R Programming Articles

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How to create smooth density curves without filling densities in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 571 Views

The density curves can be created by using stat_density function of ggplot2 package but it fills the curve with density hence it becomes difficult to recognize the curves. We can remove these densities by using geom="line" inside the stat_density function so that only the density curves will be plotted.ExampleConsider the below data frame:> G Response df dfOutputG Response 1 C 1.0229016 2 C 1.0058160 3 B 0.8831558 4 B 0.7729167 5 C 0.9130468 6 D 0.8431893 7 B 1.5003581 8 A 0.9687335 9 B 1.1139661 10 A 0.9211660 11 A 1.1790619 12 D 0.6349671 13 A 1.2616918 14 A 1.6021078 ...

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How to convert a matrix into a matrix with single column in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 1K+ Views

If we have a matrix then we might want to convert it to matrix with single column for some analytical purpose such as multiplying with a vector that has the length equal to the total number of elements as in the matrix. Thus, the matrix can be converted to a single column matrix by using matrix function itself but for this we would need to nullify the column names and row names.Example1> M1 M1Output [, 1] [, 2] [, 3] ...

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How to create a vector of lists in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 609 Views

If we have many lists but we want to use the values in the lists as a vector then we first need to combine those lists and create a vector. This can be done by using unlist function along with the combine function c to create the vector. For example, if we have two lists defined as List1 and List2 and we want to create a vector V using these lists then it can be created as:V x1 x1Output$a [1] -0.6972237 -1.5013768 -0.2451809 -0.2365569 -1.6304919 -1.1704378 [7] 1.1617054 -0.2349498 -1.2582229 0.4112065 $b [1] 2 0 2 6 0 0 ...

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How to perform chi square test for goodness of fit in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 4K+ Views

The chi square test for goodness of fit is a nonparametric test to test whether the observed values that falls into two or more categories follows a particular distribution of not. We can say that it compares the observed proportions with the expected chances. In R, we can perform this test by using chisq.test function. Check out the below examples to understand how it is done.Example1> x1 x1Output[1] 9 4 1 9 6 6 1 6 0 0 5 8 8 3 7 8 0 3 3 9 6 0 3 8 2 0 8 5 9 1 3 4 ...

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What are the different types of point available in geom_point of ggplot2 package in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 323 Views

We can create a point chart using ggplot2 package but that point not necessarily to be in circular shape, we have twenty-five shape options for those points in ggplot2. While creating a point chart using ggplot2, we can use shape argument inside geom_point to see the difference among these twenty-five shapes.ExampleConsider the below data frame:> set.seed(1957) > x y df dfOutput x y 1 0.7028704 1.6664500 2 0.9672393 1.0456639 3 1.3102736 0.2495795 4 0.3389941 0.2141513 5 0.5867095 0.4417377 6 0.4257543 0.6533757 7 0.9106756 0.3611954 8 1.0444729 1.3770588 9 ...

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How to subset nth row from an R data frame?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 2K+ Views

We can find subsets using many ways in R and the easiest way is to use single-square brackets. If we want to subset a row or a number of consecutive or non-consecutive rows then it can be directly done with the data frame name and the single-square brackets. For example, if we have a data frame called df and we want to subset 1st row of df then we can use df[1, ] and that’s it.ExampleConsider the below data frame:> set.seed(214) > x y z a b c q w df1 df1Outputx y z a b c q w 1 ...

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What is the difference between $ and @ in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 507 Views

If we have a data frame defined as df that contains column x, y, and z then extraction of these columns from df can be done by using df$x, df$y, and df$z. On the other hand, if we have an S4 object defined as Data_S4 that contains column x, y, and z then the extraction of these columns can be done by using Data_S4@x, Data_S4@y, and Data_S4@z.Example of a data frame:Example> x1 x2 df dfOutput x1 x2 1 4 2 2 7 0 3 10 2 4 3 1 5 7 1 6 2 2 7 3 4 8 4 ...

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How to create a heatmap for lower triangular matrix in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 982 Views

A heatmap is a diagrammatic representation of data where the values are represented with colours. Mostly, it is used to display data that has slight variation. We can draw it for a full matrix, an upper triangular matrix as well as a lower triangular matrix. This can be done with the help of image function.Example1> M1 M1Output   [, 1] [, 2] [, 3] [, 4] [, 5] [, 6] [1, ] 6    9    4    7    5    4 [2, ] 6    6    4    3    7    5 [3, ] 2    6 ...

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How to extract the names of list elements in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 3K+ Views

The names of list elements can be extracted by using the names function. For example, if we have a list defined as List that contains three elements with names element1, element2, and element3 then then these names can be extracted from the List by using the below command:names(List)Example1> List1 List1Output$x1 [1] -0.04518909 -0.22779868 0.24339595 -0.86189295 -0.73387277 -0.75313131 [7] 0.39694608 2.30565359 0.55670193 0.21973762 0.62968128 -0.90936921 [13] 1.33946741 -0.16315751 0.31357793 0.40365980 -0.23639612 -2.48749453 [19] 0.52152768 -1.57059863 0.51728464 0.98177111 0.65475629 0.23715538 [25] -0.71796609 -0.42731839 0.32335282 -0.90013122 -0.84549927 -0.88358214 [31] -0.32066379 -0.98945433 0.42469849 -1.63095343 0.32584448 0.10947333 [37] 0.23486625 0.28166351 1.18432843 0.94828212 0.09452671 0.56618262 [43] ...

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What is the difference between na.omit and complete.cases in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 954 Views

The na.omit function removes all the missing values in a data frame and complete.cases also does the same thing if applied to the whole data frame. The main difference between the two is that complete.cases can be applied to some columns or rows. Check out the below example to understand the difference.ExampleConsider the below data frame:> set.seed(2584) > x y df dfOutput x y 1 NA 25 2 5 5 3 8 NA 4 6 5 5 4 NA 6 4 5 7 6 NA 8 4 NA 9 4 5 10 8 5 11 8 5 12 ...

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