background-image: url("img/DAW.png") background-position: left background-size: 50% class: middle, center, .pull-right[ ## .base-blue[More Data Visualization] <br> <br> ### .purple[Kelly McConville] #### .purple[ Stat 100 | Week 3 | Fall 2022] ] --- ## Announcements * Remember that the Week 3 Lecture Quiz will open on Gradescope at 11:45am today. + Due by Friday at 11:45am. **************************** -- ## Goals for Today .pull-left[ * Learn the general structure of `ggplot2`. * Learn a few standard graphs for numerical/quantitative data: + **Scatterplot**: two numerical variables + **Linegraph**: two numerical variables ] -- .pull-right[ * And, learn the standard graphic for categorical data + **Barplot**: one categorical variable + **Segmented barplot**: two categorical variables * Also cover: + Incorporating more variables into our plots! + Adding context to our plots. ] --- <img src="img/ggplot2.png" width="15%" style="float:left; padding:10px" style="display: block; margin: auto;" /> ## Load Necessary Packages `ggplot2` is part of this collection of data science packages. ```r # Load necessary packages library(tidyverse) ``` --- # `ggplot2` example code **Guiding Principle**: We will map variables from the **data** to the **aes**thetic attributes (e.g. location, size, shape, color) of **geom**etric objects (e.g. points, lines, bars). ```r ggplot(data = ---, mapping = aes(---)) + geom_---(---) ``` --- ## Data Setting: [Eco-Totem Broadway Bicycle Count](https://data.cambridgema.gov/Transportation-Planning/Eco-Totem-Broadway-Bicycle-Count/q8v9-mcfg) .pull-left[ <img src="img/counter.jpg" width="90%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="img/bike_counter_map.png" width="90%" style="display: block; margin: auto;" /> ] --- ## Import the [Data](https://data.cambridgema.gov/Transportation-Planning/Eco-Totem-Broadway-Bicycle-Count/q8v9-mcfg) ```r bike_counter <- read_csv("https://data.cambridgema.gov/api/views/q8v9-mcfg/rows.csv") # Inspect the data glimpse(bike_counter) ``` ``` ## Rows: 245,506 ## Columns: 7 ## $ DateTime <chr> "06/24/2015 12:00:00 AM", "06/24/2015 12:15:00 AM", "06/24/2… ## $ Day <chr> "Wednesday", "Wednesday", "Wednesday", "Wednesday", "Wednesd… ## $ Date <chr> "06/24/2015", "06/24/2015", "06/24/2015", "06/24/2015", "06/… ## $ Time <time> 00:00:00, 00:15:00, 00:30:00, 00:45:00, 01:00:00, 01:15:00,… ## $ Total <dbl> 4, 3, 4, 2, 2, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, … ## $ Westbound <dbl> 1, 3, 3, 2, 2, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, … ## $ Eastbound <dbl> 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ``` --- ## Data Wrangling **We haven't learned this topic yet.** **I only included this code for completeness/transparency.** ```r # Fix Date column to be stored with the date class library(lubridate) bike_counter <- mutate(bike_counter, Date = mdy(Date)) # Filter to only include two days in July 2019 july_2019 <- filter(bike_counter, Date %in% c(mdy("07/04/2019"), mdy("07/11/2019"))) # Add an Occasion column july_2019 <- mutate(july_2019, Occasion = if_else(Date == mdy("07/04/2019"), "Fourth of July", "Normal Thursday")) ``` --- ## Scatterplots .pull-left[ * Explore relationships between numerical variables. + We will be especially interested in **linear** relationships. ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/scat-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Scatterplots .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Time, y = Total)) + geom_point(size = 2) ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/scat-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Scatterplots .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Time, y = Total)) + geom_point(size = 2, alpha = 0.6) ``` * Fix over-plotting ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/scat2-1.png" width="768" style="display: block; margin: auto;" /> ] -- **Why the weird pattern?** --- ## Scatterplots .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Time, y = Total, color = Occasion)) + geom_point(size = 2, alpha = 0.6) ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/scat3-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Linegraphs .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Time, y = Total, color = Occasion)) + geom_line(size = 2) ``` * Also called **time series plot** when time is represented on the x axis. ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/line-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Linegraphs .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Time, y = Total, color = Occasion)) + geom_line(size = 2) + theme(legend.pos = "bottom") ``` * Also called **time series plot** when time is represented on the x axis. ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/line2-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Data Setting: [Dog Names in Cambridge, MA](https://data.cambridgema.gov/General-Government/Dogs-of-Cambridge/sckh-3xyx) Based on dog license data collected by Cambridge's Animal Commission ```r # Import and inspect data dogs <- read_csv("https://data.cambridgema.gov/api/views/sckh-3xyx/rows.csv") glimpse(dogs) ``` ``` ## Rows: 3,942 ## Columns: 6 ## $ Dog_Name <chr> "Gus", "Butch", "Baxter", "Bodhi", "Ocean", "Coco", "… ## $ Dog_Breed <chr> "Boston Terrier", "Mixed Breed", "Mixed Breed", "Gold… ## $ Location_masked <chr> "POINT (-71.0849 42.3714)", "POINT (-71.1328 42.3989)… ## $ Latitude_masked <dbl> 42.3714, 42.3989, 42.3814, 42.3998, 42.3726, 42.3610,… ## $ Longitude_masked <dbl> -71.0849, -71.1328, -71.1186, -71.1308, -71.1087, -71… ## $ Neighborhood <chr> "East Cambridge", "North Cambridge", "Neighborhood Ni… ``` --- ## Data Wrangling **We haven't learned this topic yet.** **I only included this code for completeness/transparency.** ```r # Create a column for Breed dogs <- mutate(dogs, Breed = if_else( Dog_Breed == "Mixed Breed", "Mixed", "Single")) # Find the 10 top most common names top10names <- count(dogs, Dog_Name) %>% slice_max(n = 10, order_by = n) %>% select(Dog_Name) %>% pull() # Filter dataset to only the 10 top most common names dogs_top10 <- filter(dogs, Dog_Name %in% top10names) ``` --- class: , middle, center ## Before we graph the data... ### Any guesses on **popular** dog names? --- ## Bar plots .pull-left[ * Displays the frequency for each category. ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/unnamed-chunk-17-1.png" width="576" style="display: block; margin: auto;" /> ] --- ## Bar plots .pull-left[ ```r # Create barplot ggplot(data = dogs_top10, mapping = aes(x = Dog_Name)) + geom_bar() ``` * How could be make this graph better? ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/bar-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Bar plots .pull-left[ ```r # Create barplot ggplot(data = dogs_top10, mapping = aes(x = fct_infreq(Dog_Name))) + geom_bar() ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/bar2-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Segmented Barplots .pull-left[ ```r # Create segmented barplot ggplot(data = dogs_top10, mapping = aes(x = fct_infreq(Dog_Name), fill = Breed)) + geom_bar() + theme(legend.pos = "bottom") ``` * Each bar is divided into the frequencies of the `fill` variable. * Hard to make comparisons across categories. ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/seg-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Segmented Barplots .pull-left[ ```r # Create segmented barplot ggplot(data = dogs_top10, mapping = aes(x = fct_infreq(Dog_Name), fill = Breed)) + geom_bar(position = "dodge") + theme(legend.pos = "bottom") ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/seg2-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Segmented Barplots .pull-left[ ```r # Create segmented barplot ggplot(data = dogs_top10, mapping = aes(x = fct_infreq(Dog_Name), fill = Breed)) + geom_bar(position = "fill") + theme(legend.pos = "bottom") ``` * Each bar is divided into **proportions** based on the `fill` variable. ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/seg3-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Adding More Variables * Two main approaches: + Utilize other `aes`thetics of the `geom` + Facet: Create multiple plots across the categories of a categorical variable. --- ## Utilize other `aes`thetics .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Time, y = Total, color = Occasion)) + geom_line(size = 2) + theme(legend.pos = "bottom") ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/line2-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Utilize other `aes`thetics .pull-left[ ```r # A bit more wrangling # Just these three names some_dogs <- filter(dogs, Dog_Name %in% c("Charlie","Lucy", "Pepper")) ggplot(data = some_dogs, mapping = aes(x = Longitude_masked, y = Latitude_masked, color = Dog_Name)) + geom_point(size = 2) ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/all-1.png" width="768" style="display: block; margin: auto;" /> ] --- # Facet .pull-left[ ```r ggplot(data = some_dogs, mapping = aes(x = Longitude_masked, y = Latitude_masked)) + geom_point(size = 2) + facet_wrap(~Dog_Name, ncol = 2) ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/fac-1.png" width="768" style="display: block; margin: auto;" /> ] --- # Facet .pull-left[ ```r ggplot(data = some_dogs, mapping = aes(x = Longitude_masked, y = Latitude_masked)) + geom_point(size = 2) + facet_grid(Breed~Dog_Name) ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/fac2-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Consider Doing Both! .pull-left[ ```r ggplot(data = some_dogs, mapping = aes(x = Longitude_masked, y = Latitude_masked, color = Breed)) + geom_point(size = 2) + facet_wrap(~Dog_Name, ncol = 2) ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/both-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Context .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Time, y = Total, color = Occasion)) + geom_line(size = 2) + theme(legend.pos = "bottom") + labs(x = "Time of Day", y = "Number of Passes", color = "What Type of Day?", caption = "Data Collected by Eco-Totem", title = "Cycling Patterns at Broadway Bike Counter") ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/line3-1.png" width="768" style="display: block; margin: auto;" /> ] --- # Customizing your `ggplot2` Plots * There are so **many** ways you can customize the look of your `ggplot2` plots. * Let's look at some common changes: + Fussing with labels + Zooming in + Using multiple `geoms` + Color! + Themes --- # Fussing with Labels: Rotate .pull-left[ ```r # Create barplot ggplot(data = dogs_top10, mapping = aes(x = fct_infreq(Dog_Name))) + geom_bar() + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/bar4-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Zooming In .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Occasion, y = Total, fill = Occasion)) + geom_boxplot() + guides(fill = "none") ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/box5-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Zooming In .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Occasion, y = Total, fill = Occasion)) + geom_boxplot() + guides(fill = "none") + coord_cartesian(ylim = c(0, 40)) ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/box6-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Multiple `geom`s .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Occasion, y = Total, fill = Occasion)) + geom_boxplot() + guides(fill = "none") + coord_cartesian(ylim = c(0, 40)) + geom_jitter(width = .1, height = 0, alpha = 0.6) ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/box7-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Multiple `geom`s .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Time, y = Total, color = Occasion)) + geom_line(size = 2) + theme(legend.pos = "bottom") + geom_point(size = 3) ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/line4-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Change the Color ```r 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" ``` --- ## Change the Color .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Time, y = Total, color = Occasion)) + geom_line(size = 2) + theme(legend.pos = "bottom") + scale_color_manual(values = c("violetred2", "steelblue3")) ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/line5-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Change the Color .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Time, y = Total, color = Occasion)) + geom_line(size = 2) + theme(legend.pos = "bottom") + scale_color_manual(values = c("#0D6759", "#E4844A")) ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/line6-1.png" width="768" style="display: block; margin: auto;" /> ] --- ## Use a [Different Theme](https://ggplot2.tidyverse.org/reference/ggtheme.html) .pull-left[ ```r ggplot(data = july_2019, mapping = aes(x = Time, y = Total, color = Occasion)) + geom_line(size = 2) + scale_color_manual(values = c("#0D6759", "#E4844A")) + theme_bw() + theme(legend.pos = "bottom") ``` ] .pull-right[ <img src="stat100_wk03wed_files/figure-html/line7-1.png" width="768" style="display: block; margin: auto;" /> ]