Calculate a rolling percent change in R - r
I am trying to calculate a 20-day rolling percent change in R based off of a stock's closing price. Below is a sample of the most recent 100 days of closing price data. df$Close[1] is the most recent day, df$Close[2] is the previous day, and so on.
df$Close
[1] 342.94 346.22 346.18 335.24 330.45 334.20 325.45 333.79 334.90 341.66 333.74 334.49 329.75 329.82 330.56 322.81 317.87 306.84
[19] 310.39 310.60 324.46 338.03 333.12 341.06 337.25 341.01 345.30 338.69 340.77 342.96 347.56 340.89 327.74 327.64 335.37 338.62
[37] 341.13 335.85 331.62 328.08 329.98 323.57 316.92 312.22 315.81 328.69 324.61 341.88 340.78 339.99 335.34 324.76 328.53 324.54
[55] 323.77 325.45 330.05 329.22 333.64 332.96 326.23 343.01 339.39 339.61 340.65 353.58 352.96 345.96 343.21 357.48 355.70 364.72
[73] 373.06 373.92 376.53 376.51 378.69 378.00 377.57 382.18 376.26 375.28 382.05 379.38 380.66 372.63 364.38 368.39 365.51 363.35
[91] 359.37 355.12 355.45 358.45 366.56 363.18 362.65 359.96 361.13 361.61
Previously, I had used the following code to calculate the percent change:
PercChange(df, Var = 'Close', type = 'percent', NewVar = 'OneMonthChange', slideBy = 20)
which gave me the following output:
df$OneMonthChange
[1] 5.695617e-02 2.422862e-02 3.920509e-02 -1.706445e-02 -2.016308e-02 -1.997009e-02 -5.748624e-02 -1.446751e-02 -1.722569e-02
[10] -3.790530e-03 -3.976292e-02 -1.877438e-02 6.132910e-03 6.653644e-03 -1.434237e-02 -4.668950e-02 -6.818515e-02 -8.637785e-02
[19] -6.401906e-02 -5.327969e-02 -1.672829e-02 4.468894e-02 5.111700e-02 9.237076e-02 6.788892e-02 3.748213e-02 6.373802e-02
[28] -9.330759e-03 -2.934445e-05 8.735551e-03 3.644063e-02 4.966745e-02 -2.404651e-03 9.551981e-03 3.582790e-02 4.046705e-02
[37] 3.357067e-02 2.013851e-02 -6.054430e-03 -1.465642e-02 1.149496e-02 -5.667473e-02 -6.620702e-02 -8.065134e-02 -7.291942e-02
[46] -7.039425e-02 -8.032072e-02 -1.179327e-02 -7.080213e-03 -4.892581e-02 -5.723925e-02 -1.095635e-01 -1.193642e-01 -1.320603e-01
[55] -1.401216e-01 -1.356139e-01 -1.284428e-01 -1.290476e-01 -1.163493e-01 -1.287875e-01 -1.329666e-01 -8.598913e-02 -1.116608e-01
[64] -1.048289e-01 -1.051069e-01 -5.112310e-02 -3.134091e-02 -6.088656e-02 -6.101064e-02 -1.615522e-02 -1.021232e-02 2.703312e-02
[73] 4.954283e-02 4.315804e-02 2.719882e-02 3.670356e-02 4.422997e-02 5.011668e-02 4.552377e-02 5.688449e-02 3.507469e-02
[82] 3.391465e-02 6.444333e-02 8.011616e-02 8.157409e-02 4.583216e-02 1.691226e-02 -1.310009e-02 -6.253229e-03 -2.445900e-02
[91] -2.817816e-02 1.119052e-02 2.662970e-02 4.914242e-02 8.787654e-02 6.454450e-02 5.280729e-02 3.546875e-02 2.567525e-02
[100] 2.392683e-02
The PercChange function has now been deprecated and I need to find a new function to replace it. Essentially, I need a function that calculates the percent change of df$Close[1:20] (This would be Close of day 1 minus close of day 20, divided by close of day 20), then rolls to [2:21] for the next row, then [3:22],[4:23], and so on.
Thanks in advance!
A tidyverse approach
library(tidyr)
library(dplyr)
df %>% mutate(OneMonthChange=(Close-lead(Close, 20))/lead(Close, 20),
OneMonthChange=replace_na(OneMonthChange,0))
Close OneMonthChange
1 342.94 5.695617e-02
2 346.22 2.422862e-02
3 346.18 3.920509e-02
4 335.24 -1.706445e-02
5 330.45 -2.016308e-02
6 334.20 -1.997009e-02
etc...
Here is a simple Base R solution:
PercChange<- function(x, slideBy){
-diff(x, slideBy)/ tail(x, -slideBy)
}
PercChange(df$Close, slideBy = 20)
[1] 5.695617e-02 2.422862e-02 3.920509e-02 -1.706445e-02
[5] -2.016308e-02 -1.997009e-02 -5.748624e-02 -1.446751e-02
[9] -1.722569e-02 -3.790530e-03 -3.976292e-02 -1.877438e-02
If you desire a datframe back, then modify this into:
PercChange<- function(data, Var, NewVar, slideBy){
x <- data[[Var]]
data[NewVar] <- c(-diff(x, slideBy)/ tail(x, -slideBy), numeric(slideBy))
data
}
PercChange(df, Var = 'Close', NewVar = 'OneMonthChange', slideBy = 20)
data:
df <- structure(list(Close = c(342.94, 346.22, 346.18, 335.24, 330.45,
334.2, 325.45, 333.79, 334.9, 341.66, 333.74, 334.49, 329.75,
329.82, 330.56, 322.81, 317.87, 306.84, 310.39, 310.6, 324.46,
338.03, 333.12, 341.06, 337.25, 341.01, 345.3, 338.69, 340.77,
342.96, 347.56, 340.89, 327.74, 327.64, 335.37, 338.62, 341.13,
335.85, 331.62, 328.08, 329.98, 323.57, 316.92, 312.22, 315.81,
328.69, 324.61, 341.88, 340.78, 339.99, 335.34, 324.76, 328.53,
324.54, 323.77, 325.45, 330.05, 329.22, 333.64, 332.96, 326.23,
343.01, 339.39, 339.61, 340.65, 353.58, 352.96, 345.96, 343.21,
357.48, 355.7, 364.72, 373.06, 373.92, 376.53, 376.51, 378.69,
378, 377.57, 382.18, 376.26, 375.28, 382.05, 379.38, 380.66,
372.63, 364.38, 368.39, 365.51, 363.35, 359.37, 355.12, 355.45,
358.45, 366.56, 363.18, 362.65, 359.96, 361.13, 361.61)), class = "data.frame", row.names = c(NA,
-100L))
Related
R + ggplot2: plot time series with linear regression with changepoint
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Change origin for time series in r
I have a time series in R that I would like to work with, spanning from 01-01-52 to 01-01-88. (1952 to 1988). 37 observations.
However, when I read it in in R, I encounter the problem that the observations from 01-01-52 to 01-01-68 are interpreted as being in 2052 etc., rather than 1952.
How do I force R to read in all the data as being from 1952 to 1988?
Link to my data: https://www.dropbox.com/s/93foyc238skt3xj/AgricIndus.csv?dl=0
This is the code I have used. Do you know what I need to do with my code to make it read properly?
agri <- read.table("AgricIndus.csv",
sep = ",", header = TRUE, skip = 0,
stringsAsFactors = FALSE)
agri$time <- as.Date(agri$time, "%m-%d-%y")
agri.xts <- xts(agri[, 2:3], order.by = agri$time)
One way (hack) can be the following: agri$time <- as.Date(paste0(substring(agri$time,1,6), '19', substring(agri$time,7,8)), "%m-%d-%Y") agri$time # [1] "01-01-52" "01-01-53" "01-01-54" "01-01-55" "01-01-56" "01-01-57" "01-01-58" "01-01-59" "01-01-60" "01-01-61" "01-01-62" "01-01-63" "01-01-64" "01-01-65" # [15] "01-01-66" "01-01-67" "01-01-68" "01-01-69" "01-01-70" "01-01-71" "01-01-72" "01-01-73" "01-01-74" "01-01-75" "01-01-76" "01-01-77" "01-01-78" "01-01-79" # [29] "01-01-80" "01-01-81" "01-01-82" "01-01-83" "01-01-84" "01-01-85" "01-01-86" "01-01-87" "01-01-88"
If you can be sure that your time series is regular then the it is probably the easiest to generate a regular date sequence like so:
agri$time <- seq.Date(as.Date("1952-01-01"),as.Date("1988-01-01"),by='years’)
Another easy solution that would work for irregular time series as well would be to read your data as years 52 to 88 with format = %m-%d-%Y (capitalized “Y” !) and add 1900 years:
df$time <- as.POSIXlt(as.Date(df$time,format = '%m-%d-%Y'))
df$time$year <-df$time$year + 1900
df$time <- as.Date(df$time)
df$time
[1] "1952-01-01" "1953-01-01" "1954-01-01" "1955-01-01"
[5] "1956-01-01" "1957-01-01" "1958-01-01" "1959-01-01"
[9] "1960-01-01" "1961-01-01" "1962-01-01" "1963-01-01"
[13] "1964-01-01" "1965-01-01" "1966-01-01" "1967-01-01"
[17] "1968-01-01" "1969-01-01" "1970-01-01" "1971-01-01"
[21] "1972-01-01" "1973-01-01" "1974-01-01" "1975-01-01"
[25] "1976-01-01" "1977-01-01" "1978-01-01" "1979-01-01"
[29] "1980-01-01" "1981-01-01" "1982-01-01" "1983-01-01"
[33] "1984-01-01" "1985-01-01" "1986-01-01" "1987-01-01"
[37] "1988-01-01"
Dividing components of a vector into several data points in R
I am trying to turn a vector of length n (say, 14), and turn it into a vector of length N (say, 90). For example, my vector is x<-c(5,3,7,11,12,19,40,2,22,6,10,12,12,4) and I want to turn it into a vector of length 90, by creating 90 equally "spaced" points on this vector- think of x as a function. Is there any way to do that in R?
Something like this? > x<-c(5,3,7,11,12,19,40,2,22,6,10,12,12,4) > seq(min(x),max(x),length=90) [1] 2.000000 2.426966 2.853933 3.280899 3.707865 4.134831 4.561798 [8] 4.988764 5.415730 5.842697 6.269663 6.696629 7.123596 7.550562 [15] 7.977528 8.404494 8.831461 9.258427 9.685393 10.112360 10.539326 [22] 10.966292 11.393258 11.820225 12.247191 12.674157 13.101124 13.528090 [29] 13.955056 14.382022 14.808989 15.235955 15.662921 16.089888 16.516854 [36] 16.943820 17.370787 17.797753 18.224719 18.651685 19.078652 19.505618 [43] 19.932584 20.359551 20.786517 21.213483 21.640449 22.067416 22.494382 [50] 22.921348 23.348315 23.775281 24.202247 24.629213 25.056180 25.483146 [57] 25.910112 26.337079 26.764045 27.191011 27.617978 28.044944 28.471910 [64] 28.898876 29.325843 29.752809 30.179775 30.606742 31.033708 31.460674 [71] 31.887640 32.314607 32.741573 33.168539 33.595506 34.022472 34.449438 [78] 34.876404 35.303371 35.730337 36.157303 36.584270 37.011236 37.438202 [85] 37.865169 38.292135 38.719101 39.146067 39.573034 40.000000 >
Try this: #data x <- c(5,3,7,11,12,19,40,2,22,6,10,12,12,4) #expected new length N=90 #number of numbers between 2 numbers my.length.out=round((N-length(x))/(length(x)-1))+1 #new data x1 <- unlist( lapply(1:(length(x)-1), function(i) seq(x[i],x[i+1],length.out = my.length.out))) #plot par(mfrow=c(2,1)) plot(x) plot(x1)
R: simulate ts object with dates
Wondering how to generate time series and assign dates at the same time. I am trying this series = as.ts(arima.sim(model = list(ar = c(0.12, -.36)), n = 1990 - 1875, sd = sqrt(4)), start = 1875, deltat = 1) But this does not return a ts object that counts the years from 1875. By my reckoning this should work. Any advice appreciated.
You are correct. I re-typed vs cut/paste your code and it's both the start parameter and the use of as.ts vs just ts that's the issue: asim <- arima.sim(model = list(ar = c(0.12, -.36)), n = 1990 - 1875, sd = sqrt(4)) series <- ts(asim, start = c(1875, 1), deltat = 1) print(series) Time Series: Start = 1875 End = 1989 Frequency = 1 [1] -1.22873543 -2.87876290 -3.00367322 -0.93120214 1.76854684 0.93874091 -2.32494289 [8] 1.14892019 1.87773156 1.48735536 -0.84149973 -3.69650397 1.20710878 2.14151424 [15] -2.58376182 -2.97501726 2.77019523 4.50829433 0.35603642 -1.95517140 -1.12792253 [22] 1.64063413 2.25654663 -0.51293345 1.07829896 -1.77134896 2.38908172 4.29362478 [29] -1.55577635 1.17953083 3.39823289 1.11846543 -0.92758706 -1.24158935 -2.39831233 [36] 4.24302415 2.93797283 -0.75916084 -0.66967525 2.85022663 -0.18190842 -5.39057660 [43] 0.08454559 2.01667062 -3.17054706 -3.77788365 0.19987174 2.87106608 -0.33844973 [50] 1.20917997 -1.00509230 3.23130604 5.80269444 3.33781468 2.67050526 1.85130774 [57] -0.46065144 -2.79539368 0.29784271 -4.51945793 0.61091013 2.56372897 -4.66101520 [64] 2.43024521 0.04428268 -1.19454953 -3.10583191 4.55208114 6.00037902 -3.32996632 [71] 2.22167610 1.07499343 1.89873604 2.04067084 -3.43648828 -0.53093294 0.66225057 [78] -2.30214366 0.78945348 0.35241170 -0.68250626 1.39801271 -1.01914282 -0.33615058 [85] 0.92311887 1.66289752 -0.83158693 -0.74454853 6.53884660 1.53567335 -2.16745416 [92] -0.01540633 -1.25032821 -0.02958796 3.18116493 -2.07512219 -1.40620668 -0.78869155 [99] 2.30251140 -2.23997817 0.34824690 4.81898402 -0.38751197 -5.74540148 -0.37754295 [106] 2.59869857 -1.90175430 0.37994317 -1.27326292 -3.96302760 -2.01928982 2.57643462 [113] 2.62600151 -4.20987173 0.46388883 Since asim is already a ts class object, as.ts is pulling the tsp attribute from it vs creating it from the input parameters. Using ts creates a new tsp attribute.
R: Using for loop on data frame
I have a data frame, deflator.
I want to get a new data frame inflation which can be calculated by:
deflator[i] - deflator[i-4]
----------------------------- * 100
deflator [i - 4]
The data frame deflator has 71 numbers:
> deflator
[1] 0.9628929 0.9596746 0.9747274 0.9832532 0.9851884
[6] 0.9797770 0.9913502 1.0100561 1.0176906 1.0092516
[11] 1.0185932 1.0241043 1.0197975 1.0174097 1.0297328
[16] 1.0297071 1.0313232 1.0244618 1.0347808 1.0480411
[21] 1.0322142 1.0351968 1.0403264 1.0447121 1.0504402
[26] 1.0487097 1.0664664 1.0935239 1.0965951 1.1141851
[31] 1.1033155 1.1234482 1.1333870 1.1188136 1.1336276
[36] 1.1096461 1.1226584 1.1287245 1.1529588 1.1582911
[41] 1.1691221 1.1782178 1.1946234 1.1963453 1.1939922
[46] 1.2118189 1.2227960 1.2140535 1.2228828 1.2314258
[51] 1.2570788 1.2572214 1.2607763 1.2744415 1.2982076
[56] 1.3318808 1.3394186 1.3525902 1.3352815 1.3492751
[61] 1.3593859 1.3368135 1.3642940 1.3538567 1.3658135
[66] 1.3710932 1.3888638 1.4262185 1.4309707 1.4328823
[71] 1.4497201
This is a very tricky question for me.
I tried to do this using a for loop:
> d <- data.frame(deflator)
> for (i in 1:71) {d <-rbind(d,c(delfaotr ))}
I think I might be doing it wrong.
Why use data frames? This is a straightforward vector operation. inflation = 100 * (deflator[1:67] - deflator[-(1:4)])/deflator[-(1:4)]
I agree with #Fhnuzoag that your example suggests calculations on a numeric vector, not a data frame. Here's an additional way to do your calculations taking advantage of the lag argument in the diff function (with indexes that match those in your question): lagBy <- 4 # The number of indexes by which to lag laggedDiff <- diff(deflator, lag = lagBy) # The numerator above theDenom <- deflator[seq_len(length(deflator) - lagBy)] # The denominator above inflation <- laggedDiff/theDenom The first few results are: head(inflation) # [1] 0.02315470 0.02094710 0.01705379 0.02725941 0.03299085 0.03008297