使用 gsub（或类似的）从向量中提取并保留列名的最后 4 位数字
Using gsub (or similar) to extract from a vector and keep last 4 digits of column names
我正在尝试使用 gsub 或 substr 或任何类似的东西来保留由 symbols 和 date 组成的列名。 symbols.f 存储在一个向量中（可以采用不同的股票代码）；
symbols.f <- c("NVDA.f", "GOOG.f", "GE.f")
然后我从下面的 dput() 中得到以下 colnames()。
[1] "GE.f.12.31.2017"
[2] "GE.f.12.31.2016"
[3] "GE.f.12.31.2015"
[4] "GE.f.12.31.2014"
[5] "GOOG.f.12.31.2017"
[6] "GOOG.f.12.31.2016"
[7] "GOOG.f.12.31.2015"
[8] "GOOG.f.12.31.2014"
[9] "NVDA.f.1.28.2018"
[10] "NVDA.f.1.29.2017"
[11] "NVDA.f.1.31.2016"
[12] "NVDA.f.1.25.2015"
我想做的是保留 ticker 并保留 year 或列名称的最后 4 位数字。因此，例如前两个代码；
[1] "GE2017"
[2] "GE2016"
[3] "GE2015"
[4] "GE2014"
[5] "GOOG2017"
[6] "GOOG2016"
[7] "GOOG2015"
[8] "GOOG2014"
我能够提取最后 4 位数字或所有字符，但似乎无法同时或一次性完成。
数据：
df <- structure(list(GE.f.12.31.2017 = c(18211000, NA, 46549000, 21923000,
5790000, 140110000, 38696000, 53874000, 83968000, 20273000, NA,
41024000, 6207000, 377945000, 15153000, 134591000, 21400000,
61893000, 108575000, 82597000, NA, 21122000, NA, 292560000, NA,
NA, NA, 702000, 125682000, -62127000, NA, 22775000, 64257000,
-39984000), GE.f.12.31.2016 = c(10525000, NA, 42687000, 22354000,
2867000, 149029000, 44313000, 50518000, 68070000, 16436000, NA,
34449000, 1833000, 365183000, 14435000, 136211000, 20772000,
70364000, 105080000, 83040000, NA, 4688000, NA, 284667000, NA,
NA, NA, 702000, 139532000, -64412000, NA, 18626000, 75822000,
-11052000), GE.f.12.31.2015 = c(10372000, NA, 43013000, 22515000,
5109000, 280896000, 31973000, 54095000, 65526000, 17797000, NA,
42784000, 3105000, 493071000, 13680000, 197602000, 27453000,
138270000, 144659000, 79175000, NA, 4836000, NA, 389961000, NA,
NA, NA, 702000, 140020000, -42454000, NA, 21085000, 98268000,
14945000), GE.f.12.31.2014 = c(15916000, NA, 23237000, 17639000,
6566000, 460743000, 35505000, 48070000, 53207000, 13182000, NA,
44247000, 6183000, 654954000, 12067000, 261424000, 18203000,
229564000, 186596000, 70801000, NA, 8772000, NA, 518023000, NA,
NA, NA, 702000, 155333000, -27876000, NA, 14717000, 128159000,
61770000), GOOG.f.12.31.2017 = c(10715000, 91156000, 18705000,
749000, 2983000, 124308000, 7813000, 42383000, 16747000, 2692000,
NA, 3352000, 680000, 197295000, 3137000, 3969000, 10651000, 24183000,
3943000, 16641000, NA, NA, NA, 44793000, NA, NA, NA, 40247000,
113247000, -992000, NA, -992000, 152502000, 133063000), GOOG.f.12.31.2016 = c(12918000,
73415000, 15632000, 268000, 3175000, 105408000, 5878000, 34234000,
16468000, 3307000, NA, 2202000, 383000, 167497000, 2041000, 3935000,
5851000, 16756000, 3935000, 7770000, NA, NA, NA, 28461000, NA,
NA, NA, 36307000, 105131000, -2402000, NA, -2402000, 139036000,
119261000), GOOG.f.12.31.2015 = c(15409000, 56517000, 13459000,
491000, 1590000, 90114000, 5183000, 29016000, 15869000, 3847000,
NA, 3432000, 251000, 147461000, 1931000, 7648000, 4327000, 19310000,
1995000, 5825000, NA, NA, NA, 27130000, NA, NA, NA, 32982000,
89223000, -1874000, NA, -1874000, 120331000, 100615000), GOOG.f.12.31.2014 = c(16585000,
46048000, 9974000, NA, 2637000, 78656000, 3079000, 23883000,
15599000, 4607000, NA, 3363000, 176000, 129187000, 1715000, 8015000,
2803000, 16779000, 2992000, 5320000, NA, NA, NA, 25327000, NA,
NA, NA, 28767000, 75066000, 27000, NA, 27000, 103860000, 83654000
), NVDA.f.1.28.2018 = c(7108000, NA, 1265000, 796000, NA, 9255000,
NA, 997000, 618000, 52000, NA, 319000, NA, 11241000, 596000,
2e+06, NA, 1153000, 1985000, 632000, NA, NA, NA, 3770000, NA,
NA, NA, 7471000, NA, NA, NA, NA, 7471000, 6801000), NVDA.f.1.29.2017 = c(1766000,
5032000, 826000, 794000, NA, 8536000, NA, 521000, 618000, 104000,
NA, 62000, NA, 9841000, 485000, 2791000, 325000, 1788000, 1985000,
3e+05, NA, NA, NA, 4079000, NA, NA, NA, 1000, 6108000, -5055000,
4708000, -16000, 5762000, 5040000), NVDA.f.1.31.2016 = c(596000,
4441000, 505000, 418000, NA, 6053000, NA, 466000, 618000, 166000,
NA, 67000, NA, 7370000, 296000, 1434000, 532000, 2351000, 7000,
533000, NA, NA, NA, 2901000, NA, NA, NA, 1000, 4350000, -4052000,
4170000, -4000, 4469000, 3685000), NVDA.f.1.25.2015 = c(497000,
4126000, 474000, 483000, 63000, 5713000, NA, 557000, 618000,
222000, NA, 91000, NA, 7201000, 293000, 1398000, 471000, 896000,
1384000, 489000, NA, NA, NA, 2783000, NA, NA, NA, 1000, 3949000,
-3387000, 3855000, 8000, 4418000, 3578000)), .Names = c("GE.f.12.31.2017",
"GE.f.12.31.2016", "GE.f.12.31.2015", "GE.f.12.31.2014", "GOOG.f.12.31.2017",
"GOOG.f.12.31.2016", "GOOG.f.12.31.2015", "GOOG.f.12.31.2014",
"NVDA.f.1.28.2018", "NVDA.f.1.29.2017", "NVDA.f.1.31.2016", "NVDA.f.1.25.2015"
), row.names = c("Cash And Cash Equivalents", "Short Term Investments",
"Net Receivables", "Inventory", "Other Current Assets", "Total Current Assets",
"Long Term Investments", "Property Plant and Equipment", "Goodwill",
"Intangible Assets", "Accumulated Amortization", "Other Assets",
"Deferred Long Term Asset Charges", "Total Assets", "Accounts Payable",
"Short/Current Long Term Debt", "Other Current Liabilities",
"Total Current Liabilities", "Long Term Debt", "Other Liabilities",
"Deferred Long Term Liability Charges", "Minority Interest",
"Negative Goodwill", "Total Liabilities", "Misc. Stocks Options Warrants",
"Redeemable Preferred Stock", "Preferred Stock", "Common Stock",
"Retained Earnings", "Treasury Stock", "Capital Surplus", "Other Stockholder Equity",
"Total Stockholder Equity", "Net Tangible Assets"), class = "data.frame")
这个正则表达式有效吗？
gsub("\..*\.", "", colnames(df))
它删除了第一个和最后一个“.”以及介于两者之间的一切。
#[1] "GE2017" "GE2016" "GE2015" "GE2014" "GOOG2017"
#[6] "GOOG2016" "GOOG2015" "GOOG2014" "NVDA2018" "NVDA2017"
#[11] "NVDA2016" "NVDA2015"
# '\.' = match a dot, '.' = match anything, '*' = match the previous 0 or more times
# so \..*\. means "anything 0 or more times, preceded by a dot, followed by a dot")
# the \ are escapes so the regex can differentiate whether you mean the
# expression '.' (anything) or '\.' (actual dot)
这是 @Ape 使用 sub 和捕获组给出的答案的替代方法：
sub("^([^.]+).*?(\d+)$", "\1\2", colnames(df))
我正在尝试使用 gsub 或 substr 或任何类似的东西来保留由 symbols 和 date 组成的列名。 symbols.f 存储在一个向量中（可以采用不同的股票代码）；
symbols.f <- c("NVDA.f", "GOOG.f", "GE.f")
然后我从下面的 dput() 中得到以下 colnames()。
[1] "GE.f.12.31.2017"
[2] "GE.f.12.31.2016"
[3] "GE.f.12.31.2015"
[4] "GE.f.12.31.2014"
[5] "GOOG.f.12.31.2017"
[6] "GOOG.f.12.31.2016"
[7] "GOOG.f.12.31.2015"
[8] "GOOG.f.12.31.2014"
[9] "NVDA.f.1.28.2018"
[10] "NVDA.f.1.29.2017"
[11] "NVDA.f.1.31.2016"
[12] "NVDA.f.1.25.2015"
我想做的是保留 ticker 并保留 year 或列名称的最后 4 位数字。因此，例如前两个代码；
[1] "GE2017"
[2] "GE2016"
[3] "GE2015"
[4] "GE2014"
[5] "GOOG2017"
[6] "GOOG2016"
[7] "GOOG2015"
[8] "GOOG2014"
我能够提取最后 4 位数字或所有字符，但似乎无法同时或一次性完成。
数据：
df <- structure(list(GE.f.12.31.2017 = c(18211000, NA, 46549000, 21923000,
5790000, 140110000, 38696000, 53874000, 83968000, 20273000, NA,
41024000, 6207000, 377945000, 15153000, 134591000, 21400000,
61893000, 108575000, 82597000, NA, 21122000, NA, 292560000, NA,
NA, NA, 702000, 125682000, -62127000, NA, 22775000, 64257000,
-39984000), GE.f.12.31.2016 = c(10525000, NA, 42687000, 22354000,
2867000, 149029000, 44313000, 50518000, 68070000, 16436000, NA,
34449000, 1833000, 365183000, 14435000, 136211000, 20772000,
70364000, 105080000, 83040000, NA, 4688000, NA, 284667000, NA,
NA, NA, 702000, 139532000, -64412000, NA, 18626000, 75822000,
-11052000), GE.f.12.31.2015 = c(10372000, NA, 43013000, 22515000,
5109000, 280896000, 31973000, 54095000, 65526000, 17797000, NA,
42784000, 3105000, 493071000, 13680000, 197602000, 27453000,
138270000, 144659000, 79175000, NA, 4836000, NA, 389961000, NA,
NA, NA, 702000, 140020000, -42454000, NA, 21085000, 98268000,
14945000), GE.f.12.31.2014 = c(15916000, NA, 23237000, 17639000,
6566000, 460743000, 35505000, 48070000, 53207000, 13182000, NA,
44247000, 6183000, 654954000, 12067000, 261424000, 18203000,
229564000, 186596000, 70801000, NA, 8772000, NA, 518023000, NA,
NA, NA, 702000, 155333000, -27876000, NA, 14717000, 128159000,
61770000), GOOG.f.12.31.2017 = c(10715000, 91156000, 18705000,
749000, 2983000, 124308000, 7813000, 42383000, 16747000, 2692000,
NA, 3352000, 680000, 197295000, 3137000, 3969000, 10651000, 24183000,
3943000, 16641000, NA, NA, NA, 44793000, NA, NA, NA, 40247000,
113247000, -992000, NA, -992000, 152502000, 133063000), GOOG.f.12.31.2016 = c(12918000,
73415000, 15632000, 268000, 3175000, 105408000, 5878000, 34234000,
16468000, 3307000, NA, 2202000, 383000, 167497000, 2041000, 3935000,
5851000, 16756000, 3935000, 7770000, NA, NA, NA, 28461000, NA,
NA, NA, 36307000, 105131000, -2402000, NA, -2402000, 139036000,
119261000), GOOG.f.12.31.2015 = c(15409000, 56517000, 13459000,
491000, 1590000, 90114000, 5183000, 29016000, 15869000, 3847000,
NA, 3432000, 251000, 147461000, 1931000, 7648000, 4327000, 19310000,
1995000, 5825000, NA, NA, NA, 27130000, NA, NA, NA, 32982000,
89223000, -1874000, NA, -1874000, 120331000, 100615000), GOOG.f.12.31.2014 = c(16585000,
46048000, 9974000, NA, 2637000, 78656000, 3079000, 23883000,
15599000, 4607000, NA, 3363000, 176000, 129187000, 1715000, 8015000,
2803000, 16779000, 2992000, 5320000, NA, NA, NA, 25327000, NA,
NA, NA, 28767000, 75066000, 27000, NA, 27000, 103860000, 83654000
), NVDA.f.1.28.2018 = c(7108000, NA, 1265000, 796000, NA, 9255000,
NA, 997000, 618000, 52000, NA, 319000, NA, 11241000, 596000,
2e+06, NA, 1153000, 1985000, 632000, NA, NA, NA, 3770000, NA,
NA, NA, 7471000, NA, NA, NA, NA, 7471000, 6801000), NVDA.f.1.29.2017 = c(1766000,
5032000, 826000, 794000, NA, 8536000, NA, 521000, 618000, 104000,
NA, 62000, NA, 9841000, 485000, 2791000, 325000, 1788000, 1985000,
3e+05, NA, NA, NA, 4079000, NA, NA, NA, 1000, 6108000, -5055000,
4708000, -16000, 5762000, 5040000), NVDA.f.1.31.2016 = c(596000,
4441000, 505000, 418000, NA, 6053000, NA, 466000, 618000, 166000,
NA, 67000, NA, 7370000, 296000, 1434000, 532000, 2351000, 7000,
533000, NA, NA, NA, 2901000, NA, NA, NA, 1000, 4350000, -4052000,
4170000, -4000, 4469000, 3685000), NVDA.f.1.25.2015 = c(497000,
4126000, 474000, 483000, 63000, 5713000, NA, 557000, 618000,
222000, NA, 91000, NA, 7201000, 293000, 1398000, 471000, 896000,
1384000, 489000, NA, NA, NA, 2783000, NA, NA, NA, 1000, 3949000,
-3387000, 3855000, 8000, 4418000, 3578000)), .Names = c("GE.f.12.31.2017",
"GE.f.12.31.2016", "GE.f.12.31.2015", "GE.f.12.31.2014", "GOOG.f.12.31.2017",
"GOOG.f.12.31.2016", "GOOG.f.12.31.2015", "GOOG.f.12.31.2014",
"NVDA.f.1.28.2018", "NVDA.f.1.29.2017", "NVDA.f.1.31.2016", "NVDA.f.1.25.2015"
), row.names = c("Cash And Cash Equivalents", "Short Term Investments",
"Net Receivables", "Inventory", "Other Current Assets", "Total Current Assets",
"Long Term Investments", "Property Plant and Equipment", "Goodwill",
"Intangible Assets", "Accumulated Amortization", "Other Assets",
"Deferred Long Term Asset Charges", "Total Assets", "Accounts Payable",
"Short/Current Long Term Debt", "Other Current Liabilities",
"Total Current Liabilities", "Long Term Debt", "Other Liabilities",
"Deferred Long Term Liability Charges", "Minority Interest",
"Negative Goodwill", "Total Liabilities", "Misc. Stocks Options Warrants",
"Redeemable Preferred Stock", "Preferred Stock", "Common Stock",
"Retained Earnings", "Treasury Stock", "Capital Surplus", "Other Stockholder Equity",
"Total Stockholder Equity", "Net Tangible Assets"), class = "data.frame")
这个正则表达式有效吗？
gsub("\..*\.", "", colnames(df))
它删除了第一个和最后一个“.”以及介于两者之间的一切。
#[1] "GE2017" "GE2016" "GE2015" "GE2014" "GOOG2017"
#[6] "GOOG2016" "GOOG2015" "GOOG2014" "NVDA2018" "NVDA2017"
#[11] "NVDA2016" "NVDA2015"
# '\.' = match a dot, '.' = match anything, '*' = match the previous 0 or more times
# so \..*\. means "anything 0 or more times, preceded by a dot, followed by a dot")
# the \ are escapes so the regex can differentiate whether you mean the
# expression '.' (anything) or '\.' (actual dot)
这是 @Ape 使用 sub 和捕获组给出的答案的替代方法：
sub("^([^.]+).*?(\d+)$", "\1\2", colnames(df))