I would like to change in a data frame the structure of one column from this "1996-10-01" to "1996 Q4".
Here data:
narepurchasement <-
structure(list(Date = structure(c(844128000, 852076800, 859852800,
867715200, 875664000, 883612800, 891388800, 899251200, 907200000,
915148800, 922924800, 930787200, 938736000, 946684800, 954547200,
962409600, 970358400, 978307200, 986083200, 993945600, 1001894400,
1009843200, 1017619200, 1025481600, 1033430400, 1041379200, 1049155200,
1057017600, 1064966400, 1072915200, 1080777600, 1088640000, 1096588800,
1104537600, 1112313600, 1120176000, 1128124800, 1136073600, 1143849600,
1151712000, 1159660800, 1167609600, 1175385600, 1183248000, 1191196800,
1199145600, 1207008000, 1214870400, 1222819200, 1230768000, 1238544000,
1246406400, 1254355200, 1262304000, 1270080000, 1277942400, 1285891200,
1293840000, 1301616000, 1309478400, 1317427200, 1325376000, 1333238400,
1341100800, 1349049600, 1356998400, 1364774400, 1372636800, 1380585600,
1388534400, 1396310400, 1404172800, 1412121600, 1420070400, 1427846400,
1435708800, 1443657600, 1451606400, 1459468800, 1467331200, 1475280000,
1483228800, 1491004800, 1498867200, 1506816000, 1514764800, 1522540800,
1530403200, 1538352000, 1546300800, 1554076800, 1561939200, 1569888000,
1577836800, 1585699200, 1593561600, 1601510400, 1609459200, 1617235200,
1625097600, 1633046400, 1640995200, 1648771200), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), NetIssuance = c("-7450", "-13950",
"-14675", "-22875", "-25875", "-21675", "-17808", "-64840", "-111214",
"-6920", "-76700", "-26188", "-1", "27044", "-50630", "-10731",
"-83887", "-4850", "-14775", "-27350", "-1150", "-2644", "6357",
"-20316", "2098", "-10173", "-3438", "0", "-2055", "-0.802",
"-16823", "-32200", "-70730", "-43031", "-58722", "-90630", "-83784",
"-110795", "-116977", "-107859", "-137542", "-109583", "-149516",
"-162019", "-226618", "-84099", "-38612", "-73533", "-93475",
"-37950", "39311", "20920", "-62302", "-35987", "-35433", "-71238",
"-58295", "-59766", "-101392", "-133088", "-88329", "-49568",
"-99135", "-73428", "-77876", "-38256", "-73497", "-60269", "-105274",
"-101911", "-48493", "-80452", "-71090", "-116963", "-102404",
"-129399", "-104711", "-127487", "-136914", "-150658", "-80792",
"-89438", "-55464", "-119607", "-61042", "-122438", "-225035",
"-79778", "-190075", "-174006", "-46583", "-111504", "-124927",
"-95947", "-14946", "7398", "-67450", "-30403", "-133211", "-218291",
"-237670", "-227868", "-135084"), GrossIssuance = c(35393, 34426,
39963, 36586, 40630, 36993, 57637, 31110, 52737, 52487, 78711,
65846, 95574, 113349, 86067, 75480, 71906, 54552, 64094, 39824,
55322, 43624, 50257, 29329, 35664, 32098, 36084, 42285, 48634,
57955, 47497, 43892, 55599, 48385, 52197, 63692, 63159, 68401,
69557, 63825, 94723, 88627, 97967, 102944, 108022, 86316, 96002,
93730, 75885, 64674, 77307, 62616, 66705, 54873, 57173, 48392,
68703, 64334, 69966, 43637, 55198, 66678, 70380, 68331, 72198,
73702, 83784, 103945, 94138, 89471, 100239, 100418, 111302, 129933,
124281, 116589, 97678, 106734, 118234, 106262, 107965, 122679,
115625, 107485, 112226, 106358, 99560, 101952, 91526, 95447,
118912, 108570, 100615, 107853, 154908, 134115, 154227, 163567,
126579, 112180, 132474, 92327, 80342), GrossRetirement = c(42843,
48376, 54638, 59461, 66505, 58668, 75445, 95950, 163951, 59407,
155411, 92034, 96134, 86305, 136697, 86211, 155793, 59402, 78869,
67174, 56472, 46268, 43900, 49645, 33566, 42271, 39522, 42226,
50689, 58757, 64320, 76092, 126329, 91416, 110919, 154322, 146943,
179196, 186534, 171684, 232265, 198210, 247483, 264963, 334640,
170415, 134614, 167263, 169360, 102624, 37996, 41696, 129007,
90860, 92606, 119630, 126998, 124100, 171358, 176725, 143527,
116246, 169515, 141759, 150074, 111958, 157281, 164214, 199412,
191382, 148732, 180870, 182392, 246896, 226685, 245988, 202389,
234221, 255148, 256920, 188757, 212117, 171089, 227092, 173268,
228796, 324595, 181730, 281601, 269453, 165495, 220074, 225542,
203800, 169854, 126717, 221677, 193970, 259790, 330471, 370144,
320195, 215426), Repurchases = c(22263, 22638, 23514, 25005,
34369, 26643, 29082, 41095, 27253, 31805, 30779, 29350, 35972,
38084, 22859, 24761, 30152, 25245, 26623, 27689, 24038, 20954,
27243, 27314, 18885, 20208, 22000, 25993, 34329, 31567, 34011,
42358, 46643, 52980, 63201, 66599, 90778, 76295, 97243, 91990,
96248, 92541, 121025, 121251, 98213, 94359, 75799, 80943, 45745,
26459, 17862, 24888, 33600, 40277, 59624, 57199, 62624, 66172,
73022, 96186, 74495, 64511, 83483, 65770, 86040, 77135, 100169,
97375, 105120, 124551, 99652, 108215, 106062, 113685, 100343,
122057, 107005, 123418, 99546, 75010, 89025, 93073, 81638, 84879,
87762, 143170, 138764, 134874, 148169, 137193, 107400, 108922,
119371, 143785, 79929, 88312, 110984, 128796, 141252, 154680,
195502, 220050, 120000), MA = c(20579, 25738, 31124, 34456, 32136,
32025, 46364, 54855, 136698, 27602, 124632, 62684, 60162, 48221,
113837, 61450, 125641, 34157, 52246, 39486, 32434, 25314, 16657,
22331, 14681, 22063, 17522, 16233, 16360, 27191, 30309, 33735,
79686, 38436, 47718, 87723, 56166, 102901, 89291, 79694, 136016,
105669, 126458, 143711, 236427, 76055, 58816, 86320, 123615,
76166, 20134, 16809, 95407, 50583, 32982, 62430, 64373, 57928,
98336, 80539, 69032, 51735, 86032, 75988, 64033, 34823, 57112,
66838, 94292, 66831, 49079, 72655, 76330, 133211, 126342, 123931,
95384, 110803, 155602, 181911, 99732, 119044, 89451, 142213,
85506, 85626, 185832, 46856, 133432, 132260, 58095, 111152, 106172,
60015, 89925, 38404, 110693, 65174, 118539, 175791, 174642, 100146,
95426), GDP = c(8259.771, 8362.655, 8518.825, 8662.823, 8765.907,
8866.48, 8969.699, 9121.097, 9293.991, 9411.682, 9526.21, 9686.626,
9900.169, 10002.179, 10247.72, 10318.165, 10435.744, 10470.231,
10599, 10598.02, 10660.465, 10783.5, 10887.46, 10984.04, 11061.433,
11174.129, 11312.766, 11566.669, 11772.234, 11923.447, 12112.815,
12305.307, 12527.214, 12767.286, 12922.656, 13142.642, 13324.204,
13599.16, 13753.424, 13870.188, 14039.56, 14215.651, 14402.082,
14564.117, 14715.058, 14706.538, 14865.701, 14898.999, 14608.208,
14430.901, 14381.236, 14448.882, 14651.248, 14764.611, 14980.193,
15141.605, 15309.471, 15351.444, 15557.535, 15647.681, 15842.267,
16068.824, 16207.13, 16319.54, 16420.386, 16629.05, 16699.551,
16911.068, 17133.114, 17144.281, 17462.703, 17743.227, 17852.54,
17991.348, 18193.707, 18306.96, 18332.079, 18425.306, 18611.617,
18775.459, 18968.041, 19148.194, 19304.506, 19561.896, 19894.75,
20155.486, 20470.197, 20687.278, 20819.269, 21013.085, 21272.448,
21531.839, 21706.532, 21538.032, 19636.731, 21362.428, 21704.706,
22313.85, 23046.934, 23550.42, 24349.121, 24740.48, 25248.476
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-103L))
I really didn't know how to start
CodePudding user response：
Here's an approach using lubridate. I had problems importing your structure so I extracted just the Date part.
library(lubridate)
paste0(format(Dates, "%Y"), " Q", quarter(Dates))
[1] "1996 Q4" "1997 Q1" "1997 Q2" "1997 Q3" "1997 Q4" "1998 Q1" "1998 Q2"
[8] "1998 Q3" "1998 Q4" "1999 Q1" "1999 Q2" "1999 Q3" "1999 Q4" "2000 Q1"
[15] "2000 Q2" "2000 Q3" "2000 Q4" "2001 Q1" "2001 Q2" "2001 Q3" "2001 Q4"
[22] "2002 Q1" "2002 Q2" "2002 Q3" "2002 Q4" "2003 Q1" "2003 Q2" "2003 Q3"
[29] "2003 Q4" "2004 Q1" "2004 Q2" "2004 Q3" "2004 Q4" "2005 Q1" "2005 Q2"
[36] "2005 Q3" "2005 Q4" "2006 Q1" "2006 Q2" "2006 Q3" "2006 Q4" "2007 Q1"
[43] "2007 Q2" "2007 Q3" "2007 Q4" "2008 Q1" "2008 Q2" "2008 Q3" "2008 Q4"
[50] "2009 Q1" "2009 Q2" "2009 Q3" "2009 Q4" "2010 Q1" "2010 Q2" "2010 Q3"
[57] "2010 Q4" "2011 Q1" "2011 Q2" "2011 Q3" "2011 Q4" "2012 Q1" "2012 Q2"
[64] "2012 Q3" "2012 Q4" "2013 Q1" "2013 Q2" "2013 Q3" "2013 Q4" "2014 Q1"
[71] "2014 Q2" "2014 Q3" "2014 Q4" "2015 Q1" "2015 Q2" "2015 Q3" "2015 Q4"
[78] "2016 Q1" "2016 Q2" "2016 Q3" "2016 Q4" "2017 Q1" "2017 Q2" "2017 Q3"
[85] "2017 Q4" "2018 Q1" "2018 Q2" "2018 Q3" "2018 Q4" "2019 Q1" "2019 Q2"
[92] "2019 Q3" "2019 Q4" "2020 Q1" "2020 Q2" "2020 Q3" "2020 Q4" "2021 Q1"
[99] "2021 Q2" "2021 Q3" "2021 Q4" "2022 Q1" "2022 Q2"
Data
Dates <- structure(c(844128000, 852076800, 859852800, 867715200, 875664000,
883612800, 891388800, 899251200, 907200000, 915148800, 922924800,
930787200, 938736000, 946684800, 954547200, 962409600, 970358400,
978307200, 986083200, 993945600, 1001894400, 1009843200, 1017619200,
1025481600, 1033430400, 1041379200, 1049155200, 1057017600, 1064966400,
1072915200, 1080777600, 1088640000, 1096588800, 1104537600, 1112313600,
1120176000, 1128124800, 1136073600, 1143849600, 1151712000, 1159660800,
1167609600, 1175385600, 1183248000, 1191196800, 1199145600, 1207008000,
1214870400, 1222819200, 1230768000, 1238544000, 1246406400, 1254355200,
1262304000, 1270080000, 1277942400, 1285891200, 1293840000, 1301616000,
1309478400, 1317427200, 1325376000, 1333238400, 1341100800, 1349049600,
1356998400, 1364774400, 1372636800, 1380585600, 1388534400, 1396310400,
1404172800, 1412121600, 1420070400, 1427846400, 1435708800, 1443657600,
1451606400, 1459468800, 1467331200, 1475280000, 1483228800, 1491004800,
1498867200, 1506816000, 1514764800, 1522540800, 1530403200, 1538352000,
1546300800, 1554076800, 1561939200, 1569888000, 1577836800, 1585699200,
1593561600, 1601510400, 1609459200, 1617235200, 1625097600, 1633046400,
1640995200, 1648771200), class = c("POSIXct", "POSIXt"), tzone = "UTC")
CodePudding user response：
Convert to yearqtr class which represents a year and quarter internally as year and fractions of a year (0, 0.25, 0.5, 0.75) so it is easily manipulated, e.g. add 1 to get next year or add 1/4 to get next quarter.
library(dplyr)
library(zoo)
narepurchasement %>%
mutate(Date = as.yearqtr(Date))
giving
# A tibble: 103 × 7
Date NetIssuance GrossIssuance GrossRetirement Repurchases MA GDP
<yearqtr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1996 Q4 -7450 35393 42843 22263 20579 8260.
2 1997 Q1 -13950 34426 48376 22638 25738 8363.
3 1997 Q2 -14675 39963 54638 23514 31124 8519.
4 1997 Q3 -22875 36586 59461 25005 34456 8663.
5 1997 Q4 -25875 40630 66505 34369 32136 8766.
6 1998 Q1 -21675 36993 58668 26643 32025 8866.
7 1998 Q2 -17808 57637 75445 29082 46364 8970.
8 1998 Q3 -64840 31110 95950 41095 54855 9121.
9 1998 Q4 -111214 52737 163951 27253 136698 9294.
10 1999 Q1 -6920 52487 59407 31805 27602 9412.
# … with 93 more rows
# ℹ Use `print(n = ...)` to see more rows