## Zone Indiv X2007 X2008 X2009 X2010 X2011 X2012
## 1 2 373 5.1 5.1 4.8 8.7 6.3 3.2
## 2 2 379 8.1 13.3 8.6 4.9 5.9 6.3
## 3 2 383 9.3 8.5 11.7 7.9 8.0 6.3
## 4 2 389 15.0 10.3 6.8 6.9 5.9 7.6
## 5 2 390 3.5 6.2 4.7 3.8 3.5 3.0
## 6 2 395 6.1 5.6 4.4 4.5 4.5 7.6## 'data.frame': 114 obs. of 8 variables:
## $ Zone : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Indiv: int 373 379 383 389 390 395 396 408 412 421 ...
## $ X2007: num 5.1 8.1 9.3 15 3.5 6.1 7.2 6.1 4.6 7.2 ...
## $ X2008: num 5.1 13.3 8.5 10.3 6.2 5.6 5.9 8.1 6.7 5.8 ...
## $ X2009: num 4.8 8.6 11.7 6.8 4.7 4.4 5.7 7.6 4.5 5.8 ...
## $ X2010: num 8.7 4.9 7.9 6.9 3.8 4.5 5.4 6.2 2.8 5.6 ...
## $ X2011: num 6.3 5.9 8 5.9 3.5 4.5 5.5 9.6 4.2 4.3 ...
## $ X2012: num 3.2 6.3 6.3 7.6 3 7.6 4.7 10.1 5.2 3.4 ...## [1] 373 379 383 389 390 395 396 408 412 421 425 429 431 442 444
## [16] 447 458 464 486 512 530 534 538 542 549 569 572 577 581 584
## [31] 597 616 647 660 591 595 603 612 618 619 623 632 641 645 646
## [46] 648 654 657 661 663 677 678 682 1 37 38 41 49 64 71
## [61] 72 85 88 101 114 120 126 145 150 155 166 177 206 129 257
## [76] 276 303 322 349 350 355 370 454 725 766 812 817 844 863 868
## [91] 896 899 901 917 924 963 970 977 979 1000 1069 1073 1087 1109 1140
## [106] 1183 1188 1247 1286 1340 1358 1392 1477 1481## [1] 114## [1] 8.6## Zone Indiv X2007 X2008 X2009 X2010 X2011 X2012
## 6 2 395 6.1 5.6 4.4 4.5 4.5 7.6# And of course you can mix it all together!
elongation[6, ]$Indiv # returns the value in the column Indiv for the sixth observation## [1] 395## Zone Indiv X2007 X2008 X2009 X2010 X2011 X2012
## 37 4 603 2.5 3.8 3.2 2.3 3.2 5.4# Subsetting with one condition
elongation[elongation$Zone < 4, ] # returns only the data for zones 2-3## Zone Indiv X2007 X2008 X2009 X2010 X2011 X2012
## 1 2 373 5.1 5.1 4.8 8.7 6.3 3.2
## 2 2 379 8.1 13.3 8.6 4.9 5.9 6.3
## 3 2 383 9.3 8.5 11.7 7.9 8.0 6.3
## 4 2 389 15.0 10.3 6.8 6.9 5.9 7.6
## 5 2 390 3.5 6.2 4.7 3.8 3.5 3.0
## 6 2 395 6.1 5.6 4.4 4.5 4.5 7.6
## 7 2 396 7.2 5.9 5.7 5.4 5.5 4.7
## 8 2 408 6.1 8.1 7.6 6.2 9.6 10.1
## 9 2 412 4.6 6.7 4.5 2.8 4.2 5.2
## 10 2 421 7.2 5.8 5.8 5.6 4.3 3.4
## 11 2 425 6.4 8.1 7.8 6.2 6.4 7.9
## 12 2 429 8.9 11.2 8.6 8.4 8.2 6.6
## 13 2 431 3.5 5.9 5.4 4.9 3.5 4.9
## 14 2 442 5.3 9.9 9.2 6.4 5.5 5.0
## 15 2 444 8.7 5.6 5.9 5.2 6.6 7.4
## 16 3 447 8.3 6.5 8.1 7.0 7.6 8.3
## 17 3 458 5.4 8.6 6.1 5.4 7.2 9.4
## 18 3 464 9.2 9.7 7.8 5.8 7.2 6.2
## 19 3 486 9.1 6.7 6.8 6.1 8.4 7.9
## 20 3 512 9.0 10.8 7.1 5.8 8.3 6.7
## 21 3 530 9.2 11.4 4.5 3.6 6.9 5.7
## 22 3 534 7.4 6.8 7.9 3.7 5.5 6.7
## 23 3 538 8.9 12.5 10.5 6.3 7.3 7.4
## 24 3 542 5.3 6.0 5.8 8.6 6.4 7.9
## 25 3 549 4.5 5.6 5.8 4.4 7.4 4.9
## 26 3 569 4.2 6.3 6.2 6.3 6.4 6.4
## 27 3 572 7.1 10.1 10.0 10.1 9.8 13.1
## 28 3 577 6.8 12.1 11.1 8.6 6.1 8.2
## 29 3 581 6.0 6.9 6.3 3.7 5.6 5.3
## 30 3 584 5.6 6.6 5.9 4.8 6.0 6.1
## 31 3 597 8.9 7.9 6.4 7.1 8.0 9.4
## 32 3 616 9.7 10.8 8.8 6.4 9.5 8.0
## 33 3 647 8.1 6.9 6.9 4.0 7.2 7.3
## 34 3 660 6.7 5.4 6.0 4.9 6.8 6.4## Zone Indiv X2007 X2008 X2009 X2010 X2011 X2012
## 1 2 373 5.1 5.1 4.8 8.7 6.3 3.2
## 2 2 379 8.1 13.3 8.6 4.9 5.9 6.3
## 3 2 383 9.3 8.5 11.7 7.9 8.0 6.3
## 4 2 389 15.0 10.3 6.8 6.9 5.9 7.6
## 5 2 390 3.5 6.2 4.7 3.8 3.5 3.0
## 6 2 395 6.1 5.6 4.4 4.5 4.5 7.6
## 7 2 396 7.2 5.9 5.7 5.4 5.5 4.7
## 8 2 408 6.1 8.1 7.6 6.2 9.6 10.1
## 9 2 412 4.6 6.7 4.5 2.8 4.2 5.2
## 10 2 421 7.2 5.8 5.8 5.6 4.3 3.4
## 11 2 425 6.4 8.1 7.8 6.2 6.4 7.9
## 12 2 429 8.9 11.2 8.6 8.4 8.2 6.6
## 13 2 431 3.5 5.9 5.4 4.9 3.5 4.9
## 14 2 442 5.3 9.9 9.2 6.4 5.5 5.0
## 15 2 444 8.7 5.6 5.9 5.2 6.6 7.4
## 16 3 447 8.3 6.5 8.1 7.0 7.6 8.3
## 17 3 458 5.4 8.6 6.1 5.4 7.2 9.4
## 18 3 464 9.2 9.7 7.8 5.8 7.2 6.2
## 19 3 486 9.1 6.7 6.8 6.1 8.4 7.9
## 20 3 512 9.0 10.8 7.1 5.8 8.3 6.7
## 21 3 530 9.2 11.4 4.5 3.6 6.9 5.7
## 22 3 534 7.4 6.8 7.9 3.7 5.5 6.7
## 23 3 538 8.9 12.5 10.5 6.3 7.3 7.4
## 24 3 542 5.3 6.0 5.8 8.6 6.4 7.9
## 25 3 549 4.5 5.6 5.8 4.4 7.4 4.9
## 26 3 569 4.2 6.3 6.2 6.3 6.4 6.4
## 27 3 572 7.1 10.1 10.0 10.1 9.8 13.1
## 28 3 577 6.8 12.1 11.1 8.6 6.1 8.2
## 29 3 581 6.0 6.9 6.3 3.7 5.6 5.3
## 30 3 584 5.6 6.6 5.9 4.8 6.0 6.1
## 31 3 597 8.9 7.9 6.4 7.1 8.0 9.4
## 32 3 616 9.7 10.8 8.8 6.4 9.5 8.0
## 33 3 647 8.1 6.9 6.9 4.0 7.2 7.3
## 34 3 660 6.7 5.4 6.0 4.9 6.8 6.4
## 35 4 591 6.5 10.9 7.8 6.1 11.0 5.0
## 36 4 595 9.8 9.2 8.0 6.7 10.2 10.5
## 37 4 603 2.5 3.8 3.2 2.3 3.2 5.4
## 38 4 612 5.4 6.5 8.0 5.7 4.2 6.6
## 39 4 618 6.1 7.5 6.1 4.2 5.4 5.5
## 40 4 619 8.4 10.5 6.9 5.2 11.1 7.4
## 41 4 623 5.4 7.5 4.4 3.4 5.2 6.8
## 42 4 632 7.3 7.3 6.9 6.3 6.9 6.6
## 43 4 641 7.1 13.2 9.5 6.0 7.6 6.7
## 44 4 645 8.0 11.5 7.2 8.5 9.7 9.9
## 45 4 646 11.5 7.0 4.1 5.2 7.8 6.8
## 46 4 648 10.7 9.2 6.9 5.5 8.8 9.2
## 47 4 654 9.5 10.4 8.9 8.6 11.1 10.2
## 48 4 657 6.6 14.0 10.1 6.6 9.7 6.2
## 49 4 661 8.2 8.9 6.1 5.7 11.2 8.7
## 50 4 663 5.7 10.6 9.5 6.9 8.3 7.8
## 51 4 677 6.0 8.6 6.1 7.0 7.2 7.8
## 52 4 678 3.6 3.7 8.2 4.4 4.6 7.9
## 53 4 682 5.0 6.8 8.7 5.1 5.6 6.0# This is completely equivalent to the last statement
elongation[!elongation$Zone >= 5, ] # the ! means exclude## Zone Indiv X2007 X2008 X2009 X2010 X2011 X2012
## 1 2 373 5.1 5.1 4.8 8.7 6.3 3.2
## 2 2 379 8.1 13.3 8.6 4.9 5.9 6.3
## 3 2 383 9.3 8.5 11.7 7.9 8.0 6.3
## 4 2 389 15.0 10.3 6.8 6.9 5.9 7.6
## 5 2 390 3.5 6.2 4.7 3.8 3.5 3.0
## 6 2 395 6.1 5.6 4.4 4.5 4.5 7.6
## 7 2 396 7.2 5.9 5.7 5.4 5.5 4.7
## 8 2 408 6.1 8.1 7.6 6.2 9.6 10.1
## 9 2 412 4.6 6.7 4.5 2.8 4.2 5.2
## 10 2 421 7.2 5.8 5.8 5.6 4.3 3.4
## 11 2 425 6.4 8.1 7.8 6.2 6.4 7.9
## 12 2 429 8.9 11.2 8.6 8.4 8.2 6.6
## 13 2 431 3.5 5.9 5.4 4.9 3.5 4.9
## 14 2 442 5.3 9.9 9.2 6.4 5.5 5.0
## 15 2 444 8.7 5.6 5.9 5.2 6.6 7.4
## 16 3 447 8.3 6.5 8.1 7.0 7.6 8.3
## 17 3 458 5.4 8.6 6.1 5.4 7.2 9.4
## 18 3 464 9.2 9.7 7.8 5.8 7.2 6.2
## 19 3 486 9.1 6.7 6.8 6.1 8.4 7.9
## 20 3 512 9.0 10.8 7.1 5.8 8.3 6.7
## 21 3 530 9.2 11.4 4.5 3.6 6.9 5.7
## 22 3 534 7.4 6.8 7.9 3.7 5.5 6.7
## 23 3 538 8.9 12.5 10.5 6.3 7.3 7.4
## 24 3 542 5.3 6.0 5.8 8.6 6.4 7.9
## 25 3 549 4.5 5.6 5.8 4.4 7.4 4.9
## 26 3 569 4.2 6.3 6.2 6.3 6.4 6.4
## 27 3 572 7.1 10.1 10.0 10.1 9.8 13.1
## 28 3 577 6.8 12.1 11.1 8.6 6.1 8.2
## 29 3 581 6.0 6.9 6.3 3.7 5.6 5.3
## 30 3 584 5.6 6.6 5.9 4.8 6.0 6.1
## 31 3 597 8.9 7.9 6.4 7.1 8.0 9.4
## 32 3 616 9.7 10.8 8.8 6.4 9.5 8.0
## 33 3 647 8.1 6.9 6.9 4.0 7.2 7.3
## 34 3 660 6.7 5.4 6.0 4.9 6.8 6.4
## 35 4 591 6.5 10.9 7.8 6.1 11.0 5.0
## 36 4 595 9.8 9.2 8.0 6.7 10.2 10.5
## 37 4 603 2.5 3.8 3.2 2.3 3.2 5.4
## 38 4 612 5.4 6.5 8.0 5.7 4.2 6.6
## 39 4 618 6.1 7.5 6.1 4.2 5.4 5.5
## 40 4 619 8.4 10.5 6.9 5.2 11.1 7.4
## 41 4 623 5.4 7.5 4.4 3.4 5.2 6.8
## 42 4 632 7.3 7.3 6.9 6.3 6.9 6.6
## 43 4 641 7.1 13.2 9.5 6.0 7.6 6.7
## 44 4 645 8.0 11.5 7.2 8.5 9.7 9.9
## 45 4 646 11.5 7.0 4.1 5.2 7.8 6.8
## 46 4 648 10.7 9.2 6.9 5.5 8.8 9.2
## 47 4 654 9.5 10.4 8.9 8.6 11.1 10.2
## 48 4 657 6.6 14.0 10.1 6.6 9.7 6.2
## 49 4 661 8.2 8.9 6.1 5.7 11.2 8.7
## 50 4 663 5.7 10.6 9.5 6.9 8.3 7.8
## 51 4 677 6.0 8.6 6.1 7.0 7.2 7.8
## 52 4 678 3.6 3.7 8.2 4.4 4.6 7.9
## 53 4 682 5.0 6.8 8.7 5.1 5.6 6.0# Subsetting with two conditions
elongation[elongation$Zone == 2 | elongation$Zone == 7, ] # returns only data for zones 2 and 7## Zone Indiv X2007 X2008 X2009 X2010 X2011 X2012
## 1 2 373 5.1 5.1 4.8 8.7 6.3 3.2
## 2 2 379 8.1 13.3 8.6 4.9 5.9 6.3
## 3 2 383 9.3 8.5 11.7 7.9 8.0 6.3
## 4 2 389 15.0 10.3 6.8 6.9 5.9 7.6
## 5 2 390 3.5 6.2 4.7 3.8 3.5 3.0
## 6 2 395 6.1 5.6 4.4 4.5 4.5 7.6
## 7 2 396 7.2 5.9 5.7 5.4 5.5 4.7
## 8 2 408 6.1 8.1 7.6 6.2 9.6 10.1
## 9 2 412 4.6 6.7 4.5 2.8 4.2 5.2
## 10 2 421 7.2 5.8 5.8 5.6 4.3 3.4
## 11 2 425 6.4 8.1 7.8 6.2 6.4 7.9
## 12 2 429 8.9 11.2 8.6 8.4 8.2 6.6
## 13 2 431 3.5 5.9 5.4 4.9 3.5 4.9
## 14 2 442 5.3 9.9 9.2 6.4 5.5 5.0
## 15 2 444 8.7 5.6 5.9 5.2 6.6 7.4
## 95 7 924 8.1 7.8 7.1 6.8 7.0 6.6
## 96 7 963 6.8 7.2 3.7 3.5 4.0 4.5
## 97 7 970 6.2 5.2 5.9 6.2 7.7 6.3
## 98 7 977 5.5 5.3 6.4 5.7 7.7 7.9
## 99 7 979 6.6 6.3 6.3 7.7 6.6 7.1
## 100 7 1000 4.3 5.5 3.6 3.1 6.7 5.8
## 101 7 1069 6.2 3.9 4.1 3.8 5.3 7.2
## 102 7 1073 7.3 6.3 5.3 6.7 6.0 8.7
## 103 7 1087 5.9 6.7 7.4 5.1 5.3 6.2
## 104 7 1109 4.5 4.3 3.4 4.3 2.9 2.4
## 105 7 1140 4.7 3.9 5.5 5.2 5.7 3.9
## 106 7 1183 7.2 9.3 6.7 7.8 8.3 7.7
## 107 7 1188 9.4 8.9 7.0 6.9 9.5 7.8
## 108 7 1247 5.5 5.0 4.2 3.9 6.0 4.8
## 109 7 1286 7.1 9.5 7.7 5.6 8.0 8.2
## 110 7 1340 7.1 5.7 7.2 6.9 7.3 8.4
## 111 7 1358 9.0 10.0 8.6 8.5 8.3 8.3
## 112 7 1392 3.9 4.7 2.9 3.9 3.8 4.8
## 113 7 1477 5.3 5.9 6.2 4.2 5.5 6.1
## 114 7 1481 5.4 6.7 6.0 6.0 7.2 7.0elongation[elongation$Zone == 2 & elongation$Indiv %in% c(300:400), ] # returns data for shrubs in zone 2 whose ID numbers are between 300 and 400## Zone Indiv X2007 X2008 X2009 X2010 X2011 X2012
## 1 2 373 5.1 5.1 4.8 8.7 6.3 3.2
## 2 2 379 8.1 13.3 8.6 4.9 5.9 6.3
## 3 2 383 9.3 8.5 11.7 7.9 8.0 6.3
## 4 2 389 15.0 10.3 6.8 6.9 5.9 7.6
## 5 2 390 3.5 6.2 4.7 3.8 3.5 3.0
## 6 2 395 6.1 5.6 4.4 4.5 4.5 7.6
## 7 2 396 7.2 5.9 5.7 5.4 5.5 4.7## CHANGING VARIABLE NAMES AND VALUES IN A DATA FRAME
# Let's create a working copy of our object
elong2 <- elongation
# Now suppose you want to change the name of a column: you can use the names() function
# Used on its own, it returns a vector of the names of the columns. Used on the left side of the assign arrow, it overwrites all or some of the names to value(s) of your choice.
names(elong2) # returns the names of the columns## [1] "Zone" "Indiv" "X2007" "X2008" "X2009" "X2010" "X2011" "X2012"names(elong2)[1] <- "zone" # Changing Zone to zone: we call the 1st element of the names vector using brackets, and assign it a new value
names(elong2)[2] <- "ID" # Changing Indiv to ID: we call the 2nd element and assign it the desired value
# Now suppose there's a mistake in the data, and the value 5.1 for individual 373 in year 2008 should really be 5.7
## - option 1: you can use row and column number
elong2[1,4] <- 5.7
## - option 2: you can use logical conditions for more control
elong2[elong2$ID == 373, ]$X2008 <- 5.7 # completely equivalent to option 1## 'data.frame': 114 obs. of 8 variables:
## $ zone : int 2 2 2 2 2 2 2 2 2 2 ...
## $ ID : int 373 379 383 389 390 395 396 408 412 421 ...
## $ X2007: num 5.1 8.1 9.3 15 3.5 6.1 7.2 6.1 4.6 7.2 ...
## $ X2008: num 5.7 13.3 8.5 10.3 6.2 5.6 5.9 8.1 6.7 5.8 ...
## $ X2009: num 4.8 8.6 11.7 6.8 4.7 4.4 5.7 7.6 4.5 5.8 ...
## $ X2010: num 8.7 4.9 7.9 6.9 3.8 4.5 5.4 6.2 2.8 5.6 ...
## $ X2011: num 6.3 5.9 8 5.9 3.5 4.5 5.5 9.6 4.2 4.3 ...
## $ X2012: num 3.2 6.3 6.3 7.6 3 7.6 4.7 10.1 5.2 3.4 ...# The zone column shows as integer data (whole numbers), but it's really a grouping factor (the zones could have been called A, B, C, etc.) Let's turn it into a factor:
elong2$zone <- as.factor(elong2$zone) # converting and overwriting original class
str(elong2) # now zone is a factor with 6 levels## 'data.frame': 114 obs. of 8 variables:
## $ zone : Factor w/ 6 levels "2","3","4","5",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ID : int 373 379 383 389 390 395 396 408 412 421 ...
## $ X2007: num 5.1 8.1 9.3 15 3.5 6.1 7.2 6.1 4.6 7.2 ...
## $ X2008: num 5.7 13.3 8.5 10.3 6.2 5.6 5.9 8.1 6.7 5.8 ...
## $ X2009: num 4.8 8.6 11.7 6.8 4.7 4.4 5.7 7.6 4.5 5.8 ...
## $ X2010: num 8.7 4.9 7.9 6.9 3.8 4.5 5.4 6.2 2.8 5.6 ...
## $ X2011: num 6.3 5.9 8 5.9 3.5 4.5 5.5 9.6 4.2 4.3 ...
## $ X2012: num 3.2 6.3 6.3 7.6 3 7.6 4.7 10.1 5.2 3.4 ...And what if you’re not happy with the factor levels? You can see the names of the factors with the levels() function… and yes, overwrite them, too.
## [1] "2" "3" "4" "5" "6" "7"tidy datasets are arranged so that each row represents an observation and each column represents a variable.
R would be able to split the dataframe correctly, as each grouping factor has its own column.
Something not quite tidy with our previous object elongation? We have observation of the same variable, i.e. stem length, spread across multiple columns representing different years.
The gather() function from the tidyr package will let us convert this wide-format table to a tidy dataframe. We want to create a single column Year that will have years currently in the columns (2007-2012) repeated for each individual. From this, you should be able to work out that the dataframe will be six times longer than the original. We also want a column Length where all the growth data associated to each year and individual will go
library(tidyr) # load the package
elongation_long <- gather(elongation, Year, Length, # in this order: data frame, key, value
c(X2007, X2008, X2009, X2010, X2011, X2012)) # we need to specify which columns to gather
head(elongation_long)## Zone Indiv Year Length
## 1 2 373 X2007 5.1
## 2 2 379 X2007 8.1
## 3 2 383 X2007 9.3
## 4 2 389 X2007 15.0
## 5 2 390 X2007 3.5
## 6 2 395 X2007 6.1# Here we want the lengths (value) to be gathered by year (key)
# Let's reverse! spread() is the inverse function, allowing you to go from long to wide format
elongation_wide <- spread(elongation_long, Year, Length)
head(elongation_wide)## Zone Indiv X2007 X2008 X2009 X2010 X2011 X2012
## 1 2 373 5.1 5.1 4.8 8.7 6.3 3.2
## 2 2 379 8.1 13.3 8.6 4.9 5.9 6.3
## 3 2 383 9.3 8.5 11.7 7.9 8.0 6.3
## 4 2 389 15.0 10.3 6.8 6.9 5.9 7.6
## 5 2 390 3.5 6.2 4.7 3.8 3.5 3.0
## 6 2 395 6.1 5.6 4.4 4.5 4.5 7.6##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionThis lets you change the name(s) of a column or columns. The first argument is the data frame, the second (and third, etc.) takes the form New name = Old name.
elongation_long <- rename(elongation_long, zone = Zone, indiv = Indiv, year = Year, length = Length) # changes the names of the columns (getting rid of capital letters) and overwriting our data frame
# As we saw earlier, the base R equivalent would have been
names(elongation_long) <- c("zone", "indiv", "year", "length")These are some of the most routine functions that let you reduce your data frame to just the rows and columns you need. The filter() function works great for subsetting rows with logical operations. The select() function lets you specify which columns to keep. Note: the select() function often clashes with functions of the same name in other packages, and for that reason it is recommended to always use the notation dplyr::select() when calling it.
# FILTER OBSERVATIONS
# Let's keep observations from zones 2 and 3 only, and from years 2009 to 2011
elong_subset <- filter(elongation_long, zone %in% c(2, 3), year %in% c("X2009", "X2010", "X2011")) # you can use multiple different conditions separated by commas
# For comparison, the base R equivalent would be (not assigned to an object here):
elongation_long[elongation_long$zone %in% c(2,3) & elongation_long$year %in% c("X2009", "X2010", "X2011"), ]## zone indiv year length
## 229 2 373 X2009 4.8
## 230 2 379 X2009 8.6
## 231 2 383 X2009 11.7
## 232 2 389 X2009 6.8
## 233 2 390 X2009 4.7
## 234 2 395 X2009 4.4
## 235 2 396 X2009 5.7
## 236 2 408 X2009 7.6
## 237 2 412 X2009 4.5
## 238 2 421 X2009 5.8
## 239 2 425 X2009 7.8
## 240 2 429 X2009 8.6
## 241 2 431 X2009 5.4
## 242 2 442 X2009 9.2
## 243 2 444 X2009 5.9
## 244 3 447 X2009 8.1
## 245 3 458 X2009 6.1
## 246 3 464 X2009 7.8
## 247 3 486 X2009 6.8
## 248 3 512 X2009 7.1
## 249 3 530 X2009 4.5
## 250 3 534 X2009 7.9
## 251 3 538 X2009 10.5
## 252 3 542 X2009 5.8
## 253 3 549 X2009 5.8
## 254 3 569 X2009 6.2
## 255 3 572 X2009 10.0
## 256 3 577 X2009 11.1
## 257 3 581 X2009 6.3
## 258 3 584 X2009 5.9
## 259 3 597 X2009 6.4
## 260 3 616 X2009 8.8
## 261 3 647 X2009 6.9
## 262 3 660 X2009 6.0
## 343 2 373 X2010 8.7
## 344 2 379 X2010 4.9
## 345 2 383 X2010 7.9
## 346 2 389 X2010 6.9
## 347 2 390 X2010 3.8
## 348 2 395 X2010 4.5
## 349 2 396 X2010 5.4
## 350 2 408 X2010 6.2
## 351 2 412 X2010 2.8
## 352 2 421 X2010 5.6
## 353 2 425 X2010 6.2
## 354 2 429 X2010 8.4
## 355 2 431 X2010 4.9
## 356 2 442 X2010 6.4
## 357 2 444 X2010 5.2
## 358 3 447 X2010 7.0
## 359 3 458 X2010 5.4
## 360 3 464 X2010 5.8
## 361 3 486 X2010 6.1
## 362 3 512 X2010 5.8
## 363 3 530 X2010 3.6
## 364 3 534 X2010 3.7
## 365 3 538 X2010 6.3
## 366 3 542 X2010 8.6
## 367 3 549 X2010 4.4
## 368 3 569 X2010 6.3
## 369 3 572 X2010 10.1
## 370 3 577 X2010 8.6
## 371 3 581 X2010 3.7
## 372 3 584 X2010 4.8
## 373 3 597 X2010 7.1
## 374 3 616 X2010 6.4
## 375 3 647 X2010 4.0
## 376 3 660 X2010 4.9
## 457 2 373 X2011 6.3
## 458 2 379 X2011 5.9
## 459 2 383 X2011 8.0
## 460 2 389 X2011 5.9
## 461 2 390 X2011 3.5
## 462 2 395 X2011 4.5
## 463 2 396 X2011 5.5
## 464 2 408 X2011 9.6
## 465 2 412 X2011 4.2
## 466 2 421 X2011 4.3
## 467 2 425 X2011 6.4
## 468 2 429 X2011 8.2
## 469 2 431 X2011 3.5
## 470 2 442 X2011 5.5
## 471 2 444 X2011 6.6
## 472 3 447 X2011 7.6
## 473 3 458 X2011 7.2
## 474 3 464 X2011 7.2
## 475 3 486 X2011 8.4
## 476 3 512 X2011 8.3
## 477 3 530 X2011 6.9
## 478 3 534 X2011 5.5
## 479 3 538 X2011 7.3
## 480 3 542 X2011 6.4
## 481 3 549 X2011 7.4
## 482 3 569 X2011 6.4
## 483 3 572 X2011 9.8
## 484 3 577 X2011 6.1
## 485 3 581 X2011 5.6
## 486 3 584 X2011 6.0
## 487 3 597 X2011 8.0
## 488 3 616 X2011 9.5
## 489 3 647 X2011 7.2
## 490 3 660 X2011 6.8Note that here, we use %in% as a logical operator because we are looking to match a list of exact (character) values. If you want to keep observations within a range of numeric values, you either need two logical statements in your filter() function, e.g. length > 4 & length <= 6.5 or you can use the convenient between() function, e.g. between(length, 4, 6.5).
# SELECT COLUMNS
# Let's ditch the zone column just as an example
elong_no.zone <- dplyr::select(elongation_long, indiv, year, length) # or alternatively
elong_no.zone <- dplyr::select(elongation_long, -zone) # the minus sign removes the column
# For comparison, the base R equivalent would be (not assigned to an object here):
elongation_long[ , -1] # removes first column## indiv year length
## 1 373 X2007 5.1
## 2 379 X2007 8.1
## 3 383 X2007 9.3
## 4 389 X2007 15.0
## 5 390 X2007 3.5
## 6 395 X2007 6.1
## 7 396 X2007 7.2
## 8 408 X2007 6.1
## 9 412 X2007 4.6
## 10 421 X2007 7.2
## 11 425 X2007 6.4
## 12 429 X2007 8.9
## 13 431 X2007 3.5
## 14 442 X2007 5.3
## 15 444 X2007 8.7
## 16 447 X2007 8.3
## 17 458 X2007 5.4
## 18 464 X2007 9.2
## 19 486 X2007 9.1
## 20 512 X2007 9.0
## 21 530 X2007 9.2
## 22 534 X2007 7.4
## 23 538 X2007 8.9
## 24 542 X2007 5.3
## 25 549 X2007 4.5
## 26 569 X2007 4.2
## 27 572 X2007 7.1
## 28 577 X2007 6.8
## 29 581 X2007 6.0
## 30 584 X2007 5.6
## 31 597 X2007 8.9
## 32 616 X2007 9.7
## 33 647 X2007 8.1
## 34 660 X2007 6.7
## 35 591 X2007 6.5
## 36 595 X2007 9.8
## 37 603 X2007 2.5
## 38 612 X2007 5.4
## 39 618 X2007 6.1
## 40 619 X2007 8.4
## 41 623 X2007 5.4
## 42 632 X2007 7.3
## 43 641 X2007 7.1
## 44 645 X2007 8.0
## 45 646 X2007 11.5
## 46 648 X2007 10.7
## 47 654 X2007 9.5
## 48 657 X2007 6.6
## 49 661 X2007 8.2
## 50 663 X2007 5.7
## 51 677 X2007 6.0
## 52 678 X2007 3.6
## 53 682 X2007 5.0
## 54 1 X2007 6.5
## 55 37 X2007 7.3
## 56 38 X2007 5.4
## 57 41 X2007 2.0
## 58 49 X2007 5.8
## 59 64 X2007 7.5
## 60 71 X2007 4.3
## 61 72 X2007 7.0
## 62 85 X2007 5.0
## 63 88 X2007 6.6
## 64 101 X2007 3.4
## 65 114 X2007 5.4
## 66 120 X2007 6.3
## 67 126 X2007 7.7
## 68 145 X2007 4.4
## 69 150 X2007 6.0
## 70 155 X2007 4.9
## 71 166 X2007 7.4
## 72 177 X2007 7.3
## 73 206 X2007 5.0
## 74 129 X2007 8.1
## 75 257 X2007 9.9
## 76 276 X2007 4.1
## 77 303 X2007 7.1
## 78 322 X2007 6.6
## 79 349 X2007 9.6
## 80 350 X2007 4.6
## 81 355 X2007 6.7
## 82 370 X2007 6.9
## 83 454 X2007 8.4
## 84 725 X2007 4.7
## 85 766 X2007 6.3
## 86 812 X2007 9.9
## 87 817 X2007 7.4
## 88 844 X2007 4.8
## 89 863 X2007 3.5
## 90 868 X2007 6.4
## 91 896 X2007 7.2
## 92 899 X2007 2.9
## 93 901 X2007 4.3
## 94 917 X2007 6.4
## 95 924 X2007 8.1
## 96 963 X2007 6.8
## 97 970 X2007 6.2
## 98 977 X2007 5.5
## 99 979 X2007 6.6
## 100 1000 X2007 4.3
## 101 1069 X2007 6.2
## 102 1073 X2007 7.3
## 103 1087 X2007 5.9
## 104 1109 X2007 4.5
## 105 1140 X2007 4.7
## 106 1183 X2007 7.2
## 107 1188 X2007 9.4
## 108 1247 X2007 5.5
## 109 1286 X2007 7.1
## 110 1340 X2007 7.1
## 111 1358 X2007 9.0
## 112 1392 X2007 3.9
## 113 1477 X2007 5.3
## 114 1481 X2007 5.4
## 115 373 X2008 5.1
## 116 379 X2008 13.3
## 117 383 X2008 8.5
## 118 389 X2008 10.3
## 119 390 X2008 6.2
## 120 395 X2008 5.6
## 121 396 X2008 5.9
## 122 408 X2008 8.1
## 123 412 X2008 6.7
## 124 421 X2008 5.8
## 125 425 X2008 8.1
## 126 429 X2008 11.2
## 127 431 X2008 5.9
## 128 442 X2008 9.9
## 129 444 X2008 5.6
## 130 447 X2008 6.5
## 131 458 X2008 8.6
## 132 464 X2008 9.7
## 133 486 X2008 6.7
## 134 512 X2008 10.8
## 135 530 X2008 11.4
## 136 534 X2008 6.8
## 137 538 X2008 12.5
## 138 542 X2008 6.0
## 139 549 X2008 5.6
## 140 569 X2008 6.3
## 141 572 X2008 10.1
## 142 577 X2008 12.1
## 143 581 X2008 6.9
## 144 584 X2008 6.6
## 145 597 X2008 7.9
## 146 616 X2008 10.8
## 147 647 X2008 6.9
## 148 660 X2008 5.4
## 149 591 X2008 10.9
## 150 595 X2008 9.2
## 151 603 X2008 3.8
## 152 612 X2008 6.5
## 153 618 X2008 7.5
## 154 619 X2008 10.5
## 155 623 X2008 7.5
## 156 632 X2008 7.3
## 157 641 X2008 13.2
## 158 645 X2008 11.5
## 159 646 X2008 7.0
## 160 648 X2008 9.2
## 161 654 X2008 10.4
## 162 657 X2008 14.0
## 163 661 X2008 8.9
## 164 663 X2008 10.6
## 165 677 X2008 8.6
## 166 678 X2008 3.7
## 167 682 X2008 6.8
## 168 1 X2008 7.7
## 169 37 X2008 7.3
## 170 38 X2008 5.8
## 171 41 X2008 4.7
## 172 49 X2008 4.4
## 173 64 X2008 7.4
## 174 71 X2008 4.7
## 175 72 X2008 9.5
## 176 85 X2008 5.6
## 177 88 X2008 7.5
## 178 101 X2008 5.6
## 179 114 X2008 5.4
## 180 120 X2008 6.3
## 181 126 X2008 6.5
## 182 145 X2008 4.7
## 183 150 X2008 3.1
## 184 155 X2008 5.1
## 185 166 X2008 5.9
## 186 177 X2008 5.0
## 187 206 X2008 4.4
## 188 129 X2008 6.7
## 189 257 X2008 8.4
## 190 276 X2008 6.2
## 191 303 X2008 6.9
## 192 322 X2008 6.9
## 193 349 X2008 9.8
## 194 350 X2008 10.3
## 195 355 X2008 5.5
## 196 370 X2008 7.6
## 197 454 X2008 9.7
## 198 725 X2008 3.7
## 199 766 X2008 6.7
## 200 812 X2008 8.7
## 201 817 X2008 8.6
## 202 844 X2008 4.7
## 203 863 X2008 3.3
## 204 868 X2008 6.9
## 205 896 X2008 7.9
## 206 899 X2008 6.0
## 207 901 X2008 4.5
## 208 917 X2008 8.9
## 209 924 X2008 7.8
## 210 963 X2008 7.2
## 211 970 X2008 5.2
## 212 977 X2008 5.3
## 213 979 X2008 6.3
## 214 1000 X2008 5.5
## 215 1069 X2008 3.9
## 216 1073 X2008 6.3
## 217 1087 X2008 6.7
## 218 1109 X2008 4.3
## 219 1140 X2008 3.9
## 220 1183 X2008 9.3
## 221 1188 X2008 8.9
## 222 1247 X2008 5.0
## 223 1286 X2008 9.5
## 224 1340 X2008 5.7
## 225 1358 X2008 10.0
## 226 1392 X2008 4.7
## 227 1477 X2008 5.9
## 228 1481 X2008 6.7
## 229 373 X2009 4.8
## 230 379 X2009 8.6
## 231 383 X2009 11.7
## 232 389 X2009 6.8
## 233 390 X2009 4.7
## 234 395 X2009 4.4
## 235 396 X2009 5.7
## 236 408 X2009 7.6
## 237 412 X2009 4.5
## 238 421 X2009 5.8
## 239 425 X2009 7.8
## 240 429 X2009 8.6
## 241 431 X2009 5.4
## 242 442 X2009 9.2
## 243 444 X2009 5.9
## 244 447 X2009 8.1
## 245 458 X2009 6.1
## 246 464 X2009 7.8
## 247 486 X2009 6.8
## 248 512 X2009 7.1
## 249 530 X2009 4.5
## 250 534 X2009 7.9
## 251 538 X2009 10.5
## 252 542 X2009 5.8
## 253 549 X2009 5.8
## 254 569 X2009 6.2
## 255 572 X2009 10.0
## 256 577 X2009 11.1
## 257 581 X2009 6.3
## 258 584 X2009 5.9
## 259 597 X2009 6.4
## 260 616 X2009 8.8
## 261 647 X2009 6.9
## 262 660 X2009 6.0
## 263 591 X2009 7.8
## 264 595 X2009 8.0
## 265 603 X2009 3.2
## 266 612 X2009 8.0
## 267 618 X2009 6.1
## 268 619 X2009 6.9
## 269 623 X2009 4.4
## 270 632 X2009 6.9
## 271 641 X2009 9.5
## 272 645 X2009 7.2
## 273 646 X2009 4.1
## 274 648 X2009 6.9
## 275 654 X2009 8.9
## 276 657 X2009 10.1
## 277 661 X2009 6.1
## 278 663 X2009 9.5
## 279 677 X2009 6.1
## 280 678 X2009 8.2
## 281 682 X2009 8.7
## 282 1 X2009 5.6
## 283 37 X2009 7.7
## 284 38 X2009 4.9
## 285 41 X2009 4.2
## 286 49 X2009 7.9
## 287 64 X2009 6.6
## 288 71 X2009 3.9
## 289 72 X2009 5.9
## 290 85 X2009 5.4
## 291 88 X2009 5.3
## 292 101 X2009 3.5
## 293 114 X2009 4.3
## 294 120 X2009 6.0
## 295 126 X2009 7.4
## 296 145 X2009 4.4
## 297 150 X2009 5.7
## 298 155 X2009 4.8
## 299 166 X2009 8.1
## 300 177 X2009 5.2
## 301 206 X2009 4.0
## 302 129 X2009 6.3
## 303 257 X2009 8.6
## 304 276 X2009 5.5
## 305 303 X2009 7.0
## 306 322 X2009 6.5
## 307 349 X2009 8.9
## 308 350 X2009 6.5
## 309 355 X2009 7.0
## 310 370 X2009 5.1
## 311 454 X2009 7.2
## 312 725 X2009 3.4
## 313 766 X2009 5.3
## 314 812 X2009 8.0
## 315 817 X2009 8.0
## 316 844 X2009 3.4
## 317 863 X2009 4.1
## 318 868 X2009 5.5
## 319 896 X2009 7.5
## 320 899 X2009 3.0
## 321 901 X2009 4.1
## 322 917 X2009 7.0
## 323 924 X2009 7.1
## 324 963 X2009 3.7
## 325 970 X2009 5.9
## 326 977 X2009 6.4
## 327 979 X2009 6.3
## 328 1000 X2009 3.6
## 329 1069 X2009 4.1
## 330 1073 X2009 5.3
## 331 1087 X2009 7.4
## 332 1109 X2009 3.4
## 333 1140 X2009 5.5
## 334 1183 X2009 6.7
## 335 1188 X2009 7.0
## 336 1247 X2009 4.2
## 337 1286 X2009 7.7
## 338 1340 X2009 7.2
## 339 1358 X2009 8.6
## 340 1392 X2009 2.9
## 341 1477 X2009 6.2
## 342 1481 X2009 6.0
## 343 373 X2010 8.7
## 344 379 X2010 4.9
## 345 383 X2010 7.9
## 346 389 X2010 6.9
## 347 390 X2010 3.8
## 348 395 X2010 4.5
## 349 396 X2010 5.4
## 350 408 X2010 6.2
## 351 412 X2010 2.8
## 352 421 X2010 5.6
## 353 425 X2010 6.2
## 354 429 X2010 8.4
## 355 431 X2010 4.9
## 356 442 X2010 6.4
## 357 444 X2010 5.2
## 358 447 X2010 7.0
## 359 458 X2010 5.4
## 360 464 X2010 5.8
## 361 486 X2010 6.1
## 362 512 X2010 5.8
## 363 530 X2010 3.6
## 364 534 X2010 3.7
## 365 538 X2010 6.3
## 366 542 X2010 8.6
## 367 549 X2010 4.4
## 368 569 X2010 6.3
## 369 572 X2010 10.1
## 370 577 X2010 8.6
## 371 581 X2010 3.7
## 372 584 X2010 4.8
## 373 597 X2010 7.1
## 374 616 X2010 6.4
## 375 647 X2010 4.0
## 376 660 X2010 4.9
## 377 591 X2010 6.1
## 378 595 X2010 6.7
## 379 603 X2010 2.3
## 380 612 X2010 5.7
## 381 618 X2010 4.2
## 382 619 X2010 5.2
## 383 623 X2010 3.4
## 384 632 X2010 6.3
## 385 641 X2010 6.0
## 386 645 X2010 8.5
## 387 646 X2010 5.2
## 388 648 X2010 5.5
## 389 654 X2010 8.6
## 390 657 X2010 6.6
## 391 661 X2010 5.7
## 392 663 X2010 6.9
## 393 677 X2010 7.0
## 394 678 X2010 4.4
## 395 682 X2010 5.1
## 396 1 X2010 6.8
## 397 37 X2010 5.6
## 398 38 X2010 3.5
## 399 41 X2010 3.3
## 400 49 X2010 6.7
## 401 64 X2010 5.4
## 402 71 X2010 3.7
## 403 72 X2010 6.6
## 404 85 X2010 8.6
## 405 88 X2010 5.2
## 406 101 X2010 2.8
## 407 114 X2010 4.6
## 408 120 X2010 5.2
## 409 126 X2010 5.7
## 410 145 X2010 3.3
## 411 150 X2010 3.9
## 412 155 X2010 7.3
## 413 166 X2010 6.5
## 414 177 X2010 4.0
## 415 206 X2010 3.2
## 416 129 X2010 6.0
## 417 257 X2010 6.8
## 418 276 X2010 4.8
## 419 303 X2010 6.4
## 420 322 X2010 4.9
## 421 349 X2010 8.5
## 422 350 X2010 4.0
## 423 355 X2010 4.8
## 424 370 X2010 5.4
## 425 454 X2010 7.6
## 426 725 X2010 1.9
## 427 766 X2010 4.9
## 428 812 X2010 8.5
## 429 817 X2010 6.6
## 430 844 X2010 3.2
## 431 863 X2010 3.3
## 432 868 X2010 4.5
## 433 896 X2010 4.9
## 434 899 X2010 2.8
## 435 901 X2010 4.2
## 436 917 X2010 6.3
## 437 924 X2010 6.8
## 438 963 X2010 3.5
## 439 970 X2010 6.2
## 440 977 X2010 5.7
## 441 979 X2010 7.7
## 442 1000 X2010 3.1
## 443 1069 X2010 3.8
## 444 1073 X2010 6.7
## 445 1087 X2010 5.1
## 446 1109 X2010 4.3
## 447 1140 X2010 5.2
## 448 1183 X2010 7.8
## 449 1188 X2010 6.9
## 450 1247 X2010 3.9
## 451 1286 X2010 5.6
## 452 1340 X2010 6.9
## 453 1358 X2010 8.5
## 454 1392 X2010 3.9
## 455 1477 X2010 4.2
## 456 1481 X2010 6.0
## 457 373 X2011 6.3
## 458 379 X2011 5.9
## 459 383 X2011 8.0
## 460 389 X2011 5.9
## 461 390 X2011 3.5
## 462 395 X2011 4.5
## 463 396 X2011 5.5
## 464 408 X2011 9.6
## 465 412 X2011 4.2
## 466 421 X2011 4.3
## 467 425 X2011 6.4
## 468 429 X2011 8.2
## 469 431 X2011 3.5
## 470 442 X2011 5.5
## 471 444 X2011 6.6
## 472 447 X2011 7.6
## 473 458 X2011 7.2
## 474 464 X2011 7.2
## 475 486 X2011 8.4
## 476 512 X2011 8.3
## 477 530 X2011 6.9
## 478 534 X2011 5.5
## 479 538 X2011 7.3
## 480 542 X2011 6.4
## 481 549 X2011 7.4
## 482 569 X2011 6.4
## 483 572 X2011 9.8
## 484 577 X2011 6.1
## 485 581 X2011 5.6
## 486 584 X2011 6.0
## 487 597 X2011 8.0
## 488 616 X2011 9.5
## 489 647 X2011 7.2
## 490 660 X2011 6.8
## 491 591 X2011 11.0
## 492 595 X2011 10.2
## 493 603 X2011 3.2
## 494 612 X2011 4.2
## 495 618 X2011 5.4
## 496 619 X2011 11.1
## 497 623 X2011 5.2
## 498 632 X2011 6.9
## 499 641 X2011 7.6
## 500 645 X2011 9.7
## 501 646 X2011 7.8
## 502 648 X2011 8.8
## 503 654 X2011 11.1
## 504 657 X2011 9.7
## 505 661 X2011 11.2
## 506 663 X2011 8.3
## 507 677 X2011 7.2
## 508 678 X2011 4.6
## 509 682 X2011 5.6
## 510 1 X2011 6.5
## 511 37 X2011 7.2
## 512 38 X2011 3.4
## 513 41 X2011 6.3
## 514 49 X2011 8.2
## 515 64 X2011 6.8
## 516 71 X2011 3.9
## 517 72 X2011 4.9
## 518 85 X2011 6.5
## 519 88 X2011 5.8
## 520 101 X2011 7.5
## 521 114 X2011 4.6
## 522 120 X2011 6.2
## 523 126 X2011 7.2
## 524 145 X2011 3.1
## 525 150 X2011 5.8
## 526 155 X2011 6.1
## 527 166 X2011 6.0
## 528 177 X2011 4.3
## 529 206 X2011 4.5
## 530 129 X2011 7.0
## 531 257 X2011 7.7
## 532 276 X2011 5.8
## 533 303 X2011 5.7
## 534 322 X2011 5.1
## 535 349 X2011 8.7
## 536 350 X2011 5.6
## 537 355 X2011 6.0
## 538 370 X2011 6.2
## 539 454 X2011 7.1
## 540 725 X2011 3.2
## 541 766 X2011 5.2
## 542 812 X2011 7.0
## 543 817 X2011 6.9
## 544 844 X2011 4.5
## 545 863 X2011 6.1
## 546 868 X2011 6.1
## 547 896 X2011 7.5
## 548 899 X2011 6.4
## 549 901 X2011 5.0
## 550 917 X2011 6.5
## 551 924 X2011 7.0
## 552 963 X2011 4.0
## 553 970 X2011 7.7
## 554 977 X2011 7.7
## 555 979 X2011 6.6
## 556 1000 X2011 6.7
## 557 1069 X2011 5.3
## 558 1073 X2011 6.0
## 559 1087 X2011 5.3
## 560 1109 X2011 2.9
## 561 1140 X2011 5.7
## 562 1183 X2011 8.3
## 563 1188 X2011 9.5
## 564 1247 X2011 6.0
## 565 1286 X2011 8.0
## 566 1340 X2011 7.3
## 567 1358 X2011 8.3
## 568 1392 X2011 3.8
## 569 1477 X2011 5.5
## 570 1481 X2011 7.2
## 571 373 X2012 3.2
## 572 379 X2012 6.3
## 573 383 X2012 6.3
## 574 389 X2012 7.6
## 575 390 X2012 3.0
## 576 395 X2012 7.6
## 577 396 X2012 4.7
## 578 408 X2012 10.1
## 579 412 X2012 5.2
## 580 421 X2012 3.4
## 581 425 X2012 7.9
## 582 429 X2012 6.6
## 583 431 X2012 4.9
## 584 442 X2012 5.0
## 585 444 X2012 7.4
## 586 447 X2012 8.3
## 587 458 X2012 9.4
## 588 464 X2012 6.2
## 589 486 X2012 7.9
## 590 512 X2012 6.7
## 591 530 X2012 5.7
## 592 534 X2012 6.7
## 593 538 X2012 7.4
## 594 542 X2012 7.9
## 595 549 X2012 4.9
## 596 569 X2012 6.4
## 597 572 X2012 13.1
## 598 577 X2012 8.2
## 599 581 X2012 5.3
## 600 584 X2012 6.1
## 601 597 X2012 9.4
## 602 616 X2012 8.0
## 603 647 X2012 7.3
## 604 660 X2012 6.4
## 605 591 X2012 5.0
## 606 595 X2012 10.5
## 607 603 X2012 5.4
## 608 612 X2012 6.6
## 609 618 X2012 5.5
## 610 619 X2012 7.4
## 611 623 X2012 6.8
## 612 632 X2012 6.6
## 613 641 X2012 6.7
## 614 645 X2012 9.9
## 615 646 X2012 6.8
## 616 648 X2012 9.2
## 617 654 X2012 10.2
## 618 657 X2012 6.2
## 619 661 X2012 8.7
## 620 663 X2012 7.8
## 621 677 X2012 7.8
## 622 678 X2012 7.9
## 623 682 X2012 6.0
## 624 1 X2012 9.5
## 625 37 X2012 7.7
## 626 38 X2012 6.0
## 627 41 X2012 7.7
## 628 49 X2012 6.1
## 629 64 X2012 7.8
## 630 71 X2012 5.3
## 631 72 X2012 4.3
## 632 85 X2012 6.8
## 633 88 X2012 5.7
## 634 101 X2012 8.0
## 635 114 X2012 5.7
## 636 120 X2012 5.0
## 637 126 X2012 7.1
## 638 145 X2012 4.5
## 639 150 X2012 7.0
## 640 155 X2012 6.3
## 641 166 X2012 8.0
## 642 177 X2012 3.0
## 643 206 X2012 3.8
## 644 129 X2012 6.5
## 645 257 X2012 7.0
## 646 276 X2012 5.7
## 647 303 X2012 6.7
## 648 322 X2012 6.5
## 649 349 X2012 6.1
## 650 350 X2012 3.1
## 651 355 X2012 6.2
## 652 370 X2012 6.8
## 653 454 X2012 7.2
## 654 725 X2012 3.7
## 655 766 X2012 7.3
## 656 812 X2012 7.4
## 657 817 X2012 7.7
## 658 844 X2012 3.6
## 659 863 X2012 4.9
## 660 868 X2012 6.2
## 661 896 X2012 7.1
## 662 899 X2012 3.1
## 663 901 X2012 4.9
## 664 917 X2012 5.3
## 665 924 X2012 6.6
## 666 963 X2012 4.5
## 667 970 X2012 6.3
## 668 977 X2012 7.9
## 669 979 X2012 7.1
## 670 1000 X2012 5.8
## 671 1069 X2012 7.2
## 672 1073 X2012 8.7
## 673 1087 X2012 6.2
## 674 1109 X2012 2.4
## 675 1140 X2012 3.9
## 676 1183 X2012 7.7
## 677 1188 X2012 7.8
## 678 1247 X2012 4.8
## 679 1286 X2012 8.2
## 680 1340 X2012 8.4
## 681 1358 X2012 8.3
## 682 1392 X2012 4.8
## 683 1477 X2012 6.1
## 684 1481 X2012 7.0# CREATE A NEW COLUMN
elong_total <- mutate(elongation, total.growth = X2007 + X2008 + X2009 + X2010 + X2011 + X2012)
head(elong_total)## Zone Indiv X2007 X2008 X2009 X2010 X2011 X2012 total.growth
## 1 2 373 5.1 5.1 4.8 8.7 6.3 3.2 33.2
## 2 2 379 8.1 13.3 8.6 4.9 5.9 6.3 47.1
## 3 2 383 9.3 8.5 11.7 7.9 8.0 6.3 51.7
## 4 2 389 15.0 10.3 6.8 6.9 5.9 7.6 52.5
## 5 2 390 3.5 6.2 4.7 3.8 3.5 3.0 24.7
## 6 2 395 6.1 5.6 4.4 4.5 4.5 7.6 32.7Now, let’s see how we could accomplish the same thing on our long-format data elongation_long by using two functions that pair extremely well together: group_by()and summarise().
# GROUP DATA
elong_grouped <- group_by(elongation_long, indiv) # grouping our dataset by individual
head(elong_grouped)## # A tibble: 6 x 4
## # Groups: indiv [6]
## zone indiv year length
## <int> <int> <chr> <dbl>
## 1 2 373 X2007 5.1
## 2 2 379 X2007 8.1
## 3 2 383 X2007 9.3
## 4 2 389 X2007 15
## 5 2 390 X2007 3.5
## 6 2 395 X2007 6.1## zone indiv year length
## 1 2 373 X2007 5.1
## 2 2 379 X2007 8.1
## 3 2 383 X2007 9.3
## 4 2 389 X2007 15.0
## 5 2 390 X2007 3.5
## 6 2 395 X2007 6.1This function will always aggregate your original data frame, i.e. the output data frame will be shorter than the input. Here, let’s contrast summing growth increments over the study period on the original dataset vs our new grouped dataset.
# SUMMARISING OUR DATA
summary1 <- summarise(elongation_long, total.growth = sum(length))
summary2 <- summarise(elong_grouped, total.growth = sum(length))## `summarise()` ungrouping output (override with `.groups` argument)## total.growth
## 1 4448.7## # A tibble: 114 x 2
## indiv total.growth
## <int> <dbl>
## 1 1 42.6
## 2 37 42.8
## 3 38 29
## 4 41 28.2
## 5 49 39.1
## 6 64 41.5
## 7 71 25.8
## 8 72 38.2
## 9 85 37.9
## 10 88 36.1
## # … with 104 more rowsThe first summary corresponds to the sum of all growth increments in the dataset (all individuals and years). The second one gives us a breakdown of total growth per individual, our grouping variable. Amazing! We can compute all sorts of summary statistics, too, like the mean or standard deviation of growth across years:
summary3 <- summarise(elong_grouped, total.growth = sum(length),
mean.growth = mean(length),
sd.growth = sd(length))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 6 x 4
## indiv total.growth mean.growth sd.growth
## <int> <dbl> <dbl> <dbl>
## 1 1 42.6 7.1 1.35
## 2 37 42.8 7.13 0.781
## 3 38 29 4.83 1.14
## 4 41 28.2 4.7 2.05
## 5 49 39.1 6.52 1.41
## 6 64 41.5 6.92 0.868Always create a new object for summarised data, so that your full dataset doesn’t go away! You can always merge back some information at a later stage,