在 R 中加入带有重复行的 data.frame

Question

I have two dataframes. One is quality with different dates throughout the year of collection points at station st . The other is the land use of each st season for that year.

Example

library(dplyr)

 water_df=read.table(text="st Date OD pH DBO A 01/07/2005 8 6.3 3 A 02/06/2005 7 6.2 2.2 A 01/01/2005 7.3 6.5 3.1 A 03/05/2006 6 6.3 4 A 09/08/2006 6.8 7.1 1.1 A 12/12/2006 7.3 8.1 2.9 B 02/07/2005 6.8 5.4 2.6 B 03/06/2005 6.0 5.3 1.9 B 02/01/2005 6.2 5.5 2.6 B 04/05/2006 5.1 5.4 3.4 B 10/08/2006 5.8 6.0 0.9 B 13/12/2006 6.2 6.9 2.5 C 20/12/2006 6.5 7.2 2.6 C 27/12/2006 6.8 7.6 2.7 C 03/01/2007 7.2 8.0 2.9 C 10/01/2007 7.5 8.4 3.0 C 17/01/2007 7.9 8.8 3.1 C 24/01/2007 8.3 9.2 3.3 C 31/01/2007 8.7 9.7 3.5 C 07/02/2007 9.2 10.2 3.6", sep="", header=TRUE)%>%as.data.frame() land_df=read.table(text = "st year Veg Water Soil Crop Grass A 2005 100 200 80 130 70 B 2006 98 180 84 132 86 C 2007 93 175 79 127 106", sep="", header = TRUE)%>%as.data.frame()

I would like to add to the quality data.frame the land use values of that station even if for the same station st is repeated.

I tried some things but it didn't work

#I tryed cbind
join_df<-cbind(water_df, land_df)

#I tryed
library(purrr)
joind_df2<-purrr::reduce(water_df, land_df)

Answer 1

This operation it commonly called a "join" or "merge". You need columns to join on, which means we need to extract the year from your date, and then it's a left_join command. See this FAQ for more information about joining data in R .

library(dplyr)
library(lubridate)

water_df %>%
  mutate(year = year(dmy(Date))) %>%
  left_join(land_df, by = c("st", "year"))
#    st       Date  OD   pH DBO year Veg Water Soil Crop Grass
# 1   A 01/07/2005 8.0  6.3 3.0 2005 100   200   80  130    70
# 2   A 02/06/2005 7.0  6.2 2.2 2005 100   200   80  130    70
# 3   A 01/01/2005 7.3  6.5 3.1 2005 100   200   80  130    70
# 4   A 03/05/2006 6.0  6.3 4.0 2006  NA    NA   NA   NA    NA
# 5   A 09/08/2006 6.8  7.1 1.1 2006  NA    NA   NA   NA    NA
# 6   A 12/12/2006 7.3  8.1 2.9 2006  NA    NA   NA   NA    NA
# 7   B 02/07/2005 6.8  5.4 2.6 2005  NA    NA   NA   NA    NA
# 8   B 03/06/2005 6.0  5.3 1.9 2005  NA    NA   NA   NA    NA
# 9   B 02/01/2005 6.2  5.5 2.6 2005  NA    NA   NA   NA    NA
# 10  B 04/05/2006 5.1  5.4 3.4 2006  98   180   84  132    86
# 11  B 10/08/2006 5.8  6.0 0.9 2006  98   180   84  132    86
# 12  B 13/12/2006 6.2  6.9 2.5 2006  98   180   84  132    86
# 13  C 20/12/2006 6.5  7.2 2.6 2006  NA    NA   NA   NA    NA
# 14  C 27/12/2006 6.8  7.6 2.7 2006  NA    NA   NA   NA    NA
# 15  C 03/01/2007 7.2  8.0 2.9 2007  93   175   79  127   106
# 16  C 10/01/2007 7.5  8.4 3.0 2007  93   175   79  127   106
# 17  C 17/01/2007 7.9  8.8 3.1 2007  93   175   79  127   106
# 18  C 24/01/2007 8.3  9.2 3.3 2007  93   175   79  127   106
# 19  C 31/01/2007 8.7  9.7 3.5 2007  93   175   79  127   106
# 20  C 07/02/2007 9.2 10.2 3.6 2007  93   175   79  127   106

There's some missing values where your land_df didn't have observations for a particular station in a particular year. Have a look at ?tidyr::fill and this FAQ if you want to fill those in with, eg, the previous observation.