Hello,
The following works with me.
library(coronavirus)
library(dplyr)
data(coronavirus, package = "coronavirus")
#update_dataset(silence = FALSE)
coronavirus %>%
select(country, date, type, cases) %>%
filter(
country == 'Namibia',
date == '2021-10-23',
cases == 357
)
Can you post the pipe code you are running?
Hope this helps,
Rui Barradas
Às 12:25 de 25/10/21, Dr Eberhard W Lisse escreveu:
Hi,
I have data from JHU via the 'coronavirus' package which has a value for
the confirmed cases for 2021-10-23 which differs drastically (357) from
what is reported in country (23).
# A tibble: 962 × 4
country date type cases
<chr> <date> <chr> <int>
1 Namibia 2021-10-24 confirmed 23
2 Namibia 2021-10-24 death 4
3 Namibia 2021-10-23 confirmed 357
4 Namibia 2021-10-23 death 1
5 Namibia 2021-10-22 confirmed 30
6 Namibia 2021-10-22 death 1
# … with 956 more rows
I am using a '%>%' pipeline and am struggling to mutate 'cases' to NA
using something like
country == 'Namibia' & date == '2021-10-23' & cases == 357
so that if or when the data-set is corrected I don't have to change the
code (immediately), even after some googling.
I can do
cases == 357
only, but that could find other cases as well, which is obviously not
the thing to do
Any suggestions?
greetings, el
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