Programming Articles - Page 1806 of 3363

Comparison of rows is an influential part of data analysis, sometimes we compare variable with variable, value with value, case or row with another case or row, or even a complete data set with another data set. This is required to check the accuracy of data values and its consistency therefore we must do it. For this purpose, we need to select the required rows, columns etc. To select the first row for each level of a factor variable we can use duplicated function with ! sign.ExampleConsider the below data frame −> x1 x2 x3 df head(df, 20)   x1 ... Read More

To check the trend of all columns of a data frame, we need to create line charts for all of those columns. These line charts help us to understand how data points fall or rise for the columns. Once we know the trend, we can try to find the out the reasons behind them and take appropriate actions. We can plot line charts for each of the column by using plot.ts function that plots data as a time series.ExampleConsider the below data frame.> set.seed(1) > x1 x2 x3 x4 x5 x6 df head(df, 20) x1 x2 x3 x4 x5 x6 ... Read More

While doing the data exploration in an analytical project, we sometimes need to find the index of some values, mostly the indices of minimum and maximum values to check whether the corresponding data row has some crucial information or we may neglect it. Also, these values sometimes transformed to another values based on the data characteristics if we don’t want to neglect them.Example> x which(x==min(x)) [1] 1 > which(x==max(x)) [1] 25 > set.seed(2) > x1 x1  [1] 85 79 70  6  32  8 17 93 81 76 41 50 75 65  3 80 96 50 55 [20] 63  8 33 ... Read More

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