library(shinycssloaders)Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio#install.packages("fingertipsR", repos = "https://dev.ropensci.org")
library(fingertipsR)Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudiolibrary(tidyverse)Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
-- Attaching packages --------------------- tidyverse 1.3.1 --
v ggplot2 3.3.5 v purrr 0.3.4
v tibble 3.1.2 v dplyr 1.0.7
v tidyr 1.1.3 v stringr 1.4.0
v readr 1.4.0 v forcats 0.5.1
-- Conflicts ------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()Useful info here: https://rstudio-pubs-static.s3.amazonaws.com/274982_54e369ab8e5c4702a99acb15c37cae88.html
local_health_profiles <- profiles(ProfileName = "Local Health")
local_health_profiles$DomainID[1] 1938133180 1938133183 1938133184 1938133185all_local_dat <- fingertips_data(
DomainID = local_health_profiles$DomainID,
AreaTypeID = "All"
)
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|====================================================| 100%str(all_local_dat)'data.frame': 854439 obs. of 26 variables:
$ IndicatorID : int 93116 93116 93116 93116 93116 93116 93116 93116 93116 93116 ...
$ IndicatorName : chr "A&E attendances aged under 5 years old, crude rate" "A&E attendances aged under 5 years old, crude rate" "A&E attendances aged under 5 years old, crude rate" "A&E attendances aged under 5 years old, crude rate" ...
$ ParentCode : chr NA "E92000001" "E92000001" "E92000001" ...
$ ParentName : chr NA "England" "England" "England" ...
$ AreaCode : chr "E92000001" "E06000001" "E06000002" "E06000003" ...
$ AreaName : chr "England" "Hartlepool" "Middlesbrough" "Redcar and Cleveland" ...
$ AreaType : chr "England" "County & UA" "County & UA" "County & UA" ...
$ Sex : chr "Persons" "Persons" "Persons" "Persons" ...
$ Age : chr "0-4 yrs" "0-4 yrs" "0-4 yrs" "0-4 yrs" ...
$ CategoryType : chr NA NA NA NA ...
$ Category : chr NA NA NA NA ...
$ Timeperiod : chr "2017/18 - 19/20" "2017/18 - 19/20" "2017/18 - 19/20" "2017/18 - 19/20" ...
$ Value : num 643 1695 468 417 1146 ...
$ LowerCI95.0limit : num 642 1674 460 408 1134 ...
$ UpperCI95.0limit : num 643 1715 476 426 1157 ...
$ LowerCI99.8limit : num 642 1663 456 404 1128 ...
$ UpperCI99.8limit : num 643 1727 480 431 1163 ...
$ Count : num 6445297 26995 13595 9175 40125 ...
$ Denominator : num 10031289 15929 29061 22008 35024 ...
$ Valuenote : chr NA NA NA NA ...
$ RecentTrend : chr "Cannot be calculated" "Cannot be calculated" "Cannot be calculated" "Cannot be calculated" ...
$ ComparedtoEnglandvalueorpercentiles: chr "Not compared" "Worse" "Better" "Better" ...
$ ComparedtoParentvalueorpercentiles : chr "Not compared" "Not compared" "Not compared" "Not compared" ...
$ TimeperiodSortable : int 20170000 20170000 20170000 20170000 20170000 20170000 20170000 20170000 20170000 20170000 ...
$ Newdata : chr NA NA NA NA ...
$ Comparedtogoal : chr NA NA NA NA ...all_ward_dat <- all_local_dat %>%
filter(AreaType == "Ward")
nrow(all_ward_dat)[1] 432060all_profiles$DomainID
[1] 1938132885 1938132886 1938132900 1938132887 1938132888
[6] 1938132889 1938132890 1000049 1000041 1000042
[11] 1000043 1000044 1938132983 2000005 1938132829
[16] 1938133086 3000008 3000007 3000009 3000010
[21] 2000002 2000003 2000009 2000006 1938132970
[26] 2000004 1200006 1938132696 1938132695 1938132694
[31] 8000073 1938133217 3007000 1938133216 1938132701
[36] 1938132974 8000003 8000005 8000008 8000009
[41] 1938133345 1938133300 1000002 1938133209 1938132804
[46] 1938133210 1938133211 1938133212 1938133214 1938132818
[51] 8000011 8000022 1938133288 1938133368 1938133219
[56] 8000017 1938133099 1938132720 8000041 8000026
[61] 8000042 8000043 1938132954 1938132955 1938133302
[66] 1938132719 8000027 8000030 8000031 8000039
[71] 1938133369 8000057 8000035 1938133286 8000059
[76] 8000036 8000037 8000058 8000038 8000060
[81] 8000063 1938132702 1938132703 1938132704 1938132705
[86] 1938132726 1938132768 1938132770 1938132767 1938133186
[91] 1938133152 1938133151 1938133218 1938132773 1938133148
[96] 1938133149 1938133150 1938132935 1938132790 1938132791
[101] 1938132792 1938132936 1938133084 1938133052 1938132811
[106] 1938132859 1938132891 1938132897 1938132893 1938132894
[111] 1938132814 1938132984 1938132832 1938132833 1938132982
[116] 1938132848 1938132835 1938132895 1938133118 1938132828
[121] 1938132831 1938132834 1938133365 1938132830 1938133085
[126] 1938132883 1938132902 1938132882 1938132901 1938133060
[131] 1938133338 1938132951 1938132916 1938132922 1938132920
[136] 1938132921 1938132923 1938132924 1938132899 1938133001
[141] 1938133004 1938133359 1938133360 1938133361 1938133363
[146] 1938133364 1938133070 1938132908 1938132909 1938132910
[151] 1938132917 1938132929 1938132967 1938132968 1938132960
[156] 1938132915 1938132957 1938132975 1938133101 1938133228
[161] 1938133222 1938133223 1938133257 1938133224 1938133258
[166] 1938133259 1938133260 1938133225 1938133261 1938133226
[171] 1938133229 1938133230 1938133231 1938133232 1938133263
[176] 1938133236 1938133237 1938133238 1938132962 1938133121
[181] 1938132963 1938132964 1938132965 1938132966 1938132973
[186] 1938132980 1938132981 1938133009 1938133056 1938133058
[191] 1938133043 1938133042 1938133044 1938133045 1938133073
[196] 1938133071 1938133119 1938133080 1938133262 1938133081
[201] 1938133090 1938133089 1938133095 1938133096 1938133094
[206] 1938133106 1938133107 1938133108 1938133109 1938133110
[211] 1938133128 1938133138 1938133136 1938133158 1938133135
[216] 1938133137 1938133181 1938133162 1938133145 1938133161
[221] 1938133163 1938133155 1938133144 1938133159 1938133160
[226] 1938133221 1938133143 1938133156 1938133197 1938133154
[231] 1938133142 1938133198 1938133157 1938133220 1938133180
[236] 1938133183 1938133184 1938133185 1938133280 1938133250
[241] 1938133251 1938133252 1938133253 1938133264 1938133265
[246] 1938133266 1938132706 1938133282 1938133283 1938133284
[251] 1938133285 1938133299 1938133293 1938133294 1938133301
[256] 1938133344all_ward_dat <- fingertips_data(
DomainID = all_profiles$DomainID,
AreaTypeID = "8"
) %>%
filter(AreaType == "Ward")all_ward_dat %>%
group_by(IndicatorName, Timeperiod) %>%
summarise(n = n(), valid = 100 * sum(!is.na(Value)) / n) %>%
View()`summarise()` has grouped output by 'IndicatorName'. You can override using the `.groups` argument.all_ward_dat <- fingertips_data(
DomainID = all_profiles$DomainID,
AreaTypeID = "8"
) %>%
filter(AreaType == "Ward")