Server Side Programming Articles - Page 1230 of 2646

To convert an old data frame to a new data frame, we can simply set the new name. For example, if we have a data frame called df and want to convert it to a new one let’s say df_new then it can be done as df_new x1 x2 df1 df1Output x1 x2 1 8 6 2 4 9 3 3 2 4 3 5 5 7 4 6 4 8 7 8 6 8 12 12 9 8 6 10 ... Read More

Most of the times we need to deal with missing values in data science projects and these missing values can be occurred at any position. We might want to change the position of these missing values and send them to the end of the columns in the data frame. This can be done with the help of lapply function as shown in the below examples.Example1Consider the below data frame −Live Demo> x1 x2 x3 df1 df1Output   x1 x2 x3 1  0  0  2 2  1  1  NA 3  1  NA 0 4  0  NA 2 5  1  NA 2 6 ... Read More

If we have a character column in the data frame that contains string as well as numeric values and the first digit of the numeric values has some meaning that can help in data analysis then we can extract those first digits. For this purpose, we can use stri_extract_first function from stringi package.Example1Consider the below data frame −Live Demo> x1 y1 df1 df1Output x1 y1 1 1 HT14L 2 2 HT14L 3 3 HT23L 4 4 HT14L 5 5 HT32L 6 6 HT32L 7 ... Read More

With the help of addition and subtraction operations, we can swap two numbers from one memory location to another memory location.AlgorithmThe algorithm is explained below −STARTStep 1: Declare 2 variables x and y. Step 2: Read two numbers from keyboard. Step 3: Swap numbers. //Apply addition and subtraction operations to swap the numbers.    i. x=x+y    ii. y=x-y    iii. x=x-y Step 4: Print x and y values.ProgramFollowing is the C program which explains swapping of two numbers without using third variable or a temporary variable −#include int main(){    int x, y;    printf("enter x and y values:"); ... Read More

ProblemHow to print the long lines into two or more short lines based on specified length in the program using C Language?SolutionLet’s write a code to read a long line and print into two or more short lines according to the mentioned size in the program.The built in functions that we take in this program readline() function, is used to store text in array and returns the size of line.The logic we use to read a short sentence is explained below −while((charcter=readtext())>0){    if(charcter>length){       count=0;       a=0;       while(alength){          count=0;          a=0;          while(a

If we have a vector where alternate values may create a tabular form then we might want to convert the vector into a data frame. For this purpose, we first need to convert the vector into a matrix with appropriate number of columns/rows and then read it as a data frame using as.data.frame function. Check out the below examples to understand how it works.Example1Live Demo> x1 x1Output[1] "1" "male" "1" "male" "1" "male" "1" "male" [9] "1" "male" "1" "male" "1" "male" "1" "male" [17] "1" "male" "1" "male" "2" "female" "2" "female" [25] "2" "female" "2" "female" "2" "female" ... Read More

To display the upper and lower quartiles through different line in base R boxplot, we can use abline function but we need to find the quartiles inside abline using quantile for the respective quartiles. The lines created by using abline and quantiles and the boxplot function may not coincide because of the differences in calculation. The calculation method for boxplot is explained below −The two ‘hinges’ are versions of the first and third quartile. The hinges equal the quartiles for odd n (where n x boxplot(x)OutputExample> abline(h=quantile(x,c(0.25,0.75)),col="blue")Output

If we have factor columns in an R data frame then we want to find the frequency of each factor level for all the factor columns. This can be done with the help of sapply function with table function. For example, if we have a data frame called df that contains some factor columns then the frequency table for factor columns can be created by using the command sapply(df, table).Example1Consider the below data frame −Live Demo> x1 x2 df1 df1Output   x1 x2 1  D  a 2  D  b 3  D  c 4  D  b 5  D  c 6  C  a ... Read More

There is no in-built function to find the mode in R, hence we need to create one and then apply it to the rows of the matrix. The function for mode is created as follows −mode M1 M1Output     [,1] [,2] [,3] [,4] [,5] [1,]  2    2    1    2   2 [2,]  2    2    2    2   1 [3,]  2    2    1    1   1 [4,]  2    1    1    1   1 [5,]  2    1    1    2   2> apply(M1,1,mode)Output[1] 2 2 1 1 2Example2Live Demo> M2 M2Output     [,1] [,2] [,3] [,4] [,5] [1,]  1    1    2    2    1 [2,]  2    1    1    2    1 [3,]  2    2    1    1    1 [4,]  2    1    1    2    2 [5,]  2    1    1    2    2 [6,]  1    2    1    1    2 [7,]  1    1    2    1    2 [8,]  2    2    1    2    1 [9,]  2    1    1    2    2 [10,] 1    1    2    2    2 [11,] 1    1    2    1    2 [12,] 1    2    2    2    1 [13,] 2    2    2    2    1 [14,] 2    1    2    2    1 [15,] 1    2    1    1    2 [16,] 2    2    1    2    1 [17,] 2    2    1    1    1 [18,] 2    1    1    2    1 [19,] 1    1    1    2    1 [20,] 2    1    1    2    2> apply(M2,1,mode)Output[1] 1 1 1 2 2 1 1 2 2 2 1 2 2 2 1 2 1 1 1 2Example3Live Demo> M3 M3Output     [,1] [,2] [,3] [,4] [,5] [1,]  1    3    3    2    1 [2,]  2    3    1    2    2 [3,]  2    2    3    3    1 [4,]  1    3    1    3    2 [5,]  3    1    2    1    2 [6,]  2    3    1    1    1 [7,]  2    2    2    3    1 [8,]  1    2    2    2    2 [9,]  2    1    2    1    2 [10,] 1    3    1    2    1 [11,] 2    1    3    1    1 [12,] 1    1    3    2    2 [13,] 2    1    1    1    2 [14,] 2    1    3    3    2 [15,] 1    2    3    1    2 [16,] 1    2    1    2    1 [17,] 3    1    1    3    2 [18,] 3    3    3    3    1 [19,] 3    2    3    1    1 [20,] 3    3    2    2    1> apply(M3,1,mode)Output[1] 1 2 2 1 1 1 2 2 2 1 1 1 1 2 1 1 1 3 1 2Example4Live Demo> M4 M4Output      [,1] [,2] [,3] [,4] [,5] [1,]  10    10   9    10   9 [2,]  9     9    10   9    9 [3,]  9     9    9    10   10 [4,]  10    9    9    10   10 [5,]  10    10   9    10   9 [6,]  10    10   9    10   10 [7,]  9     9    9    10   9 [8,]  9     10   9    10   9 [9,]  9     9    9    9    9 [10,] 9     10   9    10   9 [11,] 10    10   9    9    9 [12,] 9     9    9    9    9 [13,] 10    10   10   9 10 [14,] 10    9    10   10 10 [15,] 9     10   9    10 9 [16,] 9     10   9    10 9 [17,] 9     10   10   9 10 [18,] 9     9    9    9 10 [19,] 10    9    9    10 9 [20,] 10    9    9    10 9> apply(M4,1,mode)Output[1] 10 9 9 10 10 10 9 9 9 9 9 9 10 10 9 9 10 9 9 9

The grouping of values can be done in many ways and one such way is if we have duplicate values or unique values then the group can be set based on that. If all the values are unique then there is no sense for grouping but if we have varying values then the grouping can be done. For this purpose, we can use rleid function as shown in the below examples.Example1Consider the below data frame −Live Demo> x df1 df1Output x 1 2 2 1 3 2 4 2 5 1 6 ... Read More