Appendix-4b-Numeric_Exploratory_Analyses.RmdNumeric exploration is necessary for quality control and to understand the structure of your data. Some numeric summaries, such as correlation tables, also provide key insights into how to model the data (Zuur et al 2010).
It is increasingly being recommended to include numeric summaries such as this to facilitate meta-analysis and help interested readers understand the structure of your data (Gerstner et al 2017).
For you independent project (in 2018) you just need to submit a script file which carries our relevant numeric summaries.
Note: original files is called “skibiel_mammalsmilk.csv” within the mammalsmilk package
file. <- "Appendix-2-Analysis-Data_mammalsmilkRA.csv"
path. <- here("/inst/extdata/",file.)
milk <- read.csv(path., skip = 3)head(milk)
#> ord fam spp mass.fem gest.mo
#> 1 Artiodactyla Bovidae Bos frontalis 800000 9.02
#> 2 Artiodactyla Bovidae Capra ibex 53000 5.60
#> 3 Artiodactyla Bovidae Connocheates taurinus taurinus 170500 8.32
#> 4 Artiodactyla Bovidae Connocheates gnou 200000 8.50
#> 5 Artiodactyla Bovidae Damaliscus pygargus phillipsi 61000 8.00
#> 6 Artiodactyla Bovidae Gazella dorcas 20600 4.74
#> lac.mo mass.litter repro.output dev.birth diet arid biome N
#> 1 4.5 26949 0.03 3 herbivore no terrestrial 4+
#> 2 7.5 3489 0.07 3 herbivore no terrestrial 24
#> 3 8.0 17717 0.10 3 herbivore yes terrestrial 5
#> 4 7.5 11110 0.06 3 herbivore yes terrestrial 3
#> 5 4.0 6500 0.11 3 herbivore yes terrestrial 4
#> 6 2.8 1771 0.09 3 herbivore yes terrestrial 16
#> fat gest.month lacat.mo prot sugar energy
#> 1 7.0 9.02 4.5 6.3 5.2 1.21
#> 2 12.4 5.60 7.5 5.7 NA NA
#> 3 7.5 8.32 8.0 4.1 5.3 1.13
#> 4 5.5 8.50 7.5 4.3 4.1 0.91
#> 5 8.6 8.00 4.0 5.6 4.9 1.31
#> 6 8.8 4.74 2.8 8.8 NA NA
tail(milk)
#> ord fam spp mass.fem gest.mo
#> 125 Rodentia Muridae Pseudomys australis 65 1.02
#> 126 Rodentia Muridae Rattus norvegicus 253 0.71
#> 127 Rodentia Octodontidae Octodon degus 235 2.96
#> 128 Rodentia Scuiridae Tamias amoenus 53 0.98
#> 129 Rodentia Scuiridae Urocitellus columbianus 406 0.84
#> 130 Soricomorpha11 Soricidae Crocidura russula 14 0.97
#> lac.mo mass.litter repro.output dev.birth diet arid biome
#> 125 0.9 13 0.20 0 herbivore yes terrestrial
#> 126 0.8 51 0.20 0 omnivore no terrestrial
#> 127 1.2 74 0.31 3 herbivore yes terrestrial
#> 128 1.5 14 0.26 0 omnivore no terrestrial
#> 129 1.0 32 0.08 0 herbivore no terrestrial
#> 130 0.8 4 0.29 0 carnivore no terrestrial
#> N fat gest.month lacat.mo prot sugar energy
#> 125 7-Jun 12.1 1.02 0.9 6.4 3.6 1.62
#> 126 18-Mar 8.8 0.71 0.8 8.1 3.8 1.43
#> 127 7 20.1 2.96 1.2 4.4 2.7 2.20
#> 128 11 21.7 0.98 1.5 8.1 4.3 2.62
#> 129 26 9.2 0.84 1.0 10.7 3.4 1.60
#> 130 3 30.0 0.97 0.8 9.4 3.0 3.40
summary(milk)
#> ord fam spp
#> Artiodactyla :23 Bovidae :13 Acomys cahirinus : 1
#> Carnivora :23 Cercopithecidae: 8 Alces alces : 1
#> Primates :22 Cervidae : 7 Aloutta palliata : 1
#> Rodentia :17 Muridae : 7 Aloutta seniculus : 1
#> Chiroptera :10 Otariidae : 7 Arctocephalus australis: 1
#> Diprotodontia:10 Phocidae : 7 Arctocephalus gazella : 1
#> (Other) :25 (Other) :81 (Other) :124
#> mass.fem gest.mo lac.mo mass.litter
#> Min. : 8 Min. : 0.400 Min. : 0.300 Min. : 0.3
#> 1st Qu.: 857 1st Qu.: 1.405 1st Qu.: 1.625 1st Qu.: 42.0
#> Median : 5716 Median : 5.000 Median : 4.500 Median : 423.5
#> Mean : 2229475 Mean : 5.624 Mean : 6.092 Mean : 52563.8
#> 3rd Qu.: 107500 3rd Qu.: 8.365 3rd Qu.: 8.225 3rd Qu.: 7038.2
#> Max. :170000000 Max. :21.460 Max. :42.000 Max. :2272500.0
#>
#> repro.output dev.birth diet arid
#> Min. :0.00003 Min. :0.000 carnivore:32 no :91
#> 1st Qu.:0.04000 1st Qu.:1.000 herbivore:61 yes:39
#> Median :0.08000 Median :2.000 omnivore :37
#> Mean :0.10374 Mean :1.831
#> 3rd Qu.:0.13750 3rd Qu.:3.000
#> Max. :0.50000 Max. :4.000
#>
#> biome N fat gest.month
#> aquatic : 22 4 :13 Min. : 0.20 Min. : 0.400
#> terrestrial:108 3 :11 1st Qu.: 4.65 1st Qu.: 1.405
#> 6 :10 Median : 8.55 Median : 5.000
#> 7 : 9 Mean :13.99 Mean : 5.624
#> 5 : 8 3rd Qu.:16.82 3rd Qu.: 8.365
#> 24 : 5 Max. :61.10 Max. :21.460
#> (Other):74
#> lacat.mo prot sugar energy
#> Min. : 0.300 Min. : 1.100 Min. : 0.02 Min. :0.360
#> 1st Qu.: 1.625 1st Qu.: 4.125 1st Qu.: 3.00 1st Qu.:0.965
#> Median : 4.500 Median : 6.750 Median : 4.70 Median :1.365
#> Mean : 6.092 Mean : 6.673 Mean : 4.94 Mean :1.680
#> 3rd Qu.: 8.225 3rd Qu.: 9.200 3rd Qu.: 6.60 3rd Qu.:2.045
#> Max. :42.000 Max. :15.800 Max. :14.00 Max. :5.890
#> NA's :16 NA's :16It can be very useful to generate numeric data summaries to help you and readers understand the data. This can also allow readers to extract information that is of interest to them but no necessarily to you. For example modelers and meta-analysts might want or need a bit of information which was not highlighted in your original analysis. Providing them the information upfront makes it more likely that they will cite you! It also saves them the trouble of asking for it if they really need it, and you the trouble of project files up and working up what they want.
This is not emphasized by Zuur et al, but is emphasized by Gerstner et al 2017.
Gerstner et al 2017. Will your paper be used in a meta-analysis? Making the reach of your research broader and longer lasting. Methods in Ecology & Evolution.
Correlation tables are excellent summaries of the response and predictor variables. This was discussed previously with regards to investigation of collinearity, along with scatter plot matrices.
milk %>%
group_by(diet) %>%
summarize(mass.mean = mean(mass.fem),
mass.sd = sd(mass.fem),
mass.n = n()
)
#> # A tibble: 3 x 4
#> diet mass.mean mass.sd mass.n
#> <fct> <dbl> <dbl> <int>
#> 1 carnivore 8646393. 32150168. 32
#> 2 herbivore 207403. 492666. 61
#> 3 omnivore 13396. 36423. 37These data appear in Figure 2a of the original publication
milk %>%
group_by(diet) %>%
summarize(fat.mean = mean(fat),
fat.sd = sd(fat),
fat.SE = plotrix::std.error(fat),
fat.n = n())
#> # A tibble: 3 x 5
#> diet fat.mean fat.sd fat.SE fat.n
#> <fct> <dbl> <dbl> <dbl> <int>
#> 1 carnivore 32.3 16.8 2.96 32
#> 2 herbivore 8.06 5.19 0.664 61
#> 3 omnivore 7.90 6.45 1.06 37