I just wanted to start off and say I really appreciate everyone's help on StackOverflow! As a new coder, a lot of solution guides tend to be confusing and everyone here is really helpful.
Now my latest question is I build this heatmap below using ggplot in R but it looks very busy (attached is the full view when all of the data is present.) I was hoping to maybe either:
A. Have the colorscale not bother with coloring sales by month totals and only color the item type sales across the rows (basically what month did the item sell well in vs. when it didn't).
B. Or a graphic way to make it look a bit less busy such as vertical lines being a different color than horizontal lines.
Thank you for your help!
> dput(head(sales, 100))
structure(list(Region = c("Sub-Saharan Africa", "Europe", "Middle East and North Africa",
"Sub-Saharan Africa", "Europe", "Sub-Saharan Africa", "Asia",
"Asia", "Sub-Saharan Africa", "Central America and the Caribbean",
"Sub-Saharan Africa", "Europe", "Europe", "Central America and the Caribbean",
"Middle East and North Africa", "Australia and Oceania", "Central America and the Caribbean",
"Europe", "Middle East and North Africa", "Europe", "Asia", "Europe",
"Europe", "Asia", "Europe", "Europe", "Europe", "Europe", "Australia and Oceania",
"Central America and the Caribbean", "Europe", "Europe", "Europe",
"Europe", "Central America and the Caribbean", "Middle East and North Africa",
"Middle East and North Africa", "Europe", "Sub-Saharan Africa",
"Europe", "Europe", "Asia", "Middle East and North Africa", "Europe",
"Middle East and North Africa", "Europe", "Europe", "Australia and Oceania",
"Australia and Oceania", "Australia and Oceania", "Europe", "Australia and Oceania",
"Sub-Saharan Africa", "Sub-Saharan Africa", "Asia", "Sub-Saharan Africa",
"Europe", "Europe", "Central America and the Caribbean", "Europe",
"Middle East and North Africa", "Central America and the Caribbean",
"Europe", "Europe", "Europe", "Sub-Saharan Africa", "Sub-Saharan Africa",
"Sub-Saharan Africa", "Europe", "Europe", "Europe", "Europe",
"Sub-Saharan Africa", "Sub-Saharan Africa", "Europe", "Sub-Saharan Africa",
"Sub-Saharan Africa", "Europe", "Asia", "Central America and the Caribbean",
"Asia", "Middle East and North Africa", "North America", "Sub-Saharan Africa",
"Sub-Saharan Africa", "Europe", "Europe", "Sub-Saharan Africa",
"Europe", "Sub-Saharan Africa", "Central America and the Caribbean",
"Sub-Saharan Africa", "Sub-Saharan Africa", "Australia and Oceania",
"Middle East and North Africa", "Sub-Saharan Africa", "Sub-Saharan Africa",
"Europe", "Sub-Saharan Africa", "Sub-Saharan Africa"), Country = c("Chad",
"Latvia", "Pakistan", "Democratic Republic of the Congo", "Czech Republic",
"South Africa", "Laos", "China", "Eritrea", "Haiti", "Cameroon",
"Bosnia and Herzegovina", "Germany", "Barbados", "Algeria", "Palau",
"Cuba", "Vatican City", "Lebanon", "Lithuania", "Myanmar", "Ukraine",
"Russia", "Japan", "Russia", "Liechtenstein", "Slovakia", "Albania",
"Federated States of Micronesia", "Dominica", "Andorra", "Switzerland",
"Lithuania", "San Marino", "Nicaragua", "Azerbaijan", "Syria",
"Serbia", "Mauritius", "Germany", "Italy", "Bhutan", "Turkey",
"Bulgaria", "Pakistan", "Poland", "France", "Fiji", "Australia",
"Nauru", "Slovenia", "Samoa", "South Africa", "Ghana", "Sri Lanka",
"Guinea", "Spain", "Moldova", "Dominican Republic", "Luxembourg",
"Kuwait", "Saint Lucia", "Georgia", "Bosnia and Herzegovina",
"Iceland", "Mauritius", "Malawi", "Seychelles", "Montenegro",
"Germany", "Estonia", "Serbia", "Madagascar", "Benin", "Hungary",
"Djibouti", "Senegal", "Ireland", "Mongolia", "Antigua and Barbuda",
"Cambodia", "Oman", "United States of America", "Mauritania",
"Central African Republic", "Albania", "Switzerland", "Ghana",
"Austria", "Democratic Republic of the Congo", "Dominican Republic",
"Mauritius", "Cote d'Ivoire", "Samoa", "Kuwait", "Uganda", "Senegal",
"Moldova", "Cote d'Ivoire", "Niger"), Item_Type = c("Office Supplies",
"Beverages", "Vegetables", "Household", "Beverages", "Beverages",
"Vegetables", "Baby Food", "Meat", "Office Supplies", "Cereal",
"Baby Food", "Office Supplies", "Vegetables", "Clothes", "Snacks",
"Beverages", "Beverages", "Personal Care", "Snacks", "Meat",
"Office Supplies", "Snacks", "Cosmetics", "Meat", "Vegetables",
"Cereal", "Baby Food", "Baby Food", "Beverages", "Office Supplies",
"Personal Care", "Clothes", "Vegetables", "Fruits", "Cosmetics",
"Baby Food", "Beverages", "Fruits", "Meat", "Cereal", "Clothes",
"Clothes", "Cosmetics", "Household", "Cereal", "Baby Food", "Beverages",
"Personal Care", "Office Supplies", "Cosmetics", "Clothes", "Cereal",
"Vegetables", "Office Supplies", "Meat", "Fruits", "Personal Care",
"Cereal", "Personal Care", "Office Supplies", "Fruits", "Vegetables",
"Cosmetics", "Snacks", "Personal Care", "Office Supplies", "Meat",
"Personal Care", "Household", "Meat", "Clothes", "Baby Food",
"Beverages", "Clothes", "Snacks", "Fruits", "Household", "Meat",
"Baby Food", "Personal Care", "Vegetables", "Baby Food", "Office Supplies",
"Cosmetics", "Baby Food", "Vegetables", "Household", "Vegetables",
"Household", "Clothes", "Baby Food", "Personal Care", "Office Supplies",
"Personal Care", "Fruits", "Beverages", "Personal Care", "Household",
"Personal Care"), Sales_Channel = c("Online", "Online", "Offline",
"Online", "Online", "Offline", "Online", "Online", "Online",
"Online", "Offline", "Offline", "Online", "Offline", "Offline",
"Offline", "Online", "Online", "Offline", "Offline", "Online",
"Online", "Offline", "Offline", "Offline", "Offline", "Offline",
"Offline", "Online", "Offline", "Online", "Online", "Offline",
"Online", "Online", "Online", "Online", "Online", "Offline",
"Online", "Offline", "Offline", "Online", "Offline", "Offline",
"Offline", "Offline", "Online", "Online", "Offline", "Online",
"Offline", "Online", "Online", "Offline", "Online", "Offline",
"Online", "Online", "Online", "Offline", "Online", "Offline",
"Offline", "Online", "Online", "Online", "Online", "Online",
"Online", "Offline", "Online", "Offline", "Offline", "Online",
"Offline", "Offline", "Offline", "Online", "Online", "Online",
"Online", "Offline", "Offline", "Offline", "Online", "Online",
"Online", "Online", "Offline", "Online", "Offline", "Online",
"Online", "Online", "Offline", "Offline", "Offline", "Online",
"Online"), Order_Priority = c("L", "C", "C", "C", "C", "H", "L",
"C", "L", "C", "M", "M", "C", "C", "C", "L", "H", "L", "H", "H",
"C", "C", "L", "H", "L", "L", "H", "C", "M", "H", "M", "M", "M",
"H", "L", "M", "L", "H", "H", "L", "H", "L", "L", "L", "M", "C",
"M", "L", "H", "H", "M", "C", "M", "L", "M", "C", "L", "M", "L",
"L", "L", "C", "H", "H", "H", "M", "C", "C", "L", "L", "H", "M",
"C", "H", "M", "H", "H", "H", "L", "H", "H", "C", "L", "L", "H",
"H", "M", "M", "H", "L", "L", "H", "H", "M", "H", "L", "C", "H",
"H", "C"), Order_Date = c("1/27/2011", "12/28/2015", "1/13/2011",
"9/11/2012", "10/27/2015", "7/10/2012", "2/20/2011", "4/10/2017",
"11/21/2014", "7/4/2015", "1/1/2016", "10/20/2012", "2/22/2015",
"1/1/2016", "6/21/2011", "9/19/2013", "11/15/2015", "4/6/2015",
"4/12/2010", "9/26/2011", "1/2/2016", "8/14/2010", "4/13/2012",
"9/19/2013", "12/2/2015", "2/26/2017", "1/2/2016", "5/20/2011",
"10/24/2013", "6/14/2011", "6/20/2015", "8/5/2011", "1/2/2016",
"7/5/2015", "3/25/2015", "8/22/2013", "1/3/2016", "6/23/2013",
"5/8/2015", "1/3/2016", "3/10/2013", "3/18/2012", "2/11/2015",
"10/30/2012", "7/6/2012", "1/4/2011", "10/25/2013", "1/3/2016",
"3/16/2014", "1/3/2016", "9/30/2010", "11/5/2010", "7/21/2017",
"7/10/2013", "10/6/2012", "6/4/2011", "4/12/2014", "10/26/2015",
"8/4/2011", "2/24/2017", "3/30/2011", "5/2/2015", "2/1/2014",
"3/3/2012", "4/22/2015", "5/12/2011", "12/21/2011", "12/2/2010",
"8/14/2010", "10/5/2010", "2/8/2012", "9/8/2012", "8/11/2011",
"10/28/2012", "10/11/2013", "1/3/2016", "7/28/2017", "1/5/2016",
"1/5/2016", "11/13/2014", "8/26/2012", "7/15/2014", "5/2/2011",
"11/11/2013", "4/14/2011", "10/4/2012", "5/14/2013", "1/12/2013",
"10/3/2012", "10/23/2010", "2/6/2014", "9/4/2011", "1/5/2016",
"7/19/2015", "10/28/2012", "1/5/2016", "10/25/2013", "2/11/2011",
"1/5/2016", "2/6/2012"), Order_ID = c(292494523, 361825549, 141515767,
500364005, 127481591, 482292354, 844532620, 564251220, 411809480,
327881228, 743598735, 479823005, 498603188, 953377091, 181401288,
500204360, 640987718, 206925189, 221503102, 878520286, 319358670,
746630275, 246883237, 967895781, 305029237, 223957431, 485685670,
121455848, 332936227, 692031657, 365978467, 392325484, 917994248,
603977954, 965943562, 233629691, 664174449, 212921321, 763686978,
520714461, 637702119, 671986758, 912333714, 540041816, 156722390,
434299266, 765008771, 593408763, 856333482, 682830178, 574837148,
365692222, 289660394, 681165492, 594943845, 956044280, 509828126,
771969211, 178453862, 835580909, 869961678, 278519999, 478492200,
257427108, 723186051, 353942859, 848183858, 374707877, 322626245,
351362788, 640653836, 540548217, 821407258, 523904788, 109027135,
113437545, 672654092, 701131856, 148230302, 230407607, 129491746,
606854999, 885983693, 260676658, 345045220, 123513209, 900816953,
452005279, 672439515, 827793490, 704053533, 157518470, 117058742,
272820842, 548818433, 198175609, 875250566, 511720263, 929683959,
923598563), Ship_Date = c("2/12/2011", "1/23/2016", "2/1/2011",
"10/6/2012", "12/5/2015", "8/21/2012", "3/20/2011", "5/12/2017",
"1/10/2015", "7/20/2015", "2/18/2016", "11/15/2012", "2/27/2015",
"1/3/2016", "7/21/2011", "10/4/2013", "11/30/2015", "4/27/2015",
"5/19/2010", "10/2/2011", "1/16/2016", "8/31/2010", "4/22/2012",
"9/28/2013", "12/26/2015", "2/28/2017", "1/10/2016", "6/19/2011",
"12/3/2013", "7/20/2011", "7/21/2015", "9/1/2011", "1/16/2016",
"7/29/2015", "5/9/2015", "8/30/2013", "1/27/2016", "7/18/2013",
"5/13/2015", "1/25/2016", "4/4/2013", "5/4/2012", "3/2/2015",
"11/3/2012", "8/1/2012", "2/21/2011", "12/10/2013", "2/20/2016",
"4/27/2014", "2/15/2016", "11/11/2010", "12/5/2010", "8/22/2017",
"7/26/2013", "10/21/2012", "7/24/2011", "4/15/2014", "12/15/2015",
"8/27/2011", "4/14/2017", "4/12/2011", "6/14/2015", "2/26/2014",
"4/10/2012", "5/13/2015", "5/15/2011", "1/18/2012", "12/25/2010",
"9/16/2010", "11/14/2010", "3/18/2012", "9/20/2012", "8/19/2011",
"11/7/2012", "10/27/2013", "1/10/2016", "7/31/2017", "2/11/2016",
"1/26/2016", "12/20/2014", "9/22/2012", "8/15/2014", "5/4/2011",
"12/17/2013", "5/20/2011", "11/21/2012", "6/10/2013", "2/2/2013",
"11/12/2012", "11/20/2010", "3/28/2014", "9/4/2011", "1/11/2016",
"8/20/2015", "11/24/2012", "2/3/2016", "11/3/2013", "2/26/2011",
"2/9/2016", "2/26/2012"), Units_Sold = c(4484, 1075, 6515, 7683,
3491, 9880, 4825, 3330, 2431, 6197, 6245, 9145, 6618, 4322, 9527,
441, 1365, 2617, 6545, 2530, 4182, 3345, 7091, 725, 3784, 2835,
4038, 339, 2083, 6401, 16, 6684, 3753, 9353, 3020, 5072, 2834,
7005, 803, 9835, 9083, 4670, 8675, 9229, 6493, 7659, 1950, 1695,
6962, 3479, 5941, 5310, 5802, 861, 5959, 3603, 8327, 1699, 7318,
5814, 9848, 9112, 5330, 7257, 5678, 8412, 5307, 3243, 1130, 4912,
2562, 9084, 1516, 3924, 2407, 7545, 2148, 9352, 3495, 1586, 8340,
735, 1118, 8871, 5403, 9158, 609, 7261, 8650, 1344, 3941, 2070,
9138, 2605, 6425, 3421, 4947, 8252, 2998, 2194), Unit_Price = c(651.21,
47.45, 154.06, 668.27, 47.45, 47.45, 154.06, 255.28, 421.89,
651.21, 205.7, 255.28, 651.21, 154.06, 109.28, 152.58, 47.45,
47.45, 81.73, 152.58, 421.89, 651.21, 152.58, 437.2, 421.89,
154.06, 205.7, 255.28, 255.28, 47.45, 651.21, 81.73, 109.28,
154.06, 9.33, 437.2, 255.28, 47.45, 9.33, 421.89, 205.7, 109.28,
109.28, 437.2, 668.27, 205.7, 255.28, 47.45, 81.73, 651.21, 437.2,
109.28, 205.7, 154.06, 651.21, 421.89, 9.33, 81.73, 205.7, 81.73,
651.21, 9.33, 154.06, 437.2, 152.58, 81.73, 651.21, 421.89, 81.73,
668.27, 421.89, 109.28, 255.28, 47.45, 109.28, 152.58, 9.33,
668.27, 421.89, 255.28, 81.73, 154.06, 255.28, 651.21, 437.2,
255.28, 154.06, 668.27, 154.06, 668.27, 109.28, 255.28, 81.73,
651.21, 81.73, 9.33, 47.45, 81.73, 668.27, 81.73), Total_Profit = c(566105,
16834.5, 411291.95, 1273303.59, 54669.06, 154720.8, 304602.25,
319213.8, 139053.2, 782371.25, 553244.55, 876639.7, 835522.5,
272847.86, 699662.88, 24316.74, 21375.9, 40982.22, 164017.7,
139504.2, 239210.4, 422306.25, 390997.74, 126055.75, 216444.8,
178973.55, 357726.42, 32496.54, 199676.38, 100239.66, 2020, 167501.04,
275620.32, 590454.89, 7278.2, 881868.64, 271667.24, 109698.3,
1935.23, 562562, 804662.97, 342964.8, 637092, 1604646.23, 1076084.89,
678510.81, 186927, 26543.7, 174467.72, 439223.75, 1032961.67,
389966.4, 513999.18, 54354.93, 752323.75, 206091.6, 20068.07,
42576.94, 648301.62, 145698.84, 1243310, 21959.92, 336482.9,
1261774.59, 313084.92, 210804.72, 670008.75, 185499.6, 28317.8,
814065.76, 146546.4, 667128.96, 145323.76, 61449.84, 176770.08,
416031.3, 5176.68, 1549906.96, 199914, 152033.96, 209000.4, 46400.55,
107171.48, 1119963.75, 939419.61, 877885.88, 38446.17, 1203365.53,
546074.5, 222741.12, 289427.04, 198430.2, 228998.28, 328881.25,
161010.5, 8244.61, 77470.02, 206795.12, 496858.54, 54981.64),
Month_RecentYear = c(NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, "January", NA, NA, "January", NA, NA, NA, NA, NA, NA,
"January", NA, NA, NA, NA, NA, "January", NA, NA, NA, NA,
NA, "January", NA, NA, NA, "January", NA, NA, "January",
NA, NA, NA, NA, NA, NA, NA, "January", NA, "January", NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, "January", NA, "January",
"January", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, "January", NA, NA, "January", NA, NA, "January", NA),
Year = c(2011, 2015, 2011, 2012, 2015, 2012, 2011, 2017,
2014, 2015, 2016, 2012, 2015, 2016, 2011, 2013, 2015, 2015,
2010, 2011, 2016, 2010, 2012, 2013, 2015, 2017, 2016, 2011,
2013, 2011, 2015, 2011, 2016, 2015, 2015, 2013, 2016, 2013,
2015, 2016, 2013, 2012, 2015, 2012, 2012, 2011, 2013, 2016,
2014, 2016, 2010, 2010, 2017, 2013, 2012, 2011, 2014, 2015,
2011, 2017, 2011, 2015, 2014, 2012, 2015, 2011, 2011, 2010,
2010, 2010, 2012, 2012, 2011, 2012, 2013, 2016, 2017, 2016,
2016, 2014, 2012, 2014, 2011, 2013, 2011, 2012, 2013, 2013,
2012, 2010, 2014, 2011, 2016, 2015, 2012, 2016, 2013, 2011,
2016, 2012), Month = c("January", "December", "January",
"September", "October", "July", "February", "April", "November",
"July", "January", "October", "February", "January", "June",
"September", "November", "April", "April", "September", "January",
"August", "April", "September", "December", "February", "January",
"May", "October", "June", "June", "August", "January", "July",
"March", "August", "January", "June", "May", "January", "March",
"March", "February", "October", "July", "January", "October",
"January", "March", "January", "September", "November", "July",
"July", "October", "June", "April", "October", "August",
"February", "March", "May", "February", "March", "April",
"May", "December", "December", "August", "October", "February",
"September", "August", "October", "October", "January", "July",
"January", "January", "November", "August", "July", "May",
"November", "April", "October", "May", "January", "October",
"October", "February", "September", "January", "July", "October",
"January", "October", "February", "January", "February")), class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -100L), spec = structure(list(
cols = list(Region = structure(list(), class = c("collector_character",
"collector")), Country = structure(list(), class = c("collector_character",
"collector")), Item_Type = structure(list(), class = c("collector_character",
"collector")), Sales_Channel = structure(list(), class = c("collector_character",
"collector")), Order_Priority = structure(list(), class = c("collector_character",
"collector")), Order_Date = structure(list(), class = c("collector_character",
"collector")), Order_ID = structure(list(), class = c("collector_double",
"collector")), Ship_Date = structure(list(), class = c("collector_character",
"collector")), Units_Sold = structure(list(), class = c("collector_double",
"collector")), Unit_Price = structure(list(), class = c("collector_double",
"collector")), Total_Profit = structure(list(), class = c("collector_double",
"collector")), Month_RecentYear = structure(list(), class = c("collector_character",
"collector")), Year = structure(list(), class = c("collector_double",
"collector")), Month = structure(list(), class = c("collector_character",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1), class = "col_spec"))
THISYEAR <- filter(sales, sales$Month_RecentYear != "NA")
df <- data.frame(
ItemType = c(THISYEAR$Item_Type),
UnitsSold = c(THISYEAR$Units_Sold),
TotalProfit = c(THISYEAR$Total_Profit),
MonthRecentYear = c(THISYEAR$Month_RecentYear))
df2 <- df %>%
group_by(MonthRecentYear, ItemType) %>%
summarise(TotalUnitsSold = sum(UnitsSold))
median(df2$TotalUnitsSold)
HEAT <- ggplot(data = df2, mapping = aes(x = factor(df2$MonthRecentYear, levels = c(month.name)), df2$ItemType)) + geom_tile(aes(fill = df2$TotalUnitsSold), color = "grey", size = 1) + geom_text(aes(label = df2$TotalUnitsSold)) + scale_fill_gradient2(low = ("red"), mid = ("yellow"), high = ("green"), midpoint = 45000)
HEAT + labs(title = "Total Item Sales per Month in 2016", fill = "Units Sold", x = "Month", y = "Item Type")
