Membatasi jumlah perbandingan string fuzzy dengan membandingkan menurut subkelompok
Dec 07 2020
Saya memiliki dua dataset sebagai berikut:
DT1 <- structure(list(Province = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3), Year = c(2000,
2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002, 2000, 2000, 2000,
2001, 2001, 2001, 2002, 2002, 2002, 2000, 2000, 2000, 2001, 2001,
2001, 2002, 2002, 2002), Municipality = c("Something", "Anything",
"Nothing", "Something", "Anything", "Nothing", "Something", "Anything",
"Nothing", "Something", "Anything", "Nothing", "Something", "Anything",
"Nothing", "Something", "Anything", "Nothing", "Something", "Anything",
"Nothing", "Something", "Anything", "Nothing", "Something", "Anything",
"Nothing"), Values = c(0.59, 0.58, 0.66, 0.53, 0.94, 0.2, 0.86,
0.85, 0.99, 0.59, 0.58, 0.66, 0.53, 0.94, 0.2, 0.86, 0.85, 0.99,
0.59, 0.58, 0.66, 0.53, 0.94, 0.2, 0.86, 0.85, 0.99)), row.names = c(NA,
-27L), class = c("tbl_df", "tbl", "data.frame"))
DT2 <- structure(list(Province = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3), Year = c(2000,
2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002, 2000, 2000, 2000,
2001, 2001, 2001, 2002, 2002, 2002, 2000, 2000, 2000, 2001, 2001,
2001, 2002, 2002, 2002), Municipality = c("Some", "Anything",
"Nothing", "Someth.", "Anything", "Not", "Something", "Anything",
"None", "Some", "Anything", "Nothing", "Someth.", "Anything",
"Not", "Something", "Anything", "None", "Some", "Anything", "Nothing",
"Someth.", "Anything", "Not", "Something", "Anything", "None"
), `Other Values` = c(0.41, 0.42, 0.34, 0.47, 0.0600000000000001,
0.8, 0.14, 0.15, 0.01, 0.41, 0.42, 0.34, 0.47, 0.0600000000000001,
0.8, 0.14, 0.15, 0.01, 0.41, 0.42, 0.34, 0.47, 0.0600000000000001,
0.8, 0.14, 0.15, 0.01)), row.names = c(NA, -27L), class = c("tbl_df",
"tbl", "data.frame"))
Saya mencoba mencocokkan mereka sebagai berikut, disarankan di tautan ini , oleh Arthur Yip.
library(fuzzyjoin); library(dplyr);
stringdist_join(DT1, DT2,
by = "Municipality",
mode = "left",
ignore_case = TRUE,
method = "jw",
max_dist = 10,
distance_col = "dist") %>%
group_by(Municipality.x) %>%
top_n(1, -dist)
Masalahnya adalah kode tersebut benar-benar menggoreng komputer saya, jadi saya ingin membagi kode menjadi beberapa grup untuk membatasi jumlah perbandingan string. Saya mencoba:
library(fuzzyjoin); library(dplyr);
stringdist_join(DT1, DT2,
by = c("Municipality","Year", "State"),
mode = "left",
ignore_case = TRUE,
method = "jw",
max_dist = 10,
distance_col = "dist") %>%
group_by(Municipality.x) %>%
top_n(1, -dist)
stringdist_join(DT1, DT2,
by = "Municipality",
mode = "left",
ignore_case = TRUE,
method = "jw",
max_dist = 10,
distance_col = "dist") %>%
group_by(Municipality, Year, Province) %>%
top_n(1, -dist)
Tapi keduanya memberi saya kesalahan masing-masing berikut:
Error: All columns in a tibble must be vectors.
x Column `col` is NULL.
Run `rlang::last_error()` to see where the error occurred.
Dan:
Error: Must group by variables found in `.data`.
* Column `Municipality` is not found.
* Column `Year` is not found.
* Column `Province` is not found.
Run `rlang::last_error()` to see where the error occurred.
Apa cara yang tepat untuk melakukan ini?
Jawaban
2 ArthurYip Dec 08 2020 at 11:59
Anda berada di jalur yang benar - hanya beberapa kesalahan ketik / bug dan Anda harus menyelesaikan perubahan / penggantian nama kolom.
Juga, di yang pertama, Anda perlu mencari tahu bagaimana Anda ingin memilih yang "paling cocok" berdasarkan Municipality.dist, Province.dist, dan Year.dist.
Mungkin yang kedua bekerja lebih baik jika Anda menyelesaikan tahun dan provinsi terlebih dahulu.
DT1 <- structure(list(Province = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3), Year = c(2000, 2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002, 2000, 2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002, 2000, 2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002), Municipality = c("Something", "Anything", "Nothing", "Something", "Anything", "Nothing", "Something", "Anything", "Nothing", "Something", "Anything", "Nothing", "Something", "Anything", "Nothing", "Something", "Anything", "Nothing", "Something", "Anything", "Nothing", "Something", "Anything", "Nothing", "Something", "Anything", "Nothing"), Values = c(0.59, 0.58, 0.66, 0.53, 0.94, 0.2, 0.86, 0.85, 0.99, 0.59, 0.58, 0.66, 0.53, 0.94, 0.2, 0.86, 0.85, 0.99, 0.59, 0.58, 0.66, 0.53, 0.94, 0.2, 0.86, 0.85, 0.99)), row.names = c(NA, -27L), class = c("tbl_df", "tbl", "data.frame"))
DT2 <- structure(list(Province = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3), Year = c(2000, 2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002, 2000, 2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002, 2000, 2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002), Municipality = c("Some", "Anything", "Nothing", "Someth.", "Anything", "Not", "Something", "Anything", "None", "Some", "Anything", "Nothing", "Someth.", "Anything", "Not", "Something", "Anything", "None", "Some", "Anything", "Nothing", "Someth.", "Anything", "Not", "Something", "Anything", "None"), `Other Values` = c(0.41, 0.42, 0.34, 0.47, 0.0600000000000001, 0.8, 0.14, 0.15, 0.01, 0.41, 0.42, 0.34, 0.47, 0.0600000000000001, 0.8, 0.14, 0.15, 0.01, 0.41, 0.42, 0.34, 0.47, 0.0600000000000001, 0.8, 0.14, 0.15, 0.01)), row.names = c(NA, -27L), class = c("tbl_df", "tbl", "data.frame"))
library(fuzzyjoin); library(dplyr);
stringdist_join(DT1, DT2,
by = c("Municipality", "Year", "Province"),
mode = "left",
ignore_case = TRUE,
method = "jw",
max_dist = 10,
distance_col = "dist") %>%
group_by(Municipality.x) %>%
slice_min(Municipality.dist)
#> # A tibble: 135 x 12
#> # Groups: Municipality.x [3]
#> Province.x Year.x Municipality.x Values Province.y Year.y Municipality.y
#> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 1 2000 Anything 0.580 1 2000 Anything
#> 2 1 2000 Anything 0.580 1 2001 Anything
#> 3 1 2000 Anything 0.580 1 2002 Anything
#> 4 1 2000 Anything 0.580 2 2000 Anything
#> 5 1 2000 Anything 0.580 2 2001 Anything
#> 6 1 2000 Anything 0.580 2 2002 Anything
#> 7 1 2000 Anything 0.580 3 2000 Anything
#> 8 1 2000 Anything 0.580 3 2001 Anything
#> 9 1 2000 Anything 0.580 3 2002 Anything
#> 10 1 2001 Anything 0.94 1 2000 Anything
#> # ... with 125 more rows, and 5 more variables: `Other Values` <dbl>,
#> # Municipality.dist <dbl>, Province.dist <dbl>, Year.dist <dbl>, dist <lgl>
stringdist_join(DT1, DT2,
by = "Municipality",
mode = "left",
ignore_case = TRUE,
method = "jw",
max_dist = 10,
distance_col = "dist") %>%
group_by(Municipality.x, Year.x, Province.x) %>%
slice_min(dist)
#> # A tibble: 135 x 9
#> # Groups: Municipality.x, Year.x, Province.x [27]
#> Province.x Year.x Municipality.x Values Province.y Year.y Municipality.y
#> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 1 2000 Anything 0.580 1 2000 Anything
#> 2 1 2000 Anything 0.580 1 2001 Anything
#> 3 1 2000 Anything 0.580 1 2002 Anything
#> 4 1 2000 Anything 0.580 2 2000 Anything
#> 5 1 2000 Anything 0.580 2 2001 Anything
#> 6 1 2000 Anything 0.580 2 2002 Anything
#> 7 1 2000 Anything 0.580 3 2000 Anything
#> 8 1 2000 Anything 0.580 3 2001 Anything
#> 9 1 2000 Anything 0.580 3 2002 Anything
#> 10 2 2000 Anything 0.580 1 2000 Anything
#> # ... with 125 more rows, and 2 more variables: `Other Values` <dbl>,
#> # dist <dbl>
Dibuat pada 2020-12-07 oleh paket reprex (v0.3.0)
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