Sometimes we have vectors with NA values, also there might be a situation that one of vector having an NA at a position and the other vector has the numerical values at the same position. For example, 1, 2, NA and 1, 2, 3. In this case, we might want to combine these two vectors to make a single vector. This can be done by using coalesce function of dplyr package.Example> library(dplyr) > x1 x1Output[1] NA 4 NA 1 2 NA 4 1 4 1 2 3 1 4 2 2 NA 2 2 1Example Live Demo> y1 y1Output[1] 1 2 ... Read More

Sometimes we want to determine the multiplication of all the elements of a vector just like the sum. This might be required to test the changes in the mathematical operations that can be applied to a numerical vector. In base R, we have prod function which works same as sum but give us the multiplication of all the elements of a vector.Example Live Demo> v1 v1Output[1] 1 2 3 4 5> prod(v1)Output[1] 120 Example Live Demo> v2 v2Output[1] -0.500466629 0.394771317 0.575743107 0.026982141 0.812697502 [6] 0.995708241 2.198243938 -0.008609976 -0.931337300 -0.073743225> prod(v2)Output[1] 3.228448e-06Example Live Demo> v3 v3Output[1] 1.7328245888 -0.5772304935 2.5161349689 3.0401656274 0.1669773313 [6] -0.0001252235 0.7649984733 ... Read More

In general, the concatenation of numerical vectors and string results in a vector of strings in R. For example, if we want to concatenate 1, 2, 3 with Tutorialspoint using paste function then it will result in a vector as: "Tutorialspoint 1" "Tutorialspoint 2" "Tutorialspoint 3". But if we want it to return as "Tutorialspoint 1 2 3" then we need to use collapse argument with paste function.Example1> x1Output[1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J"> y1Output[1] 1 9 6 3 8 1 8 5 9 1 2 8 3 8 4 5 10 1 6 3 ... Read More

When we create a bar graph using ggplot2, the color of the bars is dark grey but it can be changed to different colors or we can also give different shades of grey to them. This will be helpful if we are plotting a pattern of categorical data. For example, plotting educational level on X-axis with frequencies of years of experience on Y-axis. We can do this by using scale_fill_grey function of ggplot2 package.ExampleConsider the below data frame − Live Demo> x Freq df dfOutput x Freq 1 A 14 2 B 12 3 C 13 4 D 15> library(ggplot2) > ggplot(df, ... Read More

In base R, the barplot function easily creates a barplot but if the number of bars is large or we can say that if the categories we have for X-axis are large then some of the X-axis labels are not shown in the plot. Therefore, if we want them in the plot then we need to use las and cex.names.ExampleConsider the below data and bar graph − Live Demo> x names(x) barplot(x)OutputShowing all the X-axis labels −> barplot(x,las=2,cex.names=0.5)Output

When we perform a t test in R and the difference between two groups is very large then the p-value of the test is printed as 2.2e – 16 which is a printing behaviour of R for hypothesis testing procedures. The actual p-value can be extracted by using the t test function as t.test(“Var1”, ”Var2”, var.equal=FALSE)$p.value. This p-value is not likely to be the same as 2.2e – 16.Example1 Live Demo> x1 y1 t.test(x1, y1, var.equal=FALSE)Output   Welch Two Sample t-test data: x1 and y1 t = -3617.2, df = 10098, p-value < 2.2e-16 alternative hypothesis: true difference in means is not ... Read More

Sometimes we want to combine column values of two columns to create a new column. This is mostly used when we have a unique column that maybe combined with a numerical or any other type of column. Also, we can do this by separating the column values that is going to be created with difference characters. And it can be done with the help of apply function.ExampleConsider the below data frame − Live Demo> ID Country df1 df1Output ID Country 1 1 UK 2 2 UK 3 3 India 4 4 USA 5 5 USA 6 6 UK 7 7 Nepal 8 ... Read More

Gridlines are the horizontal and vertical dotted lines, and they help to organize the chart so that the values on the labels becomes better readable to viewers. This is helpful specially in situations where we plot a large number of data points. A graph drawn by plot function can have gridlines by defining the vertical and horizontal lines using abline.ExampleConsider the below data and scatterplot − Live Demo> x y plot(x,y)OutputAdding gridlines using abline function −> abline(h=seq(0,5,0.5),lty=5) > abline(v=seq(-2,2,0.5),lty=5)Output

Extraction or selection of data can be done in many ways such as based on an individual value, range of values, etc. This is mostly required when we want to either compare the subsets of the data set or use the subset for analysis. The selection of rows based on range of value may be done for testing as well. We can do this by subset function.ExampleConsider the below data frame − Live Demo> x1 x2 x3 df dfOutput x1 x2 x3 1 3 2 6 2 3 4 9 3 4 4 12 4 4 8 12 5 3 5 11 ... Read More

The default size of axes labels created by using plot function does not seem to be large enough and also it does not look appealing. Therefore, we might want to change their size and color because the appearance of a plot matters a lot. This can be done by setting colors with col.lab and size with cex.lab.Example Live Demo> x y plot(x,y)OutputChanging the color of axes labels and the size of those axes labels −> plot(x,y,col.lab="blue",cex.lab=2)Output> plot(x,y,col.lab="dark blue",cex.lab=3)Output