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Sometimes each row needs to be treated differently, therefore, we might want to convert those rows into list. This will help us to perform the operations on our row elements separately. To convert the rows into list, we can use split function by defining the number of rows in the data frame.Consider the below data frame −Exampleset.seed(101) x1
To create a sequence of time in minutes with date we can use seq function and define the date and time with as.POSIXct. For example, if we want to generate a sequence of time between 2 pm on tenth October 2020 to 4 pm on the same date then we can use the following code −seq(from=as.POSIXct("2020-10-10 14:00"), to=as.POSIXct("2020-10-10 16:00"), by="min")Exampleseq(from=as.POSIXct("2020-01-01 00:01"), to=as.POSIXct("2020-01-01 01:00"), by="min")Output[1] "2020-01-01 00:01:00 IST" "2020-01-01 00:02:00 IST" [3] "2020-01-01 00:03:00 IST" "2020-01-01 00:04:00 IST" [5] "2020-01-01 00:05:00 IST" "2020-01-01 00:06:00 IST" [7] "2020-01-01 00:07:00 IST" "2020-01-01 00:08:00 IST" [9] "2020-01-01 00:09:00 IST" "2020-01-01 00:10:00 IST" [11] "2020-01-01 ... Read More
The difference between regression line and the points on the scatterplot are actually the residuals, thus we need to calculate the residual from the model object. This can be simply done by using residuals function. For example, if we create a linear model defined as Model between x and y then the residuals will be found as residuals(Model).Consider the below data frame −Exampleset.seed(999) x1
Cumulative sums are mostly used in descriptive analysis of data but sometimes we might want to calculate them in understanding the time series analysis for moving sums but it is very rare. If we have a factor column in an R data frame then it would not make sense to find the cumulative sum for all factor levels together, we must find the cumulative sums for each level. This can be easily done by using ave function.ExampleConsider the below data frame −set.seed(15) x1
The relative frequency histogram can be created for the column of an R data frame or a vector that contains discrete data. For this purpose, we can use PlotRelativeFrequency function of HistogramTools package along with hist function to generate histogram. For example, if we have a vector x for which we want to create a histogram with relative frequencies then it can be done as PlotRelativeFrequency(hist(x)).ExampleConsider the below vector −x
Random samples can be generated in many ways such as using discrete and continuous distributions, using integer vectors, using numerical vectors, using character vectors and/or factor vectors, also with columns of a data set. If we have the sample that is continuous in nature then the values are likely to contain many values after decimal point and we can limit those values to 4 or use any other limit using round function.Examplex1
To create a normal random vector, we can use rnorm function with mean and standard deviation as well as without passing these arguments. If we have a different vector derived from another distribution or simply represent some numbers then we can use the mean of that vector in the rnorm function for mean argument.Exampleset.seed(101) x1
Usually, a point chart is created to assess the relationship or movement of two variables together but sometimes these points are scattered in a way that makes confusion. Hence, data analyst or researcher try to visualize this type of graph by joining the points with lines. In ggplot2, this joining can be done by using geom_line() function.Consider the below data frame −Exampleset.seed(111) x
Indexing helps us to understand the location of the value in the vector. If we have a vector that contains repeated values then we might want to figure out the last occurrence of the repeated value. For example, if we have a vector x that contains 1, 1, 2, 1, 2 then the last occurrence of repeated values will be 4 and 5 because the last 1 is at 4th position and 2 is at the 5th position. We can find this by using tapply function in R.Examplex1
If we have a matrix that contains NA or Inf values and we want to take the subset of that matrix with finite values then only the rows that do not contain NA or Inf values will be the output. We can do this in R by using rowSums and is.finite function with negation operator !.Exampleset.seed(999) M1