Question

我只是想开始说我真的很感谢大家对StackOverflow的帮助！作为一个新的编码器，很多解决方案指南都容易使人困惑，这里的每个人都非常有帮助。

现在我的最新问题是我在R中使用ggplot在下面构建此热图，但它看起来很忙（当所有数据都存在时，它是完整视图。）我希望可以：

A。使色标不影响按月总计上色的销售额，而只对行中的物料类型销售额进行上色（基本上是物料在哪个月卖得好，什么时候没卖）。

B。或采用图形方式使其显得不太忙，例如垂直线与水平线的颜色不同。

谢谢您的帮助！

> 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")

Answer 1

我认为，您的问题B.有点过分回避（或者我听不懂）。你能澄清一下吗？

关于问题A，我认为您可以通过执行以下操作将数据标准化为每一行（项目）的最大值：

library(dplyr)
DF %>% group_by(Item) %>%
  mutate(Norm_val = Value/max(Value))

# A tibble: 132 x 4
# Groups:   Item [11]
   Month     Item      Value Norm_val
   <fct>     <fct>     <int>    <dbl>
 1 January   Baby Food 61662    0.788
 2 February  Baby Food 67869    0.867
 3 March     Baby Food 12985    0.166
 4 April     Baby Food 39924    0.510
 5 May       Baby Food 78292    1    
 6 June      Baby Food 72554    0.927
 7 July      Baby Food 55403    0.708
 8 August    Baby Food 75160    0.960
 9 September Baby Food 56434    0.721
10 October   Baby Food 19641    0.251
# … with 122 more rows

然后您可以通过执行以下操作将此标准化后的值填充到geom_tile中：

DF %>% group_by(Item) %>%
  mutate(Norm_val = Value/max(Value)) %>%
  ggplot(aes(x = Month, y = Item))+
  geom_tile(aes(fill = Norm_val), color = "grey", size = 1)+
  geom_text(aes(label = Value))+
  scale_fill_gradient2(name = "Normalised value", low = "red", mid = "yellow",high = "green", midpoint = 0.5)+ 
  labs(title = "Total Item Sales per Month in 2016", fill = "Units Sold", x = "Month", y = "Item Type")

它能回答您的问题吗？

可复制示例（DF）

library(lubridate)
Month <- as.character(month(seq(ymd("2020-01-01"),ymd("2020-12-31"), by = "1 month"), label = TRUE, abbr = FALSE))
Item <- as.character(df2$ItemType)
DF <- expand.grid(Month, Item)
set.seed(123)
DF$Value <- sample(10000:100000, 132)
colnames(DF) <- c("Month", "Item", "Value")

在R中使用ggplot自定义热图外观

1 个答案: