To create a boxplot for data frame columns we can simply use boxplot function but it cannot be done directly for matrix columns. If we want to create boxplot for matrix columns then we need to convert the matrix into data frame and then use the boxplot function. For example, if we have a matrix called M then the boxplot for columns in M can be created by using boxplot(as.data.frame(M)).ExampleLive Demo> M MOutput[,1] [,2] [,3] [,4] [,5] [1,] 1.688556 1.697216 1.9469573 1.873956 2.010246 [2,] 1.655357 1.927145 2.0937415 2.273638 1.966972 [3,] 1.886917 1.182852 2.0291452 2.507944 2.338664 [4,] 2.013053 1.995526 1.8122830 2.531708 2.483359 [5,] 1.812015 1.950053 1.8902859 2.453222 2.123253 [6,] 1.781764 1.786285 2.3384120 2.275382 2.509708 [7,] 1.836378 1.192781 1.5382031 2.012324 2.290340 [8,] 2.061482 1.705481 2.5542404 1.958202 1.991252 [9,] 2.162214 1.958862 1.8096081 1.810033 1.856942 [10,] 1.897020 1.614834 2.3407207 2.199068 1.807968 [11,] 2.491147 2.317192 2.4486029 2.131722 1.947841 [12,] 1.860307 1.932982 2.2034280 1.982581 2.720482 [13,] 1.814205 2.214286 1.6917036 1.854341 2.150684 [14,] 1.224437 1.800944 1.7600398 1.503382 2.775012 [15,] 2.309462 2.534766 1.5111472 2.058761 1.823550 [16,] 2.190564 1.588298 1.8854163 1.694651 1.939035 [17,] 2.521611 2.339012 2.2959581 2.501148 1.951673 [18,] 1.808799 2.314207 1.8704730 1.937851 1.877917 [19,] 2.476626 1.806194 2.7111663 2.156506 1.521197 [20,] 1.819725 1.633549 1.9438948 2.213533 2.247944 [21,] 2.412117 1.797531 2.5320892 1.889267 2.586912 [22,] 1.679395 2.276218 1.6120445 1.648766 1.889033 [23,] 2.286285 2.221312 0.9408758 1.896072 1.996449 [24,] 2.274975 2.398884 2.0146319 1.814092 2.350100 [25,] 2.106620 1.640401 1.6416454 2.452356 1.638885 [26,] 1.556329 1.706762 1.8324196 2.348518 1.593293 [27,] 2.171867 1.707615 1.9667116 2.191344 1.595531 [28,] 1.796751 2.753674 2.1741976 1.623239 2.399018 [29,] 2.635992 2.180735 2.2114669 2.258419 2.277367 [30,] 1.874671 2.113165 2.3653358 2.231705 1.919449Example> boxplot(as.data.frame(M))Output

Sometimes we need to compare the maximum values or set some column of a data frame or data.table object to their maximums, especially in research studies that may require biasedness. Therefore, we can set all the column values to maximum. In case of a data.table object, we can use single square bracket to access and assign the column values to their maximum as shown in the below examples.ExampleLoading data.table package and creating a data.table object −> library(data.table) > x1 x2 DT1 DT1Outputx1 x2 1: 3 4 2: 3 5 3: 5 6 4: 10 5 5: 8 2 6: 3 ... Read More

The mtext function can help us to create X-axis or Y-axis labels and we can put these labels to places desired by us with the help of at argument. For example, if we want to use capital letters starting from A to J that are 10 characters on the X-axis labels then it can be done by using the below command −mtext(text=LETTERS[1:10],outer=FALSE,side=1,las=1,at=1:10)Example> plot(1:10,xaxt="n")OutputExample> mtext(text=LETTERS[1:10],outer=FALSE,side=1,las=1,at=1:10)Output

The rownames and colnames functions are used to define the corresponding names of a matrix and if we want to extract those names then the same function will be used. For example, if we have a matrix called M that has row names and column names then these names can be found by using rownames(M) and colnames(M).ExampleLive Demo> M1 M1Output[, 1] [, 2] [, 3] [, 4] [, 5] [, 6] [, 7] [, 8] [, 9] [, 10] [1, ] 1 11 21 31 41 51 61 71 81 91 [2, ] 2 12 22 32 42 52 62 ... Read More

The F statistic has two degrees of freedom, one for the numerator and one for the denominator and the F distribution is a right-tailed distribution. Therefore, we need to use the F-statistic, the degrees of freedoms, and the lower.tail=FALSE argument with pf function to find the p-value for a F statistic.ExamplesLive Demo> pf(5, 1, 99, lower.tail=F) > pf(5, 1, 24, lower.tail=F) > pf(5, 1, 239, lower.tail=F) > pf(5, 5, 239, lower.tail=F) > pf(5, 5, 49, lower.tail=F) > pf(12, 5, 49, lower.tail=F) > pf(120, 5, 49, lower.tail=F) > pf(120, 1, 49, lower.tail=F) > pf(120, 1, 149, lower.tail=F) > pf(3, 1, 149, ... Read More

To detach a package in R, we can simply use the detach function. But we need to remember that once the package will be detached there is no way to use any of the functions of that particular package. We make this mistake if we forget about detachment. For example, if we detach ggplot2 package using detach function detach(package:ggplot2, unload=TRUE) and again run the ggplot or qplot function then there will be an error.ExampleConsider the below data frame −Live Demo> x y df dfOutputx y 1 -0.09124881 0.8106691 2 -0.20521435 -1.0067072 3 -1.07904498 1.3867400 4 1.34461945 -1.4676405 5 -0.21731862 0.5801624 ... Read More

They are actually no difference between the two commands as they give the same result that is the number of objects in the current workspace. If we have five different type of objects say a data frame, a matrix, a list, a data.table object, and a vector then both the commands will give the names of these objects.ExampleConsider the below objects −Live Demo> x1 x2 df1 df1Outputx1 x2 1 A A 2 D A 3 C D 4 A A 5 B C 6 B D 7 D D 8 D C 9 B D 10 B A 11 D ... Read More

Most people get confused about levels and characters in R, especially the newbies. The difference is that levels specifically define the factor levels of a factor column and the characters are simple the character column that is not a factor or is not used as a factor but can be converted to a factor.ExampleConsider the below data frame −Live Demo> x1 x2 df1 df1Outputx1 x2 1 B B 2 B A 3 D D 4 D C 5 C A 6 D C 7 A D 8 D B 9 D C 10 B B 11 C B 12 D ... Read More

If we want to use unicode characters in the title for a plot created with the help of ggplot2 package then the ggtitle function will be used combined with functions called expression and paste. Using the expression and paste functions, we can write unicode characters for the title of the plot. Check out the below examples to understand how it works.ExampleConsider the below data frame −Live Demo> x y df dfOutput x y 1 3.4501307 6.354644 2 2.1561511 5.349282 3 4.5018653 6.080046 4 2.5512959 5.957558 5 3.6818738 5.749713 ... Read More

If a data frame contains missing value then visualising it in base R is not easily possible but we can make use of visdat package for this purpose. The vis_dat function of visdat package helps to visualize any data frame even if it contains missing values. For example, if a data frame df contains missing value then it can be visualized as vis_dat(df).Example1Consider the below data frame −Live Demo> x1 x2 x3 df1 df1Output x1 x2 x3 1 1 23 10 2 1 23 NA 3 NA NA 10 4 NA NA 10 5 1 24 NA 6 2 22 ... Read More