Я просто хотел начать и сказать, что я действительно ценю помощь всех в StackOverflow! Как новый кодер, многие руководства по решениям, как правило, сбивают с толку, и все здесь действительно полезны.
Теперь мой последний вопрос - я строю эту тепловую карту ниже, используя ggplot в R, но она выглядит очень занятой (прикреплен полное представление, когда все данные присутствуют.) Я надеялся, что либо:
A. Не используйте цветовую шкалу для раскраски продаж по итоговым месяцам и раскрасьте только продажи по типам товаров по строкам (в основном, за какой месяц товар продавался хорошо по сравнению с тем, когда он не продавался).
B. Или графический c способ заставить его выглядеть немного менее загруженным, например, вертикальные линии имеют цвет, отличный от горизонтальных.
Спасибо за вашу помощь! 
> 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")