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Instead of finding the common rows, sometimes we need to find the uncommon rows between two data frames. It is mostly used when we expect that a large number of rows are uncommon instead of few ones. We can do this by using the negation operator which is represented by exclamation sign with subset function.ExampleConsider the below data frames − Live Demo> x1 y1 df1 df1Output x1 y1 1 10 6 2 5 9 3 10 10 4 4 10 5 1 6 6 1 4 7 9 3 8 5 10 9 10 3 10 8 2 11 6 10 12 ... Read More
A vector can contain values that are increasing or decreasing in nature or they can be also random which means a higher value may come after a lower one which is followed by a higher value. An example of increasing arrangement of elements of vector is 1, 2, 3 and the opposite of that would be decreasing arrangement. We can check whether a vector is arranged in increasing order or decreasing order by checking whether the difference between all values of the vector is greater than or equal to zero or not and it can be done by using diff ... Read More
Sometimes a vector strings have patterns and sometimes we need to make patterns from a vector of strings based on the characters. For example, we might want to extract the states name of United States of America from a vector that contains all the names. This can be done by using grepl function.ExampleConsider the below vector containing states name in USA −> US_states US_states[grepl("^A", US_states)] [1] "Alabama" "Alaska" "American Samoa" "Arizona" [5] "Arkansas" > US_states[grepl("^B", US_states)] character(0) > US_states[grepl("^C", US_states)] [1] "California" "Colorado" "Connecticut" > US_states[grepl("^D", US_states)] [1] "Delaware" "District of Columbia" > US_states[grepl("^E", US_states)] character(0) > US_states[grepl("^F", US_states)] [1] ... Read More
In Data Analysis, sometimes we need to find the difference of the current value from the previous value and it can be also needed for groups. It helps us to compare the differences among the values. In R, we can use dplyr package’s group_by and mutate function with lag.ExampleConsider the below data frame − Live Demo> Group Frequency df1 df1Output Group Frequency 1 A 7 2 A 6 3 A 9 4 A 12 5 B 19 6 B 19 7 B 4 8 B 6 9 C 14 10 C 6 ... Read More
Sometimes we want to extract the count from the data frame and that count could be the number of columns that have same characteristics based on row values. For example, if we have a data frame containing three columns with fifty rows and the values are integers between 1 and 100 then we might want to find the number of columns that have value greater than 20 for each of the rows. This can be done by using rowSums function.ExampleConsider the below data frame − Live Demo> x1 x2 x3 df dfOutput x1 x2 x3 1 9 72 9 2 5 20 ... Read More
The data in simultaneous equations can be read as matrix and then we can solve those matrices to find the value of the variables. For example, if we have three equations as −x + y + z = 6 3x + 2y + 4z = 9 2x + 2y – 6z = 3then we will convert these equations into matrices and solve them using solve function in R.Example1 Live Demo> A AOutput [, 1] [, 2] [, 3] [1, ] 1 1 2 [2, ] 3 2 4 [3, ] 2 3 -6 Live Demo> b ... Read More
Normally, the gridlines on a plot created by using ggplot2 package are a little far from each other but sometimes the plot looks better if the gridlines are close to each other, therefore, we might want to do so. This can be done by setting the minor_breaks and breaks using scale_y_continuous if the Y-axis plots a continuous variable.ExampleConsider the below data frame − Live Demo> x y df dfOutput x y 1 14 16 2 36 1 3 78 18 4 61 6 5 19 11 6 2 40 7 93 23 8 10 13 9 3 21 10 55 31 ... Read More
To find the means of all columns in an R data frame, we can simply use colMeans function and it returns the mean. But for standard deviations, we do not have any direct function that can be used; therefore, we can use sd with apply and reference the columns to find the standard deviations for all column of an R data frame. For example, if we have a data frame df then the syntax using apply function to find the standard deviations for all columns will be apply(df, 2, sd), here 2 refers to the columns. If we want to ... Read More
When we take a random sample from an R data frame the sample rows have row numbers as in the original data frame, obviously it happens due to randomization. But it might create confusion while doing analysis, especially in cases when we need to use rows, therefore, we can convert the index number of rows to numbers from 1 to the number of rows in the selected sample.ExampleConsider the below data frame − Live Demo> set.seed(111) > x1 x2 x3 df1 df1Output x1 x2 x3 1 1.735220712 2.8616625 1.824274 2 1.169264128 2.8469644 ... Read More
A vector may have thousands of values and each of them could be different or same also. It is also possible that values can be grouped or randomly selected but having few similar values. Irrespective of the values in a vector, to find some largest values we need to sort the vector in ascending order then the largest values will be selected.Examples> x1 x1 [1] -1.4447473195 3.2906645299 -0.4680055849 0.1611487482 -0.7715094280 [6] 0.4442103640 0.3702444686 0.0783124252 1.3476432299 1.0140576107 [11] -0.0968917066 0.4628821017 0.3102594626 -0.2946001275 0.1498108166 [16] -0.6002154305 0.5905382364 1.3892651534 0.1008921325 -0.6486318692 [21] -0.0562831933 -0.6887431711 0.4907512082 -0.3994662410 0.7827897030 [26] 0.5294704584 -1.3802965730 -0.6159076490 -0.0009408529 1.6182294859 ... Read More