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library(tidyverse)
library(janitor)
library(skimr)
library(naniar)1. For this homework, we will use two different data sets.
Please load amniota and amphibio.
amniota data:
Myhrvold N, Baldridge E, Chan B, Sivam D, Freeman DL, Ernest SKM (2015).
“An amniote life-history database to perform comparative analyses with
birds, mammals, and reptiles.” Ecology, 96, 3109. doi:
10.1890/15-0846.1 (URL: https://doi.org/10.1890/15-0846.1).
amniota <- read_csv("data/amniota.csv")amphibio data:
Oliveira BF, São-Pedro VA, Santos-Barrera G, Penone C, Costa GC (2017).
“AmphiBIO, a global database for amphibian ecological traits.”
Scientific Data, 4, 170123. doi:
10.1038/sdata.2017.123 (URL: https://doi.org/10.1038/sdata.2017.123).
amphibio <- read_csv("data/amphibio.csv") %>% clean_names()2. Do some exploratory analysis of the amniota
data set. Use the function(s) of your choice. Try to get an idea of how
NA’s are represented in the data.
glimpse(amniota) #NA's are represented by -999## Rows: 21,322
## Columns: 36
## $ class <chr> "Aves", "Aves", "Aves", "Aves", …
## $ order <chr> "Accipitriformes", "Accipitrifor…
## $ family <chr> "Accipitridae", "Accipitridae", …
## $ genus <chr> "Accipiter", "Accipiter", "Accip…
## $ species <chr> "albogularis", "badius", "bicolo…
## $ subspecies <dbl> -999, -999, -999, -999, -999, -9…
## $ common_name <chr> "Pied Goshawk", "Shikra", "Bicol…
## $ female_maturity_d <dbl> -999.000, 363.468, -999.000, -99…
## $ litter_or_clutch_size_n <dbl> -999.000, 3.250, 2.700, -999.000…
## $ litters_or_clutches_per_y <dbl> -999, 1, -999, -999, 1, -999, -9…
## $ adult_body_mass_g <dbl> 251.500, 140.000, 345.000, 142.0…
## $ maximum_longevity_y <dbl> -999.00000, -999.00000, -999.000…
## $ gestation_d <dbl> -999, -999, -999, -999, -999, -9…
## $ weaning_d <dbl> -999, -999, -999, -999, -999, -9…
## $ birth_or_hatching_weight_g <dbl> -999, -999, -999, -999, -999, -9…
## $ weaning_weight_g <dbl> -999, -999, -999, -999, -999, -9…
## $ egg_mass_g <dbl> -999.00, 21.00, 32.00, -999.00, …
## $ incubation_d <dbl> -999.00, 30.00, -999.00, -999.00…
## $ fledging_age_d <dbl> -999.00, 32.00, -999.00, -999.00…
## $ longevity_y <dbl> -999.00000, -999.00000, -999.000…
## $ male_maturity_d <dbl> -999, -999, -999, -999, -999, -9…
## $ inter_litter_or_interbirth_interval_y <dbl> -999, -999, -999, -999, -999, -9…
## $ female_body_mass_g <dbl> 352.500, 168.500, 390.000, -999.…
## $ male_body_mass_g <dbl> 223.000, 125.000, 212.000, 142.0…
## $ no_sex_body_mass_g <dbl> -999.0, 123.0, -999.0, -999.0, -…
## $ egg_width_mm <dbl> -999, -999, -999, -999, -999, -9…
## $ egg_length_mm <dbl> -999, -999, -999, -999, -999, -9…
## $ fledging_mass_g <dbl> -999, -999, -999, -999, -999, -9…
## $ adult_svl_cm <dbl> -999.00, 30.00, 39.50, -999.00, …
## $ male_svl_cm <dbl> -999, -999, -999, -999, -999, -9…
## $ female_svl_cm <dbl> -999, -999, -999, -999, -999, -9…
## $ birth_or_hatching_svl_cm <dbl> -999, -999, -999, -999, -999, -9…
## $ female_svl_at_maturity_cm <dbl> -999, -999, -999, -999, -999, -9…
## $ female_body_mass_at_maturity_g <dbl> -999, -999, -999, -999, -999, -9…
## $ no_sex_svl_cm <dbl> -999, -999, -999, -999, -999, -9…
## $ no_sex_maturity_d <dbl> -999, -999, -999, -999, -999, -9…3. Do some exploratory analysis of the amphibio
data set. Use the function(s) of your choice. Try to get an idea of how
NA’s are represented in the data.
glimpse(amphibio) #NA's are represented by "NA"## Rows: 6,776
## Columns: 38
## $ id <chr> "Anf0001", "Anf0002", "Anf0003", "Anf0004", "A…
## $ order <chr> "Anura", "Anura", "Anura", "Anura", "Anura", "…
## $ family <chr> "Allophrynidae", "Alytidae", "Alytidae", "Alyt…
## $ genus <chr> "Allophryne", "Alytes", "Alytes", "Alytes", "A…
## $ species <chr> "Allophryne ruthveni", "Alytes cisternasii", "…
## $ fos <dbl> NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, 1, 1, 1, NA…
## $ ter <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ aqu <dbl> 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ arb <dbl> 1, 1, 1, 1, 1, 1, NA, NA, NA, NA, NA, NA, NA, …
## $ leaves <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ flowers <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ seeds <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ fruits <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ arthro <dbl> 1, 1, 1, NA, 1, 1, 1, 1, 1, NA, 1, 1, NA, NA, …
## $ vert <dbl> NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, 1, 1, N…
## $ diu <dbl> 1, NA, NA, NA, NA, NA, 1, 1, 1, NA, 1, 1, NA, …
## $ noc <dbl> 1, 1, 1, NA, 1, 1, 1, 1, 1, NA, 1, 1, 1, NA, N…
## $ crepu <dbl> 1, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, …
## $ wet_warm <dbl> NA, NA, NA, NA, 1, 1, NA, NA, NA, NA, 1, NA, N…
## $ wet_cold <dbl> 1, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, …
## $ dry_warm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dry_cold <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ body_mass_g <dbl> 31.00, 6.10, NA, NA, 2.31, 13.40, 21.80, NA, N…
## $ age_at_maturity_min_y <dbl> NA, 2.0, 2.0, NA, 3.0, 2.0, 3.0, NA, NA, NA, 4…
## $ age_at_maturity_max_y <dbl> NA, 2.0, 2.0, NA, 3.0, 3.0, 5.0, NA, NA, NA, 4…
## $ body_size_mm <dbl> 31.0, 50.0, 55.0, NA, 40.0, 55.0, 80.0, 60.0, …
## $ size_at_maturity_min_mm <dbl> NA, 27, NA, NA, NA, 35, NA, NA, NA, NA, NA, NA…
## $ size_at_maturity_max_mm <dbl> NA, 36.0, NA, NA, NA, 40.5, NA, NA, NA, NA, NA…
## $ longevity_max_y <dbl> NA, 6, NA, NA, NA, 7, 9, NA, NA, NA, NA, NA, N…
## $ litter_size_min_n <dbl> 300, 60, 40, NA, 7, 53, 300, 1500, 1000, NA, 2…
## $ litter_size_max_n <dbl> 300, 180, 40, NA, 20, 171, 1500, 1500, 1000, N…
## $ reproductive_output_y <dbl> 1, 4, 1, 4, 1, 4, 6, 1, 1, 1, 1, 1, 1, 1, NA, …
## $ offspring_size_min_mm <dbl> NA, 2.6, NA, NA, 5.4, 2.6, 1.5, NA, 1.5, NA, 1…
## $ offspring_size_max_mm <dbl> NA, 3.5, NA, NA, 7.0, 5.0, 2.0, NA, 1.5, NA, 1…
## $ dir <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N…
## $ lar <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, N…
## $ viv <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, N…
## $ obs <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…4. How many total NA’s are in each data set? Do these values make sense? Are NA’s represented by values?
amniota
For such a large data set, it is unlikely that there are no
NA’s.
miss_var_summary(amniota)## # A tibble: 36 × 3
## variable n_miss pct_miss
## <chr> <int> <num>
## 1 class 0 0
## 2 order 0 0
## 3 family 0 0
## 4 genus 0 0
## 5 species 0 0
## 6 subspecies 0 0
## 7 common_name 0 0
## 8 female_maturity_d 0 0
## 9 litter_or_clutch_size_n 0 0
## 10 litters_or_clutches_per_y 0 0
## # ℹ 26 more rowsamphibio
miss_var_summary(amphibio)## # A tibble: 38 × 3
## variable n_miss pct_miss
## <chr> <int> <num>
## 1 fruits 6774 100.
## 2 flowers 6772 99.9
## 3 seeds 6772 99.9
## 4 leaves 6752 99.6
## 5 dry_cold 6735 99.4
## 6 vert 6657 98.2
## 7 obs 6651 98.2
## 8 wet_cold 6625 97.8
## 9 crepu 6608 97.5
## 10 dry_warm 6572 97.0
## # ℹ 28 more rows5. Make any necessary replacements in the data such that all NA’s appear as “NA”.
amniota_tidy <- amniota %>%
replace_with_na_all(condition = ~.x == -999)Notice that the female_maturity_d column has a value of
-30258.711. This is likely a placeholder for missing data.
summary(amniota_tidy)## class order family genus
## Length:21322 Length:21322 Length:21322 Length:21322
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## species subspecies common_name female_maturity_d
## Length:21322 Min. : NA Length:21322 Min. :-30258.7
## Class :character 1st Qu.: NA Class :character 1st Qu.: 288.4
## Mode :character Median : NA Mode :character Median : 365.0
## Mean :NaN Mean : 691.2
## 3rd Qu.: NA 3rd Qu.: 819.3
## Max. : NA Max. : 9131.2
## NA's :21322 NA's :17849
## litter_or_clutch_size_n litters_or_clutches_per_y adult_body_mass_g
## Min. : 0.900 Min. : 0.120 Min. : 0
## 1st Qu.: 2.000 1st Qu.: 1.000 1st Qu.: 15
## Median : 2.800 Median : 1.050 Median : 44
## Mean : 3.826 Mean : 1.752 Mean : 37493
## 3rd Qu.: 4.150 3rd Qu.: 2.000 3rd Qu.: 238
## Max. :156.000 Max. :52.000 Max. :149000000
## NA's :8244 NA's :16374 NA's :4645
## maximum_longevity_y gestation_d weaning_d
## Min. : 0.083 Min. : 5.00 Min. : 1.94
## 1st Qu.: 6.000 1st Qu.: 29.91 1st Qu.: 27.75
## Median : 12.308 Median : 63.92 Median : 51.60
## Mean : 16.466 Mean : 105.28 Mean : 113.05
## 3rd Qu.: 22.000 3rd Qu.: 151.88 3rd Qu.: 129.83
## Max. :211.000 Max. :7396.92 Max. :1826.25
## NA's :15822 NA's :18926 NA's :19279
## birth_or_hatching_weight_g weaning_weight_g egg_mass_g
## Min. :0.00e+00 Min. : 1 Min. : 0.218
## 1st Qu.:1.30e+00 1st Qu.: 13 1st Qu.: 2.100
## Median :5.90e+00 Median : 43 Median : 5.100
## Mean :4.48e+03 Mean : 41386 Mean : 22.252
## 3rd Qu.:4.39e+01 3rd Qu.: 850 3rd Qu.: 20.100
## Max. :2.25e+06 Max. :17000000 Max. :1500.000
## NA's :17779 NA's :20258 NA's :15907
## incubation_d fledging_age_d longevity_y male_maturity_d
## Min. : 2.00 Min. : 1.0 Min. : 0.083 Min. : 30.44
## 1st Qu.: 17.00 1st Qu.: 16.5 1st Qu.: 5.500 1st Qu.: 365.00
## Median : 29.25 Median : 27.5 Median : 10.700 Median : 365.25
## Mean : 46.67 Mean : 36.8 Mean : 13.521 Mean : 787.16
## 3rd Qu.: 59.50 3rd Qu.: 46.0 3rd Qu.: 18.200 3rd Qu.: 913.00
## Max. :1762.00 Max. :345.0 Max. :177.000 Max. :9131.25
## NA's :17682 NA's :19478 NA's :15822 NA's :19278
## inter_litter_or_interbirth_interval_y female_body_mass_g male_body_mass_g
## Min. :0.047 Min. : 0 Min. : 0
## 1st Qu.:0.318 1st Qu.: 14 1st Qu.: 16
## Median :0.999 Median : 41 Median : 48
## Mean :0.907 Mean : 2076 Mean : 6197
## 3rd Qu.:1.000 3rd Qu.: 220 3rd Qu.: 246
## Max. :4.847 Max. :3700000 Max. :4545000
## NA's :19904 NA's :14113 NA's :14679
## no_sex_body_mass_g egg_width_mm egg_length_mm fledging_mass_g
## Min. : 0 Min. : 2.50 Min. : 2.50 Min. : 4.85
## 1st Qu.: 13 1st Qu.: 8.00 1st Qu.: 10.94 1st Qu.: 14.60
## Median : 35 Median : 13.00 Median : 19.98 Median : 24.80
## Mean : 68952 Mean : 22.99 Mean : 36.40 Mean : 452.27
## 3rd Qu.: 164 3rd Qu.: 35.90 3rd Qu.: 58.92 3rd Qu.: 107.00
## Max. :136000000 Max. :125.00 Max. :455.00 Max. :9992.00
## NA's :11663 NA's :20727 NA's :20702 NA's :21111
## adult_svl_cm male_svl_cm female_svl_cm birth_or_hatching_svl_cm
## Min. : 1.79 Min. : 1.57 Min. : 1.800 Min. : 0.400
## 1st Qu.: 9.50 1st Qu.: 21.41 1st Qu.: 5.756 1st Qu.: 2.450
## Median : 18.50 Median : 35.85 Median : 8.150 Median : 3.300
## Mean : 38.20 Mean : 50.44 Mean : 20.609 Mean : 12.099
## 3rd Qu.: 40.50 3rd Qu.: 63.39 3rd Qu.: 17.721 3rd Qu.: 5.256
## Max. :3049.00 Max. :315.20 Max. :1125.000 Max. :759.999
## NA's :14274 NA's :21040 NA's :20242 NA's :20085
## female_svl_at_maturity_cm female_body_mass_at_maturity_g no_sex_svl_cm
## Min. : 2.85 Min. : 30.0 Min. : 1.7
## 1st Qu.: 4.90 1st Qu.: 82.5 1st Qu.: 5.7
## Median : 6.00 Median : 97050.0 Median : 7.7
## Mean : 18.69 Mean : 97032.5 Mean : 20.0
## 3rd Qu.: 8.40 3rd Qu.:194000.0 3rd Qu.: 11.0
## Max. :580.00 Max. :194000.0 Max. :3300.0
## NA's :21120 NA's :21318 NA's :16052
## no_sex_maturity_d
## Min. : 33.0
## 1st Qu.: 365.3
## Median : 913.1
## Mean : 1604.5
## 3rd Qu.: 2008.9
## Max. :14610.0
## NA's :20860amniota_tidy %>%
filter(female_maturity_d=="-30258.711") ## # A tibble: 4 × 36
## class order family genus species subspecies common_name female_maturity_d
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl>
## 1 Aves Accipitri… Accip… Circ… melano… NA Pied Harri… -30259.
## 2 Aves Passerifo… Vidui… Vidua funerea NA Dusky Indi… -30259.
## 3 Aves Passerifo… Vidui… Vidua nigeri… NA Quailfinch… -30259.
## 4 Aves Passerifo… Vidui… Vidua purpur… NA Purple Ind… -30259.
## # ℹ 28 more variables: litter_or_clutch_size_n <dbl>,
## # litters_or_clutches_per_y <dbl>, adult_body_mass_g <dbl>,
## # maximum_longevity_y <dbl>, gestation_d <dbl>, weaning_d <dbl>,
## # birth_or_hatching_weight_g <dbl>, weaning_weight_g <dbl>, egg_mass_g <dbl>,
## # incubation_d <dbl>, fledging_age_d <dbl>, longevity_y <dbl>,
## # male_maturity_d <dbl>, inter_litter_or_interbirth_interval_y <dbl>,
## # female_body_mass_g <dbl>, male_body_mass_g <dbl>, …I am using ifelse here because I need to be careful with replacing values.
amniota_tidy <- amniota_tidy %>%
mutate(female_maturity_d=ifelse(female_maturity_d<0, NA, female_maturity_d))6. Use the package naniar to produce a summary,
including percentages, of missing data in each column for the
amniota data.
miss_var_summary(amniota_tidy) #how many NAs with percentages## # A tibble: 36 × 3
## variable n_miss pct_miss
## <chr> <int> <num>
## 1 subspecies 21322 100
## 2 female_body_mass_at_maturity_g 21318 100.
## 3 female_svl_at_maturity_cm 21120 99.1
## 4 fledging_mass_g 21111 99.0
## 5 male_svl_cm 21040 98.7
## 6 no_sex_maturity_d 20860 97.8
## 7 egg_width_mm 20727 97.2
## 8 egg_length_mm 20702 97.1
## 9 weaning_weight_g 20258 95.0
## 10 female_svl_cm 20242 94.9
## # ℹ 26 more rows7. Use the package naniar to produce a summary,
including percentages, of missing data in each column for the
amphibio data.
miss_var_summary(amphibio)## # A tibble: 38 × 3
## variable n_miss pct_miss
## <chr> <int> <num>
## 1 fruits 6774 100.
## 2 flowers 6772 99.9
## 3 seeds 6772 99.9
## 4 leaves 6752 99.6
## 5 dry_cold 6735 99.4
## 6 vert 6657 98.2
## 7 obs 6651 98.2
## 8 wet_cold 6625 97.8
## 9 crepu 6608 97.5
## 10 dry_warm 6572 97.0
## # ℹ 28 more rows8. For the amniota data, calculate the number of
NAs in the egg_mass_g column sorted by taxonomic class;
i.e. how many NA’s are present in the egg_mass_g column in
birds, mammals, and reptiles? Does this results make sense biologically?
How do these results affect your interpretation of NA’s?
NAs could reflect missing data (no observations were recorded) or it could reflect data that are truly not applicable.
amniota_tidy %>%
select(class, egg_mass_g) %>%
group_by(class) %>%
naniar::miss_var_summary(order=T)## # A tibble: 3 × 4
## # Groups: class [3]
## class variable n_miss pct_miss
## <chr> <chr> <int> <num>
## 1 Aves egg_mass_g 4914 50.1
## 2 Mammalia egg_mass_g 4953 100
## 3 Reptilia egg_mass_g 6040 92.09. The amphibio data have variables that
classify species as fossorial (burrowing), terrestrial, aquatic, or
arboreal.Calculate the number of NA’s in each of these variables. Do you
think that the authors intend us to think that there are NA’s in these
columns or could they represent something else? Explain.
amphibio %>%
select(fos, ter, arb, aqu) %>%
miss_var_summary()## # A tibble: 4 × 3
## variable n_miss pct_miss
## <chr> <int> <num>
## 1 fos 6053 89.3
## 2 arb 4347 64.2
## 3 aqu 2810 41.5
## 4 ter 1104 16.310. Now that we know how NA’s are represented in the
amniota data, how would you load the data such that the
values which represent NA’s are automatically converted?
#read_csv(file = "data/amniota.csv", na = c("-999"))11. Please complete the class project survey at: BIS 15L Group Project
Please be sure that you check the keep md file in the
knit preferences.