Linking t w o charts IN TE R ME D IATE IN TE R AC TIVE DATA VISU - - PowerPoint PPT Presentation

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Linking t w o charts IN TE R ME D IATE IN TE R AC TIVE DATA VISU - - PowerPoint PPT Presentation

Jun 13, 2023 155 likes •597 views

Linking t w o charts IN TE R ME D IATE IN TE R AC TIVE DATA VISU AL IZATION W ITH P L OTLY IN R Adam Lo y Statistician , Carleton College E x ploring cl u sters INTERMEDIATE INTERACTIVE DATA VISUALIZATION WITH PLOTLY IN R E x ploring longit u

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Linking two charts

IN TE R ME D IATE IN TE R AC TIVE DATA VISU AL IZATION W ITH P L OTLY IN R

Adam Loy

Statistician, Carleton College

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INTERMEDIATE INTERACTIVE DATA VISUALIZATION WITH PLOTLY IN R

Exploring clusters

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INTERMEDIATE INTERACTIVE DATA VISUALIZATION WITH PLOTLY IN R

Exploring longitudinal data

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Crosstalk

Enables linked plots via JavaScript Creates static HTML les that you can easily host Displays in the RStudio viewer pane

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INTERMEDIATE INTERACTIVE DATA VISUALIZATION WITH PLOTLY IN R

2014 world indicators

world2014 # A tibble: 193 x 11 country year income co2 military population urban life_expectancy four_regions <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> 1 Afghan… 2014 1780 0.299 1.3 32800000 8.05e6 57.8 asia 2 Albania 2014 10700 1.96 1.35 2920000 1.63e6 77.4 europe 3 Algeria 2014 13500 3.72 5.55 39100000 2.75e7 77.1 africa 4 Andorra 2014 44900 5.83 NA 79200 7.01e4 82.6 europe 5 Angola 2014 6260 1.29 4.7 26900000 1.69e7 63.3 africa 6 Antigu… 2014 19500 5.38 NA 98900 2.49e4 77.1 americas # … with 187 more rows, and 2 more variables: eight_regions <chr>, six_regions <chr>

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Separate views

p1 <- world2014 %>% plot_ly(x = ~income, y = ~co2) %>% add_markers() p2 <- world2014 %>% plot_ly(x = ~military, y = ~co2) %>% add_markers() subplot(p1, p2, titleX = TRUE, titleY = TRUE) %>% hide_legend()

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Linked views

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INTERMEDIATE INTERACTIVE DATA VISUALIZATION WITH PLOTLY IN R

Linked views

library(crosstalk) shared_data <- SharedData$new(world2014) p1 <- shared_data %>% plot_ly(x = ~income, y = ~co2) %>% add_markers() p2 <- shared_data %>% plot_ly(x = ~military, y = ~co2) %>% add_markers() subplot(p1, p2, titleX = TRUE, titleY = TRUE) %>% hide_legend()

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INTERMEDIATE INTERACTIVE DATA VISUALIZATION WITH PLOTLY IN R

Linked brushing

Enable linked brushing via highlight()

subplot(p1, p2, titleX = TRUE, titleY = TRUE) %>% hide_legend() %>% highlight(on = "plotly_selected")

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Let's practice!

IN TE R ME D IATE IN TE R AC TIVE DATA VISU AL IZATION W ITH P L OTLY IN R

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Brushing groups

IN TE R ME D IATE IN TE R AC TIVE DATA VISU AL IZATION W ITH P L OTLY IN R

Adam Loy

Statistician, Carleton College

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World indicators

world_indicators # A tibble: 11,387 x 11 country year income co2 military population urban life_expectancy four_regions <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> 1 Afghan… 1960 1210 0.0461 NA 9000000 7.56e5 38.6 asia 2 Albania 1960 2790 1.24 NA 1640000 4.94e5 62.7 europe 3 Algeria 1960 6520 0.554 NA 11100000 3.39e6 52 africa 4 Andorra 1960 15200 NA NA 13400 7.84e3 NA europe 5 Angola 1960 3860 0.0975 NA 5640000 5.89e5 42.4 africa 6 Antigu… 1960 4420 0.663 NA 55300 2.19e4 62.9 americas # … with 1.138e+04 more rows, and 2 more variables: eight_regions <chr>, # six_regions <chr>

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Selecting individual time series

world_indicators %>% plot_ly(x = ~year, y = ~income, alpha = 0.5) %>% group_by(country) %>% add_lines()

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Selecting individual time series

Create a SharedData object with a key

world_indicators %>% SharedData$new(key = ~country) %>% plot_ly(x = ~year, y = ~income, alpha = 0.5) %>% group_by(country) %>% add_lines()

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Selecting groups on a scatterplot

world_indicators %>% filter(year == 2014) %>% SharedData$new(~six_regions) %>% plot_ly(x=~military, y = ~co2, text = ~country) %>% add_markers()

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Linking a summary and detailed view

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Linking a summary and detailed view

shared_data <- world_indicators %>% filter(year == 2014) %>% SharedData$new(key = ~six_regions) p1 <- shared_data %>% plot_ly() %>% group_by(six_regions) %>% summarize(avg.military = mean(military, na.rm = TRUE)) %>% add_markers(x = ~avg.military, y = ~six_regions) p2 <- shared_data %>% plot_ly(x=~military, y = ~co2, text = ~country) %>% add_markers() subplot(p1, p2) %>% hide_legend()

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Let's practice!

IN TE R ME D IATE IN TE R AC TIVE DATA VISU AL IZATION W ITH P L OTLY IN R

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Selection strategies

IN TE R ME D IATE IN TE R AC TIVE DATA VISU AL IZATION W ITH P L OTLY IN R

Adam Loy

Statistician, Carleton College

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Two selection strategies

Transient selection previously selected cases are forgoen Persistent selection selected cases accumulate

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Example

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Generate the base chart

shared_data <- world2014 %>% SharedData$new() p1 <- shared_data %>% plot_ly(x=~urban/population, y = ~co2, text = ~country) %>% add_markers() p2 <- shared_data %>% plot_ly(x=~income, y = ~co2, text = ~country) %>% add_markers() subplot(p1, p2, titleX = TRUE, shareY = TRUE) %>% hide_legend()

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Persistent selection

Activate persistent selection via highlight()

subplot(p1, p2, titleX = TRUE, shareY = TRUE) %>% hide_legend() %>% highlight(persistent = TRUE)

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Highlighting in color

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Highlighting in color

Add dynamic = TRUE to activate a color picker

subplot(p1, p2, titleX = TRUE, shareY = TRUE) %>% hide_legend() %>% highlight(persistent = TRUE, dynamic = TRUE)

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Two manipulation types

Direct manipulation selection performed by interacting with the graphical elements Indirect manipulation selection performed via query outside of the chart

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Indirect manipulation

world_indicators %>% SharedData$new(key = ~country) %>% plot_ly(x = ~year, y = ~income, alpha = 0.5) %>% group_by(country) %>% add_lines()

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Indirect manipulation

world_indicators %>% SharedData$new(key = ~country, group = "Select a country") %>% plot_ly(x = ~year, y = ~income, alpha = 0.5) %>% group_by(country) %>% add_lines() %>% highlight(selectize = TRUE)

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Let's practice!

IN TE R ME D IATE IN TE R AC TIVE DATA VISU AL IZATION W ITH P L OTLY IN R

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Making shinier charts

IN TE R ME D IATE IN TE R AC TIVE DATA VISU AL IZATION W ITH P L OTLY IN R

Adam Loy

Statistician, Carleton College

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Apps without shiny

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bscols() for column layouts

library(plotly) library(crosstalk) shared_data <- world2014 %>% SharedData$new() p1 <- shared_data %>% plot_ly(x=~income, y = ~co2, color = ~four_regions) %>% add_markers() %>% layout(xaxis = list(type = "log"), yaxis = list(type = "log")) p2 <- shared_data %>% plot_ly(x=~income, y = ~life_expectancy, color = ~four_regions) %>% add_markers() %>% layout(xaxis = list(type = "log"))) bscols(p1, p2)

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bscols() for column layouts

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Adding filters: Checkboxes

bscols(filter_checkbox(id = "four_regions", label = "Region", sharedData = shared_data, group = ~four_regions), p1)

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Adding filters: Select box

bscols(filter_select(id = "four_regions", label = "Region", sharedData = shared_data, group = ~four_regions), p1)

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Adding filters: Sliders

bscols(filter_slider(id = "co2", label = "CO2 concentrations", sharedData = shared_data, column = ~co2), p1)

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Fixing the range of your axes

bscols(filter_slider(id = "co2", label = "CO2 concentrations", sharedData = shared_data, column = ~co2), p1 %>% layout(xaxis = list(range = c(2.5, 5)), yaxis = list(range = c(-1.4, 1.55))) )

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Putting the pieces together

bscols(widths = c(2, 5, 5), list( filter_checkbox( id = "four_regions", label = "Region", sharedData = shared_data, group = ~four_regions ), filter_slider( id = "co2", label = "CO2 concentrations", sharedData = shared_data, column = ~co2 ) ), p1, p2 )

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Putting the pieces together

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Let's practice!

IN TE R ME D IATE IN TE R AC TIVE DATA VISU AL IZATION W ITH P L OTLY IN R