How to calculate mean value in R summarize statement based on a condition?

Question

Coffee import data of several countries Problem background *Element *col has two categorical values: Import Quantity and Import Value item col has five categorical values: Coffee Green, Coffee Extracts, Coffee husks and skins, Coffee substitutes, and Roasted Coffee

Problem Statement I want to calculate the mean value for import quantity for each coffee item separately. I was trying to calculate the conditional mean but it's not working for me.

# Mean import quantity for coffee extracts for each country
mean_import_quantity <- import_data_cleaned %>%
    group_by(area) %>%
    drop_na() %>%
    summarize(mean_import_quantity=mean(...................))

new_sample <- structure(list(area = c("Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania", "Albania", 
"Albania", "Albania", "Albania", "Albania", "Albania"), element = c("Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Quantity", "Import Quantity", "Import Value", 
"Import Value", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Quantity", "Import Quantity", "Import Value", 
"Import Value", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Value", 
"Import Value", "Import Value", "Import Value", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity", "Import Quantity", "Import Quantity", "Import Quantity", 
"Import Quantity"), item = c("Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee husks and skins", "Coffee husks and skins", "Coffee husks and skins", 
"Coffee husks and skins", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee extracts", "Coffee extracts", 
"Coffee extracts", "Coffee extracts", "Coffee husks and skins", 
"Coffee husks and skins", "Coffee husks and skins", "Coffee husks and skins", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee substitutes", "Coffee substitutes", "Coffee substitutes", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, decaffeinated or roasted", "Coffee, decaffeinated or roasted", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green", "Coffee, green", "Coffee, green", 
"Coffee, green", "Coffee, green"), year = c("2000", "2001", "2002", 
"2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", 
"2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", 
"2019", "2020", "2000", "2001", "2002", "2003", "2004", "2005", 
"2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", 
"2014", "2015", "2016", "2017", "2018", "2019", "2020", "2015", 
"2018", "2015", "2018", "2014", "2016", "2017", "2019", "2020", 
"2014", "2016", "2017", "2019", "2020", "2014", "2015", "2016", 
"2017", "2018", "2019", "2020", "2014", "2015", "2016", "2017", 
"2018", "2019", "2020", "2000", "2001", "2002", "2003", "2004", 
"2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", 
"2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", 
"2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", 
"2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", 
"2016", "2017", "2018", "2019", "2020", "2000", "2001", "2002", 
"2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", 
"2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", 
"2019", "2020", "2000", "2001", "2002", "2003", "2004", "2005", 
"2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", 
"2014", "2015", "2016", "2017", "2018", "2019", "2020", "2017", 
"2019", "2017", "2019", "2000", "2001", "2002", "2003", "2004", 
"2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", 
"2013", "2015", "2016", "2017", "2019", "2000", "2001", "2002", 
"2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", 
"2011", "2012", "2013", "2015", "2016", "2017", "2019", "2000", 
"2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", 
"2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", 
"2017", "2018", "2019", "2020", "2000", "2001", "2002", "2003", 
"2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", 
"2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", 
"2020", "2000", "2001", "2002", "2003", "2004", "2005", "2006", 
"2007", "2008", "2009", "2010", "2011", "2012", "2013"), unit = c("tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "tonnes", "tonnes", "1000 US$", "1000 US$", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "tonnes", "tonnes", "1000 US$", "1000 US$", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", "1000 US$", 
"1000 US$", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", "tonnes", 
"tonnes", "tonnes"), value = c(NA, NA, 5L, 53L, 26L, 54L, 46L, 
78L, 223L, NA, NA, 24L, 107L, 70L, 227L, 80L, 86L, NA, 426L, 
435L, 718L, NA, NA, 71L, 311L, 192L, 383L, 708L, 1079L, 1159L, 
NA, NA, 491L, 1205L, 764L, 890L, 358L, 436L, NA, 3159L, 2421L, 
3940L, 1L, NA, 6L, 1L, 1L, 3L, NA, 2L, 6L, 13L, 7L, NA, 3L, 18L, 
111L, 19L, 18L, NA, 88L, 74L, 73L, 526L, 240L, 173L, NA, 891L, 
531L, 539L, 20L, NA, NA, 117L, NA, 15L, 1L, 1L, 1L, NA, NA, NA, 
NA, NA, 16L, 42L, NA, 18L, 33L, 9L, 13L, 50L, NA, NA, 280L, 1L, 
48L, 9L, 9L, 9L, NA, NA, NA, NA, NA, 40L, 241L, 1L, 62L, 289L, 
57L, 139L, 154L, 85L, 127L, 110L, 62L, 78L, 93L, 75L, 90L, 53L, 
112L, 100L, 105L, 172L, 7L, 22L, 28L, 11L, 13L, 494L, 69L, 361L, 
381L, 548L, 603L, 652L, 724L, 957L, 908L, 1450L, 709L, 1603L, 
1454L, 1624L, 2836L, 69L, 237L, 263L, 92L, 114L, 2705L, 1152L, 
NA, 18L, NA, 247L, 3L, NA, NA, NA, 6L, NA, NA, NA, NA, 1L, NA, 
3L, 2L, 2L, 1L, 2L, 1L, 2L, 6L, NA, NA, 1L, 3L, 1L, NA, 2L, 2L, 
13L, NA, 46L, 14L, 33L, 19L, 29L, 14L, 16L, 265L, 404L, 417L, 
501L, 548L, 749L, 990L, 1286L, 1461L, 1656L, 1659L, 1620L, 1692L, 
1950L, 1696L, 1848L, 2157L, 2176L, 2381L, 2639L, 2234L, 649L, 
877L, 1011L, 1313L, 1865L, 2950L, 4138L, 6185L, 8701L, 9794L, 
9864L, 12217L, 12470L, 14927L, 13085L, 12298L, 14122L, 14988L, 
17002L, 19032L, 17875L, 3537L, 3932L, 3473L, 3689L, 3320L, 2956L, 
4781L, 5015L, 5129L, 5277L, 4657L, 4922L, 5189L, 5216L), flag = c("O", 
"O", "T", "T", "T", "T", "T", "T", "T", "O", "O", "T", "T", "T", 
"T", "T", "T", "O", "T", "T", "T", "O", "O", "T", "T", "T", "T", 
"T", "T", "T", "O", "O", "T", "T", "T", "T", "T", "T", "O", "T", 
"T", "T", "T", "O", "T", "T", "T", "T", "O", "T", "T", "T", "T", 
"O", "T", "T", "T", "T", "T", "O", "T", "T", "T", "T", "T", "T", 
"O", "T", "T", "T", "T", "O", "O", "T", "O", "T", "T", "E", "E", 
"O", "O", "O", "O", "O", "T", "T", "O", "A", "T", "T", "T", "I", 
"O", "O", "T", "T", "T", "T", "E", "E", "O", "O", "O", "O", "O", 
"T", "T", "T", "A", "T", "T", "T", "A", "A", "A", "A", "A", "A", 
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", 
"T", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A", "A", "A", "A", "A", "A", "A", "T", "A", "O", "T", "O", 
"T", "A", "O", "O", "O", "A", "O", "O", "O", "O", "A", "O", "A", 
"A", "A", "A", "A", "A", "T", "A", "O", "O", "A", "A", "A", "O", 
"A", "A", "A", "O", "A", "A", "A", "A", "A", "A", "T", "A", "A", 
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A", "A", "A", "T", "A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "T", 
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A"), `flag description` = c("Estimated value", "Estimated value", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Estimated value", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Official figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Estimated value", "Estimated value", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Estimated value", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Official figure", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Official figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Official figure", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Official figure", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Official figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Unofficial figure", "Imputed value", "Estimated value", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Estimated value", "Estimated value", "Unofficial figure", 
"Estimated value", "Unofficial figure", "Estimated value", "Estimated value", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Official figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Imputed value", "Imputed value", "Estimated value", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Estimated value", "Estimated value", "Unofficial figure", 
"Estimated value", "Unofficial figure", "Estimated value", "Estimated value", 
"Unofficial figure", "Unofficial figure", "Unofficial figure", 
"Official figure", "Unofficial figure", "Unofficial figure", 
"Unofficial figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Unofficial figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Unofficial figure", "Official figure", "Official figure", 
"Unofficial figure", "Official figure", "Unofficial figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Estimated value", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Unofficial figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Unofficial figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Unofficial figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Unofficial figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Unofficial figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure", 
"Official figure", "Official figure", "Official figure", "Official figure"
)), row.names = c(NA, 250L), class = "data.frame")

Welcome to SO! Please provide a [MRE](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example), i.e. paste the output of `dput` (or the `head` of it), so that it's easier to help you, thanks! — starja, Dec 14 '22 at 13:00
So the head output is available up top. Do you see the hyperlink before the Problem background heading? — ali bokhari, Dec 14 '22 at 13:03
Please **don't** post data as a picture, but as the output of `dput` because then one can copy it directly to R, thank you. — starja, Dec 14 '22 at 13:08
Does this answer your question? [Find mean of multiple columns in R](https://stackoverflow.com/questions/68946876/find-mean-of-multiple-columns-in-r) to get the mean for each column or `group_by(area, item) |> summarize(mean_import_quantity = mean(value, na.rm = TRUE))` if they are in a single column `item`. — harre, Dec 14 '22 at 13:11
I want to upload the output of dput but I am new here so don't know what to do I have copied and pasted the output, should I just copy and paste here? It will help you more to answer my question — ali bokhari, Dec 14 '22 at 13:27
Output of `dput` is added for you. I want my output to only show mean of import quantity where item = coffee extracts — ali bokhari, Dec 14 '22 at 13:48
Using a `dput()` is great. Try to only include relevant information. Here, the columns `flag description`, `flag` are not needed to solve your problem. By only selecting the good columns, you can show your table by copy-pasting the output of `head(sample)` — Benson_YoureFired, Dec 14 '22 at 14:12

Benson_YoureFired · Accepted Answer · 2022-12-14T14:14:53.913

Before grouping by item to calculate the average value, you need to split the unit columns into two variables using pivot_wider().

I renamed 1000 USD into k_usd because R can be weird if your column names starts with numbers or has spaces.

The na.rm = TRUE parameter in mean() allows you to drop NA prior to compute the result (otherwise you will get NA)

EDIT : To also group by area, you can add the variable to the group() function

library(dplyr)
library(tidyr)

sample %>% 
  pivot_wider(names_from = unit, values_from = value) %>% 
  rename(k_usd = `1000 US$`) %>% 
  group_by(area, item) %>% 
  summarise(mean_tonnage = mean(tonnes, na.rm = T),
            mean_value = mean(k_usd, na.rm = T))

Output of the mean summary :

# A tibble: 30 x 4
# Groups:   area [29]
   area                             item                             mean_tonnage mean_k_usd
   <chr>                            <fct>                                   <dbl>      <dbl>
 1 Bolivia (Plurinational State of) Coffee extracts                           NaN       2336
 2 Canada                           Coffee, decaffeinated or roasted          NaN     165732
 3 Central African Republic         Coffee extracts                             8        NaN
 4 China                            Coffee husks and skins                    NaN         21
 5 Côte d Ivoire                    Coffee, decaffeinated or roasted          NaN       1693
 6 Croatia                          Coffee, green                             NaN      37165
 7 El Salvador                      Coffee, decaffeinated or roasted          NaN       2353
 8 Estonia                          Coffee, decaffeinated or roasted         9167        NaN
 9 Georgia                          Coffee, decaffeinated or roasted         1029        NaN
10 Greece                           Coffee substitutes                        154        NaN

I want to group by area as well. I want to see mean import values for all countries as well — ali bokhari, Dec 14 '22 at 14:14
I have a question! Do I have to change the data type of area and items to factor in order to use them in group_by()? I was under the impression that only fct type variables can go into group_by() — ali bokhari, Dec 14 '22 at 14:23
No you don't have to. You can use any type of variables in group by : dates, character, factors, integers, etc.. — Benson_YoureFired, Dec 14 '22 at 14:29

How to calculate mean value in R summarize statement based on a condition?

1 Answers1