Создать 2-х уровневую сводную таблицу в R

Question

У меня есть фрейм данных с разными значениями, и я хочу построить «сводную таблицу», используя R, но с 2 уровнями, и я знаю, как сгруппировать его по 1 уровню, а не 2.

Это ожидаемый результат: 2-х уровневая сводная таблица

Region/Country Sales Asia 3,452,710 China 3,452,710 Europe 2,881,793 Germany 1,846,392 Spain 1,035,401 South America 3,111,193 Argentina 1,059,341 Brazil 2,051,852

Это пример данных, и я хочу сгруппировать его покаРегион и Страна.

Страна Регион Продажи China Asia $109,680 China Asia $141,165 China Asia $77,516 China Asia $154,060 China Asia $38,597 China Asia $125,985 China Asia $91,850 China Asia $90,513 China Asia $51,710 China Asia $53,283 China Asia $77,352 China Asia $5,534 China Asia $6,645 China Asia $135,301 China Asia $176,095 China Asia $146,270 China Asia $54,665 China Asia $62,111 China Asia $64,390 China Asia $30,197 China Asia $132,397 China Asia $165,454 China Asia $113,084 China Asia $40,922 China Asia $161,574 China Asia $16,753 China Asia $54,616 China Asia $117,857 China Asia $193,862 China Asia $23,478 China Asia $16,135 China Asia $103,860 China Asia $72,478 China Asia $45,570 China Asia $81,337 China Asia $174,224

Answer 1

Это пример данных, и я хочу сгруппировать их по регионам и странам.

Country Region  Sales
China   Asia    $109,680
China   Asia    $141,165
China   Asia    $77,516
China   Asia    $154,060
China   Asia    $38,597
China   Asia    $125,985
China   Asia    $91,850
China   Asia    $90,513
China   Asia    $51,710
China   Asia    $53,283
China   Asia    $77,352
China   Asia    $5,534
China   Asia    $6,645
China   Asia    $135,301
China   Asia    $176,095
China   Asia    $146,270
China   Asia    $54,665
China   Asia    $62,111
China   Asia    $64,390
China   Asia    $30,197
China   Asia    $132,397
China   Asia    $165,454
China   Asia    $113,084
China   Asia    $40,922
China   Asia    $161,574
China   Asia    $16,753
China   Asia    $54,616
China   Asia    $117,857
China   Asia    $193,862
China   Asia    $23,478
China   Asia    $16,135
China   Asia    $103,860
China   Asia    $72,478
China   Asia    $45,570
China   Asia    $81,337
China   Asia    $174,224
China   Asia    $58,030
China   Asia    $188,160
Spain   Europe  $118,446
Spain   Europe  $43,783
Spain   Europe  $52,586
Spain   Europe  $42,995
Spain   Europe  $183,739
Spain   Europe  $108,905
Spain   Europe  $100,986
Spain   Europe  $155,873
Spain   Europe  $117,634
Spain   Europe  $110,454
Germany Europe  $118,446
Germany Europe  $43,783
Germany Europe  $52,586
Germany Europe  $42,995
Germany Europe  $183,739
Germany Europe  $108,905
Germany Europe  $100,986
Germany Europe  $155,873
Germany Europe  $117,634
Germany Europe  $110,454
Germany Europe  $61,951
Germany Europe  $139,379
Germany Europe  $97,083
Germany Europe  $41,821
Germany Europe  $26,241
Germany Europe  $39,150
Germany Europe  $26,485
Germany Europe  $104,995
Germany Europe  $75,915
Germany Europe  $197,971
Brazil  South America   $20,063
Brazil  South America   $96,114
Brazil  South America   $78,454
Brazil  South America   $181,765
Brazil  South America   $145,676
Brazil  South America   $133,124
Brazil  South America   $142,922
Brazil  South America   $190,313
Brazil  South America   $5,764
Brazil  South America   $77,970
Brazil  South America   $196,077
Argentina   South America   $64,547
Argentina   South America   $157,579
Argentina   South America   $87,654
Argentina   South America   $184,358
Argentina   South America   $25,555
Argentina   South America   $38,456
Argentina   South America   $6,762
Argentina   South America   $49,041
Argentina   South America   $178,130
Argentina   South America   $180,618
Argentina   South America   $86,641
Brazil  South America   $18,330
Brazil  South America   $28,143
Brazil  South America   $130,999
Brazil  South America   $100,090
Brazil  South America   $59,515
Brazil  South America   $157,308
Brazil  South America   $170,736
Brazil  South America   $106,259
Brazil  South America   $12,230

Answer 2

Трудно ответить на это без воспроизводимых наборов данных, но здесь идет.Попробуйте установить dplyr и библиотеку hflights для работы с примером кода.

#install.packages("hflights")
#install.packages("dplyr")

library(dplyr)
library(hflights)

head(hflights)

# filter data for 2011 inbound flights to DFW
data_2011_DFW <- filter(hflights, Dest == "DFW", Year == 2011, Month == 1)

# group by Destination Airport and Month
data_2011_DFW_Carrier <- group_by(data_2011_DFW, UniqueCarrier)

# create a custom summary of observations
summarize(data_2011_DFW_Carrier, count = n(), delay = mean(ArrDelay, na.rm = T))