Server Side Programming Articles - Page 1516 of 2646

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

It is common the find the correlation coefficient between columns of an R data frame but we might want to find the correlation coefficient between rows of two data frames. This might be needed in situations where we expect that there exists some relationship row of an R data frame with row of another data frame. For example, row of an R data frame showing buying trend of a customer in one year and the same row of the other data frame showing buying trend of the same customer in another year.Consider the below data frame −Example Live Demox1Read More

The default value for bins is 30 but if we don’t pass that in geom_histogram then the warning message is shown by R in most of the cases. To avoid that, we can simply put bins=30 inside the geom_histogram() function. This will stop showing the warning message.Consider the below data frame −x

If we want to use a data frame created in R in the future then it is better to save that data frame as txt file because it is obvious that data creation takes time. This can be done by using write.table function. For example, if we have a data frame df then we can save it as txt file by using the code write.table(df,"df.txt",sep="\t",row.names=FALSE)Consider the below data frame −Example Live Demoset.seed(111) x1

If we want to create a categorical vector with all unique values representing strings with numbers at the end then paste0 function can help us in the same. For example, if we want to create a vector for ID up to 10 as ID1, ID2, ID3, ID4, ID5, ID6, ID7, ID8, ID9, and ID10 then it can be done as paste0("ID",seq(1:10)).Example Live Demox1

In the whole world, the first step people teach to impute missing values is replacing them with the relevant mean. That means if we have a column which has some missing values then replace it with the mean of the remaining values. In R, we can do this by replacing the column with missing values using mean of that column and passing na.rm = TRUE argument along with the same.Consider the below data frame −Example Live Demoset.seed(121) x