ggplot2中的迷你图 [英] Sparklines in ggplot2

问题描述

作为

我不远处但我坚持最小/最大值和标签的分配：

  library（ggplot2）
 library（ ggthemes）
 library（dplyr）
 library（reshape）
 library（RCurl）
 dd<  -  read.csv（text = 
 getURL（https：/ /gist.githubusercontent.com/GeekOnAcid/da022affd36310c96cd4/raw/9c2ac2b033979fcf14a8d9b2e3e390a4bcc6f0e3/us_nr_of_crimes_1960_2014.csv））
d<  -  me lt（dd，id =Year）
名称（d）< -c（Year，Crime.Type，Crime.Rate）
 dd < -  group_by（d ，Crime.Type）％>％
 mutate（color =（min（Crime.Rate）== Crime.Rate | Max（Crime.Rate）== Crime.Rate））
 ggplot（dd，aes（x = Year，y = Crime.Rate））+ 
 facet_grid（Crime.Type〜。，scales = free_y）+ 
 geom_line（size = 0.3）+ geom_point（aes（color = color））+ 
 scale_color_manual（values = c（NA，red），guide = F）+ 
 theme_tufte（base_size = 15）+ 
 theme（axis.title = element_blank（），
 axis.text.y = element_blank（），axis.ticks = element_blank（））+ 
 theme （strip.text.y = element_text（angle = 0，vjust = 0.2，hjust = 0））
  

解决方案

作为三组标签和阴影四分位数范围：

 ＃计算最小值和最大值，which.min返回第一个（像你的例子）：
 mins < -  group_by（d，Crime.Typ e）％>％slice（which.min（Crime.Rate））
 maxs < -  group_by（d，Crime.Type）％>％slice（which.max（Crime.Rate））
 ends<  -  group_by（d，Crime.Type）％>％过滤器（年==最大（年））
夸脱<  -  d％>％
 group_by（Cr​​ime.Type ）％>％
 summary（quart1 = quantile（Crime.Rate，0.25），
 quart2 = quantile（Crime.Rate，0.75））％>％
 right_join（d）
 
 ggplot（d，aes（x = Year，y = Crime.Rate））+ 
 facet_grid（Crime.Type〜。，scales =free_y）+ 
 geom_ribbon data = quarts，aes（ymin = quart1，max = quart2），fill ='grey90'）+ 
 geom_line（size = 0.3）+ 
 geom_point（data = mins，col ='blue'）+ 
 geom_text（data = mins，aes（label = Crime.Rate），vjust = -1）+ 
 geom_point（data = maxs，col ='red'）+ 
 geom_text（data = maxs，aes（label = Crime.Rate），vjust = 2）+ 
 geom_text（data = ends，aes（label = Crime.Rate），hjust = 0）+ 
 geom_text（data = ends， aes（label = Crime.Type），hjust = 0，nudge_x = 5）+ 
 expand_limits（x = max（d $ Year）+（0.25 *（max（d $ Year） -  min（d $ Year））））+ 
 scale_x_continuous（break = seq（1960， 10））+ 
 scale_y_continuous（expand = c（0.1，0））+ 
 theme_tufte（base_size = 15）+ 
 theme（axis.title = element_blank（），
 axis .text.y = element_blank（），
 axis.ticks = element_blank（），
 strip.text = element_blank（））
  

我假设你不想在这里有个传说。几乎可以肯定的是，通过合并一些data.frames可以使事情变得更加简洁，但是多个geom调用在这里似乎是最简单的。

Tufte Sparklines (as illustrated in his Beautiful Evidence) have been replicated in base graphics as part of YaleToolkit and further perfected as a result of this question. Sparklines have also been done in lattice as a part of my small side project Tufte in R (self-promotion not intended). My goal now is to replicate Tufte sparklines in ggplot2. There are some scripts floating around on Gist and also as a reply to this question on SO, but none of those give a solid base for making replicable sets of sparklines.

Now, I would like those multiple sparklines to look like this (it was done in base graphics and the code is available here) - dots stand for maximum/minimum values, number on right end is a final value in specific time series and grey band shows a rough quantiles range:

I'm not far away but I'm stuck with the assignment of minimal/maximum values and labels:

library(ggplot2)
library(ggthemes)
library(dplyr)
library(reshape)
library(RCurl)
dd <- read.csv(text =
  getURL("https://gist.githubusercontent.com/GeekOnAcid/da022affd36310c96cd4/raw/9c2ac2b033979fcf14a8d9b2e3e390a4bcc6f0e3/us_nr_of_crimes_1960_2014.csv"))
d <- melt(dd, id="Year")
names(d) <- c("Year","Crime.Type","Crime.Rate")
dd <- group_by(d, Crime.Type) %>% 
  mutate(color = (min(Crime.Rate) == Crime.Rate | max(Crime.Rate) == Crime.Rate))
ggplot(dd, aes(x=Year, y=Crime.Rate)) + 
  facet_grid(Crime.Type ~ ., scales = "free_y") + 
  geom_line(size=0.3) + geom_point(aes(color = color)) + 
  scale_color_manual(values = c(NA, "red"), guide=F) +
  theme_tufte(base_size = 15) + 
  theme(axis.title=element_blank(), 
        axis.text.y = element_blank(), axis.ticks = element_blank()) +
  theme(strip.text.y = element_text(angle = 0, vjust=0.2, hjust=0)) 

解决方案

Here is one approach to getting single colored points, as well as the three sets of labels and shaded quartile ranges:

# Calculate the min and max values, which.min returns the first (like your example):
mins <- group_by(d, Crime.Type) %>% slice(which.min(Crime.Rate))
maxs <- group_by(d, Crime.Type) %>% slice(which.max(Crime.Rate))
ends <- group_by(d, Crime.Type) %>% filter(Year == max(Year))
quarts <- d %>%
  group_by(Crime.Type) %>%
  summarize(quart1 = quantile(Crime.Rate, 0.25),
            quart2 = quantile(Crime.Rate, 0.75)) %>%
  right_join(d)

ggplot(d, aes(x=Year, y=Crime.Rate)) + 
  facet_grid(Crime.Type ~ ., scales = "free_y") + 
  geom_ribbon(data = quarts, aes(ymin = quart1, max = quart2), fill = 'grey90') +
  geom_line(size=0.3) +
  geom_point(data = mins, col = 'blue') +
  geom_text(data = mins, aes(label = Crime.Rate), vjust = -1) +
  geom_point(data = maxs, col = 'red') +
  geom_text(data = maxs, aes(label = Crime.Rate), vjust = 2) +
  geom_text(data = ends, aes(label = Crime.Rate), hjust = 0) +
  geom_text(data = ends, aes(label = Crime.Type), hjust = 0, nudge_x = 5) +
  expand_limits(x = max(d$Year) + (0.25 * (max(d$Year) - min(d$Year)))) +
  scale_x_continuous(breaks = seq(1960, 2010, 10)) +
  scale_y_continuous(expand = c(0.1, 0)) +
  theme_tufte(base_size = 15) +
  theme(axis.title=element_blank(),
        axis.text.y = element_blank(), 
        axis.ticks = element_blank(),
        strip.text = element_blank())

I'm assuming you don't want a legend here. You can almost certainly make things more concise by merging some data.frames, but multiple geom calls seem to be easiest here.

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