tidy verse, ggplot, filter, mutate and summarise data

[anastasiawinona]

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)

[andresrcs]

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:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[jms]

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.