Tidy data. Tidy data is a standard way of mapping the meaning of a dataset to its structure. A dataset is messy or tidy depending on how rows, columns and tables are matched up with observations, variables and types. In tidy data: Every column is a variable. Every row is an observation. Every cell is a single value. My idea is to optimize somehow to generate faster. Notice that I'm using pivot_longer, which takes longer. One idea would be to continue using data.table as I did to generate SPV. However, I don't know how to do that in this case for adjusted. Can you help me? I would like to generate the same output table as in the question. The pivot_wider() function works similarly to the pivot_longer function, but the opposite. Now, we want to widen our data and spread it out instead of gathering it into a longer form. The function works like this: pivot_wider(data = data.frame, id_cols = identifying_columns, names_from = "Col with Names", values_from = "Col with Values") Using the pivot_wider () function, I ran into an error: df %>% pivot_wider (names_from = question, values_from = value) Values in `rating` are not uniquely identified; output will contain list-cols. * Use `values_fn = list (rating = list)` to suppress this warning. * Use `values_fn = list (rating = length)` to identify where the duplicates 4. Here, we can just use names_sep as the _ will be delimiter where the column names should be split. Also, specify the names_to as a vector of 3 elements i.e. alpha, beta (as column names for the prefix part from the column names, and .value for the value part from the column) library (tidyr) pivot_longer (tbl, cols = everything (), names_to First, we have to install and load the reshape2 package: install.packages("reshape2") # Install & load reshape2 library ("reshape2") Next, we can apply the dcast function to create a pivot table: data_pivot <- dcast ( data, # Create pivot table x1 + x2 ~ x4, value.var = "x3" , fun.aggregate = sum) data_pivot # Print pivot table. Table 2 shows As others have mentioned, pivot_longer needs tidyr (i.e., tidyr::pivot_longer. Then, we can use . to pipe the my_data to the melt function (this ensures that var_1_col and var_2_col are dropped before going into melt). Then, we can specify the two new column names for the pivoted data (i.e., variable and value). 1. pivot_wider () requires a set of columns that uniquely identifies each observation. Your observations are not unique, so id_cols is defaulting to all columns. You will need to create unique identified for each observation. tSG2.