I have this dataset
我有这个数据集
date<-as.Date(c("2007-01-01","2007-01-02","2007-01-03","2007-01-04","2007-01-05"))
a<-c(55,8,3,7,126)
b<-c(3,199,7,66,8)
c<-c(91,333,2,9,4)
df<-data.frame(date,a,b,c)
date a b c
2007-01-01 55 3 91
2007-01-02 8 199 333
2007-01-03 3 7 2
2007-01-04 7 66 9
2007-01-05 126 8 4
I want to 'select' the 2nd highest value from each column (a, b, and c) and get the date of occurrence, and coerce into a new dataframe as below...
我想“选择”每列中的第二个最高值(a,b和c)并获取发生日期,并强制转换为新的数据帧,如下所示......
Type Date 2nd highest
a 2007-01-01 55
b 2007-01-04 66
c 2007-01-01 91
I have tried many different verbs in dplyr, tidyr, apply functions and I really can not even get close. Please help, thanks.
我在dplyr,tidyr,apply函数中尝试了很多不同的动词,我甚至无法接近。请帮忙,谢谢。
2 个解决方案
#1
3
date<-as.Date(c("2007-01-01","2007-01-02","2007-01-03","2007-01-04","2007-01-05"))
a<-c(55,8,3,7,126)
b<-c(3,199,7,66,8)
c<-c(91,333,2,9,4)
df<-data.frame(date,a,b,c)
library(tidyverse)
df %>%
gather(Type,value,-date) %>% # reshape dataset
arrange(desc(value)) %>% # arrange in descending order
group_by(Type) %>% # for each type
slice(2) %>% # get 2nd row (2nd highest value)
ungroup() # forget the grouping
# # A tibble: 3 x 3
# date Type value
# <date> <chr> <dbl>
# 1 2007-01-01 a 55
# 2 2007-01-04 b 66
# 3 2007-01-01 c 91
#2
1
You can also use nth (forking AntoniosK's solution):
你也可以使用nth(分叉AntoniosK的解决方案):
library(tidyverse)
df %>%
gather(Type,value,-date) %>% # reshape dataset
group_by(Type) %>% # for each type
filter(value==nth(value,2,-value)) %>%
ungroup
# # A tibble: 3 x 3
# date Type value
# <date> <chr> <dbl>
# 1 2007-01-01 a 55
# 2 2007-01-04 b 66
# 3 2007-01-01 c 91
And a base R solution:
和基础R解决方案:
pos <- sapply(df[-1],function(x) which(rank(-x)==2))
rows <- lapply(1:3,function(x)
setNames(transform(df[pos[x],c(1,1+x)],Type=names(pos)[x]),c("date","value","type")))
do.call(rbind,rows)
# date value type
# 1 2007-01-01 55 a
# 4 2007-01-04 66 b
# 11 2007-01-01 91 c
#1
3
date<-as.Date(c("2007-01-01","2007-01-02","2007-01-03","2007-01-04","2007-01-05"))
a<-c(55,8,3,7,126)
b<-c(3,199,7,66,8)
c<-c(91,333,2,9,4)
df<-data.frame(date,a,b,c)
library(tidyverse)
df %>%
gather(Type,value,-date) %>% # reshape dataset
arrange(desc(value)) %>% # arrange in descending order
group_by(Type) %>% # for each type
slice(2) %>% # get 2nd row (2nd highest value)
ungroup() # forget the grouping
# # A tibble: 3 x 3
# date Type value
# <date> <chr> <dbl>
# 1 2007-01-01 a 55
# 2 2007-01-04 b 66
# 3 2007-01-01 c 91
#2
1
You can also use nth (forking AntoniosK's solution):
你也可以使用nth(分叉AntoniosK的解决方案):
library(tidyverse)
df %>%
gather(Type,value,-date) %>% # reshape dataset
group_by(Type) %>% # for each type
filter(value==nth(value,2,-value)) %>%
ungroup
# # A tibble: 3 x 3
# date Type value
# <date> <chr> <dbl>
# 1 2007-01-01 a 55
# 2 2007-01-04 b 66
# 3 2007-01-01 c 91
And a base R solution:
和基础R解决方案:
pos <- sapply(df[-1],function(x) which(rank(-x)==2))
rows <- lapply(1:3,function(x)
setNames(transform(df[pos[x],c(1,1+x)],Type=names(pos)[x]),c("date","value","type")))
do.call(rbind,rows)
# date value type
# 1 2007-01-01 55 a
# 4 2007-01-04 66 b
# 11 2007-01-01 91 c