Styjun

I'm looking for an efficient way to create multiple 2-dimension tables from an R dataframe of chi-square statistics. The code below builds on this answer to a previous question of mine about getting chi-square stats by groups. Now I want to create tables from the output by group. Here's what I have so far using the hsbdemo data frame from the UCLA R site:

ml <- foreign::read.dta("https://stats.idre.ucla.edu/stat/data/hsbdemo.dta")

str(ml)

'data.frame': 200 obs. of 13 variables:
 $ id : num 45 108 15 67 153 51 164 133 2 53 ...
 $ female : Factor w/ 2 levels "male","female": 2 1 1 1 1 2 1 1 2 1 ...
 $ ses : Factor w/ 3 levels "low","middle",..: 1 2 3 1 2 3 2 2 2 2 ...
 $ schtyp : Factor w/ 2 levels "public","private": 1 1 1 1 1 1 1 1 1 1 ...
 $ prog : Factor w/ 3 levels "general","academic",..: 3 1 3 3 3 1 3 3 3 3 ...

ml %>% 
 dplyr::select(prog, ses, schtyp) %>%
 table() %>%
 apply(3, chisq.test, simulate.p.value = TRUE) %>%
 lapply(`[`, c(6,7,9)) %>%
 reshape2::melt() %>%
 tidyr::spread(key = L2, value = value) %>%
 dplyr::rename(SchoolType = L1) %>%
 dplyr::arrange(SchoolType, prog) %>%
 dplyr::select(-observed, -expected) %>%
 reshape2::acast(., prog ~ ses ~ SchoolType ) %>%
 tbl_df() 

The output after the last arrange statement produces this tibble (showing only the first five rows):

 prog ses SchoolType expected observed stdres
1 general low private 0.37500 2 3.0404678
2 general middle private 3.56250 3 -0.5187244
3 general high private 2.06250 1 -1.0131777
4 academic low private 1.50000 0 -2.5298221
5 academic middle private 14.25000 14 -0.2078097

It's easy to select one column, for example, stdres, and pass it to acast and tbl_df, which gets pretty much what I'm after:

# A tibble: 3 x 6
 low.private middle.private high.private low.public middle.public high.public
 <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.04 -0.519 -1.01 1.47 -0.236 -1.18
2 -2.53 -0.208 1.50 -0.940 -2.06 3.21
3 -0.377 1.21 -1.06 -0.331 2.50 -2.45

Now I can repeat these steps for observed and expected frequencies and bind them by rows, but that seems inefficient. The output would observed frequencies stacked on expected, stacked on the standardized residuals. Something like this:

 low.private middle.private high.private low.public middle.public high.public
 <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2 3 1 14 17 8 
2 0 14 10 19 30 32 
3 0 2 0 12 29 7 
4 0.375 3.56 2.06 10.4 17.6 10.9 
5 1.5 14.2 8.25 21.7 36.6 22.7 
6 0.125 1.19 0.688 12.9 21.7 13.4 
7 3.04 -0.519 -1.01 1.47 -0.236 -1.18
8 -2.53 -0.208 1.50 -0.940 -2.06 3.21
9 -0.377 1.21 -1.06 -0.331 2.50 -2.45

Seems there ought to be a way to do this without repeating the code for each column, probably by creating and processing a list. Thanks in advance.

Might this be the answer?

 ml1 <- ml %>% 
 dplyr::select(prog, ses, schtyp) %>%
 table() %>%
 apply(3, chisq.test, simulate.p.value = TRUE) %>%
 lapply(`[`, c(6,7,9)) %>%
 reshape2::melt()
 ml2 <- ml1 %>%
 dplyr::mutate(type=paste(ses, L1, sep=".")) %>%
 dplyr::select(-ses, -L1) %>%
 tidyr::spread(type, value)

This gives you

 prog L2 high.private high.public low.private low.public middle.private middle.public
1 general expected 2.062500 10.910714 0.3750000 10.4464286 3.5625000 17.6428571
2 general observed 1.000000 8.000000 2.0000000 14.0000000 3.0000000 17.0000000
3 general stdres -1.013178 -1.184936 3.0404678 1.4663681 -0.5187244 -0.2360209
4 academic expected 8.250000 22.660714 1.5000000 21.6964286 14.2500000 36.6428571
5 academic observed 10.000000 32.000000 0.0000000 19.0000000 14.0000000 30.0000000
6 academic stdres 1.504203 3.212431 -2.5298221 -0.9401386 -0.2078097 -2.0607058
7 vocation expected 0.687500 13.428571 0.1250000 12.8571429 1.1875000 21.7142857
8 vocation observed 0.000000 7.000000 0.0000000 12.0000000 2.0000000 29.0000000
9 vocation stdres -1.057100 -2.445826 -0.3771236 -0.3305575 1.2081594 2.4999085

I am not sure I understand completely what you are out after... But basically, create a new variable of SES and school type, and gather based on that. And obviously, reorder it as you wish :-)