[英]R: Divide values in a dataset by another value that changes when some conditions are met
[英]Divide long data by values in another dataset in R
我有一個長數據格式的數據集:
Date Region X Y Z T D E F
01-01-2020 RegionA 2 4 2 3 2 3 4
01-01-2020 RegionB 1 3 2 2 3 3 3
01-01-2020 RegionC 1 4 4 2 3 4 2
01-01-2020 RegionD 2 4 2 3 2 4 4
01-01-2020 RegionE 1 3 2 2 2 2 2
02-01-2020 RegionA 2 4 7 3 2 3 4
02-01-2020 RegionB 1 3 2 2 2 3 3
02-01-2020 RegionC 1 4 4 8 3 4 2
02-01-2020 RegionD 2 3 2 3 2 4 4
02-01-2020 RegionE 1 3 2 2 2 2 2
日期還有很多,但這應該讓您對格式有所了解。
然后我有第二個數據集,其中包含有關這些地區人口的更多信息:
Region Pop
RegionA 2000
RegionB 4039
RegionC 24728
RegionD 3738
RegionE 2936
我想要做的是將第一個數據集中的一列除以每個地區的人口值,跨越所有日期。 例如,如果“x”是GDP
,我想將GDP
除以每個不同時間點的人口值。 對於RegionA
,這將是01-01-2020
和02-01-2020
的2/2000
和2/2000
。
我對 R 很陌生,任何幫助開始解決這個問題都會很棒。
這里有一個可重現的例子
date<-as.Date(c("2020-02-24T18:00:00", "2020-02-24T18:00:00", "2020-02-
24T18:00:00", "2020-05-02T17:00:00", "2020-05-02T17:00:00",
"2020-05-02T17:00:00"))
regions<-c("RegionA", "RegionB", "RegionC","RegionA", "RegionB", "RegionC")
total<-c(1394, 1143, 18373, 168479, 65370, 26990)
df<-data.frame(date, regions, total)
對於其他 dataframe:
regions<-c("RegionA", "RegionB", "RegionC")
pop<-c(1305283, 559084, 1935414)
mydf_pop<-data.frame(regions, pop)
現在:我嘗試了各種組合
df >%>
left_join(mydf_pop)>%>
group_by(date, regions)>%>
mutate(total/pop)
這顯然是錯誤的。
謝謝你。
您可以使用left_join
和mutate
:
library(dplyr)
new_df <- left_join(df1, df2, by = "Region")
new_df %>%
mutate_at(vars(X:F), ~ . / Pop)
我們通過公共列Region
將兩個數據集連接在一起,然后使用mutate_at
重新計算變量X
到F
。 ~
創建一個公式 object 和點.
指被引用的列。
這給了我們:
# A tibble: 10 x 10
Date Region X Y Z T D E F Pop
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 01-01-2020 RegionA 0.001 0.002 0.001 0.0015 0.001 0.0015 0.002 2000
2 01-01-2020 RegionB 0.000248 0.000743 0.000495 0.000495 0.000743 0.000743 0.000743 4039
3 01-01-2020 RegionC 0.0000404 0.000162 0.000162 0.0000809 0.000121 0.000162 0.0000809 24728
4 01-01-2020 RegionD 0.000535 0.00107 0.000535 0.000803 0.000535 0.00107 0.00107 3738
5 01-01-2020 RegionE 0.000341 0.00102 0.000681 0.000681 0.000681 0.000681 0.000681 2936
6 02-01-2020 RegionA 0.001 0.002 0.0035 0.0015 0.001 0.0015 0.002 2000
7 02-01-2020 RegionB 0.000248 0.000743 0.000495 0.000495 0.000495 0.000743 0.000743 4039
8 02-01-2020 RegionC 0.0000404 0.000162 0.000162 0.000324 0.000121 0.000162 0.0000809 24728
9 02-01-2020 RegionD 0.000535 0.000803 0.000535 0.000803 0.000535 0.00107 0.00107 3738
10 02-01-2020 RegionE 0.000341 0.00102 0.000681 0.000681 0.000681 0.000681 0.000681 2936
一基 R 選項是使用match
df1[-c(1:2)] <- df1[-(1:2)]/df2$Pop[match(df1$Region,df2$Region)]
這使
> df1
Date Region X Y Z T
1 01-01-2020 RegionA 1.000000e-03 0.0020000000 0.0010000000 1.500000e-03
2 01-01-2020 RegionB 2.475860e-04 0.0007427581 0.0004951721 4.951721e-04
3 01-01-2020 RegionC 4.043999e-05 0.0001617599 0.0001617599 8.087997e-05
4 01-01-2020 RegionD 5.350455e-04 0.0010700910 0.0005350455 8.025682e-04
5 01-01-2020 RegionE 3.405995e-04 0.0010217984 0.0006811989 6.811989e-04
6 02-01-2020 RegionA 1.000000e-03 0.0020000000 0.0035000000 1.500000e-03
7 02-01-2020 RegionB 2.475860e-04 0.0007427581 0.0004951721 4.951721e-04
8 02-01-2020 RegionC 4.043999e-05 0.0001617599 0.0001617599 3.235199e-04
9 02-01-2020 RegionD 5.350455e-04 0.0008025682 0.0005350455 8.025682e-04
10 02-01-2020 RegionE 3.405995e-04 0.0010217984 0.0006811989 6.811989e-04
D E F
1 0.0010000000 0.0015000000 2.000000e-03
2 0.0007427581 0.0007427581 7.427581e-04
3 0.0001213200 0.0001617599 8.087997e-05
4 0.0005350455 0.0010700910 1.070091e-03
5 0.0006811989 0.0006811989 6.811989e-04
6 0.0010000000 0.0015000000 2.000000e-03
7 0.0004951721 0.0007427581 7.427581e-04
8 0.0001213200 0.0001617599 8.087997e-05
9 0.0005350455 0.0010700910 1.070091e-03
10 0.0006811989 0.0006811989 6.811989e-04
數據
> dput(df1)
structure(list(Date = c("01-01-2020", "01-01-2020", "01-01-2020",
"01-01-2020", "01-01-2020", "02-01-2020", "02-01-2020", "02-01-2020",
"02-01-2020", "02-01-2020"), Region = c("RegionA", "RegionB",
"RegionC", "RegionD", "RegionE", "RegionA", "RegionB", "RegionC",
"RegionD", "RegionE"), X = c(2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 1L), Y = c(4L, 3L, 4L, 4L, 3L, 4L, 3L, 4L, 3L, 3L), Z = c(2L,
2L, 4L, 2L, 2L, 7L, 2L, 4L, 2L, 2L), T = c(3L, 2L, 2L, 3L, 2L,
3L, 2L, 8L, 3L, 2L), D = c(2L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 2L,
2L), E = c(3L, 3L, 4L, 4L, 2L, 3L, 3L, 4L, 4L, 2L), F = c(4L,
3L, 2L, 4L, 2L, 4L, 3L, 2L, 4L, 2L)), class = "data.frame", row.names = c(NA,
-10L))
> dput(df2)
structure(list(Region = c("RegionA", "RegionB", "RegionC", "RegionD",
"RegionE"), Pop = c(2000L, 4039L, 24728L, 3738L, 2936L)), class = "data.frame", row.names = c(NA,
-5L))
聲明:本站的技術帖子網頁,遵循CC BY-SA 4.0協議,如果您需要轉載,請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.