R可视化：基础图形可视化之Evolution（七）

数据分析的图形可视化是了解数据分布、波动和相关性等属性必不可少的手段。数据根据时间动态变化的可视化图形：时序图、动态堆积图和流图等等。更多知识分享请到 https://zouhua.top/。

分组线条图 grouped line chart

library(ggplot2)
library(babynames)
library(dplyr)
library(hrbrthemes)
library(viridis)

# Keep only 3 names
don <- babynames %>% 
  filter(name %in% c("Ashley", "Patricia", "Helen")) %>%
  filter(sex=="F")
  
# Plot
don %>%
  ggplot( aes(x=year, y=n, group=name, color=name)) +
    geom_line() +
    scale_color_viridis(discrete = TRUE) +
    ggtitle("Popularity of American names in the previous 30 years") +
    theme_ipsum() +
    ylab("Number of babies born")

面积图 Area

library(ggplot2)
library(hrbrthemes)

xValue <- 1:10
yValue <- abs(cumsum(rnorm(10)))
data <- data.frame(xValue,yValue)

ggplot(data, aes(x=xValue, y=yValue)) +
  geom_area( fill="#69b3a2", alpha=0.4) +
  geom_line(color="#69b3a2", size=2) +
  geom_point(size=3, color="#69b3a2") +
  theme_ipsum() +
  ggtitle("Evolution of something")

面积堆积图 Stacked area chart

library(ggplot2)
library(dplyr)
 
time <- as.numeric(rep(seq(1,7),each=7)) 
value <- runif(49, 10, 100)              
group <- rep(LETTERS[1:7],times=7)     
data <- data.frame(time, value, group)

plotdata <- data  %>%
  group_by(time, group) %>%
  summarise(n = sum(value)) %>%
  mutate(percentage = n / sum(n))

ggplot(plotdata, aes(x=time, y=percentage, fill=group)) + 
    geom_area(alpha=0.6 , size=1, colour="white")+
    scale_fill_viridis(discrete = T) +
    theme_ipsum()

Streamgraph

# devtools::install_github("hrbrmstr/streamgraph")
library(streamgraph)
library(dplyr)
library(babynames)


babynames %>%
  filter(grepl("^Kr", name)) %>%
  group_by(year, name) %>%
  tally(wt=n) %>%
  streamgraph("name", "n", "year")

babynames %>%
  filter(grepl("^I", name)) %>%
  group_by(year, name) %>%
  tally(wt=n) %>%
  streamgraph("name", "n", "year", offset="zero", interpolate="linear") %>%
  sg_legend(show=TRUE, label="I- names: ")

Time Series

library(ggplot2)
library(dplyr)
library(hrbrthemes)

data <- data.frame(
  day = as.Date("2017-06-14") - 0:364,
  value = runif(365) + seq(-140, 224)^2 / 10000
)

ggplot(data, aes(x=day, y=value)) +
  geom_line( color="steelblue") + 
  geom_point() +
  xlab("") +
  theme_ipsum() +
  theme(axis.text.x=element_text(angle=60, hjust=1)) +
  scale_x_date(limit=c(as.Date("2017-01-01"),as.Date("2017-02-11"))) +
  ylim(0,1.5)

library(dygraphs)
library(xts)
library(tidyverse)
library(lubridate)
 

data <- read.table("https://python-graph-gallery.com/wp-content/uploads/bike.csv", header=T, sep=",") %>% head(300)
data$datetime <- ymd_hms(data$datetime)
 
don <- xts(x = data$count, order.by = data$datetime)

dygraph(don) %>%
  dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors="#D8AE5A") %>%
  dyRangeSelector() %>%
  dyCrosshair(direction = "vertical") %>%
  dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE)  %>%
  dyRoller(rollPeriod = 1)

参考

1. The R Graph Gallery

参考文章如引起任何侵权问题，可以与我联系，谢谢。