Homework 12
Key
2026-03-09
Instructions
Answer the following questions and complete the exercises in
RMarkdown. Please embed all of your code and push your final work to
your repository. Your final lab report should be organized, clean, and
run free from errors. Remember, you must remove the # for
the included code chunks to run. Be sure to add your name to the author
header above.
Make sure to use the formatting conventions of RMarkdown to make your report neat and clean!
Load the libraries
library(tidyverse)
library(janitor)
library(naniar)Data
For this homework, we will use amniota data from:
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") %>%
clean_names()- Do some exploratory analysis of the
amniotadata 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…- 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)- How many total NA’s are in the data set? Use the package
naniarto produce a summary, including percentages, of missing data in each column for theamniotadata.
miss_var_summary(amniota_tidy)## # A tibble: 36 × 3
## variable n_miss pct_miss
## <chr> <int> <num>
## 1 subspecies 21322 100
## 2 female_body_mass_at_maturity_g 21318 100.0
## 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 rows- Double check your replacement using
summary. Do you see any other potential issues? If so, fix them.female_maturity_dcolumn 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. : 0.0833 Min. : 5.00 Min. : 1.94
## 1st Qu.: 6.0000 1st Qu.: 29.91 1st Qu.: 27.75
## Median : 12.3083 Median : 63.92 Median : 51.60
## Mean : 16.4657 Mean : 105.28 Mean : 113.05
## 3rd Qu.: 22.0000 3rd Qu.: 151.88 3rd Qu.: 129.83
## Max. :211.0000 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. :0.000e+00 Min. :9.000e-01 Min. : 0.218
## 1st Qu.:1.300e+00 1st Qu.:1.320e+01 1st Qu.: 2.100
## Median :5.890e+00 Median :4.310e+01 Median : 5.100
## Mean :4.480e+03 Mean :4.139e+04 Mean : 22.252
## 3rd Qu.:4.385e+01 3rd Qu.:8.503e+02 3rd Qu.: 20.100
## Max. :2.250e+06 Max. :1.700e+07 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. : 0.0833 Min. : 30.44
## 1st Qu.: 17.00 1st Qu.: 16.5 1st Qu.: 5.5000 1st Qu.: 365.00
## Median : 29.25 Median : 27.5 Median : 10.7000 Median : 365.25
## Mean : 46.67 Mean : 36.8 Mean : 13.5209 Mean : 787.16
## 3rd Qu.: 59.50 3rd Qu.: 46.0 3rd Qu.: 18.2000 3rd Qu.: 913.00
## Max. :1762.00 Max. :345.0 Max. :177.0000 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. :0.0465 Min. : 0.3 Min. : 0.3
## 1st Qu.:0.3184 1st Qu.: 14.0 1st Qu.: 16.5
## Median :0.9993 Median : 40.7 Median : 48.4
## Mean :0.9067 Mean : 2075.8 Mean : 6196.8
## 3rd Qu.:1.0000 3rd Qu.: 220.0 3rd Qu.: 245.5
## Max. :4.8467 Max. :3700000.0 Max. :4545000.0
## 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>, …amniota_tidy <- amniota_tidy %>%
mutate(female_maturity_d=ifelse(female_maturity_d<0, NA, female_maturity_d))amniota_tidy <- amniota_tidy %>%
replace_with_na(replace= list(female_maturity_d=-30258.711))- Use
naniarto visualize the number of NA’s in each variable. Which variables have the most NA’s?
amniota_tidy %>%
gg_miss_var()
- Calculate the number of NAs in the
egg_mass_gcolumn sorted by taxonomic class; i.e. how many NA’s are present in theegg_mass_gcolumn in birds, mammals, and reptiles? Does this results make sense biologically? How do these results affect your interpretation of NA’s?**
NA’s 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) %>%
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 92.0Knit and Upload
Please knit your work as an .html file and upload to Canvas. Homework is due before the start of the next lab. No late work is accepted. Make sure to use the formatting conventions of RMarkdown to make your report neat and clean!