tidy verse, ggplot, filter, mutate and summarise data

tidyverse
1

Hi All, Basically this is my code and I want to get the value of percentage, Category and HRLmean. when I run the code, it looks ok to me but the value of percentage, unrate or unemployed are zero. how do I find all these values? Thank you in advance!

labour_7 <- labour_2%>%
select(LABEL,IMMIG,LFSSTAT,HRLYEARN) %>%
mutate(Category=if_else(IMMIG==1,"immigrant",if_else(IMMIG==2,"immigrant",if_else(IMMIG==3,"native","")))) %>%
filter(HRLYEARN!="NA") %>%
filter(LFSSTAT!="4") %>%
group_by(LABEL) %>%
summarise(HRLmean=mean(HRLYEARN), HRLsd=sd(HRLYEARN),uemployed=sum(LFSSTAT=="3"), sum=sum(LFSSTAT),.groups = 'drop') %>%
mutate(unrate=uemployed/sum, percentage = unrate*100)

2

Hi!

To help us help you, could you please prepare a reproducible example (reprex) illustrating your issue? Please have a look at this guide, to see how to create one:

3

Hi!

The code itself seems alright to me, but as Andrés pointet out,it is hard to see what your problem is without knowing your data. S

If I could take a guess, however, I'd say that there are maybe no cases where the condition LFSSTAT==3 applies, is that possible?

If I run the relevant parts of youur code on some dummy data, it returns a percentage for label 1 where LFSSTAT==3 is true in one observation, and it returns 0 for label 2.

library(dplyr)

labour<-data.frame(LABEL=c(1,2,1,2,1),IMMIG=c(1,2,3,2,1),HRLYEARN=c(1,2,3,3,4),LFSSTAT=c(3,2,1,5,1))
labour
#>   LABEL IMMIG HRLYEARN LFSSTAT
#> 1     1     1        1       3
#> 2     2     2        2       2
#> 3     1     3        3       1
#> 4     2     2        3       5
#> 5     1     1        4       1

labour%>%
  group_by(LABEL) %>%
  summarise(HRLmean=mean(HRLYEARN), HRLsd=sd(HRLYEARN),uemployed=sum(LFSSTAT=="3"), sum=sum(LFSSTAT),.groups = 'drop') %>%
  mutate(unrate=uemployed/sum, percentage = unrate*100)

#> # A tibble: 2 x 7
#>   LABEL HRLmean HRLsd uemployed   sum unrate percentage
#>   <dbl>   <dbl> <dbl>     <int> <dbl>  <dbl>      <dbl>
#> 1     1    2.67 1.53          1     5    0.2         20
#> 2     2    2.5  0.707         0     7    0            0

Created on 2020-10-25 by the reprex package (v0.3.0)

Maybe just use labour%>%filter(LFSSTAT==3) to check if there are any cases where LFSSTAT is actually 3.