1.мутировать
TX.Hour <- TX.Hour %>% mutate(Year = year(Time)) TX.Hour <- TX.Hour %>% mutate(Month = month(Time)) TX.Hour <- TX.Hour %>% mutate(Day = day(Time))
2.конвертировать в xts
df$time <- as.POSIXlt(df$time, format = “%Y%m%d %H%M%S”) df_xts <- xts(df[, -1], order.by = df[, 1])
3.seq
number <- seq(2, nrow(Data), by = 5)
4.tq_get
DOW <- tq_get(x = “DOW”, get = “stock.prices.google”)
5.ggplot
Data %>% ggplot(aes(x = Date, y = Last)) + geom_line()
6.read.dta
Data <- read.dta(“Data.dta”)
7.сюжет
plot(Data$time, Data$price, type = “l”)
8.nchar
Data <- Data[nchar(Data$variable) == 6, ]
9.str_detect
TX <- TX[str_detect(TX$mark, “^TX”), ]
10. функция
read.Data <- function(y, m, d) {
paste(“Daily_”, y, “_”, m, “_”, d, “.csv”, sep = “”) %>%
read.csv(header = TRUE, sep = “,”, stringsAsFactors = FALSE)
}
TX <- read.Data(y, m, d)
11. вырезать
period.cut <- c(84500, 90000, 91500, 93000, 94500, 100000) period.cut.name <- c(“9:00”, “9:15”, “9:30”, “9:45”, “10:00”) TX$period <- cut(TX$time, breaks = period.cut, labels = period.cut.name)
12.импорт
Data <- read.csv(“Data.csv”, header = TRUE, sep = ”,”, stringsAsFactors = FALSE)
13.убрать первый "," и последний ","
Data$variable1[str_sub(string = Data$variable1, start = 1, end = 1) == “,”] <- str_replace(string = Data$variable1[str_sub(string = Data$variable1, start = 1, end = 1) == “,”], pattern = “^\\,”, replacement = “”) Data$variable1[str_sub(string = Data$variable1, start = nchar(Data$variable1), end = nchar(Data$variable1)) == “,”] <- str_replace(string = Data$variable1[str_sub(string = Data$variable1, start = nchar(Data$variable1), end = nchar(Data$variable1)) == “,”], pattern = “\\,$”, replacement = “”)
14. перекодировать
Data$variable1[Data$variable1 == “1”] <- “11” Data$variable2[Data$variable2 == “2”] <- “12” Data$variable3[Data$variable3 == “3”] <- “13”
15. перекодировать
Data$variable1 <- revalue(Data$variable1, c(“0”=”No”, “1”=”Yes”))
16. перекодировать
из http://past.rinfinance.com/agenda/2017/talk/DanielKowal.pdf
Y = Y[which(dates > as.Date(“2012–01–01”)), ];
