-Tac-Toe

Android Project: Tic-Tac-Toe is a nice game, I guess most of you all are well aware of it. This will be a game for two players. In this android game, users would be putting X and O in the given 9 parts of a box one by one. The first player to arrange X or O in an adjacent line of three wins.

To build this game, you’d need Java and XML for Android Studio. And simply apply the logic on that. This game will have a set of three matches. So, it’ll also have a scoreboard. This scoreboard will show the final result at the end of one complete set.

Upon entering the game they’ll enter their names. And that’s when the game begins. They’ll touch one of the empty boxes present there and get their turn one by one. At the end of the game, there would be a winner declared.

Source Code: Tic Tac Toe Game Project

5. Stopwatch

Android Project: A stopwatch is another simple android project idea that will work the same as a normal handheld timepiece that measures the time elapsed between its activation and deactivation. This application will have three buttons that are: start, stop, and hold.

This application would need to use Java and XML. For this application, we need to set the timer properly as it is initially set to milliseconds, and that should be converted to minutes and then hours properly. The users can use this application and all they’d need to do is, start the stopwatch and then stop it when they are done. They can also pause the timer and continue it again when they like.

6. To Do App

Android Project: This is another very simple project idea for you as a beginner. This application as the name suggests will be a To-Do list holding app. It’ll store the users schedules and their upcoming meetings or events. In this application, users will be enabled to write their important notes as well. To make it safe, provide a login page before the user can access it.

So, this app will have a login page, sign-up page, logout system, and the area to write their tasks, events, or important notes. You can build it in android studio using Java and XML at ease. Using XML you can build the user interface as user-friendly as you can. And to store the users’ data, you can use SQLite enabling the users to even delete the data permanently.

Now for users, they will sign up and get access to the write section. Here the users can note down the things and store them permanently. Users can also alter the data or delete them. Finally, they can logout and also, login again and again whenever they like.

7. Roman to decimal converter

Android Project: This app is aimed at the conversion of Roman numbers to their significant decimal number. It’ll help to check the meaning of the roman numbers. Moreover, it will be easy to develop and will help you get your hands on coding and Android.

You need to use Android Studio, Java for coding and XML for interface. The application will take input from the users and convert them to decimal. Once it converts the Roman no. into decimal, it will show the results on the screen.

The users are supposed to just enter the Roman Number and they’ll get the decimal values on the screen. This can be a good android project for final year students.

8. Virtual Dice Roller

Android Project: Well, coming to this part that is Virtual Dice or a random no. generator. It is another simple but interesting app for computer science students. The only task that it would need to do would be to generate a number randomly. This can help people who’re often confused between two or more things.

Using a simple random number generator you can actually create something as good as this. All you’d need to do is get you hands-on OnClick listeners. And a good layout would be cherry on the cake.

The user’s task would be to set the range of the numbers and then click on the roll button. And the app will show them a randomly generated number. Isn’t it interesting ? Try soon!

9. A Scientific Calculator App

Android Project: This application is very important for you as a beginner as it will let you use your logical thinking and improve your programming skills. This is a scientific calculator that will help the users to do various calculations at ease.

To make this application you’d need to use Android Studio. Here you’d need to use arithmetic logics for the calculations. The user would need to give input to the application that will be in terms of numbers. After that, the user will give the operator as an input. Then the Application will calculate and generate the result on the user screen.

10. SMS App

Android Project: An SMS app is another easy but effective idea. It will let you send the SMS to various no. just in the same way as you use the default messaging application in your phone. This project will help you with better understanding of SMSManager in Android.

For this application, you would need to implement Java class SMSManager in Android. For the Layout you can use XML or JSON. Implementing SMSManager into the app is an easy task, so you would love this.

The user would be provided with the facility to text to whichever number they wish also, they’d be able to choose the numbers from the contact list. Another thing would be the Textbox, where they’ll enter their message. Once the message is entered they can happily click on the send button.

#android tutorials #android application final year project #android mini projects #android project for beginners #android project ideas #android project ideas for beginners #android projects #android projects for students #android projects with source code #android topics list #intermediate android projects #real-time android projects

Seamus Quitzon

1595201363

Php how to delete multiple rows through checkbox using ajax in laravel

First thing, we will need a table and i am creating products table for this example. So run the following query to create table.

CREATE TABLE `products` (
 `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
 `name` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL,
 `description` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL,
 `created_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP,
 `updated_at` datetime DEFAULT NULL,
 PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=7 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci

Next, we will need to insert some dummy records in this table that will be deleted.

INSERT INTO `products` (`name`, `description`) VALUES

('Test product 1', 'Product description example1'),

('Test product 2', 'Product description example2'),

('Test product 3', 'Product description example3'),

('Test product 4', 'Product description example4'),

('Test product 5', 'Product description example5');

Now we are redy to create a model corresponding to this products table. Here we will create Product model. So let’s create a model file Product.php file under app directory and put the code below.

<?php

namespace App;

use Illuminate\Database\Eloquent\Model;

class Product extends Model
{
    protected $fillable = [
        'name','description'
    ];
}

Step 2: Create Route

Now, in this second step we will create some routes to handle the request for this example. So opeen routes/web.php file and copy the routes as given below.

routes/web.php

Route::get('product', 'ProductController@index');
Route::delete('product/{id}', ['as'=>'product.destroy','uses'=>'ProductController@destroy']);
Route::delete('delete-multiple-product', ['as'=>'product.multiple-delete','uses'=>'ProductController@deleteMultiple']);

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Shawn Durgan

1595547778

10 Writing steps to create a good project brief - Mobile app development

Developing a mobile application can often be more challenging than it seems at first glance. Whether you’re a developer, UI designer, project lead or CEO of a mobile-based startup, writing good project briefs prior to development is pivotal. According to Tech Jury, 87% of smartphone users spend time exclusively on mobile apps, with 18-24-year-olds spending 66% of total digital time on mobile apps. Of that, 89% of the time is spent on just 18 apps depending on individual users’ preferences, making proper app planning crucial for success.

Today’s audiences know what they want and don’t want in their mobile apps, encouraging teams to carefully write their project plans before they approach development. But how do you properly write a mobile app development brief without sacrificing your vision and staying within the initial budget? Why should you do so in the first place? Let’s discuss that and more in greater detail.

Why a Good Mobile App Project Brief Matters?

Why-a-Good-Mobile-App-Project-Brief-Matters

It’s worth discussing the significance of mobile app project briefs before we tackle the writing process itself. In practice, a project brief is used as a reference tool for developers to remain focused on the client’s deliverables. Approaching the development process without written and approved documentation can lead to drastic, last-minute changes, misunderstanding, as well as a loss of resources and brand reputation.

For example, developing a mobile app that filters restaurants based on food type, such as Happy Cow, means that developers should stay focused on it. Knowing that such and such features, UI elements, and API are necessary will help team members collaborate better in order to meet certain expectations. Whether you develop an app under your brand’s banner or outsource coding and design services to would-be clients, briefs can provide you with several benefits:

Clarity on what your mobile app project “is” and “isn’t” early in development
Point of reference for developers, project leads, and clients throughout the cycle
Smart allocation of available time and resources based on objective development criteria
Streamlined project data storage for further app updates and iterations

Writing Steps to Create a Good Mobile App Project Brief

Writing-Steps-to-Create-a-Good-Mobile-App-Project-Brief

1. Establish the “You” Behind the App

Depending on how “open” your project is to the public, you will want to write a detailed section about who the developers are. Elements such as company name, address, project lead, project title, as well as contact information, should be included in this introductory segment. Regardless of whether you build an in-house app or outsource developers to a client, this section is used for easy document storage and access.

#android app #ios app #minimum viable product (mvp) #mobile app development #web development #how do you write a project design #how to write a brief #how to write a project summary #how to write project summary #program brief example #project brief #project brief example #project brief template #project proposal brief #simple project brief template

Juned Ghanchi

1621508419

Laravel App Development Company in India, Hire Laravel Developers

Hire our expert team of Laravel app developers for flexible PHP applications across various cloud service providers.

With this easy build technology, we develop feature-rich apps that make your complex business process a lot easier. Our apps are,

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Extract Untranslated Strings from Laravel Projects

What is GEEK

Buddha Community

What Is R Programming Language? introduction & Basics

What is R used for?

R-environment setup

R basic Syntax

R command prompt

R data-types

Vectors

Matrices

Arrays

Dataframes

Factors

R Variables

Rules for writing Identifiers in R

Valid identifiers in R

Invalid identifiers in R

Best Practices

Constants in R

R Operators

Logical Operators

R functions

Loop Functions

R Vectors

Creating a vector using seq() function:

Extract Elements from a Vector:

Extract Using Integer as Index:

Extract Using Logical Vector as Index:

Modify a Vector in R:

Arithmetic Operations on Vectors:

Find Minimum and Maximum in a Vector:

R Lists

How to extract elements from a list?

Modifying a List in R:

R Matrices

R Arrays

Give a Name to Columns and Rows:

Accessing/Extracting Array Elements:

R Factors

R Dataframes

Creating a Data Frame:

Get the Structure of the Data Frame:

R Packages

R – CSV() files

Getting and Setting the Working Directory

Input as CSV File

Reading a CSV File

R- Charts and Graphs

R- Pie Charts

Syntax

Simple Pie chart

R -Bar Charts

Syntax

Example

Range of X and Y values

R vs SAS – Which Tool is Better?

Top Android Projects with Source Code

Android Projects with Source Code

Android Projects for beginners

1. Calculator

2. A Reminder App

3. Quiz Application

4. Simple Tic-Tac-Toe

5. Stopwatch

6. To Do App

7. Roman to decimal converter

8. Virtual Dice Roller

9. A Scientific Calculator App

10. SMS App

Php how to delete multiple rows through checkbox using ajax in laravel

Step 2: Create Route

10 Writing steps to create a good project brief - Mobile app development

Why a Good Mobile App Project Brief Matters?

Writing Steps to Create a Good Mobile App Project Brief

1. Establish the “You” Behind the App

Laravel App Development Company in India, Hire Laravel Developers