I have to randomly select 10 trading days each month from January 2019 to June 2019 (6 months total). I need a shorted code, thank you for who can help me!

[saray]

loadfile1=as.character(prigo5Years$date)
date= as.Date(loadfile1,format="%d/%m/%Y")
jan19 = prigo5Years[(date>="2019-01-01" & date<="2019-01-31"),]
feb19 = prigo5Years[(date>="2019-02-03" & date<="2019-02-28"),]
mar19 = prigo5Years[(date>="2019-03-03" & date<="2019-03-31"),]
apr19 = prigo5Years[(date>="2019-04-01" & date<="2019-04-30"),]
may19 = prigo5Years[(date>="2019-05-01" & date<="2019-05-30"),]
june19 = prigo5Years[(date>="2019-06-02" & date<="2019-06-30"),]


jan19_ChangePrecent = c(sample(jan19$Change...., size = 10))
feb19_ChangePrecent = c(sample(feb19$Change...., size = 10))
mar19_ChangePrecent = c(sample(mar19$Change...., size = 10))
apr19_ChangePrecent = c(sample(apr19$Change...., size = 10))
may19_ChangePrecent = c(sample(may19$Change...., size = 10))
june19_ChangePrecent = c(sample(june19$Change...., size = 10))

df = data.frame( jan19_ChangePrecent, feb19_ChangePrecent, mar19_ChangePrecent, apr19_ChangePrecent, may19_ChangePrecent, june19_ChangePrecent)
df

##    jan19_ChangePrecent feb19_ChangePrecent mar19_ChangePrecent
## 1                 0.88               -0.22               -0.77
## 2                 3.27               -1.23               -2.59
## 3                 2.18               -1.12                0.97
## 4                -1.13               -1.34                0.29
## 5                 4.90               -3.74               -3.07
## 6                -0.34               -0.28               -0.62
## 7                -3.22                1.97                0.12
## 8                 1.50                2.45               -1.32
## 9                 1.64               -1.83               -4.30
## 10                1.54               -0.17                1.51
##    apr19_ChangePrecent may19_ChangePrecent june19_ChangePrecent
## 1                 0.66               -1.04                 1.52
## 2                -0.17                2.75                 1.65
## 3                 0.92               -2.02                 0.25
## 4                -1.02               -0.22                 3.18
## 5                -0.11               -0.22                 0.12
## 6                 2.60                0.47                -1.70
## 7                -0.06                1.10                 1.64
## 8                -1.35               -4.66                -0.62
## 9                -0.11                1.76                 0.64
## 10                2.84               -2.00                -6.54

ata_long = stack(df, select = c("jan19_ChangePrecent", "feb19_ChangePrecent", "mar19_ChangePrecent", "apr19_ChangePrecent", "may19_ChangePrecent", "june19_ChangePrecent" ))
colnames(data_long) = c("change", "month")
data_long

##    change                month
## 1    0.88  jan19_ChangePrecent
## 2    3.27  jan19_ChangePrecent
## 3    2.18  jan19_ChangePrecent
## 4   -1.13  jan19_ChangePrecent
## 5    4.90  jan19_ChangePrecent
## 6   -0.34  jan19_ChangePrecent
## 7   -3.22  jan19_ChangePrecent
## 8    1.50  jan19_ChangePrecent
## 9    1.64  jan19_ChangePrecent
## 10   1.54  jan19_ChangePrecent
## 11  -0.22  feb19_ChangePrecent
## 12  -1.23  feb19_ChangePrecent
## 13  -1.12  feb19_ChangePrecent
## 14  -1.34  feb19_ChangePrecent
## 15  -3.74  feb19_ChangePrecent
## 16  -0.28  feb19_ChangePrecent
## 17   1.97  feb19_ChangePrecent
## 18   2.45  feb19_ChangePrecent
## 19  -1.83  feb19_ChangePrecent
## 20  -0.17  feb19_ChangePrecent
## 21  -0.77  mar19_ChangePrecent
## 22  -2.59  mar19_ChangePrecent
## 23   0.97  mar19_ChangePrecent
## 24   0.29  mar19_ChangePrecent
## 25  -3.07  mar19_ChangePrecent
## 26  -0.62  mar19_ChangePrecent
## 27   0.12  mar19_ChangePrecent
## 28  -1.32  mar19_ChangePrecent
## 29  -4.30  mar19_ChangePrecent
## 30   1.51  mar19_ChangePrecent
## 31   0.66  apr19_ChangePrecent
## 32  -0.17  apr19_ChangePrecent
## 33   0.92  apr19_ChangePrecent
## 34  -1.02  apr19_ChangePrecent
## 35  -0.11  apr19_ChangePrecent
## 36   2.60  apr19_ChangePrecent
## 37  -0.06  apr19_ChangePrecent
## 38  -1.35  apr19_ChangePrecent
## 39  -0.11  apr19_ChangePrecent
## 40   2.84  apr19_ChangePrecent
## 41  -1.04  may19_ChangePrecent
## 42   2.75  may19_ChangePrecent
## 43  -2.02  may19_ChangePrecent
## 44  -0.22  may19_ChangePrecent
## 45  -0.22  may19_ChangePrecent
## 46   0.47  may19_ChangePrecent
## 47   1.10  may19_ChangePrecent
## 48  -4.66  may19_ChangePrecent
## 49   1.76  may19_ChangePrecent
## 50  -2.00  may19_ChangePrecent
## 51   1.52 june19_ChangePrecent
## 52   1.65 june19_ChangePrecent
## 53   0.25 june19_ChangePrecent
## 54   3.18 june19_ChangePrecent
## 55   0.12 june19_ChangePrecent
## 56  -1.70 june19_ChangePrecent
## 57   1.64 june19_ChangePrecent
## 58  -0.62 june19_ChangePrecent
## 59   0.64 june19_ChangePrecent
## 60  -6.54 june19_ChangePrecent