用多列收集 r 中的数据

Question

I have some data which I am trying to use tidy R and pivot longer function in R to get the out put as mentioned below. But I am not able to do it, I am getting Data

I have data in this format. ( with many other column names )

Country State   Year 1  Population 1    Year 2  Population2
U.S.A   IL  2009    20000   2010    30000
U.S.A   VA  2009    30000   2010    40000

I want to get data in this format.

Country State   Year    Population
U.S.A   IL  2009    20000
U.S.A   IL  2010    30000
U.S.A   VA  2009    30000
U.S.A   VA  2010    40000

I am able to do it only for on column, but not able to pass other column likes like population

My code is below.

file1<-file %>%
 pivot_longer(
   cols = contains("Year"),
   names_sep = "_",
  names_to = c(".value", "repeat"),

 )

I was able to make it work on Tidyverse.

library(tidyverse)
file<-read_excel("peps300.xlsx")

names(file)<-str_replace_all(names(file), c("Year " = "Year_" , "Num " = "Num_", "DRate " = "DRate_" , "PRate " = "PRate_",  "Denom " = "Denom_"))

file<-file %>%
 pivot_longer(
   cols = c(contains("Year"),contains("Num"),contains("DRate"),contains("PRate"),contains("Denom")),
   names_sep = "_",
  names_to = c(".value", "repeat")
 )

Answer 1

An option would be to specify the cols that starts_with "Population" or "Year"

library(dplyr)
df1 %>% 
    pivot_longer(cols = c(starts_with("Population"), starts_with("Year")), 
    names_to = c(".value", "group"), names_pattern = "(.*)_(.*)")
# A tibble: 4 x 5
#  Country State group Population  Year
#  <chr>   <chr> <chr>      <int> <int>
#1 U.S.A   IL    1          20000  2009
#2 U.S.A   IL    2          30000  2010
#3 U.S.A   VA    1          30000  2009
#4 U.S.A   VA    2          40000  2010

data

df1 <- structure(list(Country = c("U.S.A", "U.S.A"), State = c("IL", 
"VA"), Year_1 = c(2009L, 2009L), Population_1 = c(20000L, 30000L
), Year_2 = c(2010L, 2010L), Population_2 = c(30000L, 40000L)), 
   class = "data.frame", row.names = c(NA, 
-2L))

Answer 2

df %>%
    pivot_longer(
        -c(Country,State),
        names_to = c(".value","group"),
        names_pattern = "(.+)_(.+)"
    )

# A tibble: 4 x 5
  Country State group Year  Population
  <chr>   <chr> <chr> <chr> <chr>     
1 U.S.A   IL    1     2009  20000     
2 U.S.A   IL    2     2010  30000     
3 U.S.A   VA    1     2009  30000     
4 U.S.A   VA    2     2010  40000 

You can then drop the group if you don't need it.

And, to do this, you will need to clean your column names first. Make sure they all follow the same pattern and words are connected with a single space or a single underscore.

df <- structure(list(Country = c("U.S.A", "U.S.A"), State = c("IL", 
"VA"), Year_1 = c("2009", "2009"), Population_1 = c("20000", 
"30000"), Year_2 = c("2010", "2010"), Population_2 = c("30000", 
"40000")), class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -2L))