Programming Articles - Page 1303 of 3366

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 ... Read More

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 − Live DemoCountry

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 − Live Demox1

To create a blank column with randomization in an R data frame, we can use sample function and pass the blanks with single space. For example, if we want to create a vector say x that will be added in the data frame can be created by using the command −x

The combination of values with repetition is the combination where the values can be repeated when creating the combination. For example, if we have three values say 1 and 2 then the combination of these values with repetition will be as follows −1 1 2 1 1 2 2 2For this purpose, we can use expand.grid function as shown in the below examples.Example 1 Live Demoexpand.grid(rep(list(1:2),2))Output  Var1 Var2 1  1   1 2  2   1 3  1   2 4  2   2Example2 Live Demoexpand.grid(rep(list(1:2),3))Output  Var1 Var2 Var3 1  1   1    1 2  2   1    1 3  1   2    1 4  2   2    1 5  1   1    2 6  2   1    2 7  1   2    2 8  2   2    2Example3 Live Demoexpand.grid(rep(list(1:2),4))Output  Var1 Var2 Var3 Var4 1  1   1    1    1 2  2   1    1    1 3  1   2    1    1 4  2   2    1    1 5  1   1    2    1 6  2   1    2    1 7  1   2    2    1 8  2   2    2    1 9  1   1    1    2 10 2   1    1    2 11 1   2    1    2 12 2   2    1    2 13 1   1    2    2 14 2   1    2    2 15 1   2    2    2 16 2   2    2    2Example4 Live Demoexpand.grid(rep(list(1:2),5))Output  Var1 Var2 Var3 Var4 Var5 1  1   1    1    1    1 2  2   1    1    1    1 3  1   2    1    1    1 4  2   2    1    1    1 5  1   1    2    1    1 6  2   1    2    1    1 7  1   2    2    1    1 8  2   2    2    1    1 9  1   1    1    2    1 10 2   1    1    2    1 11 1   2    1    2    1 12 2   2    1    2    1 13 1   1    2    2    1 14 2   1    2    2    1 15 1   2    2    2    1 16 2   2    2    2    1 17 1   1    1    1    2 18 2   1    1    1    2 19 1   2    1    1    2 20 2   2    1    1    2 21 1   1    2    1    2 22 2   1    2    1    2 23 1   2    2    1    2 24 2   2    2    1    2 25 1   1    1    2    2 26 2   1    1    2    2 27 1   2    1    2    2 28 2   2    1    2    2 29 1   1    2    2    2 30 2   1    2    2    2 31 1   2    2    2    2 32 2   2    2    2    2

To find the sum of every n values in R data frame columns, we can use rowsum function along with rep function that will repeat the sum for rows. For example, if we have a data frame called df that contains 4 columns each containing twenty values then we can find the column sums for every 5 rows by using the command rowsum(df,rep(1:5,each=4)).ExampleConsider the below data frame − Live Demox1

When we have a factor column in an R data frame that has two levels and a numerical column 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 t.test(y1~x1,data=df), where y1 is the numerical column, x1 is the factor column, and both these columns are stored in data frame called df.ExampleConsider the below data frame − Live Demox1

To extract the last element in an R matrix, we can use the length function along with single square brackets that are used for subsetting. For example, if we have a matrix called M as shown below −M1 2 3 4 5 6 7 8 9then we can extract last value of M by using the command M[length(M)].ExampleConsider the below matrix − Live DemoM1

Suppose we have two frames each having 5 columns that are stored in a list in R and the data that belongs to same columns has some kind of inherent relationship or we want to check whether there exists a relationship between them then we might want to extract those columns. Therefore, we can use lapply function for this extraction. For example, if we have a list called LIST that store two data frames then column 3 of each data frame can be extracted by using the command lapply(LIST, "[", 3).ExampleConsider the below data frames and list of these data ... Read More

To remove starting and ending zeros in an R vector, we can use min and max function to access values except 0 and then subsetting with single square brackets. For example, if we have a vector called x then we can remove starting and ending zeros by using the command −x[min(which(x!=0)):max(which(x!=0))]Example Live Demox1