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-Intro to R Data Analysis: Part 2
-2023-03-29
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-R Markdown
-This is an R Markdown document. Markdown is a simple formatting -syntax for authoring HTML, PDF, and MS Word documents. For more details -on using R Markdown see http://rmarkdown.rstudio.com. Guide to markdown syntax -https://www.markdownguide.org/basic-syntax/.
-When you click the Knit button a document will be -generated that includes both content as well as the output of any -embedded R code chunks within the document. You can embed an R code -chunk like this:
-# Simulates 100 observations from a normal distribution
-# and plots a histogram
-val <- rnorm(n = 100)
-hist(val) 
# The ggplot version of the same plot
- ggplot(data = tibble(values = val),
- mapping = aes(x = values))+
- geom_histogram(bins = 20)
Important: before running the code below, click Session -> -Set Working Directory -> To Source File Location
-
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-Exercise 3: Reading in Data
-The data we will be analyzing is from the PanTHERIA database which is -“a global species-level data set of key life-history, ecological and -geographical traits of all known extant and recently extinct mammals -(PanTHERIA) developed for a number of macroecological and -macroevolutionary research projects.”
-# The data is spread across 3 sheets in an excel file. We need to
-# combine these data into one table/data frame.
-
-# na = "NA" tells read_xlsx how missing values appear in the data
-# the default is empty cells. Run "?read_xlsx" for more info
-sheet1 <- read_xlsx(path = "PanTHERIA.xlsx", sheet = 1, na = "NA")
-sheet2 <- read_xlsx(path = "PanTHERIA.xlsx", sheet = 2, na = "NA")
-sheet3 <- read_xlsx(path = "PanTHERIA.xlsx", sheet = 3, na = "NA")
-
-# rbind (row-bind) combines data frames by row
-pantheria <- rbind(sheet1, sheet2, sheet3)# How many rows and columns are there?
-nrow(pantheria)## [1] 2161ncol(pantheria)## [1] 54# What does the data look like?
-head(pantheria)## # A tibble: 6 × 54
-## Order Family Genus Species Binomial ActivityCycle AdultBodyMass_g
-## <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
-## 1 Carnivora Canidae Canis latrans Canis l… crepuscular 11989.
-## 2 Carnivora Canidae Canis lupus Canis l… crepuscular 31757.
-## 3 Carnivora Canidae Canis simens… Canis s… diurnal 14362.
-## 4 Carnivora Canidae Atel… microt… Atelocy… <NA> 8363.
-## 5 Cetacea Balaenopteridae Bala… muscul… Balaeno… <NA> 154321304.
-## 6 Cetacea Balaenopteridae Bala… physal… Balaeno… <NA> 47506008.
-## # ℹ 47 more variables: AdultForearmLen_mm <dbl>, AdultHeadBodyLen_mm <dbl>,
-## # AgeatEyeOpening_d <dbl>, AgeatFirstBirth_d <dbl>,
-## # BasalMetRate_mLO2hr <dbl>, BasalMetRateMass_g <dbl>, DietBreadth <dbl>,
-## # DispersalAge_d <dbl>, GestationLen_d <dbl>, HabitatBreadth <dbl>,
-## # HomeRange_km2 <dbl>, HomeRange_Indiv_km2 <dbl>, InterbirthInterval_d <dbl>,
-## # LitterSize <dbl>, LittersPerYear <dbl>, MaxLongevity_m <dbl>,
-## # NeonateBodyMass_g <dbl>, NeonateHeadBodyLen_mm <dbl>, …
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-Exercise 4: Filtering and Reformatting Data
-We will exploring adult body mass from these mammals as it relates to
-their trophic level using dpylr and ggplot2.
-Download the cheatsheets for these packages at the following links:
Let’s start by subsetting the data with select()
# Pipes (%>%) work by passing the data in front of the pipe to the first argument
-# of the function after it, this prevents a lot of nested function calls and makes
-# code easier to read.
-
-pantheria <- pantheria %>% # Passes pantheria as the first argument of select
- select(Order,
- Family, # select returns the specified columns
- Genus,
- Species,
- TrophicLevel,
- AdultBodyMass_g) %>%
- drop_na() %>% # Remove any rows that have NAs
- distinct() # Remove any duplicate rowsData is almost never clean, for example there should be only 3 -trophic levels:
-unique(pantheria$TrophicLevel) # unique elements of a vector## [1] "carnivore" "herbivore" "omnivore" "Omnivore"Let’s fix the TrophicLevel column using mutate()
# mutate allows us to add columns or modify existing ones
-pantheria <- pantheria %>%
- mutate(TrophicLevel = tolower(TrophicLevel)) # Make column lowercase
-
-Exercise 5: Summarizing data
-Now we can summarize the adult body mass by trophic level by -computing standard metrics like mean and standard deviation.
-pantheria %>%
- group_by(TrophicLevel) %>% # Group observations by this column
- summarize(Mean = mean(AdultBodyMass_g), # Summarize will calculate these group wise
- `Standard Deviation` = sd(AdultBodyMass_g), # Quasi quotation lets us add spaces to column names
- Min = min(AdultBodyMass_g),
- Max = max(AdultBodyMass_g)) %>%
- ungroup() %>%
- arrange(desc(Mean)) # Order the data frame by descending mean body mass## # A tibble: 3 × 5
-## TrophicLevel Mean `Standard Deviation` Min Max
-## <chr> <dbl> <dbl> <dbl> <dbl>
-## 1 carnivore 824948. 7963841. 1.96 154321304.
-## 2 omnivore 77419. 1173853. 3.29 27324024.
-## 3 herbivore 59854. 305498. 5.55 4750000.
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-Exercise 6: Plotting Data
-According to the table above, body masses have a really wide range -across trophic levels. Let’s visualize the distribution of adult body -masses.
-# ggplot2 constructs graphics in layers, each layer is separated by "+"
-# x and y values are supplied in aes(), the type of plot is specified using
-# the "geom" functions
-
-ggplot(data = pantheria, # input data
- mapping = aes(x = log10(AdultBodyMass_g))) + # log10 transform adult body mass
- geom_histogram(fill = "#CE3274", # type of plot
- bins = 40) +
- xlab(label = "log10 Adult Body Mass (g)") + # x label
- ylab(label = "Frequency") + # y label
- labs(title = "Histogram of log10 Adult Body Mass") # title 
The data looks skewed even after log10 transformation. Let’s view the -distribution by trophic level.
-pantheria %>%
- ggplot(aes(x = log10(AdultBodyMass_g), fill = TrophicLevel)) + # Color by trophic level
- geom_histogram(bins = 40) +
- facet_grid(rows = vars(TrophicLevel)) + # Split the plot into rows by trophic level
- ylab(label = "Frequency") +
- xlab(label = "log10 Adult Body Mass (g)") +
- labs(title = "Histograms of log10 Adult Body Mass") +
- theme(plot.title = element_text(hjust = 0.5)) # Center the plot title
It is clear that trophic level does have an impact on the
-distribution of adult body mass, carnivores tend to be smaller which
-makes sense because carnivores have higher metabolic demands and so
-there might be a selection pressure towards smaller carnivores. If we
-wanted to confirm this by fitting a model, we could use the
-lm() function to fit a linear model.
-
-Exercise 7.1: Hands on coding
-An important caveat of the data is that some Orders of mammals are
-more biodiverse than others and are therefore over represented in the
-dataset. Using the dplyr cheatsheet, write code generates a
-table of Orders and what percentage of the data they are. Scroll down to
-see the hint if you are having trouble.
# Your code Exercise 7.1 hint: group_by + summarize + arrange
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-Exercise 7.2: Hands on coding
-Now that we see what the over represented Orders are, we can plot -their body masses by trophic level to see if they are skewing the -overall distributions.
-top_orders <- c("Rodentia", "Chiroptera") # Character vector of the top 2 Orders from above
-# filter uses a conditional to select rows from the data
-pantheria %>%
- filter(Order %in% top_orders) %>%
- ggplot(aes(x = log10(AdultBodyMass_g), fill = Order)) +
- geom_histogram(bins = 40) +
- facet_grid(rows = vars(TrophicLevel),
- cols = vars(Order)) +
- ylab(label = "Frequency") +
- xlab(label = "log10 Adult Body Mass (g)") +
- theme(plot.title = element_text(hjust = 0.5)) 
It looks like one of them is mostly made up of small cornivores. -Let’s remove it and redo the plot of body mass distribution by trophic -level.
-pantheria %>%
- filter(Order != "Chiroptera") %>%
- ggplot(aes(x = log10(AdultBodyMass_g),fill = TrophicLevel)) + # Color by trophic level
- geom_histogram(bins = 40) +
- facet_grid(rows = vars(TrophicLevel)) + # Split the plot into rows by trophic level
- ylab(label = "Frequency") +
- xlab(label = "log10 Adult Body Mass (g)") +
- labs(title = "Histograms of log10 Adult Body Mass")
We can see now that body mass of carnivorous mammals is much less
-skewed than the initial plots show. There is still an effect of trophic
-level on body mass, but the effect size is likely much smaller than we
-would estimate by including all 393 Chiroptera. Now that we
-have generated these plots, we can generate a full report that contains
-all of the text and code, click Knit to render the HTML
-report.
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-End of workshop exercises
-Hopefully this workshop has provided a good foundation for you to -learn R. If you would like some additional practice, check out the -resources on the workshop -wiki. R also contains many built in datasets you can use for -practice:
-| Package | -Item | -Title | -
|---|---|---|
| ggplot2 | -diamonds | -Prices of over 50,000 round cut diamonds | -
| ggplot2 | -economics | -US economic time series | -
| ggplot2 | -economics_long | -US economic time series | -
| ggplot2 | -faithfuld | -2d density estimate of Old Faithful data | -
| ggplot2 | -luv_colours | -‘colors()’ in Luv space | -
| ggplot2 | -midwest | -Midwest demographics | -
| ggplot2 | -mpg | -Fuel economy data from 1999 to 2008 for 38 popular -models of cars | -
| ggplot2 | -msleep | -An updated and expanded version of the mammals sleep -dataset | -
| ggplot2 | -presidential | -Terms of 12 presidents from Eisenhower to Trump | -
| ggplot2 | -seals | -Vector field of seal movements | -
| ggplot2 | -txhousing | -Housing sales in TX | -
| tidyr | -billboard | -Song rankings for Billboard top 100 in the year -2000 | -
| tidyr | -cms_patient_care | -Data from the Centers for Medicare & Medicaid -Services | -
| tidyr | -cms_patient_experience | -Data from the Centers for Medicare & Medicaid -Services | -
| tidyr | -construction | -Completed construction in the US in 2018 | -
| tidyr | -fish_encounters | -Fish encounters | -
| tidyr | -household | -Household data | -
| tidyr | -population | -World Health Organization TB data | -
| tidyr | -relig_income | -Pew religion and income survey | -
| tidyr | -smiths | -Some data about the Smith family | -
| tidyr | -table1 | -Example tabular representations | -
| tidyr | -table2 | -Example tabular representations | -
| tidyr | -table3 | -Example tabular representations | -
| tidyr | -table4a | -Example tabular representations | -
| tidyr | -table4b | -Example tabular representations | -
| tidyr | -table5 | -Example tabular representations | -
| tidyr | -us_rent_income | -US rent and income data | -
| tidyr | -who | -World Health Organization TB data | -
| tidyr | -who2 | -World Health Organization TB data | -
| tidyr | -world_bank_pop | -Population data from the world bank | -
| dplyr | -band_instruments | -Band membership | -
| dplyr | -band_instruments2 | -Band membership | -
| dplyr | -band_members | -Band membership | -
| dplyr | -starwars | -Starwars characters | -
| dplyr | -storms | -Storm tracks data | -
| datasets | -AirPassengers | -Monthly Airline Passenger Numbers 1949-1960 | -
| datasets | -BJsales | -Sales Data with Leading Indicator | -
| datasets | -BJsales.lead (BJsales) | -Sales Data with Leading Indicator | -
| datasets | -BOD | -Biochemical Oxygen Demand | -
| datasets | -CO2 | -Carbon Dioxide Uptake in Grass Plants | -
| datasets | -ChickWeight | -Weight versus age of chicks on different diets | -
| datasets | -DNase | -Elisa assay of DNase | -
| datasets | -EuStockMarkets | -Daily Closing Prices of Major European Stock Indices, -1991-1998 | -
| datasets | -Formaldehyde | -Determination of Formaldehyde | -
| datasets | -HairEyeColor | -Hair and Eye Color of Statistics Students | -
| datasets | -Harman23.cor | -Harman Example 2.3 | -
| datasets | -Harman74.cor | -Harman Example 7.4 | -
| datasets | -Indometh | -Pharmacokinetics of Indomethacin | -
| datasets | -InsectSprays | -Effectiveness of Insect Sprays | -
| datasets | -JohnsonJohnson | -Quarterly Earnings per Johnson & Johnson Share | -
| datasets | -LakeHuron | -Level of Lake Huron 1875-1972 | -
| datasets | -LifeCycleSavings | -Intercountry Life-Cycle Savings Data | -
| datasets | -Loblolly | -Growth of Loblolly pine trees | -
| datasets | -Nile | -Flow of the River Nile | -
| datasets | -Orange | -Growth of Orange Trees | -
| datasets | -OrchardSprays | -Potency of Orchard Sprays | -
| datasets | -PlantGrowth | -Results from an Experiment on Plant Growth | -
| datasets | -Puromycin | -Reaction Velocity of an Enzymatic Reaction | -
| datasets | -Seatbelts | -Road Casualties in Great Britain 1969-84 | -
| datasets | -Theoph | -Pharmacokinetics of Theophylline | -
| datasets | -Titanic | -Survival of passengers on the Titanic | -
| datasets | -ToothGrowth | -The Effect of Vitamin C on Tooth Growth in Guinea -Pigs | -
| datasets | -UCBAdmissions | -Student Admissions at UC Berkeley | -
| datasets | -UKDriverDeaths | -Road Casualties in Great Britain 1969-84 | -
| datasets | -UKgas | -UK Quarterly Gas Consumption | -
| datasets | -USAccDeaths | -Accidental Deaths in the US 1973-1978 | -
| datasets | -USArrests | -Violent Crime Rates by US State | -
| datasets | -USJudgeRatings | -Lawyers’ Ratings of State Judges in the US Superior -Court | -
| datasets | -USPersonalExpenditure | -Personal Expenditure Data | -
| datasets | -UScitiesD | -Distances Between European Cities and Between US -Cities | -
| datasets | -VADeaths | -Death Rates in Virginia (1940) | -
| datasets | -WWWusage | -Internet Usage per Minute | -
| datasets | -WorldPhones | -The World’s Telephones | -
| datasets | -ability.cov | -Ability and Intelligence Tests | -
| datasets | -airmiles | -Passenger Miles on Commercial US Airlines, -1937-1960 | -
| datasets | -airquality | -New York Air Quality Measurements | -
| datasets | -anscombe | -Anscombe’s Quartet of ‘Identical’ Simple Linear -Regressions | -
| datasets | -attenu | -The Joyner-Boore Attenuation Data | -
| datasets | -attitude | -The Chatterjee-Price Attitude Data | -
| datasets | -austres | -Quarterly Time Series of the Number of Australian -Residents | -
| datasets | -beaver1 (beavers) | -Body Temperature Series of Two Beavers | -
| datasets | -beaver2 (beavers) | -Body Temperature Series of Two Beavers | -
| datasets | -cars | -Speed and Stopping Distances of Cars | -
| datasets | -chickwts | -Chicken Weights by Feed Type | -
| datasets | -co2 | -Mauna Loa Atmospheric CO2 Concentration | -
| datasets | -crimtab | -Student’s 3000 Criminals Data | -
| datasets | -discoveries | -Yearly Numbers of Important Discoveries | -
| datasets | -esoph | -Smoking, Alcohol and (O)esophageal Cancer | -
| datasets | -euro | -Conversion Rates of Euro Currencies | -
| datasets | -euro.cross (euro) | -Conversion Rates of Euro Currencies | -
| datasets | -eurodist | -Distances Between European Cities and Between US -Cities | -
| datasets | -faithful | -Old Faithful Geyser Data | -
| datasets | -fdeaths (UKLungDeaths) | -Monthly Deaths from Lung Diseases in the UK | -
| datasets | -freeny | -Freeny’s Revenue Data | -
| datasets | -freeny.x (freeny) | -Freeny’s Revenue Data | -
| datasets | -freeny.y (freeny) | -Freeny’s Revenue Data | -
| datasets | -infert | -Infertility after Spontaneous and Induced Abortion | -
| datasets | -iris | -Edgar Anderson’s Iris Data | -
| datasets | -iris3 | -Edgar Anderson’s Iris Data | -
| datasets | -islands | -Areas of the World’s Major Landmasses | -
| datasets | -ldeaths (UKLungDeaths) | -Monthly Deaths from Lung Diseases in the UK | -
| datasets | -lh | -Luteinizing Hormone in Blood Samples | -
| datasets | -longley | -Longley’s Economic Regression Data | -
| datasets | -lynx | -Annual Canadian Lynx trappings 1821-1934 | -
| datasets | -mdeaths (UKLungDeaths) | -Monthly Deaths from Lung Diseases in the UK | -
| datasets | -morley | -Michelson Speed of Light Data | -
| datasets | -mtcars | -Motor Trend Car Road Tests | -
| datasets | -nhtemp | -Average Yearly Temperatures in New Haven | -
| datasets | -nottem | -Average Monthly Temperatures at Nottingham, -1920-1939 | -
| datasets | -npk | -Classical N, P, K Factorial Experiment | -
| datasets | -occupationalStatus | -Occupational Status of Fathers and their Sons | -
| datasets | -precip | -Annual Precipitation in US Cities | -
| datasets | -presidents | -Quarterly Approval Ratings of US Presidents | -
| datasets | -pressure | -Vapor Pressure of Mercury as a Function of -Temperature | -
| datasets | -quakes | -Locations of Earthquakes off Fiji | -
| datasets | -randu | -Random Numbers from Congruential Generator RANDU | -
| datasets | -rivers | -Lengths of Major North American Rivers | -
| datasets | -rock | -Measurements on Petroleum Rock Samples | -
| datasets | -sleep | -Student’s Sleep Data | -
| datasets | -stack.loss (stackloss) | -Brownlee’s Stack Loss Plant Data | -
| datasets | -stack.x (stackloss) | -Brownlee’s Stack Loss Plant Data | -
| datasets | -stackloss | -Brownlee’s Stack Loss Plant Data | -
| datasets | -state.abb (state) | -US State Facts and Figures | -
| datasets | -state.area (state) | -US State Facts and Figures | -
| datasets | -state.center (state) | -US State Facts and Figures | -
| datasets | -state.division (state) | -US State Facts and Figures | -
| datasets | -state.name (state) | -US State Facts and Figures | -
| datasets | -state.region (state) | -US State Facts and Figures | -
| datasets | -state.x77 (state) | -US State Facts and Figures | -
| datasets | -sunspot.month | -Monthly Sunspot Data, from 1749 to “Present” | -
| datasets | -sunspot.year | -Yearly Sunspot Data, 1700-1988 | -
| datasets | -sunspots | -Monthly Sunspot Numbers, 1749-1983 | -
| datasets | -swiss | -Swiss Fertility and Socioeconomic Indicators (1888) -Data | -
| datasets | -treering | -Yearly Treering Data, -6000-1979 | -
| datasets | -trees | -Diameter, Height and Volume for Black Cherry Trees | -
| datasets | -uspop | -Populations Recorded by the US Census | -
| datasets | -volcano | -Topographic Information on Auckland’s Maunga Whau -Volcano | -
| datasets | -warpbreaks | -The Number of Breaks in Yarn during Weaving | -
| datasets | -women | -Average Heights and Weights for American Women | -
