r - Create a boxplot for multiple stations and line joining median of the stations -

i have dataset follows:

 24     i=6,j=529, depth avg 129  1 129.041687011719     1.00000035762787 129.08332824707  .99999988079071 129.125015258789     1.00000011920929 129.166687011719     1.00000023841858 129.20832824707  1.00000035762787 129.250030517578     1.00000047683716 129.29167175293  1.00000035762787 129.333343505859     1.00000011920929 129.375030517578     .999998927116394 129.41667175293  .999999940395355 129.458358764648     1.00000107288361 129.5    1.00000059604645 129.541687011719     1.00000059604645 129.58332824707  1.00000059604645 129.625015258789     .999999284744263 129.666687011719     1.00000095367432 129.70832824707  1.00000107288361 129.750030517578     .999999940395355 129.79167175293  .99999988079071 129.833343505859     1.00000011920929 129.875030517578     1 129.91667175293  .99999988079071 129.958358764648     .999999761581421  24     i=7,j=505, depth avg 129  .999983608722687 129.041687011719     .999982953071594 129.08332824707  .999985218048096 129.125015258789     .999983251094818 129.166687011719     .999989926815033 129.20832824707  .999988317489624 129.250030517578     .999988853931427 129.29167175293  .999985992908478 129.333343505859     .999984502792358 129.375030517578     .999985635280609 129.41667175293  .99998551607132 129.458358764648     .999989748001099 129.5    .999991714954376 129.541687011719     .999998927116394 129.58332824707  .999999225139618 129.625015258789     .999995589256287 129.666687011719     .999993801116943 129.70832824707  .999995410442352 129.750030517578     .999995529651642 129.79167175293  .999992489814758 129.833343505859     .999987006187439 129.875030517578     .999984443187714 129.91667175293  .99998539686203 129.958358764648     .999988079071045  24     i=6,j=486, depth avg 129  .999971926212311 129.041687011719     .999973058700562 129.08332824707  .999974727630615 129.125015258789     .999973118305206 129.166687011719     .999970674514771 129.20832824707  .99997752904892 129.250030517578     .999980330467224 129.29167175293  .999976873397827 129.333343505859     .999974071979523 129.375030517578     .99997091293335 129.41667175293  .999977171421051 129.458358764648     .999985694885254 129.5    .999990105628967 129.541687011719     .999993622303009 129.58332824707  .999999344348907 129.625015258789     .999992668628693 129.666687011719     .999993085861206 129.70832824707  .999992847442627 129.750030517578     .999994277954102 129.79167175293  .999990105628967 129.833343505859     .99998152256012 129.875030517578     .999973177909851 129.91667175293  .999975740909576 129.958358764648     .999980330467224  24     i=6,j=466, depth avg 129  .999960064888 129.041687011719     .999961018562317 129.08332824707  .999964475631714 129.125015258789     .999963104724884 129.166687011719     .999962687492371 129.20832824707  .999969244003296 129.250030517578     .999969959259033 129.29167175293  .999970734119415 129.333343505859     .999963462352753 129.375030517578     .999960005283356 129.41667175293  .999967217445374 129.458358764648     .999975681304932 129.5    .999983072280884 129.541687011719     .999991953372955 129.58332824707  .999997317790985 129.625015258789     .99999213218689 129.666687011719     .999992072582245 129.70832824707  .999985218048096 129.750030517578     .99999064207077 129.79167175293  .999988555908203 129.833343505859     .99997490644455 129.875030517578     .999964952468872 129.91667175293  .999964356422424 129.958358764648     .999969661235809  24     i=6,j=447, depth avg 129  .999943792819977 129.041687011719     .999945878982544 129.08332824707  .999948799610138 129.125015258789     .999946057796478 129.166687011719     .999949932098389 129.20832824707  .999949991703033 129.250030517578     .999954700469971 129.29167175293  .99995630979538 129.333343505859     .999949872493744 129.375030517578     .999945342540741 129.41667175293  .999948680400848 129.458358764648     .999962687492371 129.5    .999973773956299 129.541687011719     .999982416629791 129.58332824707  .999990999698639 129.625015258789     .999992430210114 129.666687011719     .999982595443726 129.70832824707  .999979794025421 129.750030517578     .99998277425766 129.79167175293  .99998414516449 129.833343505859     .999970138072968 129.875030517578     .999956965446472 129.91667175293  .999948382377625 129.958358764648     .999954998493195  24     i=6,j=427, depth avg 129  .999925792217255 129.041687011719     .999931156635284 129.08332824707  .999930560588837 129.125015258789     .999935030937195 129.166687011719     .999935209751129 129.20832824707  .999935805797577 129.250030517578     .999941289424896 129.29167175293  .999940037727356 129.333343505859     .999939382076263 129.375030517578     .999930918216705 129.41667175293  .999935328960419 129.458358764648     .999944567680359 129.5    .999958515167236 129.541687011719     .999973475933075 129.58332824707  .999985992908478 129.625015258789     .999986290931702 129.666687011719     .99998140335083 129.70832824707  .999981462955475 129.750030517578     .999972283840179 129.79167175293  .999978244304657 129.833343505859     .999967753887177 129.875030517578     .999947845935822 129.91667175293  .99993896484375 129.958358764648     .999940454959869  24     i=6,j=407, depth avg 129  .999912023544312 129.041687011719     .999916732311249 129.08332824707  .999918818473816 129.125015258789     .999919652938843 129.166687011719     .999922215938568 129.20832824707  .999927818775177 129.250030517578     .999928176403046 129.29167175293  .99993222951889 129.333343505859     .999927520751953 129.375030517578     .999922752380371 129.41667175293  .99992299079895 129.458358764648     .999931871891022 129.5    .999947428703308 129.541687011719     .999964654445648 129.58332824707  .999975860118866 129.625015258789     .999980509281158 129.666687011719     .999982714653015 129.70832824707  .999972343444824 129.750030517578     .999974310398102 129.79167175293  .99997079372406 129.833343505859     .999962270259857 129.875030517578     .99994570016861 129.91667175293  .999932110309601 129.958358764648     .999929487705231  24     i=6,j=389, depth avg 129  .999896824359894 129.041687011719     .999900698661804 129.08332824707  .999906241893768 129.125015258789     .999907732009888 129.166687011719     .999911069869995 129.20832824707  .999914765357971 129.250030517578     .999916195869446 129.29167175293  .999917268753052 129.333343505859     .999915540218353 129.375030517578     .999913275241852 129.41667175293  .999912321567535 129.458358764648     .999917924404144 129.5    .999931216239929 129.541687011719     .999949038028717 129.58332824707  .999967098236084 129.625015258789     .99997490644455 129.666687011719     .99997478723526 129.70832824707  .999971747398376 129.750030517578     .999971866607666 129.79167175293  .999963641166687 129.833343505859     .999957323074341 129.875030517578     .999943554401398 129.91667175293  .999928772449493 129.958358764648     .999920845031738  24     i=6,j=368, depth avg 129  .999881386756897 129.041687011719     .999881148338318 129.08332824707  .999884247779846 129.125015258789     .999890089035034 129.166687011719     .999891340732574 129.20832824707  .999898195266724 129.250030517578     .999899566173553 129.29167175293  .99989926815033 129.333343505859     .999900281429291 129.375030517578     .999897241592407 129.41667175293  .999898076057434 129.458358764648     .999900460243225 129.5    .999909520149231 129.541687011719     .999929904937744 129.58332824707  .99994695186615 129.625015258789     .999955654144287 129.666687011719     .999966681003571 129.70832824707  .999970495700836 129.750030517578     .999961376190186 129.79167175293  .999959707260132 129.833343505859     .999947667121887 129.875030517578     .999938011169434 129.91667175293  .999923884868622 129.958358764648     .999911487102509  24     i=6,j=348, depth avg 129  .999867141246796 129.041687011719     .99986469745636 129.08332824707  .999867618083954 129.125015258789     .999873995780945 129.166687011719     .999871373176575 129.20832824707  .999880611896515 129.250030517578     .999884486198425 129.29167175293  .999886929988861 129.333343505859     .999884188175201 129.375030517578     .999882280826569 129.41667175293  .999883890151978 129.458358764648     .9998899102211 129.5    .999895036220551 129.541687011719     .999910831451416 129.58332824707  .999934732913971 129.625015258789     .999945521354675 129.666687011719     .999955534934998 129.70832824707  .999961793422699 129.750030517578     .999957978725433 129.79167175293  .999951660633087 129.833343505859     .999941468238831 129.875030517578     .999929845333099 129.91667175293  .999918639659882 129.958358764648     .999907255172729  24     i=6,j=327, depth avg 129  .999853491783142 129.041687011719     .999850451946259 129.08332824707  .999851763248444 129.125015258789     .99985283613205 129.166687011719     .999859154224396 129.20832824707  .999862432479858 129.250030517578     .99987006187439 129.29167175293  .999870896339417 129.333343505859     .999869167804718 129.375030517578     .999869883060455 129.41667175293  .999871730804443 129.458358764648     .999876856803894 129.5    .999881386756897 129.541687011719     .999897122383118 129.58332824707  .999917089939117 129.625015258789     .99993109703064 129.666687011719     .999943196773529 129.70832824707  .999952137470245 129.750030517578     .999950647354126 129.79167175293  .999947309494019 129.833343505859     .999936044216156 129.875030517578     .999920547008514 129.91667175293  .999912142753601 129.958358764648     .999901592731476  24     i=6,j=292, depth avg 129  .999843060970306 129.041687011719     .999837875366211 129.08332824707  .999838352203369 129.125015258789     .999838829040527 129.166687011719     .999844074249268 129.20832824707  .999848902225494 129.250030517578     .999852895736694 129.29167175293  .999858498573303 129.333343505859     .999856173992157 129.375030517578     .99985283613205 129.41667175293  .999858915805817 129.458358764648     .999866425991058 129.5    .999874234199524 129.541687011719     .999885022640228 129.58332824707  .99990314245224 129.625015258789     .999919414520264 129.666687011719     .99993097782135 129.70832824707  .999938011169434 129.750030517578     .999949157238007 129.79167175293  .999946355819702 129.833343505859     .999929904937744 129.875030517578     .999915540218353 129.91667175293  .999905586242676 129.958358764648     .999898970127106  24     i=6,j=259, depth avg 129  .999834001064301 129.041687011719     .999824702739716 129.08332824707  .999821126461029 129.125015258789     .999821424484253 129.166687011719     .99982613325119 129.20832824707  .999833703041077 129.250030517578     .999835669994354 129.29167175293  .999840080738068 129.333343505859     .999838411808014 129.375030517578     .999837756156921 129.41667175293  .99984335899353 129.458358764648     .999850928783417 129.5    .9998619556427 129.541687011719     .999873995780945 129.58332824707  .99988579750061 129.625015258789     .999900877475739 129.666687011719     .999914228916168 129.70832824707  .999925315380096 129.750030517578     .999936759471893 129.79167175293  .999936878681183 129.833343505859     .99992710351944 129.875030517578     .999910295009613 129.91667175293  .999898672103882 129.958358764648     .999892175197601  24     i=6,j=226, depth avg 129  .999824702739716 129.041687011719     .999814808368683 129.08332824707  .999808013439178 129.125015258789     .999804198741913 129.166687011719     .999807476997375 129.20832824707  .999813139438629 129.250030517578     .999822676181793 129.29167175293  .99982351064682 129.333343505859     .999824523925781 129.375030517578     .999823033809662 129.41667175293  .999825477600098 129.458358764648     .999838531017303 129.5    .999848604202271 129.541687011719     .999862551689148 129.58332824707  .999877154827118 129.625015258789     .999886035919189 129.666687011719     .999900817871094 129.70832824707  .999914348125458 129.750030517578     .999924421310425 129.79167175293  .999934256076813 129.833343505859     .999922454357147 129.875030517578     .999904692173004 129.91667175293  .999892711639404 129.958358764648     .999884963035584  24     i=6,j=192, depth avg 129  .99981552362442 129.041687011719     .99980354309082 129.08332824707  .999796211719513 129.125015258789     .999791741371155 129.166687011719     .999789655208588 129.20832824707  .999796688556671 129.250030517578     .999803066253662 129.29167175293  .999805808067322 129.333343505859     .999804735183716 129.375030517578     .999807178974152 129.41667175293  .99980890750885 129.458358764648     .999818205833435 129.5    .99983286857605 129.541687011719     .999846994876862 129.58332824707  .99986344575882 129.625015258789     .999871373176575 129.666687011719     .999883532524109 129.70832824707  .999895215034485 129.750030517578     .999908864498138 129.79167175293  .999918818473816 129.833343505859     .999915540218353 129.875030517578     .999901354312897 129.91667175293  .999885380268097 129.958358764648     .999875426292419  24     i=6,j=153, depth avg 129  .999805927276611 129.041687011719     .999794244766235 129.08332824707  .999784469604492 129.125015258789     .999773502349854 129.166687011719     .999773383140564 129.20832824707  .999773502349854 129.250030517578     .999780893325806 129.29167175293  .999789416790009 129.333343505859     .999786853790283 129.375030517578     .999787390232086 129.41667175293  .999790966510773 129.458358764648     .999798953533173 129.5    .999812364578247 129.541687011719     .999829947948456 129.58332824707  .999845206737518 129.625015258789     .999858379364014 129.666687011719     .999864339828491 129.70832824707  .999876201152802 129.750030517578     .999890923500061 129.79167175293  .999903082847595 129.833343505859     .999906420707703 129.875030517578     .999894261360168 129.91667175293  .999879479408264 129.958358764648     .999869167804718  24     i=6,j=128, depth avg 129  .999789476394653 129.041687011719     .999781906604767 129.08332824707  .999774098396301 129.125015258789     .999761164188385 129.166687011719     .999757647514343 129.20832824707  .999755144119263 129.250030517578     .999756097793579 129.29167175293  .99976259469986 129.333343505859     .999769032001495 129.375030517578     .999765932559967 129.41667175293  .999771893024445 129.458358764648     .999777555465698 129.5    .99978905916214 129.541687011719     .999803423881531 129.58332824707  .999822616577148 129.625015258789     .999835848808289 129.666687011719     .999845564365387 129.70832824707  .999853193759918 129.750030517578     .999869644641876 129.79167175293  .999880850315094 129.833343505859     .999887049198151 129.875030517578     .999883890151978 129.91667175293  .999871850013733 129.958358764648     .999860405921936  24     i=6,j=110, depth avg 129  .999779164791107 129.041687011719     .999770522117615 129.08332824707  .999760329723358 129.125015258789     .99975997209549 129.166687011719     .999742865562439 129.20832824707  .999745786190033 129.250030517578     .999750256538391 129.29167175293  .999748349189758 129.333343505859     .999751448631287 129.375030517578     .999754190444946 129.41667175293  .999757528305054 129.458358764648     .999769866466522 129.5    .999785482883453 129.541687011719     .999794661998749 129.58332824707  .999817728996277 129.625015258789     .999830901622772 129.666687011719     .999835133552551 129.70832824707  .999845504760742 129.750030517578     .999861538410187 129.79167175293  .999873220920563 129.833343505859     .999875068664551 129.875030517578     .999870419502258 129.91667175293  .99986457824707 129.958358764648     .999849319458008  24     i=6,j=93, depth avg 129  .999766826629639 129.041687011719     .999762296676636 129.08332824707  .999753355979919 129.125015258789     .999748826026917 129.166687011719     .999739050865173 129.20832824707  .999720871448517 129.250030517578     .999734222888947 129.29167175293  .999731242656708 129.333343505859     .999733805656433 129.375030517578     .999738991260529 129.41667175293  .999740064144135 129.458358764648     .999753355979919 129.5    .999762356281281 129.541687011719     .999780237674713 129.58332824707  .999796271324158 129.625015258789     .999814510345459 129.666687011719     .999817430973053 129.70832824707  .99983161687851 129.750030517578     .999845683574677 129.79167175293  .999861538410187 129.833343505859     .999863564968109 129.875030517578     .999863147735596 129.91667175293  .999865055084229 129.958358764648     .999856472015381  24     i=6,j=76, depth avg 129  .999753832817078 129.041687011719     .999746739864349 129.08332824707  .999741792678833 129.125015258789     .999738156795502 129.166687011719     .999724328517914 129.20832824707  .999722361564636 129.250030517578     .999713897705078 129.29167175293  .999720215797424 129.333343505859     .999716877937317 129.375030517578     .999717235565186 129.41667175293  .999724268913269 129.458358764648     .999728620052338 129.5    .999743044376373 129.541687011719     .999756336212158 129.58332824707  .999765694141388 129.625015258789     .999791264533997 129.666687011719     .999801218509674 129.70832824707  .999810576438904 129.750030517578     .999826610088348 129.79167175293  .999833583831787 129.833343505859     .999842703342438 129.875030517578     .999847292900085 129.91667175293  .999846518039703 129.958358764648     .999843716621399  24     i=6,j=57, depth avg 129  .999737977981567 129.041687011719     .999729692935944 129.08332824707  .999728322029114 129.125015258789     .999724566936493 129.166687011719     .99971330165863 129.20832824707  .999713182449341 129.250030517578     .999702155590057 129.29167175293  .999702036380768 129.333343505859     .999697923660278 129.375030517578     .999699771404266 129.41667175293  .999703884124756 129.458358764648     .999720335006714 129.5    .999739944934845 129.541687011719     .999764323234558 129.58332824707  .999775350093842 129.625015258789     .999794840812683 129.666687011719     .999800264835358 129.70832824707  .99980890750885 129.750030517578     .999824464321136 129.79167175293  .99983423948288 129.833343505859     .999840199947357 129.875030517578     .999831259250641 129.91667175293  .999834179878235 129.958358764648     .999835610389709  24     i=6,j=39, depth avg 129  .999715983867645 129.041687011719     .999719083309174 129.08332824707  .999716222286224 129.125015258789     .999715089797974 129.166687011719     .999704778194427 129.20832824707  .999699890613556 129.250030517578     .999691367149353 129.29167175293  .999691903591156 129.333343505859     .999683082103729 129.375030517578     .999683618545532 129.41667175293  .999683201313019 129.458358764648     .99969744682312 129.5    .999726533889771 129.541687011719     .999751925468445 129.58332824707  .999759078025818 129.625015258789     .99978768825531 129.666687011719     .999797701835632 129.70832824707  .999793410301209 129.750030517578     .999819159507751 129.79167175293  .999822616577148 129.833343505859     .999823331832886 129.875030517578     .999825894832611 129.91667175293  .999815165996552 129.958358764648     .999819099903107  24     i=6,j=21, depth avg 129  .999696910381317 129.041687011719     .999701023101807 129.08332824707  .999699056148529 129.125015258789     .999692380428314 129.166687011719     .999688744544983 129.20832824707  .999685347080231 129.250030517578     .999680936336517 129.29167175293  .99967360496521 129.333343505859     .999662935733795 129.375030517578     .999661505222321 129.41667175293  .99966698884964 129.458358764648     .999664664268494 129.5    .99968409538269 129.541687011719     .999708712100983 129.58332824707  .999718606472015 129.625015258789     .999738812446594 129.666687011719     .999759376049042 129.70832824707  .999764382839203 129.750030517578     .999777615070343 129.79167175293  .999795854091644 129.833343505859     .999795794487 129.875030517578     .999796092510223 129.91667175293  .999798774719238 129.958358764648     .999801099300385  24     i=6,j=4, depth avg 129  .999682068824768 129.041687011719     .999686241149902 129.08332824707  .999697148799896 129.125015258789     .999681234359741 129.166687011719     .999677836894989 129.20832824707  .999678015708923 129.250030517578     .999674320220947 129.29167175293  .999665796756744 129.333343505859     .999662160873413 129.375030517578     .99965512752533 129.41667175293  .999659299850464 129.458358764648     .999676048755646 129.5    .999703407287598 129.541687011719     .999747574329376 129.58332824707  .999796569347382 129.625015258789     .999792635440826 129.666687011719     .999805927276611 129.70832824707  .999807775020599 129.750030517578     .999792218208313 129.79167175293  .999798476696014 129.833343505859     .99979567527771 129.875030517578     .999777317047119 129.91667175293  .999795794487 129.958358764648     .99979555606842 

the dataset timeseries of dye concentration @ 24 locations. each station has 24 data points (hourly). hence, in first line of data, 24 means number of data points station , i=5,j=529, depth avg means id of station , data depth averaged. simplicity can treat first location 1 , last location 24. want plot graph such time series of each station plotted box plot , line/curve plotted joins median points data. location of 24 points defined dist <- c(0,1850,seq(from=3100,to=29350,by=1250)) . means starting point @ 0 , second point @ 1850 metres , other stations 1250m apart.

expected plot:

distance vs dye concentration (showing box plot each locations & curve joining median of 24 data points @ each station).

> datlines <- readlines(textconnection(" 24     i=6,j=529, depth avg + 129  1 + 129.041687011719     1.00000035762787 + 129.08332824707  .99999988079071 + 129.125015258789     1.00000011920929 ....... ###### snipped many lines + 129.833343505859     .99979567527771 + 129.875030517578     .999777317047119 + 129.91667175293  .999795794487 + 129.958358764648     .99979555606842"))  grp <- cumsum(grepl("depth", datlines)) # cumsum 1/0's creates group var rdlines <- lapply( split(datlines, grp), # read within each group                    function(x) read.table(text=x, skip=1) ) str(rdlines[1]) dafrm <- do.call(rbind, rdlines)  # bind in 1 dataframe dafrm$grp <- rep(1:24, each=24)   # label them bpt <- boxplot(v2~grp, data=dafrm) # save values in 'bpt' variable str(bpt) lines(1:24, bpt$stats[ 3, ])  # values of medians y-arg `lines`