Question has been edited from the original
阅读了这篇有趣的discussion篇文章后,我想知道如何在专栏中使用dplyr替换NAs,例如,Lahman击球数据:
Source: local data frame [96,600 x 3]
Groups: teamID
yearID teamID G_batting
1 2004 SFN 11
2 2006 CHN 43
3 2007 CHA 2
4 2008 BOS 5
5 2009 SEA 3
6 2010 SEA 4
7 2012 NYA NA
以下not项工作如我所料
library(dplyr)
library(Lahman)
df <- Batting[ c("yearID", "teamID", "G_batting") ]
df <- group_by(df, teamID )
df$G_batting[is.na(df$G_batting)] <- mean(df$G_batting, na.rm = TRUE)
来源:本地数据帧[20 x 3]
yearID teamID G_batting
1 2004 SFN 11.00000
2 2006 CHN 43.00000
3 2007 CHA 2.00000
4 2008 BOS 5.00000
5 2009 SEA 3.00000
6 2010 SEA 4.00000
7 2012 NYA **49.07894**
> mean(Batting$G_battin, na.rm = TRUE)
[1] **49.07894**
事实上,它估算的是总体平均数,而不是群体平均数.在dplyr链中,您将如何做到这一点?使用基数R中的transform
也可以计算not,因为它估算了总体平均值,而不是组平均值.此外,这种方法将数据转换为常规数据.镜框.有更好的方法吗?
df %.%
group_by( yearID ) %.%
transform(G_batting = ifelse(is.na(G_batting),
mean(G_batting, na.rm = TRUE),
G_batting)
)
编辑:将transform
替换为mutate
会产生以下错误
Error in mutate_impl(.data, named_dots(...), environment()) :
INTEGER() can only be applied to a 'integer', not a 'double'
编辑:添加为.integer似乎解决了错误,does产生了预期的结果.另见@eddi的答案.
df %.%
group_by( teamID ) %.%
mutate(G_batting = ifelse(is.na(G_batting), as.integer(mean(G_batting, na.rm = TRUE)), G_batting))
Source: local data frame [96,600 x 3]
Groups: teamID
yearID teamID G_batting
1 2004 SFN 11
2 2006 CHN 43
3 2007 CHA 2
4 2008 BOS 5
5 2009 SEA 3
6 2010 SEA 4
7 2012 NYA 47
> mean_NYA <- mean(filter(df, teamID == "NYA")$G_batting, na.rm = TRUE)
> as.integer(mean_NYA)
[1] 47
编辑:根据@Romain的 comments ,我从github安装了dplyr:
> head(df,10)
yearID teamID G_batting
1 2004 SFN 11
2 2006 CHN 43
3 2007 CHA 2
4 2008 BOS 5
5 2009 SEA 3
6 2010 SEA 4
7 2012 NYA NA
8 1954 ML1 122
9 1955 ML1 153
10 1956 ML1 153
> df %.%
+ group_by(teamID) %.%
+ mutate(G_batting = ifelse(is.na(G_batting), mean(G_batting, na.rm = TRUE), G_batting))
Source: local data frame [96,600 x 3]
Groups: teamID
yearID teamID G_batting
1 2004 SFN 0
2 2006 CHN 0
3 2007 CHA 0
4 2008 BOS 0
5 2009 SEA 0
6 2010 SEA 1074266112
7 2012 NYA 90693125
8 1954 ML1 122
9 1955 ML1 153
10 1956 ML1 153
.. ... ... ...
所以我没有得到错误(好),但我得到了一个(似乎)奇怪的结果.