Programming Articles - Page 1663 of 3366

Suppose we have a data frame df1 that contains 5 columns and another data frame df2 that contains only column but the data type of the columns in both the data frames is same. Now we might want to add the column of the second data frame starting at the end of the rows of the first data frame by creating the same number of columns as in first data frame. This might be required by researchers to understand the impact of an external variable on the result of the analysis and it can be done with the help of ... Read More

Random sampling is a technique used by almost every researcher, analyst, financial analyst, data scientist, or even a leader and if we way that almost everyone uses it at least once in a lifetime then it won’t be surprise. Because we use it in one or the way in our life even if we don’t know about it. To take a random sample or creating random values up to a range of values starting from 1, we can simply use sample function in R. Checkout below examples to understand how this function works for sampling with replacement.Example Live Demosample(100)Output[1] 17 76 ... Read More

A data.table object is very similar to a data frame in R, therefore, converting a data.table object to a matrix is not a difficult job. We just need to use as.matrix function and store the data.table object into a new object that will belong to the matrix, otherwise R will not be able to convert the data.object to a matrix. For example, if we have a data.table object DT then to convert it into a matrix, we should use the below example code −DT_matrix

If we have missing values/NA in our data frame and create a plot using ggplot2 without excluding those missing values then we get the warning “Removed X rows containing missing values”, here X will be the number of rows for the column that contain NA values. But the plot will be correct because it will be calculated by excluding the NA’s. To avoid this error, we just need to pass the subset of the data frame column that do not contains NA values as shown in the below example.Consider the below data frame with y column having few NA values ... Read More

The categorical variables can be easily visualized with the help of mosaic plot. In a mosaic plot, we can have one or more categorical variables and the plot is created based on the frequency of each category in the variables. To create a mosaic plot in base R, we can use mosaicplot function. The categories that have higher frequencies are displayed by a bigger size box and the categories that have less frequency are displayed by smaller size box.Consider the below data frame −Example Live Demox1

To create a line chart in base R using plot function, we need to use type = "l" so that R understand the plot needs to have a line instead of points. If we want to increase the width of the line then lwd argument can be used. The value lwd = 0 is the default value for the width.Consider the below vector and create the line chart −Examplex

The default graph created by using ggplot2 package shows the axes labels depending on the starting and ending values of the column of the data frame or vector but we might want to visualize it just like we do in paper form of graphs that shows all of the four quadrants. This can be done by using xlim, ylim, geom_hline, and geom_vline functions with ggplot function of ggplot2 package.Consider the below data frame −Example Live Demox

Subsetting can be required in many different ways, we can say that there might be infinite number of ways for subsetting as it depends on the objective of the bigger or smaller analysis. One such way is subsetting a matrix based on a certain value of column of the matrix. In R, we can easily do the same with the help of subset function as shown in below example.Example Live DemoM3)Output  [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 4 14 24 34 44 54 64 74 84 94 [2,] 5 15 25 35 45 55 65 75 85 95 [3,] 6 16 26 36 46 56 66 76 86 96 [4,] 7 17 27 37 47 57 67 77 87 97 [5,] 8 18 28 38 48 58 68 78 88 98 [6,] 9 19 29 39 49 59 69 79 89 99 [7,] 10 20 30 40 50 60 70 80 90 100Examplesubset(M,M[,1]75)Output[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 6 16 26 36 46 56 66 76 86 96 [2,] 7 17 27 37 47 57 67 77 87 97 [3,] 8 18 28 38 48 58 68 78 88 98 [4,] 9 19 29 39 49 59 69 79 89 99 [5,] 10 20 30 40 50 60 70 80 90 100Examplesubset(M,M[,9]>81)Output[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 2 12 22 32 42 52 62 72 82 92 [2,] 3 13 23 33 43 53 63 73 83 93 [3,] 4 14 24 34 44 54 64 74 84 94 [4,] 5 15 25 35 45 55 65 75 85 95 [5,] 6 16 26 36 46 56 66 76 86 96 [6,] 7 17 27 37 47 57 67 77 87 97 [7,] 8 18 28 38 48 58 68 78 88 98 [8,] 9 19 29 39 49 59 69 79 89 99 [9,] 10 20 30 40 50 60 70 80 90 100Examplesubset(M,M[,9]

The plot area in plot window is fixed by default and we can create a lint chart with extended width so that the chart covers the area of the plot from bottom left to upper right. This can be done by using very large width of the line chart with the help of lwd argument.Consider the below vector and create the very wide line chart to cover the plot area −Examplex

The anti-diagonal elements in a matrix are the elements that form straight line from right upper side to right bottom side. For example, if we have a matrix as shown below −1 2 3 4 5 6 7 8 9then the diagonal elements would be 1, 5, 9 and the anti-diagonal elements would be 3, 5, 7.To find the sum of these anti-diagonal elements, we can use apply function.Example Live DemoM1