[英]Summarize dataframe with a constraint using Dplyr
我有一個類似於以下數據框:
data <- data.frame(x = c("0", "2", "8", "1", "7", "10", "15", "14", "13", "11"),
y = c("11", "5", "14", "9", "13", "7", "4", "0", "12", "8"),
act_x = c("Short", "Buy", "Short", "Buy", "Short", "Buy", "Short", "Buy", "Short", "Buy"),
act_y = c("Buy", "Short", "Buy", "Short", "Buy", "Short", "Buy", "Short", "Buy", "Short"))
我希望根據對x和y采取的措施為x創建一個利潤列,為y創建一個利潤列。 結果應如下所示:
res <- data.frame(data,
prof_x = c(NA, -2, 6, 7, 6, -3, 5, 1, -1, 2),
prof_y = c(NA, -6, -9, -5, -4, -6, 3, -4, -12, -4))
例如,從第0天(第一行)開始,我做空x並買入y。 相應的價格在第一天(第二行)移動並結算。 x的利潤為0-2=-2
(因為我賣空了x),y的利潤為5-11=-6
(因為我買了y)。 等等...
有沒有在Dplyr管道中實現此目標的友好方法? 有人在管道之外有任何建議嗎? 在此先感謝您的指導。
使用lag
和mutate
基於dplyr
的解決方案可以實現為:
library(dplyr)
data %>% mutate(x = as.numeric(x), y = as.numeric(y)) %>%
mutate(prof_x = ifelse(act_x == "Buy", lag(x)-x, x-lag(x))) %>%
mutate(prof_y = ifelse(act_y == "Buy", lag(y)-y, y-lag(y)))
#
# x y act_x act_y prof_x prof_y
# 1 0 11 Short Buy NA NA
# 2 2 5 Buy Short -2 -6
# 3 8 14 Short Buy 6 -9
# 4 1 9 Buy Short 7 -5
# 5 7 13 Short Buy 6 -4
# 6 10 7 Buy Short -3 -6
# 7 15 4 Short Buy 5 3
# 8 14 0 Buy Short 1 -4
# 9 13 12 Short Buy -1 -12
# 10 11 8 Buy Short 2 -4
數據:
data <- data.frame(x = c("0", "2", "8", "1", "7", "10", "15", "14", "13", "11"),
y = c("11", "5", "14", "9", "13", "7", "4", "0", "12", "8"),
act_x = c("Short", "Buy", "Short", "Buy", "Short", "Buy", "Short", "Buy", "Short", "Buy"),
act_y = c("Buy", "Short", "Buy", "Short", "Buy", "Short", "Buy", "Short", "Buy", "Short"),
stringsAsFactors = FALSE)
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