output:
flexdashboard::flex_dashboard: default
pdf_document: default
html_document: default
title: “Assignment 3”
author: “Mehak Gupta (S3931990)” 
library(ggplot2)
library(gganimate)
library(png)
library(gifski)
library(gapminder)
head(gapminder)
## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.
plot_ <- ggplot(
  gapminder, 
  aes(x = gdpPercap, y=lifeExp, size = pop, colour = country)
) +
  geom_point(show.legend = FALSE, alpha = 0.7) +
  scale_color_viridis_d() +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  labs(x = "GDP per capita", y = "Life expectancy")
plot_

plot_ +
  geom_text(aes(x = min(gdpPercap), y = min(lifeExp), label = as.factor(year)) , hjust=-2, vjust = -0.2, alpha = 0.2,  col = "gray", size = 20) +
  transition_states(as.factor(year), state_length = 0)

plot_ + facet_wrap(~continent) +
  transition_time(year) +
  labs(title = "Year: {frame_time}")

plot_ <- ggplot(
  airquality,
  aes(Day, Temp, group = Month, color = factor(Month))
) +
  geom_line() +
  scale_color_viridis_d() +
  labs(x = "Day of Month", y = "Temperature") +
  theme(legend.position = "top")
plot_

plot_ + 
  geom_point() +
  transition_reveal(Day)
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

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, union
mean.temp <- airquality %>%
  group_by(Month) %>%
  summarise(Temp = mean(Temp))
mean.temp
## # A tibble: 5 × 2
##   Month  Temp
##   <int> <dbl>
## 1     5  65.5
## 2     6  79.1
## 3     7  83.9
## 4     8  84.0
## 5     9  76.9
plot_ <- ggplot(mean.temp, aes(Month, Temp, fill = Temp)) +
  geom_col() +
  scale_fill_distiller(palette = "Reds", direction = 1) +
  theme_minimal() +
  theme(
    panel.grid = element_blank(),
    panel.grid.major.y = element_line(color = "white"),
    panel.ontop = TRUE
  )
plot_

plot_ + transition_states(Month, wrap = FALSE) +
  shadow_mark()

Data Reference