C12. Logical Vectors

library(tidyverse)
library(nycflights13)
library(dplyr)

12.2. Comparisions

x <- c(1, 2, 3, 5, 7, 11, 13)
x * 2

df <- tibble(x)
df |> 
  mutate(y = x * 2)

A tibble: 7 x 2
x	y
<dbl>	<dbl>
1	2
2	4
3	6
5	10
7	14
11	22
13	26

flights |> 
  mutate(
    daytime = dep_time > 600 & dep_time < 2000,
    approx_ontime = abs(arr_delay) < 20,
    .keep = "used"
  )

A tibble: 336776 x 4
dep_time	arr_delay	daytime	approx_ontime
<int>	<dbl>	<lgl>	<lgl>
517	11	FALSE	TRUE
533	20	FALSE	FALSE
542	33	FALSE	FALSE
544	-18	FALSE	TRUE
554	-25	FALSE	FALSE
554	12	FALSE	TRUE
555	19	FALSE	TRUE
557	-14	FALSE	TRUE
557	-8	FALSE	TRUE
558	8	FALSE	TRUE
558	-2	FALSE	TRUE
558	-3	FALSE	TRUE
558	7	FALSE	TRUE
558	-14	FALSE	TRUE
559	31	FALSE	FALSE
559	-4	FALSE	TRUE
559	-8	FALSE	TRUE
600	-7	FALSE	TRUE
600	12	FALSE	TRUE
601	-6	TRUE	TRUE
602	-8	TRUE	TRUE
602	16	TRUE	TRUE
606	-12	TRUE	TRUE
606	-8	TRUE	TRUE
607	-17	TRUE	TRUE
608	32	TRUE	FALSE
611	14	TRUE	TRUE
613	4	TRUE	TRUE
615	-21	TRUE	FALSE
615	-9	TRUE	TRUE
...	...	...	...
2123	-24	FALSE	FALSE
2127	-9	FALSE	TRUE
2128	-31	FALSE	FALSE
2129	-2	FALSE	TRUE
2131	-30	FALSE	FALSE
2140	-30	FALSE	FALSE
2142	11	FALSE	TRUE
2145	-25	FALSE	FALSE
2147	3	FALSE	TRUE
2149	-23	FALSE	FALSE
2150	-16	FALSE	TRUE
2159	194	FALSE	FALSE
2203	8	FALSE	TRUE
2207	7	FALSE	TRUE
2211	57	FALSE	FALSE
2231	-21	FALSE	FALSE
2233	42	FALSE	FALSE
2235	130	FALSE	FALSE
2237	-8	FALSE	TRUE
2240	-17	FALSE	TRUE
2240	-20	FALSE	FALSE
2241	-16	FALSE	TRUE
2307	1	FALSE	TRUE
2349	-25	FALSE	FALSE
NA	NA	NA	NA
NA	NA	NA	NA
NA	NA	NA	NA
NA	NA	NA	NA
NA	NA	NA	NA
NA	NA	NA	NA

flights |> 
  mutate(
    daytime = dep_time > 600 & dep_time < 2000,
    approx_ontime = abs(arr_delay) < 20,
  ) |> 
  filter(daytime & approx_ontime)

A tibble: 172286 x 21
year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	...	tailnum	origin	dest	air_time	distance	hour	minute	time_hour	daytime	approx_ontime
<int>	<int>	<int>	<int>	<int>	<dbl>	<int>	<int>	<dbl>	<chr>	...	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dttm>	<lgl>	<lgl>
2013	1	1	601	600	1	844	850	-6	B6	...	N644JB	EWR	PBI	147	1023	6	0	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	602	610	-8	812	820	-8	DL	...	N971DL	LGA	MSP	170	1020	6	10	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	602	605	-3	821	805	16	MQ	...	N730MQ	LGA	DTW	105	502	6	5	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	606	610	-4	858	910	-12	AA	...	N633AA	EWR	MIA	152	1085	6	10	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	606	610	-4	837	845	-8	DL	...	N3739P	JFK	ATL	128	760	6	10	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	607	607	0	858	915	-17	UA	...	N53442	EWR	MIA	157	1085	6	7	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	611	600	11	945	931	14	UA	...	N532UA	JFK	SFO	366	2586	6	0	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	613	610	3	925	921	4	B6	...	N635JB	JFK	RSW	175	1074	6	10	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	615	615	0	833	842	-9	DL	...	N326NB	EWR	ATL	120	746	6	15	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	622	630	-8	1017	1014	3	US	...	N807AW	EWR	PHX	342	2133	6	30	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	623	610	13	920	915	5	AA	...	N3EMAA	LGA	MIA	153	1096	6	10	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	623	627	-4	933	932	1	UA	...	N459UA	LGA	IAH	229	1416	6	27	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	624	630	-6	840	830	10	MQ	...	N518MQ	LGA	MSP	166	1020	6	30	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	627	630	-3	1018	1018	0	US	...	N535UW	JFK	PHX	330	2153	6	30	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	628	630	-2	1137	1140	-3	AA	...	N3BAAA	JFK	SJU	192	1598	6	30	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	629	630	-1	824	810	14	AA	...	N3CYAA	LGA	ORD	140	733	6	30	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	629	630	-1	721	740	-19	WN	...	N273WN	LGA	BWI	40	185	6	30	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	629	630	-1	824	833	-9	US	...	N426US	EWR	CLT	91	529	6	30	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	632	608	24	740	728	12	EV	...	N13553	EWR	IAD	52	212	6	8	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	637	645	-8	930	935	-5	B6	...	N709JB	LGA	MCO	144	950	6	45	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	639	640	-1	739	749	-10	B6	...	N805JB	JFK	BOS	41	187	6	40	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	643	646	-3	922	940	-18	UA	...	N497UA	EWR	PBI	146	1023	6	46	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	643	645	-2	837	848	-11	US	...	N178US	EWR	CLT	91	529	6	45	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	644	636	8	931	940	-9	UA	...	N75435	EWR	FLL	151	1065	6	36	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	645	647	-2	815	810	5	B6	...	N796JB	JFK	BUF	63	301	6	47	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	646	645	1	910	916	-6	UA	...	N569UA	LGA	DEN	243	1620	6	45	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	646	645	1	1023	1030	-7	UA	...	N38727	EWR	SNA	380	2434	6	45	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	651	655	-4	936	942	-6	B6	...	N558JB	JFK	LAS	323	2248	6	55	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	652	655	-3	932	921	11	B6	...	N178JB	JFK	MSY	191	1182	6	55	2013-01-01 06:00:00	TRUE	TRUE
2013	1	1	655	655	0	1021	1030	-9	DL	...	N3763D	JFK	SLC	294	1990	6	55	2013-01-01 06:00:00	TRUE	TRUE
...	...	...	...	...	...	...	...	...	...		...	...	...	...	...	...	...	...	...	...
2013	9	30	1908	1911	-3	2107	2110	-3	EV	...	N16987	EWR	CLT	74	529	19	11	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1909	1905	4	2055	2057	-2	9E	...	N937XJ	JFK	ORD	108	740	19	5	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1909	1901	8	2212	2211	1	UA	...	N513UA	EWR	SFO	307	2565	19	1	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1915	1900	15	2138	2151	-13	B6	...	N563JB	JFK	MCO	120	944	19	0	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1915	1900	15	2025	2015	10	WN	...	N719SW	LGA	MKE	104	738	19	0	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1917	1920	-3	2053	2055	-2	9E	...	N600LR	LGA	BUF	50	292	19	20	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1925	1930	-5	2033	2049	-16	EV	...	N825AS	LGA	IAD	43	229	19	30	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1926	1930	-4	2146	2201	-15	DL	...	N303DQ	EWR	ATL	95	746	19	30	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1927	1930	-3	2054	2056	-2	EV	...	N13956	EWR	MKE	110	725	19	30	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1928	1932	-4	2137	2143	-6	EV	...	N18556	EWR	TYS	82	631	19	32	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1929	1934	-5	2146	2136	10	EV	...	N18557	EWR	CHS	92	628	19	34	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1930	1930	0	2217	2231	-14	DL	...	N365NB	LGA	TPA	133	1010	19	30	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1930	1940	-10	2120	2125	-5	MQ	...	N839MQ	JFK	RDU	70	427	19	40	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1931	1905	26	2049	2033	16	EV	...	N748EV	LGA	ROC	41	254	19	5	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1932	1935	-3	2221	2232	-11	DL	...	N927DA	LGA	MCO	132	950	19	35	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1934	1930	4	2236	2250	-14	AA	...	N3EXAA	JFK	SEA	340	2422	19	30	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1934	1935	-1	2238	2250	-12	AA	...	N3JFAA	LGA	MIA	150	1096	19	35	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1935	1935	0	2211	2229	-18	B6	...	N589JB	JFK	TPA	132	1005	19	35	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1935	1930	5	2230	2233	-3	UA	...	N38446	EWR	TPA	132	997	19	30	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1936	1910	26	2204	2203	1	DL	...	N905DL	JFK	MCO	126	944	19	10	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1939	1940	-1	2041	2100	-19	EV	...	N829AS	JFK	IAD	42	228	19	40	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1939	1950	-11	2129	2140	-11	MQ	...	N735MQ	LGA	CMH	73	479	19	50	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1940	1900	40	2228	2232	-4	DL	...	N723TW	JFK	SFO	323	2586	19	0	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1944	1950	-6	2208	2215	-7	MQ	...	N507MQ	LGA	ATL	100	762	19	50	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1953	2000	-7	2119	2132	-13	UA	...	N853UA	LGA	ORD	107	733	20	0	2013-09-30 20:00:00	TRUE	TRUE
2013	9	30	1955	2000	-5	2219	2230	-11	DL	...	N992DL	LGA	ATL	99	762	20	0	2013-09-30 20:00:00	TRUE	TRUE
2013	9	30	1955	1935	20	2141	2159	-18	9E	...	N928XJ	JFK	CVG	86	589	19	35	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1955	1942	13	2300	2250	10	B6	...	N623JB	LGA	FLL	141	1076	19	42	2013-09-30 19:00:00	TRUE	TRUE
2013	9	30	1955	2000	-5	2112	2114	-2	US	...	N957UW	LGA	BOS	35	184	20	0	2013-09-30 20:00:00	TRUE	TRUE
2013	9	30	1958	2005	-7	2119	2130	-11	MQ	...	N511MQ	EWR	ORD	102	719	20	5	2013-09-30 20:00:00	TRUE	TRUE

Floating point comparision

x <- c(1 / 49 * 49, sqrt(2) ^ 2)
x

x == c(1, 2)

<ol class=list-inline><li>FALSE</li><li>FALSE</li></ol>

print(x, digits = 16)

[1] 0.9999999999999999 2.0000000000000004

near(x, c(1, 2))

Missing values

NA > 5
10 == NA

NA == NA

<NA>

# so this code doesn't work
flights |> 
  filter(dep_time == NA)

A tibble: 0 x 19
year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute	time_hour
<int>	<int>	<int>	<int>	<int>	<dbl>	<int>	<int>	<dbl>	<chr>	<int>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dttm>

`is.na()`

flights |> 
  filter(is.na(dep_time))

A tibble: 8255 x 19
year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute	time_hour
<int>	<int>	<int>	<int>	<int>	<dbl>	<int>	<int>	<dbl>	<chr>	<int>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dttm>
2013	1	1	NA	1630	NA	NA	1815	NA	EV	4308	N18120	EWR	RDU	NA	416	16	30	2013-01-01 16:00:00
2013	1	1	NA	1935	NA	NA	2240	NA	AA	791	N3EHAA	LGA	DFW	NA	1389	19	35	2013-01-01 19:00:00
2013	1	1	NA	1500	NA	NA	1825	NA	AA	1925	N3EVAA	LGA	MIA	NA	1096	15	0	2013-01-01 15:00:00
2013	1	1	NA	600	NA	NA	901	NA	B6	125	N618JB	JFK	FLL	NA	1069	6	0	2013-01-01 06:00:00
2013	1	2	NA	1540	NA	NA	1747	NA	EV	4352	N10575	EWR	CVG	NA	569	15	40	2013-01-02 15:00:00
2013	1	2	NA	1620	NA	NA	1746	NA	EV	4406	N13949	EWR	PIT	NA	319	16	20	2013-01-02 16:00:00
2013	1	2	NA	1355	NA	NA	1459	NA	EV	4434	N10575	EWR	MHT	NA	209	13	55	2013-01-02 13:00:00
2013	1	2	NA	1420	NA	NA	1644	NA	EV	4935	N759EV	EWR	ATL	NA	746	14	20	2013-01-02 14:00:00
2013	1	2	NA	1321	NA	NA	1536	NA	EV	3849	N13550	EWR	IND	NA	645	13	21	2013-01-02 13:00:00
2013	1	2	NA	1545	NA	NA	1910	NA	AA	133	NA	JFK	LAX	NA	2475	15	45	2013-01-02 15:00:00
2013	1	2	NA	1330	NA	NA	1640	NA	AA	753	N3FBAA	LGA	DFW	NA	1389	13	30	2013-01-02 13:00:00
2013	1	2	NA	1601	NA	NA	1735	NA	UA	623	NA	EWR	ORD	NA	719	16	1	2013-01-02 16:00:00
2013	1	3	NA	645	NA	NA	757	NA	EV	4241	N14972	EWR	DCA	NA	199	6	45	2013-01-03 06:00:00
2013	1	3	NA	1030	NA	NA	1210	NA	AA	321	N487AA	LGA	ORD	NA	733	10	30	2013-01-03 10:00:00
2013	1	3	NA	1125	NA	NA	1305	NA	AA	327	N3AMAA	LGA	ORD	NA	733	11	25	2013-01-03 11:00:00
2013	1	3	NA	835	NA	NA	1150	NA	AA	717	N3GXAA	LGA	DFW	NA	1389	8	35	2013-01-03 08:00:00
2013	1	3	NA	920	NA	NA	1245	NA	AA	721	N201AA	LGA	DFW	NA	1389	9	20	2013-01-03 09:00:00
2013	1	3	NA	1020	NA	NA	1330	NA	AA	731	N3FVAA	LGA	DFW	NA	1389	10	20	2013-01-03 10:00:00
2013	1	3	NA	1220	NA	NA	1415	NA	AA	1757	N573AA	LGA	STL	NA	888	12	20	2013-01-03 12:00:00
2013	1	3	NA	630	NA	NA	830	NA	MQ	4599	N500MQ	LGA	MSP	NA	1020	6	30	2013-01-03 06:00:00
2013	1	3	NA	857	NA	NA	1209	NA	UA	714	NA	EWR	MIA	NA	1085	8	57	2013-01-03 08:00:00
2013	1	3	NA	645	NA	NA	952	NA	UA	719	NA	EWR	DFW	NA	1372	6	45	2013-01-03 06:00:00
2013	1	4	NA	845	NA	NA	1015	NA	9E	3405	NA	JFK	DCA	NA	213	8	45	2013-01-04 08:00:00
2013	1	4	NA	1830	NA	NA	2044	NA	9E	3716	NA	EWR	DTW	NA	488	18	30	2013-01-04 18:00:00
2013	1	4	NA	920	NA	NA	1245	NA	AA	721	N541AA	LGA	DFW	NA	1389	9	20	2013-01-04 09:00:00
2013	1	4	NA	1245	NA	NA	1550	NA	AA	745	N3BGAA	LGA	DFW	NA	1389	12	45	2013-01-04 12:00:00
2013	1	4	NA	1430	NA	NA	1735	NA	AA	883	N200AA	EWR	DFW	NA	1372	14	30	2013-01-04 14:00:00
2013	1	4	NA	1530	NA	NA	1725	NA	AA	2223	N569AA	LGA	STL	NA	888	15	30	2013-01-04 15:00:00
2013	1	5	NA	1400	NA	NA	1518	NA	EV	5712	N827AS	JFK	IAD	NA	228	14	0	2013-01-05 14:00:00
2013	1	5	NA	840	NA	NA	1001	NA	9E	3422	NA	JFK	BOS	NA	187	8	40	2013-01-05 08:00:00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2013	9	24	NA	1625	NA	NA	1750	NA	MQ	3622	N524MQ	LGA	BNA	NA	764	16	25	2013-09-24 16:00:00
2013	9	25	NA	1259	NA	NA	1507	NA	EV	5207	N615QX	LGA	CLT	NA	544	12	59	2013-09-25 12:00:00
2013	9	25	NA	845	NA	NA	1018	NA	EV	5286	N615QX	LGA	BTV	NA	258	8	45	2013-09-25 08:00:00
2013	9	25	NA	1755	NA	NA	1932	NA	EV	5287	N722EV	LGA	MSN	NA	812	17	55	2013-09-25 17:00:00
2013	9	25	NA	600	NA	NA	716	NA	EV	5716	N877AS	JFK	IAD	NA	228	6	0	2013-09-25 06:00:00
2013	9	25	NA	836	NA	NA	944	NA	B6	2280	N258JB	EWR	BOS	NA	200	8	36	2013-09-25 08:00:00
2013	9	25	NA	1300	NA	NA	1409	NA	US	2148	NA	LGA	BOS	NA	184	13	0	2013-09-25 13:00:00
2013	9	25	NA	1900	NA	NA	2014	NA	US	2160	NA	LGA	BOS	NA	184	19	0	2013-09-25 19:00:00
2013	9	25	NA	1300	NA	NA	1450	NA	MQ	3388	N817MQ	LGA	CMH	NA	479	13	0	2013-09-25 13:00:00
2013	9	25	NA	1655	NA	NA	1840	NA	MQ	3411	N735MQ	LGA	RDU	NA	431	16	55	2013-09-25 16:00:00
2013	9	25	NA	1559	NA	NA	1719	NA	MQ	3748	N530MQ	EWR	ORD	NA	719	15	59	2013-09-25 15:00:00
2013	9	26	NA	915	NA	NA	1141	NA	EV	5109	N748EV	LGA	CHS	NA	641	9	15	2013-09-26 09:00:00
2013	9	26	NA	1400	NA	NA	1512	NA	US	2183	NA	LGA	DCA	NA	214	14	0	2013-09-26 14:00:00
2013	9	26	NA	1240	NA	NA	1525	NA	WN	4720	N691WN	EWR	HOU	NA	1411	12	40	2013-09-26 12:00:00
2013	9	27	NA	600	NA	NA	730	NA	AA	301	N584AA	LGA	ORD	NA	733	6	0	2013-09-27 06:00:00
2013	9	27	NA	2100	NA	NA	2211	NA	US	2164	NA	LGA	BOS	NA	184	21	0	2013-09-27 21:00:00
2013	9	27	NA	1329	NA	NA	1444	NA	MQ	3760	N505MQ	EWR	ORD	NA	719	13	29	2013-09-27 13:00:00
2013	9	27	NA	1600	NA	NA	1739	NA	UA	269	NA	LGA	ORD	NA	733	16	0	2013-09-27 16:00:00
2013	9	28	NA	1803	NA	NA	1927	NA	EV	5563	N724EV	LGA	BTV	NA	258	18	3	2013-09-28 18:00:00
2013	9	28	NA	910	NA	NA	1220	NA	AA	1	N320AA	JFK	LAX	NA	2475	9	10	2013-09-28 09:00:00
2013	9	28	NA	1635	NA	NA	1827	NA	US	581	NA	EWR	CLT	NA	529	16	35	2013-09-28 16:00:00
2013	9	29	NA	2054	NA	NA	2302	NA	EV	4536	N13988	EWR	CVG	NA	569	20	54	2013-09-29 20:00:00
2013	9	29	NA	1830	NA	NA	2010	NA	MQ	3134	N508MQ	EWR	ORD	NA	719	18	30	2013-09-29 18:00:00
2013	9	29	NA	700	NA	NA	833	NA	UA	331	NA	LGA	ORD	NA	733	7	0	2013-09-29 07:00:00
2013	9	30	NA	1842	NA	NA	2019	NA	EV	5274	N740EV	LGA	BNA	NA	764	18	42	2013-09-30 18:00:00
2013	9	30	NA	1455	NA	NA	1634	NA	9E	3393	NA	JFK	DCA	NA	213	14	55	2013-09-30 14:00:00
2013	9	30	NA	2200	NA	NA	2312	NA	9E	3525	NA	LGA	SYR	NA	198	22	0	2013-09-30 22:00:00
2013	9	30	NA	1210	NA	NA	1330	NA	MQ	3461	N535MQ	LGA	BNA	NA	764	12	10	2013-09-30 12:00:00
2013	9	30	NA	1159	NA	NA	1344	NA	MQ	3572	N511MQ	LGA	CLE	NA	419	11	59	2013-09-30 11:00:00
2013	9	30	NA	840	NA	NA	1020	NA	MQ	3531	N839MQ	LGA	RDU	NA	431	8	40	2013-09-30 08:00:00

flights |> 
  filter(month == 1, day == 1) |> 
  arrange(desc(is.na(dep_time)), dep_time)

A tibble: 842 x 19
year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute	time_hour
<int>	<int>	<int>	<int>	<int>	<dbl>	<int>	<int>	<dbl>	<chr>	<int>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dttm>
2013	1	1	NA	1630	NA	NA	1815	NA	EV	4308	N18120	EWR	RDU	NA	416	16	30	2013-01-01 16:00:00
2013	1	1	NA	1935	NA	NA	2240	NA	AA	791	N3EHAA	LGA	DFW	NA	1389	19	35	2013-01-01 19:00:00
2013	1	1	NA	1500	NA	NA	1825	NA	AA	1925	N3EVAA	LGA	MIA	NA	1096	15	0	2013-01-01 15:00:00
2013	1	1	NA	600	NA	NA	901	NA	B6	125	N618JB	JFK	FLL	NA	1069	6	0	2013-01-01 06:00:00
2013	1	1	517	515	2	830	819	11	UA	1545	N14228	EWR	IAH	227	1400	5	15	2013-01-01 05:00:00
2013	1	1	533	529	4	850	830	20	UA	1714	N24211	LGA	IAH	227	1416	5	29	2013-01-01 05:00:00
2013	1	1	542	540	2	923	850	33	AA	1141	N619AA	JFK	MIA	160	1089	5	40	2013-01-01 05:00:00
2013	1	1	544	545	-1	1004	1022	-18	B6	725	N804JB	JFK	BQN	183	1576	5	45	2013-01-01 05:00:00
2013	1	1	554	600	-6	812	837	-25	DL	461	N668DN	LGA	ATL	116	762	6	0	2013-01-01 06:00:00
2013	1	1	554	558	-4	740	728	12	UA	1696	N39463	EWR	ORD	150	719	5	58	2013-01-01 05:00:00
2013	1	1	555	600	-5	913	854	19	B6	507	N516JB	EWR	FLL	158	1065	6	0	2013-01-01 06:00:00
2013	1	1	557	600	-3	709	723	-14	EV	5708	N829AS	LGA	IAD	53	229	6	0	2013-01-01 06:00:00
2013	1	1	557	600	-3	838	846	-8	B6	79	N593JB	JFK	MCO	140	944	6	0	2013-01-01 06:00:00
2013	1	1	558	600	-2	753	745	8	AA	301	N3ALAA	LGA	ORD	138	733	6	0	2013-01-01 06:00:00
2013	1	1	558	600	-2	849	851	-2	B6	49	N793JB	JFK	PBI	149	1028	6	0	2013-01-01 06:00:00
2013	1	1	558	600	-2	853	856	-3	B6	71	N657JB	JFK	TPA	158	1005	6	0	2013-01-01 06:00:00
2013	1	1	558	600	-2	924	917	7	UA	194	N29129	JFK	LAX	345	2475	6	0	2013-01-01 06:00:00
2013	1	1	558	600	-2	923	937	-14	UA	1124	N53441	EWR	SFO	361	2565	6	0	2013-01-01 06:00:00
2013	1	1	559	600	-1	941	910	31	AA	707	N3DUAA	LGA	DFW	257	1389	6	0	2013-01-01 06:00:00
2013	1	1	559	559	0	702	706	-4	B6	1806	N708JB	JFK	BOS	44	187	5	59	2013-01-01 05:00:00
2013	1	1	559	600	-1	854	902	-8	UA	1187	N76515	EWR	LAS	337	2227	6	0	2013-01-01 06:00:00
2013	1	1	600	600	0	851	858	-7	B6	371	N595JB	LGA	FLL	152	1076	6	0	2013-01-01 06:00:00
2013	1	1	600	600	0	837	825	12	MQ	4650	N542MQ	LGA	ATL	134	762	6	0	2013-01-01 06:00:00
2013	1	1	601	600	1	844	850	-6	B6	343	N644JB	EWR	PBI	147	1023	6	0	2013-01-01 06:00:00
2013	1	1	602	610	-8	812	820	-8	DL	1919	N971DL	LGA	MSP	170	1020	6	10	2013-01-01 06:00:00
2013	1	1	602	605	-3	821	805	16	MQ	4401	N730MQ	LGA	DTW	105	502	6	5	2013-01-01 06:00:00
2013	1	1	606	610	-4	858	910	-12	AA	1895	N633AA	EWR	MIA	152	1085	6	10	2013-01-01 06:00:00
2013	1	1	606	610	-4	837	845	-8	DL	1743	N3739P	JFK	ATL	128	760	6	10	2013-01-01 06:00:00
2013	1	1	607	607	0	858	915	-17	UA	1077	N53442	EWR	MIA	157	1085	6	7	2013-01-01 06:00:00
2013	1	1	608	600	8	807	735	32	MQ	3768	N9EAMQ	EWR	ORD	139	719	6	0	2013-01-01 06:00:00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2013	1	1	2128	2125	3	2243	2240	3	MQ	4449	N810MQ	JFK	DCA	54	213	21	25	2013-01-01 21:00:00
2013	1	1	2129	2120	9	2342	2351	-9	B6	97	N625JB	JFK	DEN	223	1626	21	20	2013-01-01 21:00:00
2013	1	1	2134	2045	49	20	2352	28	UA	1106	N27733	EWR	FLL	152	1065	20	45	2013-01-01 20:00:00
2013	1	1	2136	2145	-9	25	39	-14	B6	515	N198JB	EWR	FLL	154	1065	21	45	2013-01-01 21:00:00
2013	1	1	2140	2135	5	210	224	-14	B6	701	N284JB	JFK	SJU	189	1598	21	35	2013-01-01 21:00:00
2013	1	1	2157	2155	2	43	41	2	B6	43	N537JB	JFK	MCO	140	944	21	55	2013-01-01 21:00:00
2013	1	1	2158	2200	-2	2254	2307	-13	EV	4103	N14998	EWR	BWI	36	169	22	0	2013-01-01 22:00:00
2013	1	1	2205	1720	285	46	2040	246	AA	1999	N5DNAA	EWR	MIA	146	1085	17	20	2013-01-01 17:00:00
2013	1	1	2209	2145	24	58	37	21	B6	35	N608JB	JFK	PBI	143	1028	21	45	2013-01-01 21:00:00
2013	1	1	2209	2155	14	2400	2337	23	B6	1109	N216JB	JFK	RDU	86	427	21	55	2013-01-01 21:00:00
2013	1	1	2211	2145	26	2339	2311	28	B6	104	N228JB	JFK	BUF	64	301	21	45	2013-01-01 21:00:00
2013	1	1	2217	2229	-12	249	315	-26	B6	713	N547JB	JFK	SJU	191	1598	22	29	2013-01-01 22:00:00
2013	1	1	2217	2130	47	140	27	73	B6	21	N516JB	JFK	TPA	163	1005	21	30	2013-01-01 21:00:00
2013	1	1	2221	2000	141	2331	2124	127	EV	4462	N13566	EWR	BUF	56	282	20	0	2013-01-01 20:00:00
2013	1	1	2224	2200	24	2324	2316	8	EV	4206	N16561	EWR	PWM	47	284	22	0	2013-01-01 22:00:00
2013	1	1	2229	2159	30	149	100	49	B6	11	N531JB	JFK	FLL	153	1069	21	59	2013-01-01 21:00:00
2013	1	1	2240	2245	-5	2340	2356	-16	B6	608	N279JB	JFK	PWM	44	273	22	45	2013-01-01 22:00:00
2013	1	1	2250	2255	-5	2352	2359	-7	B6	1018	N521JB	JFK	BOS	37	187	22	55	2013-01-01 22:00:00
2013	1	1	2302	2200	62	2342	2253	49	EV	4276	N13903	EWR	BDL	24	116	22	0	2013-01-01 22:00:00
2013	1	1	2306	2245	21	28	5	23	B6	30	N281JB	JFK	ROC	59	264	22	45	2013-01-01 22:00:00
2013	1	1	2307	2245	22	32	2357	35	B6	128	N178JB	JFK	BTV	59	266	22	45	2013-01-01 22:00:00
2013	1	1	2310	2255	15	24	15	9	B6	112	N646JB	JFK	BUF	57	301	22	55	2013-01-01 22:00:00
2013	1	1	2312	2000	192	21	2110	191	EV	4312	N13958	EWR	DCA	44	199	20	0	2013-01-01 20:00:00
2013	1	1	2323	2200	83	22	2313	69	EV	4257	N13538	EWR	BTV	44	266	22	0	2013-01-01 22:00:00
2013	1	1	2326	2130	116	131	18	73	B6	199	N594JB	JFK	LAS	290	2248	21	30	2013-01-01 21:00:00
2013	1	1	2327	2250	37	32	2359	33	B6	22	N639JB	JFK	SYR	45	209	22	50	2013-01-01 22:00:00
2013	1	1	2343	1724	379	314	1938	456	EV	4321	N21197	EWR	MCI	222	1092	17	24	2013-01-01 17:00:00
2013	1	1	2353	2359	-6	425	445	-20	B6	739	N591JB	JFK	PSE	195	1617	23	59	2013-01-01 23:00:00
2013	1	1	2353	2359	-6	418	442	-24	B6	707	N794JB	JFK	SJU	185	1598	23	59	2013-01-01 23:00:00
2013	1	1	2356	2359	-3	425	437	-12	B6	727	N588JB	JFK	BQN	186	1576	23	59	2013-01-01 23:00:00

Exercises:

Use mutate(), is.na(), and count() together to describe how the missing values in dep_time, sched_dep_time and dep_delay are connected.

12.3 Boolean Algebra

!, &, xor, |

df <- tibble(x = c(TRUE, FALSE, NA))

df |> 
  mutate(
    and = x & NA,
    or = x | NA
  )

A tibble: 3 x 3
x	and	or
<lgl>	<lgl>	<lgl>
TRUE	NA	TRUE
FALSE	FALSE	NA
NA	NA	NA

flights |> 
  filter(month %in% c(11, 12))

A tibble: 55403 x 19
year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute	time_hour
<int>	<int>	<int>	<int>	<int>	<dbl>	<int>	<int>	<dbl>	<chr>	<int>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dttm>
2013	11	1	5	2359	6	352	345	7	B6	745	N568JB	JFK	PSE	205	1617	23	59	2013-11-01 23:00:00
2013	11	1	35	2250	105	123	2356	87	B6	1816	N353JB	JFK	SYR	36	209	22	50	2013-11-01 22:00:00
2013	11	1	455	500	-5	641	651	-10	US	1895	N192UW	EWR	CLT	88	529	5	0	2013-11-01 05:00:00
2013	11	1	539	545	-6	856	827	29	UA	1714	N38727	LGA	IAH	229	1416	5	45	2013-11-01 05:00:00
2013	11	1	542	545	-3	831	855	-24	AA	2243	N5CLAA	JFK	MIA	147	1089	5	45	2013-11-01 05:00:00
2013	11	1	549	600	-11	912	923	-11	UA	303	N595UA	JFK	SFO	359	2586	6	0	2013-11-01 06:00:00
2013	11	1	550	600	-10	705	659	6	US	2167	N748UW	LGA	DCA	57	214	6	0	2013-11-01 06:00:00
2013	11	1	554	600	-6	659	701	-2	US	2134	N742PS	LGA	BOS	40	184	6	0	2013-11-01 06:00:00
2013	11	1	554	600	-6	826	827	-1	DL	563	N912DE	LGA	ATL	126	762	6	0	2013-11-01 06:00:00
2013	11	1	554	600	-6	749	751	-2	DL	731	N315NB	LGA	DTW	93	502	6	0	2013-11-01 06:00:00
2013	11	1	555	600	-5	847	854	-7	B6	605	N640JB	EWR	FLL	149	1065	6	0	2013-11-01 06:00:00
2013	11	1	555	600	-5	839	846	-7	B6	583	N661JB	JFK	MCO	136	944	6	0	2013-11-01 06:00:00
2013	11	1	555	600	-5	929	943	-14	B6	1403	N746JB	JFK	SJU	196	1598	6	0	2013-11-01 06:00:00
2013	11	1	556	600	-4	834	851	-17	UA	407	N834UA	EWR	TPA	142	997	6	0	2013-11-01 06:00:00
2013	11	1	558	600	-2	727	730	-3	UA	279	N459UA	EWR	ORD	118	719	6	0	2013-11-01 06:00:00
2013	11	1	558	600	-2	650	658	-8	US	1909	N950UW	LGA	PHL	38	96	6	0	2013-11-01 06:00:00
2013	11	1	558	600	-2	914	905	9	AA	1175	N3GHAA	LGA	MIA	155	1096	6	0	2013-11-01 06:00:00
2013	11	1	558	600	-2	720	715	5	WN	464	N390SW	EWR	MDW	117	711	6	0	2013-11-01 06:00:00
2013	11	1	559	600	-1	756	730	26	AA	301	N4YHAA	LGA	ORD	119	733	6	0	2013-11-01 06:00:00
2013	11	1	600	600	0	709	716	-7	EV	5716	N820AS	JFK	IAD	49	228	6	0	2013-11-01 06:00:00
2013	11	1	600	600	0	725	721	4	UA	1198	N23707	LGA	ORD	121	733	6	0	2013-11-01 06:00:00
2013	11	1	601	600	1	853	856	-3	B6	371	N570JB	LGA	FLL	150	1076	6	0	2013-11-01 06:00:00
2013	11	1	601	610	-9	803	813	-10	DL	1919	N922DL	LGA	MSP	153	1020	6	10	2013-11-01 06:00:00
2013	11	1	602	600	2	843	815	28	FL	345	N353AT	LGA	ATL	131	762	6	0	2013-11-01 06:00:00
2013	11	1	603	600	3	717	711	6	EV	4533	N11109	EWR	BUF	53	282	6	0	2013-11-01 06:00:00
2013	11	1	604	610	-6	855	855	0	AA	1103	N3FTAA	LGA	DFW	205	1389	6	10	2013-11-01 06:00:00
2013	11	1	606	615	-9	746	750	-4	MQ	3525	N834MQ	LGA	RDU	74	431	6	15	2013-11-01 06:00:00
2013	11	1	606	615	-9	807	817	-10	US	1899	N199UW	JFK	CLT	93	541	6	15	2013-11-01 06:00:00
2013	11	1	606	610	-4	752	745	7	WN	2609	N440LV	LGA	STL	142	888	6	10	2013-11-01 06:00:00
2013	11	1	607	611	-4	857	912	-15	B6	601	N597JB	JFK	FLL	149	1069	6	11	2013-11-01 06:00:00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2013	12	31	2155	2039	76	253	2355	NA	B6	1205	N627JB	JFK	PDX	NA	2454	20	39	2013-12-31 20:00:00
2013	12	31	2155	2150	5	110	51	19	B6	1901	N729JB	JFK	FLL	164	1069	21	50	2013-12-31 21:00:00
2013	12	31	2159	2155	4	55	46	9	B6	2053	N593JB	JFK	PBI	155	1028	21	55	2013-12-31 21:00:00
2013	12	31	2206	2110	56	44	2339	65	B6	775	N184JB	JFK	MSY	195	1182	21	10	2013-12-31 21:00:00
2013	12	31	2211	2159	12	100	45	15	B6	1183	N715JB	JFK	MCO	148	944	21	59	2013-12-31 21:00:00
2013	12	31	2218	2219	-1	315	304	11	B6	1203	N625JB	JFK	SJU	202	1598	22	19	2013-12-31 22:00:00
2013	12	31	2235	2245	-10	2351	2355	-4	B6	234	N355JB	JFK	BTV	49	266	22	45	2013-12-31 22:00:00
2013	12	31	2245	2250	-5	2359	2356	3	B6	1816	N318JB	JFK	SYR	51	209	22	50	2013-12-31 22:00:00
2013	12	31	2310	2255	15	7	2356	11	B6	718	N279JB	JFK	BOS	40	187	22	55	2013-12-31 22:00:00
2013	12	31	2321	2250	31	46	8	38	B6	2002	N179JB	JFK	BUF	66	301	22	50	2013-12-31 22:00:00
2013	12	31	2328	2330	-2	412	409	3	B6	1389	N651JB	EWR	SJU	198	1608	23	30	2013-12-31 23:00:00
2013	12	31	2332	2245	47	58	3	55	B6	486	N334JB	JFK	ROC	60	264	22	45	2013-12-31 22:00:00
2013	12	31	2355	2359	-4	430	440	-10	B6	1503	N509JB	JFK	SJU	195	1598	23	59	2013-12-31 23:00:00
2013	12	31	2356	2359	-3	436	445	-9	B6	745	N665JB	JFK	PSE	200	1617	23	59	2013-12-31 23:00:00
2013	12	31	NA	1520	NA	NA	1705	NA	AA	341	N568AA	LGA	ORD	NA	733	15	20	2013-12-31 15:00:00
2013	12	31	NA	2025	NA	NA	2205	NA	AA	371	N482AA	LGA	ORD	NA	733	20	25	2013-12-31 20:00:00
2013	12	31	NA	1932	NA	NA	2305	NA	B6	161	N516JB	JFK	SMF	NA	2521	19	32	2013-12-31 19:00:00
2013	12	31	NA	1505	NA	NA	1725	NA	EV	4181	N24103	EWR	MCI	NA	1092	15	5	2013-12-31 15:00:00
2013	12	31	NA	1000	NA	NA	1252	NA	UA	1124	NA	EWR	EGE	NA	1725	10	0	2013-12-31 10:00:00
2013	12	31	NA	840	NA	NA	1205	NA	UA	1151	NA	EWR	SEA	NA	2402	8	40	2013-12-31 08:00:00
2013	12	31	NA	754	NA	NA	1118	NA	UA	1455	NA	EWR	LAX	NA	2454	7	54	2013-12-31 07:00:00
2013	12	31	NA	2000	NA	NA	2146	NA	UA	1482	NA	EWR	ORD	NA	719	20	0	2013-12-31 20:00:00
2013	12	31	NA	1500	NA	NA	1817	NA	UA	1483	NA	EWR	AUS	NA	1504	15	0	2013-12-31 15:00:00
2013	12	31	NA	1430	NA	NA	1750	NA	UA	1493	NA	EWR	LAX	NA	2454	14	30	2013-12-31 14:00:00
2013	12	31	NA	855	NA	NA	1142	NA	UA	1506	NA	EWR	JAC	NA	1874	8	55	2013-12-31 08:00:00
2013	12	31	NA	705	NA	NA	931	NA	UA	1729	NA	EWR	DEN	NA	1605	7	5	2013-12-31 07:00:00
2013	12	31	NA	825	NA	NA	1029	NA	US	1831	NA	JFK	CLT	NA	541	8	25	2013-12-31 08:00:00
2013	12	31	NA	1615	NA	NA	1800	NA	MQ	3301	N844MQ	LGA	RDU	NA	431	16	15	2013-12-31 16:00:00
2013	12	31	NA	600	NA	NA	735	NA	UA	219	NA	EWR	ORD	NA	719	6	0	2013-12-31 06:00:00
2013	12	31	NA	830	NA	NA	1154	NA	UA	443	NA	JFK	LAX	NA	2475	8	30	2013-12-31 08:00:00

Exercises

Find all flights where arr_delay is missing but dep_delay is not. Find all flights where neither arr_time nor sched_arr_time are missing, but arr_delay is.
How many flights have a missing dep_time? What other variables are missing in these rows? What might these rows represent?
Assuming that a missing dep_time implies that a flight is cancelled, look at the number of cancelled flights per day. Is there a pattern? Is there a connection between the proportion of cancelled flights and the average delay of non-cancelled flights?

12.4 Summarizes

Logical summarizes

Nummeric summaries of logical vectors

Logical subsetting

Exercises

12.5 Conditional transformations

`if_else()`

`case_when()`

Compatible types

Exercises

12.6 Summary

C13. Numbers

13.2 Making numbers

13.3 Counts

13.4 Numeric transformations

13.5 General transformations

13.6 Numeric summaries

13.7 Summary

C14. Strings

14.2 Creating a string

14.3 Creating many strings from data

14.4 Extracting data from strings

14.5 Letters

14.6 Non-English text

14.7 Summary

C15 Regular Expressions

library(tidyverse)
library(babynames)

15.2 Pattern basics

15.3 Key functions

15.4 Pattern details

15.5 Pattern control

15.6 Practice

15.7 Regular expressions in other places

15.8 Summary

C16. Factors

16.2 Factor basics

month_levels <- c(
  "Jan", "Feb", "Mar", "Apr", "May", "Jun", 
  "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
)

x1 <- c("Dec", "Apr", "Jan", "Mar")

y1 <- factor(x1, levels = month_levels)
y1

sort(y1)

<summary style=display:list-item;cursor:pointer> Levels: </summary> <ol class=list-inline>

'Jan'

'Feb'

'Mar'

'Apr'

'May'

'Jun'

'Jul'

'Aug'

'Sep'

'Oct'

'Nov'

'Dec'

</ol>

<summary style=display:list-item;cursor:pointer> Levels: </summary> <ol class=list-inline>

'Jan'

'Feb'

'Mar'

'Apr'

'May'

'Jun'

'Jul'

'Aug'

'Sep'

'Oct'

'Nov'

'Dec'

</ol>

x2 <- c("Dec", "Apr", "Jam", "Mar")
y2 <- factor(x2, levels = month_levels)
y2

sort(y2)

<summary style=display:list-item;cursor:pointer> Levels: </summary> <ol class=list-inline>

'Jan'

'Feb'

'Mar'

'Apr'

'May'

'Jun'

'Jul'

'Aug'

'Sep'

'Oct'

'Nov'

'Dec'

</ol>

<summary style=display:list-item;cursor:pointer> Levels: </summary> <ol class=list-inline>

'Jan'

'Feb'

'Mar'

'Apr'

'May'

'Jun'

'Jul'

'Aug'

'Sep'

'Oct'

'Nov'

'Dec'

</ol>

levels(y2)

library(readr)
csv <- "
month,value
Jan,12
Feb,56
Mar,12"

df <- read_csv(csv, col_types = cols(month = col_factor(month_levels)))
df$month

<summary style=display:list-item;cursor:pointer> Levels: </summary> <ol class=list-inline>

'Jan'

'Feb'

'Mar'

'Apr'

'May'

'Jun'

'Jul'

'Aug'

'Sep'

'Oct'

'Nov'

'Dec'

</ol>

library(tidyverse)
library(gridExtra)
gss_cat

A tibble: 21483 x 9
year	marital	age	race	rincome	partyid	relig	denom	tvhours
<int>	<fct>	<int>	<fct>	<fct>	<fct>	<fct>	<fct>	<int>
2000	Never married	26	White	$8000 to 9999	Ind,near rep	Protestant	Southern baptist	12
2000	Divorced	48	White	$8000 to 9999	Not str republican	Protestant	Baptist-dk which	NA
2000	Widowed	67	White	Not applicable	Independent	Protestant	No denomination	2
2000	Never married	39	White	Not applicable	Ind,near rep	Orthodox-christian	Not applicable	4
2000	Divorced	25	White	Not applicable	Not str democrat	None	Not applicable	1
2000	Married	25	White	$20000 - 24999	Strong democrat	Protestant	Southern baptist	NA
2000	Never married	36	White	$25000 or more	Not str republican	Christian	Not applicable	3
2000	Divorced	44	White	$7000 to 7999	Ind,near dem	Protestant	Lutheran-mo synod	NA
2000	Married	44	White	$25000 or more	Not str democrat	Protestant	Other	0
2000	Married	47	White	$25000 or more	Strong republican	Protestant	Southern baptist	3
2000	Married	53	White	$25000 or more	Not str democrat	Protestant	Other	2
2000	Married	52	White	$25000 or more	Ind,near rep	None	Not applicable	NA
2000	Married	52	White	$25000 or more	Strong democrat	Protestant	Southern baptist	1
2000	Married	51	White	$25000 or more	Strong republican	Protestant	United methodist	NA
2000	Divorced	52	White	$25000 or more	Ind,near dem	None	Not applicable	1
2000	Married	40	Black	$25000 or more	Strong democrat	Protestant	Baptist-dk which	7
2000	Widowed	77	White	Not applicable	Strong republican	Jewish	Not applicable	NA
2000	Never married	44	White	$25000 or more	Independent	None	Not applicable	3
2000	Married	40	White	$10000 - 14999	Not str democrat	Catholic	Not applicable	3
2000	Married	45	Black	Not applicable	Independent	Protestant	United methodist	NA
2000	Married	48	White	$25000 or more	Ind,near dem	Catholic	Not applicable	1
2000	Married	49	White	Refused	Strong republican	Protestant	United methodist	2
2000	Never married	19	White	Not applicable	Independent	None	Not applicable	2
2000	Widowed	54	White	$25000 or more	Ind,near rep	Christian	Not applicable	1
2000	Widowed	82	White	Not applicable	Not str democrat	Protestant	Other	3
2000	Widowed	83	White	Not applicable	Strong democrat	Protestant	Episcopal	NA
2000	Widowed	89	White	Not applicable	Not str democrat	Protestant	Other lutheran	4
2000	Widowed	88	White	Not applicable	Strong republican	Protestant	Afr meth ep zion	NA
2000	Divorced	72	White	Not applicable	Strong democrat	Protestant	Southern baptist	7
2000	Widowed	82	White	Not applicable	Independent	Protestant	Am bapt ch in usa	NA
...	...	...	...	...	...	...	...	...
2014	Divorced	38	White	$3000 to 3999	Not str republican	Protestant	Other	1
2014	Widowed	46	White	$25000 or more	Strong democrat	None	Not applicable	2
2014	Married	49	White	Not applicable	Ind,near rep	Protestant	Other	6
2014	Never married	34	White	$25000 or more	Independent	Protestant	United methodist	2
2014	Married	54	White	Not applicable	Independent	Protestant	Other	NA
2014	Married	34	White	$15000 - 19999	Ind,near dem	Buddhism	Not applicable	1
2014	Married	69	White	Not applicable	Ind,near dem	Jewish	Not applicable	3
2014	Divorced	36	White	Not applicable	Independent	None	Not applicable	0
2014	Married	65	White	$25000 or more	Not str democrat	None	Not applicable	2
2014	Married	48	White	$20000 - 24999	Strong democrat	Protestant	Other	0
2014	Married	38	White	$10000 - 14999	Not str democrat	Protestant	No denomination	2
2014	Never married	30	White	$4000 to 4999	Ind,near dem	None	Not applicable	2
2014	Married	48	White	$8000 to 9999	Not str republican	Catholic	Not applicable	0
2014	Divorced	49	White	$25000 or more	Ind,near rep	Other	Not applicable	2
2014	Married	54	White	$25000 or more	Ind,near dem	Protestant	Other	NA
2014	Married	49	White	$25000 or more	Not str republican	Catholic	Not applicable	NA
2014	Married	53	White	$25000 or more	Not str democrat	None	Not applicable	0
2014	Married	52	White	$25000 or more	Not str democrat	None	Not applicable	1
2014	Widowed	82	White	Not applicable	Strong democrat	Protestant	Other	2
2014	Married	63	White	Not applicable	Ind,near dem	No answer	No answer	2
2014	Divorced	54	White	$25000 or more	Ind,near rep	Catholic	Not applicable	3
2014	Married	62	White	$25000 or more	Ind,near rep	Protestant	Other	NA
2014	Never married	40	White	$1000 to 2999	Not str republican	None	Not applicable	2
2014	Married	33	White	Not applicable	Independent	Christian	No denomination	0
2014	Widowed	75	White	Don't know	Strong republican	Protestant	Baptist-dk which	4
2014	Widowed	89	White	Not applicable	Not str republican	Protestant	United methodist	3
2014	Divorced	56	White	$25000 or more	Independent	None	Not applicable	4
2014	Never married	24	White	$10000 - 14999	Ind,near dem	None	Not applicable	4
2014	Never married	27	White	$25000 or more	Not str democrat	Catholic	Not applicable	NA
2014	Widowed	71	White	$20000 - 24999	Ind,near rep	Protestant	Other	2

gss_cat |>
  count(race)

A tibble: 3 x 2
race	n
<fct>	<int>
Other	1959
Black	3129
White	16395

Exercises

Explore the distribution of rincome (reported income). What makes the default bar chart hard to understand? How could you improve the plot?
What is the most common relig in this survey? What’s the most common partyid?
Which relig does denom (denomination) apply to? How can you find out with a table? How can you find out with a visualization?

16.4 Modifying factor order

relig_summary <- gss_cat |>
  group_by(relig) |>
  summarize(
    tvhours = mean(tvhours, na.rm = TRUE),
    n = n()
  )

ggplot(relig_summary, aes(x = tvhours, y = relig)) + 
  geom_point()

png

ggplot(relig_summary, aes(x = tvhours, y = fct_reorder(relig, tvhours))) +
  geom_point()

png

relig_summary |>
  mutate(
    relig = fct_reorder(relig, tvhours)
  ) |>
  ggplot(aes(x = tvhours, y = relig)) +
  geom_point()

png

rincome_summary <- gss_cat |>
  group_by(rincome) |>
  summarize(
    age = mean(age, na.rm = TRUE),
    n = n()
  )

ggplot(rincome_summary, aes(x = age, y = fct_reorder(rincome, age))) + 
  geom_point()

png

ggplot(rincome_summary, aes(x = age, y = fct_relevel(rincome, "Not applicable"))) +
  geom_point()

png

by_age <- gss_cat |>
  filter(!is.na(age)) |> 
  count(age, marital) |>
  group_by(age) |>
  mutate(
    prop = n / sum(n)
  )

p1 <- ggplot(by_age, aes(x = age, y = prop, color = marital)) +
  geom_line(linewidth = 1) + 
  scale_color_brewer(palette = "Set1")

p2 <- ggplot(by_age, aes(x = age, y = prop, color = fct_reorder2(marital, age, prop))) +
  geom_line(linewidth = 1) +
  scale_color_brewer(palette = "Set1") + 
  labs(color = "marital") 

options(repr.plot.width = 20, repr.plot.height = 5)
grid.arrange(p1, p2, ncol=2)

png

gss_cat |>
  mutate(marital = marital |> fct_infreq() |> fct_rev()) |>
  ggplot(aes(x = marital)) +
  geom_bar()
  options(repr.plot.width = 10, repr.plot.height = 5)

png

There are some suspiciously high numbers in tvhours. Is the mean a good summary?
For each factor in gss_cat identify whether the order of the levels is arbitrary or principled.
Why did moving “Not applicable” to the front of the levels move it to the bottom of the plot?

16.5 Modifying factor levels

gss_cat |> count(partyid)

A tibble: 10 x 2
partyid	n
<fct>	<int>
No answer	154
Don't know	1
Other party	393
Strong republican	2314
Not str republican	3032
Ind,near rep	1791
Independent	4119
Ind,near dem	2499
Not str democrat	3690
Strong democrat	3490

gss_cat |>
  mutate(
    partyid = fct_recode(partyid,
      "Republican, strong"    = "Strong republican",
      "Republican, weak"      = "Not str republican",
      "Independent, near rep" = "Ind,near rep",
      "Independent, near dem" = "Ind,near dem",
      "Democrat, weak"        = "Not str democrat",
      "Democrat, strong"      = "Strong democrat"
    )
  ) |>
  count(partyid)

A tibble: 10 x 2
partyid	n
<fct>	<int>
No answer	154
Don't know	1
Other party	393
Republican, strong	2314
Republican, weak	3032
Independent, near rep	1791
Independent	4119
Independent, near dem	2499
Democrat, weak	3690
Democrat, strong	3490

gss_cat |>
  mutate(
    partyid = fct_recode(partyid,
      "Republican, strong"    = "Strong republican",
      "Republican, weak"      = "Not str republican",
      "Independent, near rep" = "Ind,near rep",
      "Independent, near dem" = "Ind,near dem",
      "Democrat, weak"        = "Not str democrat",
      "Democrat, strong"      = "Strong democrat",
      "Other"                 = "No answer",
      "Other"                 = "Don't know",
      "Other"                 = "Other party"
    )
  )

A tibble: 21483 x 9
year	marital	age	race	rincome	partyid	relig	denom	tvhours
<int>	<fct>	<int>	<fct>	<fct>	<fct>	<fct>	<fct>	<int>
2000	Never married	26	White	$8000 to 9999	Independent, near rep	Protestant	Southern baptist	12
2000	Divorced	48	White	$8000 to 9999	Republican, weak	Protestant	Baptist-dk which	NA
2000	Widowed	67	White	Not applicable	Independent	Protestant	No denomination	2
2000	Never married	39	White	Not applicable	Independent, near rep	Orthodox-christian	Not applicable	4
2000	Divorced	25	White	Not applicable	Democrat, weak	None	Not applicable	1
2000	Married	25	White	$20000 - 24999	Democrat, strong	Protestant	Southern baptist	NA
2000	Never married	36	White	$25000 or more	Republican, weak	Christian	Not applicable	3
2000	Divorced	44	White	$7000 to 7999	Independent, near dem	Protestant	Lutheran-mo synod	NA
2000	Married	44	White	$25000 or more	Democrat, weak	Protestant	Other	0
2000	Married	47	White	$25000 or more	Republican, strong	Protestant	Southern baptist	3
2000	Married	53	White	$25000 or more	Democrat, weak	Protestant	Other	2
2000	Married	52	White	$25000 or more	Independent, near rep	None	Not applicable	NA
2000	Married	52	White	$25000 or more	Democrat, strong	Protestant	Southern baptist	1
2000	Married	51	White	$25000 or more	Republican, strong	Protestant	United methodist	NA
2000	Divorced	52	White	$25000 or more	Independent, near dem	None	Not applicable	1
2000	Married	40	Black	$25000 or more	Democrat, strong	Protestant	Baptist-dk which	7
2000	Widowed	77	White	Not applicable	Republican, strong	Jewish	Not applicable	NA
2000	Never married	44	White	$25000 or more	Independent	None	Not applicable	3
2000	Married	40	White	$10000 - 14999	Democrat, weak	Catholic	Not applicable	3
2000	Married	45	Black	Not applicable	Independent	Protestant	United methodist	NA
2000	Married	48	White	$25000 or more	Independent, near dem	Catholic	Not applicable	1
2000	Married	49	White	Refused	Republican, strong	Protestant	United methodist	2
2000	Never married	19	White	Not applicable	Independent	None	Not applicable	2
2000	Widowed	54	White	$25000 or more	Independent, near rep	Christian	Not applicable	1
2000	Widowed	82	White	Not applicable	Democrat, weak	Protestant	Other	3
2000	Widowed	83	White	Not applicable	Democrat, strong	Protestant	Episcopal	NA
2000	Widowed	89	White	Not applicable	Democrat, weak	Protestant	Other lutheran	4
2000	Widowed	88	White	Not applicable	Republican, strong	Protestant	Afr meth ep zion	NA
2000	Divorced	72	White	Not applicable	Democrat, strong	Protestant	Southern baptist	7
2000	Widowed	82	White	Not applicable	Independent	Protestant	Am bapt ch in usa	NA
...	...	...	...	...	...	...	...	...
2014	Divorced	38	White	$3000 to 3999	Republican, weak	Protestant	Other	1
2014	Widowed	46	White	$25000 or more	Democrat, strong	None	Not applicable	2
2014	Married	49	White	Not applicable	Independent, near rep	Protestant	Other	6
2014	Never married	34	White	$25000 or more	Independent	Protestant	United methodist	2
2014	Married	54	White	Not applicable	Independent	Protestant	Other	NA
2014	Married	34	White	$15000 - 19999	Independent, near dem	Buddhism	Not applicable	1
2014	Married	69	White	Not applicable	Independent, near dem	Jewish	Not applicable	3
2014	Divorced	36	White	Not applicable	Independent	None	Not applicable	0
2014	Married	65	White	$25000 or more	Democrat, weak	None	Not applicable	2
2014	Married	48	White	$20000 - 24999	Democrat, strong	Protestant	Other	0
2014	Married	38	White	$10000 - 14999	Democrat, weak	Protestant	No denomination	2
2014	Never married	30	White	$4000 to 4999	Independent, near dem	None	Not applicable	2
2014	Married	48	White	$8000 to 9999	Republican, weak	Catholic	Not applicable	0
2014	Divorced	49	White	$25000 or more	Independent, near rep	Other	Not applicable	2
2014	Married	54	White	$25000 or more	Independent, near dem	Protestant	Other	NA
2014	Married	49	White	$25000 or more	Republican, weak	Catholic	Not applicable	NA
2014	Married	53	White	$25000 or more	Democrat, weak	None	Not applicable	0
2014	Married	52	White	$25000 or more	Democrat, weak	None	Not applicable	1
2014	Widowed	82	White	Not applicable	Democrat, strong	Protestant	Other	2
2014	Married	63	White	Not applicable	Independent, near dem	No answer	No answer	2
2014	Divorced	54	White	$25000 or more	Independent, near rep	Catholic	Not applicable	3
2014	Married	62	White	$25000 or more	Independent, near rep	Protestant	Other	NA
2014	Never married	40	White	$1000 to 2999	Republican, weak	None	Not applicable	2
2014	Married	33	White	Not applicable	Independent	Christian	No denomination	0
2014	Widowed	75	White	Don't know	Republican, strong	Protestant	Baptist-dk which	4
2014	Widowed	89	White	Not applicable	Republican, weak	Protestant	United methodist	3
2014	Divorced	56	White	$25000 or more	Independent	None	Not applicable	4
2014	Never married	24	White	$10000 - 14999	Independent, near dem	None	Not applicable	4
2014	Never married	27	White	$25000 or more	Democrat, weak	Catholic	Not applicable	NA
2014	Widowed	71	White	$20000 - 24999	Independent, near rep	Protestant	Other	2

gss_cat |>
  mutate(
    partyid = fct_collapse(partyid,
      "other" = c("No answer", "Don't know", "Other party"),
      "rep" = c("Strong republican", "Not str republican"),
      "ind" = c("Ind,near rep", "Independent", "Ind,near dem"),
      "dem" = c("Not str democrat", "Strong democrat")
    )
  ) |>
  count(partyid)

A tibble: 4 x 2
partyid	n
<fct>	<int>
other	548
rep	5346
ind	8409
dem	7180

gss_cat |>
  mutate(relig = fct_lump_lowfreq(relig)) |>
  count(relig)

A tibble: 2 x 2
relig	n
<fct>	<int>
Protestant	10846
Other	10637

gss_cat |>
  mutate(relig = fct_lump_n(relig, n = 10)) |>
  count(relig, sort = TRUE)

A tibble: 10 x 2
relig	n
<fct>	<int>
Protestant	10846
Catholic	5124
None	3523
Christian	689
Other	458
Jewish	388
Buddhism	147
Inter-nondenominational	109
Moslem/islam	104
Orthodox-christian	95

Exercises

How have the proportions of people identifying as Democrat, Republican, and Independent changed over time?
How could you collapse rincome into a small set of categories?
Notice there are 9 groups (excluding other) in the fct_lump example above. Why not 10? (Hint: type ?fct_lump, and find the default for the argument other_level is “Other”.)

16.6 Ordered factors

ordered(c("a", "b", "c"))

<summary style=display:list-item;cursor:pointer> Levels: </summary> <ol class=list-inline>

'a'

'b'

'c'

</ol>

16.7 Summary

C17. Dates and Times

library(tidyverse)
library(nycflights13)

17.2 Creating date/times

Year:

%Y 4 digit year
%y 2 digit year

Month:

%m Number
%b Abbreviated name
%B Full name

Day:

%d One or two digits
%e Two digits

Time:

%H 24-hour hour
%I 12-hour hour
%p AM/PM
%M Minutes
%S Seconds
%OS Seconds with decimal component
%Z Time zone name
%z Offset from UTC

Others:

%. Skip one non-digit
%* Skip any number of non-digits

today()
now()

2024-03-12

[1] "2024-03-12 11:34:12 AEST"

During import

csv <- "
  date
  01/02/15
"

read_csv(csv, col_types = cols(date = col_date("%m/%d/%y")))
read_csv(csv, col_types = cols(date = col_date("%d/%m/%y")))
read_csv(csv, col_types = cols(date = col_date("%y/%m/%d")))

A spec_tbl_df: 1 x 1
date
<date>
2015-01-02

A spec_tbl_df: 1 x 1
date
<date>
2015-02-01

A spec_tbl_df: 1 x 1
date
<date>
2001-02-15

From strings

ymd("2017-01-31")
mdy("January 31st, 2017")
dmy("31-Jan-2017")

ymd("2017-01-31", tz = "UTC")

2017-01-31 2017-01-31 2017-01-31

[1] "2017-01-31 UTC"

From individual components

make_date()
make_datetime()

flights |> 
  select(year, month, day, hour, minute) |> 
  mutate(departure = make_datetime(year, month, day, hour, minute))

A tibble: 336776 x 6
year	month	day	hour	minute	departure
<int>	<int>	<int>	<dbl>	<dbl>	<dttm>
2013	1	1	5	15	2013-01-01 05:15:00
2013	1	1	5	29	2013-01-01 05:29:00
2013	1	1	5	40	2013-01-01 05:40:00
2013	1	1	5	45	2013-01-01 05:45:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	5	58	2013-01-01 05:58:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	5	59	2013-01-01 05:59:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	10	2013-01-01 06:10:00
2013	1	1	6	5	2013-01-01 06:05:00
2013	1	1	6	10	2013-01-01 06:10:00
2013	1	1	6	10	2013-01-01 06:10:00
2013	1	1	6	7	2013-01-01 06:07:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	0	2013-01-01 06:00:00
2013	1	1	6	10	2013-01-01 06:10:00
2013	1	1	6	15	2013-01-01 06:15:00
2013	1	1	6	15	2013-01-01 06:15:00
...	...	...	...	...	...
2013	9	30	21	25	2013-09-30 21:25:00
2013	9	30	21	29	2013-09-30 21:29:00
2013	9	30	21	30	2013-09-30 21:30:00
2013	9	30	20	59	2013-09-30 20:59:00
2013	9	30	21	40	2013-09-30 21:40:00
2013	9	30	21	40	2013-09-30 21:40:00
2013	9	30	21	29	2013-09-30 21:29:00
2013	9	30	21	45	2013-09-30 21:45:00
2013	9	30	21	37	2013-09-30 21:37:00
2013	9	30	21	56	2013-09-30 21:56:00
2013	9	30	21	59	2013-09-30 21:59:00
2013	9	30	18	45	2013-09-30 18:45:00
2013	9	30	22	5	2013-09-30 22:05:00
2013	9	30	21	40	2013-09-30 21:40:00
2013	9	30	20	59	2013-09-30 20:59:00
2013	9	30	22	45	2013-09-30 22:45:00
2013	9	30	21	13	2013-09-30 21:13:00
2013	9	30	20	1	2013-09-30 20:01:00
2013	9	30	22	45	2013-09-30 22:45:00
2013	9	30	22	45	2013-09-30 22:45:00
2013	9	30	22	50	2013-09-30 22:50:00
2013	9	30	22	46	2013-09-30 22:46:00
2013	9	30	22	55	2013-09-30 22:55:00
2013	9	30	23	59	2013-09-30 23:59:00
2013	9	30	18	42	2013-09-30 18:42:00
2013	9	30	14	55	2013-09-30 14:55:00
2013	9	30	22	0	2013-09-30 22:00:00
2013	9	30	12	10	2013-09-30 12:10:00
2013	9	30	11	59	2013-09-30 11:59:00
2013	9	30	8	40	2013-09-30 08:40:00

make_datetime_100 <- function(year, month, day, time) {
  make_datetime(year, month, day, time %/% 100, time %% 100)
}

flights_dt <- flights |> 
  filter(!is.na(dep_time), !is.na(arr_time)) |> 
  mutate(
    dep_time = make_datetime_100(year, month, day, dep_time),
    arr_time = make_datetime_100(year, month, day, arr_time),
    sched_dep_time = make_datetime_100(year, month, day, sched_dep_time),
    sched_arr_time = make_datetime_100(year, month, day, sched_arr_time)
  ) |> 
  select(origin, dest, ends_with("delay"), ends_with("time"))

flights_dt

A tibble: 328063 x 9
origin	dest	dep_delay	arr_delay	dep_time	sched_dep_time	arr_time	sched_arr_time	air_time
<chr>	<chr>	<dbl>	<dbl>	<dttm>	<dttm>	<dttm>	<dttm>	<dbl>
EWR	IAH	2	11	2013-01-01 05:17:00	2013-01-01 05:15:00	2013-01-01 08:30:00	2013-01-01 08:19:00	227
LGA	IAH	4	20	2013-01-01 05:33:00	2013-01-01 05:29:00	2013-01-01 08:50:00	2013-01-01 08:30:00	227
JFK	MIA	2	33	2013-01-01 05:42:00	2013-01-01 05:40:00	2013-01-01 09:23:00	2013-01-01 08:50:00	160
JFK	BQN	-1	-18	2013-01-01 05:44:00	2013-01-01 05:45:00	2013-01-01 10:04:00	2013-01-01 10:22:00	183
LGA	ATL	-6	-25	2013-01-01 05:54:00	2013-01-01 06:00:00	2013-01-01 08:12:00	2013-01-01 08:37:00	116
EWR	ORD	-4	12	2013-01-01 05:54:00	2013-01-01 05:58:00	2013-01-01 07:40:00	2013-01-01 07:28:00	150
EWR	FLL	-5	19	2013-01-01 05:55:00	2013-01-01 06:00:00	2013-01-01 09:13:00	2013-01-01 08:54:00	158
LGA	IAD	-3	-14	2013-01-01 05:57:00	2013-01-01 06:00:00	2013-01-01 07:09:00	2013-01-01 07:23:00	53
JFK	MCO	-3	-8	2013-01-01 05:57:00	2013-01-01 06:00:00	2013-01-01 08:38:00	2013-01-01 08:46:00	140
LGA	ORD	-2	8	2013-01-01 05:58:00	2013-01-01 06:00:00	2013-01-01 07:53:00	2013-01-01 07:45:00	138
JFK	PBI	-2	-2	2013-01-01 05:58:00	2013-01-01 06:00:00	2013-01-01 08:49:00	2013-01-01 08:51:00	149
JFK	TPA	-2	-3	2013-01-01 05:58:00	2013-01-01 06:00:00	2013-01-01 08:53:00	2013-01-01 08:56:00	158
JFK	LAX	-2	7	2013-01-01 05:58:00	2013-01-01 06:00:00	2013-01-01 09:24:00	2013-01-01 09:17:00	345
EWR	SFO	-2	-14	2013-01-01 05:58:00	2013-01-01 06:00:00	2013-01-01 09:23:00	2013-01-01 09:37:00	361
LGA	DFW	-1	31	2013-01-01 05:59:00	2013-01-01 06:00:00	2013-01-01 09:41:00	2013-01-01 09:10:00	257
JFK	BOS	0	-4	2013-01-01 05:59:00	2013-01-01 05:59:00	2013-01-01 07:02:00	2013-01-01 07:06:00	44
EWR	LAS	-1	-8	2013-01-01 05:59:00	2013-01-01 06:00:00	2013-01-01 08:54:00	2013-01-01 09:02:00	337
LGA	FLL	0	-7	2013-01-01 06:00:00	2013-01-01 06:00:00	2013-01-01 08:51:00	2013-01-01 08:58:00	152
LGA	ATL	0	12	2013-01-01 06:00:00	2013-01-01 06:00:00	2013-01-01 08:37:00	2013-01-01 08:25:00	134
EWR	PBI	1	-6	2013-01-01 06:01:00	2013-01-01 06:00:00	2013-01-01 08:44:00	2013-01-01 08:50:00	147
LGA	MSP	-8	-8	2013-01-01 06:02:00	2013-01-01 06:10:00	2013-01-01 08:12:00	2013-01-01 08:20:00	170
LGA	DTW	-3	16	2013-01-01 06:02:00	2013-01-01 06:05:00	2013-01-01 08:21:00	2013-01-01 08:05:00	105
EWR	MIA	-4	-12	2013-01-01 06:06:00	2013-01-01 06:10:00	2013-01-01 08:58:00	2013-01-01 09:10:00	152
JFK	ATL	-4	-8	2013-01-01 06:06:00	2013-01-01 06:10:00	2013-01-01 08:37:00	2013-01-01 08:45:00	128
EWR	MIA	0	-17	2013-01-01 06:07:00	2013-01-01 06:07:00	2013-01-01 08:58:00	2013-01-01 09:15:00	157
EWR	ORD	8	32	2013-01-01 06:08:00	2013-01-01 06:00:00	2013-01-01 08:07:00	2013-01-01 07:35:00	139
JFK	SFO	11	14	2013-01-01 06:11:00	2013-01-01 06:00:00	2013-01-01 09:45:00	2013-01-01 09:31:00	366
JFK	RSW	3	4	2013-01-01 06:13:00	2013-01-01 06:10:00	2013-01-01 09:25:00	2013-01-01 09:21:00	175
JFK	SJU	0	-21	2013-01-01 06:15:00	2013-01-01 06:15:00	2013-01-01 10:39:00	2013-01-01 11:00:00	182
EWR	ATL	0	-9	2013-01-01 06:15:00	2013-01-01 06:15:00	2013-01-01 08:33:00	2013-01-01 08:42:00	120
...	...	...	...	...	...	...	...	...
LGA	DTW	5	-9	2013-09-30 21:15:00	2013-09-30 21:10:00	2013-09-30 22:46:00	2013-09-30 22:55:00	72
EWR	SDF	-8	-20	2013-09-30 21:16:00	2013-09-30 21:24:00	2013-09-30 23:17:00	2013-09-30 23:37:00	94
EWR	MCI	74	58	2013-09-30 21:19:00	2013-09-30 20:05:00	2013-09-30 23:10:00	2013-09-30 22:12:00	147
JFK	JAX	-1	-24	2013-09-30 21:19:00	2013-09-30 21:20:00	2013-09-30 23:30:00	2013-09-30 23:54:00	113
JFK	LAX	21	-25	2013-09-30 21:21:00	2013-09-30 21:00:00	2013-09-30 23:49:00	2013-09-30 00:14:00	296
EWR	DCA	-5	-11	2013-09-30 21:22:00	2013-09-30 21:27:00	2013-09-30 22:26:00	2013-09-30 22:37:00	35
LGA	CHO	-2	-24	2013-09-30 21:23:00	2013-09-30 21:25:00	2013-09-30 22:23:00	2013-09-30 22:47:00	45
EWR	CLT	-2	-9	2013-09-30 21:27:00	2013-09-30 21:29:00	2013-09-30 23:14:00	2013-09-30 23:23:00	72
JFK	DEN	-2	-31	2013-09-30 21:28:00	2013-09-30 21:30:00	2013-09-30 23:28:00	2013-09-30 23:59:00	213
LGA	RIC	30	-2	2013-09-30 21:29:00	2013-09-30 20:59:00	2013-09-30 22:30:00	2013-09-30 22:32:00	45
JFK	DCA	-9	-30	2013-09-30 21:31:00	2013-09-30 21:40:00	2013-09-30 22:25:00	2013-09-30 22:55:00	36
JFK	LAX	0	-30	2013-09-30 21:40:00	2013-09-30 21:40:00	2013-09-30 00:10:00	2013-09-30 00:40:00	298
EWR	PWM	13	11	2013-09-30 21:42:00	2013-09-30 21:29:00	2013-09-30 22:50:00	2013-09-30 22:39:00	47
JFK	SJU	0	-25	2013-09-30 21:45:00	2013-09-30 21:45:00	2013-09-30 01:15:00	2013-09-30 01:40:00	192
LGA	FLL	10	3	2013-09-30 21:47:00	2013-09-30 21:37:00	2013-09-30 00:30:00	2013-09-30 00:27:00	139
EWR	BOS	-7	-23	2013-09-30 21:49:00	2013-09-30 21:56:00	2013-09-30 22:45:00	2013-09-30 23:08:00	37
EWR	MHT	-9	-16	2013-09-30 21:50:00	2013-09-30 21:59:00	2013-09-30 22:50:00	2013-09-30 23:06:00	39
JFK	BUF	194	194	2013-09-30 21:59:00	2013-09-30 18:45:00	2013-09-30 23:44:00	2013-09-30 20:30:00	50
LGA	BGR	-2	8	2013-09-30 22:03:00	2013-09-30 22:05:00	2013-09-30 23:39:00	2013-09-30 23:31:00	61
LGA	BNA	27	7	2013-09-30 22:07:00	2013-09-30 21:40:00	2013-09-30 22:57:00	2013-09-30 22:50:00	97
EWR	STL	72	57	2013-09-30 22:11:00	2013-09-30 20:59:00	2013-09-30 23:39:00	2013-09-30 22:42:00	120
JFK	PWM	-14	-21	2013-09-30 22:31:00	2013-09-30 22:45:00	2013-09-30 23:35:00	2013-09-30 23:56:00	48
EWR	SFO	80	42	2013-09-30 22:33:00	2013-09-30 21:13:00	2013-09-30 01:12:00	2013-09-30 00:30:00	318
JFK	MCO	154	130	2013-09-30 22:35:00	2013-09-30 20:01:00	2013-09-30 00:59:00	2013-09-30 22:49:00	123
JFK	BTV	-8	-8	2013-09-30 22:37:00	2013-09-30 22:45:00	2013-09-30 23:45:00	2013-09-30 23:53:00	43
JFK	SYR	-5	-17	2013-09-30 22:40:00	2013-09-30 22:45:00	2013-09-30 23:34:00	2013-09-30 23:51:00	41
JFK	BUF	-10	-20	2013-09-30 22:40:00	2013-09-30 22:50:00	2013-09-30 23:47:00	2013-09-30 00:07:00	52
JFK	ROC	-5	-16	2013-09-30 22:41:00	2013-09-30 22:46:00	2013-09-30 23:45:00	2013-09-30 00:01:00	47
JFK	BOS	12	1	2013-09-30 23:07:00	2013-09-30 22:55:00	2013-09-30 23:59:00	2013-09-30 23:58:00	33
JFK	PSE	-10	-25	2013-09-30 23:49:00	2013-09-30 23:59:00	2013-09-30 03:25:00	2013-09-30 03:50:00	196

flights_dt |> 
  ggplot(aes(x = dep_time)) + 
  geom_freqpoly(binwidth = 86400) # 86400 seconds = 1 day

png

# or within a single day
flights_dt |> 
  filter(dep_time < ymd(20130102)) |> 
  ggplot(aes(x = dep_time)) + 
  geom_freqpoly(binwidth = 600) # 600 s = 10 minutes

png

From other types

as_date()
as_datetime()

as_datetime(today())

as_date(now())

[1] "2024-03-12 UTC"

2024-03-12

as_datetime(86300)

as_date(365 * 10 + 2)

[1] "1970-01-01 23:58:20 UTC"

1980-01-01

Exercises

today(tzone = "UTC")

2024-03-12

For each of the following date-times, show how you’d parse it using a readr column specification and a lubridate function.

d1 <- "January 1, 2010"
d2 <- "2015-Mar-07"
d3 <- "06-Jun-2017"
d4 <- c("August 19 (2015)", "July 1 (2015)")
d5 <- "12/30/14" # Dec 30, 2014
t1 <- "1705"
t2 <- "11:15:10.12 PM"


mdy(d1)
as_date(d2)
dmy(d3)
mdy(d4)
mdy(d5)

as_datetime(as.numeric(t1))
hms(t2)

2010-01-01 2015-03-07 2017-06-06

2014-12-30

[1] "1970-01-01 00:28:25 UTC"

11H 15M 10.12S

17.3 Date-time components

datetime <- ymd_hms(now())      # "2026-07-08 12:34:56"

year(datetime)
#> 2026
month(datetime)
#>  7
mday(datetime)
#>  8
yday(datetime)
#> 189
wday(datetime)
#> 4

2024

flights_dt |> 
  mutate(wday = wday(dep_time, label = TRUE)) |> 
  ggplot(aes(x = wday)) +
  geom_bar()

png

flights_dt |> 
  mutate(minute = minute(dep_time)) |> 
  group_by(minute) |> 
  summarize(
    avg_delay = mean(dep_delay, na.rm = TRUE),
    n = n()
  ) |> 
  ggplot(aes(x = minute, y = avg_delay)) +
  geom_line()

png

sched_dep <- flights_dt |> 
  mutate(minute = minute(sched_dep_time)) |> 
  group_by(minute) |> 
  summarize(
    avg_delay = mean(arr_delay, na.rm = TRUE),
    n = n()
  )

ggplot(sched_dep, aes(x = minute, y = avg_delay)) +
  geom_line()

png

flights_dt |> 
  mutate(dep_hour = hms::as_hms(dep_time - floor_date(dep_time, "day"))) |> 
  ggplot(aes(x = dep_hour)) +
  geom_freqpoly(binwidth = 60 * 30)

png

Modify components

datetime <- ymd_hms("2026-07-08 12:34:56")
datetime
year(datetime) <- 2030
datetime
month(datetime) <- 01
datetime
hour(datetime) <- hour(datetime) + 1
datetime

update(datetime, year = 2030, month = 2, mday = 2, hour = 2)
datetime

[1] "2026-07-08 12:34:56 UTC"

[1] "2030-07-08 12:34:56 UTC"

[1] "2030-01-08 12:34:56 UTC"

[1] "2030-01-08 13:34:56 UTC"

[1] "2030-02-02 02:34:56 UTC"

[1] "2030-01-08 13:34:56 UTC"

Exercises

How does the distribution of flight times within a day change over the course of the year?
Compare dep_time, sched_dep_time and dep_delay. Are they consistent? Explain your findings.
Compare air_time with the duration between the departure and arrival. Explain your findings. (Hint: consider the location of the airport.)
How does the average delay time change over the course of a day? Should you use dep_time or sched_dep_time? Why?
On what day of the week should you leave if you want to minimise the chance of a delay?
What makes the distribution of diamonds$carat and flights$sched_dep_time similar?
Confirm our hypothesis that the early departures of flights in minutes 20-30 and 50-60 are caused by scheduled flights that leave early. Hint: create a binary variable that tells you whether or not a flight was delayed.

17.4 Time spans

Durations, which represent an exact number of seconds.
Periods, which represent human units like weeks and months.
Intervals, which represent a starting and ending point.

Duration

# How old is Hadley?
h_age <- today() - ymd("1979-05-16")
h_age

Time difference of 16372 days

as.duration(h_age)

1414540800s (~44.82 years)

dseconds(15)
dminutes(10)
dhours(c(12, 24))
ddays(0:5)
dweeks(3)
dyears(1)

15s

600s (~10 minutes)

<ol class=list-inline><li>43200s (~12 hours)</li><li>86400s (~1 days)</li></ol>

1814400s (~3 weeks)

31557600s (~1 years)

# you can add and multiply durations:
2 * dyears(1)
dyears(1) + dweeks(12) + dhours(15)
## add and subtract durations to and from days

tomorrow <- today() + ddays(1)
last_year <- today() - dyears(1)

63115200s (~2 years)

38869200s (~1.23 years)

one_am <- ymd_hms("2026-03-08 01:00:00", tz = "America/New_York")

one_am
# be careful with time zone (on that day timezone has changed)
one_am + ddays(1)

[1] "2026-03-08 01:00:00 EST"

[1] "2026-03-09 02:00:00 EDT"

Periods

one_am <- ymd_hms("2026-03-08 01:00:00", tz = "America/New_York")
hours(c(12, 24))

days(7)
months(1:6)

7d 0H 0M 0S

10 * (months(6) + days(1))
days(50) + hours(25) + minutes(2)

60m 10d 0H 0M 0S

50d 25H 2M 0S

# A leap year
ymd("2024-01-01") + dyears(1)
ymd("2024-01-01") + years(1)


# Daylight saving time
one_am + ddays(1)
one_am + days(1)

[1] "2024-12-31 06:00:00 UTC"

2025-01-01

[1] "2026-03-09 02:00:00 EDT"

[1] "2026-03-09 01:00:00 EDT"

flights_dt |> 
  filter(arr_time < dep_time) 

A tibble: 10633 x 9
origin	dest	dep_delay	arr_delay	dep_time	sched_dep_time	arr_time	sched_arr_time	air_time
<chr>	<chr>	<dbl>	<dbl>	<dttm>	<dttm>	<dttm>	<dttm>	<dbl>
EWR	BQN	9	-4	2013-01-01 19:29:00	2013-01-01 19:20:00	2013-01-01 00:03:00	2013-01-01 00:07:00	192
JFK	DFW	59	NA	2013-01-01 19:39:00	2013-01-01 18:40:00	2013-01-01 00:29:00	2013-01-01 21:51:00	NA
EWR	TPA	-2	9	2013-01-01 20:58:00	2013-01-01 21:00:00	2013-01-01 00:08:00	2013-01-01 23:59:00	159
EWR	SJU	-6	-12	2013-01-01 21:02:00	2013-01-01 21:08:00	2013-01-01 01:46:00	2013-01-01 01:58:00	199
EWR	SFO	11	-14	2013-01-01 21:08:00	2013-01-01 20:57:00	2013-01-01 00:25:00	2013-01-01 00:39:00	354
LGA	FLL	-10	-2	2013-01-01 21:20:00	2013-01-01 21:30:00	2013-01-01 00:16:00	2013-01-01 00:18:00	160
EWR	MCO	41	43	2013-01-01 21:21:00	2013-01-01 20:40:00	2013-01-01 00:06:00	2013-01-01 23:23:00	143
JFK	LAX	-7	-24	2013-01-01 21:28:00	2013-01-01 21:35:00	2013-01-01 00:26:00	2013-01-01 00:50:00	338
EWR	FLL	49	28	2013-01-01 21:34:00	2013-01-01 20:45:00	2013-01-01 00:20:00	2013-01-01 23:52:00	152
EWR	FLL	-9	-14	2013-01-01 21:36:00	2013-01-01 21:45:00	2013-01-01 00:25:00	2013-01-01 00:39:00	154
JFK	SJU	5	-14	2013-01-01 21:40:00	2013-01-01 21:35:00	2013-01-01 02:10:00	2013-01-01 02:24:00	189
JFK	MCO	2	2	2013-01-01 21:57:00	2013-01-01 21:55:00	2013-01-01 00:43:00	2013-01-01 00:41:00	140
EWR	MIA	285	246	2013-01-01 22:05:00	2013-01-01 17:20:00	2013-01-01 00:46:00	2013-01-01 20:40:00	146
JFK	PBI	24	21	2013-01-01 22:09:00	2013-01-01 21:45:00	2013-01-01 00:58:00	2013-01-01 00:37:00	143
JFK	SJU	-12	-26	2013-01-01 22:17:00	2013-01-01 22:29:00	2013-01-01 02:49:00	2013-01-01 03:15:00	191
JFK	TPA	47	73	2013-01-01 22:17:00	2013-01-01 21:30:00	2013-01-01 01:40:00	2013-01-01 00:27:00	163
JFK	FLL	30	49	2013-01-01 22:29:00	2013-01-01 21:59:00	2013-01-01 01:49:00	2013-01-01 01:00:00	153
JFK	ROC	21	23	2013-01-01 23:06:00	2013-01-01 22:45:00	2013-01-01 00:28:00	2013-01-01 00:05:00	59
JFK	BTV	22	35	2013-01-01 23:07:00	2013-01-01 22:45:00	2013-01-01 00:32:00	2013-01-01 23:57:00	59
JFK	BUF	15	9	2013-01-01 23:10:00	2013-01-01 22:55:00	2013-01-01 00:24:00	2013-01-01 00:15:00	57
EWR	DCA	192	191	2013-01-01 23:12:00	2013-01-01 20:00:00	2013-01-01 00:21:00	2013-01-01 21:10:00	44
EWR	BTV	83	69	2013-01-01 23:23:00	2013-01-01 22:00:00	2013-01-01 00:22:00	2013-01-01 23:13:00	44
JFK	LAS	116	73	2013-01-01 23:26:00	2013-01-01 21:30:00	2013-01-01 01:31:00	2013-01-01 00:18:00	290
JFK	SYR	37	33	2013-01-01 23:27:00	2013-01-01 22:50:00	2013-01-01 00:32:00	2013-01-01 23:59:00	45
EWR	MCI	379	456	2013-01-01 23:43:00	2013-01-01 17:24:00	2013-01-01 03:14:00	2013-01-01 19:38:00	222
JFK	PSE	-6	-20	2013-01-01 23:53:00	2013-01-01 23:59:00	2013-01-01 04:25:00	2013-01-01 04:45:00	195
JFK	SJU	-6	-24	2013-01-01 23:53:00	2013-01-01 23:59:00	2013-01-01 04:18:00	2013-01-01 04:42:00	185
JFK	BQN	-3	-12	2013-01-01 23:56:00	2013-01-01 23:59:00	2013-01-01 04:25:00	2013-01-01 04:37:00	186
JFK	AUS	5	32	2013-01-02 20:30:00	2013-01-02 20:25:00	2013-01-02 00:13:00	2013-01-02 23:41:00	259
EWR	AUS	2	17	2013-01-02 20:30:00	2013-01-02 20:28:00	2013-01-02 00:08:00	2013-01-02 23:51:00	252
...	...	...	...	...	...	...	...	...
JFK	SJU	32	25	2013-09-28 22:17:00	2013-09-28 21:45:00	2013-09-28 02:05:00	2013-09-28 01:40:00	213
JFK	BTV	10	15	2013-09-28 22:55:00	2013-09-28 22:45:00	2013-09-28 00:08:00	2013-09-28 23:53:00	48
JFK	PSE	-8	17	2013-09-28 23:51:00	2013-09-28 23:59:00	2013-09-28 04:07:00	2013-09-28 03:50:00	218
JFK	SJU	-9	-21	2013-09-29 20:36:00	2013-09-29 20:45:00	2013-09-29 00:32:00	2013-09-29 00:53:00	212
JFK	PDX	-1	28	2013-09-29 20:54:00	2013-09-29 20:55:00	2013-09-29 00:27:00	2013-09-29 23:59:00	346
JFK	LAX	-2	3	2013-09-29 20:58:00	2013-09-29 21:00:00	2013-09-29 00:17:00	2013-09-29 00:14:00	326
JFK	AUS	5	3	2013-09-29 20:59:00	2013-09-29 20:54:00	2013-09-29 00:01:00	2013-09-29 23:58:00	202
JFK	SEA	-3	-11	2013-09-29 21:02:00	2013-09-29 21:05:00	2013-09-29 00:06:00	2013-09-29 00:17:00	336
EWR	BQN	37	21	2013-09-29 21:32:00	2013-09-29 20:55:00	2013-09-29 01:17:00	2013-09-29 00:56:00	206
JFK	LAX	-5	-34	2013-09-29 21:35:00	2013-09-29 21:40:00	2013-09-29 00:06:00	2013-09-29 00:40:00	295
JFK	SJU	9	11	2013-09-29 21:54:00	2013-09-29 21:45:00	2013-09-29 01:51:00	2013-09-29 01:40:00	208
LGA	MCO	64	39	2013-09-29 22:04:00	2013-09-29 21:00:00	2013-09-29 00:23:00	2013-09-29 23:44:00	117
JFK	DEN	39	3	2013-09-29 22:09:00	2013-09-29 21:30:00	2013-09-29 00:02:00	2013-09-29 23:59:00	208
LGA	CVG	73	54	2013-09-29 22:23:00	2013-09-29 21:10:00	2013-09-29 00:13:00	2013-09-29 23:19:00	87
LGA	FLL	47	20	2013-09-29 22:24:00	2013-09-29 21:37:00	2013-09-29 00:47:00	2013-09-29 00:27:00	128
EWR	MSP	131	110	2013-09-29 22:45:00	2013-09-29 20:34:00	2013-09-29 00:27:00	2013-09-29 22:37:00	141
JFK	LAX	173	141	2013-09-29 22:53:00	2013-09-29 20:00:00	2013-09-29 01:21:00	2013-09-29 23:00:00	303
JFK	DCA	94	76	2013-09-29 23:14:00	2013-09-29 21:40:00	2013-09-29 00:11:00	2013-09-29 22:55:00	39
JFK	PWM	33	19	2013-09-29 23:18:00	2013-09-29 22:45:00	2013-09-29 00:15:00	2013-09-29 23:56:00	44
LGA	RDU	144	124	2013-09-29 23:24:00	2013-09-29 21:00:00	2013-09-29 00:39:00	2013-09-29 22:35:00	61
LGA	FLL	225	183	2013-09-29 23:27:00	2013-09-29 19:42:00	2013-09-29 01:53:00	2013-09-29 22:50:00	129
JFK	PSE	-3	-9	2013-09-29 23:56:00	2013-09-29 23:59:00	2013-09-29 03:41:00	2013-09-29 03:50:00	204
JFK	SJU	5	-33	2013-09-30 20:50:00	2013-09-30 20:45:00	2013-09-30 00:20:00	2013-09-30 00:53:00	188
JFK	PDX	15	52	2013-09-30 21:10:00	2013-09-30 20:55:00	2013-09-30 00:51:00	2013-09-30 23:59:00	361
JFK	LAX	0	-30	2013-09-30 21:40:00	2013-09-30 21:40:00	2013-09-30 00:10:00	2013-09-30 00:40:00	298
JFK	SJU	0	-25	2013-09-30 21:45:00	2013-09-30 21:45:00	2013-09-30 01:15:00	2013-09-30 01:40:00	192
LGA	FLL	10	3	2013-09-30 21:47:00	2013-09-30 21:37:00	2013-09-30 00:30:00	2013-09-30 00:27:00	139
EWR	SFO	80	42	2013-09-30 22:33:00	2013-09-30 21:13:00	2013-09-30 01:12:00	2013-09-30 00:30:00	318
JFK	MCO	154	130	2013-09-30 22:35:00	2013-09-30 20:01:00	2013-09-30 00:59:00	2013-09-30 22:49:00	123
JFK	PSE	-10	-25	2013-09-30 23:49:00	2013-09-30 23:59:00	2013-09-30 03:25:00	2013-09-30 03:50:00	196

flights_dt <- flights_dt |> 
  mutate(
    overnight = arr_time < dep_time,
    arr_time = arr_time + days(overnight),
    sched_arr_time = sched_arr_time + days(overnight)
  )

flights_dt |> 
  filter(arr_time < dep_time) 

A tibble: 0 x 10
origin	dest	dep_delay	arr_delay	dep_time	sched_dep_time	arr_time	sched_arr_time	air_time	overnight
<chr>	<chr>	<dbl>	<dbl>	<dttm>	<dttm>	<dttm>	<dttm>	<dbl>	<lgl>

Intervals

dyears(1) / ddays(365) # 1 dyear is 365.25days
years(1) / days(1)

1.00068493150685

365.25

y2023 <- ymd("2023-01-01") %--% ymd("2024-01-01")
y2024 <- ymd("2024-01-01") %--% ymd("2025-01-01")

y2023 / days(1)
y2024 / days(1)

365

366

Exercises

Explain days(!overnight) and days(overnight) to someone who has just started learning R. What is the key fact you need to know?
Create a vector of dates giving the first day of every month in 2015. Create a vector of dates giving the first day of every month in the current year.
Write a function that given your birthday (as a date), returns how old you are in years.
Why can’t (today() %--% (today() + years(1))) / months(1) work?

(today() %--% (today() + years(1))) / months(1)

17.5 Time zones

Sys.timezone()      # {area}/{location}: {continent}/{city} or {ocian}/{city}

‘Australia/Brisbane’

length(OlsonNames())
head(OlsonNames())

597

<ol class=list-inline><li>‘Africa/Abidjan’</li><li>‘Africa/Accra’</li><li>‘Africa/Addis_Ababa’</li><li>‘Africa/Algiers’</li><li>‘Africa/Asmara’</li><li>‘Africa/Asmera’</li></ol>

x1 <- ymd_hms("2024-06-01 12:00:00", tz = "America/New_York")
x1

x2 <- ymd_hms("2024-06-01 18:00:00", tz = "Europe/Copenhagen")
x2

x3 <- ymd_hms("2024-06-02 04:00:00", tz = "Pacific/Auckland")
x3

x1 - x2
#> Time difference of 0 secs
x1 - x3
#> Time difference of 0 secs

[1] "2024-06-01 12:00:00 EDT"

[1] "2024-06-01 18:00:00 CEST"

[1] "2024-06-02 04:00:00 NZST"

Time difference of 0 secs

Time difference of 0 secs

You can change the time zone in two ways:

Keep the instant in time the same, and change how it’s displayed. Use this when the instant is correct, but you want a more natural display.
Change the underlying instant in time. Use this when you have an instant that has been labelled with the incorrect time zone, and you need to fix it.

x4 <- c(x1, x2, x3)
x4
x4a <- with_tz(x4, tzone = "Australia/Lord_Howe")
x4a

x4a - x4

[1] "2024-06-01 12:00:00 EDT" "2024-06-01 12:00:00 EDT"
[3] "2024-06-01 12:00:00 EDT"

[1] "2024-06-02 02:30:00 +1030" "2024-06-02 02:30:00 +1030"
[3] "2024-06-02 02:30:00 +1030"

Time differences in secs
[1] 0 0 0

x4b <- force_tz(x4, tzone = "Australia/Lord_Howe")
x4b

x4b - x4

[1] "2024-06-01 12:00:00 +1030" "2024-06-01 12:00:00 +1030"
[3] "2024-06-01 12:00:00 +1030"

Time differences in hours
[1] -14.5 -14.5 -14.5

C18. Missing Values

library(tidyverse)

18.2 Explicit missing values

Last observation carried forward (repeated)
Fixed values

treatment <- tribble(
  ~person,           ~treatment, ~response,
  "Derrick Whitmore", 1,         7,
  NA,                 2,         10,
  NA,                 3,         NA,
  "Katherine Burke",  1,         4
)

treatment |>
  fill(everything())        # method: last observation carried forward!

A tibble: 4 x 3
person	treatment	response
<chr>	<dbl>	<dbl>
Derrick Whitmore	1	7
Derrick Whitmore	2	10
Derrick Whitmore	3	10
Katherine Burke	1	4

x <- c(1, 4, 5, 7, NA)
coalesce(x, 0)

18.3 Implicit missing values

An explicit missing value is the presence of an absence.

An implicit missing value is the absence of a presence.

stocks <- tibble(
  year  = c(2020, 2020, 2020, 2020, 2021, 2021, 2021),
  qtr   = c(   1,    2,    3,    4,    2,    3,    4),
  price = c(1.88, 0.59, 0.35,   NA, 0.92, 0.17, 2.66)
)

Pivoting

Making data wider can make implicit missing values explicit because every combination of the rows and new columns must have some value.

stocks |>
  pivot_wider(
    names_from = qtr, 
    values_from = price
  )

A tibble: 2 x 5
year	1	2	3	4
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
2020	1.88	0.59	0.35	NA
2021	NA	0.92	0.17	2.66

Complete

tidyr::complete() allows you to generate explicit missing values by providing a set of variables that define the combination of rows that should exist.

stocks |>
  complete(year, qtr)

A tibble: 8 x 3
year	qtr	price
<dbl>	<dbl>	<dbl>
2020	1	1.88
2020	2	0.59
2020	3	0.35
2020	4	NA
2021	1	NA
2021	2	0.92
2021	3	0.17
2021	4	2.66

stocks |>
  complete(year = 2019:2021, qtr)

A tibble: 12 x 3
year	qtr	price
<dbl>	<dbl>	<dbl>
2019	1	NA
2019	2	NA
2019	3	NA
2019	4	NA
2020	1	1.88
2020	2	0.59
2020	3	0.35
2020	4	NA
2021	1	NA
2021	2	0.92
2021	3	0.17
2021	4	2.66

Joins

In some cases, the complete set of observations can’t be generated by a simple combination of variables. In that case, you can do manually what complete() does for you: create a data frame that contains all the rows that should exist (using whatever combination of techniques you need), then combine it with your original dataset with dplyr::full_join().

dplyr::anti_join(x, y) is a particularly useful tool , it selects only the rows in x that don’t have a match in y

flights |> 
  distinct(faa = dest) |> 
  anti_join(airports)

[1m[22mJoining with `by = join_by(faa)`

A tibble: 4 x 1
faa
<chr>
BQN
SJU
STT
PSE

flights |> 
  distinct(tailnum) |> 
  anti_join(planes)

[1m[22mJoining with `by = join_by(tailnum)`

A tibble: 722 x 1
tailnum
<chr>
N3ALAA
N3DUAA
N542MQ
N730MQ
N9EAMQ
N532UA
N3EMAA
N518MQ
N3BAAA
N3CYAA
N426US
N3GKAA
N4WNAA
N5FMAA
N722MQ
N3EKAA
N3ETAA
N541AA
N4WRAA
N4WPAA
N508MQ
N3HMAA
N828MQ
N3GEAA
N739MQ
N531MQ
N527JB
N846MQ
N3GVAA
N4YCAA
...
N7BAAA
N7BVAA
N626MQ
N675MQ
N580AA
N717MQ
N738MQ
N720MQ
N7ASAA
N328AT
N735MQ
N5EDAA
N5DJAA
N7ALAA
N721MQ
N7BGAA
N5ESAA
N456UW
N838MQ
N442US
N502SW
N451UW
N7BKAA
N800MQ
N7CAAA
N823MQ
N5FCAA
N5ERAA
N654MQ
N647MQ

Exercises

Can you find any relationship between the carrier and the rows that appear to be missing from planes?

18.4 Factors and empty groups

health <- tibble(
  name   = c("Ikaia", "Oletta", "Leriah", "Dashay", "Tresaun"),
  smoker = factor(c("no", "no", "no", "no", "no"), levels = c("yes", "no")),
  age    = c(34, 88, 75, 47, 56),
)

health |> count(smoker)

A tibble: 1 x 2
smoker	n
<fct>	<int>
no	5

# We can request count() to keep all the groups, even those not seen in the data by using .drop = FALSE:
health |> count(smoker, .drop = FALSE)

A tibble: 2 x 2
smoker	n
<fct>	<int>
yes	0
no	5

p1 <- ggplot(health, aes(x = smoker)) +
  geom_bar() +
  scale_x_discrete()

p2 <- ggplot(health, aes(x = smoker)) +
  geom_bar() +
  scale_x_discrete(drop = FALSE)

options(repr.plot.width = 10, repr.plot.height = 5)
grid.arrange(p1, p2, ncol=2)

png

health |> 
  group_by(smoker, .drop = FALSE) |> 
  summarize(
    n = n(),
    mean_age = mean(age),
    min_age = min(age),
    max_age = max(age),
    sd_age = sd(age)
  )

Warning message:
"[1m[22mThere were 2 warnings in `summarize()`.
The first warning was:
[1m[22m[36mi[39m In argument: `min_age = min(age)`.
[36mi[39m In group 1: `smoker = yes`.
Caused by warning in `min()`:
[33m![39m no non-missing arguments to min; returning Inf
[1m[22m[36mi[39m Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning."

A tibble: 2 x 6
smoker	n	mean_age	min_age	max_age	sd_age
<fct>	<int>	<dbl>	<dbl>	<dbl>	<dbl>
yes	0	NaN	Inf	-Inf	NA
no	5	60	34	88	21.62175

health |> 
  group_by(smoker) |> 
  summarize(
    n = n(),
    mean_age = mean(age),
    min_age = min(age),
    max_age = max(age),
    sd_age = sd(age)
  ) |> 
  complete(smoker)

A tibble: 2 x 6
smoker	n	mean_age	min_age	max_age	sd_age
<fct>	<int>	<dbl>	<dbl>	<dbl>	<dbl>
yes	NA	NA	NA	NA	NA
no	5	60	34	88	21.62175

C19. Joins

Typically you have many data frames, and you must join them together to answer the questions that you’re interested in. This chapter will introduce you to two important types of joins:

Mutating joins, which add new variables to one data frame from matching observations in another.
Filtering joins, which filter observations from one data frame based on whether or not they match an observation in another.

library(tidyverse)
library(nycflights13)

19.2 Keys

Primary and foreign keys

A primary key is a variable or set of variables that uniquely identifies each observation. When more than one variable is needed, the key is called a compound key.
A foreign key is a variable (or set of variables) that corresponds to a primary key in another table.

For examples:

carrier and faa, tailnum are primary key of corresponding tables airlines, airports, planes
origin and time_hour are the compound primary key of the table weather
flights$tailnum is a foreign key that corresponds to the primary key planes$tailnum.
flights$carrier is a foreign key that corresponds to the primary key airlines$carrier.
flights$origin is a foreign key that corresponds to the primary key airports$faa.
flights$dest is a foreign key that corresponds to the primary key airports$faa.
flights$origin-flights$time_hour is a compound foreign key that corresponds to the compound primary key weather$origin-weather$time_hour.

airlines

A tibble: 16 x 2
carrier	name
<chr>	<chr>
9E	Endeavor Air Inc.
AA	American Airlines Inc.
AS	Alaska Airlines Inc.
B6	JetBlue Airways
DL	Delta Air Lines Inc.
EV	ExpressJet Airlines Inc.
F9	Frontier Airlines Inc.
FL	AirTran Airways Corporation
HA	Hawaiian Airlines Inc.
MQ	Envoy Air
OO	SkyWest Airlines Inc.
UA	United Air Lines Inc.
US	US Airways Inc.
VX	Virgin America
WN	Southwest Airlines Co.
YV	Mesa Airlines Inc.

airports

A tibble: 1458 x 8
faa	name	lat	lon	alt	tz	dst	tzone
<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
04G	Lansdowne Airport	41.13047	-80.61958	1044	-5	A	America/New_York
06A	Moton Field Municipal Airport	32.46057	-85.68003	264	-6	A	America/Chicago
06C	Schaumburg Regional	41.98934	-88.10124	801	-6	A	America/Chicago
06N	Randall Airport	41.43191	-74.39156	523	-5	A	America/New_York
09J	Jekyll Island Airport	31.07447	-81.42778	11	-5	A	America/New_York
0A9	Elizabethton Municipal Airport	36.37122	-82.17342	1593	-5	A	America/New_York
0G6	Williams County Airport	41.46731	-84.50678	730	-5	A	America/New_York
0G7	Finger Lakes Regional Airport	42.88356	-76.78123	492	-5	A	America/New_York
0P2	Shoestring Aviation Airfield	39.79482	-76.64719	1000	-5	U	America/New_York
0S9	Jefferson County Intl	48.05381	-122.81064	108	-8	A	America/Los_Angeles
0W3	Harford County Airport	39.56684	-76.20240	409	-5	A	America/New_York
10C	Galt Field Airport	42.40289	-88.37511	875	-6	U	America/Chicago
17G	Port Bucyrus-Crawford County Airport	40.78156	-82.97481	1003	-5	A	America/New_York
19A	Jackson County Airport	34.17586	-83.56160	951	-5	U	America/New_York
1A3	Martin Campbell Field Airport	35.01581	-84.34683	1789	-5	A	America/New_York
1B9	Mansfield Municipal	42.00013	-71.19677	122	-5	A	America/New_York
1C9	Frazier Lake Airpark	54.01333	-124.76833	152	-8	A	America/Vancouver
1CS	Clow International Airport	41.69597	-88.12923	670	-6	U	America/Chicago
1G3	Kent State Airport	41.15139	-81.41511	1134	-5	A	America/New_York
1G4	Grand Canyon West Airport	35.89990	-113.81567	4813	-7	A	America/Phoenix
1H2	Effingham Memorial Airport	39.07000	-88.53400	585	-6	A	America/Chicago
1OH	Fortman Airport	40.55533	-84.38662	885	-5	U	America/New_York
1RL	Point Roberts Airpark	48.97972	-123.07889	10	-8	A	America/Los_Angeles
23M	Clarke CO	32.05170	-88.44340	320	-6	A	America/Chicago
24C	Lowell City Airport	42.95392	-85.34391	681	-5	A	America/New_York
24J	Suwannee County Airport	30.30013	-83.02469	104	-5	A	America/New_York
25D	Forest Lake Airport	45.24775	-92.99439	925	-6	A	America/Chicago
29D	Grove City Airport	41.14603	-80.16775	1371	-5	A	America/New_York
2A0	Mark Anton Airport	35.48625	-84.93108	718	-5	A	America/New_York
2B2	Plum Island Airport	42.79536	-70.83944	11	-5	A	America/New_York
...	...	...	...	...	...	...	...
X59	Valkaria Municipal	27.96086	-80.55833	26	-5	A	America/New_York
XFL	Flagler County Airport	29.28210	-81.12120	33	-5	A	America/New_York
XNA	NW Arkansas Regional	36.28187	-94.30681	1287	-6	A	America/Chicago
XZK	Amherst Amtrak Station AMM	42.37500	-72.51139	258	-5	A	America/New_York
Y51	Municipal Airport	43.57936	-90.89647	1292	-6	A	America/Chicago
Y72	Bloyer Field	43.97622	-90.48061	966	-6	A	America/Chicago
YAK	Yakutat	59.30120	-139.39370	33	-9	A	NA
YIP	Willow Run	42.23793	-83.53041	716	-5	A	America/New_York
YKM	Yakima Air Terminal McAllister Field	46.56820	-120.54400	1095	-8	A	America/Los_Angeles
YKN	Chan Gurney	42.87110	-97.39690	1200	-6	A	America/Chicago
YNG	Youngstown Warren Rgnl	41.26074	-80.67910	1196	-5	A	America/New_York
YUM	Yuma Mcas Yuma Intl	32.65658	-114.60598	216	-7	N	America/Phoenix
Z84	Clear	64.30120	-149.12014	552	-9	A	America/Anchorage
ZBP	Penn Station	39.30722	-76.61556	66	-5	A	America/New_York
ZFV	Philadelphia 30th St Station	39.95570	-75.18200	0	-5	A	America/New_York
ZPH	Municipal Airport	28.22806	-82.15583	90	-5	A	America/New_York
ZRA	Atlantic City Rail Terminal	39.36650	-74.44200	8	-5	A	America/New_York
ZRD	Train Station	37.53430	-77.42945	26	-5	A	America/New_York
ZRP	Newark Penn Station	40.73472	-74.16417	0	-5	A	America/New_York
ZRT	Hartford Union Station	41.76888	-72.68150	0	-5	A	America/New_York
ZRZ	New Carrollton Rail Station	38.94800	-76.87190	39	-5	A	America/New_York
ZSF	Springfield Amtrak Station	42.10600	-72.59305	65	-5	A	America/New_York
ZSY	Scottsdale Airport	33.62289	-111.91053	1519	-7	A	America/Phoenix
ZTF	Stamford Amtrak Station	41.04694	-73.54149	0	-5	A	America/New_York
ZTY	Boston Back Bay Station	42.34780	-71.07500	20	-5	A	America/New_York
ZUN	Black Rock	35.08323	-108.79178	6454	-7	A	America/Denver
ZVE	New Haven Rail Station	41.29867	-72.92599	7	-5	A	America/New_York
ZWI	Wilmington Amtrak Station	39.73667	-75.55167	0	-5	A	America/New_York
ZWU	Washington Union Station	38.89746	-77.00643	76	-5	A	America/New_York
ZYP	Penn Station	40.75050	-73.99350	35	-5	A	America/New_York

planes

A tibble: 3322 x 9
tailnum	year	type	manufacturer	model	engines	seats	speed	engine
<chr>	<int>	<chr>	<chr>	<chr>	<int>	<int>	<int>	<chr>
N10156	2004	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N102UW	1998	Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214	2	182	NA	Turbo-fan
N103US	1999	Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214	2	182	NA	Turbo-fan
N104UW	1999	Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214	2	182	NA	Turbo-fan
N10575	2002	Fixed wing multi engine	EMBRAER	EMB-145LR	2	55	NA	Turbo-fan
N105UW	1999	Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214	2	182	NA	Turbo-fan
N107US	1999	Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214	2	182	NA	Turbo-fan
N108UW	1999	Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214	2	182	NA	Turbo-fan
N109UW	1999	Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214	2	182	NA	Turbo-fan
N110UW	1999	Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214	2	182	NA	Turbo-fan
N11106	2002	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11107	2002	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11109	2002	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11113	2002	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11119	2002	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11121	2003	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11127	2003	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11137	2003	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11140	2003	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11150	2003	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11155	2004	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11164	2004	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11165	2004	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11176	2004	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11181	2005	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11184	2005	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11187	2005	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11189	2005	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11191	2005	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
N11192	2005	Fixed wing multi engine	EMBRAER	EMB-145XR	2	55	NA	Turbo-fan
...	...	...	...	...	...	...	...	...
N984DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N985AT	2001	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N985DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N986AT	2001	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N986DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N987AT	2001	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N987DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N988AT	2001	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N988DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N989AT	2001	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N989DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N990AT	2001	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N990DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N991AT	NA	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N991DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N992AT	2002	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N992DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N993AT	2002	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N993DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N994AT	2002	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N994DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS CORPORATION	MD-88	2	142	NA	Turbo-jet
N995AT	2002	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N995DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N996AT	2002	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N996DL	1991	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N997AT	2002	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N997DL	1992	Fixed wing multi engine	MCDONNELL DOUGLAS AIRCRAFT CO	MD-88	2	142	NA	Turbo-fan
N998AT	2002	Fixed wing multi engine	BOEING	717-200	2	100	NA	Turbo-fan
N998DL	1992	Fixed wing multi engine	MCDONNELL DOUGLAS CORPORATION	MD-88	2	142	NA	Turbo-jet
N999DN	1992	Fixed wing multi engine	MCDONNELL DOUGLAS CORPORATION	MD-88	2	142	NA	Turbo-jet

The data relationships {width=75%}

Checking primary keys

planes |> 
  count(tailnum) |> 
  filter(n > 1)

weather |> 
  count(time_hour, origin) |> 
  filter(n > 1)

A tibble: 0 x 2
tailnum	n
<chr>	<int>

A tibble: 0 x 3
time_hour	origin	n
<dttm>	<chr>	<int>

planes |> 
  filter(is.na(tailnum))

weather |> 
  filter(is.na(time_hour) | is.na(origin))

A tibble: 0 x 9
tailnum	year	type	manufacturer	model	engines	seats	speed	engine
<chr>	<int>	<chr>	<chr>	<chr>	<int>	<int>	<int>	<chr>

A tibble: 0 x 15
origin	year	month	day	hour	temp	dewp	humid	wind_dir	wind_speed	wind_gust	precip	pressure	visib	time_hour
<chr>	<int>	<int>	<int>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dttm>

Surrogate keys

flights |> 
  count(time_hour, carrier, flight) |> 
  filter(n > 1)

A tibble: 0 x 4
time_hour	carrier	flight	n
<dttm>	<chr>	<int>	<int>

flights2 <- flights |> 
  mutate(id = row_number(), .before = 1)
flights2

A tibble: 336776 x 20
id	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute	time_hour
<int>	<int>	<int>	<int>	<int>	<int>	<dbl>	<int>	<int>	<dbl>	<chr>	<int>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dttm>
1	2013	1	1	517	515	2	830	819	11	UA	1545	N14228	EWR	IAH	227	1400	5	15	2013-01-01 05:00:00
2	2013	1	1	533	529	4	850	830	20	UA	1714	N24211	LGA	IAH	227	1416	5	29	2013-01-01 05:00:00
3	2013	1	1	542	540	2	923	850	33	AA	1141	N619AA	JFK	MIA	160	1089	5	40	2013-01-01 05:00:00
4	2013	1	1	544	545	-1	1004	1022	-18	B6	725	N804JB	JFK	BQN	183	1576	5	45	2013-01-01 05:00:00
5	2013	1	1	554	600	-6	812	837	-25	DL	461	N668DN	LGA	ATL	116	762	6	0	2013-01-01 06:00:00
6	2013	1	1	554	558	-4	740	728	12	UA	1696	N39463	EWR	ORD	150	719	5	58	2013-01-01 05:00:00
7	2013	1	1	555	600	-5	913	854	19	B6	507	N516JB	EWR	FLL	158	1065	6	0	2013-01-01 06:00:00
8	2013	1	1	557	600	-3	709	723	-14	EV	5708	N829AS	LGA	IAD	53	229	6	0	2013-01-01 06:00:00
9	2013	1	1	557	600	-3	838	846	-8	B6	79	N593JB	JFK	MCO	140	944	6	0	2013-01-01 06:00:00
10	2013	1	1	558	600	-2	753	745	8	AA	301	N3ALAA	LGA	ORD	138	733	6	0	2013-01-01 06:00:00
11	2013	1	1	558	600	-2	849	851	-2	B6	49	N793JB	JFK	PBI	149	1028	6	0	2013-01-01 06:00:00
12	2013	1	1	558	600	-2	853	856	-3	B6	71	N657JB	JFK	TPA	158	1005	6	0	2013-01-01 06:00:00
13	2013	1	1	558	600	-2	924	917	7	UA	194	N29129	JFK	LAX	345	2475	6	0	2013-01-01 06:00:00
14	2013	1	1	558	600	-2	923	937	-14	UA	1124	N53441	EWR	SFO	361	2565	6	0	2013-01-01 06:00:00
15	2013	1	1	559	600	-1	941	910	31	AA	707	N3DUAA	LGA	DFW	257	1389	6	0	2013-01-01 06:00:00
16	2013	1	1	559	559	0	702	706	-4	B6	1806	N708JB	JFK	BOS	44	187	5	59	2013-01-01 05:00:00
17	2013	1	1	559	600	-1	854	902	-8	UA	1187	N76515	EWR	LAS	337	2227	6	0	2013-01-01 06:00:00
18	2013	1	1	600	600	0	851	858	-7	B6	371	N595JB	LGA	FLL	152	1076	6	0	2013-01-01 06:00:00
19	2013	1	1	600	600	0	837	825	12	MQ	4650	N542MQ	LGA	ATL	134	762	6	0	2013-01-01 06:00:00
20	2013	1	1	601	600	1	844	850	-6	B6	343	N644JB	EWR	PBI	147	1023	6	0	2013-01-01 06:00:00
21	2013	1	1	602	610	-8	812	820	-8	DL	1919	N971DL	LGA	MSP	170	1020	6	10	2013-01-01 06:00:00
22	2013	1	1	602	605	-3	821	805	16	MQ	4401	N730MQ	LGA	DTW	105	502	6	5	2013-01-01 06:00:00
23	2013	1	1	606	610	-4	858	910	-12	AA	1895	N633AA	EWR	MIA	152	1085	6	10	2013-01-01 06:00:00
24	2013	1	1	606	610	-4	837	845	-8	DL	1743	N3739P	JFK	ATL	128	760	6	10	2013-01-01 06:00:00
25	2013	1	1	607	607	0	858	915	-17	UA	1077	N53442	EWR	MIA	157	1085	6	7	2013-01-01 06:00:00
26	2013	1	1	608	600	8	807	735	32	MQ	3768	N9EAMQ	EWR	ORD	139	719	6	0	2013-01-01 06:00:00
27	2013	1	1	611	600	11	945	931	14	UA	303	N532UA	JFK	SFO	366	2586	6	0	2013-01-01 06:00:00
28	2013	1	1	613	610	3	925	921	4	B6	135	N635JB	JFK	RSW	175	1074	6	10	2013-01-01 06:00:00
29	2013	1	1	615	615	0	1039	1100	-21	B6	709	N794JB	JFK	SJU	182	1598	6	15	2013-01-01 06:00:00
30	2013	1	1	615	615	0	833	842	-9	DL	575	N326NB	EWR	ATL	120	746	6	15	2013-01-01 06:00:00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
336747	2013	9	30	2123	2125	-2	2223	2247	-24	EV	5489	N712EV	LGA	CHO	45	305	21	25	2013-09-30 21:00:00
336748	2013	9	30	2127	2129	-2	2314	2323	-9	EV	3833	N16546	EWR	CLT	72	529	21	29	2013-09-30 21:00:00
336749	2013	9	30	2128	2130	-2	2328	2359	-31	B6	97	N807JB	JFK	DEN	213	1626	21	30	2013-09-30 21:00:00
336750	2013	9	30	2129	2059	30	2230	2232	-2	EV	5048	N751EV	LGA	RIC	45	292	20	59	2013-09-30 20:00:00
336751	2013	9	30	2131	2140	-9	2225	2255	-30	MQ	3621	N807MQ	JFK	DCA	36	213	21	40	2013-09-30 21:00:00
336752	2013	9	30	2140	2140	0	10	40	-30	AA	185	N335AA	JFK	LAX	298	2475	21	40	2013-09-30 21:00:00
336753	2013	9	30	2142	2129	13	2250	2239	11	EV	4509	N12957	EWR	PWM	47	284	21	29	2013-09-30 21:00:00
336754	2013	9	30	2145	2145	0	115	140	-25	B6	1103	N633JB	JFK	SJU	192	1598	21	45	2013-09-30 21:00:00
336755	2013	9	30	2147	2137	10	30	27	3	B6	1371	N627JB	LGA	FLL	139	1076	21	37	2013-09-30 21:00:00
336756	2013	9	30	2149	2156	-7	2245	2308	-23	UA	523	N813UA	EWR	BOS	37	200	21	56	2013-09-30 21:00:00
336757	2013	9	30	2150	2159	-9	2250	2306	-16	EV	3842	N10575	EWR	MHT	39	209	21	59	2013-09-30 21:00:00
336758	2013	9	30	2159	1845	194	2344	2030	194	9E	3320	N906XJ	JFK	BUF	50	301	18	45	2013-09-30 18:00:00
336759	2013	9	30	2203	2205	-2	2339	2331	8	EV	5311	N722EV	LGA	BGR	61	378	22	5	2013-09-30 22:00:00
336760	2013	9	30	2207	2140	27	2257	2250	7	MQ	3660	N532MQ	LGA	BNA	97	764	21	40	2013-09-30 21:00:00
336761	2013	9	30	2211	2059	72	2339	2242	57	EV	4672	N12145	EWR	STL	120	872	20	59	2013-09-30 20:00:00
336762	2013	9	30	2231	2245	-14	2335	2356	-21	B6	108	N193JB	JFK	PWM	48	273	22	45	2013-09-30 22:00:00
336763	2013	9	30	2233	2113	80	112	30	42	UA	471	N578UA	EWR	SFO	318	2565	21	13	2013-09-30 21:00:00
336764	2013	9	30	2235	2001	154	59	2249	130	B6	1083	N804JB	JFK	MCO	123	944	20	1	2013-09-30 20:00:00
336765	2013	9	30	2237	2245	-8	2345	2353	-8	B6	234	N318JB	JFK	BTV	43	266	22	45	2013-09-30 22:00:00
336766	2013	9	30	2240	2245	-5	2334	2351	-17	B6	1816	N354JB	JFK	SYR	41	209	22	45	2013-09-30 22:00:00
336767	2013	9	30	2240	2250	-10	2347	7	-20	B6	2002	N281JB	JFK	BUF	52	301	22	50	2013-09-30 22:00:00
336768	2013	9	30	2241	2246	-5	2345	1	-16	B6	486	N346JB	JFK	ROC	47	264	22	46	2013-09-30 22:00:00
336769	2013	9	30	2307	2255	12	2359	2358	1	B6	718	N565JB	JFK	BOS	33	187	22	55	2013-09-30 22:00:00
336770	2013	9	30	2349	2359	-10	325	350	-25	B6	745	N516JB	JFK	PSE	196	1617	23	59	2013-09-30 23:00:00
336771	2013	9	30	NA	1842	NA	NA	2019	NA	EV	5274	N740EV	LGA	BNA	NA	764	18	42	2013-09-30 18:00:00
336772	2013	9	30	NA	1455	NA	NA	1634	NA	9E	3393	NA	JFK	DCA	NA	213	14	55	2013-09-30 14:00:00
336773	2013	9	30	NA	2200	NA	NA	2312	NA	9E	3525	NA	LGA	SYR	NA	198	22	0	2013-09-30 22:00:00
336774	2013	9	30	NA	1210	NA	NA	1330	NA	MQ	3461	N535MQ	LGA	BNA	NA	764	12	10	2013-09-30 12:00:00
336775	2013	9	30	NA	1159	NA	NA	1344	NA	MQ	3572	N511MQ	LGA	CLE	NA	419	11	59	2013-09-30 11:00:00
336776	2013	9	30	NA	840	NA	NA	1020	NA	MQ	3531	N839MQ	LGA	RDU	NA	431	8	40	2013-09-30 08:00:00

Exercises

We forgot to draw the relationship between weather and airports in Figure 19.1. What is the relationship and how should it appear in the diagram?
weather only contains information for the three origin airports in NYC. If it contained weather records for all airports in the USA, what additional connection would it make to flights?
The year, month, day, hour, and origin variables almost form a compound key for weather, but there’s one hour that has duplicate observations. Can you figure out what’s special about that hour?
We know that some days of the year are special and fewer people than usual fly on them (e.g., Christmas eve and Christmas day). How might you represent that data as a data frame? What would be the primary key? How would it connect to the existing data frames?
Draw a diagram illustrating the connections between the Batting, People, and Salaries data frames in the Lahman package. Draw another diagram that shows the relationship between People, Managers, AwardsManagers. How would you characterize the relationship between the Batting, Pitching, and Fielding data frames?

19.3 Basic joins

dplyr provides six join functions:

left_join(), to add in additional metadata.
inner_join(),
right_join(),
full_join(),
semi_join(),
anti_join()

Mutating joins:

A mutating join allows you to combine variables from two data frames: it first matches observations by their keys, then copies across variables from one data frame to the other. inner_join(), right_join(), full_join() have the same interface as left_join(). The difference is which rows they keep: left join keeps all the rows in x, the right join keeps all rows in y, the full join keeps all rows in either x or y, and the inner join only keeps rows that occur in both x and y.

flights2 <- flights |> 
  select(year, time_hour, origin, dest, tailnum, carrier)
flights2

A tibble: 336776 x 6
year	time_hour	origin	dest	tailnum	carrier
<int>	<dttm>	<chr>	<chr>	<chr>	<chr>
2013	2013-01-01 05:00:00	EWR	IAH	N14228	UA
2013	2013-01-01 05:00:00	LGA	IAH	N24211	UA
2013	2013-01-01 05:00:00	JFK	MIA	N619AA	AA
2013	2013-01-01 05:00:00	JFK	BQN	N804JB	B6
2013	2013-01-01 06:00:00	LGA	ATL	N668DN	DL
2013	2013-01-01 05:00:00	EWR	ORD	N39463	UA
2013	2013-01-01 06:00:00	EWR	FLL	N516JB	B6
2013	2013-01-01 06:00:00	LGA	IAD	N829AS	EV
2013	2013-01-01 06:00:00	JFK	MCO	N593JB	B6
2013	2013-01-01 06:00:00	LGA	ORD	N3ALAA	AA
2013	2013-01-01 06:00:00	JFK	PBI	N793JB	B6
2013	2013-01-01 06:00:00	JFK	TPA	N657JB	B6
2013	2013-01-01 06:00:00	JFK	LAX	N29129	UA
2013	2013-01-01 06:00:00	EWR	SFO	N53441	UA
2013	2013-01-01 06:00:00	LGA	DFW	N3DUAA	AA
2013	2013-01-01 05:00:00	JFK	BOS	N708JB	B6
2013	2013-01-01 06:00:00	EWR	LAS	N76515	UA
2013	2013-01-01 06:00:00	LGA	FLL	N595JB	B6
2013	2013-01-01 06:00:00	LGA	ATL	N542MQ	MQ
2013	2013-01-01 06:00:00	EWR	PBI	N644JB	B6
2013	2013-01-01 06:00:00	LGA	MSP	N971DL	DL
2013	2013-01-01 06:00:00	LGA	DTW	N730MQ	MQ
2013	2013-01-01 06:00:00	EWR	MIA	N633AA	AA
2013	2013-01-01 06:00:00	JFK	ATL	N3739P	DL
2013	2013-01-01 06:00:00	EWR	MIA	N53442	UA
2013	2013-01-01 06:00:00	EWR	ORD	N9EAMQ	MQ
2013	2013-01-01 06:00:00	JFK	SFO	N532UA	UA
2013	2013-01-01 06:00:00	JFK	RSW	N635JB	B6
2013	2013-01-01 06:00:00	JFK	SJU	N794JB	B6
2013	2013-01-01 06:00:00	EWR	ATL	N326NB	DL
...	...	...	...	...	...
2013	2013-09-30 21:00:00	LGA	CHO	N712EV	EV
2013	2013-09-30 21:00:00	EWR	CLT	N16546	EV
2013	2013-09-30 21:00:00	JFK	DEN	N807JB	B6
2013	2013-09-30 20:00:00	LGA	RIC	N751EV	EV
2013	2013-09-30 21:00:00	JFK	DCA	N807MQ	MQ
2013	2013-09-30 21:00:00	JFK	LAX	N335AA	AA
2013	2013-09-30 21:00:00	EWR	PWM	N12957	EV
2013	2013-09-30 21:00:00	JFK	SJU	N633JB	B6
2013	2013-09-30 21:00:00	LGA	FLL	N627JB	B6
2013	2013-09-30 21:00:00	EWR	BOS	N813UA	UA
2013	2013-09-30 21:00:00	EWR	MHT	N10575	EV
2013	2013-09-30 18:00:00	JFK	BUF	N906XJ	9E
2013	2013-09-30 22:00:00	LGA	BGR	N722EV	EV
2013	2013-09-30 21:00:00	LGA	BNA	N532MQ	MQ
2013	2013-09-30 20:00:00	EWR	STL	N12145	EV
2013	2013-09-30 22:00:00	JFK	PWM	N193JB	B6
2013	2013-09-30 21:00:00	EWR	SFO	N578UA	UA
2013	2013-09-30 20:00:00	JFK	MCO	N804JB	B6
2013	2013-09-30 22:00:00	JFK	BTV	N318JB	B6
2013	2013-09-30 22:00:00	JFK	SYR	N354JB	B6
2013	2013-09-30 22:00:00	JFK	BUF	N281JB	B6
2013	2013-09-30 22:00:00	JFK	ROC	N346JB	B6
2013	2013-09-30 22:00:00	JFK	BOS	N565JB	B6
2013	2013-09-30 23:00:00	JFK	PSE	N516JB	B6
2013	2013-09-30 18:00:00	LGA	BNA	N740EV	EV
2013	2013-09-30 14:00:00	JFK	DCA	NA	9E
2013	2013-09-30 22:00:00	LGA	SYR	NA	9E
2013	2013-09-30 12:00:00	LGA	BNA	N535MQ	MQ
2013	2013-09-30 11:00:00	LGA	CLE	N511MQ	MQ
2013	2013-09-30 08:00:00	LGA	RDU	N839MQ	MQ

# add the full airline name to the flights2 data
flights2 |>
  left_join(airlines)

[1m[22mJoining with `by = join_by(carrier)`

A tibble: 336776 x 7
year	time_hour	origin	dest	tailnum	carrier	name
<int>	<dttm>	<chr>	<chr>	<chr>	<chr>	<chr>
2013	2013-01-01 05:00:00	EWR	IAH	N14228	UA	United Air Lines Inc.
2013	2013-01-01 05:00:00	LGA	IAH	N24211	UA	United Air Lines Inc.
2013	2013-01-01 05:00:00	JFK	MIA	N619AA	AA	American Airlines Inc.
2013	2013-01-01 05:00:00	JFK	BQN	N804JB	B6	JetBlue Airways
2013	2013-01-01 06:00:00	LGA	ATL	N668DN	DL	Delta Air Lines Inc.
2013	2013-01-01 05:00:00	EWR	ORD	N39463	UA	United Air Lines Inc.
2013	2013-01-01 06:00:00	EWR	FLL	N516JB	B6	JetBlue Airways
2013	2013-01-01 06:00:00	LGA	IAD	N829AS	EV	ExpressJet Airlines Inc.
2013	2013-01-01 06:00:00	JFK	MCO	N593JB	B6	JetBlue Airways
2013	2013-01-01 06:00:00	LGA	ORD	N3ALAA	AA	American Airlines Inc.
2013	2013-01-01 06:00:00	JFK	PBI	N793JB	B6	JetBlue Airways
2013	2013-01-01 06:00:00	JFK	TPA	N657JB	B6	JetBlue Airways
2013	2013-01-01 06:00:00	JFK	LAX	N29129	UA	United Air Lines Inc.
2013	2013-01-01 06:00:00	EWR	SFO	N53441	UA	United Air Lines Inc.
2013	2013-01-01 06:00:00	LGA	DFW	N3DUAA	AA	American Airlines Inc.
2013	2013-01-01 05:00:00	JFK	BOS	N708JB	B6	JetBlue Airways
2013	2013-01-01 06:00:00	EWR	LAS	N76515	UA	United Air Lines Inc.
2013	2013-01-01 06:00:00	LGA	FLL	N595JB	B6	JetBlue Airways
2013	2013-01-01 06:00:00	LGA	ATL	N542MQ	MQ	Envoy Air
2013	2013-01-01 06:00:00	EWR	PBI	N644JB	B6	JetBlue Airways
2013	2013-01-01 06:00:00	LGA	MSP	N971DL	DL	Delta Air Lines Inc.
2013	2013-01-01 06:00:00	LGA	DTW	N730MQ	MQ	Envoy Air
2013	2013-01-01 06:00:00	EWR	MIA	N633AA	AA	American Airlines Inc.
2013	2013-01-01 06:00:00	JFK	ATL	N3739P	DL	Delta Air Lines Inc.
2013	2013-01-01 06:00:00	EWR	MIA	N53442	UA	United Air Lines Inc.
2013	2013-01-01 06:00:00	EWR	ORD	N9EAMQ	MQ	Envoy Air
2013	2013-01-01 06:00:00	JFK	SFO	N532UA	UA	United Air Lines Inc.
2013	2013-01-01 06:00:00	JFK	RSW	N635JB	B6	JetBlue Airways
2013	2013-01-01 06:00:00	JFK	SJU	N794JB	B6	JetBlue Airways
2013	2013-01-01 06:00:00	EWR	ATL	N326NB	DL	Delta Air Lines Inc.
...	...	...	...	...	...	...
2013	2013-09-30 21:00:00	LGA	CHO	N712EV	EV	ExpressJet Airlines Inc.
2013	2013-09-30 21:00:00	EWR	CLT	N16546	EV	ExpressJet Airlines Inc.
2013	2013-09-30 21:00:00	JFK	DEN	N807JB	B6	JetBlue Airways
2013	2013-09-30 20:00:00	LGA	RIC	N751EV	EV	ExpressJet Airlines Inc.
2013	2013-09-30 21:00:00	JFK	DCA	N807MQ	MQ	Envoy Air
2013	2013-09-30 21:00:00	JFK	LAX	N335AA	AA	American Airlines Inc.
2013	2013-09-30 21:00:00	EWR	PWM	N12957	EV	ExpressJet Airlines Inc.
2013	2013-09-30 21:00:00	JFK	SJU	N633JB	B6	JetBlue Airways
2013	2013-09-30 21:00:00	LGA	FLL	N627JB	B6	JetBlue Airways
2013	2013-09-30 21:00:00	EWR	BOS	N813UA	UA	United Air Lines Inc.
2013	2013-09-30 21:00:00	EWR	MHT	N10575	EV	ExpressJet Airlines Inc.
2013	2013-09-30 18:00:00	JFK	BUF	N906XJ	9E	Endeavor Air Inc.
2013	2013-09-30 22:00:00	LGA	BGR	N722EV	EV	ExpressJet Airlines Inc.
2013	2013-09-30 21:00:00	LGA	BNA	N532MQ	MQ	Envoy Air
2013	2013-09-30 20:00:00	EWR	STL	N12145	EV	ExpressJet Airlines Inc.
2013	2013-09-30 22:00:00	JFK	PWM	N193JB	B6	JetBlue Airways
2013	2013-09-30 21:00:00	EWR	SFO	N578UA	UA	United Air Lines Inc.
2013	2013-09-30 20:00:00	JFK	MCO	N804JB	B6	JetBlue Airways
2013	2013-09-30 22:00:00	JFK	BTV	N318JB	B6	JetBlue Airways
2013	2013-09-30 22:00:00	JFK	SYR	N354JB	B6	JetBlue Airways
2013	2013-09-30 22:00:00	JFK	BUF	N281JB	B6	JetBlue Airways
2013	2013-09-30 22:00:00	JFK	ROC	N346JB	B6	JetBlue Airways
2013	2013-09-30 22:00:00	JFK	BOS	N565JB	B6	JetBlue Airways
2013	2013-09-30 23:00:00	JFK	PSE	N516JB	B6	JetBlue Airways
2013	2013-09-30 18:00:00	LGA	BNA	N740EV	EV	ExpressJet Airlines Inc.
2013	2013-09-30 14:00:00	JFK	DCA	NA	9E	Endeavor Air Inc.
2013	2013-09-30 22:00:00	LGA	SYR	NA	9E	Endeavor Air Inc.
2013	2013-09-30 12:00:00	LGA	BNA	N535MQ	MQ	Envoy Air
2013	2013-09-30 11:00:00	LGA	CLE	N511MQ	MQ	Envoy Air
2013	2013-09-30 08:00:00	LGA	RDU	N839MQ	MQ	Envoy Air

# find out the temperature and wind speed when each plane departed:
flights2 |> 
  left_join(weather |> select(origin, time_hour, temp, wind_speed))

[1m[22mJoining with `by = join_by(time_hour, origin)`

A tibble: 336776 x 8
year	time_hour	origin	dest	tailnum	carrier	temp	wind_speed
<int>	<dttm>	<chr>	<chr>	<chr>	<chr>	<dbl>	<dbl>
2013	2013-01-01 05:00:00	EWR	IAH	N14228	UA	39.02	12.65858
2013	2013-01-01 05:00:00	LGA	IAH	N24211	UA	39.92	14.96014
2013	2013-01-01 05:00:00	JFK	MIA	N619AA	AA	39.02	14.96014
2013	2013-01-01 05:00:00	JFK	BQN	N804JB	B6	39.02	14.96014
2013	2013-01-01 06:00:00	LGA	ATL	N668DN	DL	39.92	16.11092
2013	2013-01-01 05:00:00	EWR	ORD	N39463	UA	39.02	12.65858
2013	2013-01-01 06:00:00	EWR	FLL	N516JB	B6	37.94	11.50780
2013	2013-01-01 06:00:00	LGA	IAD	N829AS	EV	39.92	16.11092
2013	2013-01-01 06:00:00	JFK	MCO	N593JB	B6	37.94	13.80936
2013	2013-01-01 06:00:00	LGA	ORD	N3ALAA	AA	39.92	16.11092
2013	2013-01-01 06:00:00	JFK	PBI	N793JB	B6	37.94	13.80936
2013	2013-01-01 06:00:00	JFK	TPA	N657JB	B6	37.94	13.80936
2013	2013-01-01 06:00:00	JFK	LAX	N29129	UA	37.94	13.80936
2013	2013-01-01 06:00:00	EWR	SFO	N53441	UA	37.94	11.50780
2013	2013-01-01 06:00:00	LGA	DFW	N3DUAA	AA	39.92	16.11092
2013	2013-01-01 05:00:00	JFK	BOS	N708JB	B6	39.02	14.96014
2013	2013-01-01 06:00:00	EWR	LAS	N76515	UA	37.94	11.50780
2013	2013-01-01 06:00:00	LGA	FLL	N595JB	B6	39.92	16.11092
2013	2013-01-01 06:00:00	LGA	ATL	N542MQ	MQ	39.92	16.11092
2013	2013-01-01 06:00:00	EWR	PBI	N644JB	B6	37.94	11.50780
2013	2013-01-01 06:00:00	LGA	MSP	N971DL	DL	39.92	16.11092
2013	2013-01-01 06:00:00	LGA	DTW	N730MQ	MQ	39.92	16.11092
2013	2013-01-01 06:00:00	EWR	MIA	N633AA	AA	37.94	11.50780
2013	2013-01-01 06:00:00	JFK	ATL	N3739P	DL	37.94	13.80936
2013	2013-01-01 06:00:00	EWR	MIA	N53442	UA	37.94	11.50780
2013	2013-01-01 06:00:00	EWR	ORD	N9EAMQ	MQ	37.94	11.50780
2013	2013-01-01 06:00:00	JFK	SFO	N532UA	UA	37.94	13.80936
2013	2013-01-01 06:00:00	JFK	RSW	N635JB	B6	37.94	13.80936
2013	2013-01-01 06:00:00	JFK	SJU	N794JB	B6	37.94	13.80936
2013	2013-01-01 06:00:00	EWR	ATL	N326NB	DL	37.94	11.50780
...	...	...	...	...	...	...	...
2013	2013-09-30 21:00:00	LGA	CHO	N712EV	EV	64.94	8.05546
2013	2013-09-30 21:00:00	EWR	CLT	N16546	EV	62.96	3.45234
2013	2013-09-30 21:00:00	JFK	DEN	N807JB	B6	62.06	9.20624
2013	2013-09-30 20:00:00	LGA	RIC	N751EV	EV	64.94	6.90468
2013	2013-09-30 21:00:00	JFK	DCA	N807MQ	MQ	62.06	9.20624
2013	2013-09-30 21:00:00	JFK	LAX	N335AA	AA	62.06	9.20624
2013	2013-09-30 21:00:00	EWR	PWM	N12957	EV	62.96	3.45234
2013	2013-09-30 21:00:00	JFK	SJU	N633JB	B6	62.06	9.20624
2013	2013-09-30 21:00:00	LGA	FLL	N627JB	B6	64.94	8.05546
2013	2013-09-30 21:00:00	EWR	BOS	N813UA	UA	62.96	3.45234
2013	2013-09-30 21:00:00	EWR	MHT	N10575	EV	62.96	3.45234
2013	2013-09-30 18:00:00	JFK	BUF	N906XJ	9E	64.04	6.90468
2013	2013-09-30 22:00:00	LGA	BGR	N722EV	EV	64.94	6.90468
2013	2013-09-30 21:00:00	LGA	BNA	N532MQ	MQ	64.94	8.05546
2013	2013-09-30 20:00:00	EWR	STL	N12145	EV	64.94	3.45234
2013	2013-09-30 22:00:00	JFK	PWM	N193JB	B6	60.98	9.20624
2013	2013-09-30 21:00:00	EWR	SFO	N578UA	UA	62.96	3.45234
2013	2013-09-30 20:00:00	JFK	MCO	N804JB	B6	62.06	8.05546
2013	2013-09-30 22:00:00	JFK	BTV	N318JB	B6	60.98	9.20624
2013	2013-09-30 22:00:00	JFK	SYR	N354JB	B6	60.98	9.20624
2013	2013-09-30 22:00:00	JFK	BUF	N281JB	B6	60.98	9.20624
2013	2013-09-30 22:00:00	JFK	ROC	N346JB	B6	60.98	9.20624
2013	2013-09-30 22:00:00	JFK	BOS	N565JB	B6	60.98	9.20624
2013	2013-09-30 23:00:00	JFK	PSE	N516JB	B6	60.08	9.20624
2013	2013-09-30 18:00:00	LGA	BNA	N740EV	EV	66.92	9.20624
2013	2013-09-30 14:00:00	JFK	DCA	NA	9E	68.00	11.50780
2013	2013-09-30 22:00:00	LGA	SYR	NA	9E	64.94	6.90468
2013	2013-09-30 12:00:00	LGA	BNA	N535MQ	MQ	69.08	5.75390
2013	2013-09-30 11:00:00	LGA	CLE	N511MQ	MQ	66.92	8.05546
2013	2013-09-30 08:00:00	LGA	RDU	N839MQ	MQ	60.98	5.75390

# what size of plane was flying:
flights2 |> 
  left_join(planes |> select(tailnum, type, engines, seats))

[1m[22mJoining with `by = join_by(tailnum)`

A tibble: 336776 x 9
year	time_hour	origin	dest	tailnum	carrier	type	engines	seats
<int>	<dttm>	<chr>	<chr>	<chr>	<chr>	<chr>	<int>	<int>
2013	2013-01-01 05:00:00	EWR	IAH	N14228	UA	Fixed wing multi engine	2	149
2013	2013-01-01 05:00:00	LGA	IAH	N24211	UA	Fixed wing multi engine	2	149
2013	2013-01-01 05:00:00	JFK	MIA	N619AA	AA	Fixed wing multi engine	2	178
2013	2013-01-01 05:00:00	JFK	BQN	N804JB	B6	Fixed wing multi engine	2	200
2013	2013-01-01 06:00:00	LGA	ATL	N668DN	DL	Fixed wing multi engine	2	178
2013	2013-01-01 05:00:00	EWR	ORD	N39463	UA	Fixed wing multi engine	2	191
2013	2013-01-01 06:00:00	EWR	FLL	N516JB	B6	Fixed wing multi engine	2	200
2013	2013-01-01 06:00:00	LGA	IAD	N829AS	EV	Fixed wing multi engine	2	55
2013	2013-01-01 06:00:00	JFK	MCO	N593JB	B6	Fixed wing multi engine	2	200
2013	2013-01-01 06:00:00	LGA	ORD	N3ALAA	AA	NA	NA	NA
2013	2013-01-01 06:00:00	JFK	PBI	N793JB	B6	Fixed wing multi engine	2	200
2013	2013-01-01 06:00:00	JFK	TPA	N657JB	B6	Fixed wing multi engine	2	200
2013	2013-01-01 06:00:00	JFK	LAX	N29129	UA	Fixed wing multi engine	2	178
2013	2013-01-01 06:00:00	EWR	SFO	N53441	UA	Fixed wing multi engine	2	191
2013	2013-01-01 06:00:00	LGA	DFW	N3DUAA	AA	NA	NA	NA
2013	2013-01-01 05:00:00	JFK	BOS	N708JB	B6	Fixed wing multi engine	2	200
2013	2013-01-01 06:00:00	EWR	LAS	N76515	UA	Fixed wing multi engine	2	149
2013	2013-01-01 06:00:00	LGA	FLL	N595JB	B6	Fixed wing multi engine	2	200
2013	2013-01-01 06:00:00	LGA	ATL	N542MQ	MQ	NA	NA	NA
2013	2013-01-01 06:00:00	EWR	PBI	N644JB	B6	Fixed wing multi engine	2	200
2013	2013-01-01 06:00:00	LGA	MSP	N971DL	DL	Fixed wing multi engine	2	142
2013	2013-01-01 06:00:00	LGA	DTW	N730MQ	MQ	NA	NA	NA
2013	2013-01-01 06:00:00	EWR	MIA	N633AA	AA	Fixed wing multi engine	2	178
2013	2013-01-01 06:00:00	JFK	ATL	N3739P	DL	Fixed wing multi engine	2	189
2013	2013-01-01 06:00:00	EWR	MIA	N53442	UA	Fixed wing multi engine	2	191
2013	2013-01-01 06:00:00	EWR	ORD	N9EAMQ	MQ	NA	NA	NA
2013	2013-01-01 06:00:00	JFK	SFO	N532UA	UA	NA	NA	NA
2013	2013-01-01 06:00:00	JFK	RSW	N635JB	B6	Fixed wing multi engine	2	200
2013	2013-01-01 06:00:00	JFK	SJU	N794JB	B6	Fixed wing multi engine	2	200
2013	2013-01-01 06:00:00	EWR	ATL	N326NB	DL	Fixed wing multi engine	2	145
...	...	...	...	...	...	...	...	...
2013	2013-09-30 21:00:00	LGA	CHO	N712EV	EV	Fixed wing multi engine	2	80
2013	2013-09-30 21:00:00	EWR	CLT	N16546	EV	Fixed wing multi engine	2	55
2013	2013-09-30 21:00:00	JFK	DEN	N807JB	B6	Fixed wing multi engine	2	200
2013	2013-09-30 20:00:00	LGA	RIC	N751EV	EV	Fixed wing multi engine	2	80
2013	2013-09-30 21:00:00	JFK	DCA	N807MQ	MQ	NA	NA	NA
2013	2013-09-30 21:00:00	JFK	LAX	N335AA	AA	Fixed wing multi engine	2	255
2013	2013-09-30 21:00:00	EWR	PWM	N12957	EV	Fixed wing multi engine	2	55
2013	2013-09-30 21:00:00	JFK	SJU	N633JB	B6	Fixed wing multi engine	2	200
2013	2013-09-30 21:00:00	LGA	FLL	N627JB	B6	Fixed wing multi engine	2	200
2013	2013-09-30 21:00:00	EWR	BOS	N813UA	UA	Fixed wing multi engine	2	179
2013	2013-09-30 21:00:00	EWR	MHT	N10575	EV	Fixed wing multi engine	2	55
2013	2013-09-30 18:00:00	JFK	BUF	N906XJ	9E	Fixed wing multi engine	2	95
2013	2013-09-30 22:00:00	LGA	BGR	N722EV	EV	Fixed wing multi engine	2	80
2013	2013-09-30 21:00:00	LGA	BNA	N532MQ	MQ	NA	NA	NA
2013	2013-09-30 20:00:00	EWR	STL	N12145	EV	Fixed wing multi engine	2	55
2013	2013-09-30 22:00:00	JFK	PWM	N193JB	B6	Fixed wing multi engine	2	20
2013	2013-09-30 21:00:00	EWR	SFO	N578UA	UA	Fixed wing multi engine	2	178
2013	2013-09-30 20:00:00	JFK	MCO	N804JB	B6	Fixed wing multi engine	2	200
2013	2013-09-30 22:00:00	JFK	BTV	N318JB	B6	Fixed wing multi engine	2	20
2013	2013-09-30 22:00:00	JFK	SYR	N354JB	B6	Fixed wing multi engine	2	20
2013	2013-09-30 22:00:00	JFK	BUF	N281JB	B6	Fixed wing multi engine	2	20
2013	2013-09-30 22:00:00	JFK	ROC	N346JB	B6	Fixed wing multi engine	2	20
2013	2013-09-30 22:00:00	JFK	BOS	N565JB	B6	Fixed wing multi engine	2	200
2013	2013-09-30 23:00:00	JFK	PSE	N516JB	B6	Fixed wing multi engine	2	200
2013	2013-09-30 18:00:00	LGA	BNA	N740EV	EV	Fixed wing multi engine	2	80
2013	2013-09-30 14:00:00	JFK	DCA	NA	9E	NA	NA	NA
2013	2013-09-30 22:00:00	LGA	SYR	NA	9E	NA	NA	NA
2013	2013-09-30 12:00:00	LGA	BNA	N535MQ	MQ	NA	NA	NA
2013	2013-09-30 11:00:00	LGA	CLE	N511MQ	MQ	NA	NA	NA
2013	2013-09-30 08:00:00	LGA	RDU	N839MQ	MQ	NA	NA	NA

# When left_join() fails to find a match for a row in x, it fills in the new variables with missing values. 
flights2 |> 
  filter(tailnum == "N535MQ") |> 
  left_join(planes |> select(tailnum, type, engines, seats))

[1m[22mJoining with `by = join_by(tailnum)`

A tibble: 264 x 9
year	time_hour	origin	dest	tailnum	carrier	type	engines	seats
<int>	<dttm>	<chr>	<chr>	<chr>	<chr>	<chr>	<int>	<int>
2013	2013-01-02 16:00:00	LGA	BNA	N535MQ	MQ	NA	NA	NA
2013	2013-01-03 06:00:00	LGA	CLT	N535MQ	MQ	NA	NA	NA
2013	2013-01-03 11:00:00	LGA	MSP	N535MQ	MQ	NA	NA	NA
2013	2013-01-03 18:00:00	LGA	MSP	N535MQ	MQ	NA	NA	NA
2013	2013-01-11 16:00:00	LGA	ATL	N535MQ	MQ	NA	NA	NA
2013	2013-01-12 12:00:00	LGA	BNA	N535MQ	MQ	NA	NA	NA
2013	2013-01-12 18:00:00	LGA	CLE	N535MQ	MQ	NA	NA	NA
2013	2013-01-13 15:00:00	LGA	CLT	N535MQ	MQ	NA	NA	NA
2013	2013-01-13 20:00:00	LGA	ATL	N535MQ	MQ	NA	NA	NA
2013	2013-01-14 08:00:00	LGA	ATL	N535MQ	MQ	NA	NA	NA
2013	2013-01-14 20:00:00	LGA	ATL	N535MQ	MQ	NA	NA	NA
2013	2013-01-18 11:00:00	JFK	DCA	N535MQ	MQ	NA	NA	NA
2013	2013-01-19 15:00:00	JFK	DCA	N535MQ	MQ	NA	NA	NA
2013	2013-01-19 19:00:00	JFK	CMH	N535MQ	MQ	NA	NA	NA
2013	2013-01-20 08:00:00	LGA	ATL	N535MQ	MQ	NA	NA	NA
2013	2013-01-20 14:00:00	LGA	MSP	N535MQ	MQ	NA	NA	NA
2013	2013-01-20 21:00:00	LGA	BNA	N535MQ	MQ	NA	NA	NA
2013	2013-01-21 09:00:00	LGA	ATL	N535MQ	MQ	NA	NA	NA
2013	2013-01-23 18:00:00	EWR	ORD	N535MQ	MQ	NA	NA	NA
2013	2013-01-25 06:00:00	LGA	MSP	N535MQ	MQ	NA	NA	NA
2013	2013-01-26 08:00:00	LGA	ATL	N535MQ	MQ	NA	NA	NA
2013	2013-01-26 15:00:00	LGA	CLT	N535MQ	MQ	NA	NA	NA
2013	2013-01-27 11:00:00	LGA	MSP	N535MQ	MQ	NA	NA	NA
2013	2013-01-28 09:00:00	LGA	CLT	N535MQ	MQ	NA	NA	NA
2013	2013-01-28 14:00:00	LGA	ATL	N535MQ	MQ	NA	NA	NA
2013	2013-01-31 12:00:00	EWR	ORD	N535MQ	MQ	NA	NA	NA
2013	2013-10-01 09:00:00	LGA	CLT	N535MQ	MQ	NA	NA	NA
2013	2013-10-01 14:00:00	LGA	MSP	N535MQ	MQ	NA	NA	NA
2013	2013-10-01 21:00:00	LGA	BNA	N535MQ	MQ	NA	NA	NA
2013	2013-10-02 15:00:00	LGA	CLT	N535MQ	MQ	NA	NA	NA
...	...	...	...	...	...	...	...	...
2013	2013-08-30 10:00:00	EWR	ORD	N535MQ	MQ	NA	NA	NA
2013	2013-08-30 15:00:00	EWR	ORD	N535MQ	MQ	NA	NA	NA
2013	2013-08-31 13:00:00	EWR	ORD	N535MQ	MQ	NA	NA	NA
2013	2013-09-03 13:00:00	EWR	ORD	N535MQ	MQ	NA	NA	NA
2013	2013-09-05 15:00:00	EWR	ORD	N535MQ	MQ	NA	NA	NA
2013	2013-09-13 13:00:00	EWR	ORD	N535MQ	MQ	NA	NA	NA
2013	2013-09-18 10:00:00	LGA	ATL	N535MQ	MQ	NA	NA	NA
2013	2013-09-18 16:00:00	LGA	BNA	N535MQ	MQ	NA	NA	NA
2013	2013-09-19 06:00:00	LGA	ATL	N535MQ	MQ	NA	NA	NA
2013	2013-09-19 12:00:00	LGA	MSP	N535MQ	MQ	NA	NA	NA
2013	2013-09-20 13:00:00	LGA	CLE	N535MQ	MQ	NA	NA	NA
2013	2013-09-20 18:00:00	LGA	CLE	N535MQ	MQ	NA	NA	NA
2013	2013-09-21 11:00:00	LGA	MSP	N535MQ	MQ	NA	NA	NA
2013	2013-09-23 11:00:00	JFK	DCA	N535MQ	MQ	NA	NA	NA
2013	2013-09-25 06:00:00	EWR	ORD	N535MQ	MQ	NA	NA	NA
2013	2013-09-25 20:00:00	JFK	CMH	N535MQ	MQ	NA	NA	NA
2013	2013-09-26 11:00:00	JFK	DCA	N535MQ	MQ	NA	NA	NA
2013	2013-09-26 15:00:00	JFK	DCA	N535MQ	MQ	NA	NA	NA
2013	2013-09-27 11:00:00	LGA	CLE	N535MQ	MQ	NA	NA	NA
2013	2013-09-27 15:00:00	LGA	RDU	N535MQ	MQ	NA	NA	NA
2013	2013-09-27 21:00:00	LGA	CLT	N535MQ	MQ	NA	NA	NA
2013	2013-09-28 11:00:00	LGA	CLE	N535MQ	MQ	NA	NA	NA
2013	2013-09-29 08:00:00	LGA	DTW	N535MQ	MQ	NA	NA	NA
2013	2013-09-29 13:00:00	LGA	RDU	N535MQ	MQ	NA	NA	NA
2013	2013-09-29 17:00:00	LGA	RDU	N535MQ	MQ	NA	NA	NA
2013	2013-09-29 21:00:00	LGA	BNA	N535MQ	MQ	NA	NA	NA
2013	2013-09-30 11:00:00	LGA	RDU	N535MQ	MQ	NA	NA	NA
2013	2013-09-30 15:00:00	LGA	RDU	N535MQ	MQ	NA	NA	NA
2013	2013-09-30 21:00:00	LGA	RDU	N535MQ	MQ	NA	NA	NA
2013	2013-09-30 12:00:00	LGA	BNA	N535MQ	MQ	NA	NA	NA

Specifying join keys

By default, left_join() will use all variables that appear in both data frames as the join key, the so called natural join. This is a useful heuristic, but it doesn’t always work, for example, both dataframes have the same column’s name (but with different meanings)

flights2 |> 
  left_join(airports, join_by(origin == faa))

A tibble: 336776 x 13
year	time_hour	origin	dest	tailnum	carrier	name	lat	lon	alt	tz	dst	tzone
<int>	<dttm>	<chr>	<chr>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
2013	2013-01-01 05:00:00	EWR	IAH	N14228	UA	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-01-01 05:00:00	LGA	IAH	N24211	UA	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-01-01 05:00:00	JFK	MIA	N619AA	AA	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-01-01 05:00:00	JFK	BQN	N804JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-01-01 06:00:00	LGA	ATL	N668DN	DL	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-01-01 05:00:00	EWR	ORD	N39463	UA	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-01-01 06:00:00	EWR	FLL	N516JB	B6	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-01-01 06:00:00	LGA	IAD	N829AS	EV	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-01-01 06:00:00	JFK	MCO	N593JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-01-01 06:00:00	LGA	ORD	N3ALAA	AA	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-01-01 06:00:00	JFK	PBI	N793JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-01-01 06:00:00	JFK	TPA	N657JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-01-01 06:00:00	JFK	LAX	N29129	UA	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-01-01 06:00:00	EWR	SFO	N53441	UA	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-01-01 06:00:00	LGA	DFW	N3DUAA	AA	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-01-01 05:00:00	JFK	BOS	N708JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-01-01 06:00:00	EWR	LAS	N76515	UA	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-01-01 06:00:00	LGA	FLL	N595JB	B6	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-01-01 06:00:00	LGA	ATL	N542MQ	MQ	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-01-01 06:00:00	EWR	PBI	N644JB	B6	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-01-01 06:00:00	LGA	MSP	N971DL	DL	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-01-01 06:00:00	LGA	DTW	N730MQ	MQ	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-01-01 06:00:00	EWR	MIA	N633AA	AA	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-01-01 06:00:00	JFK	ATL	N3739P	DL	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-01-01 06:00:00	EWR	MIA	N53442	UA	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-01-01 06:00:00	EWR	ORD	N9EAMQ	MQ	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-01-01 06:00:00	JFK	SFO	N532UA	UA	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-01-01 06:00:00	JFK	RSW	N635JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-01-01 06:00:00	JFK	SJU	N794JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-01-01 06:00:00	EWR	ATL	N326NB	DL	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
...	...	...	...	...	...	...	...	...	...	...	...	...
2013	2013-09-30 21:00:00	LGA	CHO	N712EV	EV	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-09-30 21:00:00	EWR	CLT	N16546	EV	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-09-30 21:00:00	JFK	DEN	N807JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 20:00:00	LGA	RIC	N751EV	EV	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-09-30 21:00:00	JFK	DCA	N807MQ	MQ	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 21:00:00	JFK	LAX	N335AA	AA	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 21:00:00	EWR	PWM	N12957	EV	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-09-30 21:00:00	JFK	SJU	N633JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 21:00:00	LGA	FLL	N627JB	B6	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-09-30 21:00:00	EWR	BOS	N813UA	UA	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-09-30 21:00:00	EWR	MHT	N10575	EV	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-09-30 18:00:00	JFK	BUF	N906XJ	9E	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 22:00:00	LGA	BGR	N722EV	EV	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-09-30 21:00:00	LGA	BNA	N532MQ	MQ	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-09-30 20:00:00	EWR	STL	N12145	EV	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-09-30 22:00:00	JFK	PWM	N193JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 21:00:00	EWR	SFO	N578UA	UA	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
2013	2013-09-30 20:00:00	JFK	MCO	N804JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 22:00:00	JFK	BTV	N318JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 22:00:00	JFK	SYR	N354JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 22:00:00	JFK	BUF	N281JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 22:00:00	JFK	ROC	N346JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 22:00:00	JFK	BOS	N565JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 23:00:00	JFK	PSE	N516JB	B6	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 18:00:00	LGA	BNA	N740EV	EV	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-09-30 14:00:00	JFK	DCA	NA	9E	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
2013	2013-09-30 22:00:00	LGA	SYR	NA	9E	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-09-30 12:00:00	LGA	BNA	N535MQ	MQ	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-09-30 11:00:00	LGA	CLE	N511MQ	MQ	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York
2013	2013-09-30 08:00:00	LGA	RDU	N839MQ	MQ	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York

Filtering joins

The primary action of a filtering join is to filter the rows.

semi_join() keep all rows in x that have a match in y.
anti_join() return all rows in x that don’t have a match in y. They’re useful for finding missing values that are implicit in the data.

airports |> 
  semi_join(flights2, join_by(faa == origin))

A tibble: 3 x 8
faa	name	lat	lon	alt	tz	dst	tzone
<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
EWR	Newark Liberty Intl	40.69250	-74.16867	18	-5	A	America/New_York
JFK	John F Kennedy Intl	40.63975	-73.77893	13	-5	A	America/New_York
LGA	La Guardia	40.77725	-73.87261	22	-5	A	America/New_York

airports |> 
  semi_join(flights2, join_by(faa == dest))

A tibble: 101 x 8
faa	name	lat	lon	alt	tz	dst	tzone
<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
ABQ	Albuquerque International Sunport	35.04022	-106.60919	5355	-7	A	America/Denver
ACK	Nantucket Mem	41.25305	-70.06018	48	-5	A	America/New_York
ALB	Albany Intl	42.74827	-73.80169	285	-5	A	America/New_York
ANC	Ted Stevens Anchorage Intl	61.17436	-149.99636	152	-9	A	America/Anchorage
ATL	Hartsfield Jackson Atlanta Intl	33.63672	-84.42807	1026	-5	A	America/New_York
AUS	Austin Bergstrom Intl	30.19453	-97.66989	542	-6	A	America/Chicago
AVL	Asheville Regional Airport	35.43619	-82.54181	2165	-5	A	America/New_York
BDL	Bradley Intl	41.93889	-72.68322	173	-5	A	America/New_York
BGR	Bangor Intl	44.80744	-68.82814	192	-5	A	America/New_York
BHM	Birmingham Intl	33.56294	-86.75355	644	-6	A	America/Chicago
BNA	Nashville Intl	36.12447	-86.67819	599	-6	A	America/Chicago
BOS	General Edward Lawrence Logan Intl	42.36435	-71.00518	19	-5	A	America/New_York
BTV	Burlington Intl	44.47186	-73.15328	335	-5	A	America/New_York
BUF	Buffalo Niagara Intl	42.94053	-78.73217	724	-5	A	America/New_York
BUR	Bob Hope	34.20067	-118.35867	778	-8	A	America/Los_Angeles
BWI	Baltimore Washington Intl	39.17536	-76.66833	146	-5	A	America/New_York
BZN	Gallatin Field	45.77764	-111.16015	4500	-7	A	America/Denver
CAE	Columbia Metropolitan	33.93883	-81.11953	236	-5	A	America/New_York
CAK	Akron Canton Regional Airport	40.91608	-81.44219	1228	-5	A	America/New_York
CHO	Charlottesville-Albemarle	38.13864	-78.45286	639	-5	A	America/New_York
CHS	Charleston Afb Intl	32.89865	-80.04053	45	-5	A	America/New_York
CLE	Cleveland Hopkins Intl	41.41169	-81.84979	791	-5	A	America/New_York
CLT	Charlotte Douglas Intl	35.21400	-80.94314	748	-5	A	America/New_York
CMH	Port Columbus Intl	39.99797	-82.89189	815	-5	A	America/New_York
CRW	Yeager	38.37315	-81.59319	981	-5	A	America/New_York
CVG	Cincinnati Northern Kentucky Intl	39.04884	-84.66782	896	-5	A	America/New_York
DAY	James M Cox Dayton Intl	39.90237	-84.21937	1009	-5	A	America/New_York
DCA	Ronald Reagan Washington Natl	38.85208	-77.03772	15	-5	A	America/New_York
DEN	Denver Intl	39.86166	-104.67318	5431	-7	A	America/Denver
DFW	Dallas Fort Worth Intl	32.89683	-97.03800	607	-6	A	America/Chicago
...	...	...	...	...	...	...	...
PDX	Portland Intl	45.58872	-122.59750	30	-8	A	America/Los_Angeles
PHL	Philadelphia Intl	39.87194	-75.24114	36	-5	A	America/New_York
PHX	Phoenix Sky Harbor Intl	33.43428	-112.01158	1135	-7	N	America/Phoenix
PIT	Pittsburgh Intl	40.49147	-80.23287	1204	-5	A	America/New_York
PSP	Palm Springs Intl	33.82967	-116.50669	477	-8	A	America/Los_Angeles
PVD	Theodore Francis Green State	41.73258	-71.42038	55	-5	A	America/New_York
PWM	Portland Intl Jetport	43.64616	-70.30928	77	-5	A	America/New_York
RDU	Raleigh Durham Intl	35.87764	-78.78747	435	-5	A	America/New_York
RIC	Richmond Intl	37.50517	-77.31967	167	-5	A	America/New_York
ROC	Greater Rochester Intl	43.11887	-77.67239	559	-5	A	America/New_York
RSW	Southwest Florida Intl	26.53617	-81.75517	30	-5	A	America/New_York
SAN	San Diego Intl	32.73356	-117.18967	17	-8	A	America/Los_Angeles
SAT	San Antonio Intl	29.53369	-98.46978	809	-6	A	America/Chicago
SAV	Savannah Hilton Head Intl	32.12758	-81.20214	51	-5	A	America/New_York
SBN	South Bend Rgnl	41.70866	-86.31725	799	-5	A	America/New_York
SDF	Louisville International Airport	38.17409	-85.73650	501	-5	A	America/New_York
SEA	Seattle Tacoma Intl	47.44900	-122.30931	433	-8	A	America/Los_Angeles
SFO	San Francisco Intl	37.61897	-122.37489	13	-8	A	America/Los_Angeles
SJC	Norman Y Mineta San Jose Intl	37.36260	-121.92902	62	-8	A	America/Los_Angeles
SLC	Salt Lake City Intl	40.78839	-111.97777	4227	-7	A	America/Denver
SMF	Sacramento Intl	38.69542	-121.59078	27	-8	A	America/Los_Angeles
SNA	John Wayne Arpt Orange Co	33.67567	-117.86822	56	-8	A	America/Los_Angeles
SRQ	Sarasota Bradenton Intl	27.39544	-82.55439	30	-5	A	America/New_York
STL	Lambert St Louis Intl	38.74870	-90.37003	618	-6	A	America/Chicago
SYR	Syracuse Hancock Intl	43.11119	-76.10631	421	-5	A	America/New_York
TPA	Tampa Intl	27.97547	-82.53325	26	-5	A	America/New_York
TUL	Tulsa Intl	36.19839	-95.88811	677	-6	A	America/Chicago
TVC	Cherry Capital Airport	44.74144	-85.58223	624	-5	A	America/New_York
TYS	Mc Ghee Tyson	35.81097	-83.99403	981	-5	A	America/New_York
XNA	NW Arkansas Regional	36.28187	-94.30681	1287	-6	A	America/Chicago

# find rows that are missing from airports by looking for flights that don’t have a matching destination airport.
flights2 |> 
  anti_join(airports, join_by(dest == faa)) |> 
  distinct(dest)

A tibble: 4 x 1
dest
<chr>
BQN
SJU
STT
PSE

#  find which tailnums are missing from planes
flights2 |>
  anti_join(planes, join_by(tailnum)) |> 
  distinct(tailnum)

A tibble: 722 x 1
tailnum
<chr>
N3ALAA
N3DUAA
N542MQ
N730MQ
N9EAMQ
N532UA
N3EMAA
N518MQ
N3BAAA
N3CYAA
N426US
N3GKAA
N4WNAA
N5FMAA
N722MQ
N3EKAA
N3ETAA
N541AA
N4WRAA
N4WPAA
N508MQ
N3HMAA
N828MQ
N3GEAA
N739MQ
N531MQ
N527JB
N846MQ
N3GVAA
N4YCAA
...
N7BAAA
N7BVAA
N626MQ
N675MQ
N580AA
N717MQ
N738MQ
N720MQ
N7ASAA
N328AT
N735MQ
N5EDAA
N5DJAA
N7ALAA
N721MQ
N7BGAA
N5ESAA
N456UW
N838MQ
N442US
N502SW
N451UW
N7BKAA
N800MQ
N7CAAA
N823MQ
N5FCAA
N5ERAA
N654MQ
N647MQ

Exercises

Find the 48 hours (over the course of the whole year) that have the worst delays. Cross-reference it with the weather data. Can you see any patterns?
Imagine you’ve found the top 10 most popular destinations using this code:

top_dest <- flights2 |>
  count(dest, sort = TRUE) |>
  head(10)

How can you find all flights to those destinations?

Does every departing flight have corresponding weather data for that hour?
What do the tail numbers that don’t have a matching record in planes have in common? (Hint: one variable explains ~90% of the problems.)
Add a column to planes that lists every carrier that has flown that plane. You might expect that there’s an implicit relationship between plane and airline, because each plane is flown by a single airline. Confirm or reject this hypothesis using the tools you’ve learned in previous chapters.
Add the latitude and the longitude of the origin and destination airport to flights. Is it easier to rename the columns before or after the join?
Compute the average delay by destination, then join on the airports data frame so you can show the spatial distribution of delays. Here’s an easy way to draw a map of the United States:
```
airports |>
  semi_join(flights, join_by(faa == dest)) |>
  ggplot(aes(x = lon, y = lat)) +
 borders("state") +
 geom_point() +
 coord_quickmap()
```
You might want to use the size or color of the points to display the average delay for each airport.
What happened on June 13 2013? Draw a map of the delays, and then use Google to cross-reference with the weather.

19.4 How do joins work?

Inner join {width=30%} left join {width=30%} right join {width=30%}

full join {width=30%} Venn diagram {width=30%}

x <- tribble(
  ~key, ~val_x,
     1, "x1",
     2, "x2",
     3, "x3"
)
y <- tribble(
  ~key, ~val_y,
     1, "y1",
     2, "y2",
     4, "y3"
)

x |>
inner_join(y)

[1m[22mJoining with `by = join_by(key)`

A tibble: 2 x 3
key	val_x	val_y
<dbl>	<chr>	<chr>
1	x1	y1
2	x2	y2

x |>
left_join(y)

[1m[22mJoining with `by = join_by(key)`

A tibble: 3 x 3
key	val_x	val_y
<dbl>	<chr>	<chr>
1	x1	y1
2	x2	y2
3	x3	NA

x |> 
right_join(y)

[1m[22mJoining with `by = join_by(key)`

A tibble: 3 x 3
key	val_x	val_y
<dbl>	<chr>	<chr>
1	x1	y1
2	x2	y2
4	NA	y3

full_join(x,y)

[1m[22mJoining with `by = join_by(key)`

A tibble: 4 x 3
key	val_x	val_y
<dbl>	<chr>	<chr>
1	x1	y1
2	x2	y2
3	x3	NA
4	NA	y3

Row matching

Inner join {width=30%}

one particularly dangerous case which can cause a combinatorial explosion of rows.

Filterring joins

semi join {width=30%} anti join {width=30%}

In a semi-join it only matters that there is a match; otherwise values in y don’t affect the output.
An anti-join is the inverse of a semi-join, dropping rows from x that have a match in y.

semi_join(x, y)
anti_join(x, y)

[1m[22mJoining with `by = join_by(key)`

A tibble: 2 x 2
key	val_x
<dbl>	<chr>
1	x1
2	x2

[1m[22mJoining with `by = join_by(key)`

A tibble: 1 x 2
key	val_x
<dbl>	<chr>
3	x3

19.5 Non-equi joins

Cross joins match every pair of rows.
Inequality joins use <, <=, >, and >= instead of ==.
Rolling joins are similar to inequality joins but only find the closest match.
Overlap joins are a special type of inequality join designed to work with ranges.

# equi join (where the rows match if the x key equals the y key)
x |> 
inner_join(y, join_by(key == key), keep = TRUE)

A tibble: 2 x 4
key.x	val_x	key.y	val_y
<dbl>	<chr>	<dbl>	<chr>
1	x1	1	y1
2	x2	2	y2

# non-equi join
x |> 
inner_join(y, join_by(key >= key), keep = TRUE)

A tibble: 5 x 4
key.x	val_x	key.y	val_y
<dbl>	<chr>	<dbl>	<chr>
1	x1	1	y1
2	x2	1	y1
2	x2	2	y2
3	x3	1	y1
3	x3	2	y2

Cross join {width=30%} Inequality join {width=30%} Rolling join {width=30%}

cross_join(x,y)

A tibble: 9 x 4
key.x	val_x	key.y	val_y
<dbl>	<chr>	<dbl>	<chr>
1	x1	1	y1
1	x1	2	y2
1	x1	4	y3
2	x2	1	y1
2	x2	2	y2
2	x2	4	y3
3	x3	1	y1
3	x3	2	y2
3	x3	4	y3

inner_join(x, y, join_by(key < key))

A tibble: 4 x 4
key.x	val_x	key.y	val_y
<dbl>	<chr>	<dbl>	<chr>
1	x1	2	y2
1	x1	4	y3
2	x2	4	y3
3	x3	4	y3

left_join(x, y, join_by(closest(key >= key)))

A tibble: 3 x 4
key.x	val_x	key.y	val_y
<dbl>	<chr>	<dbl>	<chr>
1	x1	1	y1
2	x2	2	y2
3	x3	2	y2

anti_join(x, y, join_by(closest(key >= key)))

A tibble: 0 x 2
key	val_x
<dbl>	<chr>

Overlap join

parties <- tibble(
  q = 1:4,
  party = ymd(c("2022-01-10", "2022-04-04", "2022-07-11", "2022-10-03")),
  start = ymd(c("2022-01-01", "2022-04-04", "2022-07-11", "2022-10-03")),
  end = ymd(c("2022-04-03", "2022-07-11", "2022-10-02", "2022-12-31"))
)
parties

A tibble: 4 x 4
q	party	start	end
<int>	<date>	<date>	<date>
1	2022-01-10	2022-01-01	2022-04-03
2	2022-04-04	2022-04-04	2022-07-11
3	2022-07-11	2022-07-11	2022-10-02
4	2022-10-03	2022-10-03	2022-12-31

parties |> 
  inner_join(parties, join_by(overlaps(start, end, start, end), q < q)) |> 
  select(start.x, end.x, start.y, end.y)

A tibble: 1 x 4
start.x	end.x	start.y	end.y
<date>	<date>	<date>	<date>
2022-04-04	2022-07-11	2022-07-11	2022-10-02

parties <- tibble(
  q = 1:4,
  party = ymd(c("2022-01-10", "2022-04-04", "2022-07-11", "2022-10-03")),
  start = ymd(c("2022-01-01", "2022-04-04", "2022-07-11", "2022-10-03")),
  end = ymd(c("2022-04-03", "2022-07-10", "2022-10-02", "2022-12-31"))
)

set.seed(123)
employees <- tibble(
  name = sample(babynames::babynames$name, 100),
  birthday = ymd("2022-01-01") + (sample(365, 100, replace = TRUE) - 1)
)
employees

A tibble: 100 x 2
name	birthday
<chr>	<date>
Kemba	2022-01-22
Orean	2022-06-26
Kirstyn	2022-02-11
Amparo	2022-11-11
Belen	2022-03-25
Rayshaun	2022-01-11
Brazil	2022-05-01
Chaston	2022-10-29
Reyn	2022-03-26
Ogechi	2022-12-31
Raylin	2022-07-13
Elisha	2022-04-17
Francico	2022-03-18
Theoplis	2022-07-17
Ashea	2022-09-06
Angela	2022-07-19
Essie	2022-06-09
Allyn	2022-09-07
Tanita	2022-10-19
Sherriann	2022-01-16
Raven	2022-02-02
Glenys	2022-02-09
Li	2022-01-10
Miana	2022-07-19
Sewell	2022-05-05
Wayland	2022-09-22
Nadean	2022-09-20
Gregoria	2022-07-05
Sumiye	2022-03-02
Norvell	2022-09-09
...	...
Liora	2022-07-31
Andrina	2022-08-10
Gay	2022-06-08
Dustyn	2022-12-15
Nasiya	2022-05-25
Belma	2022-02-26
Thena	2022-05-28
Cooper	2022-06-12
Adele	2022-08-26
Atiana	2022-06-10
Jamil	2022-03-07
Nalani	2022-01-04
Nathalie	2022-11-26
Duriel	2022-08-13
Yesenia	2022-04-27
Sherlie	2022-01-25
Brooksley	2022-05-16
Kristi	2022-02-24
Alethea	2022-08-05
Teandre	2022-03-26
Mohamedali	2022-02-14
Audray	2022-05-26
Elta	2022-06-19
Suzannah	2022-05-14
Cleve	2022-07-18
Promise	2022-12-27
Ian	2022-06-25
Shelva	2022-09-02
Aedan	2022-11-05
Dorianna	2022-04-14

employees |> 
  inner_join(parties, join_by(between(birthday, start, end)), unmatched = "error")

A tibble: 100 x 6
name	birthday	q	party	start	end
<chr>	<date>	<int>	<date>	<date>	<date>
Kemba	2022-01-22	1	2022-01-10	2022-01-01	2022-04-03
Orean	2022-06-26	2	2022-04-04	2022-04-04	2022-07-10
Kirstyn	2022-02-11	1	2022-01-10	2022-01-01	2022-04-03
Amparo	2022-11-11	4	2022-10-03	2022-10-03	2022-12-31
Belen	2022-03-25	1	2022-01-10	2022-01-01	2022-04-03
Rayshaun	2022-01-11	1	2022-01-10	2022-01-01	2022-04-03
Brazil	2022-05-01	2	2022-04-04	2022-04-04	2022-07-10
Chaston	2022-10-29	4	2022-10-03	2022-10-03	2022-12-31
Reyn	2022-03-26	1	2022-01-10	2022-01-01	2022-04-03
Ogechi	2022-12-31	4	2022-10-03	2022-10-03	2022-12-31
Raylin	2022-07-13	3	2022-07-11	2022-07-11	2022-10-02
Elisha	2022-04-17	2	2022-04-04	2022-04-04	2022-07-10
Francico	2022-03-18	1	2022-01-10	2022-01-01	2022-04-03
Theoplis	2022-07-17	3	2022-07-11	2022-07-11	2022-10-02
Ashea	2022-09-06	3	2022-07-11	2022-07-11	2022-10-02
Angela	2022-07-19	3	2022-07-11	2022-07-11	2022-10-02
Essie	2022-06-09	2	2022-04-04	2022-04-04	2022-07-10
Allyn	2022-09-07	3	2022-07-11	2022-07-11	2022-10-02
Tanita	2022-10-19	4	2022-10-03	2022-10-03	2022-12-31
Sherriann	2022-01-16	1	2022-01-10	2022-01-01	2022-04-03
Raven	2022-02-02	1	2022-01-10	2022-01-01	2022-04-03
Glenys	2022-02-09	1	2022-01-10	2022-01-01	2022-04-03
Li	2022-01-10	1	2022-01-10	2022-01-01	2022-04-03
Miana	2022-07-19	3	2022-07-11	2022-07-11	2022-10-02
Sewell	2022-05-05	2	2022-04-04	2022-04-04	2022-07-10
Wayland	2022-09-22	3	2022-07-11	2022-07-11	2022-10-02
Nadean	2022-09-20	3	2022-07-11	2022-07-11	2022-10-02
Gregoria	2022-07-05	2	2022-04-04	2022-04-04	2022-07-10
Sumiye	2022-03-02	1	2022-01-10	2022-01-01	2022-04-03
Norvell	2022-09-09	3	2022-07-11	2022-07-11	2022-10-02
...	...	...	...	...	...
Liora	2022-07-31	3	2022-07-11	2022-07-11	2022-10-02
Andrina	2022-08-10	3	2022-07-11	2022-07-11	2022-10-02
Gay	2022-06-08	2	2022-04-04	2022-04-04	2022-07-10
Dustyn	2022-12-15	4	2022-10-03	2022-10-03	2022-12-31
Nasiya	2022-05-25	2	2022-04-04	2022-04-04	2022-07-10
Belma	2022-02-26	1	2022-01-10	2022-01-01	2022-04-03
Thena	2022-05-28	2	2022-04-04	2022-04-04	2022-07-10
Cooper	2022-06-12	2	2022-04-04	2022-04-04	2022-07-10
Adele	2022-08-26	3	2022-07-11	2022-07-11	2022-10-02
Atiana	2022-06-10	2	2022-04-04	2022-04-04	2022-07-10
Jamil	2022-03-07	1	2022-01-10	2022-01-01	2022-04-03
Nalani	2022-01-04	1	2022-01-10	2022-01-01	2022-04-03
Nathalie	2022-11-26	4	2022-10-03	2022-10-03	2022-12-31
Duriel	2022-08-13	3	2022-07-11	2022-07-11	2022-10-02
Yesenia	2022-04-27	2	2022-04-04	2022-04-04	2022-07-10
Sherlie	2022-01-25	1	2022-01-10	2022-01-01	2022-04-03
Brooksley	2022-05-16	2	2022-04-04	2022-04-04	2022-07-10
Kristi	2022-02-24	1	2022-01-10	2022-01-01	2022-04-03
Alethea	2022-08-05	3	2022-07-11	2022-07-11	2022-10-02
Teandre	2022-03-26	1	2022-01-10	2022-01-01	2022-04-03
Mohamedali	2022-02-14	1	2022-01-10	2022-01-01	2022-04-03
Audray	2022-05-26	2	2022-04-04	2022-04-04	2022-07-10
Elta	2022-06-19	2	2022-04-04	2022-04-04	2022-07-10
Suzannah	2022-05-14	2	2022-04-04	2022-04-04	2022-07-10
Cleve	2022-07-18	3	2022-07-11	2022-07-11	2022-10-02
Promise	2022-12-27	4	2022-10-03	2022-10-03	2022-12-31
Ian	2022-06-25	2	2022-04-04	2022-04-04	2022-07-10
Shelva	2022-09-02	3	2022-07-11	2022-07-11	2022-10-02
Aedan	2022-11-05	4	2022-10-03	2022-10-03	2022-12-31
Dorianna	2022-04-14	2	2022-04-04	2022-04-04	2022-07-10

Exercises

x |> full_join(y, join_by(key == key))

A tibble: 4 x 3
key	val_x	val_y
<dbl>	<chr>	<chr>
1	x1	y1
2	x2	y2
3	x3	NA
4	NA	y3

x |> full_join(y, join_by(key == key), keep = TRUE)

A tibble: 4 x 4
key.x	val_x	key.y	val_y
<dbl>	<chr>	<dbl>	<chr>
1	x1	1	y1
2	x2	2	y2
3	x3	NA	NA
NA	NA	4	y3

When finding if any party period overlapped with another party period we used q < q in the join_by()? Why? What happens if you remove this inequality?`

Tags: R Data Science Data Transformation

C12. Logical Vectors

12.2. Comparisions

Floating point comparision

Missing values

is.na()

Exercises:

12.3 Boolean Algebra

Exercises

12.4 Summarizes

Logical summarizes

Nummeric summaries of logical vectors

Logical subsetting

Exercises

12.5 Conditional transformations

if_else()

case_when()

Compatible types

Exercises

12.6 Summary

C13. Numbers

13.2 Making numbers

13.3 Counts

13.4 Numeric transformations

13.5 General transformations

13.6 Numeric summaries

13.7 Summary

C14. Strings

14.2 Creating a string

14.3 Creating many strings from data

14.4 Extracting data from strings

14.5 Letters

14.6 Non-English text

14.7 Summary

C15 Regular Expressions

15.2 Pattern basics

15.3 Key functions

15.4 Pattern details

15.5 Pattern control

15.6 Practice

15.7 Regular expressions in other places

15.8 Summary

C16. Factors

16.2 Factor basics

16.3 General Social Survey

Exercises

16.4 Modifying factor order

16.5 Modifying factor levels

Exercises

16.6 Ordered factors

16.7 Summary

C17. Dates and Times

17.2 Creating date/times

During import

From strings

From individual components

From other types

Exercises

17.3 Date-time components

Modify components

Exercises

17.4 Time spans

Duration

Periods

Intervals

Exercises

17.5 Time zones

C18. Missing Values

18.2 Explicit missing values

18.3 Implicit missing values

Pivoting

Complete

Joins

Exercises

18.4 Factors and empty groups

C19. Joins

19.2 Keys

Primary and foreign keys

Checking primary keys

Surrogate keys

Exercises

`is.na()`

`if_else()`

`case_when()`