Function for generating messy data
messy_data(n = 20)
| n | the number of cases to generate |
|---|
a data frame with "messy" data
messy_data(n = 5)#> 'ID Date of Onset. DisCharge.. GENDER_ Épi.Case_définition messy/dates #> 1 ltdvnv 2018-01-10 20/01/2018 MALE Not.a.Case female #> 2 sfkczt 2018-01-11 21/01/2018 male PROBABLE <NA> #> 3 fqewjn 2018-01-03 13/01/2018 Male confirmed 2018-10-18 #> 4 qgbsqf 2018-01-10 20/01/2018 female suspected 01-12-2001 #> 5 yffwze 2018-01-02 12/01/2018 male confirmed 2018 10 19 #> lat lon #> 1 15.09329 48.22227 #> 2 10.42140 47.03783 #> 3 13.75023 48.05203 #> 4 11.88616 48.90004 #> 5 13.60485 47.60510messy_data()#> 'ID Date of Onset. DisCharge.. GENDER_ Épi.Case_définition #> 1 atwbkq 2018-01-08 18/01/2018 MALE PROBABLE #> 2 jatgwu 2018-01-09 19/01/2018 male confirmed #> 3 arclpt 2018-01-06 16/01/2018 female PROBABLE #> 4 dnpnwz 2018-01-11 21/01/2018 male Not.a.Case #> 5 dgtiqm 2018-01-02 12/01/2018 Female Not.a.Case #> 6 colnhj 2018-01-08 18/01/2018 Male probable #> 7 agrbsz 2018-01-09 19/01/2018 FEMALE confirmed #> 8 vxpjew 2018-01-11 21/01/2018 female not a case #> 9 rafnla 2018-01-05 15/01/2018 Female confirmed #> 10 vckwcu 2018-01-10 20/01/2018 FEMALE probable #> 11 majdra 2018-01-04 14/01/2018 MALE Confirmed #> 12 lmaawq 2018-01-10 20/01/2018 Female confirmed #> 13 zabxji 2018-01-11 21/01/2018 FEMALE Confirmed #> 14 scewuf 2018-01-02 12/01/2018 female suspected #> 15 xlbasn 2018-01-11 21/01/2018 male Confirmed #> 16 angwof 2018-01-11 21/01/2018 Male Not.a.Case #> 17 vtekdq 2018-01-08 18/01/2018 MALE suspected #> 18 pfndgq 2018-01-03 13/01/2018 Female not a case #> 19 dwvaav 2018-01-04 14/01/2018 FEMALE confirmed #> 20 hjgjit 2018-01-10 20/01/2018 male suspected #> messy/dates lat lon #> 1 2018 10 19 13.91259 48.96840 #> 2 that's 24/12/1989! 12.81360 48.25301 #> 3 2018-10-18 13.12241 47.72316 #> 4 that's 24/12/1989! 10.55235 46.45539 #> 5 male 12.64599 48.79495 #> 6 01-12-2001 13.31722 46.59780 #> 7 <NA> 18.29783 46.53203 #> 8 2018_10_17 13.30909 48.42130 #> 9 female 12.74878 48.77904 #> 10 that's 24/12/1989! 14.39734 46.65414 #> 11 2018-10-18 10.21107 49.31927 #> 12 // 24//12//1989 12.20306 48.36004 #> 13 2018_10_17 13.92479 46.07500 #> 14 2018_10_17 10.64110 47.66341 #> 15 female 15.78178 49.01112 #> 16 that's 24/12/1989! 11.79277 48.37401 #> 17 that's 24/12/1989! 14.41939 48.66264 #> 18 <NA> 11.35501 48.53661 #> 19 <NA> 13.49739 47.92759 #> 20 that's 24/12/1989! 14.66227 48.62119