统计摘要乐趣的值意味着

我正在尝试创建一个图来比较不同算法的运行时间.通过运行以下R代码,我得到以下情节,我通常对此表示满意.但是:从该图中读取值可能很困难,是否有办法为每个实例获取每个DBMS的绘制平均值？例如gplus-combined,值CacheDBMS大约为50,而BranchDBMS大约为200.

ggplot(dt, aes(reorder(instance, V9), V9)) + 
  geom_point(aes(group=V2, colour=V2), stat='summary', fun.y='mean') +
  geom_line(aes(group=V2, colour=V2), stat='summary', fun.y='mean') +
  scale_y_log10() +
  ylab("Mean wall time") +
  xlab("") +
  ggtitle("Comparison of Database Management Systems") +
  theme_bw() +
  theme(axis.text.x = element_text(angle=45, vjust = 1, hjust = 1)) +
  guides(color=guide_legend(title="DBMS"))

我想要每个点的y值.优选作为表格,例如

BranchDBMS    gplus-combined    213.21
CacheDBMS     gplus-combined     48.68

编辑

输入数据的小片段(超出10000行).我删除了未使用的列,因此V*不正确.但这V2是第一列,V9是第二列,也是instance最后一列.

BranchDBMS;      0.163352;  facebook-combined   
BranchDBMS;      0.169043;  facebook-combined   
BranchDBMS;      0.162545;  facebook-combined   
BranchDBMS;      0.159489;  facebook-combined   
BranchDBMS;      0.168414;  facebook-combined 
CacheDBMS ;      0.038515;  facebook-combined   
CacheDBMS ;      0.037179;  facebook-combined   
CacheDBMS ;      0.037385;  facebook-combined   
CacheDBMS ;      0.036514;  facebook-combined   
BranchDBMS;    281.149423;  gplus-combined    
BranchDBMS;    261.093502;  gplus-combined   
BranchDBMS;    258.816546;  gplus-combined     
CacheDBMS ;     22.442501;  gplus-combined    
CacheDBMS ;     22.377717;  gplus-combined   
CacheDBMS ;     22.469739;  gplus-combined   
CacheDBMS ;     22.451922;  gplus-combined

eipi10.. 5

以下是使用内置iris数据框直接将值标签添加到图表的示例:

p1 = ggplot(iris, aes(Sepal.Width, Sepal.Length, colour=Species)) +
  stat_summary(fun.y=mean, geom="line", alpha=0.5) +
  stat_summary(fun.y=mean, geom="text", aes(label=sprintf("%1.1f", ..y..)), 
               size=3, show.legend=FALSE) +
  guides(colour=guide_legend(override.aes = list(alpha=1, lwd=1)))

..y..处于的每个值的内部计算装置Sepal.Width为每个Species.因为我们用于alpha=0.5行geom,override.aes允许我们在图例中使用更大胆的线条.

添加数据值表的一种方法如下:

library(gridExtra)
library(dplyr)

# Change default fontsize for the data table
mytheme <- ttheme_default(
  core = list(fg_params=list(cex = 0.7)),
  colhead = list(fg_params=list(cex = 0.75)),
  rowhead = list(fg_params=list(cex = 0.75)))

# Create table (in this case I just show the first three values for each species)
tab = tableGrob(iris %>% group_by(Species, Sepal.Width) %>% 
             summarise(`Mean Sepal Length`=sprintf("%1.1f", mean(Sepal.Length))) %>%
               slice(1:3), theme=mytheme, rows=NULL)

# Lay out graph and table
grid.arrange(p1, tab, ncol=1)

以下是使用内置iris数据框直接将值标签添加到图表的示例:

p1 = ggplot(iris, aes(Sepal.Width, Sepal.Length, colour=Species)) +
  stat_summary(fun.y=mean, geom="line", alpha=0.5) +
  stat_summary(fun.y=mean, geom="text", aes(label=sprintf("%1.1f", ..y..)), 
               size=3, show.legend=FALSE) +
  guides(colour=guide_legend(override.aes = list(alpha=1, lwd=1)))

..y..处于的每个值的内部计算装置Sepal.Width为每个Species.因为我们用于alpha=0.5行geom,override.aes允许我们在图例中使用更大胆的线条.

添加数据值表的一种方法如下:

library(gridExtra)
library(dplyr)

# Change default fontsize for the data table
mytheme <- ttheme_default(
  core = list(fg_params=list(cex = 0.7)),
  colhead = list(fg_params=list(cex = 0.75)),
  rowhead = list(fg_params=list(cex = 0.75)))

# Create table (in this case I just show the first three values for each species)
tab = tableGrob(iris %>% group_by(Species, Sepal.Width) %>% 
             summarise(`Mean Sepal Length`=sprintf("%1.1f", mean(Sepal.Length))) %>%
               slice(1:3), theme=mytheme, rows=NULL)

# Lay out graph and table
grid.arrange(p1, tab, ncol=1)