library(tidyverse)
library(conquestr)
df <- cq_example(display = FALSE) %>% cq_itanal() %>%
select(-data)
df1 <- df %>% select(item_index:delta)
df2 <- df %>%
mutate(resp_id = id) %>%
select(item_index, resp_id, resp_stat) %>%
unnest() %>%
mutate(item_index = if_else(
item_index == lag(item_index, default = "X"), NA_character_, item_index))
df_export <- right_join(df1, df2, by = "item_index")
df_export
#> # A tibble: 50 x 17
#> item_index id case disc thrsh mnsq delta resp_id label score
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
#> 1 1 item:1~ 1475 0.4 -1.36 1.25 -1.36 item:1 ~ 1 0
#> 2 <NA> <NA> NA NA NA NA NA item:1 ~ 2 0
#> 3 <NA> <NA> NA NA NA NA NA item:1 ~ 3 1
#> 4 <NA> <NA> NA NA NA NA NA item:1 ~ 4 0
#> 5 <NA> <NA> NA NA NA NA NA item:1 ~ 5 0
#> 6 <NA> <NA> NA NA NA NA NA item:1 ~ 9 0
#> 7 2 item:2~ 1475 0.54 -1.95 0.89 -1.95 item:2 ~ 1 0
#> 8 <NA> <NA> NA NA NA NA NA item:2 ~ 2 0
#> 9 <NA> <NA> NA NA NA NA NA item:2 ~ 3 0
#> 10 <NA> <NA> NA NA NA NA NA item:2 ~ 4 1
#> # ... with 40 more rows, and 7 more variables: count <dbl>, pct_tot <dbl>,
#> # pt_bis <dbl>, t <dbl>, p <dbl>, pv_avg <dbl>, pv_sd <dbl>Created on 2018-05-10 by the reprex package (v0.2.0).