Just like numerical vectors, we can find the different elements between two string vectors if there exists any. For this purpose, we can use setdiff function. For example, if we have a vector V1 that contains a, b, c, d, e, f and the other vector V2 that contains a, e, h, k, l, p, r, u, v, w then the different elements between these two vectors can be found as setdiff(V1,V2).Example Live Demox1
Finding the sum of consecutive value while considering the sum of two values each time means the sum of first two values, then the sum of second value and the third value, then the sum of third value and the fourth value, then the sum of fourth value and the fifth value, and so on. For this purpose, we can use rollapply function from zoo package.Loading zoo packageibrary(zoo)Example Live Demox1
Suppose we have a number n, we have to find the length of the longest consecutive run of 1s in its binary representation.So, if the input is like n = 312, then the output will be 3, as 312 is 100111000 in binary and there are 3 consecutive 1s.To solve this, we will follow these steps −ret := 0, len := 0for initialize i := 0, when i < 32, update (increase i by 1), do:if n/2 is odd, then(increase len by 1)Otherwiselen := 0ret := maximum of ret and lenreturn retLet us see the following implementation to get better ... Read More
To create a horizontal bar chart using ggplot2 package, we need to use coord_flip() function along with the geom_bar and to add the labels geom_text function is used. These two functions of ggplot2 provides enough aesthetic characteristics to create the horizontal bar chart and put the labels at inside end of the bars.Example Live Demox
Suppose we have a matrix M where M[r][c] represents the height of that cell. If we are currently at top left corner and want to go to the bottom right corner. We can move to adjacent cells (up, down, left, right) only if that the height of that adjacent cell is less than or equal to the current cell's height. We can increase the height of any number of cells before we move, so we have to find the minimum total height that needs to be increased so that we can go to the bottom right cell.So, if the input ... Read More
We can use permn function from combinat package to find the permutations but if we have repeated elements in the vector then the result will not have unique permutations, therefore, we need to use unique function along with the permn function. For example, if we have a vector 1, 2, 1 then the permutations will be (1 2 1), (1 1 2), (1 1 2), (1 2 1), (2 1 1), (2 1 1) and the unique permutations will be (1 2 1), (1 1 2), (2 1 1).Example Live Demox1
A legend helps us to differentiate between the type of values or any another division of values in a data set. These legends can be represented in many ways and two of these ways are straight lines and stars. To represent the legend in a plot created by using plot function with colored straight lines or stars, we need to correct lty and pch arguments.ExampleConsider the below vectors −set.seed(199) x
Frequency polygons are the graphs of the values to understand the shape of the distribution of the values. They are useful in comparing different data sets and visualising cumulative frequency distribution of the data sets. In base R, we can use polygon function to create the frequency polygon but first we should create a line plot for the two variables under consideration.ExampleConsider the below vectors x and y −set.seed(999) x
Suppose we have a binary tree; we have to check whether its height is balanced or not. We know that for a height balanced tree, for every node in the tree, the absolute difference of the height of its left subtree and the height of its right subtree is 0 or 1.So, if the input is likethen the output will be TrueTo solve this, we will follow these steps −Define a function dfs(), this will take node, if node is null, then −return 0l := 1 + dfs(left of node)r := 1 + dfs(right of node)if |l - r| > ... Read More
Often, we find that the values that represent factor levels are recorded as numerical values, therefore, we need to convert those numerical values to factor. In this way, we can use the factor column properly in our analysis otherwise R program will treat the factors as numerical values and the analysis output will be incorrect.Example Live Demodata(mtcars) str(mtcars)Output'data.frame': 32 obs. of 11 variables: $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... $ cyl : num 6 6 4 6 8 6 8 4 4 6 ... $ disp: num 160 160 108 258 360 ... $ hp : num 110 110 93 110 175 105 245 62 95 123 ... $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ... $ wt : num 2.62 2.88 2.32 3.21 3.44 ... $ qsec: num 16.5 17 18.6 19.4 17 ... $ vs : num 0 0 1 1 0 1 0 1 1 1 ... $ am : num 1 1 1 0 0 0 0 0 0 0 ... $ gear: num 4 4 4 3 3 3 3 4 4 4 ... $ carb: num 4 4 1 1 2 1 4 2 2 4 ... mtcars$cyl
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