我在earnings上有以下数据；
# A tibble: 6 x 24
m_ticker ticker comp_name comp_name_2 exchange currency_code per_end_date_fr0
<chr> <chr> <chr> <chr> <chr> <chr> <date>
1 AAPL AAPL APPLE INC Apple Inc. NSDQ USD 2017-09-30
2 AXP AXP AMER EXPRES~ American Express~ NYSE USD 2017-12-31
3 BA BA BOEING CO The Boeing Compa~ NYSE USD 2017-12-31
4 CTR CAT CATERPILLAR~ Caterpillar Inc. NYSE USD 2017-12-31
5 CSCO CSCO CISCO SYSTE~ Cisco Systems, I~ NSDQ USD 2017-07-31
6 SD CVX CHEVRON CORP Chevron Corporat~ NYSE USD 2017-12-31
# ... with 17 more variables: per_end_date_qr1 <date>, eps_mean_est_qr1 <dbl>,
# street_mean_est_qr1 <dbl>, exp_rpt_date_qr1 <date>, exp_rpt_date_qr2 <date>,
# exp_rpt_date_fr1 <date>, exp_rpt_date_fr2 <date>, late_last_flag <dbl>,
# late_last_desc <chr>, source_flag <dbl>, source_desc <chr>, time_of_day_code <dbl>,
# time_of_day_desc <chr>, per_end_date_qr0 <date>, eps_act_qr0 <dbl>,
# per_end_date_qrm3 <date>, eps_act_qrm3 <dbl>
我还有一个ticker个符号的向量，称为tickers。
tickers <- c("PYPL", "GOOG", "AAPL", "MSFT", "CSCO")
我正在尝试创建一个ifelse语句，该语句将从earnings数据中打印出以下几列的小表：
ticker | comp_name | exp_rpt_date_qr1 | exp_rpt_date_qr2 | time_of_day_desc
因此，如果ticker与earnings$ticker相匹配，则打印以上各列。
我尝试使用grepl打印报告警告消息的基本yes / no。
ifelse(grepl(tickers, earnings$ticker), "yes", "no")
数据：
earnings <- structure(list(m_ticker = c("AAPL", "AXP", "BA", "CTR", "CSCO",
"SD", "DIS", "GE", "GS&", "HOMD", "IBM", "ITL", "JNJ", "CHL",
"KO", "MCD", "MMM", "MRK", "MSFT", "NIKE", "PFE", "PG", "SPM",
"UNIH", "UA", "VISA", "BEL", "WMS", "J"), ticker = c("AAPL",
"AXP", "BA", "CAT", "CSCO", "CVX", "DIS", "GE", "GS", "HD", "IBM",
"INTC", "JNJ", "JPM", "KO", "MCD", "MMM", "MRK", "MSFT", "NKE",
"PFE", "PG", "TRV", "UNH", "UTX", "V", "VZ", "WMT", "XOM"), comp_name = c("APPLE INC",
"AMER EXPRESS CO", "BOEING CO", "CATERPILLAR INC", "CISCO SYSTEMS",
"CHEVRON CORP", "DISNEY WALT", "GENL ELECTRIC", "GOLDMAN SACHS",
"HOME DEPOT", "INTL BUS MACH", "INTEL CORP", "JOHNSON & JOHNS",
"JPMORGAN CHASE", "COCA COLA CO", "MCDONALDS CORP", "3M CO",
"MERCK & CO INC", "MICROSOFT CORP", "NIKE INC-B", "PFIZER INC",
"PROCTER & GAMBL", "TRAVELERS COS", "UNITEDHEALTH GP", "UTD TECHS CORP",
"VISA INC-A", "VERIZON COMM", "WALMART INC", "EXXON MOBIL CRP"
), comp_name_2 = c("Apple Inc.", "American Express Company",
"The Boeing Company", "Caterpillar Inc.", "Cisco Systems, Inc.",
"Chevron Corporation", "The Walt Disney Company", "General Electric Company",
"The Goldman Sachs Group, Inc.", "The Home Depot, Inc.", "International Business Machines Corporation",
"Intel Corporation", "Johnson & Johnson", "JPMorgan Chase & Co.",
"Coca-Cola Company (The)", "McDonald's Corporation", "3M Company",
"Merck & Co., Inc.", "Microsoft Corporation", "NIKE, Inc.", "Pfizer Inc.",
"Procter & Gamble Company (The)", "The Travelers Companies, Inc.",
"UnitedHealth Group Incorporated", "United Technologies Corporation",
"Visa Inc.", "Verizon Communications Inc.", "Walmart Inc.", "Exxon Mobil Corporation"
), exchange = c("NSDQ", "NYSE", "NYSE", "NYSE", "NSDQ", "NYSE",
"NYSE", "NYSE", "NYSE", "NYSE", "NYSE", "NSDQ", "NYSE", "NYSE",
"NYSE", "NYSE", "NYSE", "NYSE", "NSDQ", "NYSE", "NYSE", "NYSE",
"NYSE", "NYSE", "NYSE", "NYSE", "NYSE", "NYSE", "NYSE"), currency_code = c("USD",
"USD", "USD", "USD", "USD", "USD", "USD", "USD", "USD", "USD",
"USD", "USD", "USD", "USD", "USD", "USD", "USD", "USD", "USD",
"USD", "USD", "USD", "USD", "USD", "USD", "USD", "USD", "USD",
"USD"), per_end_date_fr0 = structure(c(17439, 17531, 17531, 17531,
17378, 17531, 17439, 17531, 17531, 17562, 17531, 17531, 17531,
17531, 17531, 17531, 17531, 17531, 17347, 17682, 17531, 17347,
17531, 17531, 17531, 17439, 17531, 17562, 17531), class = "Date"),
per_end_date_qr1 = structure(c(17712, 17712, 17712, 17712,
17743, 17712, 17712, 17712, 17712, 17743, 17712, 17712, 17712,
17804, 17712, 17712, 17712, 17712, 17712, 17774, 17712, 17712,
17712, 17712, 17712, 17712, 17712, 17743, 17712), class = "Date"),
eps_mean_est_qr1 = c(2.19, 1.83, 3.43, 2.66, 0.63, 2.1, 2.04,
0.18, 4.67, 2.85, 3.03, 0.99, 2.06, 2.27, 0.6, 1.93, 2.59,
1.03, 1.07, 0.61, 0.75, 0.91, 2.44, 3.03, 1.86, 1.09, 1.15,
1.21, 1.24), street_mean_est_qr1 = c(2.187, 1.83, 3.434,
2.658, 0.689, 2.098, 2.043, 0.178, 4.674, 2.847, 3.031, 0.99,
2.055, 2.27, 0.601, 1.929, 2.594, 1.03, 1.074, 0.609, 0.748,
0.906, 2.436, 3.031, 1.858, 1.089, 1.145, 1.212, 1.244),
exp_rpt_date_qr1 = structure(c(17743, 17730, 17737, 17742,
17758, 17739, 17750, 17732, 17729, 17764, 17730, 17738, 17729,
17815, 17737, 17738, 17736, 17739, 17731, 17799, 17743, 17743,
17731, 17729, 17736, 17737, 17736, 17759, 17739), class = "Date"),
exp_rpt_date_qr2 = structure(c(17836, 17821, 17828, 17827,
17856, 17830, 17843, 17823, 17820, 17848, 17820, 17829, 17820,
17907, 17828, 17827, 17827, 17830, 17829, 17885, 17834, 17823,
17822, 17820, 17827, 17828, 17822, 17850, 17830), class = "Date"),
exp_rpt_date_fr1 = structure(c(17836, 17913, 17926, 17920,
17758, 17928, 17843, 17919, 17912, 17946, 17913, 17920, 17918,
17907, 17942, 17925, 17920, 17928, 17731, 18074, 17925, 17743,
17918, 17911, 17919, 17828, 17918, 17946, 17928), class = "Date"),
exp_rpt_date_fr2 = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_), class = "Date"), late_last_flag = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), late_last_desc = c("Not late",
"Not late", "Not late", "Not late", "Not late", "Not late",
"Not late", "Not late", "Not late", "Not late", "Not late",
"Not late", "Not late", "Not late", "Not late", "Not late",
"Not late", "Not late", "Not late", "Not late", "Not late",
"Not late", "Not late", "Not late", "Not late", "Not late",
"Not late", "Not late", "Not late"), source_flag = c(1, 1,
1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1,
1, 1, 1, 1, 1, 1, 2, 1), source_desc = c("Company", "Company",
"Company", "Company", "Estimated", "Company", "Company",
"Company", "Company", "Estimated", "Company", "Company",
"Company", "Estimated", "Company", "Company", "Company",
"Company", "Company", "Estimated", "Company", "Company",
"Company", "Company", "Company", "Company", "Company", "Estimated",
"Company"), time_of_day_code = c(1, 1, 2, 2, 4, 2, 1, 2,
2, 4, 1, 1, 2, 4, 2, 2, 2, 2, 1, 4, 2, 2, 2, 2, 2, 1, 2,
4, 2), time_of_day_desc = c("After market close", "After market close",
"Before the open", "Before the open", "Unknown", "Before the open",
"After market close", "Before the open", "Before the open",
"Unknown", "After market close", "After market close", "Before the open",
"Unknown", "Before the open", "Before the open", "Before the open",
"Before the open", "After market close", "Unknown", "Before the open",
"Before the open", "Before the open", "Before the open",
"Before the open", "After market close", "Before the open",
"Unknown", "Before the open"), per_end_date_qr0 = structure(c(17621,
17621, 17621, 17621, 17651, 17621, 17621, 17621, 17621, 17651,
17621, 17621, 17621, 17712, 17621, 17621, 17621, 17621, 17621,
17682, 17621, 17621, 17621, 17621, 17621, 17621, 17621, 17651,
17621), class = "Date"), eps_act_qr0 = c(2.73, 1.86, 3.64,
2.82, 0.6, 1.9, 1.84, 0.16, 6.95, 2.08, 2.45, 0.87, 2.06,
2.29, 0.47, 1.79, 2.5, 1.05, 0.95, 0.69, 0.77, 1, 2.46, 3.04,
1.77, 1.11, 1.17, 1.14, 1.09), per_end_date_qrm3 = structure(c(17347,
17347, 17347, 17347, 17378, 17347, 17347, 17347, 17347, 17378,
17347, 17347, 17347, 17439, 17347, 17347, 17347, 17347, 17347,
17409, 17347, 17347, 17347, 17347, 17347, 17347, 17347, 17378,
17347), class = "Date"), eps_act_qrm3 = c(1.67, 1.47, 2.55,
1.49, 0.55, 0.91, 1.58, 0.28, 3.95, 2.25, 2.97, 0.72, 1.83,
1.76, 0.59, 1.73, 2.58, 1.01, 0.98, 0.57, 0.67, 0.85, 1.92,
2.46, 1.85, 0.86, 0.96, 1.08, 0.78)), .Names = c("m_ticker",
"ticker", "comp_name", "comp_name_2", "exchange", "currency_code",
"per_end_date_fr0", "per_end_date_qr1", "eps_mean_est_qr1", "street_mean_est_qr1",
"exp_rpt_date_qr1", "exp_rpt_date_qr2", "exp_rpt_date_fr1", "exp_rpt_date_fr2",
"late_last_flag", "late_last_desc", "source_flag", "source_desc",
"time_of_day_code", "time_of_day_desc", "per_end_date_qr0", "eps_act_qr0",
"per_end_date_qrm3", "eps_act_qrm3"), row.names = c(NA, -29L), class = c("tbl_df",
"tbl", "data.frame"))
答案 0 :(得分：3)
您可以使用grepl代替%in%。
此外，如果您要做的只是选择要打印的特定行和列，则可以使用subset。
> keepcols = c('ticker','comp_name','exp_rpt_date_qr1','exp_rpt_date_qr2','time_of_day_desc')
> subset(earnings, ticker %in% tickers, select = keepcols)
ticker comp_name exp_rpt_date_qr1 exp_rpt_date_qr2 time_of_day_desc
1 AAPL APPLE INC 2018-07-31 2018-11-01 After market close
5 CSCO CISCO SYSTEMS 2018-08-15 2018-11-21 Unknown
19 MSFT MICROSOFT CORP 2018-07-19 2018-10-25 After market close