Azad Rasul
SmartRS

SmartRS

15- Animated graphs in R programming

15- Animated graphs in R programming
Azad Rasul's photo
Azad Rasul

Published on Jul 24, 2021

6 min read

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Download used dataset in the tutorial:

Animated timeline

Load libraries

library(ggplot2)
library(gganimate)
library(tidyr)

Animated wind timeline using 'airquality' data:

#View(airquality)
month<- factor(airquality$Month)
p <- ggplot(airquality,aes(Day, Wind, group = Month, color = month)) +
  geom_line(lwd=1.3) +
  geom_point(size=3)+
  theme_bw()+
  scale_color_viridis_d() +
  labs(x = "Day of Month", y = "Wind") +
  theme(legend.position = "top")
p
p + transition_reveal(Day)

animated_wind.gif

Animated rain timeline:

Load data:

df <- read.csv('D://R4Researchers//Erbil_Rain_Annual.csv')
#View(df)

Convert wide to long dataframe:

dflong <- gather(df, Station, Rain, Qushtapa:Shorsh)

Create graph:

p1<- ggplot(dflong, aes(x=year,y=Rain,color=Station, group=Station))+
  geom_line(lwd=1.3, show.legend = T)+
  geom_point(size=3, show.legend = T) +
  theme_bw()+
  xlab('Year')+
  ylab('Rain (mm)')
p1

Animate plot using transition_reveal() function:

anim<- p1 + transition_reveal(year)
animate(anim, height = 400, width =600)

Save the graph:

anim_save("animate_prece_Erbil_new_400&600.gif")

animate_prece_Erbil_new_400&600.gif

Animated geom_col

Load libraries:

library(ggplot2)
library(RColorBrewer)
library(gganimate)

Import data:

da<-read.csv("D:\\R4Researchers\\LULC_Change.csv")
da1<- da[,1]
da2<- da[,2]
da3<- da[,3]
da3 <- as.factor(da3)

Create the graph using 'geom_col':

p <- ggplot(da, aes(x = da1, y = da2)) +
  geom_col(aes( fill = Class), position = position_dodge(0.8), width = 0.7,show.legend =F) +
  xlab("Land use/cover category")+
  ylab("% of area")+
  theme_bw() 
p

Add text to the graph:

p1<- p + geom_text(data = da,
         aes(x = da1, group=da3, y = da2+2, 
         label = format(da2, nsmall = 0, digits=3, scientific = FALSE)), 
         color="black", position=position_dodge(.8), hjust=.5)

Animate the graph using transition_states() function:

anim<- p1 + transition_states(year, wrap = T)+
  labs(title = "Year: {closest_state}", y = 'Class %', x= 'Class')

animate(anim, height = 400, width =600)
anim_save("animate_barplot_LULC_Change_new.gif")

animate_barplot_LULC_Change_new.gif

library(tidyverse)
library(ggplot2)
library(gganimate)

df <- read.csv('D://R4Researchers//Climatic_Variables_and_Lag7R0_Australia.csv')

p1<-ggplot(df)+
  geom_col(aes(y= lag7R0, x=area , fill =area),show.legend = F) + theme_bw()+
  theme(axis.text.x = element_text(angle = 45,hjust = 1))
p1

anim<- p1 + transition_time(month) +
  labs(title = "Month: {frame_time}", y = 'R0 COVID-19', x= 'States')
anim

anim_save("animate_boxplot_covid_Australia.gif")

animate_geom_col_covid_Australia.gif

Animated boxplot

Load libraries:

library(ggplot2)
library(gganimate)

Load data:

dt <- read.csv('D://R4Researchers//Climatic_Variables_and_Lag7R0_Australia.csv')

Create boxplot:

p1<-ggplot(dt)+
  geom_boxplot(aes(y= lag7R0, x=area , fill =area),show.legend = F) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45,hjust = 1)) +
  coord_cartesian(ylim = c(0,32.2))
p1

Animate the plot using 'transition_time()' function:

anim<- p1 + transition_time(month) +
  labs(title = "Month: {frame_time}", y = 'R0 COVID-19', x= 'States') # using frmae_time for title
animate(anim, height = 400, width =600)

Save the gif:

anim_save("animate_boxplot_covid_Australia.gif")

animate_boxplot_covid_Australia.gif

Animated violin

View 'mtcars' data as table:

View(mtcars)
CYL <- factor(mtcars$cyl)

Create the graph:

p1 <- ggplot(mtcars, aes(CYL, mpg))+
  geom_violin(aes(fill = CYL))+
  theme_bw()+
  theme(legend.position = "top")
p1

Animate the graph with 'transition_states()' function:

anim<- p1 + transition_states(gear,transition_length = 3, state_length = .5, wrap = T) +
  labs(title = "Gear: {closest_state}", y = '', x= '')
animate(anim, height = 600, width =600)

Save the gif:

anim_save("animate_geom_violin.gif")

animate_geom_violin.gif

Animated jitter plot

df <- read.csv('D://R4Researchers//Climatic_Variables_and_Lag7R0_Australia.csv')

p1<-ggplot(df)+
  geom_jitter(aes(y= lag7R0, x=area , colour =area),show.legend = F) + theme_bw()+
  theme(axis.text.x = element_text(angle = 45,hjust = 1))
p1

anim<- p1 + transition_time(month) +
  labs(title = "Month: {frame_time}", y = 'R0 COVID-19', x= 'States')
animate(anim, height = 400, width =600)

anim_save("animate_geom_jitter_covid_Australia.gif")

animate_geom_jitter_covid_Australia.gif Animated geom_count

df <- read.csv('D://R4Researchers//Climatic_Variables_and_Lag7R0_Australia.csv')

p1<-ggplot(df)+
  geom_count(aes(y= lag7R0, x= area, colour=area),show.legend = F) + theme_bw()+
  scale_size_area()+
  theme(axis.text.x = element_text(angle = 45,hjust = 1))#+
#coord_cartesian(ylim = c(0,20))
p1
anim<- p1 + transition_time(month) +
  labs(title = "Month: {frame_time}", y = 'R0 COVID-19', x= 'States')
anim

anim_save("animate_geom_count_covid_Australia.gif")

animate_geom_count_covid_Australia.gif

Multiple area animated scatterplot

df <- read.csv('D://R4Researchers//Climatic_Variables_and_Lag7R0_Australia.csv')
lag <- df$lag7R0*3

p1<- ggplot(df)+
  geom_point(aes( air_temp,lag7R0,size= lag, color = area),alpha = 0.7, show.legend = T)+ theme_bw()#+
p1

anim<- p1 + transition_time(month) +
  labs(title = "2020/ {frame_time}", x = 'Air Temperature', y= 'Lag7 day R0 COVID-19')+
  enter_fade() +
  facet_wrap(~area) #Use area to create multiple scatterplot

anim1<- animate(anim, fps=2, height = 450, width =700) # select height, width and speed frame per second 'fps:2'
anim1

anim_save("animate_sactterplot_Australia_Tempwrature.gif")

animate_sactterplot_Australia_Tempwrature.gif Animated scatterplot

df <- read.csv('D://R4Researchers//Climatic_Variables_and_Lag7R0_Australia.csv')

p2<- ggplot(df)+
  geom_point(aes( air_temp,lag7R0,size= lag7R0, color = area),alpha = 0.7, show.legend = T)+ theme_bw()
p2

pp2<- p2 + transition_time(month) +
  labs(title = "Month: {frame_time}", x = 'Air Temperature', y= 'Lag7 day R0 COVID-19')+
  enter_fade() +
  exit_fade()

anim1<- animate(pp2, fps=2, height = 450, width =700)

anim_save("animate_sactterplot_Australia_Tempwrature_one.gif")

animate_sactterplot_Australia_Tempwrature_one.gif

Animated map

library("ggplot2")
theme_set(theme_bw())
library(rgeos)
library("sf")
library("rnaturalearth")
library("rnaturalearthdata")

world <- ne_countries(scale = "medium", returnclass = "sf")
#View(world)

gg<- ggplot(data = world) +
 # geom_sf(aes(fill = gdp_md_est)) +
  geom_sf(aes(fill = pop_est)) + #give different color to pop_est
  scale_fill_viridis_c(option = "plasma", trans = "sqrt")
gg

anim <- gg +  transition_manual(frames = income_grp, cumulative = F)+ #transition by incom_grp
  labs(title = "Income_grp: {frame}")+ 
  enter_fade() + 
  exit_shrink()

animate(anim, height = 400, width =600)
anim_save("animate_geom_map_pop_Income.gif")

animate_geom_map_pop_Income.gif

Animated linerange

library(tidyverse)
library(ggplot2)
library(lubridate)
library(gganimate)

df <- read.csv('D://R4Researchers//Climatic_Variables_and_Lag7R0_Australia.csv')
std <- function(x) sd(x)/sqrt(length(x))

dt<- df %>%
  group_by(area) %>%
  summarize(mean_lag = mean(lag7R0, na.rm=T), tt=std(lag7R0), month= month)
dt
df1<- df %>% mutate(dt)
#View(df1)

p1 <- ggplot() + 
  geom_linerange(data=df1, mapping=aes(x=area, ymin=mean-tt, ymax=mean+tt, colour=area),show.legend = F) + 
  geom_point(data=dt, mapping=aes(x=area, y=mean, colour=area),show.legend = F) +
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45,hjust = 1))
p1

anim<- p1 + transition_time(month) +
labs(title = "Month: {frame_time}", y = 'Mean R0 COVID-19', x= 'States')
anim
#View(df1)
animate(anim,fps = 4, height = 400, width =600)

anim_save("animate_geom_linerange_Australia.gif")

animate_geom_linerange_covid_Australia.gif

Animated bin hex

library(ggplot2)
library(RColorBrewer)
library(ggExtra)

df <- read.csv('D://R4Researchers//Climatic_Variables_and_Lag7R0_Australia.csv')

p <- ggplot(df, aes(x=air_temp, y = ws))+
  stat_bin_hex(bins=10) +
  scale_fill_gradientn(colors = brewer.pal(3,"Greens")) +
  theme_classic() +
  theme(legend.position = "top")
p

anim <- p + transition_time(month) +
  labs(title = "Month: {frame_time}", x = 'Air temperature (c)', y= 'Wind speed (m/s)')

animate(anim,fps = 2, height = 400, width =600)
anim_save("animate_stat_bin_hex_Australia.gif")

animate_stat_bin_hex_Australia.gif

Animated dotplot

df <- read.csv('D://R4Researchers//Climatic_Variables_and_Lag7R0_Australia.csv')

p1<- ggplot(df, aes(y = air_temp,x= ws, fill = area),show.legend = F, alpha=0.6) +
  geom_dotplot(binwidth = 1.5, binaxis = "y", stackdir = "center", position = "dodge", drop=T, dotsize = 0.5, alpha=0.6)+
  theme_bw()
p1

anim<- p1 + transition_time(month) +
  labs(title = "Month: {frame_time}", x = 'Wind speed (m/s)', y= 'Air temperature(c)')
animate(anim, height = 500, width =500)

anim_save("animate_geom_dotplot_Australia.gif")

animate_geom_dotplot_Australia.gif

Animated geom_segment

# Library
library(ggplot2)
library(dplyr)

# Dataset 1: one value per group
data <- data.frame(
  name=c("north","south","south-east","north-west","south-west","north-east","west","east"),
  val=sample(seq(1,10), 8 )
)

p1<-data %>%
  arrange(val) %>%
  mutate(name = factor(name, levels=c("north", "north-east", "east", "south-east", "south", "south-west", "west", "north-west"))) %>%
  ggplot( aes(x=name, y=val)) +
  geom_segment( aes(xend=name, yend=0)) +
  geom_point( size=4, color="orange") +
  theme_bw()

p1

anim<- p1 + transition_reveal(val)+
  labs(y = 'Value', x= 'Direction')
anim

anim_save("animated_geom_segment.gif")

animated_geom_segment.gif

Animated pie chart

#Load data:
df<-read.csv("D:\\R4Researchers\\LULC_Change.csv")
df

# Create piechart:
bp<- ggplot(df, aes(x="", y=percent, fill=Class))+
  geom_bar(width = 1, stat = "identity")+
  theme_bw()+ 
  geom_text(aes(label = paste0(round(percent, 1), "%")), color = "white", 
            position = position_stack(vjust = 0.5), fontface = "bold")+
  xlab('')+
  ylab('')
bp
pie <- bp + coord_polar("y", start=0)
pie

# Animate the graph:
anim<- pie + transition_time(year) + labs(title = "Year: {frame_time}") 
anim

#Save gif:
anim_save("animate_pie_chart_LULC_Chane.gif")

animate_pie_chart_LULC_Chane_500_500.gif Animated like r coding

library(magick)

Azad<- image_read("https://publons.com/media/thumbs/academic/photos/680cac60-76d2-493e-a283-41e402d9a992.jpg.200x200_q95_crop_detail_upscale.jpg")
rgif<- image_read('https://media1.tenor.com/images/be5edc14e0126d43fd7b739e9cbc82f4/tenor.gif?itemid=16753306')
rgif <- image_scale(rgif, "650")
logo <- image_read("https://jeroen.github.io/images/Rlogo.png")
lov<- image_read("http://www.iamtudor.org/wp-content/uploads/2017/08/cropped-lovecoding.png")
img <- c(logo, Azad, lov)
img <- image_scale(img, "100x100")

# Combine and flatten frames:
frames <- image_composite(banana,img,  offset = "+1+1")

# Turn frames into animation
animation <- image_animate(frames, fps = 10, optimize = TRUE)
print(animation)

I_love_R_Coding.gif

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