Randomly sampling rows from particular months in a data set - r

Question

I was given this task in R:
"Randomly select 10 trading days from each of the following months: January 2019 to June 2019 (6 months total)".
I have a CSV file of a company's stock trading history from the last 5 years (dates, opening price, closing price, changes, etc.) that I imported into R using this code (reading the file; setting date format; extracting all 6 relevant months):
SHAPIRENG5YEARS <- read.csv(file="C:\\Users\\Ron\\OneDrive\\5year.csv", header=TRUE, sep=",") #Choosing Shapir Engineering stock (last 5 years)
SHAPIRENG5YEARS$Date = as.Date(as.character(SHAPIRENG5YEARS$Date), format = "%d/%m/%Y")
January19=SHAPIRENG5YEARS[(SHAPIRENG5YEARS$Date > "2019-01-01" & SHAPIRENG5YEARS$Date < " 2019-01-31" ) ,]
February19=SHAPIRENG5YEARS[(SHAPIRENG5YEARS$Date > "2019-02-03" & SHAPIRENG5YEARS$Date < " 2019-02-28" ) ,]
March19=SHAPIRENG5YEARS[(SHAPIRENG5YEARS$Date > "2019-03-09" & SHAPIRENG5YEARS$Date < " 2019-03-31" ) ,]
April19=SHAPIRENG5YEARS[(SHAPIRENG5YEARS$Date > "2019-04-01" & SHAPIRENG5YEARS$Date < " 2019-04-30" ) ,]
May19=SHAPIRENG5YEARS[(SHAPIRENG5YEARS$Date > "2019-05-01" & SHAPIRENG5YEARS$Date < " 2019-05-30" ) ,]
June19=SHAPIRENG5YEARS[(SHAPIRENG5YEARS$Date > "2019-06-02" & SHAPIRENG5YEARS$Date < " 2019-06-30" ) ,]
Now I don't know what should I do. I can sample one month using
January19sample <-January19[sample(nrow(January19), 10), ]
but I want to avoid doing this six times (once for each month).
Ideally I'd like to sample all 10*6=60 values from the original big data frame.
Edit: I'm still struggling. I tried this (It is not good because I'm getting a list of 6 lists, each with length of 18 and not random 10 picks):
SamplesOfMonths=list(c(January19),c(February19),c(March19),c(April19),c(May19),c(June19))
TopSamples=c(1:10)
LowSamples=c(1:10)
for (i in 1:6)
{
Changer=unlist(SamplesOfMonths[i])
TopSamples[i]=sample(Changer, 10)[2]
LowSamples[i]=sample(Changer, 10)[1]
print(sample(Changer, 10))
}

Answer 1

You can use the sample() function and bracketted [ subsetting.
mtcars[sample(1:nrow(mtcars), size = 10, replace = FALSE),]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Broken down, step by step:
rows_in_data <- nrow(mtcars)
rows_in_data
#> [1] 32
# Sample from 1 to number of rows, selecting some using `size = ` argument
index_of_random_rows <- sample(1:rows_in_data, size = 10, replace = FALSE)
#use bracketted subsetting data[rows, columns]
mtcars[index_of_random_rows, ]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Created on 2020-01-13 by the reprex package (v0.3.0)
Using map:
#custom function
my_samples <- function(df = mtcars, num_rows = 10){
sample(1:nrow(df), size = num_rows)
}
purrr::map(list(mtcars, iris), my_samples)
#> [[1]]
#> [1] 16 1 4 22 30 2 21 14 23 10
#>
#> [[2]]
#> [1] 73 31 112 1 43 91 87 23 19 16
Created on 2020-01-13 by the reprex package (v0.3.0)