ANU BDSIworkshop Data Wrangling with R Part 1

🕙 Schedule

Time	Content
10:00–10:20	Tidy data
10:20–10:40	Exercise 1
10:40–10:50	Discuss exercise 1
10:50–11:30	Welcome to Tidyverse
11:30–11:40	Break
11:40–12:10	Exercise 2
12:10–12:30	Pivotting data
12:30–12:50	Exercise 3
12:50–13:00	Wrap up

Today’s learning objectives

Recognize the characteristics of tidy data
Differentiate between the Base and Tidyverse paradigms
Acquire the skills to add/modify columns, subset data by rows and columns, rename column names, and perform group operations using dplyr
Pivot data into longer or wider format using tidyr
~~Join datasets using dplyr~~

Current learning objective

Recognize the characteristics of tidy data
-Differentiate between the Base and Tidyverse paradigms
-Acquire the skills to add/modify columns, subset data by rows and columns, rename column names, and perform group operations using dplyr
-Pivot data into longer or wider format using tidyr
-~~Join datasets using dplyr~~

Tidy data

Definition of a tidy data (Wickham, 2014)

Each variable must have its own column
Each observation must have its own row
Each value must have its own cell

Wickham (2014) Tidy Data. Journal of Statistical Software

Is this tidy data? Part 1

Estimated residential population in December
state	2019	2018	2017
NSW	8130159	80366651	7919815
VIC	6655284	6528601	6387081
ACT	427892	423169	415874

Estimated residential population in December
state	year	population
NSW	2019	8130159
NSW	2018	80366651
NSW	2017	7919815
VIC	2019	6655284
VIC	2018	6528601
VIC	2017	6387081
ACT	2019	427892
ACT	2018	423169
ACT	2017	415874

Values adapted from Australian Bureau of Statistics. (2020). Table 04. Estimated Resident Population, States and Territories [Time series spreadsheet]. National, state and territory population, Australia Mar 2020. Retrieved Nov 24, 2020. https://www.abs.gov.au/statistics/people/population/national-state-and-territory-population/mar-2020/310104.xls

Is this tidy data? Part 2

Total weekly personal income by age and sex from ABS 2016 Census

STE_CODE_2016	M_Neg_Nil_income_15_19_yrs	M_Neg_Nil_income_20_24_yrs	M_Neg_Nil_income_25_34_yrs	M_Neg_Nil_income_35_44_yrs	M_Neg_Nil_income_45_54_yrs	M_Neg_Nil_income_55_64_yrs	M_Neg_Nil_income_65_74_yrs	M_Neg_Nil_income_75_84_yrs	M_Negtve_Nil_incme_85_yrs_ovr	M_Neg_Nil_income_Tot	M_1_149_15_19_yrs	M_1_149_20_24_yrs	M_1_149_25_34_yrs	M_1_149_35_44_yrs	M_1_149_45_54_yrs	M_1_149_55_64_yrs	M_1_149_65_74_yrs	M_1_149_75_84_yrs	M_1_149_85ov	M_1_149_Tot	M_150_299_15_19_yrs	M_150_299_20_24_yrs	M_150_299_25_34_yrs	M_150_299_35_44_yrs	M_150_299_45_54_yrs	M_150_299_55_64_yrs	M_150_299_65_74_yrs	M_150_299_75_84_yrs	M_150_299_85ov	M_150_299_Tot	M_300_399_15_19_yrs	M_300_399_20_24_yrs	M_300_399_25_34_yrs	M_300_399_35_44_yrs	M_300_399_45_54_yrs	M_300_399_55_64_yrs	M_300_399_65_74_yrs	M_300_399_75_84_yrs	M_300_399_85ov	M_300_399_Tot	M_400_499_15_19_yrs	M_400_499_20_24_yrs	M_400_499_25_34_yrs	M_400_499_35_44_yrs	M_400_499_45_54_yrs	M_400_499_55_64_yrs	M_400_499_65_74_yrs	M_400_499_75_84_yrs	M_400_499_85ov	M_400_499_Tot	M_500_649_15_19_yrs	M_500_649_20_24_yrs	M_500_649_25_34_yrs	M_500_649_35_44_yrs	M_500_649_45_54_yrs	M_500_649_55_64_yrs	M_500_649_65_74_yrs	M_500_649_75_84_yrs	M_500_649_85ov	M_500_649_Tot	M_650_799_15_19_yrs	M_650_799_20_24_yrs	M_650_799_25_34_yrs	M_650_799_35_44_yrs	M_650_799_45_54_yrs	M_650_799_55_64_yrs	M_650_799_65_74_yrs	M_650_799_75_84_yrs	M_650_799_85ov	M_650_799_Tot	M_800_999_15_19_yrs	M_800_999_20_24_yrs	M_800_999_25_34_yrs	M_800_999_35_44_yrs	M_800_999_45_54_yrs	M_800_999_55_64_yrs	M_800_999_65_74_yrs	M_800_999_75_84_yrs	M_800_999_85ov	M_800_999_Tot	M_1000_1249_15_19_yrs	M_1000_1249_20_24_yrs	M_1000_1249_25_34_yrs	M_1000_1249_35_44_yrs	M_1000_1249_45_54_yrs	M_1000_1249_55_64_yrs	M_1000_1249_65_74_yrs	M_1000_1249_75_84_yrs	M_1000_1249_85ov	M_1000_1249_Tot	M_1250_1499_15_19_yrs	M_1250_1499_20_24_yrs	M_1250_1499_25_34_yrs	M_1250_1499_35_44_yrs	M_1250_1499_45_54_yrs	M_1250_1499_55_64_yrs	M_1250_1499_65_74_yrs	M_1250_1499_75_84_yrs	M_1250_1499_85ov	M_1250_1499_Tot	M_1500_1749_15_19_yrs	M_1500_1749_20_24_yrs	M_1500_1749_25_34_yrs	M_1500_1749_35_44_yrs	M_1500_1749_45_54_yrs	M_1500_1749_55_64_yrs	M_1500_1749_65_74_yrs	M_1500_1749_75_84_yrs	M_1500_1749_85ov	M_1500_1749_Tot	M_1750_1999_15_19_yrs	M_1750_1999_20_24_yrs	M_1750_1999_25_34_yrs	M_1750_1999_35_44_yrs	M_1750_1999_45_54_yrs	M_1750_1999_55_64_yrs	M_1750_1999_65_74_yrs	M_1750_1999_75_84_yrs	M_1750_1999_85ov	M_1750_1999_Tot	M_2000_2999_15_19_yrs	M_2000_2999_20_24_yrs	M_2000_2999_25_34_yrs	M_2000_2999_35_44_yrs	M_2000_2999_45_54_yrs	M_2000_2999_55_64_yrs	M_2000_2999_65_74_yrs	M_2000_2999_75_84_yrs	M_2000_2999_85ov	M_2000_2999_Tot	M_3000_more_15_19_yrs	M_3000_more_20_24_yrs	M_3000_more_25_34_yrs	M_3000_more_35_44_yrs	M_3000_more_45_54_yrs	M_3000_more_55_64_yrs	M_3000_more_65_74_yrs	M_3000_more_75_84_yrs	M_3000_more_85ov	M_3000_more_Tot	M_PI_NS_15_19_yrs	M_PI_NS_ns_20_24_yrs	M_PI_NS_ns_25_34_yrs	M_PI_NS_ns_35_44_yrs	M_PI_NS_ns_45_54_yrs	M_PI_NS_ns_55_64_yrs	M_PI_NS_ns_65_74_yrs	M_PI_NS_ns_75_84_yrs	M_PI_NS_ns_85_yrs_ovr	M_PI_NS_ns_Tot	M_Tot_15_19_yrs	M_Tot_20_24_yrs	M_Tot_25_34_yrs	M_Tot_35_44_yrs	M_Tot_45_54_yrs	M_Tot_55_64_yrs	M_Tot_65_74_yrs	M_Tot_75_84_yrs	M_Tot_85ov	M_Tot_Tot	F_Neg_Nil_income_15_19_yrs	F_Neg_Nil_income_20_24_yrs	F_Neg_Nil_income_25_34_yrs	F_Neg_Nil_income_35_44_yrs	F_Neg_Nil_income_45_54_yrs	F_Neg_Nil_income_55_64_yrs	F_Neg_Nil_income_65_74_yrs	F_Neg_Nil_income_75_84_yrs	F_Neg_Nil_incme_85_yrs_ovr	F_Neg_Nil_income_Tot	F_1_149_15_19_yrs	F_1_149_20_24_yrs	F_1_149_25_34_yrs	F_1_149_35_44_yrs	F_1_149_45_54_yrs	F_1_149_55_64_yrs	F_1_149_65_74_yrs	F_1_149_75_84_yrs	F_1_149_85ov	F_1_149_Tot	F_150_299_15_19_yrs	F_150_299_20_24_yrs	F_150_299_25_34_yrs	F_150_299_35_44_yrs	F_150_299_45_54_yrs	F_150_299_55_64_yrs	F_150_299_65_74_yrs	F_150_299_75_84_yrs	F_150_299_85ov	F_150_299_Tot	F_300_399_15_19_yrs	F_300_399_20_24_yrs	F_300_399_25_34_yrs	F_300_399_35_44_yrs	F_300_399_45_54_yrs	F_300_399_55_64_yrs	F_300_399_65_74_yrs	F_300_399_75_84_yrs	F_300_399_85ov	F_300_399_Tot	F_400_499_15_19_yrs	F_400_499_20_24_yrs	F_400_499_25_34_yrs	F_400_499_35_44_yrs	F_400_499_45_54_yrs	F_400_499_55_64_yrs	F_400_499_65_74_yrs	F_400_499_75_84_yrs	F_400_499_85ov	F_400_499_Tot	F_500_649_15_19_yrs	F_500_649_20_24_yrs	F_500_649_25_34_yrs	F_500_649_35_44_yrs	F_500_649_45_54_yrs	F_500_649_55_64_yrs	F_500_649_65_74_yrs	F_500_649_75_84_yrs	F_500_649_85ov	F_500_649_Tot	F_650_799_15_19_yrs	F_650_799_20_24_yrs	F_650_799_25_34_yrs	F_650_799_35_44_yrs	F_650_799_45_54_yrs	F_650_799_55_64_yrs	F_650_799_65_74_yrs	F_650_799_75_84_yrs	F_650_799_85ov	F_650_799_Tot	F_800_999_15_19_yrs	F_800_999_20_24_yrs	F_800_999_25_34_yrs	F_800_999_35_44_yrs	F_800_999_45_54_yrs	F_800_999_55_64_yrs	F_800_999_65_74_yrs	F_800_999_75_84_yrs	F_800_999_85ov	F_800_999_Tot	F_1000_1249_15_19_yrs	F_1000_1249_20_24_yrs	F_1000_1249_25_34_yrs	F_1000_1249_35_44_yrs	F_1000_1249_45_54_yrs	F_1000_1249_55_64_yrs	F_1000_1249_65_74_yrs	F_1000_1249_75_84_yrs	F_1000_1249_85ov	F_1000_1249_Tot	F_1250_1499_15_19_yrs	F_1250_1499_20_24_yrs	F_1250_1499_25_34_yrs	F_1250_1499_35_44_yrs	F_1250_1499_45_54_yrs	F_1250_1499_55_64_yrs	F_1250_1499_65_74_yrs	F_1250_1499_75_84_yrs	F_1250_1499_85ov	F_1250_1499_Tot	F_1500_1749_15_19_yrs	F_1500_1749_20_24_yrs	F_1500_1749_25_34_yrs	F_1500_1749_35_44_yrs	F_1500_1749_45_54_yrs	F_1500_1749_55_64_yrs	F_1500_1749_65_74_yrs	F_1500_1749_75_84_yrs	F_1500_1749_85ov	F_1500_1749_Tot	F_1750_1999_15_19_yrs	F_1750_1999_20_24_yrs	F_1750_1999_25_34_yrs	F_1750_1999_35_44_yrs	F_1750_1999_45_54_yrs	F_1750_1999_55_64_yrs	F_1750_1999_65_74_yrs	F_1750_1999_75_84_yrs	F_1750_1999_85ov	F_1750_1999_Tot	F_2000_2999_15_19_yrs	F_2000_2999_20_24_yrs	F_2000_2999_25_34_yrs	F_2000_2999_35_44_yrs	F_2000_2999_45_54_yrs	F_2000_2999_55_64_yrs	F_2000_2999_65_74_yrs	F_2000_2999_75_84_yrs	F_2000_2999_85ov	F_2000_2999_Tot	F_3000_more_15_19_yrs	F_3000_more_20_24_yrs	F_3000_more_25_34_yrs	F_3000_more_35_44_yrs	F_3000_more_45_54_yrs	F_3000_more_55_64_yrs	F_3000_more_65_74_yrs	F_3000_more_75_84_yrs	F_3000_more_85ov	F_3000_more_Tot	F_PI_NS_15_19_yrs	F_PI_NS_ns_20_24_yrs	F_PI_NS_ns_25_34_yrs	F_PI_NS_ns_35_44_yrs	F_PI_NS_ns_45_54_yrs	F_PI_NS_ns_55_64_yrs	F_PI_NS_ns_65_74_yrs	F_PI_NS_ns_75_84_yrs	F_PI_NS_ns_85_yrs_ovr	F_PI_NS_ns_Tot	F_Tot_15_19_yrs	F_Tot_20_24_yrs	F_Tot_25_34_yrs	F_Tot_35_44_yrs	F_Tot_45_54_yrs	F_Tot_55_64_yrs	F_Tot_65_74_yrs	F_Tot_75_84_yrs	F_Tot_85ov	F_Tot_Tot	P_Neg_Nil_income_15_19_yrs	P_Neg_Nil_income_20_24_yrs	P_Neg_Nil_income_25_34_yrs	P_Neg_Nil_income_35_44_yrs	P_Neg_Nil_income_45_54_yrs	P_Neg_Nil_income_55_64_yrs	P_Neg_Nil_income_65_74_yrs	P_Neg_Nil_income_75_84_yrs	P_Negtve_Nil_incme_85_yrs_ovr	P_Neg_Nil_income_Tot	P_1_149_15_19_yrs	P_1_149_20_24_yrs	P_1_149_25_34_yrs	P_1_149_35_44_yrs	P_1_149_45_54_yrs	P_1_149_55_64_yrs	P_1_149_65_74_yrs	P_1_149_75_84_yrs	P_1_149_85ov	P_1_149_Tot	P_150_299_15_19_yrs	P_150_299_20_24_yrs	P_150_299_25_34_yrs	P_150_299_35_44_yrs	P_150_299_45_54_yrs	P_150_299_55_64_yrs	P_150_299_65_74_yrs	P_150_299_75_84_yrs	P_150_299_85ov	P_150_299_Tot	P_300_399_15_19_yrs	P_300_399_20_24_yrs	P_300_399_25_34_yrs	P_300_399_35_44_yrs	P_300_399_45_54_yrs	P_300_399_55_64_yrs	P_300_399_65_74_yrs	P_300_399_75_84_yrs	P_300_399_85ov	P_300_399_Tot	P_400_499_15_19_yrs	P_400_499_20_24_yrs	P_400_499_25_34_yrs	P_400_499_35_44_yrs	P_400_499_45_54_yrs	P_400_499_55_64_yrs	P_400_499_65_74_yrs	P_400_499_75_84_yrs	P_400_499_85ov	P_400_499_Tot	P_500_649_15_19_yrs	P_500_649_20_24_yrs	P_500_649_25_34_yrs	P_500_649_35_44_yrs	P_500_649_45_54_yrs	P_500_649_55_64_yrs	P_500_649_65_74_yrs	P_500_649_75_84_yrs	P_500_649_85ov	P_500_649_Tot	P_650_799_15_19_yrs	P_650_799_20_24_yrs	P_650_799_25_34_yrs	P_650_799_35_44_yrs	P_650_799_45_54_yrs	P_650_799_55_64_yrs	P_650_799_65_74_yrs	P_650_799_75_84_yrs	P_650_799_85ov	P_650_799_Tot	P_800_999_15_19_yrs	P_800_999_20_24_yrs	P_800_999_25_34_yrs	P_800_999_35_44_yrs	P_800_999_45_54_yrs	P_800_999_55_64_yrs	P_800_999_65_74_yrs	P_800_999_75_84_yrs	P_800_999_85ov	P_800_999_Tot	P_1000_1249_15_19_yrs	P_1000_1249_20_24_yrs	P_1000_1249_25_34_yrs	P_1000_1249_35_44_yrs	P_1000_1249_45_54_yrs	P_1000_1249_55_64_yrs	P_1000_1249_65_74_yrs	P_1000_1249_75_84_yrs	P_1000_1249_85ov	P_1000_1249_Tot	P_1250_1499_15_19_yrs	P_1250_1499_20_24_yrs	P_1250_1499_25_34_yrs	P_1250_1499_35_44_yrs	P_1250_1499_45_54_yrs	P_1250_1499_55_64_yrs	P_1250_1499_65_74_yrs	P_1250_1499_75_84_yrs	P_1250_1499_85ov	P_1250_1499_Tot	P_1500_1749_15_19_yrs	P_1500_1749_20_24_yrs	P_1500_1749_25_34_yrs	P_1500_1749_35_44_yrs	P_1500_1749_45_54_yrs	P_1500_1749_55_64_yrs	P_1500_1749_65_74_yrs	P_1500_1749_75_84_yrs	P_1500_1749_85ov	P_1500_1749_Tot	P_1750_1999_15_19_yrs	P_1750_1999_20_24_yrs	P_1750_1999_25_34_yrs	P_1750_1999_35_44_yrs	P_1750_1999_45_54_yrs	P_1750_1999_55_64_yrs	P_1750_1999_65_74_yrs	P_1750_1999_75_84_yrs	P_1750_1999_85ov	P_1750_1999_Tot	P_2000_2999_15_19_yrs	P_2000_2999_20_24_yrs	P_2000_2999_25_34_yrs	P_2000_2999_35_44_yrs	P_2000_2999_45_54_yrs	P_2000_2999_55_64_yrs	P_2000_2999_65_74_yrs	P_2000_2999_75_84_yrs	P_2000_2999_85ov	P_2000_2999_Tot	P_3000_more_15_19_yrs	P_3000_more_20_24_yrs	P_3000_more_25_34_yrs	P_3000_more_35_44_yrs	P_3000_more_45_54_yrs	P_3000_more_55_64_yrs	P_3000_more_65_74_yrs	P_3000_more_75_84_yrs	P_3000_more_85ov	P_3000_more_Tot	P_PI_NS_15_19_yrs	P_PI_NS_ns_20_24_yrs	P_PI_NS_ns_25_34_yrs	P_PI_NS_ns_35_44_yrs	P_PI_NS_ns_45_54_yrs	P_PI_NS_ns_55_64_yrs	P_PI_NS_ns_65_74_yrs	P_PI_NS_ns_75_84_yrs	P_PI_NS_ns_85_yrs_ovr	P_PI_NS_ns_Tot	P_Tot_15_19_yrs	P_Tot_20_24_yrs	P_Tot_25_34_yrs	P_Tot_35_44_yrs	P_Tot_45_54_yrs	P_Tot_55_64_yrs	P_Tot_65_74_yrs	P_Tot_75_84_yrs	P_Tot_85ov	P_Tot_Tot
1	109473	33797	25398	14472	15235	22736	15465	6576	2320	245480	41865	14976	5674	4202	5561	8627	8244	3449	1119	93723	18444	25465	19864	15614	18341	25927	27772	16496	4870	172799	9018	17374	17339	14480	20156	29997	60479	43533	13188	225558	10497	18935	20677	16252	20964	27706	43545	27376	11489	197429	9213	24033	26629	19382	21431	26066	31057	16175	5043	179031	5040	27515	41764	29222	30239	30707	23657	11167	3568	202871	2600	26711	61986	44391	44280	40397	22083	7154	2296	251897	1151	20070	73023	54800	51333	44385	21655	6139	1931	274488	385	9340	53495	43951	38954	30553	12202	2936	923	192736	184	4420	44394	43349	37858	26989	9013	2165	716	169085	90	1878	29445	36276	30618	20560	5783	1349	503	126501	87	1514	42982	68876	55456	34158	9018	2108	672	214871	241	779	15687	48586	49374	29279	9539	2774	1008	157264	21813	21685	48201	40406	38664	34973	30961	21518	12098	270327	230103	248490	526560	494264	478458	433059	330471	170912	61753	2974063	95424	32797	53643	46531	40486	51106	21012	8866	4068	353937	51928	16390	14381	16661	15217	18126	11252	4423	1562	149938	23031	28295	30674	30305	30488	41208	34625	17970	6087	242685	9541	20982	28659	29309	32941	44933	73308	45792	15552	301014	7222	21547	32701	31009	34903	38925	61759	48408	27794	304260	5884	26247	46536	43262	43802	38121	35855	18431	10845	268982	3355	26587	50935	45197	46748	36994	22959	10531	4470	247778	1606	23106	56094	45396	48155	37096	16239	6573	3196	237465	576	16429	60714	45794	45889	35036	12968	4623	2287	224322	169	6349	44811	33903	31641	21973	6725	1910	866	148357	90	2023	33527	30863	28217	19438	4998	1507	794	121458	59	602	18558	23047	21168	14263	3335	1030	505	82559	76	481	21559	33731	27799	16469	4389	1491	707	106694	224	411	6038	18714	17426	8948	3891	1747	982	58377	19126	18948	42136	34906	34646	34068	33252	28889	26038	272003	218321	241181	540962	508625	499528	456703	346555	202200	105755	3119834	204894	66595	79047	61006	55721	73844	36478	15448	6389	599413	93787	31363	20056	20868	20778	26750	19501	7870	2681	243659	41477	53758	50544	45919	48828	67136	62396	34466	10963	415486	18559	38357	45993	43786	53101	74931	133782	89322	28739	526570	17725	40481	53377	47257	55870	66626	105300	75784	39280	501698	15101	50283	73161	62645	65235	64188	66904	34607	15895	448014	8395	54097	92698	74420	76984	67698	46609	21701	8040	450649	4211	49813	118084	89786	92434	77495	38326	13730	5497	489370	1732	36494	133738	100591	97224	79423	34625	10760	4216	498807	554	15688	98307	77854	70595	52528	18922	4853	1786	341092	272	6442	77918	74217	66074	46427	14015	3673	1510	290547	148	2477	47997	59326	51780	34823	9116	2374	1011	209058	164	1998	64540	102608	83247	50630	13407	3602	1379	321562	465	1190	21722	67297	66796	38226	13424	4523	1990	215643	40942	40631	90340	75312	73314	69037	64211	50411	38132	542326	448425	489673	1067524	1002886	977984	889763	677020	373115	167506	6093895
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9	37	3	4	10	7	13	15	3	0	97	12	3	0	5	3	5	6	0	0	36	7	3	3	6	7	24	19	11	3	82	0	0	5	8	6	12	31	20	0	94	3	3	8	4	10	17	24	16	9	92	6	13	5	18	28	25	26	3	6	129	5	5	20	22	29	35	17	9	3	145	3	16	24	35	32	31	17	3	0	163	0	10	34	31	33	27	19	0	0	159	0	3	22	21	27	21	19	0	0	124	0	7	18	21	25	28	13	3	0	109	0	0	20	27	28	22	8	0	0	104	0	3	27	38	49	35	12	0	0	153	0	0	7	13	7	11	10	0	0	43	27	58	139	97	79	57	38	15	4	514	106	136	330	345	376	364	263	95	28	2037	36	6	25	26	22	45	14	3	0	182	21	3	7	10	12	13	8	0	0	73	11	3	16	12	20	29	24	11	0	126	3	0	16	15	24	26	45	24	3	160	10	7	19	16	25	25	29	21	10	154	0	4	31	35	28	36	19	9	3	158	0	10	28	31	38	25	10	7	0	154	0	12	14	35	28	19	14	0	0	119	0	11	18	28	27	23	7	0	0	113	0	0	8	18	14	18	3	3	0	65	0	3	11	16	17	14	3	0	0	62	0	0	5	17	9	12	3	0	0	48	0	3	12	9	18	13	0	0	0	48	0	0	3	3	4	0	0	0	0	15	26	10	35	27	45	47	33	13	4	243	110	72	244	296	334	350	214	91	33	1730	78	14	30	32	29	58	31	5	0	280	30	4	7	21	11	20	11	3	3	116	21	5	18	25	27	47	44	28	3	214	3	4	23	17	30	41	73	48	7	247	12	6	25	19	36	42	55	32	16	247	7	16	36	49	56	56	43	10	8	282	8	20	46	57	69	57	30	13	3	300	3	20	43	68	57	58	25	3	0	281	0	15	49	56	63	58	26	0	0	276	3	8	30	37	38	42	24	0	0	185	0	12	24	32	44	36	17	5	0	172	0	0	23	43	36	32	11	0	0	152	0	4	31	48	64	41	12	0	0	206	0	0	7	15	11	13	6	0	0	59	49	72	179	124	125	102	68	30	12	762	215	210	573	642	711	709	478	182	58	3773

What do you think the headers mean?

Based on Australian Bureau of Statistics 2016 Census Data

STE Code
code	name	abbrv
1	New South Wales	NSW
2	Victoria	Vic
3	Queensland	Qld
4	South Australia	SA
5	Western Australia	WA
6	Tasmania	Tas
7	Northern Territory	NT
8	Australian Capital Territory	ACT
9	Other Territories	OT

Is this tidy data? Part 3

Total weekly personal income by age and sex
state	sex	income_category	age_category	count
New South Wales	M	$0 or negative income	[15, 19)	109473
New South Wales	M	$0 or negative income	[20, 24)	33797
New South Wales	M	$0 or negative income	[25, 34)	25398
New South Wales	M	$0 or negative income	[35, 44)	14472
New South Wales	M	$0 or negative income	[45, 54)	15235
New South Wales	M	$0 or negative income	[55, 64)	22736
New South Wales	M	$0 or negative income	[65, 74)	15465
New South Wales	M	$0 or negative income	[75, 84)	6576
New South Wales	M	$0 or negative income	>=85	2320

Based on Australian Bureau of Statistics 2016 Census Data

Is this tidy data? Part 4

U.S. historical crop yields by state
year	state	crop	yield
1900	Iowa	barley	28.5
1900	Kansas	barley	18.0
2000	Kansas	barley	35.0
1900	Iowa	wheat	14.4
1900	Kansas	wheat	18.2
2000	Iowa	wheat	47.0
2000	Kansas	wheat	37.0

U.S. historical crop yields by state
year	state	barley_yield	wheat_yield
1900	Iowa	28.5	14.4
1900	Kansas	18.0	18.2
2000	Kansas	35.0	37.0
2000	Iowa	NA	47.0

United States Department of Agriculture, National Agricultural Statistics Service. http://quickstats.nass.usda.gov/

Kevin Wright (2020). agridat: Agricultural Datasets. R package version 1.17

Is this tidy data? Part 5

U.S. historical crop yields by state
year	state	crop	metric	value
1900	Iowa	barley	yield	28.5
1900	Iowa	barley	acres	620,000.0
1900	Kansas	barley	yield	18.0
1900	Kansas	barley	acres	127,000.0
2000	Kansas	barley	yield	35.0
2000	Kansas	barley	acres	7,000.0
1900	Iowa	wheat	yield	14.4
1900	Iowa	wheat	acres	1,450,000.0
1900	Kansas	wheat	yield	18.2
1900	Kansas	wheat	acres	4,290,000.0
2000	Iowa	wheat	yield	47.0
2000	Iowa	wheat	acres	18,000.0
2000	Kansas	wheat	yield	37.0
2000	Kansas	wheat	acres	9,400,000.0

U.S. historical crop yields by state
year	state	barley_yield	wheat_yield	barley_acres	wheat_acres
1900	Iowa	28.5	14.4	620000	1450000
1900	Kansas	18.0	18.2	127000	4290000
2000	Kansas	35.0	37.0	7000	9400000
2000	Iowa	NA	47.0	NA	18000

United States Department of Agriculture, National Agricultural Statistics Service. http://quickstats.nass.usda.gov/

Kevin Wright (2020). agridat: Agricultural Datasets. R package version 1.17

United States Department of Agriculture, National Agricultural Statistics Service. http://quickstats.nass.usda.gov/

Kevin Wright (2020). agridat: Agricultural Datasets. R package version 1.17

year	state	crop	yield	acres
1900	Iowa	barley	28.5	620000
1900	Kansas	barley	18.0	127000
2000	Kansas	barley	35.0	7000
1900	Iowa	wheat	14.4	1450000
1900	Kansas	wheat	18.2	4290000
2000	Iowa	wheat	47.0	18000
2000	Kansas	wheat	37.0	9400000

Summary

Tidy data

Each variable must have its own column
Each observation must have its own row
Each value must have its own cell

Tidy data

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Tidy data

Is this tidy data? Part 1

Is this tidy data? Part 2

Is this tidy data? Part 3

Is this tidy data? Part 4

Is this tidy data? Part 5

Summary

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