why date format parsing the data in wrong format

library(dygraphs)
library(xts)
library(lubridate)
library(timetk)
library(dplyr)
library(readr)

data1<-read.csv("c:/users/nic user/downloads/report.csv")
a<-aggregate(. ~data1$MthYr , data = data1, sum)
# 
# str(data1)
# glimpse(data1)
# a<-data1 %>% group_by(data1$month)
# a
#aggregate(. ~data1$month, data=data1, FUN = sum)
#s<-parse_datetime(data1$month, "%d/%m/%y")
#s<-as.Date(data1$month)
# s<-as.POSIXct(as.numeric(as.character(data1$month)),origin = "2017-01-01")
# s
qxts <- xts(a[, -1], order.by=as.Date(as.POSIXct(a$`data1$MthYr`)))
qxts
ad <- cbind(qxts$RegCount,qxts$AbortionCount,qxts$MaternalDeathCount)
dygraph(ad, main = "Deaths from Lung Disease (UK)") %>%
  dySeries("RegCount", stepPlot = TRUE, color = "red") %>%
  dyGroup(c("AbortionCount", "MaternalDeathCount"), drawPoints = TRUE, color = c("blue", "green"))

why is it looking like this...

MthYr UnitName RegCount AbortionCount MaternalDeathCount
0001-01-19    34      595   139251          5465                293
0001-01-19    68      595   143688          5825                348
0001-01-19   102      595   120622          3223                404
0001-02-19   136      595   134003          5157                376
0001-02-19   170      595   145692          5091                421
0001-02-19   204      595   104335          2076                333
0001-03-19   238      595   123052          5121                334
0001-03-19   272      595   115471          5251                325
0001-03-19   306      595    54319          1108                390
0001-04-19   340      595   161201          5216                375
0001-04-19   374      595   161791          4992                357
0001-05-19   408      595   150065          5880                354
0001-05-19   442      595   142824          5499                349
0001-06-19   476      595   146671          5781                411
0001-06-19   510      595   144185          5316                329
0001-07-19   544      595   142286          5958                402
0001-07-19   578      595   135974          5022                316
0001-08-19   612      595   127708          5968                409
0001-08-19   646      595   118690          4804                410
0001-09-19   680      595   117502          5134                416
0001-09-19   714      595   122510          4309                319
0001-10-19   748      595   115698          5208                316
0001-10-19   782      595    99298          4371                362
0001-11-19   816      595   125748          5420                355
0001-11-19   850      595   115744          3954                309
0001-12-19   884      595   140958          5927                398
0001-12-19   918      595   105097          3689                274

while the real data looks like this...

MthYr UnitName RegCount AbortionCount MaternalDeathCount
01-01-2018 a 4979 187 5
01-01-2018 b 7168 170 3
01-01-2018 c 4276 314 5
01-01-2018 d 2148 92 1
01-01-2018 e 7256 182 4
01-01-2018 f 5309 117 3
01-01-2018 g 5153 211 1
01-01-2018 h 5214 102 3
01-01-2018 i 2074 76 1
01-01-2018 j 2958 262 2
01-01-2018 k 4006 136 2
01-01-2018 l 3047 87 1
01-01-2018 m 3557 164 1
01-01-2018 n 3155 244 5
01-01-2018 o 3194 171 1
01-01-2018 p 2985 132 2
01-01-2018 q 6795 140 4
01-01-2018 r 3862 99 2
01-01-2018 s 1567 12 1
01-01-2018 t 4339 81 3
01-01-2018 u 3159 137 2
01-01-2018 v 3744 262 1
01-01-2018 w 6950 104 6
01-01-2018 x 3596 164 1
01-01-2018 y 2724 81 9
01-01-2018 z 7178 377 6
01-01-2018 a 4254 128 4
01-01-2018 b 1955 38 3
01-01-2018 c 2835 173 4
01-01-2018 d 3130 159 2
01-01-2018 e 4609 242 5
01-01-2018 f 2535 113 1
01-01-2018 g 2904 100 2
01-01-2018 h 6636 408 5
01-02-2018 i 5017 170 2
01-02-2018 j 7806 211 5
01-02-2018 k 4218 245 4
01-02-2018 l 2779 113 2
01-02-2018 m 6672 213 3
01-02-2018 n 5804 112 4
01-02-2018 o 4458 179 3
01-02-2018 p 4889 97 3
01-02-2018 q 2190 67 1
01-02-2018 r 2631 205 NULL
01-02-2018 s 4147 133 4
01-02-2018 t 3136 83 2
01-02-2018 u 3543 176 NULL
01-02-2018 v 2867 217 6
01-02-2018 w 3277 174 1
01-02-2018 x 2844 143 3
01-02-2018 y 6172 178 3
01-02-2018 z 3587 102 4
01-02-2018 a 1521 9 NULL
01-02-2018 b 4103 90 2
01-02-2018 c 3436 138 3
01-02-2018 d 3304 197 3
01-02-2018 e 6495 91 5
01-02-2018 f 3590 130 2
01-02-2018 g 3069 67 2
01-02-2018 h 6181 340 2
01-02-2018 i 3930 126 4
01-02-2018 j 1872 41 NULL
01-02-2018 k 2508 164 2
01-02-2018 l 2930 153 1
01-02-2018 m 4100 218 2
01-02-2018 n 2239 91 3
01-02-2018 o 2694 155 2
01-02-2018 p 5994 329 5
01-03-2018 q 4739 191 4
01-03-2018 r 7420 235 5
01-03-2018 s 3790 275 1
01-03-2018 t 2288 99 1
01-03-2018 u 5818 178 2
01-03-2018 v 5250 102 NULL
01-03-2018 w 4011 176 2
01-03-2018 x 4323 128 NULL
01-03-2018 y 2089 63 NULL
01-03-2018 z 2539 189 1
01-03-2018 a 3718 145 5
01-03-2018 b 2917 92 1
01-03-2018 c 3390 166 4
01-03-2018 d 3036 177 6
01-03-2018 e 2836 176 1
01-03-2018 f 2768 166 NULL
01-01-2019 g 6330 177 1
01-01-2019 h 3553 111 2
01-01-2019 i 1671 10 NULL
01-01-2019 j 3465 86 1
01-01-2019 k 2743 122 1
01-01-2019 l 2993 178 NULL
01-01-2019 m 5992 91 6
01-01-2019 n 3335 129 2
01-01-2019 o 2923 95 2
01-01-2019 p 5390 314 6
01-01-2019 q 3784 130 1
01-01-2019 r 1696 44 3
01-01-2019 s 2315 172 2
01-01-2019 t 2467 117 NULL
01-02-2019 u 3492 232 1
01-02-2019 v 2002 104 2
01-02-2019 w 2527 124 4
01-02-2019 x 5442 327 5
01-02-2019 y 5947 183 3
01-02-2019 z 8122 214 1
01-02-2019 a 4739 248 3
01-02-2019 b 2650 127 2
01-02-2019 c 9221 178 4
01-02-2019 d 6352 134 4
01-02-2019 e 5501 237 1
01-02-2019 f 5682 85 2
01-02-2019 g 2617 85 3
01-02-2019 h 3233 166 NULL
01-02-2019 i 4367 130 NULL
01-02-2019 j 3142 88 3
01-02-2019 k 3562 166 NULL
01-02-2019 l 3610 168 2
01-02-2019 m 3832 185 NULL
01-02-2019 n 3205 158 3
01-02-2019 o 6994 167 5
01-02-2019 p 4315 121 2
01-02-2019 q 2522 9 1
01-02-2019 r 5760 78 2
01-02-2019 s 3412 135 2
01-02-2019 t 4233 158 3
01-02-2019 u 8364 118 3
01-02-2019 v 3682 145 3
01-02-2019 w 3162 91 7
01-02-2019 x 8250 323 5
01-02-2019 y 5522 119 2
01-02-2019 z 2381 41 NULL
01-02-2019 a 3280 213 3
01-02-2019 b 3349 128 3
01-03-2019 c 6335 232 6
01-03-2019 d 2919 110 2
01-03-2019 e 3235 147 1
01-03-2019 f 7704 329 4
01-03-2019 g 5274 218 4
01-03-2019 h 7557 235 1
01-03-2019 i 4703 268 4
01-03-2019 j 2446 111 1
01-03-2019 k 8107 198 5
01-03-2019 l 5918 153 5
01-03-2019 m 5115 264 4
01-03-2019 n 5895 93 5
01-03-2019 o 2440 82 1
01-03-2019 p 3257 250 5
01-03-2019 q 4144 164 1
01-03-2019 r 3786 99 5
01-03-2019 s 3535 180 2
01-03-2019 t 3467 175 3
01-03-2019 u 3219 202 3
01-03-2019 v 2915 175 2
01-03-2019 w 6756 184 15
01-03-2019 x 4031 116 1
01-03-2019 y 2348 26 NULL
01-03-2019 z 5369 92 7
01-03-2019 a 3327 161 7
01-03-2019 b 3637 207 3
01-03-2019 c 7993 120 9
01-03-2019 d 3918 169 2
01-03-2019 e 3154 104 2
01-03-2019 f 6882 361 4
01-03-2019 g 4902 112 3
01-03-2019 h 2358 52 NULL
01-03-2019 i 2971 237 3
01-03-2019 j 3222 146 4
01-04-2019 k 4630 234 4
01-04-2019 l 2837 116 5
01-04-2019 m 2838 172 2
01-04-2019 n 7114 404 12
01-04-2019 o 5216 211 4
01-04-2019 p 7283 232 4
01-04-2019 q 5080 301 5
01-04-2019 r 2761 102 6
01-04-2019 s 8121 238 6
01-04-2019 t 5760 166 5
01-04-2019 u 5066 227 3
01-04-2019 v 5487 103 5
01-04-2019 w 2297 70 2
01-04-2019 x 3163 228 NULL
01-04-2019 y 3893 139 5
01-04-2019 z 3129 107 2

could anyone please tell me how to rectify this error. please guide me through this issue.
any help and suggestion would be a great help.

Try replacing the line where you read the data with this:

data1 <- read_csv("c:/users/nic user/downloads/report.csv", 
                  col_types = cols(MthYr = col_date("%d-%m-%Y"))

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.