我的销售数据集包括3列：国家/地区，销售类型/方法，季度总收入。这是显示前几行的更好主意：
Retailer.country Order.method.type Qtr.Rev
<fctr> <fctr> <dbl>
1 Australia E-mail 171407.28
2 Australia Sales visit 2013909.18
3 Australia Special 158795.34
4 Australia Telephone 2289201.87
5 Australia Web 1738303.59
6 Austria Sales visit 66926.18
7 Austria Telephone 1671887.40
8 Austria Web 7050164.50
9 Belgium Sales visit 1655507.05
10 Belgium Web 6222440.26
etc.........
以下是此数据的输入：
structure(list(Retailer.country = structure(c(1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 3L, 3L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L,
7L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L, 11L, 11L,
11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 14L,
14L, 14L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 17L, 17L, 17L,
17L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L,
21L, 21L, 21L, 21L, 21L, 21L), .Label = c("Australia", "Austria",
"Belgium", "Brazil", "Canada", "China", "Denmark", "Finland",
"France", "Germany", "Italy", "Japan", "Korea", "Mexico",
"Netherlands",
"Singapore", "Spain", "Sweden", "Switzerland", "United Kingdom",
"United States"), class = "factor"), Order.method.type =
structure(c(1L,
4L, 5L, 6L, 7L, 4L, 6L, 7L, 4L, 7L, 7L, 1L, 2L, 4L, 7L, 2L, 4L,
6L, 7L, 4L, 7L, 4L, 7L, 2L, 4L, 6L, 7L, 1L, 3L, 4L, 7L, 1L, 2L,
4L, 5L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 4L, 6L, 7L, 4L, 5L, 7L,
2L, 3L, 6L, 7L, 2L, 5L, 6L, 7L, 2L, 3L, 6L, 7L, 1L, 7L, 2L, 4L,
5L, 6L, 7L, 1L, 2L, 4L, 6L, 7L, 2L, 3L, 4L, 5L, 6L, 7L), .Label =
c("E-mail",
"Fax", "Mail", "Sales visit", "Special", "Telephone", "Web"), class =
"factor"),
Qtr.Rev = c(171407.28, 2013909.18, 158795.34, 2289201.87,
1738303.59, 66926.18, 1671887.4, 7050164.5, 1655507.05,
6222440.26,
7746789.52, 6864270.12, 195549.5, 450628.79, 12376528.53,
415128.31, 1453194.14, 2735416.3, 15777880.11, 413978.16,
3776833.13, 308638.6, 12328172.97, 709194.65, 1304167.86,
5897377.14, 11048160.97, 1546079.43, 1247170.05, 2373591.15,
12102240.99, 2461322.51, 165800.42, 1397604.56, 198705.05,
7413833.64, 2662351.94, 289704.5, 680467.87, 87186.72, 343708.86,
1802166.73, 16990817.52, 2821127.32, 431860.34, 10144353.75,
5063353.42, 1725508.54, 3571760.87, 593828.88, 1074860.66,
2981026.86, 5254137.56, 469627.61, 908725.05, 1625096.56,
9677070.09, 88788.41, 337710.73, 254360.21, 7835117.44,
1292812.39,
4818848.86, 217936.39, 792168.42, 790344.28, 109161.04,
4565896.64,
697619.35, 264500.2, 189218.02, 2022968.96, 13756025.4,
1357389.56,
2352483.29, 2842600.85, 685752.21, 13437403.28, 29573813.7
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-79L), .Names = c("Retailer.country", "Order.method.type", "Qtr.Rev"
))
我在R中创建一个列联表，显示每个国家/地区每种销售方法产生的季度收入。最终输出应该类似于：
Retailer.country E-mail Fax Mail Sales visit Special Telephone Web TOTAL.cn
1 Australia 171407.3 0.00 0.0 2013909.18 158795.3 2289201.9 1738304 6371617
2 Austria 0.0 0.00 0.0 66926.18 0.0 1671887.4 7050164 8788978
3 Belgium 0.0 0.00 0.0 1655507.05 0.0 0.0 6222440 7877947
4 Brazil 0.0 0.00 0.0 0.00 0.0 0.0 7746790 7746790
5 Canada 6864270.1 195549.50 0.0 450628.79 0.0 0.0 12376529 19886977
6 China 0.0 415128.31 0.0 1453194.14 0.0 2735416.3 15777880 20381619
7 Denmark 0.0 0.00 0.0 413978.16
...
20 United Kingdom 697619.3 264500.20 0.0 189218.02 0.0 2022969.0 13756025 16930332
21 United States 0.0 1357389.56 2352483.3 2842600.85 685752.2 13437403.3 29573814 50249443
22 TOTAL.type 15695863.0 4767448.43 5692692.6 23233800.42 4811539.3 35257926.7 203769190 293228461
reshape库中的cast（）函数完成大部分工作，只留下要计算的所有值的汇总列和行。
cast(sales.by.country, Retailer.country ~ Order.method.type,
fill=0) -> sales.by.country
将行汇总到名为“TOTAL.cn”的新列中非常简单：
sales.by.country$TOTAL.cn <- rowSums(sales.by.country[,c(2:8)])
但总结列成为一个主要问题，因为最后一行的第一个组成部分必须是因素或字符。我将第一列“Retailer.country”转换为字符类型，因为它实际上只是一个视觉标签。
在讨论了几个函数后，这是我能够创建的最佳代码，以实现预期的行总和：
# Sum the numeric columns, which is everything *except* column 1
total.by.ordertype <- (colSums(sales.by.country[,-1]))
# Create the Total by Order row
total.by.ordertype.row <- list("TOTAL.type", total.by.ordertype[1],
total.by.ordertype[2], total.by.ordertype[3], total.by.ordertype[4],
total.by.ordertype[5], total.by.ordertype[6], total.by.ordertype[7],
total.by.ordertype[8])
# Add the Total by Order row to the bottom of the table
sales.by.country[22, ] <- total.by.ordertype.row
它在所有列中工作并维护正确的数据类型...但我认为必须有一种更有效的方法，可能是通过使用apply系列函数，来自dplyr等等。也许唯一的方法是写我自己的功能？
例如，未来的数据集可能有50多种不同的销售方法。在创建“按订单总计”行（上图）的列表时，我不得不调出向量中的每个单元格，用逗号分隔，以便成功将其添加到现有表格中。其他努力将所有其他列的数据类型转换为字符，这使得一切都搞砸了。
我不介意复制/粘贴“total.by.ordertype”8次这么多。但是当我处理50-100个订单类型时会发生什么？是否有更简洁的方法来重现这些？
谢谢！
答案 0 :(得分：3)
cast()库中的reshape函数可以完成整个工作。使用参数margin = TRUE，将计算所有行和列总计：
reshape::cast(sales.by.country, Retailer.country ~ Order.method.type, fun.aggregate = sum,
fill = 0, margins = TRUE)
Retailer.country E-mail Fax Mail Sales visit Special Telephone Web (all) 1 Australia 171407.3 0.00 0.0 2013909.18 158795.3 2289201.9 1738304 6371617 2 Austria 0.0 0.00 0.0 66926.18 0.0 1671887.4 7050164 8788978 3 Belgium 0.0 0.00 0.0 1655507.05 0.0 0.0 6222440 7877947 4 Brazil 0.0 0.00 0.0 0.00 0.0 0.0 7746790 7746790 5 Canada 6864270.1 195549.50 0.0 450628.79 0.0 0.0 12376529 19886977 6 China 0.0 415128.31 0.0 1453194.14 0.0 2735416.3 15777880 20381619 7 Denmark 0.0 0.00 0.0 413978.16 0.0 0.0 3776833 4190811 8 Finland 0.0 0.00 0.0 308638.60 0.0 0.0 12328173 12636812 9 France 0.0 709194.65 0.0 1304167.86 0.0 5897377.1 11048161 18958901 10 Germany 1546079.4 0.00 1247170.1 2373591.15 0.0 0.0 12102241 17269082 11 Italy 2461322.5 165800.42 0.0 1397604.56 198705.0 0.0 7413834 11637266 12 Japan 2662351.9 289704.50 680467.9 87186.72 343708.9 1802166.7 16990818 22856404 13 Korea 0.0 0.00 0.0 2821127.32 0.0 431860.3 10144354 13397341 14 Mexico 0.0 0.00 0.0 5063353.42 1725508.5 0.0 3571761 10360623 15 Netherlands 0.0 593828.88 1074860.7 0.00 0.0 2981026.9 5254138 9903854 16 Singapore 0.0 469627.61 0.0 0.00 908725.1 1625096.6 9677070 12680519 17 Spain 0.0 88788.41 337710.7 0.00 0.0 254360.2 7835117 8515977 18 Sweden 1292812.4 0.00 0.0 0.00 0.0 0.0 4818849 6111661 19 Switzerland 0.0 217936.39 0.0 792168.42 790344.3 109161.0 4565897 6475507 20 United Kingdom 697619.3 264500.20 0.0 189218.02 0.0 2022969.0 13756025 16930332 21 United States 0.0 1357389.56 2352483.3 2842600.85 685752.2 13437403.3 29573814 50249443 22 (all) 15695863.0 4767448.43 5692692.6 23233800.42 4811539.3 35257926.7 203769190 293228461
当然，还必须指定fun.aggregate。
同样的功能也来自reshape2包，即reshape的后续版本，但对于这个小样本大小，速度提高了约4倍。
reshape2::dcast(sales.by.country, Retailer.country ~ Order.method.type, fun.aggregate = sum,
fill = 0, margins = TRUE)
dcast()也可以从data.table包中获得，声称速度超过reshape2::dcast()。遗憾的是，margins参数尚未实现（当前CRAN版本1.10.4）。因此，边距必须单独计算并与原始数据结合：
DT2 <- rbind(
DT,
DT[, .(Qtr.Rev = sum(Qtr.Rev)), by = Retailer.country],
DT[, .(Qtr.Rev = sum(Qtr.Rev)), by = Order.method.type],
DT[, .(Qtr.Rev = sum(Qtr.Rev))],
fill = TRUE
)
dcast(DT2, Retailer.country ~ Order.method.type, fill = 0)
Retailer.country E-mail Fax Mail Sales visit Special Telephone Web NA 1: Australia 171407.3 0.00 0.0 2013909.18 158795.3 2289201.9 1738304 6371617 2: Austria 0.0 0.00 0.0 66926.18 0.0 1671887.4 7050164 8788978 3: Belgium 0.0 0.00 0.0 1655507.05 0.0 0.0 6222440 7877947 4: Brazil 0.0 0.00 0.0 0.00 0.0 0.0 7746790 7746790 5: Canada 6864270.1 195549.50 0.0 450628.79 0.0 0.0 12376529 19886977 6: China 0.0 415128.31 0.0 1453194.14 0.0 2735416.3 15777880 20381619 7: Denmark 0.0 0.00 0.0 413978.16 0.0 0.0 3776833 4190811 8: Finland 0.0 0.00 0.0 308638.60 0.0 0.0 12328173 12636812 9: France 0.0 709194.65 0.0 1304167.86 0.0 5897377.1 11048161 18958901 10: Germany 1546079.4 0.00 1247170.1 2373591.15 0.0 0.0 12102241 17269082 11: Italy 2461322.5 165800.42 0.0 1397604.56 198705.0 0.0 7413834 11637266 12: Japan 2662351.9 289704.50 680467.9 87186.72 343708.9 1802166.7 16990818 22856404 13: Korea 0.0 0.00 0.0 2821127.32 0.0 431860.3 10144354 13397341 14: Mexico 0.0 0.00 0.0 5063353.42 1725508.5 0.0 3571761 10360623 15: Netherlands 0.0 593828.88 1074860.7 0.00 0.0 2981026.9 5254138 9903854 16: Singapore 0.0 469627.61 0.0 0.00 908725.1 1625096.6 9677070 12680519 17: Spain 0.0 88788.41 337710.7 0.00 0.0 254360.2 7835117 8515977 18: Sweden 1292812.4 0.00 0.0 0.00 0.0 0.0 4818849 6111661 19: Switzerland 0.0 217936.39 0.0 792168.42 790344.3 109161.0 4565897 6475507 20: United Kingdom 697619.3 264500.20 0.0 189218.02 0.0 2022969.0 13756025 16930332 21: United States 0.0 1357389.56 2352483.3 2842600.85 685752.2 13437403.3 29573814 50249443 22: NA 15695863.0 4767448.43 5692692.6 23233800.42 4811539.3 35257926.7 203769190 293228461 Retailer.country E-mail Fax Mail Sales visit Special Telephone Web NA
答案 1 :(得分：0)
使用tidyr和dt4中的函数的解决方案。 summarise_if是最终输出。请注意sum的使用。当我们只想为适合预定条件的列应用函数时，它很有用。在这种情况下，我们只能将# Create example data frame
library(dplyr)
library(tidyr)
# sales.by.country is created by OP's dput dataset
dt2 <- sales.by.country %>%
mutate(Retailer.country = as.character(Retailer.country)) %>%
# Spread the data frame
spread(Order.method.type, Qtr.Rev, fill = 0) %>%
# Calcualte Total.cn by rowSums
mutate(TOTAL.cn = rowSums(.[, 2:ncol(.)]))
# Calculate the sum of each column if it is numeric
dt3 <- dt2 %>% summarise_if(is.numeric, sum)
# Combine dt3 (the summary) to dt2
dt4 <- dt2 %>%
bind_rows(dt3) %>%
# Replace the na in Retailer.country to be "TOTAL.type"
replace_na(list(Retailer.country = "TOTAL.type"))
函数应用于数字列。
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答案 2 :(得分：0)
使用tidyr进行分摊，并使用janitor添加列和行的总计：
library(janitor)
library(tidyr)
sales.by.country %>%
spread(Order.method.type, Qtr.Rev, fill = 0) %>%
adorn_totals(c("row", "col"))