erd2023_explore_for_misty_rmd
2024-10-31
Misty Start Here:
Inits
erd2023_filename <- "erd_2023.WI.data.parquet"
srd2023_filename <- "srd_2023.WI.data.parquet"
solar_filename <- "pv-1000kW-updated.csv"Define WI Region & Season of interest
max_lat <- 47
max_lon <- -86.5
min_lat <- 42.4
min_lon <- -93
day_max <- 210
day_min <- 150Load 2023 ERD & SRD & Solar data
erd <- read_parquet(erd2023_filename)
srd <- read_parquet(srd2023_filename)
solar <- read.csv(solar_filename)dim(erd)## [1] 174084 527 dim(srd)## [1] 24912 122 dim(solar)## [1] 93 13Identify which ERD checklists (in B) are within a specified distance of any Solar installations (in A)
A <- data.frame(
lat = solar$latitude,
lon = solar$longitude )
A <- A[ complete.cases(A), ]
# In any column in the data frame, remove rows with missing values. B <- data.frame(
lat = erd$latitude,
lon = erd$longitude,
checklist_id = erd$checklist_id ) # (each element is the distance between one point in A and one in B)
dist_matrix <- distm(
cbind(B$lon, B$lat),
cbind(A$lon, A$lat),
fun = distHaversine)
head(dist_matrix) distance_threshold <- 10000 # 10k meters or 10 km
within_distance <- apply(
dist_matrix, 1,
function(distances) any(distances <= distance_threshold)) B_within_distance <- B[within_distance, ]head(B_within_distance)
head(within_distance)Maps
plot checklists, solar arrays, and solar circles
# Plot Locations
par(cex = 0.5)
#mfrow = c(1,2),
xxx <- erd$longitude
yyy <- erd$latitude
plot(xxx, yyy,
xlab = "Lon",
ylab = "Lat",
main = "Breeding Season ERD2023",
pch = 20,
cex = 0.15,
col = alpha("blue", 0.20 ))
map("state", add=T, col="black")
# Add points to plot
points(
A$lon, A$lat,
col=alpha("green", 0.5), pch = 19, cex = 2.5)
points(
B_within_distance$lon, B_within_distance$lat,
col=alpha("orange", 0.25),
pch = 19, cex = 0.25)plot ??
xxx <- srd$longitude
yyy <- srd$latitude
plot(xxx, yyy,
xlab = "Lon",
ylab = "Lat",
main = "SRD2023",
pch = 20,
cex = 0.15,
col = alpha("orange", 1.0 ))
map("state", add=T, col="black")plot circles and orange only
# Plot Locations
par(cex = 0.5)
#mfrow = c(1,2),
plot(A$lon, A$lat,
xlab = "Lon",
ylab = "Lat",
col=alpha("green", 0.5), pch = 19, cex = 5,
xlim = c(-92, -91), ylim = c(45.5,46.5))
map("state", add=T, col="black")
# Add points to plot
points(
B_within_distance$lon, B_within_distance$lat,
col=alpha("orange", 0.5),
pch = 19, cex = 0.25)# Look how much data there are!
dim(B_within_distance)
dim(unique(B_within_distance[,c(1:2)]))
# [1] 33982 3
# [1] 14329 2Determine how many checklists within 10 km of array
array46_91.2 = A %>% filter(A$lat > -91.7 & A$lat < 91.2)
dim(array46_91.2)head(erd$latitude)——————————————————————–
Checklist feature names
These include environmental and observation process features
——————————————————————–
range(erd$longitude)## [1] -92.99995 -86.50023range(erd$latitude)## [1] 42.40009 46.99900names(erd)## [1] "checklist_id" "observer_id"
## [3] "loc_id" "longitude"
## [5] "latitude" "year"
## [7] "day_of_year" "hours_of_day"
## [9] "solar_noon_diff" "is_stationary"
## [11] "effort_hrs" "effort_distance_km"
## [13] "effort_speed_kmph" "num_observers"
## [15] "cci" "moon_fraction"
## [17] "moon_altitude" "cds_u10"
## [19] "cds_v10" "cds_d2m"
## [21] "cds_t2m" "cds_hcc"
## [23] "cds_i10fg" "cds_mcc"
## [25] "cds_lcc" "cds_sf"
## [27] "cds_rf" "cds_slc"
## [29] "cds_msl" "eastness_1km_median"
## [31] "eastness_1km_sd" "eastness_90m_median"
## [33] "eastness_90m_sd" "northness_1km_median"
## [35] "northness_1km_sd" "northness_90m_median"
## [37] "northness_90m_sd" "bathymetry_elevation_median"
## [39] "bathymetry_elevation_sd" "elevation_30m_median"
## [41] "elevation_30m_sd" "island"
## [43] "mountain" "astwbd_c1_ed"
## [45] "astwbd_c1_pland" "astwbd_c2_ed"
## [47] "astwbd_c2_pland" "astwbd_c3_ed"
## [49] "astwbd_c3_pland" "gsw_c2_pland"
## [51] "gsw_c2_ed" "ntl_mean"
## [53] "ntl_sd" "road_density_c1"
## [55] "road_density_c2" "road_density_c3"
## [57] "road_density_c4" "road_density_c5"
## [59] "mcd12q1_lccs1_c1_ed" "mcd12q1_lccs1_c1_pland"
## [61] "mcd12q1_lccs1_c2_ed" "mcd12q1_lccs1_c2_pland"
## [63] "mcd12q1_lccs1_c11_ed" "mcd12q1_lccs1_c11_pland"
## [65] "mcd12q1_lccs1_c12_ed" "mcd12q1_lccs1_c12_pland"
## [67] "mcd12q1_lccs1_c13_ed" "mcd12q1_lccs1_c13_pland"
## [69] "mcd12q1_lccs1_c14_ed" "mcd12q1_lccs1_c14_pland"
## [71] "mcd12q1_lccs1_c15_ed" "mcd12q1_lccs1_c15_pland"
## [73] "mcd12q1_lccs1_c16_ed" "mcd12q1_lccs1_c16_pland"
## [75] "mcd12q1_lccs1_c21_ed" "mcd12q1_lccs1_c21_pland"
## [77] "mcd12q1_lccs1_c22_ed" "mcd12q1_lccs1_c22_pland"
## [79] "mcd12q1_lccs1_c31_ed" "mcd12q1_lccs1_c31_pland"
## [81] "mcd12q1_lccs1_c32_ed" "mcd12q1_lccs1_c32_pland"
## [83] "mcd12q1_lccs1_c41_ed" "mcd12q1_lccs1_c41_pland"
## [85] "mcd12q1_lccs1_c42_ed" "mcd12q1_lccs1_c42_pland"
## [87] "mcd12q1_lccs1_c43_ed" "mcd12q1_lccs1_c43_pland"
## [89] "mcd12q1_lccs1_c255_ed" "mcd12q1_lccs1_c255_pland"
## [91] "mcd12q1_lccs2_c9_ed" "mcd12q1_lccs2_c9_pland"
## [93] "mcd12q1_lccs2_c25_ed" "mcd12q1_lccs2_c25_pland"
## [95] "mcd12q1_lccs2_c35_ed" "mcd12q1_lccs2_c35_pland"
## [97] "mcd12q1_lccs2_c36_ed" "mcd12q1_lccs2_c36_pland"
## [99] "mcd12q1_lccs3_c27_ed" "mcd12q1_lccs3_c27_pland"
## [101] "mcd12q1_lccs3_c50_ed" "mcd12q1_lccs3_c50_pland"
## [103] "mcd12q1_lccs3_c51_ed" "mcd12q1_lccs3_c51_pland"
## [105] "has_shoreline" "shoreline_waveheight_mean"
## [107] "shoreline_waveheight_sd" "shoreline_tidal_range_mean"
## [109] "shoreline_tidal_range_sd" "shoreline_chlorophyll_mean"
## [111] "shoreline_chlorophyll_sd" "shoreline_turbidity_mean"
## [113] "shoreline_turbidity_sd" "shoreline_sinuosity_mean"
## [115] "shoreline_sinuosity_sd" "shoreline_slope_mean"
## [117] "shoreline_slope_sd" "shoreline_outflow_density_mean"
## [119] "shoreline_outflow_density_sd" "shoreline_erodibility_n"
## [121] "shoreline_erodibility_c1_density" "shoreline_erodibility_c2_density"
## [123] "shoreline_erodibility_c3_density" "shoreline_erodibility_c4_density"
## [125] "shoreline_emu_physical_n" "shoreline_emu_physical_c1_density"
## [127] "shoreline_emu_physical_c2_density" "shoreline_emu_physical_c3_density"
## [129] "shoreline_emu_physical_c4_density" "shoreline_emu_physical_c5_density"
## [131] "shoreline_emu_physical_c6_density" "shoreline_emu_physical_c7_density"
## [133] "shoreline_emu_physical_c8_density" "shoreline_emu_physical_c9_density"
## [135] "shoreline_emu_physical_c10_density" "shoreline_emu_physical_c11_density"
## [137] "shoreline_emu_physical_c12_density" "shoreline_emu_physical_c13_density"
## [139] "shoreline_emu_physical_c14_density" "shoreline_emu_physical_c15_density"
## [141] "shoreline_emu_physical_c16_density" "shoreline_emu_physical_c17_density"
## [143] "shoreline_emu_physical_c18_density" "shoreline_emu_physical_c19_density"
## [145] "shoreline_emu_physical_c20_density" "shoreline_emu_physical_c21_density"
## [147] "shoreline_emu_physical_c22_density" "shoreline_emu_physical_c23_density"
## [149] "has_evi" "evi_median"
## [151] "evi_sd" "wilsni1"
## [153] "comloo" "virrai2"
## [155] "mallar3" "pibgre"
## [157] "easwpw1" "cedwax"
## [159] "dowwoo" "amerob"
## [161] "baleag" "rthhum"
## [163] "rewbla" "olsfly"
## [165] "easblu" "easpho"
## [167] "blujay" "boboli"
## [169] "eursta" "bnhcow"
## [171] "moudov" "comrav"
## [173] "rocpig" "whbnut"
## [175] "yebsap" "balori"
## [177] "purfin" "comgra"
## [179] "treswa" "easkin"
## [181] "amegfi" "amecro"
## [183] "haiwoo" "amebit"
## [185] "eastow" "eawpew"
## [187] "barswa" "swaspa"
## [189] "sancra" "norcar"
## [191] "sora" "robgro"
## [193] "indbun" "buggna"
## [195] "comyel" "veery"
## [197] "reevir1" "houwre"
## [199] "rehwoo" "sonspa"
## [201] "nrwswa" "comnig"
## [203] "grycat" "wooduc"
## [205] "brdowl" "cangoo"
## [207] "blkter" "amwpel"
## [209] "purmar" "y00475"
## [211] "grnher" "forter"
## [213] "dickci" "wilfly"
## [215] "graspa" "easmea"
## [217] "rebwoo" "grbher3"
## [219] "killde" "warvir"
## [221] "belkin1" "grcfly"
## [223] "yelwar" "bkcchi"
## [225] "ribgul" "hergul"
## [227] "chiswi" "houspa"
## [229] "mouwar" "amered"
## [231] "chswar" "chispa"
## [233] "wiltur" "pilwoo"
## [235] "pinsis" "turvul"
## [237] "houfin" "scatan"
## [239] "yetvir" "ovenbi1"
## [241] "osprey" "yebcha"
## [243] "brebla" "leafly"
## [245] "grhowl" "rethaw"
## [247] "fiespa" "baisan"
## [249] "cliswa" "litgul"
## [251] "shbdow" "bongul"
## [253] "lesyel" "redkno"
## [255] "caster1" "sposan"
## [257] "stisan" "gresca"
## [259] "comter" "banswa"
## [261] "amekes" "greegr"
## [263] "woothr" "brnthr"
## [265] "carwre" "savspa"
## [267] "rudduc" "buwtea"
## [269] "norsho" "leabit"
## [271] "gnwtea" "coohaw"
## [273] "solsan" "doccor"
## [275] "amewig" "sedwre1"
## [277] "herthr" "hoomer"
## [279] "amewoo" "loeowl"
## [281] "norfli" "dunlin"
## [283] "semsan" "pinwar"
## [285] "rebnut" "commer"
## [287] "aldfly" "whtspa"
## [289] "buhvir" "attwoo1"
## [291] "rufgro" "yerwar"
## [293] "naswar" "lessca"
## [295] "merlin" "perfal"
## [297] "rebmer" "y00478"
## [299] "sander" "phivir"
## [301] "bkbplo" "retloo"
## [303] "rudtur" "whrsan"
## [305] "semplo" "marwre"
## [307] "brwhaw" "acafly"
## [309] "hoowar" "winwre3"
## [311] "norwat" "chwwid"
## [313] "btnwar" "bawwar"
## [315] "tuftit" "gadwal"
## [317] "orcori" "norgos"
## [319] "clcspa" "henspa"
## [321] "buwwar" "norhar2"
## [323] "norpar" "bkbwar"
## [325] "canwar" "bkbcuc"
## [327] "larspa" "redhea"
## [329] "ambduc" "horlar"
## [331] "blugrb1" "purgal2"
## [333] "rengre" "norpin"
## [335] "leasan" "willet1"
## [337] "pecsan" "greyel"
## [339] "bubsan" "wesmea"
## [341] "magwar" "rusbla"
## [343] "fragul" "normoc"
## [345] "evegro" "reshaw"
## [347] "tenwar" "canvas"
## [349] "yehbla" "bcnher"
## [351] "bknsti" "buffle"
## [353] "lbbgul" "palwar"
## [355] "gryjay" "yebfly"
## [357] "linspa" "gockin"
## [359] "rinduc" "rinphe1"
## [361] "sheowl" "comgal1"
## [363] "lobdow" "margod"
## [365] "categr1" "gbbgul"
## [367] "brncre" "btbwar"
## [369] "wilpha" "vesspa"
## [371] "gowwar" "whimbr"
## [373] "comgol" "bkbwoo"
## [375] "shtgro" "uplsan"
## [377] "yebcuc" "wlswar"
## [379] "conwar" "snoegr"
## [381] "renpha" "whocra"
## [383] "glagul" "mutswa"
## [385] "calgul" "prowar"
## [387] "laugul" "hudgod"
## [389] "swathr" "whcspa"
## [391] "kirwar" "amgplo"
## [393] "shshaw" "truswa"
## [395] "lecspa" "louwat"
## [397] "cerwar" "woewar1"
## [399] "prawar" "whevir"
## [401] "belvir" "camwar"
## [403] "daejun" "logshr"
## [405] "easowl1" "larbun"
## [407] "ruckin" "horgre"
## [409] "bkpwar" "snogoo"
## [411] "kenwar" "parjae"
## [413] "eucdov" "sprgro"
## [415] "whfibi" "kinrai4"
## [417] "gloibi" "yetwar"
## [419] "norbob" "tunswa"
## [421] "swahaw" "snoplo5"
## [423] "ycnher" "babwar"
## [425] "wesgre" "ameavo"
## [427] "pipplo" "whwcro"
## [429] "nswowl" "sagthr"
## [431] "gycthr" "eargre"
## [433] "sumtan" "blksco2"
## [435] "neocor" "whwdov"
## [437] "amepip2" "orcwar"
## [439] "whwsco2" "whiibi"
## [441] "triher" "clagre"
## [443] "comred" "sctfly"
## [445] "redcro" "grpchi"
## [447] "lazbun" "eugplo"
## [449] "weskin" "yelrai"
## [451] "borchi2" "bkchum"
## [453] "eurgol" "foxspa"
## [455] "goleag" "sprpip"
## [457] "sursco" "pacloo"
## [459] "arcter" "gwfgoo"
## [461] "calhum" "monpar"
## [463] "y00678" "lotduc"
## [465] "gretit1" "pomjae"
## [467] "swtkit" "bbcdov1"
## [469] "redpha1" "bohwax"
## [471] "lotjae" "grypar"
## [473] "bkbmag1" "bubhum"
## [475] "virrai" "rolhaw"
## [477] "wessan" "cavswa"
## [479] "trokin" "eutspa"
## [481] "bargoo" "spotow"
## [483] "bulori" "amtspa"
## [485] "snoowl1" "pingro"
## [487] "hooori" "grtgra"
## [489] "rufhum" "bbwduc"
## [491] "libher" "moublu"
## [493] "bkhgro" "rocpar2"
## [495] "harduc" "limpki"
## [497] "arcloo" "towwar"
## [499] "annhum" "rosgoo"
## [501] "whtkit" "cintea"
## [503] "fiscro" "savhaw1"
## [505] "harspa" "sedwre"
## [507] "blkrai" "grgowl"
## [509] "saypho" "caskin"
## [511] "bkhgul" "brbhum"
## [513] "acowoo" "towsol"
## [515] "rosgul" "blkvul"
## [517] "brnpel" "stejay"
## [519] "carchi" "whwbec1"
## [521] "audori" "lesgol"
## [523] "redwin" "miskit"
## [525] "norshr4" "rosspo1"
## [527] "bewwre"fiddlings to determine # checklists within 10k of a given array
solar_f = solar %>% select("latitude", "longitude", "Customer.ProjectName")
solar_f = solar_f %>% filter(!is.na(latitude))determine max/min lat for 10 km
# 1 deg = 110.574 km
solar_f$ lat.plus.10k = solar_f$ latitude + ((1/110)*10)
solar_f$ lat.less.10k = solar_f$ latitude - ((1/110)*10)determine max/min long for 10 km
# 1. convert degrees to radians
## function that converts degree to radians
deg2rad <- function(x) {(x * pi) / (180)}
# create variable of converted values
solar_f$ lat.deg2radians = deg2rad(solar_f$ latitude)
solar_f$ long.deg2radians = deg2rad(solar_f$ longitude)
# 1 deg = 111.320*cos(latitude) km
# 1 degree = 111.320*cos(lat in radians) km
solar_f$ km_per_1deglong = 111.32*cos(solar_f$ lat.deg2radians)
# calculate 1 degree longitude with formula
# 1 degree = 111.320*cos(lat in radians) km
solar_f$ deg.in.10km = (1/(solar_f$ km_per_1deglong))*10
solar_f$ long.plus.10k = solar_f$ longitude + solar_f$ deg.in.10km
solar_f$ long.less.10k = solar_f$ longitude - solar_f$ deg.in.10km
head(solar_f)## latitude longitude Customer.ProjectName lat.plus.10k lat.less.10k
## 1 45.41171 -92.63882 Interstate Park 45.50262 45.32081
## 2 45.36830 -92.55854 Sand Lake Solar 45.45920 45.27739
## 3 45.44526 -92.54959 Sand Lake Solar (Polk County) 45.53617 45.35435
## 4 44.95917 -92.51738 Warren Solar 45.05008 44.86826
## 5 45.52665 -92.49619 Community Insurance 45.61756 45.43574
## 6 46.70992 -92.08766 UW Superior 46.80083 46.61901
## lat.deg2radians long.deg2radians km_per_1deglong deg.in.10km long.plus.10k
## 1 0.7925839 -1.616852 78.14747 0.1279632 -92.51086
## 2 0.7918261 -1.615451 78.20753 0.1278649 -92.43067
## 3 0.7931694 -1.615295 78.10104 0.1280393 -92.42155
## 4 0.7846855 -1.614733 78.77120 0.1269499 -92.39043
## 5 0.7945899 -1.614363 77.98829 0.1282244 -92.36797
## 6 0.8152419 -1.607233 76.33127 0.1310079 -91.95665
## long.less.10k
## 1 -92.76678
## 2 -92.68640
## 3 -92.67763
## 4 -92.64433
## 5 -92.62441
## 6 -92.21867# rad2deg <- function(rad) {(rad * 180) / (pi)}
# deg2rad <- function(deg) {(deg * pi) / (180)}Isolate array and see what checklists are within 10 km
Hill Construction
Hill Construction
2016
lat 46.007367 max 46.09827609 min 45.91645791 long -91.492534 max
-91.62186819 min -91.36319981
Hill_constrxn = erd %>% select (latitude, longitude, checklist_id, year)max(solar_f$lat.less.10k)## [1] 46.61901min(solar_f$lat.less.10k)## [1] 42.44924max(solar_f$lat.plus.10k)## [1] 46.80083min(solar_f$lat.plus.10k)## [1] 42.63105max(solar_f$long.plus.10k)## [1] -87.41756min(solar_f$long.plus.10k)## [1] -92.51086max(solar_f$long.less.10k)## [1] -87.66846min(solar_f$long.less.10k)## [1] -92.76678# selecting checklists between the max and min lat longs for Hill Construction
## select lat
Hill_constrxn.code = Hill_constrxn %>% filter (Hill_constrxn$latitude < max(solar_f$lat.plus.10k) & Hill_constrxn$latitude > min(solar_f$lat.less.10k))
#Hill_constrxn.manual = Hill_constrxn %>% filter (Hill_constrxn$latitude < 46.8 & Hill_constrxn$latitude < 42.4)## select long
Hill_constrxn.code = Hill_constrxn %>% filter (Hill_constrxn$longitude < max(solar_f$long.plus.10k) & Hill_constrxn$longitude > min(solar_f$long.less.10k))
#Hill_constrxn.manual = Hill_constrxn %>% filter (Hill_constrxn$longitude < -87.4 & Hill_constrxn$longitude > -92.8)#### create table with the total number of checklists per year - created by coding min and max
checklists_per_year.code <- Hill_constrxn.code %>% group_by(year) %>%
summarise(total.sample.code=length(year))
checklists_per_year.code## # A tibble: 15 × 2
## year total.sample.code
## <dbl> <int>
## 1 2009 3982
## 2 2010 2830
## 3 2011 1888
## 4 2012 3704
## 5 2013 4979
## 6 2014 4399
## 7 2015 7726
## 8 2016 13424
## 9 2017 15179
## 10 2018 16495
## 11 2019 18193
## 12 2020 13316
## 13 2021 15727
## 14 2022 17137
## 15 2023 20220#### create table with the total number of checklists per year
#checklists_per_year.manual <- Hill_constrxn.manual %>% group_by(year) %>%
# summarise(total.sample.manual=length(year))
#checklists_per_year.manualggplot(checklists_per_year.code,aes(x=year,y=total.sample.code))+
geom_jitter() +
labs(x="Year",y="Total checklists",title="Number of checklists submitted per year at Hill Construction") +
geom_vline(xintercept = 2016)
#ggplot(checklists_per_year.manual,aes(x=year,y=total.sample.manual))+
# geom_jitter() +
# labs(x="Year",y="Total checklists",title="Number of checklists submitted per year at Hill Construction") +
# geom_vline(xintercept = 2016)NOW NEED TO FIND OUT HOW MANY SPECIES DETECTED PER CHECKLIST! NEED TO ACCOUNT FOR GREATER EFFORT (MORE CHECKLISTS) AFTER THE PANELS WENT IN
Hill Construction
2016
lat 46.007367 max 46.09827609 min 45.91645791 long -91.492534 max
-91.62186819 min -91.36319981
create table with the total number of observations per year
columns are: year, spp1, spp2…sppn rows are year, # observed …
# calculate observations per species for a given year
## create vector for each year
spp.per.2009 = colSums(Hill_constrxn.spp[which(Hill_constrxn.spp$year=='2009'), 7:382])
spp.per.2010 = colSums(Hill_constrxn.spp[which(Hill_constrxn.spp$year=='2010'), 7:382])
spp.per.2011 = colSums(Hill_constrxn.spp[which(Hill_constrxn.spp$year=='2011'), 7:382])
spp.per.2012 = colSums(Hill_constrxn.spp[which(Hill_constrxn.spp$year=='2012'), 7:382])
spp.per.2013 = colSums(Hill_constrxn.spp[which(Hill_constrxn.spp$year=='2013'), 7:382])
spp.per.2014 = colSums(Hill_constrxn.spp[which(Hill_constrxn.spp$year=='2014'), 7:382])
## create vector with all years
spp.per.year = c(colSums(Hill_constrxn.spp[which(Hill_constrxn.spp$year=='2009'), 7:382]), colSums(Hill_constrxn.spp[which(Hill_constrxn.spp$year=='2010'), 7:382]), colSums(Hill_constrxn.spp[which(Hill_constrxn.spp$year=='2011'), 7:382]))
## create matrix with vectors
spp.per.year.matrix = matrix(spp.per.year, nrow = 3, byrow = TRUE)
dim(spp.per.year.matrix)
## add a column for year
spp.per.year.matrix <- cbind(spp.per.year.matrix,c(2009, 2010, 2011))
dim(spp.per.year.matrix)
# dimnames = list(rown, coln))
spp.per.year.matrix[, 375:377]
## Now need to add spp names!!!Now isolate for Hill construction
Hill_constrxn.spp = erd %>% select(1:6, 152:527)
checklists_per_year.code <- Hill_constrxn.spp %>% group_by(year) %>%
summarise(total.sample.code=length(year))
checklists_per_year.codenames(Hill_constrxn.spp)# selecting observations between the max and min lat longs for Hill Construction
## select lat
Hill_constrxn.spp = Hill_constrxn.spp %>% filter (Hill_constrxn.spp$latitude < max(solar_f$lat.plus.10k) & Hill_constrxn.spp$latitude > min(solar_f$lat.less.10k))
#Hill_constrxn.manual = Hill_constrxn %>% filter (Hill_constrxn$latitude < 46.8 & Hill_constrxn$latitude < 42.4)## select long
Hill_constrxn.spp = Hill_constrxn.spp %>% filter (Hill_constrxn.spp$longitude < max(solar_f$long.plus.10k) & Hill_constrxn.spp$longitude > min(solar_f$long.less.10k))
#Hill_constrxn.manual = Hill_constrxn %>% filter (Hill_constrxn$longitude < -87.4 & Hill_constrxn$longitude > -92.8)year.f = factor(erd$year)
levels(year.f)# calc total observations per spp
spp.sum=colSums(Hill_constrxn.spp[,7:382])
spp.sum
spp.per.year <- Hill_constrxn.spp %>% group_by(year) %>%
reframe(spp.sum=colSums(Hill_constrxn.spp[,7:382]))
spp.per.yearggplot(checklists_per_year.code,aes(x=year,y=total.sample.code))+
geom_jitter() +
labs(x="Year",y="Total checklists",title="Number of checklists submitted per year at Hill Construction") +
geom_vline(xintercept = 2016)`