The main title of a histogram in base R can be inserted by using title function and if we want to have it in the outer margin then outer argument must be set to TRUE. If the outer argument does not fulfil our requirement then we can use par function to adjust the outer margin area and create the histogram. Check out the below example to understand how it works.Example> x hist(x) > title('Normal Distribution',outer=TRUE)OutputExample> par(oma=c(0,0,2,0)) > hist(x) > title('Normal Distribution',outer=TRUE)Output

When the assumptions of parametric analysis are not satisfied then we move on to non-parametric analysis and non-parametric analysis often deals with the calculation of median because the data is not normally distributed. If we want to find the group-wise median and the data is stored in a data.table object then lapply function can be used as shown in the below examples.ExampleLoading data.table package:> library(data.table)Consider the below data.table object:Example> Group x1 x2 x3 x4 dt1 dt1OutputGroup x1 x2 x3 x4 1: B 0.515370827 6.174187 542.9350 50.28300 2: B 0.522858146 6.976872 510.5568 49.71331 3: A 1.055456751 3.192242 476.7693 48.88280 4: A ... Read More

If we have a matrix then we might want to convert it to matrix with single column for some analytical purpose such as multiplying with a vector that has the length equal to the total number of elements as in the matrix. Thus, the matrix can be converted to a single column matrix by using matrix function itself but for this we would need to nullify the column names and row names.Example1Live Demo> M1 M1Output [, 1] [, 2] [, ... Read More

The density curves can be created by using stat_density function of ggplot2 package but it fills the curve with density hence it becomes difficult to recognize the curves. We can remove these densities by using geom="line" inside the stat_density function so that only the density curves will be plotted.ExampleConsider the below data frame:Live Demo> G Response df dfOutputG Response 1 C 1.0229016 2 C 1.0058160 3 B 0.8831558 4 B 0.7729167 5 C 0.9130468 6 D 0.8431893 7 B 1.5003581 8 A 0.9687335 9 B 1.1139661 10 A 0.9211660 11 A 1.1790619 12 D 0.6349671 13 A 1.2616918 14 A ... Read More

If we have a large amount of data in a data frame and we suspect that some of the data columns are repeated or some of them are equal to a particular column then we can use sapply function in base R to figure it out. In this way, we can remove duplicated columns that does not suppose to help in our data analysis objective.Example1Consider the below data frame:Live Demo> set.seed(354) > x1 x2 x3 x4 x5 df1 df1Outputx1 x2 x3 x4 x5 1 4 5 4 4 6 2 6 4 8 7 5 3 5 6 4 7 ... Read More

To convert a date or date vector to POSIXct, we can use as.POSIXct function but we also need to pass the appropriate date format inside the function. For example, if we have a date "2020-11-14" then it can be converted to POSIXct by using as.POSIXct("2020-11-14", format="%Y-%m-%d").Example1Live Demo> date1 as.POSIXct(date1, format="%Y-%m-%d")Output[1] "2020-04-01 IST"Example2Live Demo> date2 date2Output[1] "2020-02-12" "2020-06-01" "2020-04-01" "2020-05-01" "2020-01-21" [6] "2020-01-21" "2020-06-01" "2020-04-27" "2020-05-11" "2020-06-01" [11] "2020-01-21" "2020-03-31" "2020-05-01" "2020-02-12" "2020-01-21" [16] "2020-05-01" "2020-03-31" "2020-04-01" "2020-05-01" "2020-01-21" [21] "2020-05-01" "2020-04-11" "2020-05-11" "2020-04-01" "2020-03-31" [26] "2020-04-11" "2020-04-01" "2020-03-31" "2020-04-01" "2020-04-11" [31] "2020-05-11" "2020-06-01" "2020-03-31" "2020-04-27" "2020-01-21" [36] "2020-01-21" "2020-04-01" "2020-06-01" ... Read More

To create a bar plot in base R, we can directly use barplot function but the table of frequencies should be passed inside this function. If we want to create the barplot in horizontal manner then horiz=TRUE argument must be added. For example, if we have a vector x that contains repeating values then the horizontal bar plot of x can be created by using barplot(table(x),horiz=TRUE).Example1> x barplot(table(x),horiz=TRUE)OutputExample2> y barplot(table(y),horiz=TRUE)OutputExample3> z barplot(table(z),horiz=TRUE)Output

If we have missing data in our data frame then some of them can be replaced if we have enough information about the characteristic of the case for which the information is missing. But if that information is not available and we do not find any suitable way to replace the missing values then complete.cases function can be used with the columns that has missing values.ExampleConsider the below data frame:Live Demo> set.seed(19991) > x1 x2 x3 x4 x5 df1 df1Output x1 x2 x3 ... Read More

Sometimes we want to created repeated values, this is helpful in different scenarios such as measuring an effect of a constant on multiple variables. The list values can be also replicated for similar purpose of analysis. The replication of list of a list can be created by using rep function. For example, if we have a list called x and we want to create five times replicated list of this list then we can use the code rep(list(x), 5).Example1Live Demo> List1 List1Output$x1 [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ... Read More

The subsetting of a data frame can be done by using column names as well as column number. Also, we can subset by subsequent as well as non-subsequent column numbers. For example, if we have a data frame df that contains column x, y, z then we can make a subset of x and z by using df[, c(1, 3)].ExampleConsider the below data frame:> set.seed(191) > x1 x2 x3 x4 df1 df1Output x1 x2 x3 ... Read More