Nizamuddin Siddiqui

Nizamuddin Siddiqui

1,958 Articles Published

Articles by Nizamuddin Siddiqui

Page 29 of 196

How to display p-value with coefficients in stargazer output for linear regression model in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 3K+ Views

To display p-value in stargazer output for linear regression model, we can use the report argument. For example, if we have a model called RegressionModel then to display the p-value with coefficients can be done by using the below command −stargazer(RegressionModel,type="text",report=("vc*p"))ExampleConsider the below data frame −x1

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How to apply a manually created function to two columns in an R data frame?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 785 Views

Suppose we created a function that can take two different values at a time then we can apply that function to two columns of an R data frame by using mapply. For example, if we have a manually created function say func that multiply two values then we can apply it to a data frame called df that has two columns x and y by using the below command −mapply(func, df$x, df$y) Manually created function named as func: func mapply(func, df1$x1, df1$x2)Output[1] 24 35 18 5 56 25 4 48 16 28 30 7 24 30 30 25 12 ...

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How to create a frequency column for categorical variable in an R data frame?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 6K+ Views

To create a frequency column for categorical variable in an R data frame, we can use the transform function by defining the length of categorical variable using ave function. The output will have the duplicated frequencies as one value in the categorical column is likely to be repeated. Check out the below examples to understand how it can be done.ExampleConsider the below data frame −Country

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How to find the number of unique values in each row of an R data frame?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 2K+ Views

To find the number of unique values in each row of an R data frame, we can use apply function with length and unique function. For example, if we have a data frame called df that contains multiple columns then the number of unique values in each row of df can be found by using the command apply(df, 1, function(x) length(unique(x))).Example1Consider the below data frame −> x1 x2 x3 x4 df1 df1Output   x1 x2 x3 x4 1   3  1  1  2 2   3  2  0  2 3   3  2  0  1 4   3  0  3  1 ...

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How to perform paired t test for multiple columns in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 2K+ Views

When we have a factor column in an R data frame that has two levels and multiple numerical columns then we can apply paired-test on this data frame but the data must be collected for same subjects, otherwise it will not be a paired data. The t.test application on the data discussed here can be done by using the command lapply(df[-1], function(x) t.test(x~df$group)), where group is the factor column and lies at the first position in the data frame, x contains all the numerical columns in the data frame, and all these columns are stored in data frame called df.ExampleConsider ...

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How to extract the last row from list of a data frame in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 634 Views

Suppose we have two frames each having 5 columns that are stored in a list in R and we want to extract the last row from each data frame then we can use the lapply function. For example, if we have a list called LIST that contains the data frames described above then we can extract the last row from each data frame using the command lapply(LIST, tail, 1).ExampleConsider the below list of data frames −> x1 x2 df1 y1 y2 df2 z1 z2 df3 List ListOutput[[1]]    x1 x2 1   6  5 2   6  5 3   ...

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How to convert a column with missing values to binary with 0 for missing values in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 355 Views

To convert a column with missing values to binary with 0 for missing values, we can use as.integer function with complete.cases for the data frame column. For example, if we have a data frame called df that contains a column x which has some missing values then the column x can be converted to binary with 0 for missing values by using the command −as.integer(complete.cases(df$x))Example1Consider the below data frame −> x1 y1 df1 df1Output   x1 y1 1  NA  2 2   2  5 3   2 10 4   2  2 5   2  4 6  NA  7 7  NA ...

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What to do if numeric values are being read as character in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 4K+ Views

If the numeric values are being read as character then we need to convert them into numeric values by using the function as.numeric. For example, if we have a data frame called df that contains a column say x which has numerical values stored in character format then we can convert them into numeric values using the command as.numeric(df$x).ExampleConsider the below data frame −x1

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How to find the column that has the largest sum in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 597 Views

To find the column that has the largest sum, we can use sort function for sorting in decreasing order with colSums and accessing the first element of the output which will be the largest sum. For example, if we have a data frame called df that contains multiple columns then the column that has the largest sum can be found by using the command −str(sort(colSums(df[, 1:length(df)]), decreasing=TRUE)[1])Example1Consider the below data frame −> x1 x2 x3 x4 df1 df1Output   x1 x2 x3 x4 1   3  4  4  5 2   6 10  3  3 3   6  5  2  5 ...

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How to calculate monthly average for time series object in R?

Nizamuddin Siddiqui
Nizamuddin Siddiqui
Updated on 11-Mar-2026 3K+ Views

To calculate monthly average for time series object, we can use tapply function with mean. For example, if we have a time series object called TimeData then the monthly average for this series can be found by using the command tapply(TimeData, cycle(TimeData), mean).Example1Consider the below time series object −> Data1 Data1Output   Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1  988 695 867 211 915 348 729 518 592 447 448 880 2  551 410 427 134 133 572 637 800 630 878 642 940 3  603 335 638 639 595 512 671 863 752 568 ...

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