Plotting years over same x-axis in months - Reprex

Hi,

I have a dataset which I am trying to plot Revenue by year over the 12 month period of each year.

After exploring the data and decided to generate a simple Revenue X Time simple plot.

My issue is that when I group by year, I lose the "roughness" / "jaggedness" of the daily variations as well as the x-axis comes out of format. Any insight on what might be the issue here and how to get the plot to have x-axis Jan-Dez grouped by year.

Please find my below reprex:

library(tidyverse)
library(lubridate)

# Fictitious sample data of total revenue or amount billed by date.
x <- tribble(
~ActualInvDate,	~Tot.Billed.Amt,

"2-20-2018",	2315,
"2-22-2018", 	825,
"2-23-2018", 	710,
"2-26-2018", 	1032,
"2-27-2018", 	2519,
"3-1-2018", 	5709,
"3-6-2018", 	3457,
"3-9-2018", 	2380,
"3-19-2018", 	2515,
"3-23-2018", 	1399,
"3-26-2018", 	915,
"3-28-2018", 	2846,
"4-3-2018", 	2995,
"4-6-2018", 	1848,
"4-16-2018", 	2163,
"4-17-2018", 	3316,
"4-18-2018", 	4051,
"4-19-2018", 	9872,
"4-20-2018", 	3258,
"4-23-2018", 	3248,
"4-24-2018", 	2609,
"4-25-2018", 	3436,
"4-26-2018", 	1128,
"4-27-2018", 	7194,
"4-30-2018", 	1725,
"5-1-2018", 	8568,
"5-2-2018", 	5845,
"5-3-2018", 	8665,
"5-4-2018", 	12012,
"5-7-2018", 	6676,
"5-8-2018", 	12266,
"5-9-2018", 	20535,
"5-10-2018", 	15335,
"5-11-2018", 	65970,
"5-14-2018", 	2230,
"5-15-2018", 	7316,
"5-16-2018", 	8852,
"5-17-2018", 	63012,
"5-18-2018", 	34252,
"5-21-2018", 	7421,
"5-22-2018", 	6600,
"5-23-2018", 	19201,
"5-24-2018", 	11100,
"5-25-2018", 	50899,
"5-26-2018", 	3800,
"5-29-2018", 	13308,
"5-30-2018", 	7512,
"5-31-2018", 	22619,
"6-1-2018", 	39321,
"6-4-2018", 	3566,
"6-5-2018", 	13047,
"6-6-2018", 	138839,
"6-7-2018", 	26474,
"6-8-2018", 	29660,
"6-11-2018", 	8637,
"6-12-2018", 	22942,
"6-13-2018", 	30953,
"6-14-2018", 	126284,
"6-15-2018", 	55290,
"6-18-2018", 	9670,
"6-19-2018", 	42982,
"6-20-2018", 	88784,
"6-21-2018", 	41224,
"6-22-2018", 	16450,
"6-25-2018", 	26242,
"6-26-2018", 	256503,
"6-27-2018", 	18127,
"6-28-2018", 	27626,
"6-29-2018", 	45475,
"7-2-2018", 	53976,
"7-3-2018", 	25175,
"7-5-2018", 	37834,
"7-6-2018", 	33602,
"7-9-2018", 	79154,
"7-10-2018", 	16275,
"7-11-2018", 	105632,
"7-12-2018", 	22028,
"7-13-2018", 	22039,
"7-16-2018", 	63600,
"7-17-2018", 	12807,
"7-18-2018", 	28392,
"7-19-2018", 	24746,
"7-20-2018", 	44127,
"7-23-2018", 	18321,
"7-24-2018", 	58024,
"7-25-2018", 	17775,
"7-26-2018", 	29174,
"7-27-2018", 	35527,
"7-30-2018", 	52932,
"7-31-2018", 	17608,
"8-1-2018", 	73385,
"8-2-2018", 	31679,
"8-3-2018", 	53366,
"8-6-2018", 	107639,
"8-7-2018", 	10754,
"8-8-2018", 	36492,
"8-9-2018", 	78571,
"8-10-2018", 	43583,
"8-13-2018", 	30338,
"8-14-2018", 	38666,
"8-15-2018", 	46697,
"8-16-2018", 	68630,
"8-17-2018", 	58900,
"8-20-2018", 	33593,
"8-21-2018", 	33823,
"8-22-2018", 	25898,
"8-23-2018", 	21140,
"8-24-2018", 	26173,
"8-27-2018", 	57885,
"8-28-2018", 	27521,
"8-29-2018", 	93063,
"8-30-2018", 	40859,
"8-31-2018", 	14984,
"9-4-2018", 	53205,
"9-5-2018", 	84689,
"9-6-2018", 	51083,
"9-7-2018", 	64887,
"9-10-2018", 	16876,
"9-11-2018", 	34002,
"9-12-2018", 	75343,
"9-13-2018", 	35299,
"9-14-2018", 	24313,
"9-16-2018", 	12994,
"9-17-2018", 	23038,
"9-18-2018", 	69125,
"9-19-2018", 	115924,
"9-20-2018", 	77321,
"9-21-2018", 	71522,
"9-24-2018", 	54669,
"9-25-2018", 	166560,
"9-26-2018", 	32937,
"9-27-2018", 	45888,
"9-28-2018", 	55788,
"10-1-2018", 	76223,
"10-2-2018", 	38322,
"10-3-2018", 	131780,
"10-4-2018", 	24949,
"10-5-2018", 	72824,
"10-8-2018", 	32683,
"10-9-2018", 	11963,
"10-10-2018", 	48699,
"10-11-2018", 	21639,
"10-12-2018", 	67049,
"10-15-2018", 	29531,
"10-16-2018", 	33038,
"10-17-2018", 	27702,
"10-18-2018", 	108123,
"10-19-2018", 	37491,
"10-22-2018", 	42021,
"10-23-2018", 	436226,
"10-24-2018", 	85408,
"10-25-2018", 	48811,
"10-26-2018", 	48260,
"10-29-2018", 	115547,
"10-30-2018", 	87492,
"10-31-2018", 	102753,
"11-1-2018", 	40049,
"11-2-2018", 	99763,
"11-3-2018", 	1583,
"11-5-2018", 	31273,
"11-6-2018", 	32896,
"11-7-2018", 	44155,
"11-8-2018", 	50792,
"11-9-2018", 	35316,
"11-12-2018", 	13245,
"11-13-2018", 	36688,
"11-14-2018", 	58860,
"11-15-2018", 	120772,
"11-16-2018", 	72833,
"11-19-2018", 	65900,
"11-20-2018", 	57201,
"11-21-2018", 	89682,
"11-23-2018", 	25196,
"11-26-2018", 	26029,
"11-27-2018", 	177255,
"11-28-2018", 	192322,
"11-29-2018", 	90096,
"11-30-2018", 	40402,
"12-3-2018", 	41149,
"12-4-2018", 	36235,
"12-5-2018", 	193966,
"12-6-2018", 	140907,
"12-7-2018", 	335491,
"12-10-2018", 	26606,
"12-11-2018", 	52543,
"12-12-2018", 	71969,
"12-13-2018", 	42853,
"12-14-2018", 	194443,
"12-17-2018", 	31843,
"12-18-2018", 	37400,
"12-19-2018", 	68673,
"12-20-2018", 	96150,
"12-21-2018", 	106817,
"12-24-2018", 	9951,
"12-26-2018", 	47133,
"12-27-2018", 	53725,
"12-28-2018", 	91384,
"12-31-2018", 	46223,
"1-2-2019", 	134153,
"1-3-2019", 	25835,
"1-4-2019", 	105737,
"1-7-2019", 	117669,
"1-8-2019", 	64041,
"1-9-2019", 	56282,
"1-10-2019", 	57692,
"1-11-2019", 	62629,
"1-14-2019", 	19359,
"1-15-2019", 	90822,
"1-16-2019", 	105597,
"1-17-2019", 	109124,
"1-18-2019", 	67683,
"1-21-2019", 	138530,
"1-22-2019", 	111611,
"1-23-2019", 	129246,
"1-24-2019", 	73279,
"1-25-2019", 	87689,
"1-28-2019", 	35619,
"1-29-2019", 	23879,
"1-30-2019", 	74315,
"1-31-2019", 	93815,
"2-1-2019", 	20004,
"2-4-2019", 	39538,
"2-5-2019", 	21436,
"2-6-2019", 	50803,
"2-7-2019", 	53845,
"2-8-2019", 	91022,
"2-11-2019", 	60783,
"2-12-2019", 	54061,
"2-13-2019", 	115675,
"2-14-2019", 	26104,
"2-15-2019", 	172181,
"2-18-2019", 	103545,
"2-19-2019", 	56036,
"2-20-2019", 	102366,
"2-21-2019", 	105714,
"2-22-2019", 	128733,
"2-24-2019", 	4915,
"2-25-2019", 	46749,
"2-26-2019", 	76465,
"2-27-2019", 	73043,
"2-28-2019", 	65527,
"3-1-2019", 	76577,
"3-2-2019", 	1275,
"3-3-2019", 	1056,
"3-4-2019", 	84193,
"3-5-2019", 	39162,
"3-6-2019", 	45854,
"3-7-2019", 	52611,
"3-8-2019", 	202706,
"3-9-2019", 	1574,
"3-11-2019", 	191915,
"3-12-2019", 	66025,
"3-13-2019", 	50385,
"3-14-2019", 	104651,
"3-15-2019", 	178230,
"3-16-2019", 	1819,
"3-18-2019", 	176311,
"3-19-2019", 	113162,
"3-20-2019", 	193697,
"3-21-2019", 	236478,
"3-22-2019", 	137631,
"3-24-2019", 	4494,
"3-25-2019", 	172074,
"3-26-2019", 	286207,
"3-27-2019", 	134305,
"3-28-2019", 	158184,
"3-29-2019", 	82140,
"4-1-2019", 	113498,
"4-2-2019", 	180771,
"4-3-2019", 	80910,
"4-4-2019", 	81043,
"4-5-2019", 	79580,
"4-6-2019", 	1249,
"4-7-2019", 	25047,
"4-8-2019", 	112628,
"4-9-2019", 	92044,
"4-10-2019", 	107552,
"4-11-2019", 	102431,
"4-12-2019", 	83815,
"4-14-2019", 	7185,
"4-15-2019", 	79806,
"4-16-2019", 	221777,
"4-17-2019", 	94871,
"4-18-2019", 	112382,
"4-19-2019", 	58334,
"4-22-2019", 	83021,
"4-23-2019", 	111868,
"4-24-2019", 	77150,
"4-25-2019", 	84455,
"4-26-2019", 	104736,
"4-29-2019", 	76470,
"4-30-2019", 	140846,
"5-1-2019", 	127323,
"5-2-2019", 	139238,
"5-3-2019", 	92862,
"5-6-2019", 	283460,
"5-7-2019", 	127881,
"5-8-2019", 	79966,
"5-9-2019", 	159864,
"5-10-2019", 	87209,
"5-13-2019", 	107573,
"5-14-2019", 	171410,
"5-15-2019", 	108894,
"5-16-2019", 	89317,
"5-17-2019", 	74481,
"5-20-2019", 	148184,
"5-21-2019", 	215174,
"5-22-2019", 	51308,
"5-23-2019", 	110106,
"5-24-2019", 	92743,
"5-26-2019", 	29351,
"5-27-2019", 	18849,
"5-28-2019", 	240982,
"5-29-2019", 	134842,
"5-30-2019", 	106541,
"5-31-2019", 	99460,
"6-2-2019", 	8412,
"6-3-2019", 	118405,
"6-4-2019", 	144759,
"6-5-2019", 	123294,
"6-6-2019", 	67532,
"6-7-2019", 	202912,
"6-10-2019", 	164081,
"6-11-2019", 	367452,
"6-12-2019", 	189177,
"6-13-2019", 	90003,
"6-14-2019", 	119539,
"6-17-2019", 	169452,
"6-18-2019", 	196062,
"6-19-2019", 	104542,
"6-20-2019", 	62737,
"6-21-2019", 	75287,
"6-24-2019", 	127385,
"6-25-2019", 	295774,
"6-26-2019", 	279868,
"6-27-2019", 	100558,
"6-28-2019", 	78487,
"7-1-2019", 	127187,
"7-2-2019", 	285203,
"7-3-2019", 	48705,
"7-5-2019", 	115280,
"7-7-2019", 	1207,
"7-8-2019", 	124428,
"7-9-2019", 	251938,
"7-10-2019", 	104617,
"7-11-2019", 	141377,
"7-12-2019", 	187875,
"7-15-2019", 	117554,
"7-16-2019", 	212808,
"7-17-2019", 	147232,
"7-18-2019", 	149241,
"7-19-2019", 	94922,
"7-22-2019", 	209061,
"7-23-2019", 	192436,
"7-24-2019", 	258726,
"7-25-2019", 	272500,
"7-26-2019", 	183545,
"7-28-2019", 	3450,
"7-29-2019", 	150473,
"7-30-2019", 	322693,
"7-31-2019", 	154438,
"8-1-2019", 	118792,
"8-2-2019", 	127683,
"8-5-2019", 	193117,
"8-6-2019", 	258290,
"8-7-2019", 	149324,
"8-8-2019", 	134461,
"8-9-2019", 	58165,
"8-10-2019", 	1728,
"8-12-2019", 	100383,
"8-13-2019", 	204296,
"8-14-2019", 	107806,
"8-15-2019", 	198694,
"8-16-2019", 	203261,
"8-19-2019", 	186531,
"8-20-2019", 	312579,
"8-21-2019", 	99326,
"8-22-2019", 	190502,
"8-23-2019", 	155308,
"8-26-2019", 	324124,
"8-27-2019", 	293942,
"8-28-2019", 	183753,
"8-29-2019", 	217908,
"8-30-2019", 	81776,
"8-31-2019", 	11544,
"9-3-2019", 	191848,
"9-4-2019", 	315857,
"9-5-2019", 	231207,
"9-6-2019", 	125868,
"9-7-2019", 	2337,
"9-9-2019", 	146405,
"9-10-2019", 	252506,
"9-11-2019", 	124663,
"9-12-2019", 	198406,
"9-13-2019", 	137195,
"9-14-2019", 	972,
"9-16-2019", 	124401,
"9-17-2019", 	246132,
"9-18-2019", 	127232,
"9-19-2019", 	216183,
"9-20-2019", 	177872,
"9-22-2019", 	2755,
"9-23-2019", 	232763,
"9-24-2019", 	257909,
"9-25-2019", 	169977,
"9-26-2019", 	205241,
"9-27-2019", 	144566,
"9-30-2019", 	269271,
"10-1-2019", 	208830,
"10-2-2019", 	159483,
"10-3-2019", 	213372,
"10-4-2019", 	148954,
"10-5-2019", 	514,
"10-7-2019", 	146648,
"10-8-2019", 	299011,
"10-9-2019", 	254048,
"10-10-2019", 	256041,
"10-11-2019", 	119752,
"10-14-2019", 	115786,
"10-15-2019", 	180412,
"10-16-2019", 	305405,
"10-17-2019", 	106641,
"10-18-2019", 	105346,
"10-20-2019", 	3150,
"10-21-2019", 	240890,
"10-22-2019", 	317188,
"10-23-2019", 	189734,
"10-24-2019", 	77177,
"10-25-2019", 	165337,
"10-28-2019", 	180917,
"10-29-2019", 	78138,
"10-30-2019", 	176064,
"10-31-2019", 	108147,
"11-1-2019", 	107501,
"11-2-2019", 	2467,
"11-3-2019", 	894,
"11-4-2019", 	217879,
"11-5-2019", 	179882,
"11-6-2019", 	52904,
"11-7-2019", 	119438,
"11-8-2019", 	167452,
"11-9-2019", 	504,
"11-10-2019", 	4232,
"11-11-2019", 	231509,
"11-12-2019", 	214653,
"11-13-2019", 	261901,
"11-14-2019", 	36632,
"11-15-2019", 	264392,
"11-17-2019", 	2794,
"11-18-2019", 	236941,
"11-19-2019", 	272816,
"11-20-2019", 	180210,
"11-21-2019", 	168248,
"11-22-2019", 	198985,
"11-23-2019", 	80153,
"11-24-2019", 	6696,
"11-25-2019", 	145417,
"11-26-2019", 	233239,
"11-27-2019", 	-1281939,
"11-29-2019", 	15402,
"12-2-2019", 	75270,
"12-3-2019", 	215988,
"12-4-2019", 	210551,
"12-5-2019", 	174883,
"12-6-2019", 	181061,
"12-7-2019", 	504,
"12-9-2019", 	147408,
"12-10-2019", 	310209,
"12-11-2019", 	182883,
"12-12-2019", 	157903,
"12-13-2019", 	145769,
"12-14-2019", 	800,
"12-16-2019", 	136420,
"12-17-2019", 	213845,
"12-18-2019", 	175403,
"12-19-2019", 	73617,
"12-20-2019", 	254792,
"12-22-2019", 	3800,
"12-23-2019", 	210088,
"12-24-2019", 	102881,
"12-26-2019", 	163274,
"12-27-2019", 	88936,
"12-29-2019", 	15859,
"12-30-2019", 	170950,
"12-31-2019", 	331191,
"1-1-2020", 	9963,
"1-2-2020", 	100166,
"1-3-2020", 	166192,
"1-4-2020", 	4248,
"1-6-2020", 	90837,
"1-7-2020", 	186577,
"1-8-2020", 	95335,
"1-9-2020", 	143383,
"1-10-2020", 	169206,
"1-13-2020", 	171433,
"1-14-2020", 	223742,
"1-15-2020", 	213288,
"1-16-2020", 	143010,
"1-17-2020", 	398692,
"1-20-2020", 	212388,
"1-21-2020", 	420029,
"1-22-2020", 	147302,
"1-23-2020", 	191470,
"1-24-2020", 	266806,
"1-27-2020", 	203302,
"1-28-2020", 	267417,
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"2-7-2020", 	215664,
"2-8-2020", 	4441,
"2-10-2020", 	284293,
"2-11-2020", 	352285,
"2-12-2020", 	149764,
"2-13-2020", 	225254,
"2-14-2020", 	159852,
"2-17-2020", 	195529,
"2-18-2020", 	186526,
"2-19-2020", 	345102,
"2-20-2020", 	179146,
"2-21-2020", 	322430,
"2-23-2020", 	660,
"2-24-2020", 	301683,
"2-25-2020", 	250295,
"2-26-2020", 	148598,
"2-27-2020", 	190384,
"2-28-2020", 	265806,
"3-2-2020", 	135718,
"3-3-2020", 	363251,
"3-4-2020", 	164761,
"3-5-2020", 	184143,
"3-6-2020", 	45863,
"3-9-2020", 	248728,
"3-10-2020", 	206968,
"3-11-2020", 	186121,
"3-12-2020", 	225251,
"3-13-2020", 	124201,
"3-16-2020", 	230773,
"3-17-2020", 	253656,
"3-18-2020", 	137603,
"3-19-2020", 	181515,
"3-20-2020", 	44376,
"3-21-2020", 	521,
"3-23-2020", 	93516,
"3-24-2020", 	410314,
"3-25-2020", 	120042,
"3-26-2020", 	179452,
"3-27-2020", 	62426,
"3-30-2020", 	141558,
"3-31-2020", 	245599,
"4-1-2020", 	279977,
"4-2-2020", 	159775,
"4-3-2020", 	79169,
"4-6-2020", 	133797,
"4-7-2020", 	140002,
"4-8-2020", 	385024,
"4-9-2020", 	151364,
"4-10-2020", 	70551,
"4-12-2020", 	3814,
"4-13-2020", 	331806,
"4-14-2020", 	300080,
"4-15-2020", 	223719,
"4-16-2020", 	235440,
"4-17-2020", 	42567,
"4-20-2020", 	154566,
"4-21-2020", 	323098,
"4-22-2020", 	39385,
"4-23-2020", 	298896,
"4-24-2020", 	234195,
"4-27-2020", 	177914,
"4-28-2020", 	441017,
"4-29-2020", 	223406,
"4-30-2020", 	278052,
"5-1-2020", 	226830,
"5-3-2020", 	1898,
"5-4-2020", 	314639,
"5-5-2020", 	228052,
"5-6-2020", 	110759,
"5-7-2020", 	204911,
"5-8-2020", 	239334,
"5-10-2020", 	2020,
"5-11-2020", 	167912,
"5-12-2020", 	250557,
"5-13-2020", 	161858,
"5-14-2020", 	277749,
"5-15-2020", 	117101,
"5-17-2020", 	3864,
"5-18-2020", 	192543,
"5-19-2020", 	274125,
"5-20-2020", 	162523,
"5-21-2020", 	147519,
"5-22-2020", 	157107,
"5-26-2020", 	349935,
"5-27-2020", 	58701,
"5-28-2020", 	182074,
"5-29-2020", 	355316,
"5-30-2020", 	1570,
"5-31-2020", 	19866,
"6-1-2020", 	488207,
"6-2-2020", 	207082,
"6-3-2020", 	286356,
"6-4-2020", 	280647,
"6-5-2020", 	208814,
"6-8-2020", 	257497,
"6-9-2020", 	171638,
"6-10-2020", 	142506,
"6-11-2020", 	181565,
"6-12-2020", 	136672,
"6-13-2020", 	29165,
"6-15-2020", 	607336,
"6-16-2020", 	389575,
"6-17-2020", 	183919,
"6-18-2020", 	295681,
"6-19-2020", 	199838,
"6-22-2020", 	380438,
"6-23-2020", 	121738,
"6-24-2020", 	101299,
"6-25-2020", 	216002,
"6-26-2020", 	137952,
"6-27-2020", 	107081,
"6-29-2020", 	336415,
"6-30-2020", 	396493,
"7-1-2020", 	179598,
"7-2-2020", 	95814,
"7-3-2020", 	20702,
"7-6-2020", 	285842,
"7-7-2020", 	232135,
"7-8-2020", 	215388,
"7-9-2020", 	153461,
"7-10-2020", 	176932,
"7-13-2020", 	208367)

# Breaking into y/m/d and rename it:
x <- x %>% mutate(`ActualInvDate` = mdy(`ActualInvDate`)) %>% 
  mutate_at(vars(`ActualInvDate`),
                funs(year, month, day)) %>%
  rename("Act.Inv.Year" = `year`,
         "Act.Inv.Month" = `month`,
         "Act.Inv.Day" = `day`
  ) %>% mutate(`Act.Inv.Year` = as.factor(`Act.Inv.Year`)) # Ensure Factor for plotting by year
               

#Plot 1
# Simple revenue x time 
ggplot(data = x,
       aes(
         x = `ActualInvDate`, y = `Tot.Billed.Amt`/1000
       )) + geom_line()


# Plot2
# grouped by year, issue on x-axis (should be the months),
# lost the "granularity" of first 
ggplot(data = x,
       aes(x = `Act.Inv.Month`, y = `Tot.Billed.Amt`/1000,
           color = `Act.Inv.Year`
       )) + 
  geom_line() +   
  labs(x = "Invoice Date", y = "Amount Billed (Thousands $)")



Any feedback would be much appreciated.

Thanks.

I think this gets you close to what you want.

x <- x %>% mutate(`ActualInvDate` = mdy(`ActualInvDate`)) %>% 
  mutate_at(vars(`ActualInvDate`),
            funs(year, month, day)) %>%
  rename("Act.Inv.Year" = `year`,
         "Act.Inv.Month" = `month`,
         "Act.Inv.Day" = `day`
  ) %>% mutate(`Act.Inv.Year` = as.factor(`Act.Inv.Year`)) # Ensure Factor for plotting by year

x <- mutate(x, DayofYear = yday(ActualInvDate))

ggplot(x, aes(DayofYear, Tot.Billed.Amt/1000, color = Act.Inv.Year)) + geom_line()

I am not sure how you would want to label the x axis due to 2020 being a leap year. March 1 is the 60th day of the in 2018 and 2019 but it is the 61st in 2020. I would leave the axis as Day of Year but the choice is yours.

Not sure what you're after

library(tidyverse)
library(lubridate)
#> 
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#> 
#>     date, intersect, setdiff, union

# Fictitious sample data of total revenue or amount billed by date.
x <- tribble(
    ~ActualInvDate, ~Tot.Billed.Amt,
    
    "2-20-2018",    2315,
    "2-22-2018",    825,
    "2-23-2018",    710,
    "2-26-2018",    1032,
    "2-27-2018",    2519,
    "3-1-2018",     5709,
    "3-6-2018",     3457,
    "3-9-2018",     2380,
    "3-19-2018",    2515,
    "3-23-2018",    1399,
    "3-26-2018",    915,
    "3-28-2018",    2846,
    "4-3-2018",     2995,
    "4-6-2018",     1848,
    "4-16-2018",    2163,
    "4-17-2018",    3316,
    "4-18-2018",    4051,
    "4-19-2018",    9872,
    "4-20-2018",    3258,
    "4-23-2018",    3248,
    "4-24-2018",    2609,
    "4-25-2018",    3436,
    "4-26-2018",    1128,
    "4-27-2018",    7194,
    "4-30-2018",    1725,
    "5-1-2018",     8568,
    "5-2-2018",     5845,
    "5-3-2018",     8665,
    "5-4-2018",     12012,
    "5-7-2018",     6676,
    "5-8-2018",     12266,
    "5-9-2018",     20535,
    "5-10-2018",    15335,
    "5-11-2018",    65970,
    "5-14-2018",    2230,
    "5-15-2018",    7316,
    "5-16-2018",    8852,
    "5-17-2018",    63012,
    "5-18-2018",    34252,
    "5-21-2018",    7421,
    "5-22-2018",    6600,
    "5-23-2018",    19201,
    "5-24-2018",    11100,
    "5-25-2018",    50899,
    "5-26-2018",    3800,
    "5-29-2018",    13308,
    "5-30-2018",    7512,
    "5-31-2018",    22619,
    "6-1-2018",     39321,
    "6-4-2018",     3566,
    "6-5-2018",     13047,
    "6-6-2018",     138839,
    "6-7-2018",     26474,
    "6-8-2018",     29660,
    "6-11-2018",    8637,
    "6-12-2018",    22942,
    "6-13-2018",    30953,
    "6-14-2018",    126284,
    "6-15-2018",    55290,
    "6-18-2018",    9670,
    "6-19-2018",    42982,
    "6-20-2018",    88784,
    "6-21-2018",    41224,
    "6-22-2018",    16450,
    "6-25-2018",    26242,
    "6-26-2018",    256503,
    "6-27-2018",    18127,
    "6-28-2018",    27626,
    "6-29-2018",    45475,
    "7-2-2018",     53976,
    "7-3-2018",     25175,
    "7-5-2018",     37834,
    "7-6-2018",     33602,
    "7-9-2018",     79154,
    "7-10-2018",    16275,
    "7-11-2018",    105632,
    "7-12-2018",    22028,
    "7-13-2018",    22039,
    "7-16-2018",    63600,
    "7-17-2018",    12807,
    "7-18-2018",    28392,
    "7-19-2018",    24746,
    "7-20-2018",    44127,
    "7-23-2018",    18321,
    "7-24-2018",    58024,
    "7-25-2018",    17775,
    "7-26-2018",    29174,
    "7-27-2018",    35527,
    "7-30-2018",    52932,
    "7-31-2018",    17608,
    "8-1-2018",     73385,
    "8-2-2018",     31679,
    "8-3-2018",     53366,
    "8-6-2018",     107639,
    "8-7-2018",     10754,
    "8-8-2018",     36492,
    "8-9-2018",     78571,
    "8-10-2018",    43583,
    "8-13-2018",    30338,
    "8-14-2018",    38666,
    "8-15-2018",    46697,
    "8-16-2018",    68630,
    "8-17-2018",    58900,
    "8-20-2018",    33593,
    "8-21-2018",    33823,
    "8-22-2018",    25898,
    "8-23-2018",    21140,
    "8-24-2018",    26173,
    "8-27-2018",    57885,
    "8-28-2018",    27521,
    "8-29-2018",    93063,
    "8-30-2018",    40859,
    "8-31-2018",    14984,
    "9-4-2018",     53205,
    "9-5-2018",     84689,
    "9-6-2018",     51083,
    "9-7-2018",     64887,
    "9-10-2018",    16876,
    "9-11-2018",    34002,
    "9-12-2018",    75343,
    "9-13-2018",    35299,
    "9-14-2018",    24313,
    "9-16-2018",    12994,
    "9-17-2018",    23038,
    "9-18-2018",    69125,
    "9-19-2018",    115924,
    "9-20-2018",    77321,
    "9-21-2018",    71522,
    "9-24-2018",    54669,
    "9-25-2018",    166560,
    "9-26-2018",    32937,
    "9-27-2018",    45888,
    "9-28-2018",    55788,
    "10-1-2018",    76223,
    "10-2-2018",    38322,
    "10-3-2018",    131780,
    "10-4-2018",    24949,
    "10-5-2018",    72824,
    "10-8-2018",    32683,
    "10-9-2018",    11963,
    "10-10-2018",   48699,
    "10-11-2018",   21639,
    "10-12-2018",   67049,
    "10-15-2018",   29531,
    "10-16-2018",   33038,
    "10-17-2018",   27702,
    "10-18-2018",   108123,
    "10-19-2018",   37491,
    "10-22-2018",   42021,
    "10-23-2018",   436226,
    "10-24-2018",   85408,
    "10-25-2018",   48811,
    "10-26-2018",   48260,
    "10-29-2018",   115547,
    "10-30-2018",   87492,
    "10-31-2018",   102753,
    "11-1-2018",    40049,
    "11-2-2018",    99763,
    "11-3-2018",    1583,
    "11-5-2018",    31273,
    "11-6-2018",    32896,
    "11-7-2018",    44155,
    "11-8-2018",    50792,
    "11-9-2018",    35316,
    "11-12-2018",   13245,
    "11-13-2018",   36688,
    "11-14-2018",   58860,
    "11-15-2018",   120772,
    "11-16-2018",   72833,
    "11-19-2018",   65900,
    "11-20-2018",   57201,
    "11-21-2018",   89682,
    "11-23-2018",   25196,
    "11-26-2018",   26029,
    "11-27-2018",   177255,
    "11-28-2018",   192322,
    "11-29-2018",   90096,
    "11-30-2018",   40402,
    "12-3-2018",    41149,
    "12-4-2018",    36235,
    "12-5-2018",    193966,
    "12-6-2018",    140907,
    "12-7-2018",    335491,
    "12-10-2018",   26606,
    "12-11-2018",   52543,
    "12-12-2018",   71969,
    "12-13-2018",   42853,
    "12-14-2018",   194443,
    "12-17-2018",   31843,
    "12-18-2018",   37400,
    "12-19-2018",   68673,
    "12-20-2018",   96150,
    "12-21-2018",   106817,
    "12-24-2018",   9951,
    "12-26-2018",   47133,
    "12-27-2018",   53725,
    "12-28-2018",   91384,
    "12-31-2018",   46223,
    "1-2-2019",     134153,
    "1-3-2019",     25835,
    "1-4-2019",     105737,
    "1-7-2019",     117669,
    "1-8-2019",     64041,
    "1-9-2019",     56282,
    "1-10-2019",    57692,
    "1-11-2019",    62629,
    "1-14-2019",    19359,
    "1-15-2019",    90822,
    "1-16-2019",    105597,
    "1-17-2019",    109124,
    "1-18-2019",    67683,
    "1-21-2019",    138530,
    "1-22-2019",    111611,
    "1-23-2019",    129246,
    "1-24-2019",    73279,
    "1-25-2019",    87689,
    "1-28-2019",    35619,
    "1-29-2019",    23879,
    "1-30-2019",    74315,
    "1-31-2019",    93815,
    "2-1-2019",     20004,
    "2-4-2019",     39538,
    "2-5-2019",     21436,
    "2-6-2019",     50803,
    "2-7-2019",     53845,
    "2-8-2019",     91022,
    "2-11-2019",    60783,
    "2-12-2019",    54061,
    "2-13-2019",    115675,
    "2-14-2019",    26104,
    "2-15-2019",    172181,
    "2-18-2019",    103545,
    "2-19-2019",    56036,
    "2-20-2019",    102366,
    "2-21-2019",    105714,
    "2-22-2019",    128733,
    "2-24-2019",    4915,
    "2-25-2019",    46749,
    "2-26-2019",    76465,
    "2-27-2019",    73043,
    "2-28-2019",    65527,
    "3-1-2019",     76577,
    "3-2-2019",     1275,
    "3-3-2019",     1056,
    "3-4-2019",     84193,
    "3-5-2019",     39162,
    "3-6-2019",     45854,
    "3-7-2019",     52611,
    "3-8-2019",     202706,
    "3-9-2019",     1574,
    "3-11-2019",    191915,
    "3-12-2019",    66025,
    "3-13-2019",    50385,
    "3-14-2019",    104651,
    "3-15-2019",    178230,
    "3-16-2019",    1819,
    "3-18-2019",    176311,
    "3-19-2019",    113162,
    "3-20-2019",    193697,
    "3-21-2019",    236478,
    "3-22-2019",    137631,
    "3-24-2019",    4494,
    "3-25-2019",    172074,
    "3-26-2019",    286207,
    "3-27-2019",    134305,
    "3-28-2019",    158184,
    "3-29-2019",    82140,
    "4-1-2019",     113498,
    "4-2-2019",     180771,
    "4-3-2019",     80910,
    "4-4-2019",     81043,
    "4-5-2019",     79580,
    "4-6-2019",     1249,
    "4-7-2019",     25047,
    "4-8-2019",     112628,
    "4-9-2019",     92044,
    "4-10-2019",    107552,
    "4-11-2019",    102431,
    "4-12-2019",    83815,
    "4-14-2019",    7185,
    "4-15-2019",    79806,
    "4-16-2019",    221777,
    "4-17-2019",    94871,
    "4-18-2019",    112382,
    "4-19-2019",    58334,
    "4-22-2019",    83021,
    "4-23-2019",    111868,
    "4-24-2019",    77150,
    "4-25-2019",    84455,
    "4-26-2019",    104736,
    "4-29-2019",    76470,
    "4-30-2019",    140846,
    "5-1-2019",     127323,
    "5-2-2019",     139238,
    "5-3-2019",     92862,
    "5-6-2019",     283460,
    "5-7-2019",     127881,
    "5-8-2019",     79966,
    "5-9-2019",     159864,
    "5-10-2019",    87209,
    "5-13-2019",    107573,
    "5-14-2019",    171410,
    "5-15-2019",    108894,
    "5-16-2019",    89317,
    "5-17-2019",    74481,
    "5-20-2019",    148184,
    "5-21-2019",    215174,
    "5-22-2019",    51308,
    "5-23-2019",    110106,
    "5-24-2019",    92743,
    "5-26-2019",    29351,
    "5-27-2019",    18849,
    "5-28-2019",    240982,
    "5-29-2019",    134842,
    "5-30-2019",    106541,
    "5-31-2019",    99460,
    "6-2-2019",     8412,
    "6-3-2019",     118405,
    "6-4-2019",     144759,
    "6-5-2019",     123294,
    "6-6-2019",     67532,
    "6-7-2019",     202912,
    "6-10-2019",    164081,
    "6-11-2019",    367452,
    "6-12-2019",    189177,
    "6-13-2019",    90003,
    "6-14-2019",    119539,
    "6-17-2019",    169452,
    "6-18-2019",    196062,
    "6-19-2019",    104542,
    "6-20-2019",    62737,
    "6-21-2019",    75287,
    "6-24-2019",    127385,
    "6-25-2019",    295774,
    "6-26-2019",    279868,
    "6-27-2019",    100558,
    "6-28-2019",    78487,
    "7-1-2019",     127187,
    "7-2-2019",     285203,
    "7-3-2019",     48705,
    "7-5-2019",     115280,
    "7-7-2019",     1207,
    "7-8-2019",     124428,
    "7-9-2019",     251938,
    "7-10-2019",    104617,
    "7-11-2019",    141377,
    "7-12-2019",    187875,
    "7-15-2019",    117554,
    "7-16-2019",    212808,
    "7-17-2019",    147232,
    "7-18-2019",    149241,
    "7-19-2019",    94922,
    "7-22-2019",    209061,
    "7-23-2019",    192436,
    "7-24-2019",    258726,
    "7-25-2019",    272500,
    "7-26-2019",    183545,
    "7-28-2019",    3450,
    "7-29-2019",    150473,
    "7-30-2019",    322693,
    "7-31-2019",    154438,
    "8-1-2019",     118792,
    "8-2-2019",     127683,
    "8-5-2019",     193117,
    "8-6-2019",     258290,
    "8-7-2019",     149324,
    "8-8-2019",     134461,
    "8-9-2019",     58165,
    "8-10-2019",    1728,
    "8-12-2019",    100383,
    "8-13-2019",    204296,
    "8-14-2019",    107806,
    "8-15-2019",    198694,
    "8-16-2019",    203261,
    "8-19-2019",    186531,
    "8-20-2019",    312579,
    "8-21-2019",    99326,
    "8-22-2019",    190502,
    "8-23-2019",    155308,
    "8-26-2019",    324124,
    "8-27-2019",    293942,
    "8-28-2019",    183753,
    "8-29-2019",    217908,
    "8-30-2019",    81776,
    "8-31-2019",    11544,
    "9-3-2019",     191848,
    "9-4-2019",     315857,
    "9-5-2019",     231207,
    "9-6-2019",     125868,
    "9-7-2019",     2337,
    "9-9-2019",     146405,
    "9-10-2019",    252506,
    "9-11-2019",    124663,
    "9-12-2019",    198406,
    "9-13-2019",    137195,
    "9-14-2019",    972,
    "9-16-2019",    124401,
    "9-17-2019",    246132,
    "9-18-2019",    127232,
    "9-19-2019",    216183,
    "9-20-2019",    177872,
    "9-22-2019",    2755,
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    "9-24-2019",    257909,
    "9-25-2019",    169977,
    "9-26-2019",    205241,
    "9-27-2019",    144566,
    "9-30-2019",    269271,
    "10-1-2019",    208830,
    "10-2-2019",    159483,
    "10-3-2019",    213372,
    "10-4-2019",    148954,
    "10-5-2019",    514,
    "10-7-2019",    146648,
    "10-8-2019",    299011,
    "10-9-2019",    254048,
    "10-10-2019",   256041,
    "10-11-2019",   119752,
    "10-14-2019",   115786,
    "10-15-2019",   180412,
    "10-16-2019",   305405,
    "10-17-2019",   106641,
    "10-18-2019",   105346,
    "10-20-2019",   3150,
    "10-21-2019",   240890,
    "10-22-2019",   317188,
    "10-23-2019",   189734,
    "10-24-2019",   77177,
    "10-25-2019",   165337,
    "10-28-2019",   180917,
    "10-29-2019",   78138,
    "10-30-2019",   176064,
    "10-31-2019",   108147,
    "11-1-2019",    107501,
    "11-2-2019",    2467,
    "11-3-2019",    894,
    "11-4-2019",    217879,
    "11-5-2019",    179882,
    "11-6-2019",    52904,
    "11-7-2019",    119438,
    "11-8-2019",    167452,
    "11-9-2019",    504,
    "11-10-2019",   4232,
    "11-11-2019",   231509,
    "11-12-2019",   214653,
    "11-13-2019",   261901,
    "11-14-2019",   36632,
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# Breaking into y/m/d and rename it:
x <- x %>% mutate(`ActualInvDate` = mdy(`ActualInvDate`)) %>% 
    mutate_at(vars(`ActualInvDate`),
              funs(year, month, day)) %>%
    rename("Act.Inv.Year" = `year`,
           "Act.Inv.Month" = `month`,
           "Act.Inv.Day" = `day`
    ) %>% mutate(`Act.Inv.Year` = as.factor(`Act.Inv.Year`)) # Ensure Factor for plotting by year
#> Warning: `funs()` is deprecated as of dplyr 0.8.0.
#> Please use a list of either functions or lambdas: 
#> 
#>   # Simple named list: 
#>   list(mean = mean, median = median)
#> 
#>   # Auto named with `tibble::lst()`: 
#>   tibble::lst(mean, median)
#> 
#>   # Using lambdas
#>   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.


#Plot 1
# Simple revenue x time 
ggplot(data = x,
       aes(
           x = `ActualInvDate`, y = `Tot.Billed.Amt`/1000
       )) + geom_line()



# Plot2
# grouped by year, issue on x-axis (should be the months),
# lost the "granularity" of first 
ggplot(data = x,
       aes(x = ActualInvDate, y = Tot.Billed.Amt/1000,
           color = Act.Inv.Year
       )) + 
    geom_line() +   
    labs(x = "Invoice Date", y = "Amount Billed (Thousands $)") +
    theme_minimal()

Created on 2020-08-04 by the reprex package (v0.3.0)

Thank you for you reply!

You got the right idea and I understand you point regarding leap years etc, do you think maybe is it possible to have it by months instead of days then? I'm open to better ways to visualize this information over time.

Sorry if I weren't clear, FJCC got the idea but his solution has the x-axis in days of the year.

Basically x-axis being from Jan through Dec and the colored line plots for each year.

ggplot(data = x,
       aes(x = `Act.Inv.Month`, y = `Tot.Billed.Amt`/1000,
           color = `Act.Inv.Year`
       )) + 
  geom_line() +   
  labs(x = "Invoice Date", y = "Amount Billed (Thousands $)") 

Looks like:

I'm open for suggestions on better ways to visualize this data. Basically total revenue month by month, each year a different color.

Thanks for your time!

minor tweak to fjcc :


x <- mutate(x, week_of_year = week(ActualInvDate))

ggplot(x, aes(week_of_year, 
              Tot.Billed.Amt/1000, 
              color = Act.Inv.Year)) + geom_point(size=.5) +
  geom_smooth() +
  scale_x_continuous(breaks = 0:17*3+1)

1 Like

@nirgrahamuk very nice! This is actually awesome!

Do you mind explaning a little how you came up with the numbers 0:17*3+1 for:

  scale_x_continuous(breaks = 0:17*3+1)

I see that it is where you want the breaks and how they create a vector from 0 to 52.

>  0:17*3
 [1]  0  3  6  9 12 15 18 21 24 27 30 33 36 39 42 45 48 51
> 0:17*3+1
 [1]  1  4  7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52

Just never seen this and maybe there is some underlying fundamental principal which I'm unaware. Like I get what you did, but never seen or would have thought of managing the breaks like that.

Thanks for that!

I want to control the breaks, and I want the first to be 1 and the last to be 52.
I conceptualise it as 0 to 51 (all plus 1), If I used seq() with length, it wont respect my desire to have integers, so some factorisation will be required.
Here is a convenient tool


1,3,17,51
(in practice I didnt use the above but rather a very simple and fast 'trial and error' where I saw that 51 could be divided by 3 17 times. Therefore I was able to generate a sequence with the properties I wanted.
2 Likes

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