In data analysis, time series is one of the common data we have to deal with and it might also contain dates data along with other variables. We might want to find the difference between two times to check how many days or weeks have changed the time series. This can be easily done with the help of difftime function.Example> difftime(strptime("25/07/2021", format = "%d/%m/%Y"), + strptime("25/07/2020", format = "%d/%m/%Y"), units="weeks") Time difference of 52.14286 weeks > difftime(strptime("25.07.2021", format = "%d.%m.%Y"), + strptime("25.07.2020", format = "%d.%m.%Y"), units="weeks") Time difference of 52.14286 weeks > difftime(strptime("25.07.2021", format = "%d.%m.%Y"), + strptime("25.07.2020", format = ... Read More

Regression analysis output in R gives us so many values but if we believe that our model is good enough, we might want to extract only coefficients, standard errors, and t-scores or p-values because these are the values that ultimately matters, specifically the coefficients as they help us to interpret the model. We can extract these values from the regression model summary with delta $ operator.ExampleConsider the below data −> set.seed(99) > x1 x2 x3 x4 x5 x6 x7 y Regression_Model summary(Regression_Model) Call: lm(formula = y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7) ... Read More

In data analysis, there are many situations we have to deal with and one of them is creating a new column that has the row sums of only some rows. These sums will be repeated so that we get the total number of values equal to the number of rows in the data frame. We can use rowSums with rep function to create such type of columns.ExampleConsider the below data frame −> set.seed(99) > x1 x2 x3 x4 x5 df df x1 x2 x3 x4 x5 1 0.7139625 4 9.321058 0.33297863 4 2 0.9796581 2 4.298837 -1.47926432 11 3 0.5878287 ... Read More

The error “undefined columns selected when subsetting data frame” means that R does not understand the column that you want to use while subsetting the data frame. Generally, this happens when we forget to use comma while subsetting with single square brackets.ExampleConsider the below data frame −> set.seed(99) > x1 x2 x3 x4 x5 df df x1 x2 x3 x4 x5 1 0.7139625 4 9.321058 0.33297863 4 2 0.9796581 2 4.298837 -1.47926432 11 3 0.5878287 3 7.389898 -0.07847958 5 4 0.9438585 4 7.873764 -1.35241100 6 5 0.1371621 2 5.534758 -1.17969925 4 6 0.6226740 4 8.786676 -1.15705659 5 7 -0.3638452 1 ... Read More

When we have multiple lists but they have similar type of data then we might want to combine or merge those lists. This will be helpful to use because we can perform the calculations using one list name instead of applying them on multiple ones. We can combine multiple lists with the help of mapply function.ExampleConsider the below lists −> List1 List1 [[1]]   [1] "a" "b" "c" "d" "e" [[2]]   [1]  1   2   3   4   5 [[3]]   [1]  5   4   3   2   1 [[4]]   [1] 25 [[5]]   ... Read More

Categorical variables have multiple categories but if the data set is large and the categories are also large in numbers then it becomes a little difficult to recognize them. Therefore, we can extract unique values for categorical variables that will help us to easily recognize the categories of a categorical variable. We can do this by using unique for every column of an R data frame.ExampleConsider the below data frame −> x1 x2 x3 x4 df df    x1 x2  x3     x4 1  A  5 India     a 2  A  5 India     b 3  A ... Read More

An R data frame can have a large number of categorical variables and these categorical form different combinations. For example, one value of a variable could be linked with two or more values of the other variable. Also, one categorical variable can have all unique categories. We can find this unique combination for as many variables as we want and it can be done with the help of unique function.ExampleConsider the below data frame −> x1 x2 x3 x4 df df x1 x2 x3 x4 1 1 A a 5 2 2 A b 5 3 3 A c 10 ... Read More

Creating a data frame with a column as a list is not difficult but we need to use I with the list so that the list elements do not work as an individual column. Here, you will find the common method to create a list which is incorrect if we want to insert that list in our data, also the correct method is mentioned at the end.The incorrect way −Example> x1 x2 df df      x1 c.1..1. c.2..2. c.3..3. c.4..4. c.5..5. c.6..6. c.7..7. c.8..8. c.9..9. 1     1    1       2       3 ... Read More

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