You get this error from this kind of operation on tibbles:
library(tibble)
t1 <- tibble(x = c(TRUE, FALSE))
t2 <- tibble(x = c(1.2, 1.3))
t1[1,] <- t2[1,]
#> Error: Assigned data `t2[1, ]` must be compatible with existing data.
#> ℹ Error occurred for column `x`.
#> x Can't convert from <double> to <logical> due to loss of precision.
#> * Locations: 1.
If t1 had been a data.frame instead of a tibble, this would convert t1$x
to type double. So it is possible some code you are using assumes
things inheriting from class "data.frame" act like dataframes. Or maybe
they were just sloppy. In any case, you might be able to fix it by
changing single_study_df to a dataframe using
single_study_df <- as.data.frame(single_study_df)
Duncan Murdoch
On 06/09/2021 12:34 p.m., Duncan Murdoch wrote:
On 06/09/2021 10:16 a.m., John Tully wrote:
Dear colleagues
in conducting a meta-analysis (of MRI data) I am running into the repeated
issue:
Error: Assigned data `single_study_df` must be compatible with existing data. ℹ Error
occurred for column `accumbens_sd`. x Can't convert from <double> to <logical>
due to loss of precision. * Locations: 1, 2. Run `rlang::last_error()` to see where the
error occurred.
That certainly looks like a tidyverse error, specifically from the
tibble package.
Duncan Murdoch
This follows the commands
for (region in regions){
for (study in unique(df$studyid)){
single_study_df <- df %>% filter(studyid==study)
if (is.na(single_study_df[sprintf('%s_mn', region)][[1]]) &
!is.na(single_study_df[sprintf('%s_mn_l', region)])){
df <- calc_bilat(study, region, r, df)
}
}
}
My colleague (cc'd) believed it may be an issue with tidyverse version, however
using an older version (1.2.1), the issue persists. note 'accumbens' is the
first of many columns so I suspect this is why it flags this up.
I would greatly value your input on this matter
Kind regards
John Tully
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