A bar graph plotted with ggplot function of ggplot2 shows horizontal and vertical gridlines. If we are interested only in the bar heights then we might prefer to remove the horizontal gridlines. In this way, we can have X-axis that helps us to look at the different categories we have in our variable of interest and get rid of the unnecessary information. This can be done by setting breaks argument to NULL in scale_y_discrete function.ExampleConsider the below data frame −> x y df library(ggplot2)Creating the plot with all gridlines −> ggplot(df, aes(x, y))+ + geom_bar(stat='identity')OutputCreating the plot without horizontal gridlines ... Read More

Since operations with data.table are sometimes faster than the data frames, we might want to convert a data frame to a data.table object. The main difference between data frame and data.table is that data frame is available in the base R but to use data.table we have to install the package data.table. We can do this with the help setDT function in the data.table package.ExampleConsider the below data frame −> set.seed(1) > x1 x2 x3 x4 x5 df df x1 x2 x3 x4 x5 1  -0.1264538 1.7189774 2 6 9.959193 2   0.6836433  1.5821363 3 4 7.477968 3  -0.3356286 ... Read More

In a plot, the axes labels help us to understand the range of the variables for which the plot is created. While creating a plot in R using plot function, the axes labels are automatically chosen but we can change them. To do this, firstly we have to remove the axes then add each of the axes with the labels we want and then create the box for the plot.ExampleConsider the below data −> x y plot(x, y)OutputChanging the axes labels for X and Y axes −> plot(x, y, axes=FALSE)+ + axis(side = 1, at = c(2, 5, 10))+ + ... Read More

We might prefer to use row index or column index during the analysis instead of using their numbers, therefore, we can get them with the help of grep function. While dealing with a large data set it becomes helpful because large data sets have large number of rows and columns so it is easier to recall them with their indexes instead of numbers. Specifically, column indexes are needed, on the other hand, rows are required in special cases only such as analysing a particular case.ExampleConsider the below data frame −> set.seed(1) > x1 x2 x3 x4 x5 df head(df, 20) ... Read More

Bar plot is frequently used to analyze the number of times a level of factor variable occurs in a data set and the Y-axis values are crucial to the bar plot. Sometimes these values are not in the form we want, therefore, we want to replace them with the new ones. This can be done with the help of breaks argument of scale_y_continuous function in ggplot2.ExampleConsider the below data frame −> set.seed(1) > x df library(ggplot2)Creating the plot without specifying the Y-axis values −> ggplot(df, aes(x))+ + geom_bar()OutputPlotting with new Y-axis values −> ggplot(df, aes(x))+ + geom_bar()+ + scale_y_continuous(breaks=c(0, 2, ... Read More

In Text analysis, we might want to extract characters from a single string or from a vector of strings. This extraction might be required to create a new string with some specific words required for further analysis. We can do this with the help of str_sub function of stringr package.ExampleConsider the below string −> x1 library(stringr) > str_sub(x1, 1, 8) [1] "Removing" > str_sub(x1, 1, 23) [1] "Removing harmful things" > str_sub(x1, 29, 37) [1] " the road" > str_sub(x1, 30, 37) [1] "the road" > str_sub(x1, -58, -51) [1] "Removing" > str_sub(x1, -58, -1) [1] "Removing harmful things from ... Read More

When we have two categorical variables then each of them is likely to have different number of rows for the other variable. This helps us to understand the combinatorial values of those two categorical variables. We can find such type of rows using count function of dplyr package.ExampleConsider the CO2 data in base R −> head(CO2, 20) > head(CO2, 20)       Plant    Type    Treatment    conc     uptake 1     Qn1     Quebec   nonchilled     95      16.0 2     Qn1     Quebec   nonchilled    175   ... Read More

Some vectors are randomly created and some are not randomly created in R but we can do randomization for both of these types of vectors. Randomization ensures unbiasedness therefore it is necessary especially when the vector is created with an objective that tends to change the result of the analysis. The randomization in R can be simply done with the help of sample function.Randomization of vectors that are not randomly created −> x1 x1 [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ... Read More

There are two methods to create a vector with repeated values in R but both of them have different approaches, first one is by repeating each element of the vector and the second repeats the elements by a specified number of times. Both of these methods use rep function to create the vectors.ExampleConsider the below examples −> x1 x1 [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 [39] 4 ... Read More

A queue is an abstract data structure that contains a collection of elements. Queue implements the FIFO mechanism i.e the element that is inserted first is also deleted first.Queue cane be one linear data structure. But it may create some problem if we implement queue using array. Sometimes by using some consecutive insert and delete operation, the front and rear position will change. In that moment, it will look like the queue has no space to insert elements into it. Even if there are some free spaces, that will not be used due to some logical problems. To overcome this ... Read More