👀 Views: 0 💬 Answers: 1 📅 Created: 2025-06-18

r tidyr data-wrangling R

I've been working on this all day and This might be a silly question, but I'm trying to reshape my data frame using `tidyr::pivot_wider()`, but I'm encountering unexpected `NA` values in the output. My original data frame contains monthly sales data grouped by product and region, and I want to spread the `month` column into multiple columns while summarizing the `sales` values. Here’s a simplified version of my data: ```r library(dplyr) library(tidyr) sales_data <- data.frame( product = c('A', 'A', 'B', 'B'), region = c('North', 'South', 'North', 'South'), month = c('Jan', 'Jan', 'Feb', 'Feb'), sales = c(100, 200, 150, 250) ) ``` I want to reshape this so that I have a separate column for each month with the corresponding sales figures. Here’s the code I used: ```r reshaped_data <- sales_data %>% pivot_wider( names_from = month, values_from = sales, values_fill = list(sales = 0) ) ``` However, the result includes rows with `NA` values that I didn't expect. The output looks like this: ``` # A tibble: 2 x 4 product region Jan Feb <chr> <chr> <dbl> <dbl> 1 A North 100 0 2 A South 200 0 3 B North NA 150 4 B South NA 250 ``` As you can see, the `NA` values are showing up for the product B in the North region for January. I thought the `values_fill` argument would take care of this, but it seems like I'm missing something. I've tried checking for duplicates and filtering out any unnecessary rows, but the NA values persist. What could be going wrong here? Could someone point out what I might have overlooked or how I can avoid these `NA` values in my reshaped data? What am I doing wrong? I'm working on a application that needs to handle this.