DRUM_Gannon_020322
Katie_Gannon
2/3/2022
#Input Data
#all_lakes <- read_xlsx("Data/All_Lakes_KAG_020322.xlsx")
all_lakes <- read_csv("All_Lakes_KAG_020322.csv")## Rows: 1550 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Sample_Code, Lake, Taxa, Strain
## dbl (2): Year, Year_TP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.all_lakes <- as.data.frame(all_lakes)
#Break lakes into separate dataframes to be analyzed individually
all_lakes$Lake <- as.factor(all_lakes$Lake)
levels(all_lakes$Lake)## [1] "Bald_Eagle" "Christmas" "Grays_Bay" "Ham" "Independence"
## [6] "North_Arm" "Phelps_Bay" "Smiths_Bay" eagle <- subset(all_lakes, all_lakes$Lake == "Bald_Eagle")
head(eagle)## Sample_Code Lake Taxa Year Year_TP Strain
## 239 MYR-7079 Bald_Eagle EWM 2018 2018 MC-E-1
## 240 MYR-7080 Bald_Eagle NWM 2018 2018 BE-N-2
## 241 MYR-7081 Bald_Eagle NWM 2018 2018 BE-N-2
## 242 MYR-7082 Bald_Eagle EWM 2018 2018 MC-E-1
## 243 MYR-7083 Bald_Eagle EWM 2018 2018 MC-E-1
## 244 MYR-7084 Bald_Eagle EWM 2018 2018 MC-E-1 christmas <- subset(all_lakes, all_lakes$Lake == "Christmas")
head(christmas)## Sample_Code Lake Taxa Year Year_TP Strain
## 1 MYR-6915 Christmas EWM 2018 2018 MC-E-1
## 2 MYR-6916 Christmas EWM 2018 2018 MC-E-1
## 3 MYR-6917 Christmas NWM 2018 2018 CH-N-1
## 4 MYR-6918 Christmas EWM 2018 2018 MC-E-1
## 5 MYR-6919 Christmas NWM 2018 2018 CH-N-1
## 6 MYR-6920 Christmas NWM 2018 2018 CH-N-1 grays_bay <- subset(all_lakes, all_lakes$Lake == "Grays_Bay")
head(grays_bay)## Sample_Code Lake Taxa Year Year_TP Strain
## 502 MYR-7276 Grays_Bay HWM 2018 2018 MC-H-7
## 503 MYR-7283 Grays_Bay HWM 2018 2018 MC-H-7
## 504 MYR-7284 Grays_Bay HWM 2018 2018 MC-H-7
## 505 MYR-7288 Grays_Bay HWM 2018 2018 MC-H-7
## 506 MYR-7290 Grays_Bay HWM 2018 2018 MC-H-7
## 507 MYR-7295 Grays_Bay HWM 2018 2018 MC-H-7 ham <- subset(all_lakes, all_lakes$Lake == "Ham")
head(ham)## Sample_Code Lake Taxa Year Year_TP Strain
## 730 MYR-7166 Ham HWM 2018 2018.5 HM-H-14
## 731 MYR-7167 Ham HWM 2018 2018.5 HM-H-14
## 732 MYR-7168 Ham HWM 2018 2018.5 HM-H-14
## 733 MYR-7169 Ham HWM 2018 2018.5 HM-H-14
## 734 MYR-7170 Ham HWM 2018 2018.5 HM-H-14
## 735 MYR-7171 Ham HWM 2018 2018.5 HM-H-14 indp <- subset(all_lakes, all_lakes$Lake == "Independence")
head(indp)## Sample_Code Lake Taxa Year Year_TP Strain
## 922 MYR-6855 Independence HWM 2018 2018 IN-H-99
## 923 MYR-6856 Independence HWM 2018 2018 IN-H-99
## 924 MYR-6857 Independence HWM 2018 2018 IN-H-99
## 925 MYR-6858 Independence HWM 2018 2018 IN-H-99
## 926 MYR-6859 Independence HWM 2018 2018 IN-H-99
## 927 MYR-6860 Independence HWM 2018 2018 IN-H-99 north_arm <- subset(all_lakes, all_lakes$Lake == "North_Arm")
head(north_arm)## Sample_Code Lake Taxa Year Year_TP Strain
## 1136 MYR-7856 North_Arm HWM 2019 2019.5 MC-H-7
## 1137 MYR-7857 North_Arm HWM 2019 2019.5 MC-H-12
## 1138 MYR-7858 North_Arm HWM 2019 2019.5 MC-H-9
## 1139 MYR-7859 North_Arm HWM 2019 2019.5 MC-H-7
## 1140 MYR-7860 North_Arm HWM 2019 2019.5 MC-H-7
## 1141 MYR-7861 North_Arm HWM 2019 2019.5 MC-H-9 phelps <- subset(all_lakes, all_lakes$Lake == "Phelps_Bay")
head(phelps)## Sample_Code Lake Taxa Year Year_TP Strain
## 1243 MYR-8125 Phelps_Bay HWM 2019 2019.5 PB-H-7
## 1244 MYR-8253 Phelps_Bay HWM 2019 2019.5 PB-H-8
## 1245 MYR-8254 Phelps_Bay HWM 2019 2019.5 PB-H-7
## 1246 MYR-8256 Phelps_Bay HWM 2019 2019.5 PB-H-7
## 1247 MYR-8257 Phelps_Bay HWM 2019 2019.5 PB-H-8
## 1248 MYR-8258 Phelps_Bay NWM 2019 2019.5 PB-N-10 smith_geno <- subset(all_lakes, all_lakes$Lake == "Smiths_Bay")
head(smith_geno)## Sample_Code Lake Taxa Year Year_TP Strain
## 1354 MYR-6935 Smiths_Bay EWM 2018 2018 MC-E-1
## 1355 MYR-6936 Smiths_Bay EWM 2018 2018 MC-E-1
## 1356 MYR-6937 Smiths_Bay EWM 2018 2018 MC-E-1
## 1357 MYR-6939 Smiths_Bay EWM 2018 2018 MC-E-1
## 1358 MYR-6940 Smiths_Bay EWM 2018 2018 MC-E-1
## 1359 MYR-7368 Smiths_Bay EWM 2018 2018 MC-E-1Analyze Bald Eagle Lake
#1 Graph with y-axis as percent of total sites visited
#_________________________________________________________________________________________________
head(eagle)## Sample_Code Lake Taxa Year Year_TP Strain
## 239 MYR-7079 Bald_Eagle EWM 2018 2018 MC-E-1
## 240 MYR-7080 Bald_Eagle NWM 2018 2018 BE-N-2
## 241 MYR-7081 Bald_Eagle NWM 2018 2018 BE-N-2
## 242 MYR-7082 Bald_Eagle EWM 2018 2018 MC-E-1
## 243 MYR-7083 Bald_Eagle EWM 2018 2018 MC-E-1
## 244 MYR-7084 Bald_Eagle EWM 2018 2018 MC-E-1 eagle_counts <- eagle %>% count(Year_TP,Strain, Taxa)
eagle## Sample_Code Lake Taxa Year Year_TP Strain
## 239 MYR-7079 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 240 MYR-7080 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 241 MYR-7081 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 242 MYR-7082 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 243 MYR-7083 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 244 MYR-7084 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 245 MYR-7085 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 246 MYR-7086 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 247 MYR-7087 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 248 MYR-7088 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 249 MYR-7089 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 250 MYR-7090 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 251 MYR-7091 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 252 MYR-7092 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 253 MYR-7093 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 254 MYR-7094 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 255 MYR-7095 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 256 MYR-7096 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 257 MYR-7097 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 258 MYR-7098 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 259 MYR-7099 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 260 MYR-7100 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 261 MYR-7101 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 262 MYR-7102 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 263 MYR-7103 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 264 MYR-7104 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 265 MYR-7105 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 266 MYR-7106 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 267 MYR-7107 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 268 MYR-7108 Bald_Eagle NWM 2018 2018.0 BE-N-4
## 269 MYR-7110 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 270 MYR-7111 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 271 MYR-7112 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 272 MYR-7113 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 273 MYR-7114 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 274 MYR-7115 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 275 MYR-7116 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 276 MYR-7117 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 277 MYR-7118 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 278 MYR-7119 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 279 MYR-7120 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 280 MYR-7187 Bald_Eagle EWM 2018 2018.5 MC-E-1
## 281 MYR-7188 Bald_Eagle EWM 2018 2018.5 MC-E-1
## 282 MYR-7189 Bald_Eagle NWM 2018 2018.5 BE-N-2
## 283 MYR-7190 Bald_Eagle EWM 2018 2018.5 MC-E-1
## 284 MYR-7191 Bald_Eagle EWM 2018 2018.5 MC-E-1
## 285 MYR-7192 Bald_Eagle EWM 2018 2018.5 MC-E-1
## 286 MYR-7193 Bald_Eagle HWM 2018 2018.5 BE-H-3
## 287 MYR-7194 Bald_Eagle HWM 2018 2018.5 BE-H-3
## 288 MYR-7195 Bald_Eagle NWM 2018 2018.5 BE-N-4
## 289 MYR-7196 Bald_Eagle NWM 2018 2018.5 BE-N-2
## 290 MYR-7197 Bald_Eagle NWM 2018 2018.5 BE-N-2
## 291 MYR-7198 Bald_Eagle NWM 2018 2018.5 BE-N-2
## 292 MYR-7199 Bald_Eagle NWM 2018 2018.5 BE-N-2
## 293 MYR-7200 Bald_Eagle NWM 2018 2018.5 BE-N-2
## 294 MYR-7201 Bald_Eagle NWM 2018 2018.5 BE-N-2
## 295 MYR-7202 Bald_Eagle HWM 2018 2018.5 BE-H-3
## 296 MYR-7764 Bald_Eagle HWM 2019 2019.0 BE-H-3
## 297 MYR-7765 Bald_Eagle HWM 2019 2019.0 BE-H-3
## 298 MYR-7766 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 299 MYR-7767 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 300 MYR-7768 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 301 MYR-7769 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 302 MYR-7770 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 303 MYR-7771 Bald_Eagle HWM 2019 2019.0 BE-H-3
## 304 MYR-7773 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 305 MYR-7774 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 306 MYR-7775 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 307 MYR-7776 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 308 MYR-7777 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 309 MYR-7778 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 310 MYR-7779 Bald_Eagle HWM 2019 2019.0 BE-H-3
## 311 MYR-7781 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 312 MYR-7782 Bald_Eagle HWM 2019 2019.0 BE-H-3
## 313 MYR-7783 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 314 MYR-7784 Bald_Eagle NWM 2019 2019.0 BE-N-2
## 315 MYR-7848 Bald_Eagle NWM? 2019 2019.5 BE-N-6
## 316 MYR-7850 Bald_Eagle HWM 2019 2019.5 BE-H-3
## 317 MYR-7851 Bald_Eagle NWM 2019 2019.5 BE-N-2
## 318 MYR-7852 Bald_Eagle NWM 2019 2019.5 BE-N-2
## 319 MYR-7853 Bald_Eagle NWM 2019 2019.5 BE-N-2
## 320 MYR-7854 Bald_Eagle EWM 2019 2019.5 MC-E-1
## 321 MYR-7855 Bald_Eagle NWM 2019 2019.5 BE-N-2
## 322 MYR-7883 Bald_Eagle NWM 2019 2019.5 BE-N-2
## 323 MYR-7885 Bald_Eagle NWM 2019 2019.5 BE-N-2
## 324 MYR-7886 Bald_Eagle NWM 2019 2019.5 BE-N-2
## 325 MYR-7887 Bald_Eagle NWM 2019 2019.5 BE-N-2
## 326 MYR-7888 Bald_Eagle NWM 2019 2019.5 BE-N-2
## 327 MYR-7889 Bald_Eagle HWM 2019 2019.5 BE-H-3
## 328 MYR-7890 Bald_Eagle NWM 2019 2019.5 BE-N-2
## 329 MYR-7891 Bald_Eagle NWM 2019 2019.5 BE-N-2
## 330 MYR-7893 Bald_Eagle NWM 2019 2019.5 BE-N-2
## 331 MYR-8623 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 332 MYR-8624 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 333 MYR-8625 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 334 MYR-8626 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 335 MYR-8627 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 336 MYR-8628 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 337 MYR-8629 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 338 MYR-8630 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 339 MYR-8631 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 340 MYR-8632 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 341 MYR-8633 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 342 MYR-8634 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 343 MYR-8635 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 344 MYR-8636 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 345 MYR-8637 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 346 MYR-8638 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 347 MYR-8639 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 348 MYR-8640 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 349 MYR-8641 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 350 MYR-8642 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 351 MYR-8643 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 352 MYR-8644 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 353 MYR-8645 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 354 MYR-8646 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 355 MYR-8647 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 356 MYR-8648 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 357 MYR-8649 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 358 MYR-8650 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 359 MYR-8651 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 360 MYR-8652 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 361 MYR-8653 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 362 MYR-8654 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 363 MYR-8655 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 364 MYR-8656 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 365 MYR-8657 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 366 MYR-8658 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 367 MYR-8659 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 368 MYR-8660 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 369 MYR-8661 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 370 MYR-8662 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 371 MYR-8663 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 372 MYR-8664 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 373 MYR-8665 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 374 MYR-8666 Bald_Eagle EWM 2018 2018.0 MC-E-1
## 375 MYR-8667 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 376 MYR-8668 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 377 MYR-8669 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 378 MYR-8670 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 379 MYR-8671 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 380 MYR-8672 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 381 MYR-8673 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 382 MYR-8674 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 383 MYR-8675 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 384 MYR-8676 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 385 MYR-8677 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 386 MYR-8678 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 387 MYR-8679 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 388 MYR-8680 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 389 MYR-8681 Bald_Eagle NWM 2018 2018.0 BE-N-2
## 390 MYR-8682 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 391 MYR-8683 Bald_Eagle HWM 2018 2018.0 BE-H-3
## 392 MYR-9084 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 393 MYR-9085 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 394 MYR-9086 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 395 MYR-9087 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 396 MYR-9089 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 397 MYR-9091 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 398 MYR-9092 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 399 MYR-9093 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 400 MYR-9095 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 401 MYR-9096 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 402 MYR-9097 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 403 MYR-9098 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 404 MYR-9099 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 405 MYR-9101 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 406 MYR-9102 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 407 MYR-9104 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 408 MYR-9106 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 409 MYR-9109 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 410 MYR-9110 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 411 MYR-9111 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 412 MYR-9112 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 413 MYR-9113 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 414 MYR-9114 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 415 MYR-9116 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 416 MYR-9117 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 417 MYR-9118 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 418 MYR-9119 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 419 MYR-9120 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 420 MYR-9122 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 421 MYR-9123 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 422 MYR-9125 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 423 MYR-9126 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 424 MYR-9127 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 425 MYR-9129 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 426 MYR-9130 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 427 MYR-9131 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 428 MYR-9132 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 429 MYR-9134 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 430 MYR-9137 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 431 MYR-9138 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 432 MYR-9139 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 433 MYR-9140 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 434 MYR-9519 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 435 MYR-9520 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 436 MYR-9522 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 437 MYR-9523 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 438 MYR-9525 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 439 MYR-9526 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 440 MYR-9527 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 441 MYR-9528 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 442 MYR-9727 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 443 MYR-9729 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 444 MYR-9731 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 445 MYR-9732 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 446 MYR-9733 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 447 MYR-9734 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 448 MYR-9736 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 449 MYR-9737 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 450 MYR-9738 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 451 MYR-9739 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 452 MYR-9742 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 453 MYR-9744 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 454 MYR-9745 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 455 MYR-9746 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 456 MYR-9749 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 457 MYR-9750 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 458 MYR-9752 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 459 MYR-9753 Bald_Eagle NWM 2020 2020.0 BE-N-2
## 460 MYR-9755 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 461 MYR-9758 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 462 MYR-9759 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 463 MYR-9760 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 464 MYR-9761 Bald_Eagle NWM 2020 2020.5 BE-N-2
## 465 MYR-9088 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 466 MYR-9090 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 467 MYR-9094 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 468 MYR-9100 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 469 MYR-9103 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 470 MYR-9105 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 471 MYR-9115 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 472 MYR-9121 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 473 MYR-9124 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 474 MYR-9128 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 475 MYR-9133 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 476 MYR-9136 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 477 MYR-9141 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 478 MYR-9518 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 479 MYR-9521 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 480 MYR-9524 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 481 MYR-9529 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 482 MYR-9725 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 483 MYR-9726 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 484 MYR-9728 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 485 MYR-9740 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 486 MYR-9741 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 487 MYR-9743 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 488 MYR-9747 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 489 MYR-9748 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 490 MYR-9751 Bald_Eagle HWM 2020 2020.0 BE-H-3
## 491 MYR-9754 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 492 MYR-9756 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 493 MYR-9757 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 494 MYR-9762 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 495 MYR-9764 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 496 MYR-9765 Bald_Eagle HWM 2020 2020.5 BE-H-3
## 497 MYR-9730 Bald_Eagle NWM 2020 2020.0 BE-N-4
## 498 MYR-9735 Bald_Eagle NWM 2020 2020.0 BE-N-4
## 499 MYR-9108 Bald_Eagle NWM? 2020 2020.5 BE-N-5
## 500 MYR-9763 Bald_Eagle NWM? 2020 2020.5 BE-N-5
## 501 MYR-9135 Bald_Eagle NWM? 2020 2020.0 BE-N-6 eagle_counts$Percent_Ocupied <- ((eagle_counts$n)/151)*100
eagle_plot_Percent_Ocupied <- eagle_counts %>%
ggplot()+geom_line(aes(x=Year_TP,y=Percent_Ocupied,col=Strain))+geom_point(aes(x=Year_TP,y=Percent_Ocupied, col=Strain), size = 2)
eagle_PO_annotated <- print(eagle_plot_Percent_Ocupied +
ggtitle("A. Bald Eagle") +
theme_light() +
geom_vline(xintercept = 2018.25, linetype="dashed", color = "red") +
guides(col=guide_legend(ncol=1,byrow=TRUE)) +
labs(y="Percent of Sampled Sites (%)", x = "Year") ) 
#2) CHI SQUARED ANALYSIS WITHIN YEAR
#_________________________________________________________________________________________________
set.seed(1025)
head(eagle)## Sample_Code Lake Taxa Year Year_TP Strain
## 239 MYR-7079 Bald_Eagle EWM 2018 2018 MC-E-1
## 240 MYR-7080 Bald_Eagle NWM 2018 2018 BE-N-2
## 241 MYR-7081 Bald_Eagle NWM 2018 2018 BE-N-2
## 242 MYR-7082 Bald_Eagle EWM 2018 2018 MC-E-1
## 243 MYR-7083 Bald_Eagle EWM 2018 2018 MC-E-1
## 244 MYR-7084 Bald_Eagle EWM 2018 2018 MC-E-1 eagle$Strain <- as.factor(eagle$Strain)
eagle$Year_TP_F <- as.factor(eagle$Year_TP)
eagle$Year_F <- as.factor(eagle$Year)
eagle_2018 <- eagle[eagle$Year == 2018 , ]
eagle_2019 <- eagle[eagle$Year == 2019 , ]
eagle_2020 <- eagle[eagle$Year == 2020 , ]
#CHI SQUARED 2018
eagle_chi_geno_2018 <- chisq.test(eagle_2018$Strain, eagle_2018$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
eagle_chi_geno_2018$p.value## [1] 0.133933 #CHI SQUARED 2019
eagle_chi_geno_2019 <- chisq.test(eagle_2019$Strain, eagle_2019$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
eagle_chi_geno_2019$p.value## [1] 0.3558221 #CHI SQUARED 2020
eagle_chi_geno_2020 <- chisq.test(eagle_2020$Strain, eagle_2020$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
eagle_chi_geno_2020$p.value## [1] 0.2648676 #3) CHI SQUARED ANALYSIS BTWN YEARS
#_________________________________________________________________________________________________
head(eagle)## Sample_Code Lake Taxa Year Year_TP Strain Year_TP_F Year_F
## 239 MYR-7079 Bald_Eagle EWM 2018 2018 MC-E-1 2018 2018
## 240 MYR-7080 Bald_Eagle NWM 2018 2018 BE-N-2 2018 2018
## 241 MYR-7081 Bald_Eagle NWM 2018 2018 BE-N-2 2018 2018
## 242 MYR-7082 Bald_Eagle EWM 2018 2018 MC-E-1 2018 2018
## 243 MYR-7083 Bald_Eagle EWM 2018 2018 MC-E-1 2018 2018
## 244 MYR-7084 Bald_Eagle EWM 2018 2018 MC-E-1 2018 2018 tally(~Year_F, data = eagle)## Year_F
## 2018 2019 2020
## 118 35 110 eagle$Strain <- as.factor(eagle$Strain)
eagle$Year_TP_F <- as.factor(eagle$Year_TP_F)
eagle$Year_F <- as.factor(eagle$Year)
time_one <- c("2018", "2019", "2020")
eagle_btwn <- eagle[eagle$Year_TP_F %in% time_one , ]
eagle_btwn <- droplevels(eagle_btwn)
tally(~Year_TP_F, data = eagle_btwn)## Year_TP_F
## 2018 2019 2020
## 102 19 59 eagle_btwn_18_19 <-eagle_btwn[eagle_btwn$Year == 2018 | eagle_btwn$Year == 2019 , ]
eagle_btwn_18_19 <- droplevels(eagle_btwn_18_19)
tally(~Year, data = eagle_btwn_18_19)## Year
## 2018 2019
## 102 19 eagle_btwn_18_19$Strain <- as.factor(eagle_btwn_18_19$Strain)
eagle_btwn_18_19$Year_F <- as.factor(eagle_btwn_18_19$Year)
eagle_btwn_19_20 <-eagle_btwn[eagle_btwn$Year == 2019 | eagle_btwn$Year == 2020 , ]
eagle_btwn_19_20 <- droplevels(eagle_btwn_19_20)
tally(~Year_TP_F, data = eagle_btwn_19_20)## Year_TP_F
## 2019 2020
## 19 59 eagle_btwn_19_20$Strain <- as.factor(eagle_btwn_19_20$Strain)
eagle_btwn_19_20$Year_F <- as.factor(eagle_btwn_19_20$Year)
#CHI SQUARED COMPARING 2018 TO 2019
eagle_chi_btwn_18_19 <- chisq.test(eagle_btwn_18_19$Strain, eagle_btwn_18_19$Year_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
eagle_chi_btwn_18_19$p.value## [1] 0.0009995002 #CHI SQUARED COMPARING 2019 TO 2020
eagle_chi_btwn_19_20 <- chisq.test(eagle_btwn_19_20$Strain, eagle_btwn_19_20$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
eagle_chi_btwn_19_20$p.value## [1] 1 #4) ALL THREE YEARS (first t1 to last t2)
#_________________________________________________________________________________________________
head(eagle)## Sample_Code Lake Taxa Year Year_TP Strain Year_TP_F Year_F
## 239 MYR-7079 Bald_Eagle EWM 2018 2018 MC-E-1 2018 2018
## 240 MYR-7080 Bald_Eagle NWM 2018 2018 BE-N-2 2018 2018
## 241 MYR-7081 Bald_Eagle NWM 2018 2018 BE-N-2 2018 2018
## 242 MYR-7082 Bald_Eagle EWM 2018 2018 MC-E-1 2018 2018
## 243 MYR-7083 Bald_Eagle EWM 2018 2018 MC-E-1 2018 2018
## 244 MYR-7084 Bald_Eagle EWM 2018 2018 MC-E-1 2018 2018 tally(~Year_TP_F, data = eagle)## Year_TP_F
## 2018 2018.5 2019 2019.5 2020 2020.5
## 102 16 19 16 59 51 list_three_yr <- c( 2018, 2020.5 )
eagle_three_yr_2018 <- as.data.frame(eagle[eagle$Year_TP_F == 2018 , ])
eagle_three_yr_2020 <- as.data.frame(eagle[eagle$Year_TP_F == 2020.5 , ])
eagle_three_yr <- rbind(eagle_three_yr_2018, eagle_three_yr_2020)
eagle_chi_three_yr <- chisq.test(eagle_three_yr$Strain, eagle_three_yr$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
eagle_chi_three_yr##
## Pearson's Chi-squared test with simulated p-value (based on 2000
## replicates)
##
## data: eagle_three_yr$Strain and eagle_three_yr$Year_TP_F
## X-squared = 34.386, df = NA, p-value = 0.0004998Analyze Christmas Lake
#1 Graph with y-axis as percent of total sites visited
#_________________________________________________________________________________________________
head(christmas)## Sample_Code Lake Taxa Year Year_TP Strain
## 1 MYR-6915 Christmas EWM 2018 2018 MC-E-1
## 2 MYR-6916 Christmas EWM 2018 2018 MC-E-1
## 3 MYR-6917 Christmas NWM 2018 2018 CH-N-1
## 4 MYR-6918 Christmas EWM 2018 2018 MC-E-1
## 5 MYR-6919 Christmas NWM 2018 2018 CH-N-1
## 6 MYR-6920 Christmas NWM 2018 2018 CH-N-1 christmas_counts <- christmas %>% count(Year_TP,Strain, Taxa)
christmas_counts## Year_TP Strain Taxa n
## 1 2018.0 CH-N-1 NWM 18
## 2 2018.0 CH-N-3 NWM 2
## 3 2018.0 MC-E-1 EWM 41
## 4 2018.5 CH-N-1 NWM 19
## 5 2018.5 CH-N-3 NWM 3
## 6 2018.5 MC-E-1 EWM 21
## 7 2019.0 CH-N-1 NWM 19
## 8 2019.0 MC-E-1 EWM 20
## 9 2019.5 CH-N-1 NWM 26
## 10 2019.5 CH-N-3 NWM 4
## 11 2019.5 MC-E-1 EWM 13
## 12 2020.0 CH-N-1 NWM 36
## 13 2020.0 CH-N-3 NWM 2
## 14 2020.0 MC-E-1 EWM 14 christmas_counts$Percent_Ocupied <- ((christmas_counts$n)/113)*100
christmas_plot_Percent_Ocupied <- christmas_counts %>%
ggplot()+geom_line(aes(x=Year_TP,y=Percent_Ocupied,col=Strain))+geom_point(aes(x=Year_TP,y=Percent_Ocupied, col=Strain), size = 2)
Christmas_PO_annotated <- print(christmas_plot_Percent_Ocupied +
ggtitle("H. Christmas") +
theme_light() +
ylim(0, 40) +
guides(col=guide_legend(ncol=1,byrow=TRUE)) +
labs(y="Percent of Sampled Sites (%)", x = "Year") ) 
Christmas_PO_annotated
#2) CHI SQUARED ANALYSIS WITHIN YEAR
#_________________________________________________________________________________________________
set.seed(1025)
head(christmas)## Sample_Code Lake Taxa Year Year_TP Strain
## 1 MYR-6915 Christmas EWM 2018 2018 MC-E-1
## 2 MYR-6916 Christmas EWM 2018 2018 MC-E-1
## 3 MYR-6917 Christmas NWM 2018 2018 CH-N-1
## 4 MYR-6918 Christmas EWM 2018 2018 MC-E-1
## 5 MYR-6919 Christmas NWM 2018 2018 CH-N-1
## 6 MYR-6920 Christmas NWM 2018 2018 CH-N-1 christmas$Strain <- as.factor(christmas$Strain)
christmas$Year_TP_F <- as.factor(christmas$Year_TP)
christmas$Year_F <- as.factor(christmas$Year)
christmas_2018 <- christmas[christmas$Year == 2018 , ]
christmas_2019 <- christmas[christmas$Year == 2019 , ]
christmas_2020 <- christmas[christmas$Year == 2020 , ]
#CHI SQUARED 2018
head(christmas)## Sample_Code Lake Taxa Year Year_TP Strain Year_TP_F Year_F
## 1 MYR-6915 Christmas EWM 2018 2018 MC-E-1 2018 2018
## 2 MYR-6916 Christmas EWM 2018 2018 MC-E-1 2018 2018
## 3 MYR-6917 Christmas NWM 2018 2018 CH-N-1 2018 2018
## 4 MYR-6918 Christmas EWM 2018 2018 MC-E-1 2018 2018
## 5 MYR-6919 Christmas NWM 2018 2018 CH-N-1 2018 2018
## 6 MYR-6920 Christmas NWM 2018 2018 CH-N-1 2018 2018 christmas$Year_TP_F <- as.factor(christmas$Year_TP)
christmas_chi_geno_2018 <- chisq.test(christmas_2018$Strain, christmas_2018$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
christmas_chi_geno_2018$p.value## [1] 0.183908 #CHI SQUARED 2019
christmas_chi_geno_2019 <- chisq.test(christmas_2019$Strain, christmas_2019$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
christmas_chi_geno_2019$p.value## [1] 0.04197901 #3) CHI SQUARED ANALYSIS BTWN YEARS
#_________________________________________________________________________________________________
head(christmas)## Sample_Code Lake Taxa Year Year_TP Strain Year_TP_F Year_F
## 1 MYR-6915 Christmas EWM 2018 2018 MC-E-1 2018 2018
## 2 MYR-6916 Christmas EWM 2018 2018 MC-E-1 2018 2018
## 3 MYR-6917 Christmas NWM 2018 2018 CH-N-1 2018 2018
## 4 MYR-6918 Christmas EWM 2018 2018 MC-E-1 2018 2018
## 5 MYR-6919 Christmas NWM 2018 2018 CH-N-1 2018 2018
## 6 MYR-6920 Christmas NWM 2018 2018 CH-N-1 2018 2018 tally(~Year_F, data = christmas)## Year_F
## 2018 2019 2020
## 104 82 52 christmas$Strain <- as.factor(christmas$Strain)
christmas$Year_TP_F <- as.factor(christmas$Year_TP_F)
christmas$Year_F <- as.factor(christmas$Year)
time_one <- c("2018", "2019", "2020")
christmas_btwn <- christmas[christmas$Year_TP_F %in% time_one , ]
christmas_btwn <- droplevels(christmas_btwn)
tally(~Year_TP_F, data = christmas_btwn)## Year_TP_F
## 2018 2019 2020
## 61 39 52 christmas_btwn_18_19 <-christmas_btwn[christmas_btwn$Year == 2018 | christmas_btwn$Year == 2019 , ]
christmas_btwn_18_19 <- droplevels(christmas_btwn_18_19)
tally(~Year, data = christmas_btwn_18_19)## Year
## 2018 2019
## 61 39 christmas_btwn_18_19$Strain <- as.factor(christmas_btwn_18_19$Strain)
christmas_btwn_18_19$Year_F <- as.factor(christmas_btwn_18_19$Year)
christmas_btwn_19_20 <-christmas_btwn[christmas_btwn$Year == 2019 | christmas_btwn$Year == 2020 , ]
christmas_btwn_19_20 <- droplevels(christmas_btwn_19_20)
tally(~Year_TP_F, data = christmas_btwn_19_20)## Year_TP_F
## 2019 2020
## 39 52 christmas_btwn_19_20$Strain <- as.factor(christmas_btwn_19_20$Strain)
christmas_btwn_19_20$Year_F <- as.factor(christmas_btwn_19_20$Year)
#CHI SQUARED COMPARING 2018 TO 2019
christmas_chi_btwn_18_19 <- chisq.test(christmas_btwn_18_19$Strain, christmas_btwn_18_19$Year_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
christmas_chi_btwn_18_19$p.value## [1] 0.07846077 #CHI SQUARED COMPARING 2019 TO 2020
christmas_chi_btwn_19_20 <- chisq.test(christmas_btwn_19_20$Strain, christmas_btwn_19_20$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
christmas_chi_btwn_19_20$p.value## [1] 0.01999 #4) ALL THREE YEARS (first t1 to last t2)
#_________________________________________________________________________________________________
head(christmas)## Sample_Code Lake Taxa Year Year_TP Strain Year_TP_F Year_F
## 1 MYR-6915 Christmas EWM 2018 2018 MC-E-1 2018 2018
## 2 MYR-6916 Christmas EWM 2018 2018 MC-E-1 2018 2018
## 3 MYR-6917 Christmas NWM 2018 2018 CH-N-1 2018 2018
## 4 MYR-6918 Christmas EWM 2018 2018 MC-E-1 2018 2018
## 5 MYR-6919 Christmas NWM 2018 2018 CH-N-1 2018 2018
## 6 MYR-6920 Christmas NWM 2018 2018 CH-N-1 2018 2018 tally(~Year_TP_F, data = christmas)## Year_TP_F
## 2018 2018.5 2019 2019.5 2020
## 61 43 39 43 52 christmas_three_yr_2018 <- as.data.frame(christmas[christmas$Year_TP_F == 2018 , ])
christmas_three_yr_2020 <- as.data.frame(christmas[christmas$Year_TP_F == 2020 , ])
christmas_three_yr <- rbind(christmas_three_yr_2018, christmas_three_yr_2020)
christmas_chi_three_yr <- chisq.test(christmas_three_yr$Strain, christmas_three_yr$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
christmas_chi_three_yr$p.value## [1] 0.0004997501Analyze Grays Bay of Lake Minnetonka
#1 Graph with y-axis as percent of total sites visited
#_________________________________________________________________________________________________
head(grays_bay)## Sample_Code Lake Taxa Year Year_TP Strain
## 502 MYR-7276 Grays_Bay HWM 2018 2018 MC-H-7
## 503 MYR-7283 Grays_Bay HWM 2018 2018 MC-H-7
## 504 MYR-7284 Grays_Bay HWM 2018 2018 MC-H-7
## 505 MYR-7288 Grays_Bay HWM 2018 2018 MC-H-7
## 506 MYR-7290 Grays_Bay HWM 2018 2018 MC-H-7
## 507 MYR-7295 Grays_Bay HWM 2018 2018 MC-H-7 #grays_bay$Year_TP <- grays_bay$Time_Point
grays_bay_counts <- grays_bay %>% count(Year_TP,Strain, Taxa)
grays_bay_counts## Year_TP Strain Taxa n
## 1 2018.0 MC-H-12 HWM 13
## 2 2018.0 MC-H-136 HWM 1
## 3 2018.0 MC-H-137 HWM 2
## 4 2018.0 MC-H-138 HWM 1
## 5 2018.0 MC-H-7 HWM 6
## 6 2018.5 MC-H-12 HWM 19
## 7 2018.5 MC-H-136 HWM 1
## 8 2018.5 MC-H-137 HWM 4
## 9 2018.5 MC-H-138 HWM 3
## 10 2018.5 MC-H-7 HWM 4
## 11 2019.0 MC-H-12 HWM 30
## 12 2019.0 MC-H-136 HWM 1
## 13 2019.0 MC-H-137 HWM 4
## 14 2019.0 MC-H-7 HWM 7
## 15 2019.5 MC-H-12 HWM 31
## 16 2019.5 MC-H-136 HWM 1
## 17 2019.5 MC-H-137 HWM 3
## 18 2019.5 MC-H-7 HWM 9
## 19 2020.0 MC-H-12 HWM 49
## 20 2020.0 MC-H-136 HWM 2
## 21 2020.0 MC-H-137 HWM 4
## 22 2020.0 MC-H-138 HWM 1
## 23 2020.0 MC-H-7 HWM 10
## 24 2020.5 MC-H-12 HWM 18
## 25 2020.5 MC-H-138 HWM 2
## 26 2020.5 MC-H-7 HWM 2 grays_bay_counts$Percent_Ocupied <- ((grays_bay_counts$n)/125)*100
grays_bay_counts## Year_TP Strain Taxa n Percent_Ocupied
## 1 2018.0 MC-H-12 HWM 13 10.4
## 2 2018.0 MC-H-136 HWM 1 0.8
## 3 2018.0 MC-H-137 HWM 2 1.6
## 4 2018.0 MC-H-138 HWM 1 0.8
## 5 2018.0 MC-H-7 HWM 6 4.8
## 6 2018.5 MC-H-12 HWM 19 15.2
## 7 2018.5 MC-H-136 HWM 1 0.8
## 8 2018.5 MC-H-137 HWM 4 3.2
## 9 2018.5 MC-H-138 HWM 3 2.4
## 10 2018.5 MC-H-7 HWM 4 3.2
## 11 2019.0 MC-H-12 HWM 30 24.0
## 12 2019.0 MC-H-136 HWM 1 0.8
## 13 2019.0 MC-H-137 HWM 4 3.2
## 14 2019.0 MC-H-7 HWM 7 5.6
## 15 2019.5 MC-H-12 HWM 31 24.8
## 16 2019.5 MC-H-136 HWM 1 0.8
## 17 2019.5 MC-H-137 HWM 3 2.4
## 18 2019.5 MC-H-7 HWM 9 7.2
## 19 2020.0 MC-H-12 HWM 49 39.2
## 20 2020.0 MC-H-136 HWM 2 1.6
## 21 2020.0 MC-H-137 HWM 4 3.2
## 22 2020.0 MC-H-138 HWM 1 0.8
## 23 2020.0 MC-H-7 HWM 10 8.0
## 24 2020.5 MC-H-12 HWM 18 14.4
## 25 2020.5 MC-H-138 HWM 2 1.6
## 26 2020.5 MC-H-7 HWM 2 1.6 grays_bay_plot_Percent_Ocupied <- grays_bay_counts %>%
ggplot()+geom_line(aes(x=Year_TP,y=Percent_Ocupied,col=Strain))+geom_point(aes(x=Year_TP,y=Percent_Ocupied, col=Strain), size = 2)
grays_PO_annotated <- print(grays_bay_plot_Percent_Ocupied +
ggtitle("B. Grays Bay") +
theme_light() +
geom_vline(xintercept = 2018.25, linetype="dashed", color = "red") +
geom_vline(xintercept = 2019.25, linetype="dashed", color = "red") +
geom_vline(xintercept = 2020.25, linetype="dashed", color = "red") +
guides(col=guide_legend(ncol=1,byrow=TRUE)) +
labs(y="Percent of Sampled Sites (%)", x = "Year") ) 
grays_PO_annotated
#2) CHI SQUARED ANALYSIS WITHIN YEAR
#_________________________________________________________________________________________________
head(grays_bay)## Sample_Code Lake Taxa Year Year_TP Strain
## 502 MYR-7276 Grays_Bay HWM 2018 2018 MC-H-7
## 503 MYR-7283 Grays_Bay HWM 2018 2018 MC-H-7
## 504 MYR-7284 Grays_Bay HWM 2018 2018 MC-H-7
## 505 MYR-7288 Grays_Bay HWM 2018 2018 MC-H-7
## 506 MYR-7290 Grays_Bay HWM 2018 2018 MC-H-7
## 507 MYR-7295 Grays_Bay HWM 2018 2018 MC-H-7 grays_bay$Genotype <- as.factor(grays_bay$Strain)
grays_bay$Year_TP_F <- as.factor(grays_bay$Year_TP)
grays_bay$Year_F <- as.factor(grays_bay$Year)
grays_bay_2018 <- grays_bay[grays_bay$Year == 2018 , ]
grays_bay_2019 <- grays_bay[grays_bay$Year == 2019 , ]
grays_bay_2020 <- grays_bay[grays_bay$Year == 2020 , ]
grays_bay$Year_TP_F <- as.factor(grays_bay$Year_TP)
#CHI SQUARED 2018
grays_bay_chi_geno_2018 <- chisq.test(grays_bay_2018$Strain, grays_bay_2018$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
grays_bay_chi_geno_2018$p.value## [1] 0.7876062 #CHI SQUARED 2019
grays_bay_chi_geno_2019 <- chisq.test(grays_bay_2019$Strain, grays_bay_2019$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
grays_bay_chi_geno_2019$p.value## [1] 0.9365317 #CHI SQUARED 2020
grays_bay_chi_geno_2020 <- chisq.test(grays_bay_2020$Strain, grays_bay_2020$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
grays_bay_chi_geno_2020$p.value## [1] 0.2183908 #3) CHI SQUARED ANALYSIS BTWN YEARS
#_________________________________________________________________________________________________
head(grays_bay)## Sample_Code Lake Taxa Year Year_TP Strain Genotype Year_TP_F Year_F
## 502 MYR-7276 Grays_Bay HWM 2018 2018 MC-H-7 MC-H-7 2018 2018
## 503 MYR-7283 Grays_Bay HWM 2018 2018 MC-H-7 MC-H-7 2018 2018
## 504 MYR-7284 Grays_Bay HWM 2018 2018 MC-H-7 MC-H-7 2018 2018
## 505 MYR-7288 Grays_Bay HWM 2018 2018 MC-H-7 MC-H-7 2018 2018
## 506 MYR-7290 Grays_Bay HWM 2018 2018 MC-H-7 MC-H-7 2018 2018
## 507 MYR-7295 Grays_Bay HWM 2018 2018 MC-H-7 MC-H-7 2018 2018 tally(~Year_F, data = grays_bay)## Year_F
## 2018 2019 2020
## 54 86 88 grays_bay$Strain <- as.factor(grays_bay$Strain)
grays_bay$Year_TP_F <- as.factor(grays_bay$Year_TP_F)
grays_bay$Year_F <- as.factor(grays_bay$Year)
time_one <- c("2018", "2019", "2020")
grays_bay_btwn <- grays_bay[grays_bay$Year_TP_F %in% time_one , ]
grays_bay_btwn <- droplevels(grays_bay_btwn)
tally(~Year_TP_F, data = grays_bay_btwn)## Year_TP_F
## 2018 2019 2020
## 23 42 66 grays_bay_btwn_18_19 <-grays_bay_btwn[grays_bay_btwn$Year == 2018 | grays_bay_btwn$Year == 2019 , ]
grays_bay_btwn_18_19 <- droplevels(grays_bay_btwn_18_19)
tally(~Year, data = grays_bay_btwn_18_19)## Year
## 2018 2019
## 23 42 grays_bay_btwn_18_19$Strain <- as.factor(grays_bay_btwn_18_19$Strain)
grays_bay_btwn_18_19$Year_F <- as.factor(grays_bay_btwn_18_19$Year)
grays_bay_btwn_19_20 <-grays_bay_btwn[grays_bay_btwn$Year == 2019 | grays_bay_btwn$Year == 2020 , ]
grays_bay_btwn_19_20 <- droplevels(grays_bay_btwn_19_20)
tally(~Year_TP_F, data = grays_bay_btwn_19_20)## Year_TP_F
## 2019 2020
## 42 66 grays_bay_btwn_19_20$Strain <- as.factor(grays_bay_btwn_19_20$Strain)
grays_bay_btwn_19_20$Year_F <- as.factor(grays_bay_btwn_19_20$Year)
#CHI SQUARED COMPARING 2018 TO 2019
grays_bay_chi_btwn_18_19 <- chisq.test(grays_bay_btwn_18_19$Strain, grays_bay_btwn_18_19$Year_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
grays_bay_chi_btwn_18_19$p.value## [1] 0.5682159 #CHI SQUARED COMPARING 2019 TO 2020
grays_bay_chi_btwn_19_20 <- chisq.test(grays_bay_btwn_19_20$Strain, grays_bay_btwn_19_20$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
grays_bay_chi_btwn_19_20$p.value## [1] 0.9635182 #4) ALL THREE YEARS (first t1 to last t2)
#_________________________________________________________________________________________________
head(grays_bay)## Sample_Code Lake Taxa Year Year_TP Strain Genotype Year_TP_F Year_F
## 502 MYR-7276 Grays_Bay HWM 2018 2018 MC-H-7 MC-H-7 2018 2018
## 503 MYR-7283 Grays_Bay HWM 2018 2018 MC-H-7 MC-H-7 2018 2018
## 504 MYR-7284 Grays_Bay HWM 2018 2018 MC-H-7 MC-H-7 2018 2018
## 505 MYR-7288 Grays_Bay HWM 2018 2018 MC-H-7 MC-H-7 2018 2018
## 506 MYR-7290 Grays_Bay HWM 2018 2018 MC-H-7 MC-H-7 2018 2018
## 507 MYR-7295 Grays_Bay HWM 2018 2018 MC-H-7 MC-H-7 2018 2018 tally(~Year_TP_F, data = grays_bay)## Year_TP_F
## 2018 2018.5 2019 2019.5 2020 2020.5
## 23 31 42 44 66 22 grays_bay_three_yr_2018 <- as.data.frame(grays_bay[grays_bay$Year_TP_F == 2018 , ])
grays_bay_three_yr_2020 <- as.data.frame(grays_bay[grays_bay$Year_TP_F == 2020.5 , ])
grays_bay_three_yr <- rbind(grays_bay_three_yr_2018, grays_bay_three_yr_2020)
grays_bay_chi_three_yr <- chisq.test(grays_bay_three_yr$Strain, grays_bay_three_yr$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
grays_bay_chi_three_yr##
## Pearson's Chi-squared test with simulated p-value (based on 2000
## replicates)
##
## data: grays_bay_three_yr$Strain and grays_bay_three_yr$Year_TP_F
## X-squared = 6.1206, df = NA, p-value = 0.1639Analyze Ham Lake
#1 Graph with y-axis as percent of total sites visited
#_________________________________________________________________________________________________
names(ham)## [1] "Sample_Code" "Lake" "Taxa" "Year" "Year_TP"
## [6] "Strain" ham_counts <- ham %>% count(Year_TP,Strain, Taxa)
ham_counts## Year_TP Strain Taxa n
## 1 2018.0 HM-H-14 HWM 31
## 2 2018.0 HM-N-15 NWM 3
## 3 2018.5 HM-H-14 HWM 48
## 4 2018.5 HM-N-15 NWM 1
## 5 2019.0 HM-H-14 HWM 6
## 6 2019.0 HM-N-15 NWM 6
## 7 2019.5 HM-H-14 HWM 18
## 8 2019.5 HM-N-15 NWM 6
## 9 2020.0 HM-H-14 HWM 17
## 10 2020.0 HM-N-15 NWM 5
## 11 2020.5 HM-H-14 HWM 42
## 12 2020.5 HM-N-15 NWM 9 is.data.frame(ham_counts)## [1] TRUE ham_counts$Percent_Ocupied <- ((ham_counts$n)/147)*100
ham_counts$Strain <- ham_counts$Strain
ham_plot_Percent_Ocupied <- ham_counts %>%
ggplot()+geom_line(aes(x=Year_TP,y=Percent_Ocupied,col=Strain))+geom_point(aes(x=Year_TP,y=Percent_Ocupied, col=Strain), size = 2)
Ham_PO_annotated <- print(ham_plot_Percent_Ocupied +
ggtitle("C. Ham") +
theme_light() +
geom_vline(xintercept = 2018.65, linetype="dashed", color = "red") +
guides(col=guide_legend(ncol=1,byrow=TRUE)) +
labs(y="Percent of Sampled Sites (%)", x = "Year") ) 
Ham_PO_annotated
#2) CHI SQUARED ANALYSIS WITHIN YEAR
#_________________________________________________________________________________________________
set.seed(1025)
head(ham)## Sample_Code Lake Taxa Year Year_TP Strain
## 730 MYR-7166 Ham HWM 2018 2018.5 HM-H-14
## 731 MYR-7167 Ham HWM 2018 2018.5 HM-H-14
## 732 MYR-7168 Ham HWM 2018 2018.5 HM-H-14
## 733 MYR-7169 Ham HWM 2018 2018.5 HM-H-14
## 734 MYR-7170 Ham HWM 2018 2018.5 HM-H-14
## 735 MYR-7171 Ham HWM 2018 2018.5 HM-H-14 ham$Strain <- as.factor(ham$Strain)
ham$Year_TP_F <- as.factor(ham$Year_TP)
ham$Year_F <- as.factor(ham$Year)
ham_2018 <- ham[ham$Year == 2018 , ]
ham_2019 <- ham[ham$Year == 2019 , ]
ham_2020 <- ham[ham$Year == 2020 , ]
#CHI SQUARED 2018
ham_chi_geno_2018 <- chisq.test(ham_2018$Strain, ham_2018$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
ham_chi_geno_2018$p.value## [1] 0.2823588 #CHI SQUARED 2019
ham_chi_geno_2019 <- chisq.test(ham_2019$Strain, ham_2019$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
ham_chi_geno_2019$p.value## [1] 0.2543728 #CHI SQUARED 2020
ham_chi_geno_2020 <- chisq.test(ham_2020$Strain, ham_2020$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
ham_chi_geno_2020$p.value## [1] 0.7551224 #3) CHI SQUARED ANALYSIS BTWN YEARS
#_________________________________________________________________________________________________
head(ham)## Sample_Code Lake Taxa Year Year_TP Strain Year_TP_F Year_F
## 730 MYR-7166 Ham HWM 2018 2018.5 HM-H-14 2018.5 2018
## 731 MYR-7167 Ham HWM 2018 2018.5 HM-H-14 2018.5 2018
## 732 MYR-7168 Ham HWM 2018 2018.5 HM-H-14 2018.5 2018
## 733 MYR-7169 Ham HWM 2018 2018.5 HM-H-14 2018.5 2018
## 734 MYR-7170 Ham HWM 2018 2018.5 HM-H-14 2018.5 2018
## 735 MYR-7171 Ham HWM 2018 2018.5 HM-H-14 2018.5 2018 tally(~Year_F, data = ham)## Year_F
## 2018 2019 2020
## 83 36 73 ham$Strain <- as.factor(ham$Strain)
ham$Year_TP_F <- as.factor(ham$Year_TP_F)
ham$Year_F <- as.factor(ham$Year)
time_one <- c("2018", "2019", "2020")
ham_btwn <- ham[ham$Year_TP_F %in% time_one , ]
ham_btwn <- droplevels(ham_btwn)
tally(~Year_TP_F, data = ham_btwn)## Year_TP_F
## 2018 2019 2020
## 34 12 22 ham_btwn_18_19 <-ham_btwn[ham_btwn$Year == 2018 | ham_btwn$Year == 2019 , ]
ham_btwn_18_19 <- droplevels(ham_btwn_18_19)
tally(~Year, data = ham_btwn_18_19)## Year
## 2018 2019
## 34 12 ham_btwn_18_19$Strain <- as.factor(ham_btwn_18_19$Strain)
ham_btwn_18_19$Year_F <- as.factor(ham_btwn_18_19$Year)
ham_btwn_19_20 <-ham_btwn[ham_btwn$Year == 2019 | ham_btwn$Year == 2020 , ]
ham_btwn_19_20 <- droplevels(ham_btwn_19_20)
tally(~Year_TP_F, data = ham_btwn_19_20)## Year_TP_F
## 2019 2020
## 12 22 ham_btwn_19_20$Strain <- as.factor(ham_btwn_19_20$Strain)
ham_btwn_19_20$Year_F <- as.factor(ham_btwn_19_20$Year)
#CHI SQUARED COMPARING 2018 TO 2019
ham_chi_btwn_18_19 <- chisq.test(ham_btwn_18_19$Strain, ham_btwn_18_19$Year_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
ham_chi_btwn_18_19$p.value## [1] 0.008495752 #CHI SQUARED COMPARING 2019 TO 2020
ham_chi_btwn_19_20 <- chisq.test(ham_btwn_19_20$Strain, ham_btwn_19_20$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
ham_chi_btwn_19_20$p.value## [1] 0.1414293 #4) ALL THREE YEARS (first t1 to last t2)
#_________________________________________________________________________________________________
ham_three_yr_2018 <- as.data.frame(ham[ham$Year_TP_F == 2018 , ])
ham_three_yr_2020 <- as.data.frame(ham[ham$Year_TP_F == 2020.5 , ])
ham_three_yr <- rbind(ham_three_yr_2018, ham_three_yr_2020)
ham_chi_three_yr <- chisq.test(ham_three_yr$Strain, ham_three_yr$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
ham_chi_three_yr##
## Pearson's Chi-squared test with simulated p-value (based on 2000
## replicates)
##
## data: ham_three_yr$Strain and ham_three_yr$Year_TP_F
## X-squared = 1.3099, df = NA, p-value = 0.3313Analyze Independence Lake
#1 Graph with y-axis as percent of total sites visited
#_________________________________________________________________________________________________
head(indp)## Sample_Code Lake Taxa Year Year_TP Strain
## 922 MYR-6855 Independence HWM 2018 2018 IN-H-99
## 923 MYR-6856 Independence HWM 2018 2018 IN-H-99
## 924 MYR-6857 Independence HWM 2018 2018 IN-H-99
## 925 MYR-6858 Independence HWM 2018 2018 IN-H-99
## 926 MYR-6859 Independence HWM 2018 2018 IN-H-99
## 927 MYR-6860 Independence HWM 2018 2018 IN-H-99 indp_counts <- indp %>% count(Year_TP,Strain, Taxa)
indp_counts## Year_TP Strain Taxa n
## 1 2018.0 IN-H-99 HWM 22
## 2 2018.0 MC-E-1 EWM 25
## 3 2018.5 IN-H-99 HWM 24
## 4 2018.5 MC-E-1 EWM 22
## 5 2019.0 IN-H-99 HWM 15
## 6 2019.0 MC-E-1 EWM 10
## 7 2019.5 IN-H-99 HWM 14
## 8 2019.5 MC-E-1 EWM 10
## 9 2020.0 IN-H-99 HWM 31
## 10 2020.0 MC-E-1 EWM 11
## 11 2020.5 IN-H-99 HWM 18
## 12 2020.5 MC-E-1 EWM 12 is.data.frame(indp_counts)## [1] TRUE indp_counts$Percent_Ocupied <- ((indp_counts$n)/198)*100
indp_counts$Strain <- indp_counts$Strain
indp_plot_Percent_Ocupied <- indp_counts %>%
ggplot()+geom_line(aes(x=Year_TP,y=Percent_Ocupied,col=Strain))+geom_point(aes(x=Year_TP,y=Percent_Ocupied, col=Strain), size = 2)
indp_PO_annotated <- print(indp_plot_Percent_Ocupied +
ggtitle("G. Independence") +
theme_light() +
ylim(0, 18) +
guides(col=guide_legend(ncol=1,byrow=TRUE)) +
labs(y="Percent of Sampled Sites (%)", x = "Year") ) 
#2) CHI SQUARED ANALYSIS WITHIN YEAR
#_________________________________________________________________________________________________
head(indp)## Sample_Code Lake Taxa Year Year_TP Strain
## 922 MYR-6855 Independence HWM 2018 2018 IN-H-99
## 923 MYR-6856 Independence HWM 2018 2018 IN-H-99
## 924 MYR-6857 Independence HWM 2018 2018 IN-H-99
## 925 MYR-6858 Independence HWM 2018 2018 IN-H-99
## 926 MYR-6859 Independence HWM 2018 2018 IN-H-99
## 927 MYR-6860 Independence HWM 2018 2018 IN-H-99 indp$Year_TP_F <- as.factor(indp$Year_TP)
indp$Strain <- as.factor(indp$Strain)
indp_2018 <- indp[indp$Year == 2018 , ]
indp_2019 <- indp[indp$Year == 2019 , ]
indp_2020 <- indp[indp$Year == 2020 , ]
#CHI SQUARED 2018
indp_chi_geno_2018 <- chisq.test(indp_2018$Strain, indp_2018$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
indp_chi_geno_2018$p.value## [1] 0.6721639 #CHI SQUARED 2019
indp_chi_geno_2019 <- chisq.test(indp_2019$Strain, indp_2019$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
indp_chi_geno_2019$p.value## [1] 1 #CHI SQUARED 2020
indp_chi_geno_2020 <- chisq.test(indp_2020$Strain, indp_2020$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
indp_chi_geno_2020$p.value## [1] 0.3133433 #3) CHI SQUARED ANALYSIS BTWN YEARS
#_________________________________________________________________________________________________
head(indp)## Sample_Code Lake Taxa Year Year_TP Strain Year_TP_F
## 922 MYR-6855 Independence HWM 2018 2018 IN-H-99 2018
## 923 MYR-6856 Independence HWM 2018 2018 IN-H-99 2018
## 924 MYR-6857 Independence HWM 2018 2018 IN-H-99 2018
## 925 MYR-6858 Independence HWM 2018 2018 IN-H-99 2018
## 926 MYR-6859 Independence HWM 2018 2018 IN-H-99 2018
## 927 MYR-6860 Independence HWM 2018 2018 IN-H-99 2018 tally(~Year_TP_F, data = indp)## Year_TP_F
## 2018 2018.5 2019 2019.5 2020 2020.5
## 47 46 25 24 42 30 time_one <- c("2018", "2019", "2020")
indp_btwn <- indp[indp$Year_TP_F %in% time_one , ]
indp_btwn <- droplevels(indp_btwn)
tally(~Year_TP_F, data = indp_btwn)## Year_TP_F
## 2018 2019 2020
## 47 25 42 indp_btwn_18_19 <-indp_btwn[indp_btwn$Year == 2018 | indp_btwn$Year == 2019 , ]
indp_btwn_18_19 <- droplevels(indp_btwn_18_19)
tally(~Year_TP_F, data = indp_btwn_18_19)## Year_TP_F
## 2018 2019
## 47 25 indp_btwn_19_20 <-indp_btwn[indp_btwn$Year == 2019 | indp_btwn$Year == 2020 , ]
indp_btwn_19_20 <- droplevels(indp_btwn_19_20)
tally(~Year_TP_F, data = indp_btwn_19_20)## Year_TP_F
## 2019 2020
## 25 42 #CHI SQUARED COMPARING 2018 TO 2019
indp_chi_btwn_18_19 <- chisq.test(indp_btwn_18_19$Strain, indp_btwn_18_19$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
indp_chi_btwn_18_19$p.value## [1] 0.3293353 #CHI SQUARED COMPARING 2019 TO 2020
indp_chi_btwn_19_20 <- chisq.test(indp_btwn_19_20$Strain, indp_btwn_19_20$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
indp_chi_btwn_19_20$p.value## [1] 0.2793603 #4) ALL THREE YEARS (first t1 to last t2)
#_________________________________________________________________________________________________
head(indp)## Sample_Code Lake Taxa Year Year_TP Strain Year_TP_F
## 922 MYR-6855 Independence HWM 2018 2018 IN-H-99 2018
## 923 MYR-6856 Independence HWM 2018 2018 IN-H-99 2018
## 924 MYR-6857 Independence HWM 2018 2018 IN-H-99 2018
## 925 MYR-6858 Independence HWM 2018 2018 IN-H-99 2018
## 926 MYR-6859 Independence HWM 2018 2018 IN-H-99 2018
## 927 MYR-6860 Independence HWM 2018 2018 IN-H-99 2018 tally(~Year_TP, data = indp)## Year_TP
## 2018 2018.5 2019 2019.5 2020 2020.5
## 47 46 25 24 42 30 indp$Strain <- as.factor(indp$Strain)
indp$Year_TP_F <- as.factor(indp$Year_TP)
indp_three_yr_2018 <- as.data.frame(indp[indp$Year_TP_F == 2018 , ])
indp_three_yr_2020 <- as.data.frame(indp[indp$Year_TP_F == 2020.5 , ])
indp_three_yr <- rbind(indp_three_yr_2018, indp_three_yr_2020)
indp_chi_three_yr <- chisq.test(indp_three_yr$Strain, indp_three_yr$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
indp_chi_three_yr##
## Pearson's Chi-squared test with simulated p-value (based on 2000
## replicates)
##
## data: indp_three_yr$Strain and indp_three_yr$Year_TP_F
## X-squared = 1.2765, df = NA, p-value = 0.3498Analyze North Arm of Lake Minnetonka
#1 Graph with y-axis as percent of total sites visited
#_________________________________________________________________________________________________
head(north_arm)## Sample_Code Lake Taxa Year Year_TP Strain
## 1136 MYR-7856 North_Arm HWM 2019 2019.5 MC-H-7
## 1137 MYR-7857 North_Arm HWM 2019 2019.5 MC-H-12
## 1138 MYR-7858 North_Arm HWM 2019 2019.5 MC-H-9
## 1139 MYR-7859 North_Arm HWM 2019 2019.5 MC-H-7
## 1140 MYR-7860 North_Arm HWM 2019 2019.5 MC-H-7
## 1141 MYR-7861 North_Arm HWM 2019 2019.5 MC-H-9 north_arm$Genotype <- as.factor(north_arm$Strain)
north_arm$Year_TP_F <- as.factor(north_arm$Year_TP)
north_arm$Year_F <- as.factor(north_arm$Year)
north_arm$Year <- as.numeric(north_arm$Year)
head(north_arm)## Sample_Code Lake Taxa Year Year_TP Strain Genotype Year_TP_F Year_F
## 1136 MYR-7856 North_Arm HWM 2019 2019.5 MC-H-7 MC-H-7 2019.5 2019
## 1137 MYR-7857 North_Arm HWM 2019 2019.5 MC-H-12 MC-H-12 2019.5 2019
## 1138 MYR-7858 North_Arm HWM 2019 2019.5 MC-H-9 MC-H-9 2019.5 2019
## 1139 MYR-7859 North_Arm HWM 2019 2019.5 MC-H-7 MC-H-7 2019.5 2019
## 1140 MYR-7860 North_Arm HWM 2019 2019.5 MC-H-7 MC-H-7 2019.5 2019
## 1141 MYR-7861 North_Arm HWM 2019 2019.5 MC-H-9 MC-H-9 2019.5 2019 tally(~Year, data = north_arm)## Year
## 2018 2019 2020
## 84 7 16 north_arm$Year_TP <- north_arm$Time_Point
north_arm_counts <- north_arm %>% count(Year_TP_F,Strain)
north_arm_counts## Year_TP_F Strain n
## 1 2018 MC-H-7 8
## 2 2018.2 MC-H-106 2
## 3 2018.2 MC-H-12 2
## 4 2018.2 MC-H-7 58
## 5 2018.2 MC-H-9 1
## 6 2018.2 NB-H_14 2
## 7 2018.2 NB-H_3 11
## 8 2019.5 MC-H-106 1
## 9 2019.5 MC-H-12 1
## 10 2019.5 MC-H-7 3
## 11 2019.5 MC-H-9 2
## 12 2020.5 MC-H-12 2
## 13 2020.5 MC-H-7 10
## 14 2020.5 MC-H-9 3
## 15 2020.5 NB-H_14 1 north_arm_counts$Percent_Ocupied <- ((north_arm_counts$n)/229)*100
north_arm_counts## Year_TP_F Strain n Percent_Ocupied
## 1 2018 MC-H-7 8 3.4934498
## 2 2018.2 MC-H-106 2 0.8733624
## 3 2018.2 MC-H-12 2 0.8733624
## 4 2018.2 MC-H-7 58 25.3275109
## 5 2018.2 MC-H-9 1 0.4366812
## 6 2018.2 NB-H_14 2 0.8733624
## 7 2018.2 NB-H_3 11 4.8034934
## 8 2019.5 MC-H-106 1 0.4366812
## 9 2019.5 MC-H-12 1 0.4366812
## 10 2019.5 MC-H-7 3 1.3100437
## 11 2019.5 MC-H-9 2 0.8733624
## 12 2020.5 MC-H-12 2 0.8733624
## 13 2020.5 MC-H-7 10 4.3668122
## 14 2020.5 MC-H-9 3 1.3100437
## 15 2020.5 NB-H_14 1 0.4366812 north_arm$Strain <- as.factor(north_arm$Strain)
north_arm_counts$Year_TP_F <- as.character(north_arm_counts$Year_TP_F)
north_arm_counts$Year_TP_F <- as.numeric(north_arm_counts$Year_TP_F)
north_arm_plot_Percent_Ocupied <- north_arm_counts %>%
ggplot() + geom_line(aes(x=Year_TP_F,y=Percent_Ocupied,col=Strain))+
geom_point(aes(x=Year_TP_F,y=Percent_Ocupied, col=Strain), size = 2)
north_arm_PO_annotated <- print(north_arm_plot_Percent_Ocupied +
ggtitle("E. North Arm") +
theme_light() +
geom_vline(xintercept = 2018.25, linetype="dashed", color = "red") +
guides(col=guide_legend(ncol=1,byrow=TRUE)) +
labs(y="Percent of Sampled Sites (%)", x = "Year") )
north_arm_PO_annotated
#2) CHI SQUARED ANALYSIS WITHIN YEAR
#_________________________________________________________________________________________________
#Only one timepoint per year for North Arm Lake
set.seed(1025)
#3) CHI SQUARED ANALYSIS BTWN YEARS
#_________________________________________________________________________________________________
head(north_arm)## Sample_Code Lake Taxa Year Strain Genotype Year_TP_F Year_F
## 1136 MYR-7856 North_Arm HWM 2019 MC-H-7 MC-H-7 2019.5 2019
## 1137 MYR-7857 North_Arm HWM 2019 MC-H-12 MC-H-12 2019.5 2019
## 1138 MYR-7858 North_Arm HWM 2019 MC-H-9 MC-H-9 2019.5 2019
## 1139 MYR-7859 North_Arm HWM 2019 MC-H-7 MC-H-7 2019.5 2019
## 1140 MYR-7860 North_Arm HWM 2019 MC-H-7 MC-H-7 2019.5 2019
## 1141 MYR-7861 North_Arm HWM 2019 MC-H-9 MC-H-9 2019.5 2019 tally(~Year_F, data = north_arm)## Year_F
## 2018 2019 2020
## 84 7 16 north_arm$Genotype <- as.factor(north_arm$Genotype)
north_arm$Year_TP_F <- as.factor(north_arm$Year_TP_F)
north_arm$Year_F <- as.factor(north_arm$Year)
time_one <- c("2018", "2019", "2020")
north_arm_btwn <- north_arm[north_arm$Year %in% time_one , ]
north_arm_btwn <- droplevels(north_arm_btwn)
tally(~Year, data = north_arm_btwn)## Year
## 2018 2019 2020
## 84 7 16 north_arm_btwn_18_19 <-north_arm_btwn[north_arm_btwn$Year == 2018 | north_arm_btwn$Year == 2019 , ]
north_arm_btwn_18_19 <- droplevels(north_arm_btwn_18_19)
tally(~Year, data = north_arm_btwn_18_19)## Year
## 2018 2019
## 84 7 north_arm_btwn_18_19$Genotype <- as.factor(north_arm_btwn_18_19$Genotype)
north_arm_btwn_18_19$Year_F <- as.factor(north_arm_btwn_18_19$Year)
north_arm_btwn_19_20 <-north_arm_btwn[north_arm_btwn$Year == 2019 | north_arm_btwn$Year == 2020 , ]
north_arm_btwn_19_20 <- droplevels(north_arm_btwn_19_20)
tally(~Year_TP_F, data = north_arm_btwn_19_20)## Year_TP_F
## 2019.5 2020.5
## 7 16 north_arm_btwn_19_20$Genotype <- as.factor(north_arm_btwn_19_20$Genotype)
north_arm_btwn_19_20$Year_F <- as.factor(north_arm_btwn_19_20$Year)
#CHI SQUARED COMPARING 2018 TO 2019
north_arm_chi_btwn_18_19 <- chisq.test(north_arm_btwn_18_19$Genotype, north_arm_btwn_18_19$Year, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
north_arm_chi_btwn_18_19$p.value## [1] 0.005997001 #CHI SQUARED COMPARING 2019 TO 2020
north_arm_chi_btwn_19_20 <- chisq.test(north_arm_btwn_19_20$Genotype, north_arm_btwn_19_20$Year, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
north_arm_chi_btwn_19_20$p.value## [1] 0.6546727 #4) ALL THREE YEARS (first t1 to last t2)
#_________________________________________________________________________________________________
head(north_arm)## Sample_Code Lake Taxa Year Strain Genotype Year_TP_F Year_F
## 1136 MYR-7856 North_Arm HWM 2019 MC-H-7 MC-H-7 2019.5 2019
## 1137 MYR-7857 North_Arm HWM 2019 MC-H-12 MC-H-12 2019.5 2019
## 1138 MYR-7858 North_Arm HWM 2019 MC-H-9 MC-H-9 2019.5 2019
## 1139 MYR-7859 North_Arm HWM 2019 MC-H-7 MC-H-7 2019.5 2019
## 1140 MYR-7860 North_Arm HWM 2019 MC-H-7 MC-H-7 2019.5 2019
## 1141 MYR-7861 North_Arm HWM 2019 MC-H-9 MC-H-9 2019.5 2019 north_arm$Genotype <- as.factor(north_arm$Genotype)
north_arm$Year_TP_F <- as.factor(north_arm$Year_TP)
tally(~Genotype + Year_TP_F, data = north_arm)## Year_TP_F
## Genotype 2018 2018.2 2019.5 2020.5
## MC-H-106 0 2 1 0
## MC-H-12 0 2 1 2
## MC-H-7 8 58 3 10
## MC-H-9 0 1 2 3
## NB-H_14 0 2 0 1
## NB-H_3 0 11 0 0 levels(north_arm$Year_TP_F)## [1] "2018" "2018.2" "2019.5" "2020.5" north_arm_three_yr_2018 <- as.data.frame(north_arm[north_arm$Year_TP_F == 2018.2 , ])
tally(~Genotype + Year_TP_F, data = north_arm_three_yr_2018)## Year_TP_F
## Genotype 2018 2018.2 2019.5 2020.5
## MC-H-106 0 2 0 0
## MC-H-12 0 2 0 0
## MC-H-7 0 58 0 0
## MC-H-9 0 1 0 0
## NB-H_14 0 2 0 0
## NB-H_3 0 11 0 0 north_arm_three_yr_2020 <- as.data.frame(north_arm[north_arm$Year_TP_F == 2020.5 , ])
tally(~Genotype + Year_TP_F, data = north_arm_three_yr_2020)## Year_TP_F
## Genotype 2018 2018.2 2019.5 2020.5
## MC-H-106 0 0 0 0
## MC-H-12 0 0 0 2
## MC-H-7 0 0 0 10
## MC-H-9 0 0 0 3
## NB-H_14 0 0 0 1
## NB-H_3 0 0 0 0 north_arm_three_yr <- rbind(north_arm_three_yr_2018, north_arm_three_yr_2020)
tally(~Genotype + Year_TP_F, data = north_arm_three_yr)## Year_TP_F
## Genotype 2018 2018.2 2019.5 2020.5
## MC-H-106 0 2 0 0
## MC-H-12 0 2 0 2
## MC-H-7 0 58 0 10
## MC-H-9 0 1 0 3
## NB-H_14 0 2 0 1
## NB-H_3 0 11 0 0 north_arm_chi_three_yr <- chisq.test(north_arm_three_yr$Genotype, north_arm_three_yr$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
north_arm_chi_three_yr##
## Pearson's Chi-squared test with simulated p-value (based on 2000
## replicates)
##
## data: north_arm_three_yr$Genotype and north_arm_three_yr$Year_TP_F
## X-squared = 15.81, df = NA, p-value = 0.01449Analyze Phelps Bay of Lake Minnetonka
#1 Graph with y-axis as percent of total sites visited
#_________________________________________________________________________________________________
head(phelps)## Sample_Code Lake Taxa Year Year_TP Strain
## 1243 MYR-8125 Phelps_Bay HWM 2019 2019.5 PB-H-7
## 1244 MYR-8253 Phelps_Bay HWM 2019 2019.5 PB-H-8
## 1245 MYR-8254 Phelps_Bay HWM 2019 2019.5 PB-H-7
## 1246 MYR-8256 Phelps_Bay HWM 2019 2019.5 PB-H-7
## 1247 MYR-8257 Phelps_Bay HWM 2019 2019.5 PB-H-8
## 1248 MYR-8258 Phelps_Bay NWM 2019 2019.5 PB-N-10 tally(~Year_TP, data = phelps)## Year_TP
## 2019.5 2020 2020.5
## 40 63 8 phelps_counts <- phelps %>% count(Year_TP,Strain)
phelps_counts## Year_TP Strain n
## 1 2019.5 PB-H-2 1
## 2 2019.5 PB-H-7 24
## 3 2019.5 PB-H-8 10
## 4 2019.5 PB-N-10 5
## 5 2020.0 PB-H-2 1
## 6 2020.0 PB-H-3 2
## 7 2020.0 PB-H-7 27
## 8 2020.0 PB-H-8 21
## 9 2020.0 PB-N-10 12
## 10 2020.5 PB-H-8 1
## 11 2020.5 PB-N-10 7 phelps_counts$Percent_Ocupied <- ((phelps_counts$n)/148)*100
phelps_counts## Year_TP Strain n Percent_Ocupied
## 1 2019.5 PB-H-2 1 0.6756757
## 2 2019.5 PB-H-7 24 16.2162162
## 3 2019.5 PB-H-8 10 6.7567568
## 4 2019.5 PB-N-10 5 3.3783784
## 5 2020.0 PB-H-2 1 0.6756757
## 6 2020.0 PB-H-3 2 1.3513514
## 7 2020.0 PB-H-7 27 18.2432432
## 8 2020.0 PB-H-8 21 14.1891892
## 9 2020.0 PB-N-10 12 8.1081081
## 10 2020.5 PB-H-8 1 0.6756757
## 11 2020.5 PB-N-10 7 4.7297297 phelps_plot_Percent_Ocupied <- phelps_counts %>%
ggplot()+geom_line(aes(x=Year_TP,y=Percent_Ocupied,col=Strain))+geom_point(aes(x=Year_TP,y=Percent_Ocupied, col=Strain), size = 2)
phelps_PO_annotated <- print(phelps_plot_Percent_Ocupied +
ggtitle("D. Phelps") +
theme_light() +
xlim(2018, 2020.5) +
geom_vline(xintercept = 2019.95, linetype="dashed", color = "red") +
guides(col=guide_legend(ncol=1,byrow=TRUE)) +
labs(y="Percent of Sampled Sites (%)", x = "Year") )
#2) CHI SQUARED ANALYSIS WITHIN YEAR
#_________________________________________________________________________________________________
set.seed(1025)
head(phelps)## Sample_Code Lake Taxa Year Year_TP Strain
## 1243 MYR-8125 Phelps_Bay HWM 2019 2019.5 PB-H-7
## 1244 MYR-8253 Phelps_Bay HWM 2019 2019.5 PB-H-8
## 1245 MYR-8254 Phelps_Bay HWM 2019 2019.5 PB-H-7
## 1246 MYR-8256 Phelps_Bay HWM 2019 2019.5 PB-H-7
## 1247 MYR-8257 Phelps_Bay HWM 2019 2019.5 PB-H-8
## 1248 MYR-8258 Phelps_Bay NWM 2019 2019.5 PB-N-10 phelps$Strain <- as.factor(phelps$Strain)
phelps$Year_TP_F <- as.factor(phelps$Year_TP)
head(phelps)## Sample_Code Lake Taxa Year Year_TP Strain Year_TP_F
## 1243 MYR-8125 Phelps_Bay HWM 2019 2019.5 PB-H-7 2019.5
## 1244 MYR-8253 Phelps_Bay HWM 2019 2019.5 PB-H-8 2019.5
## 1245 MYR-8254 Phelps_Bay HWM 2019 2019.5 PB-H-7 2019.5
## 1246 MYR-8256 Phelps_Bay HWM 2019 2019.5 PB-H-7 2019.5
## 1247 MYR-8257 Phelps_Bay HWM 2019 2019.5 PB-H-8 2019.5
## 1248 MYR-8258 Phelps_Bay NWM 2019 2019.5 PB-N-10 2019.5 tally(~Year_TP_F, data = phelps)## Year_TP_F
## 2019.5 2020 2020.5
## 40 63 8 head(phelps)## Sample_Code Lake Taxa Year Year_TP Strain Year_TP_F
## 1243 MYR-8125 Phelps_Bay HWM 2019 2019.5 PB-H-7 2019.5
## 1244 MYR-8253 Phelps_Bay HWM 2019 2019.5 PB-H-8 2019.5
## 1245 MYR-8254 Phelps_Bay HWM 2019 2019.5 PB-H-7 2019.5
## 1246 MYR-8256 Phelps_Bay HWM 2019 2019.5 PB-H-7 2019.5
## 1247 MYR-8257 Phelps_Bay HWM 2019 2019.5 PB-H-8 2019.5
## 1248 MYR-8258 Phelps_Bay NWM 2019 2019.5 PB-N-10 2019.5 tally(~ Year, data = phelps)## Year
## 2019 2020
## 40 71 phelps_2019 <- phelps[phelps$Year == 2019 , ]
phelps_2020 <- phelps[phelps$Year == 2020 , ]
tally(~ Strain + Year_TP_F, data = phelps_2020)## Year_TP_F
## Strain 2019.5 2020 2020.5
## PB-H-2 0 1 0
## PB-H-3 0 2 0
## PB-H-7 0 27 0
## PB-H-8 0 21 1
## PB-N-10 0 12 7 #CHI SQUARED 2020
tally(~Strain + Year_TP_F, data = phelps_2020)## Year_TP_F
## Strain 2019.5 2020 2020.5
## PB-H-2 0 1 0
## PB-H-3 0 2 0
## PB-H-7 0 27 0
## PB-H-8 0 21 1
## PB-N-10 0 12 7 phelps_chi_geno_2020 <- chisq.test(phelps_2020$Strain, phelps_2020$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
phelps_chi_geno_2020$p.value## [1] 0.01349325 #3) CHI SQUARED ANALYSIS BTWN YEARS
#_________________________________________________________________________________________________
head(phelps)## Sample_Code Lake Taxa Year Year_TP Strain Year_TP_F
## 1243 MYR-8125 Phelps_Bay HWM 2019 2019.5 PB-H-7 2019.5
## 1244 MYR-8253 Phelps_Bay HWM 2019 2019.5 PB-H-8 2019.5
## 1245 MYR-8254 Phelps_Bay HWM 2019 2019.5 PB-H-7 2019.5
## 1246 MYR-8256 Phelps_Bay HWM 2019 2019.5 PB-H-7 2019.5
## 1247 MYR-8257 Phelps_Bay HWM 2019 2019.5 PB-H-8 2019.5
## 1248 MYR-8258 Phelps_Bay NWM 2019 2019.5 PB-N-10 2019.5 tally(~Year_TP_F, data = phelps)## Year_TP_F
## 2019.5 2020 2020.5
## 40 63 8 phelps$Strain <- as.factor(phelps$Strain)
phelps$Year_TP_F <- as.factor(phelps$Year_TP_F)
phelps$Year_F <- as.factor(phelps$Year)
phelps_time <- c("2019.5", "2020.5")
phelps_btwn <- phelps[phelps$Year_TP_F %in% phelps_time , ]
phelps_btwn <- droplevels(phelps_btwn)
tally(~Year_TP_F, data = phelps_btwn)## Year_TP_F
## 2019.5 2020.5
## 40 8 phelps_chi_btwn_19_20 <- chisq.test(phelps_btwn$Strain, phelps_btwn$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
phelps_chi_btwn_19_20$p.value## [1] 0.0004997501 #4) ALL THREE YEARS (first t1 to last t2)
#_________________________________________________________________________________________________
head(phelps)## Sample_Code Lake Taxa Year Year_TP Strain Year_TP_F Year_F
## 1243 MYR-8125 Phelps_Bay HWM 2019 2019.5 PB-H-7 2019.5 2019
## 1244 MYR-8253 Phelps_Bay HWM 2019 2019.5 PB-H-8 2019.5 2019
## 1245 MYR-8254 Phelps_Bay HWM 2019 2019.5 PB-H-7 2019.5 2019
## 1246 MYR-8256 Phelps_Bay HWM 2019 2019.5 PB-H-7 2019.5 2019
## 1247 MYR-8257 Phelps_Bay HWM 2019 2019.5 PB-H-8 2019.5 2019
## 1248 MYR-8258 Phelps_Bay NWM 2019 2019.5 PB-N-10 2019.5 2019 names(phelps)[names(phelps)== "Time_Point"] <- "Year_TP_F"
phelps$Strain <- as.factor(phelps$Strain)
phelps$Year_TP_F <- as.factor(phelps$Year_TP_F)
phelps_three_yr_2018 <- as.data.frame(phelps[phelps$Year_TP_F == 2019.5 , ])
phelps_three_yr_2020 <- as.data.frame(phelps[phelps$Year_TP_F == 2020.5 , ])
phelps_three_yr <- rbind(phelps_three_yr_2018, phelps_three_yr_2020)
phelps_chi_three_yr <- chisq.test(phelps_three_yr$Strain, phelps_three_yr$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
phelps_chi_three_yr##
## Pearson's Chi-squared test with simulated p-value (based on 2000
## replicates)
##
## data: phelps_three_yr$Strain and phelps_three_yr$Year_TP_F
## X-squared = 20.455, df = NA, p-value = 0.0009995Analyze Smiths Bay of Lake Minnetonka
#1 Graph with y-axis as percent of total sites visited
#_________________________________________________________________________________________________
head(smith_geno)## Sample_Code Lake Taxa Year Year_TP Strain
## 1354 MYR-6935 Smiths_Bay EWM 2018 2018 MC-E-1
## 1355 MYR-6936 Smiths_Bay EWM 2018 2018 MC-E-1
## 1356 MYR-6937 Smiths_Bay EWM 2018 2018 MC-E-1
## 1357 MYR-6939 Smiths_Bay EWM 2018 2018 MC-E-1
## 1358 MYR-6940 Smiths_Bay EWM 2018 2018 MC-E-1
## 1359 MYR-7368 Smiths_Bay EWM 2018 2018 MC-E-1 smith_geno_counts <- smith_geno %>% count(Year,Strain, Taxa)
smith_geno_counts## Year Strain Taxa n
## 1 2018 MC-E-1 EWM 14
## 2 2018 MC-H-106 HWM 4
## 3 2018 MC-H-107 HWM 3
## 4 2018 MC-H-108 HWM 3
## 5 2018 MC-H-12 HWM 2
## 6 2018 MC-H-142 HWM 1
## 7 2018 MC-H-7 HWM 19
## 8 2018 MC-H-9 HWM 2
## 9 2018 MC-N-109 NWM 1
## 10 2019 MC-E-1 EWM 14
## 11 2019 MC-H-106 HWM 5
## 12 2019 MC-H-108 HWM 9
## 13 2019 MC-H-12 HWM 7
## 14 2019 MC-H-142 HWM 3
## 15 2019 MC-H-7 HWM 44
## 16 2019 MC-N-109 NWM 1
## 17 2020 MC-E-1 EWM 9
## 18 2020 MC-H-106 HWM 7
## 19 2020 MC-H-107 HWM 1
## 20 2020 MC-H-108 HWM 7
## 21 2020 MC-H-12 HWM 3
## 22 2020 MC-H-142 HWM 1
## 23 2020 MC-H-7 HWM 29
## 24 2020 MC-H-9 HWM 6
## 25 2020 SB-H-2 HWM 2 is.data.frame(smith_geno_counts)## [1] TRUE smith_geno_counts$Percent_Ocupied <- ((smith_geno_counts$n)/127)*100
smith_geno_plot_Percent_Ocupied <- smith_geno_counts %>%
ggplot()+geom_line(aes(x=Year,y=Percent_Ocupied,col=Strain))+geom_point(aes(x=Year,y=Percent_Ocupied, col=Strain), size = 2)
smith_PO_annotated <- print(smith_geno_plot_Percent_Ocupied +
ggtitle("F. Smith") +
theme_light() +
guides(col=guide_legend(ncol=1,byrow=TRUE)) +
labs(y="Percent of Sampled Sites (%)", x = "Year") ) 
#2) CHI SQUARED ANALYSIS WITHIN YEAR
#_________________________________________________________________________________________________
#Only have data for one timepoint for each year in Smith's Bay
set.seed(1025)
head(smith_geno)## Sample_Code Lake Taxa Year Year_TP Strain
## 1354 MYR-6935 Smiths_Bay EWM 2018 2018 MC-E-1
## 1355 MYR-6936 Smiths_Bay EWM 2018 2018 MC-E-1
## 1356 MYR-6937 Smiths_Bay EWM 2018 2018 MC-E-1
## 1357 MYR-6939 Smiths_Bay EWM 2018 2018 MC-E-1
## 1358 MYR-6940 Smiths_Bay EWM 2018 2018 MC-E-1
## 1359 MYR-7368 Smiths_Bay EWM 2018 2018 MC-E-1 smith_geno$Strain <- as.factor(smith_geno$Strain)
smith_geno$Year_F <- as.factor(smith_geno$Year)
tally(~Year_F, data = smith_geno)## Year_F
## 2018 2019 2020
## 49 83 65 smith_geno_2018 <- smith_geno[smith_geno$Year == 2018 , ]
smith_geno_2019 <- smith_geno[smith_geno$Year == 2019 , ]
smith_geno_2020 <- smith_geno[smith_geno$Year == 2020 , ]
#3) CHI SQUARED ANALYSIS BTWN YEARS
#_________________________________________________________________________________________________
head(smith_geno)## Sample_Code Lake Taxa Year Year_TP Strain Year_F
## 1354 MYR-6935 Smiths_Bay EWM 2018 2018 MC-E-1 2018
## 1355 MYR-6936 Smiths_Bay EWM 2018 2018 MC-E-1 2018
## 1356 MYR-6937 Smiths_Bay EWM 2018 2018 MC-E-1 2018
## 1357 MYR-6939 Smiths_Bay EWM 2018 2018 MC-E-1 2018
## 1358 MYR-6940 Smiths_Bay EWM 2018 2018 MC-E-1 2018
## 1359 MYR-7368 Smiths_Bay EWM 2018 2018 MC-E-1 2018 tally(~Year_F, data = smith_geno)## Year_F
## 2018 2019 2020
## 49 83 65 smith_geno$Strain <- as.factor(smith_geno$Strain)
smith_geno$Year_F <- as.factor(smith_geno$Year)
time_one <- c("2018", "2019", "2020")
smith_geno_btwn <- smith_geno[smith_geno$Year_F %in% time_one , ]
smith_geno_btwn <- droplevels(smith_geno_btwn)
smith_geno_btwn_18_19 <-smith_geno_btwn[smith_geno_btwn$Year == 2018 | smith_geno_btwn$Year == 2019 , ]
smith_geno_btwn_18_19 <- droplevels(smith_geno_btwn_18_19)
tally(~Year, data = smith_geno_btwn_18_19)## Year
## 2018 2019
## 49 83 smith_geno_btwn_18_19$Strain <- as.factor(smith_geno_btwn_18_19$Strain)
smith_geno_btwn_18_19$Year_F <- as.factor(smith_geno_btwn_18_19$Year)
smith_geno_btwn_19_20 <-smith_geno_btwn[smith_geno_btwn$Year == 2019 | smith_geno_btwn$Year == 2020 , ]
smith_geno_btwn_19_20 <- droplevels(smith_geno_btwn_19_20)
tally(~Year_F, data = smith_geno_btwn_19_20)## Year_F
## 2019 2020
## 83 65 smith_geno_btwn_19_20$Strain <- as.factor(smith_geno_btwn_19_20$Strain)
smith_geno_btwn_19_20$Year_F <- as.factor(smith_geno_btwn_19_20$Year)
#CHI SQUARED COMPARING 2018 TO 2019
smith_geno_chi_btwn_18_19 <- chisq.test(smith_geno_btwn_18_19$Strain, smith_geno_btwn_18_19$Year_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
smith_geno_chi_btwn_18_19$p.value## [1] 0.06146927 #CHI SQUARED COMPARING 2019 TO 2020
smith_geno_chi_btwn_19_20 <- chisq.test(smith_geno_btwn_19_20$Strain, smith_geno_btwn_19_20$Year_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
smith_geno_chi_btwn_19_20$p.value## [1] 0.04697651 #4) ALL THREE YEARS (first t1 to last t2)
#_________________________________________________________________________________________________
head(smith_geno)## Sample_Code Lake Taxa Year Year_TP Strain Year_F
## 1354 MYR-6935 Smiths_Bay EWM 2018 2018 MC-E-1 2018
## 1355 MYR-6936 Smiths_Bay EWM 2018 2018 MC-E-1 2018
## 1356 MYR-6937 Smiths_Bay EWM 2018 2018 MC-E-1 2018
## 1357 MYR-6939 Smiths_Bay EWM 2018 2018 MC-E-1 2018
## 1358 MYR-6940 Smiths_Bay EWM 2018 2018 MC-E-1 2018
## 1359 MYR-7368 Smiths_Bay EWM 2018 2018 MC-E-1 2018 tally(~Year, data = smith_geno)## Year
## 2018 2019 2020
## 49 83 65 smith_geno$Year_TP_F <- smith_geno$Year_F
smith_geno$Strain <- as.factor(smith_geno$Strain)
smith_geno$Year_TP_F <- as.factor(smith_geno$Year_TP)
smith_geno_three_yr_2018 <- as.data.frame(smith_geno[smith_geno$Year_TP_F == 2018 , ])
smith_geno_three_yr_2020 <- as.data.frame(smith_geno[smith_geno$Year_TP_F == 2020 , ])
smith_geno_three_yr <- rbind(smith_geno_three_yr_2018, smith_geno_three_yr_2020)
smith_geno_chi_three_yr <- chisq.test(smith_geno_three_yr$Strain, smith_geno_three_yr$Year_TP_F, correct = TRUE,
p = rep(1/length(x), length(x)), rescale.p = FALSE,
simulate.p.value = TRUE, B = 2000)
smith_geno_chi_three_yr ##
## Pearson's Chi-squared test with simulated p-value (based on 2000
## replicates)
##
## data: smith_geno_three_yr$Strain and smith_geno_three_yr$Year_TP_F
## X-squared = 9.7346, df = NA, p-value = 0.3738The End
Session information
Note: These are the package versions used to reproduce the RMD file during curation
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: x86_64-apple-darwin20
## Running under: macOS Sonoma 14.4.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/Chicago
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] writexl_1.5.0 effects_4.2-2 car_3.1-2 carData_3.0-5
## [5] lme4_1.1-35.3 devtools_2.4.5 usethis_2.2.3 lubridate_1.9.3
## [9] forcats_1.0.0 stringr_1.5.1 purrr_1.0.2 readr_2.1.5
## [13] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0 readxl_1.4.3
## [17] mosaic_1.9.1 mosaicData_0.20.4 ggformula_0.12.0 Matrix_1.7-0
## [21] ggplot2_3.5.1 lattice_0.22-6 ape_5.8 raster_3.6-26
## [25] sp_2.1-4 sf_1.0-16 dplyr_1.1.4
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.2 remotes_2.5.0 rlang_1.1.4 magrittr_2.0.3
## [5] ggridges_0.5.6 e1071_1.7-14 compiler_4.4.1 vctrs_0.6.5
## [9] profvis_0.3.8 crayon_1.5.3 pkgconfig_2.0.3 fastmap_1.2.0
## [13] ellipsis_0.3.2 labeling_0.4.3 utf8_1.2.4 promises_1.3.0
## [17] rmarkdown_2.27 sessioninfo_1.2.2 tzdb_0.4.0 haven_2.5.4
## [21] nloptr_2.0.3 bit_4.0.5 xfun_0.46 cachem_1.1.0
## [25] labelled_2.13.0 jsonlite_1.8.8 highr_0.11 later_1.3.2
## [29] terra_1.7-78 parallel_4.4.1 R6_2.5.1 bslib_0.7.0
## [33] stringi_1.8.4 boot_1.3-30 pkgload_1.4.0 jquerylib_0.1.4
## [37] cellranger_1.1.0 Rcpp_1.0.13 knitr_1.48 httpuv_1.6.15
## [41] splines_4.4.1 nnet_7.3-19 timechange_0.3.0 tidyselect_1.2.1
## [45] rstudioapi_0.16.0 abind_1.4-5 yaml_2.3.9 codetools_0.2-20
## [49] miniUI_0.1.1.1 pkgbuild_1.4.4 shiny_1.8.1.1 withr_3.0.0
## [53] evaluate_0.24.0 survival_3.6-4 survey_4.4-2 units_0.8-5
## [57] proxy_0.4-27 urlchecker_1.0.1 pillar_1.9.0 KernSmooth_2.23-24
## [61] insight_0.19.10 generics_0.1.3 vroom_1.6.5 hms_1.1.3
## [65] munsell_0.5.1 scales_1.3.0 minqa_1.2.6 xtable_1.8-4
## [69] class_7.3-22 glue_1.7.0 tools_4.4.1 fs_1.6.4
## [73] grid_4.4.1 mitools_2.4 colorspace_2.1-0 nlme_3.1-164
## [77] cli_3.6.3 fansi_1.0.6 mosaicCore_0.9.4.0 gtable_0.3.5
## [81] sass_0.4.9 digest_0.6.36 classInt_0.4-10 farver_2.1.2
## [85] htmlwidgets_1.6.4 memoise_2.0.1 htmltools_0.5.8.1 lifecycle_1.0.4
## [89] mime_0.12 bit64_4.0.5 MASS_7.3-60.2