从数据到图表
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。基于R和Python做的源代码,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。
原图地址:https://www.data-to-viz.com/img/poster/poster_big.png
基于网站我们来做一个示例
大部分情况,我们的数据都是二维数据框:下面就二维数据框的数据,变量指定为有顺序的变量,我们进行出图。
这是基于时间序列的一份二维数据。作者提供了数据下载地址. as.Date函数将数据转化为时间序列。
# 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)
# 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 ($)") +
theme_ipsum()
这里做了折线图和点线图。我们ggplot出图就是这么随意,图形相加就是拼图。
# Plot
p1 <- data %>%
tail(60) %>%
ggplot( aes(x=date, y=value)) +
geom_line(color="#69b3a2") +
ggtitle("Line chart") +
ylab("bitcoin price ($)") +
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 ($)") +
theme_ipsum()
p = p1 + p2
p
# Plot
data %>%
tail(60) %>%
ggplot( aes(x=date, y=value)) +
geom_point(color="#69b3a2", size=2) +
ggtitle("Line chart") +
ylab("bitcoin price ($)") +
theme_ipsum()
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()
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
https://www.data-to-viz.com/graph/connectedscatter.html
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