Customize your traces IN TERACTIVE DATA VIS UALIZ ATION W ITH P - - PowerPoint PPT Presentation

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Customize your traces IN TERACTIVE DATA VIS UALIZ ATION W ITH P - - PowerPoint PPT Presentation

Jan 24, 2024 286 likes •626 views

Customize your traces IN TERACTIVE DATA VIS UALIZ ATION W ITH P LOTLY IN R Adam Loy Statistician, Carleton College Wine quality data glimpse(winequality) Observations: 325 Variables: 14 $ type <chr> "red",

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Customize your traces

IN TERACTIVE DATA VIS UALIZ ATION W ITH P LOTLY IN R

Adam Loy

Statistician, Carleton College

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INTERACTIVE DATA VISUALIZATION WITH PLOTLY IN R

Wine quality data

glimpse(winequality) Observations: 325 Variables: 14 $ type <chr> "red", "red", "red", "red", "red", "red", ... $ fixed_acidity <dbl> 8.2, 8.2, 8.0, 10.2, 8.6, 6.1, 10.7, 9.1, 7.2... $ volatile_acidity <dbl> 0.885, 0.640, 0.715, 0.360, 0.520, 0.590, 0.6... $ citric_acid <dbl> 0.20, 0.27, 0.22, 0.64, 0.38, 0.01, 0.22, 0.3... $ residual_sugar <dbl> 1.40, 2.00, 2.30, 2.90, 1.50, 2.10, 2.70, 2.1... ... $ sulphates <dbl> 0.46, 0.62, 0.54, 0.66, 0.52, 0.56, 0.98, 0.8... $ alcohol <dbl> 10.0, 9.1, 9.5, 12.5, 9.4, 11.4, 9.9, 11.2, 1... $ quality <int> 5, 6, 6, 6, 5, 5, 6, 6, 6, 7, 6, 5, 4, 6, 6, ... $ quality_label <chr> "low", "medium", "medium", "medium", "low", ...

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INTERACTIVE DATA VISUALIZATION WITH PLOTLY IN R

Color

winequality %>% plot_ly(x = ~fixed_acidity) %>% add_histogram()

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Color

winequality %>% plot_ly(x = ~fixed_acidity) %>% add_histogram(color = I("red")) # Setting color

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Opacity

winequality %>% plot_ly(x = ~residual_sugar, y = ~fixed_acidity) %>% add_markers()

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Opacity

winequality %>% plot_ly(x = ~residual_sugar, y = ~fixed_acidity) %>% add_markers(marker = list(opacity = 0.2)) # Adjust opacity

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INTERACTIVE DATA VISUALIZATION WITH PLOTLY IN R

Symbols

winequality %>% plot_ly(x = ~residual_sugar, y = ~fixed_acidity) %>% add_markers(marker = list(symbol = "circle-open")) # Change symbol

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Marker options

  • pacity

color symbol (scatter/box) size (scatter) width (bar/histogram)

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Let's practice!

IN TERACTIVE DATA VIS UALIZ ATION W ITH P LOTLY IN R

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Thoughtful use of color

IN TERACTIVE DATA VIS UALIZ ATION W ITH P LOTLY IN R

Adam Loy

Statistician, Carleton College

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Adding a third variable

wine %>% plot_ly(x = ~Flavanoids, y = ~Alcohol, color = ~Type) %>% add_markers()

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Adding a quantitative variable

wine %>% plot_ly(x = ~Flavanoids, y = ~Alcohol, color = ~Color) %>% add_markers()

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RColorBrewer palettes

wine %>% plot_ly(x = ~Flavanoids, y = ~Alcohol, color = ~Type) %>% add_markers(colors = "Dark2")

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Manual palettes

wine %>% plot_ly(x = ~Flavanoids, y = ~Alcohol, color = ~Type) %>% add_markers(colors = c("orange", "black", "skyblue"))

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Let's practice!

IN TERACTIVE DATA VIS UALIZ ATION W ITH P LOTLY IN R

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Labeling your data

IN TERACTIVE DATA VIS UALIZ ATION W ITH P LOTLY IN R

Adam Loy

Statistician, Carleton College

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Changing the default

wine %>% count(Type) %>% plot_ly(x = ~Type, y = ~n, # Changing the default info displaye hoverinfo = "y" ) %>% add_bars()

hoverinfo = "all" "x" "y" "x+y" "x+y+z"

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Custom hover text

wine %>% plot_ly(x = ~Flavanoids, y = ~Alcohol, hoverinfo = "text", text = ~paste("Flavanoids:", Flavanoids, "<br "Alcohol:", Alcohol) ) %>% add_markers()

tilde, ~ , to map columns to aesthetic parameters

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Let's practice!

IN TERACTIVE DATA VIS UALIZ ATION W ITH P LOTLY IN R

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Customizing your layout

IN TERACTIVE DATA VIS UALIZ ATION W ITH P LOTLY IN R

Adam Loy

Statistician, Carleton College

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layout()

axes: type, labels, tick marks, transformations, etc. legend: position canvas: grid lines, background color size: height, width, margins

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Axis labels

winequality %>% plot_ly(x = ~free_so2, y = ~total_so2) %>% add_markers(marker = list(opacity = 0.2))

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Axis labels

winequality %>% plot_ly(x = ~free_so2, y = ~total_so2) %>% add_markers(marker = list(opacity = 0.2)) %>% layout(xaxis = list(title = "Free SO2 (ppm)"), yaxis = list(title = "Total SO2 (ppm)"))

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Titles

winequality %>% plot_ly(x = ~free_so2, y = ~total_so2) %>% add_markers(marker = list(opacity = 0.2)) %>% layout(xaxis = list(title = "Free SO2 (ppm)"), yaxis = list(title = "Total SO2 (ppm)"), title = "Does free SO2 predict total SO2 in wine?")

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Transforming axes

winequality %>% plot_ly(x = ~free_so2, y = ~total_so2) %>% add_markers(marker = list(opacity = 0.2)) %>% layout(xaxis = list(title = "Free SO2 (ppm, log scale)", type = "log"), yaxis = list(title = "Total SO2 (ppm, log scale)", type = "log"), title = "Does free SO2 predict total SO2 in wine?")

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Customizing the grid

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Customizing the grid

winequality %>% plot_ly(x = ~free_so2, y = ~total_so2) %>% add_markers(marker = list(opacity = 0.5)) %>% layout(xaxis = list(title = "Free SO2 (ppm)", zeroline = FALSE), yaxis = list(title = "Total SO2 (ppm)", zeroline = FALSE, showgrid = FALSE))

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Customizing the canvas

winequality %>% plot_ly(x = ~free_so2, y = ~total_so2) %>% add_markers(marker = list(opacity = 0.5)) %>% layout(xaxis = list(title = "Free SO2 (ppm)"), yaxis = list(title = "Total SO2 (ppm)"), plot_bgcolor = toRGB("gray90"),

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Let's practice!

IN TERACTIVE DATA VIS UALIZ ATION W ITH P LOTLY IN R