Module 06
by Anthony Di Fiore and Christopher A. Schmitt
Exploratory Data Analysis
Objectives
The objective of this module to begin exploring data using the summary functions and graphing abilities of R.
Preliminaries
GO TO: https://github.com/fuzzyatelin/fuzzyatelin.github.io/tree/master/AN588_Fall23/, select the
.csvversion of the Country-Data-2016 file, then press theRAWbutton, highlight, and copy the text to a text editor and save it locally. Do the same for the KamilarAndCooperData file.Install these packages in R: {dplyr}, {ggplot2}. These are part of the {tidyverse}, which you can also install as a shorthand for them.
The Backstory
R has some very easy to use functions for taking a quick tour of your data. We have seen some of these already (e.g., head(), tail(), and str()), and you should always use these right after loading in a dataset to work with. Also useful are dim() to return the number of rows and columns in a data frame, names(), colnames(), and sometimes rownames().
As an aside, you can use the
attach()function to make variables within data frames accessible in R with fewer keystrokes. Theattach()function binds the variables from the data frame named as an argument to the local namespace so that as long as the data frame is attached, variables can be called by their names without explicitly referring to the data frame. That is, if youattach()a data frame, then you do not need to use the $ operator or bracket notation to refer to a particular variable. It is important to remember todetach()data frames when finished. It is also possible to attach multiple data frames (and the same data frame multiple times), and, if these share variable names, then the more recently attached one will mask the other. Thus, it is best to attach only one data frame at a time (or none at all).
The
with()function accomplishes much the same thing asattach()but is self-contained and cleaner, especially for use in functions. If you usewith(), all code to be run should be included as an argument of the function.
Some Useful Procedures for Exploratory Data Analysis
For univariate data
CHALLENGE 1
Summary: The summary() function provides a quick overview of each column in a data frame. For numeric variables, this includes the minimum, 25th percentile, median, mean, 75th percentile, and maximum of the data, as well as a count of NA (missing values). For factors, it includes a count of each factor.
Load the Country-Data-2016 dataset into a data frame variable, d, and summarize the variables in that data frame. You can load the file any way you want, e.g., load from a local file, or you can access the data straight from GitHub, as in the code below.
library(curl)## Using libcurl 7.54.0 with LibreSSL/2.6.5f <- curl("https://raw.githubusercontent.com/fuzzyatelin/fuzzyatelin.github.io/master/AN588_Fall23/Country-Data-2016.csv")
d <- read.csv(f, header = TRUE, sep = ",", stringsAsFactors = FALSE)
head(d)## country population area govt_form birthrate
## 1 Afghanistan 32564342 652230 islamic republic 38.6
## 2 Albania 3029278 28748 republic 12.9
## 3 Algeria 39542166 2381741 republic 23.7
## 4 American Samoa 54343 199 territory of the United States 22.9
## 5 Andorra 85580 468 constitutional monarchy 8.1
## 6 Angola 19625353 1246700 republic 38.8
## deathrate life_expect mammals birds reptiles amphibians fishes mollucs
## 1 13.9 50.9 11 17 1 1 5 0
## 2 6.6 78.1 3 10 4 2 44 49
## 3 4.3 76.6 14 14 8 3 40 10
## 4 4.8 75.1 1 8 6 0 12 5
## 5 7.0 82.7 2 2 1 0 0 3
## 6 11.5 55.6 17 26 5 0 51 5
## other_inverts plants fungi_protists
## 1 2 5 0
## 2 13 0 0
## 3 26 18 0
## 4 59 1 0
## 5 4 0 0
## 6 4 34 0summary(d)## country population area govt_form
## Length:248 Min. :3.000e+01 Min. : 0 Length:248
## Class :character 1st Qu.:2.991e+05 1st Qu.: 1769 Class :character
## Mode :character Median :4.912e+06 Median : 69700 Mode :character
## Mean :2.999e+07 Mean : 610952
## 3rd Qu.:1.803e+07 3rd Qu.: 398754
## Max. :1.367e+09 Max. :17098242
## NA's :6 NA's :1
## birthrate deathrate life_expect mammals
## Min. : 0.00 Min. : 0.00 Min. :49.80 Min. : 0.00
## 1st Qu.:11.40 1st Qu.: 5.65 1st Qu.:67.40 1st Qu.: 3.00
## Median :16.40 Median : 7.40 Median :74.70 Median : 8.00
## Mean :18.95 Mean : 7.61 Mean :72.19 Mean : 13.85
## 3rd Qu.:24.35 3rd Qu.: 9.40 3rd Qu.:78.40 3rd Qu.: 15.00
## Max. :45.50 Max. :14.90 Max. :89.50 Max. :188.00
## NA's :17 NA's :17 NA's :19 NA's :3
## birds reptiles amphibians fishes
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.00
## 1st Qu.: 6.00 1st Qu.: 2.000 1st Qu.: 0.000 1st Qu.: 11.00
## Median : 12.00 Median : 5.000 Median : 0.000 Median : 25.00
## Mean : 17.82 Mean : 8.331 Mean : 9.849 Mean : 32.84
## 3rd Qu.: 19.00 3rd Qu.: 8.000 3rd Qu.: 4.000 3rd Qu.: 43.00
## Max. :165.00 Max. :139.000 Max. :215.000 Max. :249.00
## NA's :3 NA's :3 NA's :3 NA's :3
## mollucs other_inverts plants fungi_protists
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.0000
## 1st Qu.: 0.00 1st Qu.: 3.00 1st Qu.: 2.00 1st Qu.: 0.0000
## Median : 1.00 Median : 11.00 Median : 10.00 Median : 0.0000
## Mean : 9.62 Mean : 32.57 Mean : 60.78 Mean : 0.6082
## 3rd Qu.: 6.00 3rd Qu.: 33.00 3rd Qu.: 44.00 3rd Qu.: 0.0000
## Max. :301.00 Max. :340.00 Max. :1856.00 Max. :12.0000
## NA's :3 NA's :3 NA's :3 NA's :3names(d)## [1] "country" "population" "area" "govt_form"
## [5] "birthrate" "deathrate" "life_expect" "mammals"
## [9] "birds" "reptiles" "amphibians" "fishes"
## [13] "mollucs" "other_inverts" "plants" "fungi_protists"- What are the median area and population size of all countries in the dataset? HINT: There are a couple of ways to do this… try
summary()andmedian()(for the latter, you’ll need to use thena.rm = TRUEargument) - Create a new pop_density variable in your data frame which is population / area. What are the 10 most dense countries? The 10 least dense? HINT: check out the
order()function
d$density <- d$population/d$area
d <- d[order(-d$density), ]
d[1:10, ]## country population area
## 130 Macau 592731 28.0
## 145 Monaco 30535 2.0
## 97 Holy See (Vatican City) 842 0.1
## 199 Singapore 5674472 697.0
## 99 Hong Kong 7141106 1108.0
## 84 Gibraltar 29258 7.0
## 17 Bahrain 1346613 760.0
## 135 Maldives 393253 298.0
## 137 Malta 413965 316.0
## 24 Bermuda 70196 54.0
## govt_form birthrate deathrate life_expect
## 130 special administrative region of China 8.9 4.2 84.5
## 145 constitutional monarchy 6.7 9.2 89.5
## 97 monarchy NA NA NA
## 199 republic 8.3 3.4 84.7
## 99 special administrative region of China 9.2 7.1 82.9
## 84 British overseas territory 14.1 8.4 79.3
## 17 constitutional monarchy 13.7 2.7 78.7
## 135 republic 15.8 3.9 75.4
## 137 republic 10.2 9.1 80.2
## 24 British overseas territory 11.3 8.2 81.2
## mammals birds reptiles amphibians fishes mollucs other_inverts plants
## 130 0 4 1 0 5 0 1 0
## 145 3 0 0 0 15 0 3 0
## 97 1 0 0 0 0 0 0 0
## 199 13 17 6 0 27 0 173 58
## 99 3 20 5 5 13 1 7 9
## 84 4 5 0 0 18 3 2 0
## 17 3 6 4 0 10 0 13 0
## 135 2 0 3 0 24 0 46 0
## 137 2 5 1 0 22 3 2 4
## 24 4 1 4 0 26 0 28 8
## fungi_protists density
## 130 0 21168.964
## 145 0 15267.500
## 97 0 8420.000
## 199 0 8141.280
## 99 0 6445.042
## 84 0 4179.714
## 17 0 1771.859
## 135 0 1319.641
## 137 0 1310.016
## 24 0 1299.926d <- d[order(d$density), ]
d[1:10, ]## country population area
## 206 South Georgia and South Sandwich Islands 30 3903
## 86 Greenland 57733 2166086
## 70 Falkland Islands 3361 12173
## 175 Pitcairn Islands 48 47
## 146 Mongolia 2992908 1564116
## 245 Western Sahara 570866 266000
## 76 French Guiana 181000 83534
## 152 Namibia 2212307 824292
## 13 Australia 22751014 7741220
## 101 Iceland 331918 103000
## govt_form birthrate deathrate life_expect mammals birds
## 206 British overseas territory NA NA NA 3 6
## 86 autonomous region of Denmark 14.5 8.5 72.1 9 3
## 70 British overseas territory NA NA NA 4 9
## 175 British overseas territory NA NA NA 1 10
## 146 republic 20.3 6.4 69.3 11 24
## 245 autonomous region of Morocco 30.2 8.3 62.6 10 3
## 76 overseas territory of France 0.0 0.0 76.1 8 8
## 152 republic 19.8 13.9 51.6 14 28
## 13 parliamentary monarchy 12.2 7.1 82.2 63 50
## 101 republic 13.9 6.3 83.0 6 4
## reptiles amphibians fishes mollucs other_inverts plants fungi_protists
## 206 0 0 0 0 0 0 0
## 86 0 0 9 0 0 1 0
## 70 0 0 5 0 0 5 0
## 175 0 0 10 5 11 7 0
## 146 0 0 2 0 3 0 0
## 245 1 0 31 0 3 0 0
## 76 7 3 29 0 0 18 0
## 152 5 1 33 0 4 28 0
## 13 43 47 118 174 340 92 1
## 101 0 0 16 0 0 0 0
## density
## 206 0.007686395
## 86 0.026653143
## 70 0.276102851
## 175 1.021276596
## 146 1.913482120
## 245 2.146112782
## 76 2.166782388
## 152 2.683887506
## 13 2.938944249
## 101 3.222504854- Extract data from the 20 largest countries into a new variable. What are the median area and population size of these countries?
- Extract data from all countries beginning with the letters “A” through “F”. What are the mean area and population size of these countries?
new <- d[grep("^[A-F]", d$country), ]
summary(new)## country population area govt_form
## Length:78 Min. :5.960e+02 Min. : 14 Length:78
## Class :character 1st Qu.:2.991e+05 1st Qu.: 4066 Class :character
## Mode :character Median :4.785e+06 Median : 51148 Mode :character
## Mean :3.507e+07 Mean : 918248
## 3rd Qu.:1.469e+07 3rd Qu.: 466498
## Max. :1.367e+09 Max. :14000000
## NA's :4
## birthrate deathrate life_expect mammals
## Min. : 0.00 Min. : 0.000 Min. :49.80 Min. : 0.0
## 1st Qu.:11.65 1st Qu.: 5.850 1st Qu.:68.75 1st Qu.: 3.0
## Median :15.90 Median : 7.700 Median :75.50 Median : 7.0
## Mean :18.77 Mean : 7.861 Mean :72.25 Mean :13.4
## 3rd Qu.:23.30 3rd Qu.: 9.500 3rd Qu.:78.40 3rd Qu.:14.0
## Max. :42.00 Max. :14.400 Max. :82.70 Max. :81.0
## NA's :7 NA's :7 NA's :7
## birds reptiles amphibians fishes
## Min. : 0.00 Min. : 0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 6.00 1st Qu.: 2.000 1st Qu.: 0.00 1st Qu.: 10.00
## Median : 11.00 Median : 5.000 Median : 0.00 Median : 24.50
## Mean : 18.62 Mean : 7.397 Mean : 11.86 Mean : 29.54
## 3rd Qu.: 18.00 3rd Qu.: 8.000 3rd Qu.: 3.00 3rd Qu.: 41.50
## Max. :165.00 Max. :43.000 Max. :215.00 Max. :133.00
##
## mollucs other_inverts plants fungi_protists
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. :0.0000
## 1st Qu.: 0.00 1st Qu.: 4.00 1st Qu.: 2.25 1st Qu.:0.0000
## Median : 1.00 Median : 11.00 Median : 10.00 Median :0.0000
## Mean : 10.17 Mean : 23.63 Mean : 70.64 Mean :0.6026
## 3rd Qu.: 5.00 3rd Qu.: 25.25 3rd Qu.: 41.75 3rd Qu.:0.0000
## Max. :174.00 Max. :340.00 Max. :1856.00 Max. :8.0000
##
## density
## Min. : 0.2761
## 1st Qu.: 24.5932
## Median : 75.0297
## Mean : 162.4785
## 3rd Qu.: 140.7140
## Max. :1771.8592
## NA's :4Or, alternatively…
mean(new$population, na.rm = TRUE)## [1] 35065172mean(new$area, na.rm = TRUE)## [1] 918247.7CHALLENGE 2
Boxplots: The boxplot() function provides a box-and-whiskers visual representation of the five-number summary plus outliers that go beyond the bulk of the data. The function balks if you pass it nonnumeric data, so you may need to reference columns specifically using either bracket notation or the $ operator.
Barplots : The barplot() function is useful for crude data, with bar height proportional to the value of the variable. The function dotchart() provides a similar graphical summary.
Make boxplots of the raw population and area data, then do the same after log() transforming these variables.
NOTE: The par() command will let you set up a grid of panel in which to plot. Here, I set up a two row by three column grid.
par(mfrow = c(2, 3))
boxplot(d$population)
boxplot(log(d$population))
boxplot(d$area)
boxplot(log(d$area))
barplot(d$population)
barplot(d$area)
CHALLENGE 3
Histograms : The hist() function returns a histogram showing the complete empirical distribution of the data in binned categories, which is useful for checking skewwness of the data, symmetry, multi-modality, etc. Setting the argument freq=FALSE will scale the Y axis to represent the proportion of observations falling into each bin rather than the count.
Make histograms of the log() transformed population and area data from the Country-Data-2016 file. Explore what happens if you set freq=FALSE versus the default of freq=TRUE. Try looking at other variables as well.
par(mfrow = c(1, 2)) # gives us two panels
attach(d)
hist(log(population), freq = FALSE, col = "red", main = "Plot 1", xlab = "log(population size)",
ylab = "density", ylim = c(0, 0.2))
hist(log(area), freq = FALSE, col = "red", main = "Plot 2", xlab = "log(area)",
ylab = "density", ylim = c(0, 0.2))
NOTE: You can add a line to your histograms (e.g., to show the mean value for a variable) using the abline() command, with arguments. For exmaple, to show a single vertical line representing the mean log(population size), you would add the argument v=mean(log(population)))
Density plot : The density() function computes a non-parametric estimate of the distribution of a variable, which can be combined with plot() to also yield a graphical view of the distribution of the data. If your data have missing values, then you need to add the argument na.rm=TRUE to the density() function. To superimpose a density() curve on a histogram, you can use the lines(density()) function.
par(mfrow = c(1, 1)) # set up one panel and redraw the log(population) histogram
hist(log(population), freq = FALSE, col = "white", main = "My Plot with Mean and Density",
xlab = "log(population size)", ylab = "density", ylim = c(0, 0.2))
abline(v = mean(log(population), na.rm = TRUE), col = "blue")
lines(density(log(population), na.rm = TRUE), col = "green")
detach(d)CHALLENGE 4
Tables : the table() function can be used to summarize counts and proportions for categorical variables in your dataset.
Using the table() function, find what is the most common form of government in the Country-Data-2016 dataset. How many countries have that form? HINT: We can combine table() with sort() and the argument decreasing=TRUE to get the desired answered straight away:
sort(table(d$govt_form), decreasing = TRUE)##
## republic constitutional monarchy
## 127 33
## British overseas territory overseas territory of France
## 12 7
## presidential federal republic monarchy
## 7 6
## parliamentary monarchy territory of the United States
## 5 5
## parliamentary democracy territory of Australia
## 4 4
## autonomous territory of the Netherlands federal republic
## 3 3
## islamic republic socialist republic
## 3 3
## absolute monarchy autonomous region of Denmark
## 2 2
## autonomous territory of New Zealand overseas community of France
## 2 2
## parliamentary federal republic special administrative region of China
## 2 2
## territory of Norway autonomous region
## 2 1
## autonomous region of France autonomous region of Morocco
## 1 1
## federation foreign-administrated territory
## 1 1
## islamic-socialist peoples republic parliamentary republic
## 1 1
## peoples republic territory of France
## 1 1
## territory of New Zealand territory of the Netherlands
## 1 1For multivariate data
CHALLENGE 5
Multiple boxplots or histograms can be laid out side-by-side or overlaid. For boxplots, the ~ operator can be read as “by”.
Read in the dataset KamilarAndCooperData, which contains a host of information from about 213 living primate species.
Spend some time exploring the data and then make boxplots of log(female body mass) ~ family. Try doing this with {base} graphics and then look at how we might do in in {ggplot2}, which provides a standard “grammar of graphics” (see the {ggplot2} documentation)
f <- curl("https://raw.githubusercontent.com/fuzzyatelin/fuzzyatelin.github.io/master/AN588_Fall23/KamilarAndCooperData.csv")
d <- read.csv(f, header = TRUE, stringsAsFactors = FALSE)
attach(d)
head(d)## Scientific_Name Family Genus Species
## 1 Allenopithecus_nigroviridis Cercopithecidae Allenopithecus nigroviridis
## 2 Allocebus_trichotis Cercopithecidae Allocebus trichotis
## 3 Alouatta_belzebul Atelidae Alouatta belzebul
## 4 Alouatta_caraya Atelidae Alouatta caraya
## 5 Alouatta_guariba Atelidae Alouatta guariba
## 6 Alouatta_palliata Atelidae Alouatta palliata
## Brain_Size_Species_Mean Brain_Size_Female_Mean Brain_size_Ref
## 1 58.02 53.70 Isler et al 2008
## 2 NA NA
## 3 52.84 51.19 Isler et al 2008
## 4 52.63 47.80 Isler et al 2008
## 5 51.70 49.08 Isler et al 2008
## 6 49.88 48.04 Isler et al 2008
## Body_mass_male_mean Body_mass_female_mean Mass_Dimorphism
## 1 6130 3180 1.928
## 2 92 84 1.095
## 3 7270 5520 1.317
## 4 6525 4240 1.539
## 5 5800 4550 1.275
## 6 7150 5350 1.336
## Mass_Ref MeanGroupSize AdultMales AdultFemale AdultSexRatio
## 1 Isler et al 2008 NA NA NA NA
## 2 Smith and Jungers 1997 1.00 1.00 1.0 NA
## 3 Isler et al 2008 7.00 1.00 1.0 1.00
## 4 Isler et al 2008 8.00 2.30 3.3 1.43
## 5 Isler et al 2008 6.53 1.37 2.2 1.61
## 6 Isler et al 2008 12.00 2.90 6.3 2.17
## Social_Organization_Ref
## 1
## 2 Kappeler 1997
## 3 Campbell et al 2007
## 4 van Schaik et al. 1999; Kappeler and Pereira 2003; Nunn & van Schaik 2000
## 5 Campbell et al 2007
## 6 van Schaik et al. 1999; Kappeler and Pereira 2003; Nunn & van Schaik 2000
## InterbirthInterval_d Gestation WeaningAge_d MaxLongevity_m LitterSz
## 1 NA NA 106.15 276.0 1.01
## 2 NA NA NA NA 1.00
## 3 NA NA NA NA NA
## 4 337.62 187 323.16 243.6 1.01
## 5 NA NA NA NA NA
## 6 684.37 186 495.60 300.0 1.02
## Life_History_Ref GR_MidRangeLat_dd Precip_Mean_mm Temp_Mean_degC AET_Mean_mm
## 1 Jones et al. 2009 -0.17 1574.0 25.2 1517.8
## 2 -16.59 1902.3 20.3 1388.2
## 3 -6.80 1643.5 24.9 1286.6
## 4 Jones et al. 2009 -20.34 1166.4 22.9 1193.1
## 5 -21.13 1332.3 19.6 1225.7
## 6 Jones et al. 2009 6.95 1852.6 23.7 1300.0
## PET_Mean_mm Climate_Ref HomeRange_km2 HomeRangeRef DayLength_km
## 1 1589.4 Jones et al. 2009 NA NA
## 2 1653.7 Jones et al. 2009 NA NA
## 3 1549.8 Jones et al. 2009 NA NA
## 4 1404.9 Jones et al. 2009 NA 0.40
## 5 1332.2 Jones et al. 2009 0.03 Jones et al. 2009 NA
## 6 1633.9 Jones et al. 2009 0.19 Jones et al. 2009 0.32
## DayLengthRef Territoriality Fruit Leaves Fauna DietRef1
## 1 NA NA
## 2 NA NA
## 3 NA 57.3 19.1 0.0 Campbell et al. 2007
## 4 Nunn et al. 2003 NA 23.8 67.7 0.0 Campbell et al. 2007
## 5 NA 5.2 73.0 0.0 Campbell et al. 2007
## 6 Nunn et al. 2003 0.6506 33.1 56.4 0.0 Campbell et al. 2007
## Canine_Dimorphism Canine_Dimorphism_Ref Feed Move Rest Social
## 1 2.210 Plavcan & Ruff 2008 NA NA NA NA
## 2 NA NA NA NA NA
## 3 1.811 Plavcan & Ruff 2008 13.75 18.75 57.30 10.00
## 4 1.542 Plavcan & Ruff 2008 15.90 17.60 61.60 4.90
## 5 1.783 Plavcan & Ruff 2008 18.33 14.33 64.37 3.00
## 6 1.703 Plavcan & Ruff 2008 17.94 12.32 66.14 3.64
## Activity_Budget_Ref
## 1
## 2
## 3 Campbell et al. 2007
## 4 Campbell et al. 2007
## 5 Campbell et al. 2007
## 6 Campbell et al. 2007summary(d)## Scientific_Name Family Genus Species
## Length:213 Length:213 Length:213 Length:213
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Brain_Size_Species_Mean Brain_Size_Female_Mean Brain_size_Ref
## Min. : 1.63 Min. : 1.66 Length:213
## 1st Qu.: 13.95 1st Qu.: 13.23 Class :character
## Median : 61.45 Median : 57.20 Mode :character
## Mean : 68.11 Mean : 65.24
## 3rd Qu.: 88.48 3rd Qu.: 86.80
## Max. :491.27 Max. :480.15
## NA's :42 NA's :48
## Body_mass_male_mean Body_mass_female_mean Mass_Dimorphism Mass_Ref
## Min. : 31 Min. : 30.0 Min. :0.841 Length:213
## 1st Qu.: 865 1st Qu.: 835.5 1st Qu.:1.013 Class :character
## Median : 4290 Median : 3039.0 Median :1.109 Mode :character
## Mean : 8112 Mean : 5396.5 Mean :1.246
## 3rd Qu.: 7815 3rd Qu.: 6390.0 3rd Qu.:1.409
## Max. :170400 Max. :97500.0 Max. :2.688
## NA's :18 NA's :18 NA's :18
## MeanGroupSize AdultMales AdultFemale AdultSexRatio
## Min. : 1.00 Min. : 0.900 Min. : 1.000 Min. : 0.500
## 1st Qu.: 4.00 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 1.000
## Median : 7.80 Median : 1.500 Median : 2.900 Median : 1.450
## Mean :15.06 Mean : 2.516 Mean : 5.049 Mean : 2.305
## 3rd Qu.:18.50 3rd Qu.: 3.100 3rd Qu.: 7.100 3rd Qu.: 2.770
## Max. :90.00 Max. :16.000 Max. :25.200 Max. :15.600
## NA's :60 NA's :68 NA's :68 NA's :84
## Social_Organization_Ref InterbirthInterval_d Gestation WeaningAge_d
## Length:213 Min. : 144.5 Min. : 59.99 Min. : 40.0
## Class :character 1st Qu.: 365.0 1st Qu.:142.00 1st Qu.: 122.4
## Mode :character Median : 476.9 Median :165.04 Median : 237.7
## Mean : 572.1 Mean :163.47 Mean : 310.0
## 3rd Qu.: 741.4 3rd Qu.:180.75 3rd Qu.: 383.4
## Max. :2007.5 Max. :256.00 Max. :1260.8
## NA's :103 NA's :75 NA's :95
## MaxLongevity_m LitterSz Life_History_Ref GR_MidRangeLat_dd
## Min. :103.0 Min. :0.990 Length:213 Min. :-24.500
## 1st Qu.:233.4 1st Qu.:1.010 Class :character 1st Qu.:-13.625
## Median :303.6 Median :1.010 Mode :character Median : -0.760
## Mean :327.3 Mean :1.181 Mean : -1.796
## 3rd Qu.:394.2 3rd Qu.:1.050 3rd Qu.: 6.785
## Max. :720.0 Max. :2.520 Max. : 35.880
## NA's :66 NA's :47 NA's :38
## Precip_Mean_mm Temp_Mean_degC AET_Mean_mm PET_Mean_mm
## Min. : 419 Min. : 2.60 Min. : 453.1 Min. : 842.5
## 1st Qu.:1190 1st Qu.:21.95 1st Qu.:1091.0 1st Qu.:1512.3
## Median :1542 Median :24.30 Median :1291.1 Median :1566.9
## Mean :1543 Mean :23.13 Mean :1253.1 Mean :1553.2
## 3rd Qu.:1857 3rd Qu.:25.20 3rd Qu.:1444.2 3rd Qu.:1622.2
## Max. :2794 Max. :27.40 Max. :1828.3 Max. :1927.3
## NA's :38 NA's :38 NA's :38 NA's :38
## Climate_Ref HomeRange_km2 HomeRangeRef DayLength_km
## Length:213 Min. : 0.0020 Length:213 Min. : 0.250
## Class :character 1st Qu.: 0.0600 Class :character 1st Qu.: 0.708
## Mode :character Median : 0.2750 Mode :character Median : 1.212
## Mean : 1.9379 Mean : 1.551
## 3rd Qu.: 0.8995 3rd Qu.: 1.800
## Max. :28.2400 Max. :11.000
## NA's :65 NA's :104
## DayLengthRef Territoriality Fruit Leaves
## Length:213 Min. : 0.2250 Min. : 1.00 Length:213
## Class :character 1st Qu.: 0.8555 1st Qu.:27.00 Class :character
## Mode :character Median : 1.5923 Median :48.00 Mode :character
## Mean : 2.2289 Mean :47.74
## 3rd Qu.: 2.6867 3rd Qu.:68.00
## Max. :15.5976 Max. :97.00
## NA's :109 NA's :98
## Fauna DietRef1 Canine_Dimorphism Canine_Dimorphism_Ref
## Length:213 Length:213 Min. :0.880 Length:213
## Class :character Class :character 1st Qu.:1.109 Class :character
## Mode :character Mode :character Median :1.560 Mode :character
## Mean :1.617
## 3rd Qu.:1.883
## Max. :5.263
## NA's :92
## Feed Move Rest Social
## Min. : 9.00 Min. : 3.00 Min. : 4.00 Min. : 0.900
## 1st Qu.:21.75 1st Qu.:14.93 1st Qu.:18.38 1st Qu.: 3.500
## Median :33.30 Median :21.00 Median :30.48 Median : 5.400
## Mean :33.08 Mean :21.67 Mean :34.26 Mean : 7.369
## 3rd Qu.:43.08 3rd Qu.:26.90 3rd Qu.:52.40 3rd Qu.:10.000
## Max. :63.90 Max. :70.60 Max. :78.50 Max. :23.500
## NA's :141 NA's :143 NA's :143 NA's :136
## Activity_Budget_Ref
## Length:213
## Class :character
## Mode :character
##
##
##
## Plotting using {base} graphics…
boxplot(log(Body_mass_female_mean) ~ Family, d)
detach(d)Alternatively, plotting using {ggplot2}… notice how each novel command is being added to the already-saved initial command? This is for ease of reading and understanding how we’re building the figure. All of this could also go on one line (and is read by the computer as such in the final version of the object p), but to make it easier to understand what each component it doing, we often build graphs in {ggplot2} using the below method:
library(ggplot2)
p <- ggplot(data = d, aes(x = Family, y = log(Body_mass_female_mean))) #define the variables
p <- p + geom_boxplot() #graph them in a boxplot
p <- p + theme(axis.text.x = element_text(angle = 90)) #put x-axis names at 90deg
p <- p + ylab("log(Female Body Mass)") #rename y-axis title
p #show me the graph
CHALLENGE 6
Scatterplots : Scatterplots are a natural tool for visualizing two continuous variables and can be made easily with the plot(x=XXX, y=YYY) function in {base} graphics (where XXX* and YYY** denote the names of the two variables you wish to plot). Transformations of the variables, e.g., log or square-root (sqrt()), may be necessary for effective visualization.
Again using data from the KamilarAndCooperData dataset, plot the relationship between female body size and female brain size. Then, play with log transforming the data and plot again.
attach(d)
par(mfrow = c(1, 2))
plot(x = Body_mass_female_mean, y = Brain_Size_Female_Mean)
plot(x = log(Body_mass_female_mean), y = log(Brain_Size_Female_Mean))
detach(d)The grammar for {ggplot2} is a bit more complicated… see if you can follow it in the example below.
p <- ggplot(data = d, aes(x = log(Body_mass_female_mean), y = log(Brain_Size_Female_Mean),
color = factor(Family))) # first, we build a plot object and color points by Family
p <- p + xlab("log(Female Body Mass)") + ylab("log(Female Brain Size)") # then we modify the axis labels
p <- p + geom_point() # then we make a scatterplot
p <- p + theme(legend.position = "bottom", legend.title = element_blank()) # then we modify the legend
p # and, finally, we plot the object
Using {ggplot2}, we can also easily set up a grid for “faceting”" by a grouping variable…
p <- p + facet_wrap(~Family, ncol = 4)
p <- p + theme(legend.position = "none")
p
And we can easily add regression lines to our plot. Here, we add a linear model to each facet.
p <- p + geom_smooth(method = "lm", fullrange = TRUE)
p## `geom_smooth()` using formula 'y ~ x'
CHALLENGE 7
Build your own bivariate scatterplot using the KamilarAndCooperData dataset.
p <- ggplot(data = d, aes(x = log(Body_mass_female_mean), y = log(MaxLongevity_m)))
p <- p + geom_point()
p <- p + geom_smooth(method = "lm")
p## `geom_smooth()` using formula 'y ~ x'
Aggregate Statistics and the {dplyr} Package
To calculate summary statistics for groups of observations in a data frame, there are many different approaches. One is to use the aggregate() function from the {stats} package (a standard package), which provides a quick way to look at summary statistics for sets of observations, though it requires a bit of clunky code. Here, we apply a particular function (FUN = "mean") to mean female body mass, grouped by Family.
aggregate(d$Body_mass_female_mean ~ d$Family, FUN = "mean", na.rm = TRUE)## d$Family d$Body_mass_female_mean
## 1 Atelidae 6616.2000
## 2 Cebidae 876.3323
## 3 Cercopithecidae 6327.8247
## 4 Cheirogalidae 186.0286
## 5 Daubentonidae 2490.0000
## 6 Galagidae 371.6143
## 7 Hominidae 53443.7167
## 8 Hylobatidae 6682.1200
## 9 Indriidae 3886.5333
## 10 Lemuridae 1991.1200
## 11 Lepilemuridae 813.5000
## 12 Lorisidae 489.8625
## 13 Pitheciidae 1768.5000
## 14 Tarsiidae 120.0000Or, alternatively…
aggregate(x = d["Body_mass_female_mean"], by = d["Family"], FUN = "mean", na.rm = TRUE)## Family Body_mass_female_mean
## 1 Atelidae 6616.2000
## 2 Cebidae 876.3323
## 3 Cercopithecidae 6327.8247
## 4 Cheirogalidae 186.0286
## 5 Daubentonidae 2490.0000
## 6 Galagidae 371.6143
## 7 Hominidae 53443.7167
## 8 Hylobatidae 6682.1200
## 9 Indriidae 3886.5333
## 10 Lemuridae 1991.1200
## 11 Lepilemuridae 813.5000
## 12 Lorisidae 489.8625
## 13 Pitheciidae 1768.5000
## 14 Tarsiidae 120.0000Another, EASIER, way to summarize data is to use the package {dplyr}, which provides “a flexible grammar of data manipulation” that includes a set of verbs that can be used to perform useful operations on data frames. Before using {dplyr} for this, let’s look in general at what it can do…
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unions <- filter(d, Family == "Hominidae" & Mass_Dimorphism > 2)
head(s) # filtering a data frame for certain rows...## Scientific_Name Family Genus Species Brain_Size_Species_Mean
## 1 Gorilla_gorilla Hominidae Gorilla gorilla 490.41
## 2 Pongo_abelii Hominidae Pongo abelii 389.50
## 3 Pongo_pygmaeus Hominidae Pongo pygmaeus 377.38
## Brain_Size_Female_Mean Brain_size_Ref Body_mass_male_mean
## 1 455.89 Isler et al 2008 170400.0
## 2 341.21 Isler et al 2008 84482.5
## 3 337.72 Isler et al 2008 80136.9
## Body_mass_female_mean Mass_Dimorphism Mass_Ref MeanGroupSize
## 1 71500.0 2.383 Isler et al 2008 7.1
## 2 41148.0 2.053 Isler et al 2008 NA
## 3 36947.6 2.169 Isler et al 2008 2.0
## AdultMales AdultFemale AdultSexRatio
## 1 1.8 4.4 2.44
## 2 NA NA NA
## 3 1.0 1.0 1.00
## Social_Organization_Ref
## 1 van Schaik et al. 1999; Kappeler and Pereira 2003; Nunn & van Schaik 2000
## 2
## 3 van Schaik et al. 1999; Kappeler and Pereira 2003; Nunn & van Schaik 2000
## InterbirthInterval_d Gestation WeaningAge_d MaxLongevity_m LitterSz
## 1 1430.8 256 920.35 648 1.05
## 2 NA NA NA NA NA
## 3 2007.5 249 1088.80 720 1.07
## Life_History_Ref GR_MidRangeLat_dd Precip_Mean_mm Temp_Mean_degC AET_Mean_mm
## 1 Jones et al. 2009 0.88 1528.8 24.2 1291.1
## 2 NA NA NA NA
## 3 Jones et al. 2009 1.33 2587.0 23.8 1676.6
## PET_Mean_mm Climate_Ref HomeRange_km2 HomeRangeRef DayLength_km
## 1 1502.7 Jones et al. 2009 4.03 Jones et al. 2009 0.86
## 2 NA NA NA
## 3 1691.7 Jones et al. 2009 3.88 Jones et al. 2009 0.50
## DayLengthRef Territoriality Fruit Leaves Fauna DietRef1
## 1 Nunn et al. 2003 0.3797 11 93 0 Nunn et al 2003
## 2 NA NA
## 3 Nunn et al. 2003 0.2250 64 18 3 Nunn et al 2003
## Canine_Dimorphism Canine_Dimorphism_Ref Feed Move Rest Social
## 1 1.739 Plavcan & Ruff 2008 52.6 9.55 35.5 5.8
## 2 1.710 Plavcan & Ruff 2008 NA NA NA NA
## 3 1.693 Plavcan & Ruff 2008 42.4 15.40 41.0 1.2
## Activity_Budget_Ref
## 1 wendy dataset; Sussman et al. 2005
## 2
## 3 Kamilar 2006s <- arrange(d, Family, Genus, Body_mass_male_mean) # rearranging a data frame...
head(s)## Scientific_Name Family Genus Species Brain_Size_Species_Mean
## 1 Alouatta_guariba Atelidae Alouatta guariba 51.70
## 2 Alouatta_caraya Atelidae Alouatta caraya 52.63
## 3 Alouatta_seniculus Atelidae Alouatta seniculus 55.22
## 4 Alouatta_palliata Atelidae Alouatta palliata 49.88
## 5 Alouatta_belzebul Atelidae Alouatta belzebul 52.84
## 6 Alouatta_pigra Atelidae Alouatta pigra 51.13
## Brain_Size_Female_Mean Brain_size_Ref Body_mass_male_mean
## 1 49.08 Isler et al 2008 5800
## 2 47.80 Isler et al 2008 6525
## 3 54.26 Isler et al 2008 6690
## 4 48.04 Isler et al 2008 7150
## 5 51.19 Isler et al 2008 7270
## 6 48.75 Isler et al 2008 11400
## Body_mass_female_mean Mass_Dimorphism Mass_Ref MeanGroupSize
## 1 4550 1.275 Isler et al 2008 6.53
## 2 4240 1.539 Isler et al 2008 8.00
## 3 5210 1.284 Isler et al 2008 7.10
## 4 5350 1.336 Isler et al 2008 12.00
## 5 5520 1.317 Isler et al 2008 7.00
## 6 6430 1.773 Isler et al 2008 6.60
## AdultMales AdultFemale AdultSexRatio
## 1 1.370 2.200 1.61
## 2 2.300 3.300 1.43
## 3 1.700 2.200 1.29
## 4 2.900 6.300 2.17
## 5 1.000 1.000 1.00
## 6 1.925 2.175 1.13
## Social_Organization_Ref
## 1 Campbell et al 2007
## 2 van Schaik et al. 1999; Kappeler and Pereira 2003; Nunn & van Schaik 2000
## 3 van Schaik et al. 1999; Kappeler and Pereira 2003; Nunn & van Schaik 2000
## 4 van Schaik et al. 1999; Kappeler and Pereira 2003; Nunn & van Schaik 2000
## 5 Campbell et al 2007
## 6 Campbell et al 2007
## InterbirthInterval_d Gestation WeaningAge_d MaxLongevity_m LitterSz
## 1 NA NA NA NA NA
## 2 337.62 187 323.16 243.6 1.01
## 3 507.35 190 370.04 300.0 1.42
## 4 684.37 186 495.60 300.0 1.02
## 5 NA NA NA NA NA
## 6 NA 187 NA 240.0 1.01
## Life_History_Ref GR_MidRangeLat_dd Precip_Mean_mm Temp_Mean_degC AET_Mean_mm
## 1 -21.13 1332.3 19.6 1225.7
## 2 Jones et al. 2009 -20.34 1166.4 22.9 1193.1
## 3 Jones et al. 2009 0.68 1823.4 25.1 1449.8
## 4 Jones et al. 2009 6.95 1852.6 23.7 1300.0
## 5 -6.80 1643.5 24.9 1286.6
## 6 Jones et al. 2009 18.80 1341.3 25.1 1373.8
## PET_Mean_mm Climate_Ref HomeRange_km2 HomeRangeRef DayLength_km
## 1 1332.2 Jones et al. 2009 0.03 Jones et al. 2009 NA
## 2 1404.9 Jones et al. 2009 NA 0.40
## 3 1574.9 Jones et al. 2009 0.10 Jones et al. 2009 0.55
## 4 1633.9 Jones et al. 2009 0.19 Jones et al. 2009 0.32
## 5 1549.8 Jones et al. 2009 NA NA
## 6 1580.8 Jones et al. 2009 0.30 Jones et al. 2009 NA
## DayLengthRef Territoriality Fruit Leaves Fauna DietRef1
## 1 NA 5.2 73.0 0.0 Campbell et al. 2007
## 2 Nunn et al. 2003 NA 23.8 67.7 0.0 Campbell et al. 2007
## 3 Nunn et al. 2003 1.5414 40.0 48.1 0.0 Campbell et al. 2007
## 4 Nunn et al. 2003 0.6506 33.1 56.4 0.0 Campbell et al. 2007
## 5 NA 57.3 19.1 0.0 Campbell et al. 2007
## 6 NA 40.8 45.1 0.0 Campbell et al. 2007
## Canine_Dimorphism Canine_Dimorphism_Ref Feed Move Rest Social
## 1 1.783 Plavcan & Ruff 2008 18.33 14.33 64.37 3.00
## 2 1.542 Plavcan & Ruff 2008 15.90 17.60 61.60 4.90
## 3 1.464 Plavcan & Ruff 2008 12.70 6.20 78.50 2.50
## 4 1.703 Plavcan & Ruff 2008 17.94 12.32 66.14 3.64
## 5 1.811 Plavcan & Ruff 2008 13.75 18.75 57.30 10.00
## 6 1.109 Plavcan & Ruff 2008 24.40 9.80 61.90 3.80
## Activity_Budget_Ref
## 1 Campbell et al. 2007
## 2 Campbell et al. 2007
## 3 Campbell et al. 2007
## 4 Campbell et al. 2007
## 5 Campbell et al. 2007
## 6 Campbell et al. 2007s <- select(d, Family, Genus, Body_mass_male_mean) # selecting specific columns...
head(s)## Family Genus Body_mass_male_mean
## 1 Cercopithecidae Allenopithecus 6130
## 2 Cercopithecidae Allocebus 92
## 3 Atelidae Alouatta 7270
## 4 Atelidae Alouatta 6525
## 5 Atelidae Alouatta 5800
## 6 Atelidae Alouatta 7150s <- rename(d, Female_Mass = Body_mass_female_mean)
head(s$Female_Mass) # renaming columns...## [1] 3180 84 5520 4240 4550 5350s <- mutate(d, Binomial = paste(Genus, Species, sep = " "))
head(s$Binomial) # and adding new columns...## [1] "Allenopithecus nigroviridis" "Allocebus trichotis"
## [3] "Alouatta belzebul" "Alouatta caraya"
## [5] "Alouatta guariba" "Alouatta palliata"The {dplyr} package also makes it easy to summarize data using more convenient functions than aggregate(). For example:
s <- summarise(d, avgF = mean(Body_mass_female_mean, na.rm = TRUE), avgM = mean(Body_mass_male_mean,
na.rm = TRUE))
s## avgF avgM
## 1 5396.474 8111.801The group_by() function allows us to do apply summary functions to sets of observations defined by a categorical variable, as we did above with aggregate().
byFamily <- group_by(d, Family)
byFamily## # A tibble: 213 × 44
## # Groups: Family [14]
## Scientific_Name Family Genus Species Brain_Size_Speci… Brain_Size_Fema…
## <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Allenopithecus_n… Cercopi… Alleno… nigrov… 58.0 53.7
## 2 Allocebus_tricho… Cercopi… Alloce… tricho… NA NA
## 3 Alouatta_belzebul Atelidae Alouat… belzeb… 52.8 51.2
## 4 Alouatta_caraya Atelidae Alouat… caraya 52.6 47.8
## 5 Alouatta_guariba Atelidae Alouat… guariba 51.7 49.1
## 6 Alouatta_palliata Atelidae Alouat… pallia… 49.9 48.0
## 7 Alouatta_pigra Atelidae Alouat… pigra 51.1 48.8
## 8 Alouatta_senicul… Atelidae Alouat… senicu… 55.2 54.3
## 9 Aotus_azarai Cebidae Aotus azarai 20.7 20.7
## 10 Aotus_brumbacki Cebidae Aotus brumba… NA NA
## # … with 203 more rows, and 38 more variables: Brain_size_Ref <chr>,
## # Body_mass_male_mean <dbl>, Body_mass_female_mean <dbl>,
## # Mass_Dimorphism <dbl>, Mass_Ref <chr>, MeanGroupSize <dbl>,
## # AdultMales <dbl>, AdultFemale <dbl>, AdultSexRatio <dbl>,
## # Social_Organization_Ref <chr>, InterbirthInterval_d <dbl>, Gestation <dbl>,
## # WeaningAge_d <dbl>, MaxLongevity_m <dbl>, LitterSz <dbl>,
## # Life_History_Ref <chr>, GR_MidRangeLat_dd <dbl>, Precip_Mean_mm <dbl>, …s <- summarise(byFamily, avgF = mean(Body_mass_female_mean, na.rm = TRUE), avgM = mean(Body_mass_male_mean,
na.rm = TRUE))
s## # A tibble: 14 × 3
## Family avgF avgM
## <chr> <dbl> <dbl>
## 1 Atelidae 6616. 7895.
## 2 Cebidae 876. 1012.
## 3 Cercopithecidae 6328. 9543.
## 4 Cheirogalidae 186. 193.
## 5 Daubentonidae 2490 2620
## 6 Galagidae 372. 395.
## 7 Hominidae 53444. 98681.
## 8 Hylobatidae 6682. 6926.
## 9 Indriidae 3887. 3638.
## 10 Lemuridae 1991. 2077.
## 11 Lepilemuridae 814. 792
## 12 Lorisidae 490. 512.
## 13 Pitheciidae 1768. 1955.
## 14 Tarsiidae 120 131Piping
One other cool thing about the {dplyr} package is that it provides a convenient way to “pipe” together operations on a data frame using the %>% operator. This means that each line of code after the operator is implemented on the product of the line of code before the operator.In this way, you can use piping to build, step by step, a more complicated output.
As an example, the line of code, below, accomplishes the same as the multiple line of code in the previous chunk (although it is only one line of code, I’ve separated it by pipes for ease of reading and understanding (see hashes for a descriptor of what each pipe section accomplishes)… it could also be written as one continuous line):
s <- #to create dataframe "s"
d %>% #take dataframe "d"
group_by(Family) %>% #Group it by Family
summarise(avgF = mean(Body_mass_female_mean, na.rm=TRUE), #And calculate mean male BM
avgM = mean(Body_mass_male_mean, na.rm=TRUE)) #And mean female BM
s## # A tibble: 14 × 3
## Family avgF avgM
## <chr> <dbl> <dbl>
## 1 Atelidae 6616. 7895.
## 2 Cebidae 876. 1012.
## 3 Cercopithecidae 6328. 9543.
## 4 Cheirogalidae 186. 193.
## 5 Daubentonidae 2490 2620
## 6 Galagidae 372. 395.
## 7 Hominidae 53444. 98681.
## 8 Hylobatidae 6682. 6926.
## 9 Indriidae 3887. 3638.
## 10 Lemuridae 1991. 2077.
## 11 Lepilemuridae 814. 792
## 12 Lorisidae 490. 512.
## 13 Pitheciidae 1768. 1955.
## 14 Tarsiidae 120 131Piping allows us to keep a clean and readable workflow without having to create numerous intermediate dataframes, as well as offering us a shorthand that accomplishes one complicated process with one simple-to-breakdown command.
Although this may at first seem cumbersome (many students despise piping at first!), it will quickly become one of the best ways to make your code more readable and simpler to implement.
CHALLENGE 8
In one line of code, do the following:
- Add a variable, Binomial to our data frame d, which is a concatenation of the Genus and Species…
- Trim the data frame to only include the variables Binomial, Family, Body_mass_female_mean, Body_mass_male_mean and Mass_Dimorphism…
- Group these by Binomial (in other words, by species but with full binomial nomenclature)…
- And calculate the average value for female body mass, male body mass, and mass dimorphism.
s <- d %>%
mutate(Binomial = paste(Genus, Species, sep = " ")) %>%
select(Binomial, Body_mass_female_mean, Body_mass_male_mean, Mass_Dimorphism) %>%
group_by(Binomial) %>%
summarise(avgF = mean(Body_mass_female_mean, na.rm = TRUE), avgM = mean(Body_mass_male_mean,
na.rm = TRUE), avgBMD = mean(Mass_Dimorphism, na.rm = TRUE))
s## # A tibble: 213 × 4
## Binomial avgF avgM avgBMD
## <chr> <dbl> <dbl> <dbl>
## 1 Allenopithecus nigroviridis 3180 6130 1.93
## 2 Allocebus trichotis 84 92 1.10
## 3 Alouatta belzebul 5520 7270 1.32
## 4 Alouatta caraya 4240 6525 1.54
## 5 Alouatta guariba 4550 5800 1.27
## 6 Alouatta palliata 5350 7150 1.34
## 7 Alouatta pigra 6430 11400 1.77
## 8 Alouatta seniculus 5210 6690 1.28
## 9 Aotus azarai 1230 1180 0.959
## 10 Aotus brumbacki NaN NaN NaN
## # … with 203 more rowsAcccording to Kamilar & Cooper’s (2013) dataset, what is the average male and female size, and body mass dimorphism of my two main study species (vervet monkeys, Chlorocebus pygerythrus; and woolly monkeys, Lagothrix lagotricha)? Which has a larger average female body mass? Which is more sexually dimorphic?
Compare the body size of my two main study taxa at the Family level (i.e., Cercopithecidae vs. Atelidae) by plotting (using {ggplot2}) the body mass of males and females and sexual dimorphism. If you can, make the Cercopithecid boxes green, and the Atelid boxes purple.