Data Viz Project
Monica Campos
2024-12-01
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library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(maps)
library(tibble)
library(tidyr)
library(RColorBrewer)
#Load Dataset
trade_data <- read.csv("Merged.csv")
head(trade_data)## Country Partner.Name Trade.Flow Product.Group Indicator Year
## 1 China Bangladesh Import All Products Import (US$ Thousand) 1996
## 2 China Bangladesh Import All Products Import (US$ Thousand) 1996
## 3 China Iran Import All Products Import (US$ Thousand) 1996
## 4 China Iran Import All Products Import (US$ Thousand) 1996
## 5 China Iran Import All Products Import (US$ Thousand) 1996
## 6 China Iran Import All Products Import (US$ Thousand) 1996
## Amount SIPRI.AT.Database.ID Supplier Recipient Designation
## 1 34217.72 27731 China Bangladesh F-7M Airguard
## 2 34217.72 27731 China Bangladesh F-7M Airguard
## 3 386141.35 32698 China Iran JY-14
## 4 386141.35 32698 China Iran JY-14
## 5 386141.35 32698 China Iran JY-14
## 6 386141.35 39941 China Iran Type-86 APC
## Description Armament.category Order.date Order.date.is.estimate
## 1 fighter aircraft Aircraft 1996 Yes
## 2 fighter aircraft Aircraft 1996 Yes
## 3 air search radar Sensors 1996 Yes
## 4 air search radar Sensors 1996 Yes
## 5 air search radar Sensors 1996 Yes
## 6 APC Armoured vehicles 1996 Yes
## Numbers.delivered Numbers.delivered.is.estimate Delivery.year
## 1 3 No 1999
## 2 1 No 2000
## 3 1 Yes 1999
## 4 1 Yes 2000
## 5 1 Yes 2001
## 6 10 Yes 2001
## Delivery.year.is.estimate Status SIPRI.estimate TIV.deal.unit
## 1 No New 11.0 11.0
## 2 No New 11.0 11.0
## 3 Yes New 13.5 13.5
## 4 Yes New 13.5 13.5
## 5 Yes New 13.5 13.5
## 6 No New 0.3 0.3
## TIV.delivery.values Local.production TII TII.Score
## 1 33.0 No TII 4.128203
## 2 11.0 No TII 4.128203
## 3 13.5 No TII 1.198521
## 4 13.5 No TII 1.198521
## 5 13.5 No TII 1.198521
## 6 3.0 Yes TII 1.198521# Coordinates
region_mapping <- tibble(
Region = c("North America", "North America", "North America", "South and Central America", "Europe & Eurasia", "Asia", "Asia",
"South and Central America", "Asia", "Asia", "Africa", "Europe & Eurasia", "Africa", "Middle East", "Europe & Eurasia", "Middle East",
"Africa", "Asia Pacific", "Europe & Eurasia"),
Country = c("United States", "Canada", "Mexico", "Brazil", "Germany", "China", "India",
"Ecuador","Bangladesh", "Pakistan","Nigeria", "Netherlands", "Kenya", "Yemen", "United Kingdom", "Saudi Arabia",
"South Africa", "Australia", "Russia"),
Latitude = c(37.0902, 56.130366, 23.634501, -14.235004, 51.165691, 35.8617, 20.5937,
-1.8312, 23.6850, 30.3753,9.0820, 52.1326, -1.2864, 15.552727, 55.378051,23.885942,
-30.559482, -25.274398, 61.52401),
Longitude = c(-95.7129, -106.346771, -102.552784, -51.92528, 10.451526, 104.1954, 78.96288,
-78.1834, 90.3563, 69.3451, 8.6753, 5.2913,37.9062, 48.5164,-3.4359, 45.0792,
22.937506, 133.775136, 105.318756))
trade_data_coords <- trade_data %>%
left_join(region_mapping, by = c("Supplier" = "Country")) %>%
rename(Export_Lat = Latitude, Export_Lon = Longitude, Region_Export = Supplier) %>%
left_join(region_mapping, by = c("Recipient" = "Country")) %>%
rename(Import_Lat = Latitude, Import_Lon = Longitude, Region_Import = Recipient)
head(trade_data_coords)## Country Partner.Name Trade.Flow Product.Group Indicator Year
## 1 China Bangladesh Import All Products Import (US$ Thousand) 1996
## 2 China Bangladesh Import All Products Import (US$ Thousand) 1996
## 3 China Iran Import All Products Import (US$ Thousand) 1996
## 4 China Iran Import All Products Import (US$ Thousand) 1996
## 5 China Iran Import All Products Import (US$ Thousand) 1996
## 6 China Iran Import All Products Import (US$ Thousand) 1996
## Amount SIPRI.AT.Database.ID Region_Export Region_Import Designation
## 1 34217.72 27731 China Bangladesh F-7M Airguard
## 2 34217.72 27731 China Bangladesh F-7M Airguard
## 3 386141.35 32698 China Iran JY-14
## 4 386141.35 32698 China Iran JY-14
## 5 386141.35 32698 China Iran JY-14
## 6 386141.35 39941 China Iran Type-86 APC
## Description Armament.category Order.date Order.date.is.estimate
## 1 fighter aircraft Aircraft 1996 Yes
## 2 fighter aircraft Aircraft 1996 Yes
## 3 air search radar Sensors 1996 Yes
## 4 air search radar Sensors 1996 Yes
## 5 air search radar Sensors 1996 Yes
## 6 APC Armoured vehicles 1996 Yes
## Numbers.delivered Numbers.delivered.is.estimate Delivery.year
## 1 3 No 1999
## 2 1 No 2000
## 3 1 Yes 1999
## 4 1 Yes 2000
## 5 1 Yes 2001
## 6 10 Yes 2001
## Delivery.year.is.estimate Status SIPRI.estimate TIV.deal.unit
## 1 No New 11.0 11.0
## 2 No New 11.0 11.0
## 3 Yes New 13.5 13.5
## 4 Yes New 13.5 13.5
## 5 Yes New 13.5 13.5
## 6 No New 0.3 0.3
## TIV.delivery.values Local.production TII TII.Score Region.x Export_Lat
## 1 33.0 No TII 4.128203 Asia 35.8617
## 2 11.0 No TII 4.128203 Asia 35.8617
## 3 13.5 No TII 1.198521 Asia 35.8617
## 4 13.5 No TII 1.198521 Asia 35.8617
## 5 13.5 No TII 1.198521 Asia 35.8617
## 6 3.0 Yes TII 1.198521 Asia 35.8617
## Export_Lon Region.y Import_Lat Import_Lon
## 1 104.1954 Asia 23.685 90.3563
## 2 104.1954 Asia 23.685 90.3563
## 3 104.1954 <NA> NA NA
## 4 104.1954 <NA> NA NA
## 5 104.1954 <NA> NA NA
## 6 104.1954 <NA> NA NAtrade_data_clean = trade_data_coords %>%
filter(!is.na(Region_Export), !is.na(Region_Import)) %>%
mutate(TradeRoute = paste(Region_Export, "to", Region_Import)) %>%
filter(!is.na(Export_Lon), !is.na(Export_Lat), !is.na(Import_Lon), !is.na(Import_Lat)) %>%
filter(!(Export_Lon == Import_Lon & Export_Lat == Import_Lat))
View(trade_data_clean)
# create map
world <- map_data("world")
world_regions <- world %>%
mutate(Region = case_when(
region %in% c("USA", "Canada", "Mexico") ~ "North America",
region %in% c("Brazil", "Argentina", "Ecuador") ~ "South and Central America",
region %in% c("Germany", "Russia", "France", "Netherlands", "UK") ~ "Europe & Eurasia",
region %in% c("China", "India", "Bangladesh", "Pakistan") ~ "Asia",
region %in% c("Japan", "Australia") ~ "Asia Pacific",
region %in% c("Nigeria", "South Africa", "Kenya") ~ "Africa",
region %in% c("Saudi Arabia", "Yemen") ~ "Middle East",
TRUE ~ "Other"
))
# Color palette for regions
region_colors <- c(
"North America" = "mistyrose1",
"South and Central America" = "tomato",
"Europe & Eurasia" = "lightskyblue3",
"Asia" = "antiquewhite",
"Asia Pacific" = "sandybrown",
"Middle East" = "pink",
"Africa" = "aquamarine",
"Other" = "gray90")
summary(trade_data_clean)## Country Partner.Name Trade.Flow Product.Group
## Length:3122 Length:3122 Length:3122 Length:3122
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Indicator Year Amount SIPRI.AT.Database.ID
## Length:3122 Min. :1996 Min. : 1410 Min. :17856
## Class :character 1st Qu.:2001 1st Qu.: 1610552 1st Qu.:34289
## Mode :character Median :2007 Median : 5596013 Median :50209
## Mean :2007 Mean : 18113453 Mean :44862
## 3rd Qu.:2012 3rd Qu.: 18960148 3rd Qu.:55380
## Max. :2021 Max. :568174902 Max. :66265
## NA's :56 NA's :104
## Region_Export Region_Import Designation Description
## Length:3122 Length:3122 Length:3122 Length:3122
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Armament.category Order.date Order.date.is.estimate Numbers.delivered
## Length:3122 Min. :1996 Length:3122 Min. : 0.0
## Class :character 1st Qu.:2001 Class :character 1st Qu.: 5.0
## Mode :character Median :2007 Mode :character Median : 16.0
## Mean :2007 Mean : 111.8
## 3rd Qu.:2012 3rd Qu.: 70.0
## Max. :2021 Max. :10000.0
## NA's :52
## Numbers.delivered.is.estimate Delivery.year Delivery.year.is.estimate
## Length:3122 Min. : 0 Length:3122
## Class :character 1st Qu.:2007 Class :character
## Mode :character Median :2013 Mode :character
## Mean :1978
## 3rd Qu.:2017
## Max. :2022
## NA's :52
## Status SIPRI.estimate TIV.deal.unit TIV.delivery.values
## Length:3122 Min. : 0.00 Min. : 0.00 Min. : 0.00
## Class :character 1st Qu.: 0.25 1st Qu.: 0.20 1st Qu.: 7.70
## Mode :character Median : 1.40 Median : 1.40 Median : 24.00
## Mean : 14.07 Mean : 13.65 Mean : 75.50
## 3rd Qu.: 5.00 3rd Qu.: 5.00 3rd Qu.: 65.25
## Max. :1250.00 Max. :1250.00 Max. :1950.00
## NA's :52 NA's :52 NA's :52
## Local.production TII TII.Score Region.x
## Length:3122 Length:3122 Min. :0.1089 Length:3122
## Class :character Class :character 1st Qu.:0.9335 Class :character
## Mode :character Mode :character Median :1.2742 Mode :character
## Mean :1.4163
## 3rd Qu.:1.7969
## Max. :4.1282
##
## Export_Lat Export_Lon Region.y Import_Lat
## Min. :35.86 Min. :-95.71 Length:3122 Min. :20.59
## 1st Qu.:37.09 1st Qu.:-95.71 Class :character 1st Qu.:20.59
## Median :37.09 Median :104.20 Mode :character Median :23.89
## Mean :47.95 Mean : 44.26 Mean :27.34
## 3rd Qu.:61.52 3rd Qu.:105.32 3rd Qu.:30.38
## Max. :61.52 Max. :105.32 Max. :61.52
##
## Import_Lon TradeRoute
## Min. :-95.71 Length:3122
## 1st Qu.: 69.35 Class :character
## Median : 78.96 Mode :character
## Mean : 73.38
## 3rd Qu.: 90.36
## Max. :105.32
## head(trade_data_clean)## Country Partner.Name Trade.Flow Product.Group Indicator Year
## 1 China Bangladesh Import All Products Import (US$ Thousand) 1996
## 2 China Bangladesh Import All Products Import (US$ Thousand) 1996
## 3 China Pakistan Import All Products Import (US$ Thousand) 1996
## 4 China Pakistan Import All Products Import (US$ Thousand) 1996
## 5 China United States Import All Products Import (US$ Thousand) 1996
## 6 China Bangladesh Export All Products Export (US$ Thousand) 1996
## Amount SIPRI.AT.Database.ID Region_Export Region_Import Designation
## 1 34217.72 27731 China Bangladesh F-7M Airguard
## 2 34217.72 27731 China Bangladesh F-7M Airguard
## 3 342263.08 28922 China Pakistan Type-347G
## 4 342263.08 28922 China Pakistan Type-347G
## 5 16155120.43 NA China United States 0
## 6 653039.64 27731 China Bangladesh F-7M Airguard
## Description Armament.category Order.date Order.date.is.estimate
## 1 fighter aircraft Aircraft 1996 Yes
## 2 fighter aircraft Aircraft 1996 Yes
## 3 fire control radar Sensors 1996 Yes
## 4 fire control radar Sensors 1996 Yes
## 5 0 0 1996 0
## 6 fighter aircraft Aircraft 1996 Yes
## Numbers.delivered Numbers.delivered.is.estimate Delivery.year
## 1 3 No 1999
## 2 1 No 2000
## 3 1 No 1997
## 4 1 No 1999
## 5 0 0 0
## 6 3 No 1999
## Delivery.year.is.estimate Status SIPRI.estimate TIV.deal.unit
## 1 No New 11.0 11.0
## 2 No New 11.0 11.0
## 3 No New 2.5 2.5
## 4 No New 2.5 2.5
## 5 0 0 0.0 0.0
## 6 No New 11.0 11.0
## TIV.delivery.values Local.production TII TII.Score Region.x Export_Lat
## 1 33.0 No TII 4.128203 Asia 35.8617
## 2 11.0 No TII 4.128203 Asia 35.8617
## 3 2.5 No TII 2.369284 Asia 35.8617
## 4 2.5 No TII 2.369284 Asia 35.8617
## 5 0.0 0 TII 1.184385 Asia 35.8617
## 6 33.0 No TII 4.128203 Asia 35.8617
## Export_Lon Region.y Import_Lat Import_Lon TradeRoute
## 1 104.1954 Asia 23.6850 90.3563 China to Bangladesh
## 2 104.1954 Asia 23.6850 90.3563 China to Bangladesh
## 3 104.1954 Asia 30.3753 69.3451 China to Pakistan
## 4 104.1954 Asia 30.3753 69.3451 China to Pakistan
## 5 104.1954 North America 37.0902 -95.7129 China to United States
## 6 104.1954 Asia 23.6850 90.3563 China to Bangladesh# Create base map
tradebasic <- ggplot() +
geom_polygon(data = world_regions, aes(x = long, y = lat, group = group, fill = Region), color = "black") +
geom_curve(data = trade_data_clean, aes(x = Export_Lon, y = Export_Lat,
xend = Import_Lon, yend = Import_Lat, color = TradeRoute, size = Amount),
size = 0.3 ,curvature = 0.1, arrow = arrow(length = unit(0.1, "cm"))) +
scale_fill_manual(values = region_colors, name = "Region") +
coord_fixed(1.3) +
labs(title = "China, US, Russia Trade Routes", x = "Longitude", y = "Latitude") +
theme_minimal() +
theme(legend.position = "bottom")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.# create plotly map
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(dplyr)
library(tidyr)
trade_data_clean = trade_data_coords %>%
filter(!is.na(Region_Export), !is.na(Region_Import)) %>%
mutate(TradeRoute = paste(Region_Export, "to", Region_Import)) %>%
filter(!is.na(Export_Lon), !is.na(Export_Lat), !is.na(Import_Lon), !is.na(Import_Lat)) %>%
filter(!(Export_Lon == Import_Lon & Export_Lat == Import_Lat))
View(trade_data_clean)
trade_data_plot <- trade_data_clean %>%
rowwise() %>%
mutate(
lon = list(c(Export_Lon, Import_Lon, NA)),
lat = list(c(Export_Lat, Import_Lat, NA))
) %>%
unnest(c(lon, lat))
View(trade_data_plot)
plot <- plot_geo() %>%
add_trace(
type = "scattergeo",
mode = "lines",
lon = ~lon,
lat = ~lat,
split = ~TradeRoute,
line = list(width = 2.5, color = "steelblue"),
color = ~TradeRoute,
colors = "viridis",
data = trade_data_plot
) %>%
add_trace(
type = "scattergeo",
mode = "markers",
lon = ~Export_Lon,
lat = ~Export_Lat,
text = ~paste("Country", Country),
marker = list(size = 5, color = "hotpink2"),
name = "Export"
) %>%
add_trace(
type = "scattergeo",
mode = "markers",
lon = ~Import_Lon,
lat = ~Import_Lat,
text = ~paste("Country", Partner.Name),
marker = list(size = 5, color = "red"),
name = "Import"
) %>%
layout(
title = "Interactive US and China Trade Route",
geo = list(
showland = TRUE,
landcolor = "darkseagreen",
showcoastlines = "black",
projection = list(type = "equirectagular")
)
)
#animated interactive map
plot_animate <- plot_geo() %>%
add_trace(
type = "scattergeo",
mode = "lines",
lon = ~lon,
lat = ~lat,
split = ~TradeRoute,
line = list(width = 2.5, color = "steelblue"),
color = ~TradeRoute,
colors = "viridis",
frame = ~Year,
data = trade_data_plot
) %>%
add_trace(
type = "scattergeo",
mode = "markers",
lon = ~Export_Lon,
lat = ~Export_Lat,
text = ~paste("Country", Country),
marker = list(size = 5, color = "hotpink2"),
name = "Export"
) %>%
add_trace(
type = "scattergeo",
mode = "markers",
lon = ~Import_Lon,
lat = ~Import_Lat,
text = ~paste("Country", Partner.Name),
marker = list(size = 5, color = "red"),
name = "Import"
) %>%
layout(
title = "Interactive US and China Trade Route",
geo = list(
showland = TRUE,
landcolor = "darkseagreen4",
showcoastlines = "black",
projection = list(type = "equirectagular")
),
updatemenus = list(
list(
type = "buttons",
showactive = FALSE,
buttons = list(
list(label = "Play",
method = "animate",
args = list(NULL, list(frame = list(duration = 700, redraw = TRUE), fromcurrent = TRUE))),
list(label = "Play",
method = "animate",
args = list(NULL, list(mode = "immediate", frame = list(duration = 0))))
)
)
)
)
#animated plotly
trade_data_clean = trade_data_coords %>%
filter(!is.na(Region_Export), !is.na(Region_Import)) %>%
mutate(TradeRoute = paste(Region_Export, "to", Region_Import)) %>%
filter(!is.na(Export_Lon), !is.na(Export_Lat), !is.na(Import_Lon), !is.na(Import_Lat)) %>%
filter(!(Export_Lon == Import_Lon & Export_Lat == Import_Lat))
head(trade_data_clean)## Country Partner.Name Trade.Flow Product.Group Indicator Year
## 1 China Bangladesh Import All Products Import (US$ Thousand) 1996
## 2 China Bangladesh Import All Products Import (US$ Thousand) 1996
## 3 China Pakistan Import All Products Import (US$ Thousand) 1996
## 4 China Pakistan Import All Products Import (US$ Thousand) 1996
## 5 China United States Import All Products Import (US$ Thousand) 1996
## 6 China Bangladesh Export All Products Export (US$ Thousand) 1996
## Amount SIPRI.AT.Database.ID Region_Export Region_Import Designation
## 1 34217.72 27731 China Bangladesh F-7M Airguard
## 2 34217.72 27731 China Bangladesh F-7M Airguard
## 3 342263.08 28922 China Pakistan Type-347G
## 4 342263.08 28922 China Pakistan Type-347G
## 5 16155120.43 NA China United States 0
## 6 653039.64 27731 China Bangladesh F-7M Airguard
## Description Armament.category Order.date Order.date.is.estimate
## 1 fighter aircraft Aircraft 1996 Yes
## 2 fighter aircraft Aircraft 1996 Yes
## 3 fire control radar Sensors 1996 Yes
## 4 fire control radar Sensors 1996 Yes
## 5 0 0 1996 0
## 6 fighter aircraft Aircraft 1996 Yes
## Numbers.delivered Numbers.delivered.is.estimate Delivery.year
## 1 3 No 1999
## 2 1 No 2000
## 3 1 No 1997
## 4 1 No 1999
## 5 0 0 0
## 6 3 No 1999
## Delivery.year.is.estimate Status SIPRI.estimate TIV.deal.unit
## 1 No New 11.0 11.0
## 2 No New 11.0 11.0
## 3 No New 2.5 2.5
## 4 No New 2.5 2.5
## 5 0 0 0.0 0.0
## 6 No New 11.0 11.0
## TIV.delivery.values Local.production TII TII.Score Region.x Export_Lat
## 1 33.0 No TII 4.128203 Asia 35.8617
## 2 11.0 No TII 4.128203 Asia 35.8617
## 3 2.5 No TII 2.369284 Asia 35.8617
## 4 2.5 No TII 2.369284 Asia 35.8617
## 5 0.0 0 TII 1.184385 Asia 35.8617
## 6 33.0 No TII 4.128203 Asia 35.8617
## Export_Lon Region.y Import_Lat Import_Lon TradeRoute
## 1 104.1954 Asia 23.6850 90.3563 China to Bangladesh
## 2 104.1954 Asia 23.6850 90.3563 China to Bangladesh
## 3 104.1954 Asia 30.3753 69.3451 China to Pakistan
## 4 104.1954 Asia 30.3753 69.3451 China to Pakistan
## 5 104.1954 North America 37.0902 -95.7129 China to United States
## 6 104.1954 Asia 23.6850 90.3563 China to Bangladeshinter_trade <- trade_data_clean %>%
rowwise() %>%
mutate(
interpolated = list(
tibble(
lon = seq(Export_Lon, Import_Lon, length.out = 25),
lat = seq(Export_Lat, Import_Lat, length.out = 25),
step = 1:25
)
)
)%>%
unnest(interpolated)
head(inter_trade)## # A tibble: 6 × 36
## Country Partner.Name Trade.Flow Product.Group Indicator Year Amount
## <chr> <chr> <chr> <chr> <chr> <int> <dbl>
## 1 China Bangladesh Import " All Products" Import (US$ Tho… 1996 34218.
## 2 China Bangladesh Import " All Products" Import (US$ Tho… 1996 34218.
## 3 China Bangladesh Import " All Products" Import (US$ Tho… 1996 34218.
## 4 China Bangladesh Import " All Products" Import (US$ Tho… 1996 34218.
## 5 China Bangladesh Import " All Products" Import (US$ Tho… 1996 34218.
## 6 China Bangladesh Import " All Products" Import (US$ Tho… 1996 34218.
## # ℹ 29 more variables: SIPRI.AT.Database.ID <int>, Region_Export <chr>,
## # Region_Import <chr>, Designation <chr>, Description <chr>,
## # Armament.category <chr>, Order.date <int>, Order.date.is.estimate <chr>,
## # Numbers.delivered <int>, Numbers.delivered.is.estimate <chr>,
## # Delivery.year <int>, Delivery.year.is.estimate <chr>, Status <chr>,
## # SIPRI.estimate <dbl>, TIV.deal.unit <dbl>, TIV.delivery.values <dbl>,
## # Local.production <chr>, TII <chr>, TII.Score <dbl>, Region.x <chr>, …unique(inter_trade$step)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25custom_colors <- colorRampPalette(brewer.pal(9, "Set1"))(length(unique(inter_trade$Indicator)))
animated_plot <- plot_ly() %>%
add_trace(
type = "scattergeo",
mode = "lines",
lon = ~lon,
lat = ~lat,
frame = ~Year,
color = ~Indicator,
line = list(width = 5),
data = inter_trade) %>%
layout(
title = "Animated US, Russia, and China Trade Route",
geo = list(
showland = TRUE,
landcolor = "tan",
showcoastlines = "black",
projection = list(type = "equirectagular")
),
updatemenus = list(
list(
type = "buttons",
showactive = FALSE,
buttons = list(
list(label = "Play",
method = "animate",
args = list(NULL, list(frame = list(duration = 700, redraw = TRUE), fromcurrent = TRUE))),
list(label = "Play",
method = "animate",
args = list(NULL, list(mode = "immediate", frame = list(duration = 0))))
)
)
)
)Including Plots
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## Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
## replace is not a multiple of replacement length