Data Science http://r4ds.had.co.nz/
https://www.tidyverse.org/
Transform data
Tools for data science
library(tidyverse) ## ── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ── ## ✔ ggplot2 3.0.0 ✔ purrr 0.2.5 ## ✔ tibble 1.4.2 ✔ dplyr 0.7.6 ## ✔ tidyr 0.8.1 ✔ stringr 1.3.1 ## ✔ readr 1.1.1 ✔ forcats 0.3.0 ## ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ── ## ✖ dplyr::filter() masks stats::filter() ## ✖ dplyr::lag() masks stats::lag()
Example dataset
library(fgeo.data) dim(luquillo_tree6_random) ## [1] 1004 19
Overview
luquillo_tree6_random ## # A tibble: 1,004 x 19 ## treeID stemID tag StemTag sp quadrat gx gy MeasureID CensusID ## <int> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <int> ## 1 104 143 10009 10009 DACE… 113 10.3 245. 582850 6 ## 2 119 158 1001… 100104 MYRS… 1021 183. 410. 578696 6 ## 3 180 225 1001… 100174 CASA… 921 165. 410. 617049 6 ## 4 602 736 1006… 100649 GUAG… 821 149. 414. 614253 6 ## 5 631 775 10069 10069 PREM… 213 38.3 245. 598429 6 ## 6 647 793 1007… 100708 SCHM… 821 143. 411. 614211 6 ## 7 1086 1339 10122 10122 DRYG… 413 68.9 253. 603131 6 ## 8 1144 1410 1012… 101285 SCHM… 920 161. 395. 616923 6 ## 9 1168 1438 10131 10131 DACE… 413 70.6 252. 603151 6 ## 10 1380 1702 1015… 101560 CASA… 820 142. 386. 614023 6 ## # ... with 994 more rows, and 9 more variables: dbh <dbl>, pom <chr>, ## # hom <dbl>, ExactDate <dbl>, DFstatus <chr>, codes <chr>, ## # nostems <dbl>, status <chr>, date <dbl> tree <- luquillo_tree6_random
dplyr: Main verbs
Filter rows with filter()
filter(tree, sp == "PREMON", quadrat == "1017") ## # A tibble: 5 x 19 ## treeID stemID tag StemTag sp quadrat gx gy MeasureID CensusID ## <int> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <int> ## 1 23886 29878 14617 14617 PREM… 1017 182. 330. 578115 6 ## 2 23903 29911 14637 14637 PREM… 1017 194. 334. 578189 6 ## 3 23909 29927 14642 14642 PREM… 1017 191. 337. 578196 6 ## 4 122801 160013 38574 38574 PREM… 1017 185. 324. 578109 6 ## 5 125513 NA 14617 <NA> PREM… 1017 182. 330. NA NA ## # ... with 9 more variables: dbh <dbl>, pom <chr>, hom <dbl>, ## # ExactDate <dbl>, DFstatus <chr>, codes <chr>, nostems <dbl>, ## # status <chr>, date <dbl>
Equivalent
tree[tree$sp == "PREMON" & tree$quadrat == "1017", ] ## # A tibble: 5 x 19 ## treeID stemID tag StemTag sp quadrat gx gy MeasureID CensusID ## <int> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <int> ## 1 23886 29878 14617 14617 PREM… 1017 182. 330. 578115 6 ## 2 23903 29911 14637 14637 PREM… 1017 194. 334. 578189 6 ## 3 23909 29927 14642 14642 PREM… 1017 191. 337. 578196 6 ## 4 122801 160013 38574 38574 PREM… 1017 185. 324. 578109 6 ## 5 125513 NA 14617 <NA> PREM… 1017 182. 330. NA NA ## # ... with 9 more variables: dbh <dbl>, pom <chr>, hom <dbl>, ## # ExactDate <dbl>, DFstatus <chr>, codes <chr>, nostems <dbl>, ## # status <chr>, date <dbl>
Arrange rows with arrange()
arrange(tree, sp, quadrat) ## # A tibble: 1,004 x 19 ## treeID stemID tag StemTag sp quadrat gx gy MeasureID CensusID ## <int> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <int> ## 1 78221 95215 91969 91969 ALCF… 518 90.4 340. 606006 6 ## 2 104760 139181 1495… 149560 ALCF… 820 140. 395. 614037 6 ## 3 59773 72453 69811 69811 ALCF… 914 160. 268. 616272 6 ## 4 19961 24771 12354 12354 ALCL… 914 167. 270. 616286 6 ## 5 19965 24786 1235… 123547 ANDI… 425 70.4 481. 604320 6 ## 6 39681 48857 33319 33319 ANDI… 810 146. 191. 612949 6 ## 7 120711 157828 1687… 168752 ARDG… 1605 315. 91.6 595640 6 ## 8 33658 42059 25092 25092 ARDG… 1606 305. 107. 595666 6 ## 9 49466 59981 57479 57479 ARTA… 412 62.3 235. 603020 6 ## 10 37703 46647 30594 30594 BRUP… 1407 265. 123. 590198 6 ## # ... with 994 more rows, and 9 more variables: dbh <dbl>, pom <chr>, ## # hom <dbl>, ExactDate <dbl>, DFstatus <chr>, codes <chr>, ## # nostems <dbl>, status <chr>, date <dbl>
Arrange in descending order desc()
arrange(tree, desc(sp), quadrat) ## # A tibble: 1,004 x 19 ## treeID stemID tag StemTag sp quadrat gx gy MeasureID ## <int> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> ## 1 21971 27365 12600 12600 ZANM… 115 0.01 280. 583015 ## 2 98940 132754 1387… 138778 UREB… 1014 196. 262. 577799 ## 3 58640 71135 68475 68475 UREB… 1114 210. 269. 581419 ## 4 35856 44596 28156 28156 UREB… 306 45.7 103. 600399 ## 5 43923 53793 46485 46485 UREB… 409 78.0 161. 602836 ## 6 34977 43627 27108 27108 TRIP… 1005 188. 83.0 576789 ## 7 37918 46881 30831 30831 TRIP… 1207 221. 137. 584220 ## 8 70362 85902 82942 82942 TRIP… 1516 295. 315. 594238 ## 9 21157 26316 1250… 125043 TRIP… 225 39.3 490. 599894 ## 10 62931 76415 73761 73761 TRIP… 317 56.2 330. 601329 ## # ... with 994 more rows, and 10 more variables: CensusID <int>, ## # dbh <dbl>, pom <chr>, hom <dbl>, ExactDate <dbl>, DFstatus <chr>, ## # codes <chr>, nostems <dbl>, status <chr>, date <dbl>
Select columns with select()
select(tree, sp, quadrat, treeID, status, dbh) ## # A tibble: 1,004 x 5 ## sp quadrat treeID status dbh ## <chr> <chr> <int> <chr> <dbl> ## 1 DACEXC 113 104 A 195 ## 2 MYRSPL 1021 119 A 44.9 ## 3 CASARB 921 180 A 46.1 ## 4 GUAGUI 821 602 A 33.1 ## 5 PREMON 213 631 A 139 ## 6 SCHMOR 821 647 A 248 ## 7 DRYGLA 413 1086 A 176 ## 8 SCHMOR 920 1144 A 75 ## 9 DACEXC 413 1168 A 613 ## 10 CASARB 820 1380 D NA ## # ... with 994 more rows
Select range
select(tree, treeID:quadrat) ## # A tibble: 1,004 x 6 ## treeID stemID tag StemTag sp quadrat ## <int> <int> <chr> <chr> <chr> <chr> ## 1 104 143 10009 10009 DACEXC 113 ## 2 119 158 100104 100104 MYRSPL 1021 ## 3 180 225 100171 100174 CASARB 921 ## 4 602 736 100649 100649 GUAGUI 821 ## 5 631 775 10069 10069 PREMON 213 ## 6 647 793 100708 100708 SCHMOR 821 ## 7 1086 1339 10122 10122 DRYGLA 413 ## 8 1144 1410 101285 101285 SCHMOR 920 ## 9 1168 1438 10131 10131 DACEXC 413 ## 10 1380 1702 101560 101560 CASARB 820 ## # ... with 994 more rows
Exclude range
select(tree, -(treeID:quadrat)) ## # A tibble: 1,004 x 13 ## gx gy MeasureID CensusID dbh pom hom ExactDate DFstatus ## <dbl> <dbl> <int> <int> <dbl> <chr> <dbl> <dbl> <chr> ## 1 10.3 245. 582850 6 195 1.45 1.45 16911 alive ## 2 183. 410. 578696 6 44.9 1.25 1.26 17017 alive ## 3 165. 410. 617049 6 46.1 1.35 1.34 17017 alive ## 4 149. 414. 614253 6 33.1 1.3 1.3 17011 alive ## 5 38.3 245. 598429 6 139 1.25 1.25 16912 alive ## 6 143. 411. 614211 6 248 1.35 1.35 17011 alive ## 7 68.9 253. 603131 6 176 1.4 1.42 16913 alive ## 8 161. 395. 616923 6 75 1.3 1.3 17015 alive ## 9 70.6 252. 603151 6 613 1.25 1.25 16913 alive ## 10 142. 386. 614023 6 NA <NA> NA 17014 dead ## # ... with 994 more rows, and 4 more variables: codes <chr>, ## # nostems <dbl>, status <chr>, date <dbl>
Rename columns with rename()
rename(tree, tree_id = treeID) ## # A tibble: 1,004 x 19 ## tree_id stemID tag StemTag sp quadrat gx gy MeasureID ## <int> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> ## 1 104 143 10009 10009 DACE… 113 10.3 245. 582850 ## 2 119 158 1001… 100104 MYRS… 1021 183. 410. 578696 ## 3 180 225 1001… 100174 CASA… 921 165. 410. 617049 ## 4 602 736 1006… 100649 GUAG… 821 149. 414. 614253 ## 5 631 775 10069 10069 PREM… 213 38.3 245. 598429 ## 6 647 793 1007… 100708 SCHM… 821 143. 411. 614211 ## 7 1086 1339 10122 10122 DRYG… 413 68.9 253. 603131 ## 8 1144 1410 1012… 101285 SCHM… 920 161. 395. 616923 ## 9 1168 1438 10131 10131 DACE… 413 70.6 252. 603151 ## 10 1380 1702 1015… 101560 CASA… 820 142. 386. 614023 ## # ... with 994 more rows, and 10 more variables: CensusID <int>, ## # dbh <dbl>, pom <chr>, hom <dbl>, ExactDate <dbl>, DFstatus <chr>, ## # codes <chr>, nostems <dbl>, status <chr>, date <dbl>
Add new columns with mutate()
mutate(tree, dbh_mm = dbh, dbh_m = dbh_mm / 1000 ) ## # A tibble: 1,004 x 21 ## treeID stemID tag StemTag sp quadrat gx gy MeasureID CensusID ## <int> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <int> ## 1 104 143 10009 10009 DACE… 113 10.3 245. 582850 6 ## 2 119 158 1001… 100104 MYRS… 1021 183. 410. 578696 6 ## 3 180 225 1001… 100174 CASA… 921 165. 410. 617049 6 ## 4 602 736 1006… 100649 GUAG… 821 149. 414. 614253 6 ## 5 631 775 10069 10069 PREM… 213 38.3 245. 598429 6 ## 6 647 793 1007… 100708 SCHM… 821 143. 411. 614211 6 ## 7 1086 1339 10122 10122 DRYG… 413 68.9 253. 603131 6 ## 8 1144 1410 1012… 101285 SCHM… 920 161. 395. 616923 6 ## 9 1168 1438 10131 10131 DACE… 413 70.6 252. 603151 6 ## 10 1380 1702 1015… 101560 CASA… 820 142. 386. 614023 6 ## # ... with 994 more rows, and 11 more variables: dbh <dbl>, pom <chr>, ## # hom <dbl>, ExactDate <dbl>, DFstatus <chr>, codes <chr>, ## # nostems <dbl>, status <chr>, date <dbl>, dbh_mm <dbl>, dbh_m <dbl>
Only new variables with transmute()
transmute(tree, dbh_mm = dbh, dbh_m = dbh_mm / 1000 ) ## # A tibble: 1,004 x 2 ## dbh_mm dbh_m ## <dbl> <dbl> ## 1 195 0.195 ## 2 44.9 0.0449 ## 3 46.1 0.0461 ## 4 33.1 0.0331 ## 5 139 0.139 ## 6 248 0.248 ## 7 176 0.176 ## 8 75 0.075 ## 9 613 0.613 ## 10 NA NA ## # ... with 994 more rows
Summarise values with summarise()
by_quad_sp <- group_by(tree, quadrat, sp) summarise(by_quad_sp, mean_dbh = mean(dbh, na.rm = TRUE), mean_gx = mean(gx, na.rm = TRUE), mean_gy = mean(gy, na.rm = TRUE) ) ## # A tibble: 853 x 5 ## # Groups: quadrat [?] ## quadrat sp mean_dbh mean_gx mean_gy ## <chr> <chr> <dbl> <dbl> <dbl> ## 1 1001 PREMON 135 181. 13 ## 2 1002 DACEXC 423 196. 33.5 ## 3 1005 DACEXC 60 185. 88.0 ## 4 1005 TRIPAL NaN 188. 83.0 ## 5 1006 MANBID 30.5 184. 106. ## 6 1007 EUGSTA 56 182. 139. ## 7 1007 MANBID 14.5 198. 131. ## 8 1007 MATDOM 240 191. 132. ## 9 1007 MICPRA NaN 186. 132. ## 10 1007 SLOBER NaN 182. 123. ## # ... with 843 more rows
Anything by groups with group_by()
by_sp <- group_by(tree, quadrat) summarise(by_sp, mean_dbh = mean(dbh, na.rm = TRUE) ) ## # A tibble: 359 x 2 ## quadrat mean_dbh ## <chr> <dbl> ## 1 1001 135 ## 2 1002 423 ## 3 1005 60 ## 4 1006 30.5 ## 5 1007 81.8 ## 6 1008 149 ## 7 101 98 ## 8 1010 198. ## 9 1011 15.6 ## 10 1013 15.4 ## # ... with 349 more rows
Learn more
https://www.rstudio.com/
https://rstudio.cloud/learn/primers
https://www.rstudio.com/resources/cheatsheets/
