Data Visualization: ggplot
part 5
Learning Goals
At the end of this exercise, you will be able to:
1. Build histograms and density plots. 2. Adjust colors using R’s
built-in color options.
3. Create new categories with case_when() and use those
categories to build plots.
4. Build line plots.
Resources
Load the libraries
library(tidyverse)
library(janitor)options(scipen=999) #cancels the use of scientific notation for the sessionData
For this tutorial, we will use two data sets.
Desert ecology. The data are from: S. K. Morgan Ernest, Thomas J. Valone, and James H. Brown. 2009. Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA. Ecology 90:1708.
deserts <- read_csv("data/surveys_complete.csv")Homerange. The data are from: Tamburello N, Cote IM, Dulvy NK (2015) Energy and the scaling of animal space use. The American Naturalist 186(2):196-211. http://dx.doi.org/10.1086/682070.
homerange <- read_csv("data/Tamburelloetal_HomeRangeDatabase.csv", na = c("", "NA", "\\"))Review
- Use the
homerangedata to make a plot that shows the range oflog10.massby taxonomic class.
homerange %>%
ggplot(aes(x=class, y=log10.mass))+
geom_boxplot()
- Now, add a layer to this plot that fills color for each box by taxonomic class.
homerange %>%
ggplot(aes(x=class, y=log10.mass, fill=class))+
geom_boxplot()
- Use
fillto show the range oflog10.massby taxonomic class with color codes.
homerange %>%
ggplot(aes(x = class, y = log10.mass, fill = taxon)) +
geom_boxplot()
Line plots
Line plots are great when you need to show changes over time. Here we
look at the number of samples for species DM and DS over the years
represented in the deserts data. This takes some careful
thought- we want to know how sampling has changed over time for these
two species.
Let’s start by making a clear x and y so we know what we are going to plot.
deserts %>%
filter(species_id=="DM" | species_id=="DS") %>%
mutate(year = as.factor(year)) %>% #year isn't a numeric, so we are converting it to a factor
group_by(year, species_id) %>%
summarise(n=n(), .groups='keep') %>%
pivot_wider(names_from = species_id, values_from = n)## # A tibble: 26 × 3
## # Groups: year [26]
## year DM DS
## <fct> <int> <int>
## 1 1977 264 98
## 2 1978 389 320
## 3 1979 209 204
## 4 1980 493 346
## 5 1981 559 354
## 6 1982 609 354
## 7 1983 528 280
## 8 1984 396 76
## 9 1985 667 98
## 10 1986 406 88
## # ℹ 16 more rowsdeserts %>%
filter(species_id=="DM" | species_id=="DS") %>%
mutate(year = as.factor(year)) %>% #year isn't a numeric, so we are converting it to a factor
group_by(year) %>%
summarise(n=n()) %>%
ggplot(aes(x=year, y=n, group=1))+
geom_line()
?geom_linedeserts %>%
filter(species_id=="DM" | species_id=="DS") %>%
mutate(year = as.factor(year)) %>%
group_by(year, species_id) %>%
summarise(n=n(), .groups='keep') %>%
ggplot(aes(x=year, y=n, group=species_id, color=species_id))+
geom_line()+
geom_point(shape=5)+ # you can experiment with shapes
theme(axis.text.x = element_text(angle = 60, hjust = 1))+
labs(title = "Number of samples for species DM & DS",
x = "Year",
fill = "n")
Histograms
Histograms show the distribution of continuous variables. As
students, you have seen histograms of grade distributions. A histogram
bins the data and you specify the number of bins that
encompass a range of observations. For something like grades, this is
easy because the number of bins corresponds to the grades A-F. By
default, R uses a formula to calculate the number of bins but some
adjustment may be required.
What does the distribution of body mass look like in the
homerange data?
homerange %>%
ggplot(aes(log10.mass)) +
geom_histogram()+ #we can adjust the number of bins with the bins argument
labs(title = "Distribution of Body Mass")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Let’s play with the colors. This shows all 657 of R’s built-in
colors. Notice that color and fill do
different things!
colors()## [1] "white" "aliceblue" "antiquewhite"
## [4] "antiquewhite1" "antiquewhite2" "antiquewhite3"
## [7] "antiquewhite4" "aquamarine" "aquamarine1"
## [10] "aquamarine2" "aquamarine3" "aquamarine4"
## [13] "azure" "azure1" "azure2"
## [16] "azure3" "azure4" "beige"
## [19] "bisque" "bisque1" "bisque2"
## [22] "bisque3" "bisque4" "black"
## [25] "blanchedalmond" "blue" "blue1"
## [28] "blue2" "blue3" "blue4"
## [31] "blueviolet" "brown" "brown1"
## [34] "brown2" "brown3" "brown4"
## [37] "burlywood" "burlywood1" "burlywood2"
## [40] "burlywood3" "burlywood4" "cadetblue"
## [43] "cadetblue1" "cadetblue2" "cadetblue3"
## [46] "cadetblue4" "chartreuse" "chartreuse1"
## [49] "chartreuse2" "chartreuse3" "chartreuse4"
## [52] "chocolate" "chocolate1" "chocolate2"
## [55] "chocolate3" "chocolate4" "coral"
## [58] "coral1" "coral2" "coral3"
## [61] "coral4" "cornflowerblue" "cornsilk"
## [64] "cornsilk1" "cornsilk2" "cornsilk3"
## [67] "cornsilk4" "cyan" "cyan1"
## [70] "cyan2" "cyan3" "cyan4"
## [73] "darkblue" "darkcyan" "darkgoldenrod"
## [76] "darkgoldenrod1" "darkgoldenrod2" "darkgoldenrod3"
## [79] "darkgoldenrod4" "darkgray" "darkgreen"
## [82] "darkgrey" "darkkhaki" "darkmagenta"
## [85] "darkolivegreen" "darkolivegreen1" "darkolivegreen2"
## [88] "darkolivegreen3" "darkolivegreen4" "darkorange"
## [91] "darkorange1" "darkorange2" "darkorange3"
## [94] "darkorange4" "darkorchid" "darkorchid1"
## [97] "darkorchid2" "darkorchid3" "darkorchid4"
## [100] "darkred" "darksalmon" "darkseagreen"
## [103] "darkseagreen1" "darkseagreen2" "darkseagreen3"
## [106] "darkseagreen4" "darkslateblue" "darkslategray"
## [109] "darkslategray1" "darkslategray2" "darkslategray3"
## [112] "darkslategray4" "darkslategrey" "darkturquoise"
## [115] "darkviolet" "deeppink" "deeppink1"
## [118] "deeppink2" "deeppink3" "deeppink4"
## [121] "deepskyblue" "deepskyblue1" "deepskyblue2"
## [124] "deepskyblue3" "deepskyblue4" "dimgray"
## [127] "dimgrey" "dodgerblue" "dodgerblue1"
## [130] "dodgerblue2" "dodgerblue3" "dodgerblue4"
## [133] "firebrick" "firebrick1" "firebrick2"
## [136] "firebrick3" "firebrick4" "floralwhite"
## [139] "forestgreen" "gainsboro" "ghostwhite"
## [142] "gold" "gold1" "gold2"
## [145] "gold3" "gold4" "goldenrod"
## [148] "goldenrod1" "goldenrod2" "goldenrod3"
## [151] "goldenrod4" "gray" "gray0"
## [154] "gray1" "gray2" "gray3"
## [157] "gray4" "gray5" "gray6"
## [160] "gray7" "gray8" "gray9"
## [163] "gray10" "gray11" "gray12"
## [166] "gray13" "gray14" "gray15"
## [169] "gray16" "gray17" "gray18"
## [172] "gray19" "gray20" "gray21"
## [175] "gray22" "gray23" "gray24"
## [178] "gray25" "gray26" "gray27"
## [181] "gray28" "gray29" "gray30"
## [184] "gray31" "gray32" "gray33"
## [187] "gray34" "gray35" "gray36"
## [190] "gray37" "gray38" "gray39"
## [193] "gray40" "gray41" "gray42"
## [196] "gray43" "gray44" "gray45"
## [199] "gray46" "gray47" "gray48"
## [202] "gray49" "gray50" "gray51"
## [205] "gray52" "gray53" "gray54"
## [208] "gray55" "gray56" "gray57"
## [211] "gray58" "gray59" "gray60"
## [214] "gray61" "gray62" "gray63"
## [217] "gray64" "gray65" "gray66"
## [220] "gray67" "gray68" "gray69"
## [223] "gray70" "gray71" "gray72"
## [226] "gray73" "gray74" "gray75"
## [229] "gray76" "gray77" "gray78"
## [232] "gray79" "gray80" "gray81"
## [235] "gray82" "gray83" "gray84"
## [238] "gray85" "gray86" "gray87"
## [241] "gray88" "gray89" "gray90"
## [244] "gray91" "gray92" "gray93"
## [247] "gray94" "gray95" "gray96"
## [250] "gray97" "gray98" "gray99"
## [253] "gray100" "green" "green1"
## [256] "green2" "green3" "green4"
## [259] "greenyellow" "grey" "grey0"
## [262] "grey1" "grey2" "grey3"
## [265] "grey4" "grey5" "grey6"
## [268] "grey7" "grey8" "grey9"
## [271] "grey10" "grey11" "grey12"
## [274] "grey13" "grey14" "grey15"
## [277] "grey16" "grey17" "grey18"
## [280] "grey19" "grey20" "grey21"
## [283] "grey22" "grey23" "grey24"
## [286] "grey25" "grey26" "grey27"
## [289] "grey28" "grey29" "grey30"
## [292] "grey31" "grey32" "grey33"
## [295] "grey34" "grey35" "grey36"
## [298] "grey37" "grey38" "grey39"
## [301] "grey40" "grey41" "grey42"
## [304] "grey43" "grey44" "grey45"
## [307] "grey46" "grey47" "grey48"
## [310] "grey49" "grey50" "grey51"
## [313] "grey52" "grey53" "grey54"
## [316] "grey55" "grey56" "grey57"
## [319] "grey58" "grey59" "grey60"
## [322] "grey61" "grey62" "grey63"
## [325] "grey64" "grey65" "grey66"
## [328] "grey67" "grey68" "grey69"
## [331] "grey70" "grey71" "grey72"
## [334] "grey73" "grey74" "grey75"
## [337] "grey76" "grey77" "grey78"
## [340] "grey79" "grey80" "grey81"
## [343] "grey82" "grey83" "grey84"
## [346] "grey85" "grey86" "grey87"
## [349] "grey88" "grey89" "grey90"
## [352] "grey91" "grey92" "grey93"
## [355] "grey94" "grey95" "grey96"
## [358] "grey97" "grey98" "grey99"
## [361] "grey100" "honeydew" "honeydew1"
## [364] "honeydew2" "honeydew3" "honeydew4"
## [367] "hotpink" "hotpink1" "hotpink2"
## [370] "hotpink3" "hotpink4" "indianred"
## [373] "indianred1" "indianred2" "indianred3"
## [376] "indianred4" "ivory" "ivory1"
## [379] "ivory2" "ivory3" "ivory4"
## [382] "khaki" "khaki1" "khaki2"
## [385] "khaki3" "khaki4" "lavender"
## [388] "lavenderblush" "lavenderblush1" "lavenderblush2"
## [391] "lavenderblush3" "lavenderblush4" "lawngreen"
## [394] "lemonchiffon" "lemonchiffon1" "lemonchiffon2"
## [397] "lemonchiffon3" "lemonchiffon4" "lightblue"
## [400] "lightblue1" "lightblue2" "lightblue3"
## [403] "lightblue4" "lightcoral" "lightcyan"
## [406] "lightcyan1" "lightcyan2" "lightcyan3"
## [409] "lightcyan4" "lightgoldenrod" "lightgoldenrod1"
## [412] "lightgoldenrod2" "lightgoldenrod3" "lightgoldenrod4"
## [415] "lightgoldenrodyellow" "lightgray" "lightgreen"
## [418] "lightgrey" "lightpink" "lightpink1"
## [421] "lightpink2" "lightpink3" "lightpink4"
## [424] "lightsalmon" "lightsalmon1" "lightsalmon2"
## [427] "lightsalmon3" "lightsalmon4" "lightseagreen"
## [430] "lightskyblue" "lightskyblue1" "lightskyblue2"
## [433] "lightskyblue3" "lightskyblue4" "lightslateblue"
## [436] "lightslategray" "lightslategrey" "lightsteelblue"
## [439] "lightsteelblue1" "lightsteelblue2" "lightsteelblue3"
## [442] "lightsteelblue4" "lightyellow" "lightyellow1"
## [445] "lightyellow2" "lightyellow3" "lightyellow4"
## [448] "limegreen" "linen" "magenta"
## [451] "magenta1" "magenta2" "magenta3"
## [454] "magenta4" "maroon" "maroon1"
## [457] "maroon2" "maroon3" "maroon4"
## [460] "mediumaquamarine" "mediumblue" "mediumorchid"
## [463] "mediumorchid1" "mediumorchid2" "mediumorchid3"
## [466] "mediumorchid4" "mediumpurple" "mediumpurple1"
## [469] "mediumpurple2" "mediumpurple3" "mediumpurple4"
## [472] "mediumseagreen" "mediumslateblue" "mediumspringgreen"
## [475] "mediumturquoise" "mediumvioletred" "midnightblue"
## [478] "mintcream" "mistyrose" "mistyrose1"
## [481] "mistyrose2" "mistyrose3" "mistyrose4"
## [484] "moccasin" "navajowhite" "navajowhite1"
## [487] "navajowhite2" "navajowhite3" "navajowhite4"
## [490] "navy" "navyblue" "oldlace"
## [493] "olivedrab" "olivedrab1" "olivedrab2"
## [496] "olivedrab3" "olivedrab4" "orange"
## [499] "orange1" "orange2" "orange3"
## [502] "orange4" "orangered" "orangered1"
## [505] "orangered2" "orangered3" "orangered4"
## [508] "orchid" "orchid1" "orchid2"
## [511] "orchid3" "orchid4" "palegoldenrod"
## [514] "palegreen" "palegreen1" "palegreen2"
## [517] "palegreen3" "palegreen4" "paleturquoise"
## [520] "paleturquoise1" "paleturquoise2" "paleturquoise3"
## [523] "paleturquoise4" "palevioletred" "palevioletred1"
## [526] "palevioletred2" "palevioletred3" "palevioletred4"
## [529] "papayawhip" "peachpuff" "peachpuff1"
## [532] "peachpuff2" "peachpuff3" "peachpuff4"
## [535] "peru" "pink" "pink1"
## [538] "pink2" "pink3" "pink4"
## [541] "plum" "plum1" "plum2"
## [544] "plum3" "plum4" "powderblue"
## [547] "purple" "purple1" "purple2"
## [550] "purple3" "purple4" "red"
## [553] "red1" "red2" "red3"
## [556] "red4" "rosybrown" "rosybrown1"
## [559] "rosybrown2" "rosybrown3" "rosybrown4"
## [562] "royalblue" "royalblue1" "royalblue2"
## [565] "royalblue3" "royalblue4" "saddlebrown"
## [568] "salmon" "salmon1" "salmon2"
## [571] "salmon3" "salmon4" "sandybrown"
## [574] "seagreen" "seagreen1" "seagreen2"
## [577] "seagreen3" "seagreen4" "seashell"
## [580] "seashell1" "seashell2" "seashell3"
## [583] "seashell4" "sienna" "sienna1"
## [586] "sienna2" "sienna3" "sienna4"
## [589] "skyblue" "skyblue1" "skyblue2"
## [592] "skyblue3" "skyblue4" "slateblue"
## [595] "slateblue1" "slateblue2" "slateblue3"
## [598] "slateblue4" "slategray" "slategray1"
## [601] "slategray2" "slategray3" "slategray4"
## [604] "slategrey" "snow" "snow1"
## [607] "snow2" "snow3" "snow4"
## [610] "springgreen" "springgreen1" "springgreen2"
## [613] "springgreen3" "springgreen4" "steelblue"
## [616] "steelblue1" "steelblue2" "steelblue3"
## [619] "steelblue4" "tan" "tan1"
## [622] "tan2" "tan3" "tan4"
## [625] "thistle" "thistle1" "thistle2"
## [628] "thistle3" "thistle4" "tomato"
## [631] "tomato1" "tomato2" "tomato3"
## [634] "tomato4" "turquoise" "turquoise1"
## [637] "turquoise2" "turquoise3" "turquoise4"
## [640] "violet" "violetred" "violetred1"
## [643] "violetred2" "violetred3" "violetred4"
## [646] "wheat" "wheat1" "wheat2"
## [649] "wheat3" "wheat4" "whitesmoke"
## [652] "yellow" "yellow1" "yellow2"
## [655] "yellow3" "yellow4" "yellowgreen"Let’s rebuild the histogram, but this time we will specify the color and fill. Do a little experimentation on your own with the different colors.
homerange %>%
ggplot(aes(x = log10.mass)) +
geom_histogram(color = "black", fill = "turquoise4", bins=10)+
labs(title = "Distribution of Body Mass")
Density plots
Density plots are similar to histograms but they use a smoothing function to make the distribution more even and clean looking. They do not use bins.
homerange %>%
ggplot(aes(x = log10.mass)) +
geom_density(fill="deepskyblue4", alpha =0.4, color = "black")+ #alpha is the transparency
labs(title = "Distribution of Body Mass")
I like to see both the histogram and the density curve so I often plot them together. Note that I assign the density plot a different color.
homerange %>%
ggplot(aes(x=log10.mass)) +
geom_histogram(aes(y = after_stat(density)), fill = "deepskyblue4", alpha = 0.4, color = "black")+
geom_density(color = "red")+
labs(title = "Distribution of Body Mass")
Practice
- Make a histogram of
log10.hra. Make sure to add a title.
homerange %>%
ggplot(aes(x = log10.hra)) +
geom_histogram(color = "black", fill = "gray70")+
labs(title = "Distribution of Home Range")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
- Now plot the same variable using
geom_density().
homerange %>%
ggplot(aes(x = log10.hra)) +
geom_density(fill="deepskyblue4", alpha =0.4, color = "black")+
labs(title = "Distribution of Body Mass")
- Combine them both!
homerange %>%
ggplot(aes(x=log10.hra)) +
geom_histogram(aes(y = after_stat(density)), fill = "deepskyblue4", alpha = 0.4, color = "black")+
geom_density(color = "orange")+
labs(title = "Distribution of Homernge")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Create Categories with mutate and case_when()
case_when() is a very handy function from
dplyr which allows us to calculate a new variable from
other variables. We use case_when() within
mutate() to do this.case_when() allows us to
specify multiple conditions. Let’s reclassify the body mass variable
into a new factor variable with small, medium, and large animals. In
this case, we are making a continuous variable into a categorical
variable.
glimpse(homerange)## Rows: 569
## Columns: 24
## $ taxon <chr> "lake fishes", "river fishes", "river fishe…
## $ common.name <chr> "american eel", "blacktail redhorse", "cent…
## $ class <chr> "actinopterygii", "actinopterygii", "actino…
## $ order <chr> "anguilliformes", "cypriniformes", "cyprini…
## $ family <chr> "anguillidae", "catostomidae", "cyprinidae"…
## $ genus <chr> "anguilla", "moxostoma", "campostoma", "cli…
## $ species <chr> "rostrata", "poecilura", "anomalum", "fundu…
## $ primarymethod <chr> "telemetry", "mark-recapture", "mark-recapt…
## $ N <chr> "16", NA, "20", "26", "17", "5", "2", "2", …
## $ mean.mass.g <dbl> 887.00, 562.00, 34.00, 4.00, 4.00, 3525.00,…
## $ log10.mass <dbl> 2.9479236, 2.7497363, 1.5314789, 0.6020600,…
## $ alternative.mass.reference <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ mean.hra.m2 <dbl> 282750.00, 282.10, 116.11, 125.50, 87.10, 3…
## $ log10.hra <dbl> 5.4514026, 2.4504031, 2.0648696, 2.0986437,…
## $ hra.reference <chr> "Minns, C. K. 1995. Allometry of home range…
## $ realm <chr> "aquatic", "aquatic", "aquatic", "aquatic",…
## $ thermoregulation <chr> "ectotherm", "ectotherm", "ectotherm", "ect…
## $ locomotion <chr> "swimming", "swimming", "swimming", "swimmi…
## $ trophic.guild <chr> "carnivore", "carnivore", "carnivore", "car…
## $ dimension <dbl> 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3…
## $ preymass <dbl> NA, NA, NA, NA, NA, NA, 1.39, NA, NA, NA, N…
## $ log10.preymass <dbl> NA, NA, NA, NA, NA, NA, 0.1430148, NA, NA, …
## $ PPMR <dbl> NA, NA, NA, NA, NA, NA, 530, NA, NA, NA, NA…
## $ prey.size.reference <chr> NA, NA, NA, NA, NA, NA, "Brose U, et al. 20…summary(homerange$log10.mass)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.6576 1.6990 2.5185 2.5947 3.3324 6.6021library(gtools)
quartiles <- quantcut(homerange$log10.mass)
table(quartiles)## quartiles
## [-0.658,1.7] (1.7,2.52] (2.52,3.33] (3.33,6.6]
## 143 142 143 141homerange <- homerange %>%
mutate(mass_category = case_when(log10.mass <= 1.7 ~ "small",
log10.mass > 1.7 & log10.mass <= 3.33 ~ "medium",
log10.mass > 3.33 ~ "large"))Here we check how the newly created body mass categories compare
across trophic.guild.
homerange %>%
ggplot(aes(x = mass_category, fill = trophic.guild)) +
geom_bar(position="dodge")+
labs(title = "Observations by Taxon and Mass Category in Homerange Data",
x = "Mass Category",
fill = "Trophic Guild")
Practice
- Use case_when() to make a new column
range_categorythat breaks downlog10.hrainto very small, small, medium, and large classes based on quartile.
summary(homerange$log10.hra)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.523 3.653 4.595 4.709 6.016 9.550quartiles <- quantcut(homerange$log10.hra)
table(quartiles)## quartiles
## [-1.52,3.65] (3.65,4.59] (4.59,6.02] (6.02,9.55]
## 143 142 142 142homerange <- homerange %>%
mutate(range_category=case_when(log10.hra<3.65 ~ "very_small",
log10.hra>=3.65 & log10.hra<=4.59 ~ "small",
log10.hra>=4.59 & log10.hra<=6.02 ~ "medium",
log10.hra>=6.02 ~ "large"))- Make a plot that shows how many and which taxonomic classes are
represented in each
range_category.
homerange %>%
ggplot(aes(x=range_category, fill=class))+
geom_bar(position="dodge", alpha=0.6, color="black")+
labs(title="Observations by Range Category",
x="Range Category",
y="Class")
- Isolate the small
range_categoryand plot the range oflog10.massby taxonomic class.
homerange %>%
filter(range_category=="small") %>%
ggplot(aes(x=class, y= log10.mass, fill=class))+
geom_boxplot()
That’s it! Let’s take a break!
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