2

这是我的数据结构:

Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   60 obs. of  3 variables:
$ year     : num  1990 1990 1991 1992 1993 ...
$ studyType: Factor w/ 4 levels "meta","observational",..: 2 3 3 1 3 3 3 1 3 3 ...
$ N        : int  1 4 4 1 2 5 3 1 2 6 ...

我正在尝试使用以下代码将其放入堆积面积图中:

ggplot(evidence.summary.main, aes(x = year, y = N, fill=studyType)) + 
geom_area(alpha=.80) 

任何想法为什么我不能让它正确堆叠?

在此处输入图像描述

我的数据来自dput

structure(list(year = c(1990, 1990, 1991, 1992, 1993, 1994, 1995, 
1996, 1996, 1997, 1997, 1998, 1998, 1999, 1999, 2000, 2000, 2000, 
2001, 2001, 2002, 2002, 2003, 2003, 2003, 2004, 2004, 2004, 2005, 
2005, 2006, 2006, 2006, 2007, 2007, 2007, 2007, 2008, 2008, 2008, 
2009, 2009, 2009, 2009, 2010, 2010, 2010, 2010, 2011, 2011, 2011, 
2011, 2012, 2012, 2012, 2012, 2013, 2013, 2013, 2013), studyType = structure(c(2L, 
3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 4L, 1L, 3L, 1L, 3L, 1L, 2L, 
3L, 1L, 3L, 1L, 3L, 1L, 3L, 4L, 1L, 2L, 3L, 3L, 4L, 1L, 3L, 4L, 
1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L), .Label = c("meta", 
"observational", "rct", "sr"), class = "factor"), N = c(1L, 4L, 
4L, 1L, 2L, 5L, 3L, 1L, 2L, 6L, 1L, 2L, 2L, 1L, 5L, 3L, 1L, 1L, 
4L, 6L, 1L, 10L, 2L, 6L, 3L, 1L, 2L, 8L, 12L, 2L, 3L, 12L, 4L, 
3L, 1L, 9L, 5L, 4L, 7L, 4L, 4L, 1L, 16L, 8L, 9L, 2L, 17L, 7L, 
6L, 2L, 15L, 16L, 3L, 2L, 19L, 14L, 8L, 2L, 28L, 20L)), row.names = c(NA, 
-60L), .Names = c("year", "studyType", "N"), class = c("tbl_df", 
"tbl", "data.frame"))
4

2 回答 2

1

我同意@untitled 的观点,即问题可能是缺失值。我一直在想必须有一个更清洁的转变,但我能想出的就是这个

ggplot(as.data.frame(xtabs(N~year+studyType, evidence.summary.main)), 
    aes(x = as.numeric(year), y = Freq, fill=studyType)) + 
geom_area(alpha=.80)

在此处输入图像描述

所以我过去xtabs()基本上把所有缺失的年份/学习组合填零。

于 2015-04-23T04:41:13.577 回答
1

我认为有一个稍微优雅的解决方案,它依赖于tidyr::complete一个仅用于填充缺失级别的函数。

evidence.summary.main %>%
    tidyr::complete(year, studyType, fill = list(N = 0)) %>%
    ggplot(aes(x = as.numeric(year), y = N, fill = studyType)) + 
    geom_area(alpha = .8)
于 2018-08-28T23:49:22.250 回答