I'm trying to extract the year and month from a POSIXct variable using data wrangling skills I recently learned. However it doesn't seem to work. I can do it the old way (just using format) but my pipes seem to be not working. Any help would be greatly appreciated. The output from my pipes looks kinda weird.
I posted information about the variable below.
-- Variable type: POSIXct ----------------------
# A tibble: 1 x 7
skim_variable n_missing complete_rate
* <chr> <int> <dbl>
1 date 0 1
min max
* <dttm> <dttm>
1 2014-05-02 00:00:00 2015-05-27 00:00:00
median n_unique
* <dttm> <int>
1 2014-10-16 00:00:00 372
My code which doesn't work.
dataset %>%
select(date) %>%
format(format = '%Y%m')
output:
[1] "\033[38;5;246m# A tibble: 21,613 x 1\033[39m"
[2] " date "
[3] " \033[3m\033[38;5;246m<dttm>\033[39m\033[23m "
[4] "\033[38;5;250m 1\033[39m 2014-10-13 \033[38;5;246m00:00:00\033[39m"
[5] "\033[38;5;250m 2\033[39m 2014-12-09 \033[38;5;246m00:00:00\033[39m"
[6] "\033[38;5;250m 3\033[39m 2015-02-25 \033[38;5;246m00:00:00\033[39m"
[7] "\033[38;5;250m 4\033[39m 2014-12-09 \033[38;5;246m00:00:00\033[39m"
[8] "\033[38;5;250m 5\033[39m 2015-02-18 \033[38;5;246m00:00:00\033[39m"
[9] "\033[38;5;250m 6\033[39m 2014-05-12 \033[38;5;246m00:00:00\033[39m"
[10] "\033[38;5;250m 7\033[39m 2014-06-27 \033[38;5;246m00:00:00\033[39m"
[11] "\033[38;5;250m 8\033[39m 2015-01-15 \033[38;5;246m00:00:00\033[39m"
[12] "\033[38;5;250m 9\033[39m 2015-04-15 \033[38;5;246m00:00:00\033[39m"
[13] "\033[38;5;250m10\033[39m 2015-03-12 \033[38;5;246m00:00:00\033[39m"
[14] "\033[38;5;246m# ... with 21,603 more rows\033[39m"
code that works:
format(dataset$date, format = '%Y%m')
output:
[1] "201410" "201412" "201502" "201412"
[5] "201502" "201405" "201406" "201501"
[9] "201504" "201503" "201504" "201405"
[13] "201405" "201410" "201503" "201501"
[17] "201407" "201405" "201412" "201504"
[21] "201405" "201408" "201407" "201405"
[25] "201411" "201411" "201406" "201412"
[29] "201406" "201503" "201411" "201412"
[33] "201406" "201411" "201412" "201406"
[37] "201405" "201412" "201502" "201406"
[41] "201407" "201408" "201407" "201410"
[45] "201407" "201407" "201503" "201407"
[49] "201504" "201503" "201409" "201502"
[53] "201412" "201502" "201503" "201405"
[57] "201408" "201504" "201408" "201502"
[61] "201412" "201408" "201410" "201412"
[65] "201406" "201411" "201409" "201410"
[69] "201408" "201406" "201409" "201501"
[73] "201406" "201407" "201503" "201411"
[77] "201410" "201504" "201406" "201503"
[81] "201412" "201412" "201410" "201501"
[85] "201406" "201411" "201411" "201406"
[89] "201405" "201409" "201405" "201503"
[93] "201502" "201407" "201412" "201409"
[97] "201503" "201409" "201407" "201405"
[101] "201406" "201410" "201412" "201410"
[105] "201409" "201504" "201405" "201407"
[109] "201503" "201408" "201407" "201503"
[113] "201409" "201411" "201410" "201411"
[117] "201406" "201406" "201501" "201505"
[121] "201501" "201411" "201504" "201411"
[125] "201406" "201503" "201407" "201407"
[129] "201406" "201504" "201501" "201504"
[133] "201406" "201405" "201501" "201408"
[137] "201408" "201405" "201407" "201405"
[141] "201406" "201407" "201505" "201504"
[145] "201502" "201412" "201406" "201408"
[149] "201410" "201411" "201408" "201504"
[153] "201503" "201504" "201405" "201407"
[157] "201411" "201408" "201411" "201410"
[161] "201405" "201503" "201503" "201406"
[165] "201408" "201412" "201502" "201503"
[169] "201406" "201406" "201503" "201501"
[173] "201405" "201501" "201409" "201502"
[177] "201407" "201405" "201411" "201501"
[181] "201407" "201504" "201406" "201407"
[185] "201409" "201411" "201504" "201405"
[189] "201504" "201412" "201408" "201411"
[193] "201409" "201406" "201412" "201405"
[197] "201411" "201407" "201501" "201410"
[201] "201503" "201411" "201407" "201408"
[205] "201503" "201408" "201409" "201502"
[209] "201411" "201407" "201504" "201407"
[213] "201405" "201501" "201405" "201501"
[217] "201502" "201407" "201408" "201410"
[221] "201408" "201406" "201409" "201409"
[225] "201409" "201406" "201503" "201405"
[229] "201501" "201503" "201504" "201406"
[233] "201411" "201411" "201409" "201406"
[237] "201504" "201503" "201407" "201405"
[241] "201412" "201410" "201501" "201504"
[245] "201408" "201502" "201409" "201502"
[249] "201503" "201504" "201409" "201405"
[253] "201504" "201504" "201409" "201405"
[257] "201410" "201502" "201411" "201408"
[261] "201412" "201410" "201412" "201504"
[265] "201410" "201405" "201409" "201408"
[269] "201504" "201505" "201410" "201505"
[273] "201410" "201409" "201411" "201503"
[277] "201501" "201411" "201505" "201505"
[281] "201407" "201502" "201505" "201406"
[285] "201504" "201409" "201410" "201502"
[289] "201407" "201408" "201407" "201502"
[293] "201410" "201410" "201407" "201407"
[297] "201408" "201503" "201406" "201405"
[301] "201406" "201408" "201406" "201504"
[305] "201501" "201502" "201504" "201406"
[309] "201502" "201501" "201502" "201410"
[313] "201405" "201406" "201412" "201411"
[317] "201504" "201505" "201407" "201411"
[321] "201405" "201410" "201407" "201502"
[325] "201409" "201503" "201410" "201409"
[329] "201504" "201407" "201409" "201504"
[333] "201407" "201504" "201407" "201410"
[337] "201406" "201407" "201408" "201505"
[341] "201504" "201411" "201409" "201410"
[345] "201407" "201407" "201412" "201412"
[349] "201410" "201504" "201406" "201409"
[353] "201501" "201412" "201503" "201406"
[357] "201408" "201410" "201405" "201408"
[361] "201406" "201408" "201412" "201408"
[365] "201409" "201501" "201503" "201410"
[369] "201504" "201411" "201412" "201410"
[373] "201503" "201412" "201408" "201405"
[377] "201406" "201410" "201504" "201504"
[381] "201409" "201408" "201503" "201501"
[385] "201407" "201411" "201406" "201405"
[389] "201409" "201408" "201412" "201409"
[393] "201406" "201405" "201407" "201504"
[397] "201503" "201406" "201406" "201406"
[401] "201503" "201408" "201504" "201502"
[405] "201406" "201409" "201408" "201504"
[409] "201504" "201409" "201503" "201407"
[413] "201411" "201407" "201502" "201409"
[417] "201406" "201501" "201412" "201407"
[421] "201412" "201407" "201412" "201501"
[425] "201407" "201504" "201411" "201502"
[429] "201405" "201505" "201410" "201409"
[433] "201407" "201409" "201409" "201409"
[437] "201408" "201503" "201407" "201409"
[441] "201409" "201407" "201407" "201504"
[445] "201408" "201412" "201411" "201503"
[449] "201410" "201411" "201505" "201412"
[453] "201502" "201504" "201410" "201405"
[457] "201501" "201411" "201406" "201501"
[461] "201406" "201503" "201411" "201412"
[465] "201411" "201405" "201412" "201410"
[469] "201501" "201501" "201406" "201502"
[473] "201411" "201410" "201408" "201408"
[477] "201407" "201409" "201504" "201407"
[481] "201411" "201406" "201503" "201410"
[485] "201504" "201504" "201407" "201407"
[489] "201412" "201502" "201411" "201502"
[493] "201501" "201410" "201406" "201405"
[497] "201501" "201409" "201408" "201411"
[501] "201505" "201405" "201405" "201410"
[505] "201502" "201405" "201405" "201502"
[509] "201405" "201406" "201505" "201503"
[513] "201407" "201411" "201408" "201503"
[517] "201410" "201505" "201411" "201408"
[521] "201503" "201409" "201503" "201411"
[525] "201408" "201411" "201406" "201406"
[529] "201411" "201410" "201504" "201505"
[533] "201502" "201406" "201502" "201502"
[537] "201505" "201412" "201504" "201503"
[541] "201502" "201410" "201410" "201409"
[545] "201411" "201504" "201405" "201503"
[549] "201409" "201405" "201407" "201410"
[553] "201505" "201409" "201411" "201407"
[557] "201406" "201407" "201504" "201410"
[561] "201410" "201407" "201412" "201406"
[565] "201412" "201407" "201412" "201408"
[569] "201410" "201412" "201407" "201501"
[573] "201504" "201406" "201501" "201412"
[577] "201412" "201412" "201501" "201412"
[581] "201405" "201408" "201407" "201410"
[585] "201411" "201407" "201408" "201407"
[589] "201409" "201503" "201409" "201410"
[593] "201503" "201409" "201502" "201406"
[597] "201407" "201410" "201410" "201408"
[601] "201410" "201502" "201408" "201408"
[605] "201502" "201504" "201405" "201502"
[609] "201411" "201504" "201412" "201411"
[613] "201409" "201406" "201406" "201501"
[617] "201505" "201409" "201501" "201406"
[621] "201504" "201406" "201410" "201502"
[625] "201405" "201408" "201408" "201504"
[629] "201410" "201405" "201405" "201406"
[633] "201412" "201409" "201409" "201406"
[637] "201408" "201501" "201407" "201504"
[641] "201503" "201407" "201503" "201505"
[645] "201409" "201504" "201503" "201407"
[649] "201411" "201504" "201411" "201411"
[653] "201405" "201504" "201407" "201504"
[657] "201411" "201405" "201501" "201409"
[661] "201503" "201411" "201501" "201405"
[665] "201411" "201411" "201405" "201409"
[669] "201407" "201408" "201406" "201409"
[673] "201503" "201407" "201408" "201407"
[677] "201406" "201501" "201409" "201407"
[681] "201408" "201405" "201406" "201410"
[685] "201410" "201501" "201405" "201503"
[689] "201409" "201502" "201408" "201409"
[693] "201407" "201503" "201407" "201406"
[697] "201412" "201409" "201406" "201410"
[701] "201504" "201505" "201411" "201412"
[705] "201408" "201408" "201504" "201410"
[709] "201408" "201407" "201406" "201409"
[713] "201407" "201409" "201503" "201412"
[717] "201407" "201407" "201501" "201504"
[721] "201408" "201407" "201408" "201504"
[725] "201409" "201406" "201406" "201409"
[729] "201411" "201405" "201406" "201409"
[733] "201405" "201407" "201501" "201410"
[737] "201502" "201408" "201411" "201411"
[741] "201407" "201412" "201411" "201409"
[745] "201406" "201501" "201407" "201406"
[749] "201502" "201409" "201504" "201412"
[753] "201407" "201408" "201406" "201409"
[757] "201407" "201501" "201502" "201410"
[761] "201405" "201409" "201503" "201502"
[765] "201412" "201411" "201503" "201407"
[769] "201501" "201406" "201406" "201411"
[773] "201406" "201405" "201502" "201405"
[777] "201406" "201410" "201405" "201406"
[781] "201502" "201406" "201411" "201501"
[785] "201412" "201503" "201504" "201411"
[789] "201407" "201405" "201406" "201406"
[793] "201407" "201410" "201503" "201407"
[797] "201410" "201409" "201410" "201502"
[801] "201408" "201405" "201501" "201501"
[805] "201406" "201504" "201411" "201406"
[809] "201409" "201408" "201501" "201410"
[813] "201505" "201410" "201503" "201412"
[817] "201412" "201505" "201408" "201407"
[821] "201410" "201408" "201409" "201410"
[825] "201502" "201408" "201505" "201409"
[829] "201405" "201503" "201502" "201406"
[833] "201406" "201409" "201411" "201503"
[837] "201407" "201505" "201407" "201501"
[841] "201412" "201411" "201405" "201501"
[845] "201412" "201405" "201411" "201407"
[849] "201503" "201412" "201405" "201410"
[853] "201407" "201407" "201406" "201408"
[857] "201410" "201503" "201406" "201411"
[861] "201406" "201505" "201408" "201503"
[865] "201411" "201503" "201504" "201407"
[869] "201412" "201504" "201504" "201409"
[873] "201503" "201502" "201405" "201406"
[877] "201502" "201411" "201412" "201405"
[881] "201408" "201502" "201410" "201409"
[885] "201406" "201406" "201411" "201407"
[889] "201408" "201407" "201406" "201411"
[893] "201405" "201407" "201408" "201411"
[897] "201405" "201408" "201503" "201504"
[901] "201406" "201408" "201405" "201407"
[905] "201502" "201406" "201503" "201410"
[909] "201410" "201408" "201411" "201410"
[913] "201409" "201409" "201405" "201407"
[917] "201410" "201412" "201408" "201405"
[921] "201406" "201501" "201503" "201406"
[925] "201405" "201412" "201405" "201503"
[929] "201505" "201408" "201409" "201503"
[933] "201504" "201502" "201407" "201503"
[937] "201502" "201406" "201410" "201501"
[941] "201503" "201406" "201410" "201408"
[945] "201408" "201501" "201502" "201504"
[949] "201503" "201410" "201505" "201503"
[953] "201407" "201405" "201504" "201409"
[957] "201503" "201409" "201409" "201412"
[961] "201411" "201504" "201409" "201412"
[965] "201405" "201407" "201406" "201405"
[969] "201406" "201408" "201410" "201505"
[973] "201502" "201407" "201405" "201408"
[977] "201408" "201503" "201501" "201503"
[981] "201503" "201504" "201503" "201409"
[985] "201405" "201405" "201410" "201408"
[989] "201504" "201502" "201410" "201503"
[993] "201501" "201505" "201407" "201408"
[997] "201409" "201408" "201504" "201409"
[ reached getOption("max.print") -- omitted 20613 entries ]
CodePudding user response：
select returns a dataframe whereas format requires a vector. The correct function for you would be pull if you want to use pipes -
library(dplyr)
dataset %>%
pull(date) %>%
format(format = '%Y%m')