如何使用R从另外两个不同大小的向量中构建具有特定数据的新向量
我有一个向量，它是一个巴西州的列表，我想形成一个data.frame，告诉你哪个州位于哪个地区 数据框的数据将从一个不一定列出所有州的网站导入，因此，它必须逐个选择一个州，并将根据自己的地区进行调整 然后，我有5个不同的区域向量和它自己的状态如何使用R从另外两个不同大小的向量中构建具有特定数据的新向量,r,R,我有一个向量，它是一个巴西州的列表，我想形成一个data.frame，告诉你哪个州位于哪个地区 数据框的数据将从一个不一定列出所有州的网站导入，因此，它必须逐个选择一个州，并将根据自己的地区进行调整 然后，我有5个不同的区域向量和它自己的状态 states <- c("AC", "AL", "AM", "AP", "BA", "CE", "DF", "ES", "GO", "MA", "MG", "MS", "MT", "PA", "PB", "PE", "PI", "PR", "RJ"
states <- c("AC", "AL", "AM", "AP", "BA", "CE", "DF", "ES", "GO", "MA", "MG", "MS", "MT", "PA", "PB", "PE", "PI", "PR", "RJ", "RN", "RO", "RR", "RS", "SC", "SE", "SP", "TO")
no <- c("AC","AP","AM","PA","RO","RR","TO")
su <- c("PR","SC","RS")
co <- c("GO","MG","MS","DF")
ne <- c("AL","BA","CE","MA","PB","PE","PI","RN","SE")
mges <- c("MG","ES")
rj <- c("RJ")
sp <- c("SP")
状态我建议将区域信息存储在列表中。此列表可用于创建数据帧
regions <- list(no = c("AC","AP","AM","PA","RO","RR","TO"),
su = c("PR","SC","RS"),
co = c("GO","MG","MS","DF"),
ne = c("AL","BA","CE","MA","PB","PE","PI","RN","SE"),
mges = c("MG","ES"),
rj = c("RJ"),
sp = c("SP"))
region_dat <- data.frame(state = unlist(regions),
region = rep(names(regions), lengths(regions)))
结果是：
state region
1 AC no
2 AL ne
3 AM no
4 AP no
5 BA ne
6 CE ne
7 DF co
8 ES mges
9 GO co
10 MA ne
11 MG co
12 MG mges
13 MS co
14 PA no
15 PB ne
16 PE ne
17 PI ne
18 PR su
19 RJ rj
20 RN ne
21 RO no
22 RR no
23 RS su
24 SC su
25 SE ne
26 SP sp
27 TO no
我建议将区域信息存储在列表中。此列表可用于创建数据帧
regions <- list(no = c("AC","AP","AM","PA","RO","RR","TO"),
su = c("PR","SC","RS"),
co = c("GO","MG","MS","DF"),
ne = c("AL","BA","CE","MA","PB","PE","PI","RN","SE"),
mges = c("MG","ES"),
rj = c("RJ"),
sp = c("SP"))
region_dat <- data.frame(state = unlist(regions),
region = rep(names(regions), lengths(regions)))
结果是：
state region
1 AC no
2 AL ne
3 AM no
4 AP no
5 BA ne
6 CE ne
7 DF co
8 ES mges
9 GO co
10 MA ne
11 MG co
12 MG mges
13 MS co
14 PA no
15 PB ne
16 PE ne
17 PI ne
18 PR su
19 RJ rj
20 RN ne
21 RO no
22 RR no
23 RS su
24 SC su
25 SE ne
26 SP sp
27 TO no
如果你能稍微重组一下你的数据，这里有一个tidyverse
方法来做你想做的事情
library(tidyverse)
df1 = data.frame(states = c("AC","AP","AM","PA","RO","RR","TO"), region='no')
df2 = data.frame(states = c("PR","SC","RS"), region='su')
df3 = data.frame(states = c("GO","MG","MS","DF"), region='co')
df4 = data.frame(states = c("AL","BA","CE","MA","PB","PE","PI","RN","SE"), region='ne')
df5 = data.frame(states = c("MG","ES"), region='mges')
df6 = data.frame(states = c("RJ"), region='rj')
df7 = data.frame(states = c("SP"), region='sp')
dfs = list(df1,df2,df3,df4,df5,df6,df7)
state_regions = bind_rows(dfs)
为每个区域创建一个具有关联状态的数据帧。然后合并所有数据帧行
states region
1 AC no
2 AP no
3 AM no
4 PA no
5 RO no
6 RR no
7 TO no
8 PR su
9 SC su
10 RS su
11 GO co
12 MG co
13 MS co
14 DF co
15 AL ne
16 BA ne
17 CE ne
18 MA ne
19 PB ne
20 PE ne
21 PI ne
22 RN ne
23 SE ne
24 MG mges
25 ES mges
26 RJ rj
27 SP sp
如果你能稍微重组一下你的数据，这里有一个tidyverse
方法来做你想做的事情
library(tidyverse)
df1 = data.frame(states = c("AC","AP","AM","PA","RO","RR","TO"), region='no')
df2 = data.frame(states = c("PR","SC","RS"), region='su')
df3 = data.frame(states = c("GO","MG","MS","DF"), region='co')
df4 = data.frame(states = c("AL","BA","CE","MA","PB","PE","PI","RN","SE"), region='ne')
df5 = data.frame(states = c("MG","ES"), region='mges')
df6 = data.frame(states = c("RJ"), region='rj')
df7 = data.frame(states = c("SP"), region='sp')
dfs = list(df1,df2,df3,df4,df5,df6,df7)
state_regions = bind_rows(dfs)
为每个区域创建一个具有关联状态的数据帧。然后合并所有数据帧行
states region
1 AC no
2 AP no
3 AM no
4 PA no
5 RO no
6 RR no
7 TO no
8 PR su
9 SC su
10 RS su
11 GO co
12 MG co
13 MS co
14 DF co
15 AL ne
16 BA ne
17 CE ne
18 MA ne
19 PB ne
20 PE ne
21 PI ne
22 RN ne
23 SE ne
24 MG mges
25 ES mges
26 RJ rj
27 SP sp
这个代码工作得很好！非常感谢@Sven Hohenstein！！这个代码工作得很好！非常感谢@Sven Hohenstein！！