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从数据到图表

有什么样的数据做什么样的图

作者提供了一张树状图，帮助并引导我们找到合适自己数据的的可视化方式

What kind of data do you have? Pick the main type using the buttons below.
Then let the decision tree guide you toward your graphic possibilities.

这便是有名的网站：https://www.data-to-viz.com/。

Yan Holtz 和Conor Healys两个人关系很好，一起在业余时间开发了这个网站。和，这里我们不仅可以得到大量优秀的源代码，同时我们可以得到一张决策树，用于知道如何使用代码。这两个人相当厉害了，不仅仅给大家了工具，还叫大家如何使用。作为无私的分享，如果对大家有用，请在文章中致谢他们。如果我们需要交流代码，和谁交流呢？那必须是Yan Holtz，这位主要负责代码部分。Conor Healys负责图形设计工作。

可视化架构

基于网站我们来做一个示例

基于有顺序的二维数据框的出图

# Libraries
library(tidyverse)

## -- Attaching packages ----------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --

## √ ggplot2 3.2.0     √ purrr   0.3.2
## √ tibble  2.1.3     √ dplyr   0.8.3
## √ tidyr   0.8.3     √ stringr 1.4.0
## √ readr   1.3.1     √ forcats 0.4.0

## -- Conflicts -------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(hrbrthemes)

## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.

##       Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and

##       if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow

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':
##
##     layout

library(patchwork)
# install.packages("babynames")
library(babynames)
library(viridis)

## Loading required package: viridisLite

# ?as.Date
# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/3_TwoNumOrdered.csv", header=T)
data$date <- as.Date(data$date)

这里仅仅提取最后的十个数据进行点线图的可视化

# Plot
data %>%
  tail(10) %>%
  ggplot( aes(x=date, y=value)) +
    geom_line(color="#69b3a2") +
    geom_point(color="#69b3a2", size=4) +
    ggtitle("Evolution of Bitcoin price") +
    ylab("bitcoin price ($)") +
    theme_ipsum()

这里使用最后的60个数据进行可视化

 # Plot
p1 <- data %>%
  tail(60) %>%
  ggplot( aes(x=date, y=value)) +
    geom_line(color="#69b3a2") +
    ggtitle("Line chart") +
    ylab("bitcoin price ($)") +
    theme_ipsum()

p2 <- data %>%
  tail(60) %>%
  ggplot( aes(x=date, y=value)) +
    geom_line(color="#69b3a2") +
    geom_point(color="#69b3a2", size=2) +
    ggtitle("Connected scatterplot") +
    ylab("bitcoin price ($)") +
    theme_ipsum()

p = p1 + p2
p

散点图展示时间序列

# Plot
data %>%
  tail(60) %>%
  ggplot( aes(x=date, y=value)) +
    geom_point(color="#69b3a2", size=2) +
    ggtitle("Line chart") +
    ylab("bitcoin price ($)") +
    theme_ipsum()

分组时间序列可视化

 library(babynames)

# Load dataset
data <- babynames %>%
  filter(name %in% c("Ashley", "Amanda")) %>%
  filter(sex=="F")

#plot
data %>%
  ggplot( aes(x=year, y=n, group=name, color=name)) +
    geom_line() +
    scale_color_viridis(discrete = TRUE, name="") +
    theme(legend.position="none") +
    ggtitle("Popularity of American names in the previous 30 years") +
    theme_ipsum()

geom_segment函数突出展示变化趋势

 library(grid) # needed for arrow function
library(ggrepel)

# data
tmp <- data %>%
  filter(year>1970) %>%
  select(year, name, n) %>%
  spread(key = name, value=n, -1)

# data for date
tmp_date <- tmp %>% sample_frac(0.3)

tmp%>%
  ggplot(aes(x=Amanda, y=Ashley, label=year)) +
     geom_point(color="#69b3a2") +
     geom_text_repel(data=tmp_date) +
     geom_segment(color="#69b3a2",
                  aes(
                    xend=c(tail(Amanda, n=-1), NA),
                    yend=c(tail(Ashley, n=-1), NA)
                  ),
                  arrow=arrow(length=unit(0.3,"cm"))
      ) +
      theme_ipsum()

 data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/3_TwoNumOrdered.csv", header=T)
data$date <- as.Date(data$date)

p1 <- data %>%
  tail(10) %>%
  ggplot( aes(x=date, y=value)) +
    geom_line(color="#69b3a2") +
    geom_point(color="#69b3a2", size=4) +
    ggtitle("Not cuting") +
    ylab("bitcoin price ($)") +
    theme_ipsum() +
    ylim(0,10000)

p2 <- data %>%
  tail(10) %>%
  ggplot( aes(x=date, y=value)) +
    geom_line(color="#69b3a2") +
    geom_point(color="#69b3a2", size=4) +
    ggtitle("Cuting") +
    ylab("bitcoin price ($)") +
    theme_ipsum()

p1 + p2

reference

https://www.data-to-viz.com/graph/connectedscatter.html

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