使用查找表將多個列添加到數據框
[英]Adding multiple columns to a dataframe using lookup table
問題描述
我有一個數據表，想使用查找表對其進行修改。 我想遍歷數據中的代碼列，並根據匹配的datayear列和匹配查找表的field列中正確行值的代碼列的名稱，為每個代碼列添加一個新的對應value列。
我嘗試將lapply與left_join一起使用，但是我無法鍛煉如何使用數據列名稱在查找的field列中引用正確的值。 我還考慮了查找表在寬格式下是否可能更好，因此您至少要具有匹配的列名，但是我仍然無法生成可行的函數。
示例數據和所需的輸出：
數據（編輯：實際數據將包含更多代碼列）：
structure(list(id = 1:10, datayear = c(2007L, 2007L, 2007L, 2007L,
2007L, 2008L, 2008L, 2008L, 2008L, 2008L), nationalitycode = c(1L,
1L, 1L, 2L, 3L, 5L, 4L, 3L, 2L, 1L), subjectcode = c(2L, 5L,
5L, 5L, 2L, 5L, 4L, 2L, 1L, 4L)), .Names = c("id", "datayear",
"nationalitycode", "subjectcode"), class = "data.frame", row.names = c(NA,
-10L))
id datayear nationalitycode subjectcode
1 1 2007 1 2
2 2 2007 1 5
3 3 2007 1 5
4 4 2007 2 5
5 5 2007 3 2
6 6 2008 5 5
7 7 2008 4 4
8 8 2008 3 2
9 9 2008 2 1
10 10 2008 1 4
查找表：
structure(list(datayear = c(2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2008L, 2008L, 2008L, 2008L,
2008L, 2008L, 2008L, 2008L, 2008L, 2008L), field = structure(c(1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L), .Label = c("nationalitycode", "subjectcode"), class = "factor"),
code = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), lookupvalue = structure(c(10L,
16L, 9L, 4L, 5L, 2L, 7L, 13L, 1L, 14L, 5L, 16L, 4L, 6L, 11L,
17L, 3L, 15L, 8L, 12L), .Label = c("Algebra", "Art", "Beekeeping",
"Chinese", "English", "French", "Geography", "H.E.", "Indian",
"Irish", "Italian", "Latin", "Maths", "P.E.", "Rivetting",
"Scottish", "Sewing"), class = "factor")), class = "data.frame", row.names = c(NA,
-20L), .Names = c("datayear", "field", "code", "lookupvalue"))
datayear field code lookupvalue
1 2007 nationalitycode 1 Irish
2 2007 nationalitycode 2 Scottish
3 2007 nationalitycode 3 Indian
4 2007 nationalitycode 4 Chinese
5 2007 nationalitycode 5 English
6 2007 subjectcode 1 Art
7 2007 subjectcode 2 Geography
8 2007 subjectcode 3 Maths
9 2007 subjectcode 4 Algebra
10 2007 subjectcode 5 P.E.
11 2008 nationalitycode 1 English
12 2008 nationalitycode 2 Scottish
13 2008 nationalitycode 3 Chinese
14 2008 nationalitycode 4 French
15 2008 nationalitycode 5 Italian
16 2008 subjectcode 1 Sewing
17 2008 subjectcode 2 Beekeeping
18 2008 subjectcode 3 Rivetting
19 2008 subjectcode 4 H.E.
20 2008 subjectcode 5 Latin
所需的輸出：
id datayear nationalitycode subjectcode nationalityvalue subjectvalue
1 1 2007 1 2 Irish Geography
2 2 2007 1 5 Irish P.E.
3 3 2007 1 5 Irish P.E.
4 4 2007 2 5 Scottish P.E.
5 5 2007 3 2 Indian Geography
6 6 2008 5 5 Italian Latin
7 7 2008 4 4 French H.E.
8 8 2008 3 2 Chinese Beekeeping
9 9 2008 2 1 Scottish Sewing
10 10 2008 1 4 English H.E.
非常感謝您的協助！
2 個解決方案
解決方案1
1 已采納 2017-08-08 11:37:30
訣竅是根據您的查找表的適當子集進行聯接。 那就是通過使用正確的字段值進行子集化。
library(dplyr)
dt1 = structure(list(id = 1:10, datayear = c(2007L, 2007L, 2007L, 2007L,
2007L, 2008L, 2008L, 2008L, 2008L, 2008L), nationalitycode = c(1L,
1L, 1L, 2L, 3L, 5L, 4L, 3L, 2L, 1L), subjectcode = c(2L, 5L,
5L, 5L, 2L, 5L, 4L, 2L, 1L, 4L)), .Names = c("id", "datayear",
"nationalitycode", "subjectcode"), class = "data.frame", row.names = c(NA, -10L))
dt2 = structure(list(datayear = c(2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2008L, 2008L, 2008L, 2008L,
2008L, 2008L, 2008L, 2008L, 2008L, 2008L), field = structure(c(1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L), .Label = c("nationalitycode", "subjectcode"), class = "factor"),
code = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), lookupvalue = structure(c(10L,
16L, 9L, 4L, 5L, 2L, 7L, 13L, 1L, 14L, 5L, 16L, 4L, 6L, 11L,
17L, 3L, 15L, 8L, 12L), .Label = c("Algebra", "Art", "Beekeeping",
"Chinese", "English", "French", "Geography", "H.E.", "Indian",
"Irish", "Italian", "Latin", "Maths", "P.E.", "Rivetting",
"Scottish", "Sewing"), class = "factor")), class = "data.frame", row.names = c(NA,
-20L), .Names = c("datayear", "field", "code", "lookupvalue"))
dt1 %>%
left_join(dt2 %>% filter(field == "nationalitycode"), by=c("datayear"="datayear","nationalitycode"="code")) %>%
left_join(dt2 %>% filter(field == "subjectcode"), by=c("datayear"="datayear","subjectcode"="code")) %>%
rename(nationalityvalue = lookupvalue.x,
subjectvalue = lookupvalue.y) %>%
select(-field.x, -field.y)
# id datayear nationalitycode subjectcode nationalityvalue subjectvalue
# 1 1 2007 1 2 Irish Geography
# 2 2 2007 1 5 Irish P.E.
# 3 3 2007 1 5 Irish P.E.
# 4 4 2007 2 5 Scottish P.E.
# 5 5 2007 3 2 Indian Geography
# 6 6 2008 5 5 Italian Latin
# 7 7 2008 4 4 French H.E.
# 8 8 2008 3 2 Chinese Beekeeping
# 9 9 2008 2 1 Scottish Sewing
# 10 10 2008 1 4 English H.E.
對於使用循環請求的更一般的情況，我需要重塑您的查找表，以便可以使用列名。 該過程將自動檢測您的查找表中有多少個唯一字段，並將使用for循環（順序）執行聯接。
library(dplyr)
library(tidyr)
dt1 = structure(list(id = 1:10, datayear = c(2007L, 2007L, 2007L, 2007L,
2007L, 2008L, 2008L, 2008L, 2008L, 2008L), nationalitycode = c(1L,
1L, 1L, 2L, 3L, 5L, 4L, 3L, 2L, 1L), subjectcode = c(2L, 5L,
5L, 5L, 2L, 5L, 4L, 2L, 1L, 4L)), .Names = c("id", "datayear",
"nationalitycode", "subjectcode"), class = "data.frame", row.names = c(NA, -10L))
dt2 = structure(list(datayear = c(2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2008L, 2008L, 2008L, 2008L,
2008L, 2008L, 2008L, 2008L, 2008L, 2008L), field = structure(c(1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L), .Label = c("nationalitycode", "subjectcode"), class = "factor"),
code = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), lookupvalue = structure(c(10L,
16L, 9L, 4L, 5L, 2L, 7L, 13L, 1L, 14L, 5L, 16L, 4L, 6L, 11L,
17L, 3L, 15L, 8L, 12L), .Label = c("Algebra", "Art", "Beekeeping",
"Chinese", "English", "French", "Geography", "H.E.", "Indian",
"Irish", "Italian", "Latin", "Maths", "P.E.", "Rivetting",
"Scottish", "Sewing"), class = "factor")), class = "data.frame", row.names = c(NA,
-20L), .Names = c("datayear", "field", "code", "lookupvalue"))
# reshape your lookup data
dt2 %>%
spread(field, code) -> dt2_reshaped
# start dataset (to join every field you have)
dt_temp = dt1
# for every field you have do the join
for (fld in as.character(unique(dt2$field))) {
dt_temp %>% left_join(dt2_reshaped %>% select_("datayear", "lookupvalue", fld), by=c("datayear",fld)) -> dt_temp
names(dt_temp)[names(dt_temp) == "lookupvalue" ] = gsub("code","value",fld)
}
dt_temp
# id datayear nationalitycode subjectcode nationalityvalue subjectvalue
# 1 1 2007 1 2 Irish Geography
# 2 2 2007 1 5 Irish P.E.
# 3 3 2007 1 5 Irish P.E.
# 4 4 2007 2 5 Scottish P.E.
# 5 5 2007 3 2 Indian Geography
# 6 6 2008 5 5 Italian Latin
# 7 7 2008 4 4 French H.E.
# 8 8 2008 3 2 Chinese Beekeeping
# 9 9 2008 2 1 Scottish Sewing
# 10 10 2008 1 4 English H.E.
解決方案2
1 2017-08-08 11:48:28
如果X是您的第一個data.frame而LU是您的第二個，則data.table和merge使此過程變得簡單，而且很重要的一點是要使其清晰。
library(data.table)
# Convert the data.frames into data.tables
setDT(X)
setDT(LU)
# Join the tables on datayear and the appropriate code, for the
# nationality data only.
X1 <- merge(X, LU[field == "nationalitycode"],
by.x=c("datayear", "nationalitycode"),
by.y=c("datayear", "code"))
# Now join the resulting table by subjectcode.
X2 <- merge(X1, LU[field == "subjectcode"],
by.x=c("datayear", "subjectcode"),
by.y=c("datayear", "code"))
# Now subset the data.table to the columns you want, set the key
# (order) by id, and rename some columns.
M <- X2[, c("id", "datayear", "nationalitycode", "subjectcode",
"lookupvalue.x", "lookupvalue.y"), with=FALSE]
setkey(M, "id")
setnames(M, c("lookupvalue.x", "lookupvalue.y"),
c("nationalityvalue", "subjectvalue"))
M
# id datayear nationalitycode subjectcode nationalityvalue subjectvalue
# 1: 1 2007 1 2 Irish Geography
# 2: 2 2007 1 5 Irish P.E.
# 3: 3 2007 1 5 Irish P.E.
# 4: 4 2007 2 5 Scottish P.E.
# 5: 5 2007 3 2 Indian Geography
# 6: 6 2008 5 5 Italian Latin
# 7: 7 2008 4 4 French H.E.
# 8: 8 2008 3 2 Chinese Beekeeping
# 9: 9 2008 2 1 Scottish Sewing
# 10: 10 2008 1 4 English H.E.
您可以采取一些措施來縮短時間，但是我認為這很清楚發生了什么。
編輯：這是一個應該可以幫助您入門的功能：
merge_fn <- function(column, data=X, lookup=LU)
{
value_nm <- paste0(gsub("code", "", column),
"value")
X1 <- merge(data, LU[field == column],
by.x=c("datayear", column),
by.y=c("datayear", "code"))
setnames(X1, "lookupvalue", value_nm)
X1[, !"field", with=FALSE]
}
M <- merge_fn("subjectcode", data=merge_fn("nationalitycode"))
setkey(M, "id")
M
# datayear subjectcode nationalitycode id nationalityvalue subjectvalue
# 1: 2007 2 1 1 Irish Geography
# 2: 2007 5 1 2 Irish P.E.
# 3: 2007 5 1 3 Irish P.E.
# 4: 2007 5 2 4 Scottish P.E.
# 5: 2007 2 3 5 Indian Geography
# 6: 2008 5 5 6 Italian Latin
# 7: 2008 4 4 7 French H.E.
# 8: 2008 2 3 8 Chinese Beekeeping
# 9: 2008 1 2 9 Scottish Sewing
# 10: 2008 4 1 10 English H.E.
使用R中的查找表選擇多列並更改其類？
[英]Choosing multiple columns and changing their classes using a lookup table in R?
根據數據框中的多個列向r數據框中添加新列
[英]Adding new columns to r dataframe based on multiple columns within the dataframe
使用多列的值將新列添加到 dataframe 並在另一個 dataframe 中查詢
[英]Adding new column to dataframe using values of multiple columns and querying in another dataframe
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