Lets take a look at report for individual farmer
The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
Introduction: This function will generate roster for a week (168 hrs. or 24X7). Flow time of a watercourse to an individual farmer calculate based on their holding area (bigga or hectares), This flow time roster generated by this function known as “warabandi” in canal irrigated agricultural areas (Punjab, Rajasthan, Haryana and Some areas of Pakistan)
library(warabandi)head(warabandi_data)## Name_of_Farmer Nkka.No. x1 MuNo x2 MuNo.1 x3 MuNo.2 x4 MuNo.3 x5
## 1 Kahan Singh 5 4.5 5 2.0 1 5.0 13 12.5 23 NA
## 2 Harvinderpal Singh 5 4.5 5 24.5 14 12.0 57 NA NA
## 3 Jasvant Singh 5 5.3 5 9.7 26 8.5 55 NA NA
## 4 Mukhtyar Singh 5 7.8 5 11.5 6 9.8 10 24.5 27 9.8
## 5 Diggi NA NA NA NA NA NA
## 6 Pooran Singh 6 11.5 6 NA NA NA NA
## MuNo.4 x6 MuNo.5 x7 MuNo.6 Bharai Katai
## 1 NA NA NA NA 0:00 0:00
## 2 NA NA NA NA 0:00 0:00
## 3 NA NA NA NA 0:00 0:04
## 4 26 10 55 0.5 44 0:00 0:00
## 5 NA NA NA NA 0:12 0:00
## 6 NA NA NA NA 0:15 0:00#Calulation of roster Here we are not writing output to reduce vignettes and space
output<-warabandi(file = warabandi_data, output = FALSE, nof = "my_file.csv")## Name Nakka
## 1 Kahan Singh 5
## 2 Harvinderpal Singh 5
## 3 Jasvant Singh 5
## 4 Mukhtyar Singh 5
## 5 Diggi NA
## 6 Pooran Singh 6
## 7 Nirmal Singh 9
## 8 Jagtar Singh 4
## 9 Harbhinder singh/Rulda Singh 3
## 10 Chamkaur Singh 3
## 11 Preetam Sing 11
## 12 Ikbaal Singh 22
## 13 Diggi NA
## 14 Gurpreet Singh 23
## 15 Chamkaur Singh 12
## 16 Diggi NA
## 17 Diggi NA
## 18 Jagtar Singh 12
## 19 Jagtar Singh 12
## 20 Jagtar Singh 21
## 21 Chamkaur Singh 21
## 22 Hargovind Singh 21
## 23 Hargovind Singh 21
## 24 Diggi NA
## 25 Hargovind Singh 24
## 26 Mewa Singh 33
## 27 Kapal Singh 33
## 28 Gurbachan Singh 33
## 29 Harbhajan Singh 13
## 30 Surendra Singh 19
## 31 Harvinderpal Singh 26
## 32 Diggi NA
## 33 Hoshiyar Singh 32
## 34 Kulvinder Singh 31
## 35 Shri Gurudwara 31
## 36 Saudagar Singh 31
## 37 Saudagar Singh 34
## 38 Sukhwant Singh 35
## 39 Harbhajan Singh 40
## 40 Baljinder Singh 42
## 41 Baljinder Singh 43
## 42 Ikbaal Singh 52
## 43 Abaadi Diggi 39
## 44 Hoshiyar Singh 44
## 45 Mukhtyar Singh 44
## 46 budhraam 51
## 47 Bant Singh 51
## 48 Tej Singh, Gej Singh 55
## 49 Ikbaal Singh 56
## 50 Saudagar Singh 48
## 51 Preetam Singh 48
## 52 Unknown NA
## 53 Rulda Singh 57
## 54 Harbhinderpal Singh/Rulda Singh 47
## 55 Rulda Singh 15
## 56 Rulda Singh 18
## 57 Gurjeet Singh 16
## 58 Jagdev Singh 17
## 59 Abadi NA
## Drkva
## 1 4.5, 2, 5, 12.5, 0, 0, 0 \n MNo 5, 1, 13, 23, 0, 0,
## 2 4.5, 24.5, 12, 0, 0, 0, 0 \n MNo 5, 14, 57, , 0, 0,
## 3 5.3, 9.7, 8.5, 0, 0, 0, 0 \n MNo 5, 26, 55, , 0, 0,
## 4 7.8, 11.5, 9.8, 24.5, 9.8, 10, 0.5 \n MNo 5, 6, 10, 27, 26, 55, 44
## 5 0, 0, 0, 0, 0, 0, 0 \n MNo , , , , 0, 0,
## 6 11.5, 0, 0, 0, 0, 0, 0 \n MNo 6, , , , 0, 0,
## 7 25, 4.8, 24.5, 7.35, 6, 2.95, 0 \n MNo 9, 10, 28, 29, 47, 16,
## 8 3.5, 0, 0, 0, 0, 0, 0 \n MNo 3TW, , , , 0, 0,
## 9 1.05, 0, 0, 0, 0, 0, 0 \n MNo 3TW, , , , 0, 0,
## 10 3.5, 0, 0, 0, 0, 0, 0 \n MNo 3TW, , , , 0, 0,
## 11 25, 12, 7, 0.6, 0, 0, 0 \n MNo 11, 4, 56, 4TW, 0, 0,
## 12 24, 20, 0, 0, 0, 0, 0 \n MNo 22, 52, , , 0, 0,
## 13 0, 0, 0, 0, 0, 0, 0 \n MNo , , , , 0, 0,
## 14 12.5, 10, 14.25, 10.35, 1, 1.6, 0 \n MNo 23, 10, 20, 53, 31, 35,
## 15 10.5, 2.9, 1.1, 0, 0, 0, 0 \n MNo 12, 3, 21, , 0, 0,
## 16 0, 0, 0, 0, 0, 0, 0 \n MNo , , , , 0, 0,
## 17 0, 0, 0, 0, 0, 0, 0 \n MNo , , , , 0, 0,
## 18 9.6, 5.3, 0, 0, 0, 0, 0 \n MNo 12, 4, , , 0, 0,
## 19 15.6, 0, 0, 0, 0, 0, 0 \n MNo 12BAG, , , , 0, 0,
## 20 15.6, 0, 0, 0, 0, 0, 0 \n MNo 21BAG, , , , 0, 0,
## 21 30.4, 0, 0, 0, 0, 0, 0 \n MNo 21BAG, , , , 0, 0,
## 22 30.4, 0, 0, 0, 0, 0, 0 \n MNo 21BAG, , , , 0, 0,
## 23 3.2, 2, 0.5, 0, 0, 0, 0 \n MNo 21, 3, 30, , 0, 0,
## 24 0, 0, 0, 0, 0, 0, 0 \n MNo , , , , 0, 0,
## 25 19.6, 0, 0, 0, 0, 0, 0 \n MNo 24, , , , 0, 0,
## 26 5, 4.9, 0, 0, 0, 0, 0 \n MNo 33, 24, , , 0, 0,
## 27 10, 0, 0, 0, 0, 0, 0 \n MNo 33, , , , 0, 0,
## 28 10, 0, 0, 0, 0, 0, 0 \n MNo 33, , , , 0, 0,
## 29 20, 12.5, 16.15, 0, 0, 0, 0 \n MNo 13, 25, 30, , 0, 0,
## 30 24, 11.3, 12, 15, 0, 0, 0 \n MNo 19, 40, 56, 54, 0, 0,
## 31 5, 0, 0, 0, 0, 0, 0 \n MNo 26, , , , 0, 0,
## 32 0, 0, 0, 0, 0, 0, 0 \n MNo , , , , 0, 0,
## 33 25, 12.5, 5, 4.4, 7.2, 6.55, 0.5 \n MNo 32, 25, 48, 35, 40, 43, 44
## 34 13.7, 0.6, 7.85, 14.9, 0.25, 0, 8.4 \n MNo 31, 35, 30, 42, 43, 0, 53
## 35 4.8, 0, 0, 0, 0, 0, 0 \n MNo 31, , , , 0, 0,
## 36 9.8, 6, 1.6, 10.25, 0, 0, 0 \n MNo 31, 41, 35, 20, 0, 0,
## 37 17, 6.25, 10, 6, 0, 0, 0 \n MNo 34, 53, 54, 47, 0, 0,
## 38 15.6, 9.8, 2, 0, 0, 0, 0 \n MNo 35, 43, 51, , 0, 0,
## 39 5, 0, 0, 0, 0, 0, 0 \n MNo 40, , , , 0, 0,
## 40 5.1, 6.5, 0, 0, 0, 0, 0 \n MNo 42, 41, , , 0, 0,
## 41 8.1, 7.5, 0, 0, 0, 0, 0 \n MNo 43, 34, , , 0, 0,
## 42 5, 0, 0, 0, 0, 0, 0 \n MNo 52, , , , 0, 0,
## 43 0, 0, 0, 0, 0, 0, 0 \n MNo , , , , 0, 0,
## 44 4.5, 0, 0, 0, 0, 0, 0 \n MNo 44, , , , 0, 0,
## 45 4.5, 0, 0, 0, 0, 0, 0 \n MNo 44, , , , 0, 0,
## 46 2.9, 0.35, 0, 0, 0, 0, 0 \n MNo 51, 44, , , 0, 0,
## 47 6.7, 5.75, 0, 0, 0, 0, 0 \n MNo 51, 49, , , 0, 0,
## 48 6, 0, 0, 0, 0, 0, 0 \n MNo 55, , , , 0, 0,
## 49 5, 0, 0, 0, 0, 0, 0 \n MNo 56, , , , 0, 0,
## 50 19.5, 0, 0, 0, 0, 0, 0 \n MNo 48, , , , 0, 0,
## 51 5, 2.4, 0.8, 2.4, 0.85, 0, 0 \n MNo 48, 51, 44, 51, 44, 0,
## 52 0, 0, 0, 0, 0, 0, 0 \n MNo , , , , 0, 0,
## 53 13, 0, 0, 0, 0, 0, 0 \n MNo 57, , , , 0, 0,
## 54 19, 0, 0, 0, 0, 0, 0 \n MNo 47, , , , 0, 0,
## 55 14.5, 7, 0, 0, 0, 0, 0 \n MNo 15, 15TW, , , 0, 0,
## 56 24, 1.1, 17.15, 10, 7.25, 1.05, 2.9 \n MNo 18, 16, 29, 2, 3, 16, 16TW
## 57 1.5, 2.8, 7.5, 0, 0, 0, 0 \n MNo 16, 51, 16TW, , 0, 0,
## 58 24.5, 6, 12.5, 7, 5, 0, 0 \n MNo 17, 16, 41, 50, 50, 0,
## 59 0, 0, 0, 0, 0, 0, 0 \n MNo , , , , 0, 0,
## RakvaSum TotalTurn Bharai Jharai Time_schedule
## 1 24.00 3:13 0:00 0:00 Monday, 18:0-Monday, 21:13
## 2 41.00 5:31 0:00 0:00 Monday, 21:13-Tuesday, 2:44
## 3 23.50 3:5 0:00 0:04 Tuesday, 2:44-Tuesday, 5:50
## 4 73.90 9:56 0:00 0:00 Tuesday, 5:50-Tuesday, 15:47
## 5 0.00 0:12 0:12 0:00 Tuesday, 15:47-Tuesday, 15:59
## 6 11.50 1:47 0:15 0:00 Tuesday, 15:59-Tuesday, 17:47
## 7 70.60 9:30 0:10 0:10 Tuesday, 17:47-Wednesday, 3:17
## 8 3.50 0:28 0:00 0:00 Wednesday, 3:17-Wednesday, 3:45
## 9 1.05 0:23 0:15 0:00 Wednesday, 3:45-Wednesday, 4:9
## 10 3.50 0:28 0:00 0:00 Wednesday, 4:9-Wednesday, 4:37
## 11 44.60 6:15 0:15 0:00 Wednesday, 4:37-Wednesday, 10:52
## 12 44.00 6:10 0:15 0:00 Wednesday, 10:52-Wednesday, 17:2
## 13 0.00 0:12 0:12 0:00 Wednesday, 17:2-Wednesday, 17:14
## 14 49.70 6:41 0:15 0:15 Wednesday, 17:14-Wednesday, 23:56
## 15 14.50 2:21 0:24 0:00 Wednesday, 23:56-Thursday, 2:17
## 16 0.00 0:4 0:04 0:00 Thursday, 2:17-Thursday, 2:21
## 17 0.00 0:4 0:04 0:00 Thursday, 2:21-Thursday, 2:25
## 18 14.90 2:0 0:00 0:00 Thursday, 2:25-Thursday, 4:25
## 19 15.60 2:5 0:00 0:00 Thursday, 4:25-Thursday, 6:31
## 20 15.60 2:20 0:15 0:00 Thursday, 6:31-Thursday, 8:52
## 21 30.40 4:5 0:00 0:00 Thursday, 8:52-Thursday, 12:57
## 22 30.40 4:5 0:00 0:00 Thursday, 12:57-Thursday, 17:3
## 23 5.70 0:46 0:00 0:00 Thursday, 17:3-Thursday, 17:49
## 24 0.00 0:5 0:05 0:00 Thursday, 17:49-Thursday, 17:54
## 25 19.60 2:53 0:15 0:00 Thursday, 17:54-Thursday, 20:47
## 26 9.90 1:34 0:15 0:00 Thursday, 20:47-Thursday, 22:22
## 27 10.00 1:20 0:00 0:00 Thursday, 22:22-Thursday, 23:43
## 28 10.00 1:2 0:00 0:18 Thursday, 23:43-Friday, 0:46
## 29 48.65 6:44 0:12 0:00 Friday, 0:46-Friday, 7:31
## 30 62.30 8:33 0:10 0:00 Friday, 7:31-Friday, 16:4
## 31 5.00 0:50 0:10 0:00 Friday, 16:4-Friday, 16:54
## 32 0.00 0:12 0:12 0:00 Friday, 16:54-Friday, 17:6
## 33 61.15 8:23 0:10 0:00 Friday, 17:6-Saturday, 1:30
## 34 45.70 6:9 0:00 0:00 Saturday, 1:30-Saturday, 7:39
## 35 4.80 0:38 0:00 0:00 Saturday, 7:39-Saturday, 8:18
## 36 27.65 3:43 0:00 0:00 Saturday, 8:18-Saturday, 12:1
## 37 39.25 5:26 0:10 0:00 Saturday, 12:1-Saturday, 17:28
## 38 27.40 3:41 0:00 0:00 Saturday, 17:28-Saturday, 21:9
## 39 5.00 0:50 0:10 0:00 Saturday, 21:9-Saturday, 21:59
## 40 11.60 1:43 0:10 0:00 Saturday, 21:59-Saturday, 23:43
## 41 15.60 2:5 0:00 0:00 Saturday, 23:43-Sunday, 1:49
## 42 5.00 0:45 0:15 0:10 Sunday, 1:49-Sunday, 2:34
## 43 0.00 2:40 2:40 0:00 Sunday, 2:34-Sunday, 5:14
## 44 4.50 0:51 0:15 0:00 Sunday, 5:14-Sunday, 6:6
## 45 4.50 0:36 0:00 0:00 Sunday, 6:6-Sunday, 6:42
## 46 3.25 0:51 0:25 0:00 Sunday, 6:42-Sunday, 7:33
## 47 12.45 1:40 0:00 0:00 Sunday, 7:33-Sunday, 9:14
## 48 6.00 1:7 0:19 0:00 Sunday, 9:14-Sunday, 10:21
## 49 5.00 0:50 0:10 0:00 Sunday, 10:21-Sunday, 11:12
## 50 19.50 2:37 0:00 0:00 Sunday, 11:12-Sunday, 13:49
## 51 11.45 1:32 0:00 0:00 Sunday, 13:49-Sunday, 15:22
## 52 0.00 0:0 0:00 0:00 Sunday, 15:22-Sunday, 15:22
## 53 13.00 0:53 0:10 1:01 Sunday, 15:22-Sunday, 16:16
## 54 19.00 2:33 0:00 0:00 Sunday, 16:16-Sunday, 18:49
## 55 21.50 3:8 0:15 0:00 Sunday, 18:49-Sunday, 21:58
## 56 63.45 8:48 0:16 0:00 Sunday, 21:58-Monday, 6:46
## 57 11.80 1:35 0:00 0:00 Monday, 6:46-Monday, 8:21
## 58 55.00 7:28 0:04 0:00 Monday, 8:21-Monday, 15:50
## 59 0.00 2:10 2:55 0:45 Monday, 15:50-Monday, 18:0#Lets take a look at output Output generated by package is a list of different objects. 1. Flow time per unit area in minutes 2. Rakva or total area under command subject to be irrigated 3. Bharai or fillig time; as a compensation 4. Jharai or empty time 5. Weekdays or total duration has to be divided 6. Final table calculated by warabandi saved as a output file to the working directory
output[1:5]## [[1]]
## [1] 8
##
## [[2]]
## [1] 1176.95
##
## [[3]]
## [1] "12H 19M 0S"
##
## [[4]]
## [1] "2H 43M 0S"
##
## [[5]]
## [1] "7d 0H 0M 0S"Report_Table<-data.frame(output[6])
head(Report_Table)## Name Nakka
## 1 Kahan Singh 5
## 2 Harvinderpal Singh 5
## 3 Jasvant Singh 5
## 4 Mukhtyar Singh 5
## 5 Diggi NA
## 6 Pooran Singh 6
## Drkva RakvaSum
## 1 4.5, 2, 5, 12.5, 0, 0, 0 \n MNo 5, 1, 13, 23, 0, 0, 24.0
## 2 4.5, 24.5, 12, 0, 0, 0, 0 \n MNo 5, 14, 57, , 0, 0, 41.0
## 3 5.3, 9.7, 8.5, 0, 0, 0, 0 \n MNo 5, 26, 55, , 0, 0, 23.5
## 4 7.8, 11.5, 9.8, 24.5, 9.8, 10, 0.5 \n MNo 5, 6, 10, 27, 26, 55, 44 73.9
## 5 0, 0, 0, 0, 0, 0, 0 \n MNo , , , , 0, 0, 0.0
## 6 11.5, 0, 0, 0, 0, 0, 0 \n MNo 6, , , , 0, 0, 11.5
## TotalTurn Bharai Jharai Time_schedule
## 1 3:13 0:00 0:00 Monday, 18:0-Monday, 21:13
## 2 5:31 0:00 0:00 Monday, 21:13-Tuesday, 2:44
## 3 3:5 0:00 0:04 Tuesday, 2:44-Tuesday, 5:50
## 4 9:56 0:00 0:00 Tuesday, 5:50-Tuesday, 15:47
## 5 0:12 0:12 0:00 Tuesday, 15:47-Tuesday, 15:59
## 6 1:47 0:15 0:00 Tuesday, 15:59-Tuesday, 17:47Generation of the report in printable format which can be supplied or distribut- ed among all the farmers. This step is highle depend on the external packages (knitr, flextable). Code chunk provide below is a sample which can be modified as per user need like header and fotters. Lets read output file generated by warabandi package
data2<-Report_Tableflextable::flextable(head(data2))Name | Nakka | Drkva | RakvaSum | TotalTurn | Bharai | Jharai | Time_schedule |
Kahan Singh | 5 | 4.5, 2, 5, 12.5, 0, 0, 0 | 24.0 | 3:13 | 0:00 | 0:00 | Monday, 18:0-Monday, 21:13 |
Harvinderpal Singh | 5 | 4.5, 24.5, 12, 0, 0, 0, 0 | 41.0 | 5:31 | 0:00 | 0:00 | Monday, 21:13-Tuesday, 2:44 |
Jasvant Singh | 5 | 5.3, 9.7, 8.5, 0, 0, 0, 0 | 23.5 | 3:5 | 0:00 | 0:04 | Tuesday, 2:44-Tuesday, 5:50 |
Mukhtyar Singh | 5 | 7.8, 11.5, 9.8, 24.5, 9.8, 10, 0.5 | 73.9 | 9:56 | 0:00 | 0:00 | Tuesday, 5:50-Tuesday, 15:47 |
Diggi | 0, 0, 0, 0, 0, 0, 0 | 0.0 | 0:12 | 0:12 | 0:00 | Tuesday, 15:47-Tuesday, 15:59 | |
Pooran Singh | 6 | 11.5, 0, 0, 0, 0, 0, 0 | 11.5 | 1:47 | 0:15 | 0:00 | Tuesday, 15:59-Tuesday, 17:47 |
data3 <- data.frame(head(data2))
invisible(by(data3, seq_len(nrow((data3))), function(row) {
chak<-"Chak-38 R.B"
distributries<- "R.B II"
dateOI<-date()
dateOd<-"Date of distribution __/__/____"
note1<-"Note: All schedules issued before this, are cancelled"
Year_o_Valid<-"Year: April 2019 to 2025"
Issue<-"Issue Serial No:________/___"
Jal_upbhokta<-"Jal Upbhokta Sangam:"
d5 <-flextable::fit_to_width(flextable::flextable(row), max_width = 7,
inc=1L, max_iter = 20)
d5 <- flextable::add_header_lines(d5, c(paste(Year_o_Valid,"\t","\t", chak), paste(Jal_upbhokta,
distributries, "\t","\t", Issue, "\t","\t","\t","\t", dateOI)))
d5 <-flextable::add_footer_lines(d5,
c(paste(dateOd, "\t", "\t", note1),
paste("President", "\n", "\n", "\n",
Jal_upbhokta, distributries)))
#flextable::flextable_to_rmd(d5)
}))
That’s all! Thanks for being here. Let me know if you heve any doubt about this package or issue Harvinder Singh Department of pharmacology PGIMER, Chandigarh (160012) harvindermaan4@gmail.com ORCID https://orcid.org/0000-0002-6349-3143
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.