stitched-output/manipulation/selection-munge.md
In OuhscBbmc/usnavy-billets: Matching Billets and Officers
This report was automatically generated with the R package knitr
(version 1.15.1).
# knitr::stitch_rmd(script="./manipulation/selection-munge.R", output="./stitched-output/manipulation/selection-munge.md")
rm(list=ls(all=TRUE)) #Clear the variables from previous runs.
# Call `base::source()` on any repo file that defines functions needed below. Ideally, no real operations are performed.
# Attach these package(s) so their functions don't need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
library(magrittr, quietly=TRUE)
# Verify these packages are available on the machine, but their functions need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
requireNamespace("readr" , quietly=TRUE)
requireNamespace("tidyr" , quietly=TRUE)
requireNamespace("dplyr" , quietly=TRUE) #Avoid attaching dplyr, b/c its function names conflict with a lot of packages (esp base, stats, and plyr).
requireNamespace("testit" , quietly=TRUE) #For asserting conditions meet expected patterns.
# Constant values that won't change.
set.seed(43) #So the random sampling won't change.
path_in_command_roster <- "data-phi-free/raw/command-roster.csv"
path_in_command_wide <- "data-phi-free/raw/command-wide.csv"
path_out_command_wide <- "data-phi-free/derived/command.csv"
path_in_officer_roster <- "data-phi-free/raw/officer-roster.csv"
path_in_officer_wide <- "data-phi-free/raw/officer-wide.csv"
path_out_officer_wide <- "data-phi-free/derived/officer.csv"
col_types_command_roster <- readr::cols_only(
command_id = readr::col_integer(),
command_name = readr::col_character(),
billet_count_max = readr::col_integer()
)
col_types_officer_roster <- readr::cols_only(
officer_id = readr::col_integer(),
officer_tag = readr::col_character(),
officer_name_last = readr::col_character()
)
col_types_wide <- readr::cols(
.default = readr::col_integer()
)
# Read the CSVs
ds_command_roster <- readr::read_csv(path_in_command_roster, col_types=col_types_command_roster)
ds_command_wide <- readr::read_csv(path_in_command_wide, col_types=col_types_wide)
ds_officer_roster <- readr::read_csv(path_in_officer_roster, col_types=col_types_officer_roster)
ds_officer_wide <- readr::read_csv(path_in_officer_wide, col_types=col_types_wide)
## Warning: 1 parsing failure.
## row col expected actual
## 41 2nd MEU an integer .
rm(path_in_command_roster, col_types_command_roster)
rm( path_in_command_wide)
rm(path_in_officer_roster, col_types_officer_roster)
rm(path_in_officer_wide, col_types_wide)
# Print the first few rows of each table, especially if you're stitching with knitr (see first line of this file).
# If you print, make sure that the datasets don't contain any PHI.
ds_command_roster
## # A tibble: 20 × 3
## command_id command_name billet_count_max
## <int> <chr> <int>
## 1 201 Guam 0
## 2 202 NH Oki 1
## 3 203 Oki MEU 2
## 4 204 3rd MLG 3
## 5 205 NH Yoko 2
## 6 206 Rota 1
## 7 207 Napl 2
## 8 208 Sig 1
## 9 209 NMCP 5
## 10 210 Jax 4
## 11 211 Gitmo 1
## 12 212 NHCL 5
## 13 213 2nd MLG 2
## 14 214 2nd MEU 2
## 15 215 CBIRF 1
## 16 216 NMCSD 4
## 17 217 NH 29P 3
## 18 218 NH Pend 1
## 19 219 1st MLG 3
## 20 220 1st MEU 3
ds_command_wide
## # A tibble: 46 × 21
## Code Guam `NH Oki` `Oki MEU` `3rd MLG` `NH Yoko` Rota Napl Sig
## <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 401 1 10 NA NA NA NA NA NA
## 2 402 NA 18 NA NA NA 2 6 1
## 3 403 NA 5 NA NA NA NA NA NA
## 4 404 NA 7 NA NA NA NA NA NA
## 5 405 NA 8 NA NA NA NA NA NA
## 6 406 NA 19 NA NA NA NA NA NA
## 7 407 NA 16 NA NA NA NA NA NA
## 8 408 NA NA NA NA NA NA NA NA
## 9 409 NA 12 NA NA NA NA NA NA
## 10 410 NA NA NA NA NA NA NA NA
## # ... with 36 more rows, and 12 more variables: NMCP <int>, Jax <int>,
## # Gitmo <int>, NHCL <int>, `2nd MLG` <int>, `2nd MEU` <int>,
## # CBIRF <int>, NMCSD <int>, `NH 29P` <int>, `NH Pend` <int>, `1st
## # MLG` <int>, `1st MEU` <int>
ds_officer_roster
## # A tibble: 62 × 3
## officer_id officer_tag officer_name_last
## <int> <chr> <chr>
## 1 401 xvxe Abraham
## 2 402 kobl Bailey
## 3 403 x0qi Carr
## 4 404 pd7n Davidson
## 5 405 87cl Ellison
## 6 406 m8w6 Forsyth
## 7 407 wnaf Glover
## 8 408 as5q Harris
## 9 409 pb8x Ince
## 10 410 5t8r Jones
## # ... with 52 more rows
ds_officer_wide
## # A tibble: 46 × 21
## Code Guam `NH Oki` `Oki MEU` `3rd MLG` `NH Yoko` Rota Napl Sig
## <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 401 NA NA NA NA NA NA NA NA
## 2 402 4 NA NA NA NA 2 1 3
## 3 403 NA 5 NA NA NA NA NA NA
## 4 404 9 NA NA NA 5 6 7 8
## 5 405 NA NA NA NA NA NA NA NA
## 6 406 NA NA NA NA NA 2 1 3
## 7 407 NA 8 NA NA 4 5 6 7
## 8 408 NA NA NA NA NA NA NA NA
## 9 409 NA NA NA NA NA 3 4 5
## 10 410 NA NA NA NA NA NA NA NA
## # ... with 36 more rows, and 12 more variables: NMCP <int>, Jax <int>,
## # Gitmo <int>, NHCL <int>, `2nd MLG` <int>, `2nd MEU` <int>,
## # CBIRF <int>, NMCSD <int>, `NH 29P` <int>, `NH Pend` <int>, `1st
## # MLG` <int>, `1st MEU` <int>
# OuhscMunge::column_rename_headstart(ds_county) #Spit out columns to help write call ato `dplyr::rename()`.
# colnames(ds_command_wide) <- make.names(colnames(ds_command_wide))
ds_command_long <- ds_command_wide %>%
dplyr::rename_(
"officer_id" = "Code"
) %>%
tidyr::gather(key=command_name, value=rank, -officer_id) %>%
dplyr::left_join(ds_command_roster, by="command_name") %>%
dplyr::arrange(command_id, officer_id)
ds_officer_long <- ds_officer_wide %>%
dplyr::rename_(
"officer_id" = "Code"
) %>%
tidyr::gather(key=command_name, value=rank, -officer_id) %>%
dplyr::left_join(
ds_command_roster %>%
dplyr::select(command_id, command_name),
by="command_name"
) %>%
dplyr::left_join(ds_officer_roster, by="officer_id") %>%
dplyr::arrange(command_id, officer_id)
ds_command_missing_ranks <- ds_command_long %>%
dplyr::group_by(command_id) %>%
dplyr::summarize(
nonmissing_rank_count = sum(!is.na(rank))
) %>%
dplyr::ungroup() %>%
dplyr::filter(nonmissing_rank_count == 0L)
ds_officer_missing_ranks <- ds_officer_long %>%
dplyr::group_by(officer_id) %>%
dplyr::summarize(
nonmissing_rank_count = sum(!is.na(rank))
) %>%
dplyr::ungroup() %>%
# as.data.frame() %>%
dplyr::filter(nonmissing_rank_count == 0L)
testit::assert("All commands should rank at least one officer.", nrow(ds_command_missing_ranks) == 0L)
testit::assert("All officers should rank at least one command.", nrow(ds_officer_missing_ranks) == 0L)
# Sniff out problems
testit::assert("The officer_id must be nonmissing.", all(!is.na(ds_command_long$officer_id)))
testit::assert("The command_name must be nonmissing.", all(!is.na(ds_command_long$command_name)))
# testit::assert("The rank must be nonmissing.", all(!is.na(ds_command_long$rank)))
testit::assert("The command_id must be nonmissing.", all(!is.na(ds_command_long$command_id)))
testit::assert("The billet_count_max must be nonmissing.", all(!is.na(ds_command_long$billet_count_max)))
testit::assert("The officer_id-command_id combination should be unique.", all(!duplicated(paste(ds_command_long$officer_id, ds_command_long$command_id))))
# testit::assert("The command_id-rank combination should be unique.", all(!duplicated(paste(ds_command_long$command_id, ds_command_long$rank))))
# Sniff out problems
testit::assert("The officer_id must be nonmissing.", all(!is.na(ds_officer_long$officer_id)))
testit::assert("The command_name must be nonmissing.", all(!is.na(ds_officer_long$command_name)))
# testit::assert("The rank must be nonmissing.", all(!is.na(ds_officer_long$rank)))
testit::assert("The command_id must be nonmissing.", all(!is.na(ds_officer_long$command_id)))
testit::assert("The officer_id-command_id combination should be unique.", all(!duplicated(paste(ds_officer_long$officer_id, ds_officer_long$command_id))))
# testit::assert("The officer_id-rank combination should be unique.", all(!duplicated(paste(ds_officer_long$officer_id, ds_officer_long$rank))))
as.data.frame(ds_officer_long[duplicated(paste(ds_officer_long$officer_id, ds_officer_long$rank)), ])
## officer_id command_name rank command_id officer_tag officer_name_last
## 1 401 NH Oki NA 202 xvxe Abraham
## 2 405 NH Oki NA 202 87cl Ellison
## 3 406 NH Oki NA 202 m8w6 Forsyth
## 4 408 NH Oki NA 202 as5q Harris
## 5 409 NH Oki NA 202 pb8x Ince
## 6 410 NH Oki NA 202 5t8r Jones
## 7 413 NH Oki NA 202 5sjg Murray
## 8 414 NH Oki NA 202 k1lv Nash
## 9 415 NH Oki NA 202 cn7q Oliver
## 10 419 NH Oki NA 202 pt9y Sutherland
## 11 420 NH Oki NA 202 paed Taylor
## 12 421 NH Oki NA 202 zaif Underwood
## 13 423 NH Oki NA 202 vqdq Walker
## 14 424 NH Oki NA 202 d6hs Xiong
## 15 425 NH Oki NA 202 tb5t Young
## 16 426 NH Oki NA 202 d2nz Zimmer
## 17 427 NH Oki NA 202 9je3 Aikman
## 18 429 NH Oki NA 202 85i6 Coolie
## 19 430 NH Oki NA 202 zkff Dulles
## 20 431 NH Oki NA 202 6ust Estes
## 21 432 NH Oki NA 202 785o Flag
## 22 433 NH Oki NA 202 6zmz Grind
## 23 435 NH Oki NA 202 cbku Instance
## 24 436 NH Oki NA 202 0cot Jack
## 25 438 NH Oki NA 202 tun2 Laurence
## 26 439 NH Oki NA 202 ob7u Michaels
## 27 440 NH Oki NA 202 27vk Never
## 28 441 NH Oki NA 202 ovvy Object
## 29 443 NH Oki NA 202 yocg Quincy
## 30 444 NH Oki NA 202 yk8a Rolls
## 31 446 NH Oki NA 202 wfv3 Tut
## 32 401 Oki MEU NA 203 xvxe Abraham
## 33 402 Oki MEU NA 203 kobl Bailey
## 34 403 Oki MEU NA 203 x0qi Carr
## 35 404 Oki MEU NA 203 pd7n Davidson
## 36 405 Oki MEU NA 203 87cl Ellison
## 37 406 Oki MEU NA 203 m8w6 Forsyth
## 38 407 Oki MEU NA 203 wnaf Glover
## 39 408 Oki MEU NA 203 as5q Harris
## 40 409 Oki MEU NA 203 pb8x Ince
## 41 410 Oki MEU NA 203 5t8r Jones
## 42 414 Oki MEU NA 203 k1lv Nash
## 43 415 Oki MEU NA 203 cn7q Oliver
## 44 419 Oki MEU NA 203 pt9y Sutherland
## 45 420 Oki MEU NA 203 paed Taylor
## 46 421 Oki MEU NA 203 zaif Underwood
## 47 422 Oki MEU NA 203 h8pg Vaughan
## 48 423 Oki MEU NA 203 vqdq Walker
## 49 424 Oki MEU NA 203 d6hs Xiong
## 50 425 Oki MEU NA 203 tb5t Young
## 51 426 Oki MEU NA 203 d2nz Zimmer
## 52 427 Oki MEU NA 203 9je3 Aikman
## 53 429 Oki MEU NA 203 85i6 Coolie
## 54 430 Oki MEU NA 203 zkff Dulles
## 55 431 Oki MEU NA 203 6ust Estes
## 56 432 Oki MEU NA 203 785o Flag
## 57 433 Oki MEU NA 203 6zmz Grind
## 58 434 Oki MEU NA 203 525l Hyer
## 59 435 Oki MEU NA 203 cbku Instance
## 60 436 Oki MEU NA 203 0cot Jack
## 61 437 Oki MEU NA 203 ojoz Kippler
## 62 438 Oki MEU NA 203 tun2 Laurence
## 63 439 Oki MEU NA 203 ob7u Michaels
## 64 440 Oki MEU NA 203 27vk Never
## 65 441 Oki MEU NA 203 ovvy Object
## 66 443 Oki MEU NA 203 yocg Quincy
## 67 444 Oki MEU NA 203 yk8a Rolls
## 68 446 Oki MEU NA 203 wfv3 Tut
## 69 401 3rd MLG NA 204 xvxe Abraham
## 70 402 3rd MLG NA 204 kobl Bailey
## 71 403 3rd MLG NA 204 x0qi Carr
## 72 404 3rd MLG NA 204 pd7n Davidson
## 73 405 3rd MLG NA 204 87cl Ellison
## 74 406 3rd MLG NA 204 m8w6 Forsyth
## 75 407 3rd MLG NA 204 wnaf Glover
## 76 408 3rd MLG NA 204 as5q Harris
## 77 409 3rd MLG NA 204 pb8x Ince
## 78 410 3rd MLG NA 204 5t8r Jones
## 79 412 3rd MLG NA 204 9aa1 Lambert
## 80 414 3rd MLG NA 204 k1lv Nash
## 81 415 3rd MLG NA 204 cn7q Oliver
## 82 418 3rd MLG NA 204 bakn Rampling
## 83 419 3rd MLG NA 204 pt9y Sutherland
## 84 420 3rd MLG NA 204 paed Taylor
## 85 421 3rd MLG NA 204 zaif Underwood
## 86 422 3rd MLG NA 204 h8pg Vaughan
## 87 423 3rd MLG NA 204 vqdq Walker
## 88 424 3rd MLG NA 204 d6hs Xiong
## 89 425 3rd MLG NA 204 tb5t Young
## 90 426 3rd MLG NA 204 d2nz Zimmer
## 91 427 3rd MLG NA 204 9je3 Aikman
## 92 429 3rd MLG NA 204 85i6 Coolie
## 93 430 3rd MLG NA 204 zkff Dulles
## 94 431 3rd MLG NA 204 6ust Estes
## 95 432 3rd MLG NA 204 785o Flag
## 96 433 3rd MLG NA 204 6zmz Grind
## 97 434 3rd MLG NA 204 525l Hyer
## 98 435 3rd MLG NA 204 cbku Instance
## 99 436 3rd MLG NA 204 0cot Jack
## 100 437 3rd MLG NA 204 ojoz Kippler
## 101 438 3rd MLG NA 204 tun2 Laurence
## 102 439 3rd MLG NA 204 ob7u Michaels
## 103 440 3rd MLG NA 204 27vk Never
## 104 441 3rd MLG NA 204 ovvy Object
## 105 443 3rd MLG NA 204 yocg Quincy
## 106 444 3rd MLG NA 204 yk8a Rolls
## 107 446 3rd MLG NA 204 wfv3 Tut
## 108 401 NH Yoko NA 205 xvxe Abraham
## 109 402 NH Yoko NA 205 kobl Bailey
## 110 403 NH Yoko NA 205 x0qi Carr
## 111 405 NH Yoko NA 205 87cl Ellison
## 112 406 NH Yoko NA 205 m8w6 Forsyth
## 113 408 NH Yoko NA 205 as5q Harris
## 114 409 NH Yoko NA 205 pb8x Ince
## 115 410 NH Yoko NA 205 5t8r Jones
## 116 412 NH Yoko NA 205 9aa1 Lambert
## 117 413 NH Yoko NA 205 5sjg Murray
## 118 414 NH Yoko NA 205 k1lv Nash
## 119 415 NH Yoko NA 205 cn7q Oliver
## 120 420 NH Yoko NA 205 paed Taylor
## 121 421 NH Yoko NA 205 zaif Underwood
## 122 423 NH Yoko NA 205 vqdq Walker
## 123 424 NH Yoko NA 205 d6hs Xiong
## 124 425 NH Yoko NA 205 tb5t Young
## 125 426 NH Yoko NA 205 d2nz Zimmer
## 126 427 NH Yoko NA 205 9je3 Aikman
## 127 429 NH Yoko NA 205 85i6 Coolie
## 128 430 NH Yoko NA 205 zkff Dulles
## 129 431 NH Yoko NA 205 6ust Estes
## 130 432 NH Yoko NA 205 785o Flag
## 131 433 NH Yoko NA 205 6zmz Grind
## 132 435 NH Yoko NA 205 cbku Instance
## 133 437 NH Yoko NA 205 ojoz Kippler
## 134 438 NH Yoko NA 205 tun2 Laurence
## 135 439 NH Yoko NA 205 ob7u Michaels
## 136 440 NH Yoko NA 205 27vk Never
## 137 441 NH Yoko NA 205 ovvy Object
## 138 442 NH Yoko NA 205 bqrp Power
## 139 443 NH Yoko NA 205 yocg Quincy
## 140 444 NH Yoko NA 205 yk8a Rolls
## 141 401 Rota NA 206 xvxe Abraham
## 142 403 Rota NA 206 x0qi Carr
## 143 405 Rota NA 206 87cl Ellison
## 144 408 Rota NA 206 as5q Harris
## 145 410 Rota NA 206 5t8r Jones
## 146 412 Rota NA 206 9aa1 Lambert
## 147 414 Rota NA 206 k1lv Nash
## 148 415 Rota NA 206 cn7q Oliver
## 149 420 Rota NA 206 paed Taylor
## 150 421 Rota NA 206 zaif Underwood
## 151 423 Rota NA 206 vqdq Walker
## 152 424 Rota NA 206 d6hs Xiong
## 153 425 Rota NA 206 tb5t Young
## 154 426 Rota NA 206 d2nz Zimmer
## 155 427 Rota NA 206 9je3 Aikman
## 156 429 Rota NA 206 85i6 Coolie
## 157 430 Rota NA 206 zkff Dulles
## 158 431 Rota NA 206 6ust Estes
## 159 432 Rota NA 206 785o Flag
## 160 433 Rota NA 206 6zmz Grind
## 161 435 Rota NA 206 cbku Instance
## 162 437 Rota NA 206 ojoz Kippler
## 163 438 Rota NA 206 tun2 Laurence
## 164 439 Rota NA 206 ob7u Michaels
## 165 440 Rota NA 206 27vk Never
## 166 441 Rota NA 206 ovvy Object
## [ reached getOption("max.print") -- omitted 315 rows ]
# dput(colnames(ds_command_long)) # Print colnames for line below.
ds_command_long <- ds_command_long %>%
dplyr::select_(.dots=c("command_id", "officer_id", "rank"))#, "command_name", "billet_count_max"))
ds_officer_long <- ds_officer_long %>%
dplyr::select_(.dots=c("command_id", "officer_id", "rank"))#, "officer_tag" , "officer_name_last"))
readr::write_csv(ds_command_long, path_out_command_wide)
readr::write_csv(ds_officer_long, path_out_officer_wide)
The R session information (including the OS info, R version and all
packages used):
sessionInfo()
## R version 3.3.2 Patched (2017-01-07 r71934)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 7 x64 (build 7601) Service Pack 1
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] magrittr_1.5
##
## loaded via a namespace (and not attached):
## [1] readr_1.0.0 lazyeval_0.2.0 R6_2.2.0 assertthat_0.1
## [5] DBI_0.5-1 tools_3.3.2 dplyr_0.5.0.9000 tibble_1.2
## [9] Rcpp_0.12.8 stringi_1.1.2 knitr_1.15.1 stringr_1.1.0
## [13] testit_0.6 tidyr_0.6.1 evaluate_0.10
Sys.time()
## [1] "2017-01-17 17:33:15 CST"
OuhscBbmc/usnavy-billets documentation built on April 7, 2024, 12:32 a.m.
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
This report was automatically generated with the R package knitr (version 1.15.1).
# knitr::stitch_rmd(script="./manipulation/selection-munge.R", output="./stitched-output/manipulation/selection-munge.md")
rm(list=ls(all=TRUE)) #Clear the variables from previous runs.
# Call `base::source()` on any repo file that defines functions needed below. Ideally, no real operations are performed.
# Attach these package(s) so their functions don't need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
library(magrittr, quietly=TRUE)
# Verify these packages are available on the machine, but their functions need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
requireNamespace("readr" , quietly=TRUE)
requireNamespace("tidyr" , quietly=TRUE)
requireNamespace("dplyr" , quietly=TRUE) #Avoid attaching dplyr, b/c its function names conflict with a lot of packages (esp base, stats, and plyr).
requireNamespace("testit" , quietly=TRUE) #For asserting conditions meet expected patterns.
# Constant values that won't change.
set.seed(43) #So the random sampling won't change.
path_in_command_roster <- "data-phi-free/raw/command-roster.csv"
path_in_command_wide <- "data-phi-free/raw/command-wide.csv"
path_out_command_wide <- "data-phi-free/derived/command.csv"
path_in_officer_roster <- "data-phi-free/raw/officer-roster.csv"
path_in_officer_wide <- "data-phi-free/raw/officer-wide.csv"
path_out_officer_wide <- "data-phi-free/derived/officer.csv"
col_types_command_roster <- readr::cols_only(
command_id = readr::col_integer(),
command_name = readr::col_character(),
billet_count_max = readr::col_integer()
)
col_types_officer_roster <- readr::cols_only(
officer_id = readr::col_integer(),
officer_tag = readr::col_character(),
officer_name_last = readr::col_character()
)
col_types_wide <- readr::cols(
.default = readr::col_integer()
)
# Read the CSVs
ds_command_roster <- readr::read_csv(path_in_command_roster, col_types=col_types_command_roster)
ds_command_wide <- readr::read_csv(path_in_command_wide, col_types=col_types_wide)
ds_officer_roster <- readr::read_csv(path_in_officer_roster, col_types=col_types_officer_roster)
ds_officer_wide <- readr::read_csv(path_in_officer_wide, col_types=col_types_wide)
## Warning: 1 parsing failure.
## row col expected actual
## 41 2nd MEU an integer .
rm(path_in_command_roster, col_types_command_roster)
rm( path_in_command_wide)
rm(path_in_officer_roster, col_types_officer_roster)
rm(path_in_officer_wide, col_types_wide)
# Print the first few rows of each table, especially if you're stitching with knitr (see first line of this file).
# If you print, make sure that the datasets don't contain any PHI.
ds_command_roster
## # A tibble: 20 × 3
## command_id command_name billet_count_max
## <int> <chr> <int>
## 1 201 Guam 0
## 2 202 NH Oki 1
## 3 203 Oki MEU 2
## 4 204 3rd MLG 3
## 5 205 NH Yoko 2
## 6 206 Rota 1
## 7 207 Napl 2
## 8 208 Sig 1
## 9 209 NMCP 5
## 10 210 Jax 4
## 11 211 Gitmo 1
## 12 212 NHCL 5
## 13 213 2nd MLG 2
## 14 214 2nd MEU 2
## 15 215 CBIRF 1
## 16 216 NMCSD 4
## 17 217 NH 29P 3
## 18 218 NH Pend 1
## 19 219 1st MLG 3
## 20 220 1st MEU 3
ds_command_wide
## # A tibble: 46 × 21
## Code Guam `NH Oki` `Oki MEU` `3rd MLG` `NH Yoko` Rota Napl Sig
## <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 401 1 10 NA NA NA NA NA NA
## 2 402 NA 18 NA NA NA 2 6 1
## 3 403 NA 5 NA NA NA NA NA NA
## 4 404 NA 7 NA NA NA NA NA NA
## 5 405 NA 8 NA NA NA NA NA NA
## 6 406 NA 19 NA NA NA NA NA NA
## 7 407 NA 16 NA NA NA NA NA NA
## 8 408 NA NA NA NA NA NA NA NA
## 9 409 NA 12 NA NA NA NA NA NA
## 10 410 NA NA NA NA NA NA NA NA
## # ... with 36 more rows, and 12 more variables: NMCP <int>, Jax <int>,
## # Gitmo <int>, NHCL <int>, `2nd MLG` <int>, `2nd MEU` <int>,
## # CBIRF <int>, NMCSD <int>, `NH 29P` <int>, `NH Pend` <int>, `1st
## # MLG` <int>, `1st MEU` <int>
ds_officer_roster
## # A tibble: 62 × 3
## officer_id officer_tag officer_name_last
## <int> <chr> <chr>
## 1 401 xvxe Abraham
## 2 402 kobl Bailey
## 3 403 x0qi Carr
## 4 404 pd7n Davidson
## 5 405 87cl Ellison
## 6 406 m8w6 Forsyth
## 7 407 wnaf Glover
## 8 408 as5q Harris
## 9 409 pb8x Ince
## 10 410 5t8r Jones
## # ... with 52 more rows
ds_officer_wide
## # A tibble: 46 × 21
## Code Guam `NH Oki` `Oki MEU` `3rd MLG` `NH Yoko` Rota Napl Sig
## <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 401 NA NA NA NA NA NA NA NA
## 2 402 4 NA NA NA NA 2 1 3
## 3 403 NA 5 NA NA NA NA NA NA
## 4 404 9 NA NA NA 5 6 7 8
## 5 405 NA NA NA NA NA NA NA NA
## 6 406 NA NA NA NA NA 2 1 3
## 7 407 NA 8 NA NA 4 5 6 7
## 8 408 NA NA NA NA NA NA NA NA
## 9 409 NA NA NA NA NA 3 4 5
## 10 410 NA NA NA NA NA NA NA NA
## # ... with 36 more rows, and 12 more variables: NMCP <int>, Jax <int>,
## # Gitmo <int>, NHCL <int>, `2nd MLG` <int>, `2nd MEU` <int>,
## # CBIRF <int>, NMCSD <int>, `NH 29P` <int>, `NH Pend` <int>, `1st
## # MLG` <int>, `1st MEU` <int>
# OuhscMunge::column_rename_headstart(ds_county) #Spit out columns to help write call ato `dplyr::rename()`.
# colnames(ds_command_wide) <- make.names(colnames(ds_command_wide))
ds_command_long <- ds_command_wide %>%
dplyr::rename_(
"officer_id" = "Code"
) %>%
tidyr::gather(key=command_name, value=rank, -officer_id) %>%
dplyr::left_join(ds_command_roster, by="command_name") %>%
dplyr::arrange(command_id, officer_id)
ds_officer_long <- ds_officer_wide %>%
dplyr::rename_(
"officer_id" = "Code"
) %>%
tidyr::gather(key=command_name, value=rank, -officer_id) %>%
dplyr::left_join(
ds_command_roster %>%
dplyr::select(command_id, command_name),
by="command_name"
) %>%
dplyr::left_join(ds_officer_roster, by="officer_id") %>%
dplyr::arrange(command_id, officer_id)
ds_command_missing_ranks <- ds_command_long %>%
dplyr::group_by(command_id) %>%
dplyr::summarize(
nonmissing_rank_count = sum(!is.na(rank))
) %>%
dplyr::ungroup() %>%
dplyr::filter(nonmissing_rank_count == 0L)
ds_officer_missing_ranks <- ds_officer_long %>%
dplyr::group_by(officer_id) %>%
dplyr::summarize(
nonmissing_rank_count = sum(!is.na(rank))
) %>%
dplyr::ungroup() %>%
# as.data.frame() %>%
dplyr::filter(nonmissing_rank_count == 0L)
testit::assert("All commands should rank at least one officer.", nrow(ds_command_missing_ranks) == 0L)
testit::assert("All officers should rank at least one command.", nrow(ds_officer_missing_ranks) == 0L)
# Sniff out problems
testit::assert("The officer_id must be nonmissing.", all(!is.na(ds_command_long$officer_id)))
testit::assert("The command_name must be nonmissing.", all(!is.na(ds_command_long$command_name)))
# testit::assert("The rank must be nonmissing.", all(!is.na(ds_command_long$rank)))
testit::assert("The command_id must be nonmissing.", all(!is.na(ds_command_long$command_id)))
testit::assert("The billet_count_max must be nonmissing.", all(!is.na(ds_command_long$billet_count_max)))
testit::assert("The officer_id-command_id combination should be unique.", all(!duplicated(paste(ds_command_long$officer_id, ds_command_long$command_id))))
# testit::assert("The command_id-rank combination should be unique.", all(!duplicated(paste(ds_command_long$command_id, ds_command_long$rank))))
# Sniff out problems
testit::assert("The officer_id must be nonmissing.", all(!is.na(ds_officer_long$officer_id)))
testit::assert("The command_name must be nonmissing.", all(!is.na(ds_officer_long$command_name)))
# testit::assert("The rank must be nonmissing.", all(!is.na(ds_officer_long$rank)))
testit::assert("The command_id must be nonmissing.", all(!is.na(ds_officer_long$command_id)))
testit::assert("The officer_id-command_id combination should be unique.", all(!duplicated(paste(ds_officer_long$officer_id, ds_officer_long$command_id))))
# testit::assert("The officer_id-rank combination should be unique.", all(!duplicated(paste(ds_officer_long$officer_id, ds_officer_long$rank))))
as.data.frame(ds_officer_long[duplicated(paste(ds_officer_long$officer_id, ds_officer_long$rank)), ])
## officer_id command_name rank command_id officer_tag officer_name_last
## 1 401 NH Oki NA 202 xvxe Abraham
## 2 405 NH Oki NA 202 87cl Ellison
## 3 406 NH Oki NA 202 m8w6 Forsyth
## 4 408 NH Oki NA 202 as5q Harris
## 5 409 NH Oki NA 202 pb8x Ince
## 6 410 NH Oki NA 202 5t8r Jones
## 7 413 NH Oki NA 202 5sjg Murray
## 8 414 NH Oki NA 202 k1lv Nash
## 9 415 NH Oki NA 202 cn7q Oliver
## 10 419 NH Oki NA 202 pt9y Sutherland
## 11 420 NH Oki NA 202 paed Taylor
## 12 421 NH Oki NA 202 zaif Underwood
## 13 423 NH Oki NA 202 vqdq Walker
## 14 424 NH Oki NA 202 d6hs Xiong
## 15 425 NH Oki NA 202 tb5t Young
## 16 426 NH Oki NA 202 d2nz Zimmer
## 17 427 NH Oki NA 202 9je3 Aikman
## 18 429 NH Oki NA 202 85i6 Coolie
## 19 430 NH Oki NA 202 zkff Dulles
## 20 431 NH Oki NA 202 6ust Estes
## 21 432 NH Oki NA 202 785o Flag
## 22 433 NH Oki NA 202 6zmz Grind
## 23 435 NH Oki NA 202 cbku Instance
## 24 436 NH Oki NA 202 0cot Jack
## 25 438 NH Oki NA 202 tun2 Laurence
## 26 439 NH Oki NA 202 ob7u Michaels
## 27 440 NH Oki NA 202 27vk Never
## 28 441 NH Oki NA 202 ovvy Object
## 29 443 NH Oki NA 202 yocg Quincy
## 30 444 NH Oki NA 202 yk8a Rolls
## 31 446 NH Oki NA 202 wfv3 Tut
## 32 401 Oki MEU NA 203 xvxe Abraham
## 33 402 Oki MEU NA 203 kobl Bailey
## 34 403 Oki MEU NA 203 x0qi Carr
## 35 404 Oki MEU NA 203 pd7n Davidson
## 36 405 Oki MEU NA 203 87cl Ellison
## 37 406 Oki MEU NA 203 m8w6 Forsyth
## 38 407 Oki MEU NA 203 wnaf Glover
## 39 408 Oki MEU NA 203 as5q Harris
## 40 409 Oki MEU NA 203 pb8x Ince
## 41 410 Oki MEU NA 203 5t8r Jones
## 42 414 Oki MEU NA 203 k1lv Nash
## 43 415 Oki MEU NA 203 cn7q Oliver
## 44 419 Oki MEU NA 203 pt9y Sutherland
## 45 420 Oki MEU NA 203 paed Taylor
## 46 421 Oki MEU NA 203 zaif Underwood
## 47 422 Oki MEU NA 203 h8pg Vaughan
## 48 423 Oki MEU NA 203 vqdq Walker
## 49 424 Oki MEU NA 203 d6hs Xiong
## 50 425 Oki MEU NA 203 tb5t Young
## 51 426 Oki MEU NA 203 d2nz Zimmer
## 52 427 Oki MEU NA 203 9je3 Aikman
## 53 429 Oki MEU NA 203 85i6 Coolie
## 54 430 Oki MEU NA 203 zkff Dulles
## 55 431 Oki MEU NA 203 6ust Estes
## 56 432 Oki MEU NA 203 785o Flag
## 57 433 Oki MEU NA 203 6zmz Grind
## 58 434 Oki MEU NA 203 525l Hyer
## 59 435 Oki MEU NA 203 cbku Instance
## 60 436 Oki MEU NA 203 0cot Jack
## 61 437 Oki MEU NA 203 ojoz Kippler
## 62 438 Oki MEU NA 203 tun2 Laurence
## 63 439 Oki MEU NA 203 ob7u Michaels
## 64 440 Oki MEU NA 203 27vk Never
## 65 441 Oki MEU NA 203 ovvy Object
## 66 443 Oki MEU NA 203 yocg Quincy
## 67 444 Oki MEU NA 203 yk8a Rolls
## 68 446 Oki MEU NA 203 wfv3 Tut
## 69 401 3rd MLG NA 204 xvxe Abraham
## 70 402 3rd MLG NA 204 kobl Bailey
## 71 403 3rd MLG NA 204 x0qi Carr
## 72 404 3rd MLG NA 204 pd7n Davidson
## 73 405 3rd MLG NA 204 87cl Ellison
## 74 406 3rd MLG NA 204 m8w6 Forsyth
## 75 407 3rd MLG NA 204 wnaf Glover
## 76 408 3rd MLG NA 204 as5q Harris
## 77 409 3rd MLG NA 204 pb8x Ince
## 78 410 3rd MLG NA 204 5t8r Jones
## 79 412 3rd MLG NA 204 9aa1 Lambert
## 80 414 3rd MLG NA 204 k1lv Nash
## 81 415 3rd MLG NA 204 cn7q Oliver
## 82 418 3rd MLG NA 204 bakn Rampling
## 83 419 3rd MLG NA 204 pt9y Sutherland
## 84 420 3rd MLG NA 204 paed Taylor
## 85 421 3rd MLG NA 204 zaif Underwood
## 86 422 3rd MLG NA 204 h8pg Vaughan
## 87 423 3rd MLG NA 204 vqdq Walker
## 88 424 3rd MLG NA 204 d6hs Xiong
## 89 425 3rd MLG NA 204 tb5t Young
## 90 426 3rd MLG NA 204 d2nz Zimmer
## 91 427 3rd MLG NA 204 9je3 Aikman
## 92 429 3rd MLG NA 204 85i6 Coolie
## 93 430 3rd MLG NA 204 zkff Dulles
## 94 431 3rd MLG NA 204 6ust Estes
## 95 432 3rd MLG NA 204 785o Flag
## 96 433 3rd MLG NA 204 6zmz Grind
## 97 434 3rd MLG NA 204 525l Hyer
## 98 435 3rd MLG NA 204 cbku Instance
## 99 436 3rd MLG NA 204 0cot Jack
## 100 437 3rd MLG NA 204 ojoz Kippler
## 101 438 3rd MLG NA 204 tun2 Laurence
## 102 439 3rd MLG NA 204 ob7u Michaels
## 103 440 3rd MLG NA 204 27vk Never
## 104 441 3rd MLG NA 204 ovvy Object
## 105 443 3rd MLG NA 204 yocg Quincy
## 106 444 3rd MLG NA 204 yk8a Rolls
## 107 446 3rd MLG NA 204 wfv3 Tut
## 108 401 NH Yoko NA 205 xvxe Abraham
## 109 402 NH Yoko NA 205 kobl Bailey
## 110 403 NH Yoko NA 205 x0qi Carr
## 111 405 NH Yoko NA 205 87cl Ellison
## 112 406 NH Yoko NA 205 m8w6 Forsyth
## 113 408 NH Yoko NA 205 as5q Harris
## 114 409 NH Yoko NA 205 pb8x Ince
## 115 410 NH Yoko NA 205 5t8r Jones
## 116 412 NH Yoko NA 205 9aa1 Lambert
## 117 413 NH Yoko NA 205 5sjg Murray
## 118 414 NH Yoko NA 205 k1lv Nash
## 119 415 NH Yoko NA 205 cn7q Oliver
## 120 420 NH Yoko NA 205 paed Taylor
## 121 421 NH Yoko NA 205 zaif Underwood
## 122 423 NH Yoko NA 205 vqdq Walker
## 123 424 NH Yoko NA 205 d6hs Xiong
## 124 425 NH Yoko NA 205 tb5t Young
## 125 426 NH Yoko NA 205 d2nz Zimmer
## 126 427 NH Yoko NA 205 9je3 Aikman
## 127 429 NH Yoko NA 205 85i6 Coolie
## 128 430 NH Yoko NA 205 zkff Dulles
## 129 431 NH Yoko NA 205 6ust Estes
## 130 432 NH Yoko NA 205 785o Flag
## 131 433 NH Yoko NA 205 6zmz Grind
## 132 435 NH Yoko NA 205 cbku Instance
## 133 437 NH Yoko NA 205 ojoz Kippler
## 134 438 NH Yoko NA 205 tun2 Laurence
## 135 439 NH Yoko NA 205 ob7u Michaels
## 136 440 NH Yoko NA 205 27vk Never
## 137 441 NH Yoko NA 205 ovvy Object
## 138 442 NH Yoko NA 205 bqrp Power
## 139 443 NH Yoko NA 205 yocg Quincy
## 140 444 NH Yoko NA 205 yk8a Rolls
## 141 401 Rota NA 206 xvxe Abraham
## 142 403 Rota NA 206 x0qi Carr
## 143 405 Rota NA 206 87cl Ellison
## 144 408 Rota NA 206 as5q Harris
## 145 410 Rota NA 206 5t8r Jones
## 146 412 Rota NA 206 9aa1 Lambert
## 147 414 Rota NA 206 k1lv Nash
## 148 415 Rota NA 206 cn7q Oliver
## 149 420 Rota NA 206 paed Taylor
## 150 421 Rota NA 206 zaif Underwood
## 151 423 Rota NA 206 vqdq Walker
## 152 424 Rota NA 206 d6hs Xiong
## 153 425 Rota NA 206 tb5t Young
## 154 426 Rota NA 206 d2nz Zimmer
## 155 427 Rota NA 206 9je3 Aikman
## 156 429 Rota NA 206 85i6 Coolie
## 157 430 Rota NA 206 zkff Dulles
## 158 431 Rota NA 206 6ust Estes
## 159 432 Rota NA 206 785o Flag
## 160 433 Rota NA 206 6zmz Grind
## 161 435 Rota NA 206 cbku Instance
## 162 437 Rota NA 206 ojoz Kippler
## 163 438 Rota NA 206 tun2 Laurence
## 164 439 Rota NA 206 ob7u Michaels
## 165 440 Rota NA 206 27vk Never
## 166 441 Rota NA 206 ovvy Object
## [ reached getOption("max.print") -- omitted 315 rows ]
# dput(colnames(ds_command_long)) # Print colnames for line below.
ds_command_long <- ds_command_long %>%
dplyr::select_(.dots=c("command_id", "officer_id", "rank"))#, "command_name", "billet_count_max"))
ds_officer_long <- ds_officer_long %>%
dplyr::select_(.dots=c("command_id", "officer_id", "rank"))#, "officer_tag" , "officer_name_last"))
readr::write_csv(ds_command_long, path_out_command_wide)
readr::write_csv(ds_officer_long, path_out_officer_wide)
The R session information (including the OS info, R version and all packages used):
sessionInfo()
## R version 3.3.2 Patched (2017-01-07 r71934)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 7 x64 (build 7601) Service Pack 1
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] magrittr_1.5
##
## loaded via a namespace (and not attached):
## [1] readr_1.0.0 lazyeval_0.2.0 R6_2.2.0 assertthat_0.1
## [5] DBI_0.5-1 tools_3.3.2 dplyr_0.5.0.9000 tibble_1.2
## [9] Rcpp_0.12.8 stringi_1.1.2 knitr_1.15.1 stringr_1.1.0
## [13] testit_0.6 tidyr_0.6.1 evaluate_0.10
Sys.time()
## [1] "2017-01-17 17:33:15 CST"
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