# how does health vary with education
ed_table<-
meps_2012%>%
filter(rthlth42>0 & !is.na(ednew))%>%
srvyr::survey_count(rthlth42,ednew)%>%
select(-n_se)%>%
group_by(ednew)%>%
mutate(prop=prop.table(n))%>%
pivot_wider(names_from=ednew,values_from=c(n,prop))%>% # make the names "pretty"
inner_join(srh_labels,by=c("rthlth42"="level"))%>%
select(label,starts_with("prop"))%>%
rename_with(~str_replace(.,"prop_[0-9] - ",""))
# use pcs42 and compute the mean
ed_table<-
ed_table%>%
bind_rows(
meps_2012%>%
filter(pcs42>0 & !is.na(ednew))%>%
group_by(ednew)%>%
srvyr::summarize(pcs42=srvyr::survey_mean(pcs42))%>%
select(-pcs42_se)%>%
pivot_wider(names_from=ednew,values_from=pcs42)%>%
rename_with(~str_replace(.,"[0-9] - ",""))%>%
mutate(label="Mean PCS")
)
ed_table
ed_table%>%
write.csv(file="./output/2.5.7.table1.csv")
age_table<-
meps_2012%>%
filter(rthlth42>0 & !is.na(age_bands))%>%
srvyr::survey_count(rthlth42,age_bands)%>%
select(-ends_with("se"))%>%
group_by(age_bands)%>%
mutate(prop=prop.table(n))%>%
pivot_wider(names_from=age_bands,values_from=c(n,prop))%>% # make the names "pretty"
inner_join(srh_labels,by=c("rthlth42"="level"))%>%
select(label,starts_with("prop"))%>%
rename_with(~str_replace(.,"prop_",""))
# use pcs42 and compute the mean
age_table<-
age_table%>%
bind_rows(
meps_2012%>%
filter(pcs42>0 & !is.na(age_bands))%>%
group_by(age_bands)%>%
srvyr::summarize(pcs42=srvyr::survey_mean(pcs42))%>%
select(-pcs42_se)%>%
pivot_wider(names_from=age_bands,values_from=pcs42)%>%
rename_with(~str_replace(.,"[0-9] - ",""))%>%
mutate(label="Mean PCS")
)
age_table
age_table%>%
write.csv(file="./output/2.5.7.table2.csv")
# how does health vary with education among 25-64 yo
ed_table2<-
meps_2012%>%
filter(rthlth42>0 & !is.na(ednew) & age31x %in% 25:64)%>%
srvyr::survey_count(rthlth42,ednew)%>%
select(-n_se)%>%
group_by(ednew)%>%
mutate(prop=prop.table(n))%>%
pivot_wider(names_from=ednew,values_from=c(n,prop))%>% # make the names "pretty"
inner_join(srh_labels,by=c("rthlth42"="level"))%>%
select(label,starts_with("prop"))%>%
rename_with(~str_replace(.,"prop_[0-9] - ",""))
# use pcs42 and compute the mean
ed_table2<-
ed_table2%>%
bind_rows(
meps_2012%>%
filter(pcs42>0 & !is.na(ednew) & age31x %in% 25:64)%>%
group_by(ednew)%>%
srvyr::summarize(pcs42=srvyr::survey_mean(pcs42))%>%
select(-pcs42_se)%>%
pivot_wider(names_from=ednew,values_from=pcs42)%>%
rename_with(~str_replace(.,"[0-9] - ",""))%>%
mutate(label="Mean PCS")
)
ed_table2
ed_table2%>%
write.csv(file="./output/2.5.7.table3.csv")
Perhaps you can explain what the code does, and we could point you to equivalent SAS code.
Build your skills. Make connections. Enjoy creative freedom. Maybe change the world. Registration is now open through August 30th. Visit the SAS Hackathon homepage.
Register today!Need to connect to databases in SAS Viya? SAS’ David Ghan shows you two methods – via SAS/ACCESS LIBNAME and SAS Data Connector SASLIBS – in this video.
Find more tutorials on the SAS Users YouTube channel.