如何在现有data.frame中添加其他列，这些列在data.frame中已有的一个特定列上对齐？

Question

I'm a new R user and I'm having trouble trying to replicate a left basic join and update that I would normally do in SQL. I've checked several previously asked questions on Stackoverflow but still cannot quite get this code right.
I've been trying to build out a data.frame, starting with a single data.frame representing only all possible zip codes. I have several additional data.frames each each of which that count construction years over a certain range (say 1990-1999), grouped by zip code. Note that each subsequent data.frame is only a subset of zip codes from the first data.frame. Essentially, what I'm trying to do is build out a table, starting with a data.frame representing of all possible zip codes, and link each individual range data.frame to the table so that my final table will show all ranges for each zip code. Each range data.frame will need to be aligned with the "ZIPS_ALL" variable. The 1990-1999, 2000-2009 and Zips_ALL data.frames are below:

    1990-1999           2000-2009         zip_codes_all
    ZIP     Count       ZIP     Count     ZIPS_ALL
    19145     1         19145     1       19145
    19146     2         19147     3       19146
    19147     2         19148     1       19147 
                                          19148

I've tried using several different Left_Joins or merge from dplyr/base_r but when trying to attach each range, it overwrites the previous range so that my final table is all zip codes and the final range only. I need to keep all ranges of my table so that the final table shows all zip codes from "All Zip Codes", aligned with the ZIPS_ALL variable.

    1990_1999_df <- left_join(x = zip_codes_all, y = 1990-1999, by = 
    c("ZIP_ALL" = "ZIP"))
    2000_2009_df <- left_join(x = zip_codes_all, y = 2000-2009, by = 
    c("ZIP_ALL" = "ZIP"))

Expected results would have all range data.frames lined up with all possible zip codes data.frame where missing entries, would just be NA values; See below:

    1990-1999   2000-2009   zip_codes_all
    Count       Count       ZIPS_ALL
    1           1           19145
    2           NA          19146
    2           1           19147
    NA          1           19148

The dput code for my zip_codes_all variable is:

dput(droplevels(zip_codes_all[1:10,]))
structure(list(ZIP_ALL = c(23115L, 22960L, 22578L, 23936L, 23308L, 
23875L, 23518L, 23139L, 23917L, 22967L)), row.names = c(NA, -10L
), .internal.selfref = <pointer: 0x0000000000201ef0>, class = 
c("data.table", 
"data.frame"))

My updated code with actual variable names. This code worked but I am wondering if there is a more efficient way of doing this where I don't have to add each range manually, since I have numerous ranges I need to build out.

#create your range counts by group
nn_data_1939_range <- nn_data[yearbuilt <= 1939 ,.N, by = ZIP][order(ZIP)]
nn_data_1949_range <- nn_data[yearbuilt >= 1940 & yearbuilt <= 1949 ,.N, by = ZIP][order(ZIP)]
nn_data_1959_range <- nn_data[yearbuilt >= 1950 & yearbuilt <= 1959 ,.N, by = ZIP][order(ZIP)]
nn_data_1969_range <- nn_data[yearbuilt >= 1960 & yearbuilt <= 1969 ,.N, by = ZIP][order(ZIP)]
nn_data_1979_range <- nn_data[yearbuilt >= 1970 & yearbuilt <= 1979 ,.N, by = ZIP][order(ZIP)]
nn_data_1989_range <- nn_data[yearbuilt >= 1980 & yearbuilt <= 1989 ,.N, by = ZIP][order(ZIP)]
nn_data_1999_range <- nn_data[yearbuilt >= 1990 & yearbuilt <= 1999 ,.N, by = ZIP][order(ZIP)]
nn_data_2004_range <- nn_data[yearbuilt >= 2000 & yearbuilt <= 2004 ,.N, by = ZIP][order(ZIP)]
nn_data_2005_range <- nn_data[yearbuilt >= 2005,.N, by = ZIP][order(ZIP)]


#Build your table by each range; adding each range to the previously created data.frame; join zip_all to zip
tbl_LessThan_1939 <- left_join(x = zip_codes_all, y = nn_data_1939_range, by = c("ZIP_ALL" = "ZIP"))
tbl_0_1949 <- left_join(x = tbl_LessThan_1939, nn_data_1949_range, by = c("ZIP_ALL" = "ZIP"))
tbl_0_1959 <- left_join(x = tbl_0_1949, nn_data_1959_range, by = c("ZIP_ALL" = "ZIP"))
tbl_0_1969 <- left_join(x = tbl_0_1959, nn_data_1969_range, by = c("ZIP_ALL" = "ZIP"))
tbl_0_1979 <- left_join(x = tbl_0_1969, nn_data_1979_range, by = c("ZIP_ALL" = "ZIP"))
tbl_0_1989 <- left_join(x = tbl_0_1979, nn_data_1989_range, by = c("ZIP_ALL" = "ZIP"))
tbl_0_1999 <- left_join(x = tbl_0_1989, nn_data_1999_range, by = c("ZIP_ALL" = "ZIP"))
tbl_0_2004 <- left_join(x = tbl_0_1999, nn_data_2004_range, by = c("ZIP_ALL" = "ZIP"))
tbl_0_present <- left_join(x = tbl_0_2004, nn_data_2005_range, by = c("ZIP_ALL" = "ZIP"))

Answer 1

All right, my best guess is your data looks something like this (though probably much bigger):

library(data.table)
set.seed(47)
nn_data_sample = data.table(
  yearbuilt = rep(c(1938, 1942, 1951, 1963), each = 4),
  ZIP = sample(c(90210, 19145, 19146, 19147, 19148, 19149), size = 16, replace = TRUE)
)
nn_data_sample
 #    yearbuilt   ZIP
 # 1:      1938 19149
 # 2:      1938 19146
 # 3:      1938 19148
 # 4:      1938 19148
 # 5:      1942 19147
 # 6:      1942 19148
 # 7:      1942 19146
 # 8:      1942 19146
 # 9:      1951 19147

This is nicely formatted data, in long format , which is easy to work with. You seem to want to (a) count rows by zipcode and by the decade they were built (more-or-less, with a little more granularity recently), and then (b) convert the long data (with one zipcode column and one time column) into a wide format , where the times are spread across many columns.

For (a), we will use the cut function to divide the years into the decade-like intervals you want, and then aggregate the rows by zip code and decade.

decade_data = nn_data_sample[, decade_built := cut(
  yearbuilt,
  breaks = c(0, seq(1939, 1999, by = 10), 2004, Inf))
][, .(n = .N), by = .(decade_built, ZIP)]

decade_data
 #    decade_built   ZIP n
 # 1:     (0,1939] 19149 1
 # 2:     (0,1939] 19146 1
 # 3:     (0,1939] 19148 2
 # 4:  (1939,1949] 19147 1
 # 5:  (1939,1949] 19148 1
 # 6:  (1939,1949] 19146 2
 # 7:  (1949,1959] 19147 1
 # 8:  (1949,1959] 19149 1
 # ...

For a lot of use cases, this is a great format to work with---data.table makes it easy to do things "by group", so if you have more operations you want to do to each decade, this should be your starting point. (Since we used := the decade_built column became part of the original data, you can look at it to verify that it worked right.)

But, if you want to change to wide format, dcast does that for us:

dcast(decade_data, ZIP ~ decade_built, value.var = "n")
#      ZIP (0,1939] (1939,1949] (1949,1959] (1959,1969]
# 1: 19146        1           2          NA          NA
# 2: 19147       NA           1           1           2
# 3: 19148        2           1           1          NA
# 4: 19149        1          NA           1           1
# 5: 90210       NA          NA           1           1

If you want to edit the column names, you can either specify what you want from the top, using the labels argument of the cut function, or simply rename the columns at the end. Or do it in the middle, modifying the values of the decade_built column after it's created---do it wherever feels easiest.