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Dealing with NA values is one of the boring and almost day to day task for an analyst and hence we need to replace it with the appropriate value. If in an R data frame, we have a Boolean column that represents TRUE and FALSE values, and we have only FALSE values then we might want to replace NA’s with TRUE. In this case, we can use single square bracket and is.na to set all NA’s to TRUE.Exampleset.seed(999) S.No.
The default color of boxplot area in R using ggplot2 is white but we might want to change that color to something more attracting, for example blue or red. To do this purpose, we can use geom_boxplot function of ggplot2 package with fill argument by passing the color names.Consider the below data frame −Exampleset.seed(1321) v1
To find the root mean square of a vector we can find the mean of the squared values then take the square root of the resulting vector. This can be done in a single and very short line of code. For example, if we have a vector x and we want to find the root mean square of this vector then it can be done as sqrt(mean(x^2)).Examplex1
To find the correlation matrix, we simply need to use cor function with the data frame object name. For example, if we have a data frame named as df then the correlation matrix can be found by using cor(df). But the result will have too many decimal places to represent the correlation. If we want to avoid the values after decimal places, we can use round function.Consider the mtcars data in base R −Exampledata(mtcars) cor(mtcars)Output mpg cyl disp hp drat ... Read More
An unordered combination of elements means that the combination of the values in a way that does not make any particular arrangement. For example, if we have three values one, two, and three then they can be arranged in the following way which is unordered −"one" "two" "three" "one" "two" "one" three" "two" "three" "one" "two" "three"Examplex
If we have two data frames with same number of columns of same data type and equal number of rows then we might want to find the difference between the corresponding values of the data frames. To do this, we simply need to use minus sign. For example, if we have data-frames df1 and df2 then the subtraction can be found as df1-df2.Consider the below data frame −Examplex1
When we have repeated elements of strings and we want to use them as factor levels then it is okay but if we want to treat them individually then it is better to make each value a unique element. To do this, we can use make.unique function. For example, if we have a vector x that contains repeated string values then to make them unique, we can use make.unique(x).Examplex1
Let’s say the following is our first select − John David Bob Mike Sam Carol Following is our second select − David Mike Carol We need to remove the options in the first select, which are similar to options in the second select. For this, use val(). Following is the complete code −Example Document John David Bob Mike Sam Carol David Mike Carol $('#remaining_name > option').each(function (i, el) { var value = $(el).val(); $('#all_present_name > option[value="' + value + '"]').remove(); }); OutputThe output is as follows −
Let’s say we have the following button −On click of the above button, call the function() with on() −$('#buttonId').on('click', function () { displayTheData(); })The above function is using the html() to display the following div − I am inside the div tag... ExampleFollowing is the complete code to print div content − Document I am inside the div tag... I am not inside the div tag... $('#buttonId').on('click', function () { ... Read More
Mostly, we need to import the data from an outside source in R environment for analysis and these data can be recorded as comma separated values that represent rows. If we want to create the columns for the comma separated values then cSplit function of splitstackshape package can be used. In the below example, we have created a data frame with comma separated values then splitting those values as single value in each column.Consider the below data frame −Exampledf=data.frame(x=apply(matrix(rpois(200, 10), 20, 10), 1, paste, collapse=", ")) dfoutputx 1 8, 12, 7, 12, 10, 8, 11, 6, 8, 7 2 9, ... Read More