ggplot2::workshop(…)
Tufts Friedman Symposium 2021
written by: M.A. Hartwick
Set up the environment
(check if tidyverse installed)
packages <- c("tidyverse", "skimr", "RColorBrewer")
install.packages(setdiff(packages, rownames(installed.packages())))library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.0.6 v dplyr 1.0.4
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(skimr)
library(RColorBrewer)
search()## [1] ".GlobalEnv" "package:RColorBrewer" "package:skimr"
## [4] "package:forcats" "package:stringr" "package:dplyr"
## [7] "package:purrr" "package:readr" "package:tidyr"
## [10] "package:tibble" "package:ggplot2" "package:tidyverse"
## [13] "package:stats" "package:graphics" "package:grDevices"
## [16] "package:utils" "package:datasets" "package:methods"
## [19] "Autoloads" "package:base"1. Data
iris[1:20,]Data Structure (tidyverse)
iris %>%
skimr::skim()| Name | Piped data |
| Number of rows | 150 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Species | 0 | 1 | FALSE | 3 | set: 50, ver: 50, vir: 50 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Sepal.Length | 0 | 1 | 5.84 | 0.83 | 4.3 | 5.1 | 5.80 | 6.4 | 7.9 | ▆▇▇▅▂ |
| Sepal.Width | 0 | 1 | 3.06 | 0.44 | 2.0 | 2.8 | 3.00 | 3.3 | 4.4 | ▁▆▇▂▁ |
| Petal.Length | 0 | 1 | 3.76 | 1.77 | 1.0 | 1.6 | 4.35 | 5.1 | 6.9 | ▇▁▆▇▂ |
| Petal.Width | 0 | 1 | 1.20 | 0.76 | 0.1 | 0.3 | 1.30 | 1.8 | 2.5 | ▇▁▇▅▃ |
tidyverse
(data cleaning)
set.seed(100)
iris_clean <- iris %>%
mutate(Trial = sample(3, n(), replace = TRUE))iris_clean %>% skim()| Name | Piped data |
| Number of rows | 150 |
| Number of columns | 6 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Species | 0 | 1 | FALSE | 3 | set: 50, ver: 50, vir: 50 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Sepal.Length | 0 | 1 | 5.84 | 0.83 | 4.3 | 5.10 | 5.80 | 6.4 | 7.9 | ▆▇▇▅▂ |
| Sepal.Width | 0 | 1 | 3.06 | 0.44 | 2.0 | 2.80 | 3.00 | 3.3 | 4.4 | ▁▆▇▂▁ |
| Petal.Length | 0 | 1 | 3.76 | 1.77 | 1.0 | 1.60 | 4.35 | 5.1 | 6.9 | ▇▁▆▇▂ |
| Petal.Width | 0 | 1 | 1.20 | 0.76 | 0.1 | 0.30 | 1.30 | 1.8 | 2.5 | ▇▁▇▅▃ |
| Trial | 0 | 1 | 2.13 | 0.79 | 1.0 | 1.25 | 2.00 | 3.0 | 3.0 | ▅▁▇▁▇ |
2. Function
iris_clean %>%
ggplot()
3. Coordinates
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length ))
4. Mapping
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Width, color = Species))
5. Geometries
geom_histogram(…), geom_point(…), geom_bar(…), geom_col(…)
- geom_histogram(…) is a count geom, so only takes one coordinate
iris_clean %>%
ggplot(.,aes(x = Sepal.Length, color = Species)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
- color sets the outline here, what happens if we try fill instead?
- what happens when we specify bin size?
- where should that argument go?
iris_clean %>%
ggplot(.,aes(x = Sepal.Length, fill = Species)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
- The geom ‘fill’ is specified
- Add an outline and transparency outside the mapping.
- Could we specify the fill aesthetic inside the geom_histogram?, how?
iris_clean %>%
ggplot(.,aes(x = Sepal.Length, fill = Species)) +
geom_histogram(aes(color = Species), alpha = 0.4)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
- Try out geom_point with an x and y coordinate.
- What happens if we remove the ‘as.character()’ wrapper from Trial?
iris_clean %>%
ggplot(.,aes(x = Sepal.Width, y = Sepal.Length, color = Species,
shape = as.character(Trial))) +
geom_point()
- Let’s try a barchart with x and y coordinates, mapped to species.
- The default for geom_barchart(…) is count (similiar to geom_histogram(…)).
- What happens if we remove stat = ‘identity’?
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_bar(stat = 'identity')
- geom_col(…) is the geometry for x and y barcharts.
- The default here is stat = ‘identity’, so no need to specify.
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col()
- By changing position from the default ‘stacked’ to dodge we can look at the
species as separate columns.
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge')
- Before we move on to the next layer, is there another geom_* you want to try?
- Add your own geom_* below from some that we’ve discussed (boxplot, violin?)
- What have we learned about default arguments that could help here
- Remember to run ’?geom_*()’ to find defaults.
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species))
6. Scales
scale_y_continuous(…), scale_x_discrete(…), scale_color_manual(…), scale_color_brewer(…)
- Let’s adjust the breaks in the y axis for each major break.
- The data are continuous so we use scale_y_continouus(…).
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8))
- Let’s also expand the x-axis so the data fills the plotting area.
- Remember, at this point the Trial data is still datatype continuous,
- So we use: scale_x_continuous(…)
- What happens if we use scale_x_discrete(…)
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
scale_x_continuous(expand = c(0.01,0.01))
- Want to apply scales to some other attributes?
- Lets add another layer of geom below and adjust that.
- what are some other things we could do to improve this? (keep in mind for later)
- What happens if we change the order of the geoms_*(…)?
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') +
geom_point(aes(shape = Species, color = Trial), position = position_dodge(width = 1))+
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
scale_x_continuous(expand = c(0.01,0.01)) +
scale_shape_manual(values = c(21,22,23))
- Time to adjust the default colors.
- First let’s take a look at the basic Rcolors
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"- First we’ll try to manually set our selected colors with scale_color_manual(…)
- That changed something, but what?
- What’s going on with our data that Trial is continuous
- and it didn’t use the colors argument?
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') +
geom_point(aes(shape = Species, color = as.character(Trial)), position = position_dodge(width = 1))+
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
scale_x_continuous(expand = c(0.01,0.01)) +
scale_shape_manual(values = c(21,22,23)) +
scale_color_manual(values = c('plum2', 'lemonchiffon', 'skyblue3'))
- Let’s try using scale_fill_manual(…) to map to Species, which is character class
- Then, we’ll wrap Trial in as.character, add a scale_color_manual(…)
- Why are we using both fill and color scales here? (hint: think back to
- geom_histogram(…))
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') +
geom_point(aes(shape = Species, color = as.character(Trial)),
position =position_dodge(width = 1))+
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
scale_x_continuous(expand = c(0.01,0.01)) +
scale_shape_manual(values = c(21,22,23)) +
scale_fill_manual(values = c('plum2', 'lemonchiffon', 'skyblue3'))+
scale_color_manual(values = c('forestgreen', 'grey80', 'aquamarine2'))
What do some of the Pre-Built color scales look like?
display.brewer.all()
- Let’s take RcolorBrewer for a spin
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') +
geom_point(aes(shape = Species, color = as.character(Trial)), position = position_dodge(width = 1))+
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
scale_x_continuous(expand = c(0.01,0.01)) +
scale_shape_manual(values = c(21,22,23)) +
scale_fill_brewer(palette = 'Set2') +
scale_color_brewer(palette = 'Set3')
7. Facets
facet_wrap(…), facet_grid(…)
- Start off with facet_wrap(…) by Trial.
- What happens with the x-axis?
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') +
geom_point(aes(shape = Species, color = as.character(Trial)), position = position_dodge(width = 1))+
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
scale_x_continuous(expand = c(0.01,0.01)) +
scale_shape_manual(values = c(21,22,23)) +
scale_fill_brewer(palette = 'Set2') +
scale_color_brewer(palette = 'Set3') +
facet_wrap(~Trial)
- Adjust the x-axis scale
- Lets view by rows instead of columns and move the facet labels
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') +
geom_point(aes(shape = Species, color = as.character(Trial)), position = position_dodge(width = 1))+
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
scale_x_continuous(expand = c(0.01,0.01)) +
scale_shape_manual(values = c(21,22,23)) +
scale_fill_brewer(palette = 'Set2') +
scale_color_brewer(palette = 'Set3') +
facet_wrap(~Trial, scales = "free_x", nrow = 3, strip.position = 'right')
- Facet_grid(…) is similiar, is there any varaible that we should look at
- Adjust the code below and add another varaible to the before the ‘~’, what happens?
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') +
geom_point(aes(shape = Species, color = as.character(Trial)), position = position_dodge(width = 1))+
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
scale_x_continuous(expand = c(0.01,0.01)) +
scale_shape_manual(values = c(21,22,23)) +
scale_fill_brewer(palette = 'Set2') +
scale_color_brewer(palette = 'Set3') +
facet_wrap(~Trial, scales = "free_x", nrow = 3, strip.position = 'right')
8. Themes
- Start by adding some Pre-Built themes.
- What other Pre-Built themes are availble, start typing ‘theme’ in the console below.
- What pops up? What are their defaults?
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') +
geom_point(aes(shape = Species, color = as.character(Trial)), position = position_dodge(width = 1))+
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
scale_x_continuous(expand = c(0.01,0.01)) +
scale_shape_manual(values = c(21,22,23)) +
scale_fill_brewer(palette = 'Set2') +
scale_color_brewer(palette = 'Set3') +
facet_wrap(~Trial, scales = "free_x", nrow = 3, strip.position = 'right') +
theme_minimal()
- Let’s try out setting some of the elements ourselves.
- What else would you adjust?
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') +
geom_point(aes(shape = Species, color = as.character(Trial)), position = position_dodge(width = 1))+
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
scale_x_continuous(expand = c(0.01,0.01)) +
scale_shape_manual(values = c(21,22,23)) +
scale_fill_brewer(palette = 'Set2') +
scale_color_brewer(palette = 'Set3') +
facet_wrap(~Trial, scales = "free_x", nrow = 3, strip.position = 'right')+
theme(legend.text = element_text(face = 'bold'),
text = element_text(family = 'mono', color = 'grey7', size = 10),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = 'grey10'))
From Sketch to Story, your turn:
Challenge(No Hints)
-Facetted Width and Length
-Bargraph
-Trial axis breaks as ‘2010’, 2015’, ‘2020’
-Y axis at 2, 4, 6, 8
-Ditch the points, and accompanying scale
-Choose a different palette for species
-Dodge position
-Species in italics
-No grey in facet
-Use a more elegant font
iris_clean %>%
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') +
geom_point(aes(shape = Species, color = as.character(Trial)), position = position_dodge(width = 1))+
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
scale_x_continuous(expand = c(0.01,0.01)) +
scale_shape_manual(values = c(21,22,23)) +
scale_fill_brewer(palette = 'Set2') +
scale_color_brewer(palette = 'Set3') +
facet_wrap(~Trial, scales = "free_x", nrow = 3, strip.position = 'right')+
theme(legend.text = element_text(face = 'bold'),
text = element_text(family = 'mono', color = 'grey7', size = 10),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = 'grey10'))
Challenge(Hints)
-Faceted Width and Length - add a tidyr::pivot_longer(…) pipe before ggplot2::
-Bargraph - choose the best option from the geoms we covered
-Trial axis breaks as 2010, 2015, 2020 - transform Trial column to character,use scale*
-Y axis at 2, 4, 6, 8 - Modify existing scale*
-Ditch the points, and accompanying scale - delete the correct geom_*()
-Choose the correct scale and change color palette - Modify Existing Scale*
-Dodge position - set from in geom*
-Species in italics - modify the theme
-No grey in facet - modify the theme
-Use a more elegant font - modify the theme, solution uses a different package
iris_clean %>% # Faceted Width and Length - add a tidyr::pivot_longer(...) %>%
#Trial axis breaks as 2010, 2015, 2020 - transform Trial column to character
# mutate(Trial = as.character(Trial))
ggplot(.,aes(x = Trial, y = Sepal.Length, fill = Species)) +
geom_col(position = 'dodge') + # Bargraph and Dodge - choose the best option from the geoms we covered
geom_point(aes(shape = Species, color = as.character(Trial)), position = position_dodge(width = 1))+ # delete the points geom
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
scale_x_continuous(expand = c(0.01,0.01)) + #Trial axis breaks as 2010, 2015, 2020, change scale*
scale_shape_manual(values = c(21,22,23)) + # geom_point() scale, delete
scale_fill_brewer(palette = 'Set2') + #change this palette
scale_color_brewer(palette = 'Set3') + #delete this scale, was for points
facet_wrap(~Trial, scales = "free_x", nrow = 3, strip.position = 'right')+
theme(legend.text = element_text(face = 'bold'), #Species in italics - modify the theme
text = element_text(family = 'mono', color = 'grey7', size = 10),# Use a more elegant font - modify the theme,
#No grey in facet - modify the theme
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = 'grey10'))
Solution
-Facetted Width and Length - add a tidyr::pivot_longer(…) pipe before ggplot2::
-Bargraph - choose the best option from the geoms we covered
-Trial axis breaks as 2010, 2015, 2020 - transform Trial column to character,use scale*
-Y axis at 2, 4, 6, 8 - Modify existing scale*
-Ditch the points, and accompanying scale - delete the correct geom_*()
-Choose the correct scale and change color palette - Modify Existing Scale*
-Dodge position - set from in geom*
-Species in italics - modify the theme
-No grey in facet - modify the theme
-Use a more elegant font - modify the theme, solution uses a different package
#library(extrafont)
iris_clean %>% tidyr::pivot_longer(cols = c('Petal.Length', 'Petal.Width'),
names_to = 'Petal_Attribute',
values_to = 'Petal_Measurement') %>%
dplyr::mutate(Trial = as.character(Trial)) %>%
ggplot(.,aes(x = Trial, y = Petal_Measurement, fill = Species)) +
geom_col(position = 'dodge') +
scale_y_continuous(breaks = c(2,4,6,8,10,12,14), expand = c(0,0), limits = c(0, 15)) +
scale_x_discrete(breaks = c(1,2,3),
labels = c('2010', '2015', '2020')) +
scale_fill_manual(values = c("skyblue3", "peachpuff", 'mediumaquamarine')) +
scale_color_manual(values = c("skyblue3", "peachpuff", 'mediumaquamarine')) +
facet_wrap(~Petal_Attribute,
scales = 'free',
ncol = 2, strip.position = 'top')+
theme(legend.text = element_text(face = 'italic'),
text = element_text(color = 'grey7',
size = 10),
#family = 'Palatino Linotype'
strip.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = 'grey10')) +
ylab('Petal Measurement')