Why doesn't `pivot_wider` work on a `data.table`
A simple example of long to wide pivot:
library(tidyverse)
library(data.table)
df <- data.frame(names=letters[1:3],values=1:3)
df %>% pivot_wider(names_from=names,values_from=values)
#works
dt <- data.table(names=letters[1:3],values=1:3)
dt %>% pivot_wider(names_from=names,values_from=values)
Error in data.frame(row = row_id, col = col_id) :
arguments imply differing number of rows: 0, 3
Why does this error happen?
PS: one fix is to remove the data tableness with as.data.frame(dt).
dt %>% as.data.frame %>% pivot_wider(names_from=names,values_from=values)
#works
1 answer
-
answered 2020-11-28 09:37
jangorecki
Manual entry of this function in
tidyrmentions only "data frame" without specifying other classes like tibble or data.table. So addressing your question, function is either not designed to handle data.table, or there is a bug inpivot_wider.As a workaround you can now use
as.data.frame(as you already mentioned), if your data is big then possiblysetDFto avoid extra in-memory copy.You can also use
data.tablefunctiondcastto perform same kind of transformation.library(tidyr) library(data.table) df <- data.frame(names=letters[1:3],values=1:3) pivot_wider(df, names_from=names,values_from=values) ## A tibble: 1 x 3 # a b c # <int> <int> <int> #1 1 2 3 setDT(df) dcast(df, .~names, value.var="values")[, ".":=NULL][] # a b c #1: 1 2 3
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For the remaining parties (the columns with labels "other_party") it doesn't matter the order (which party will turn to be "other_party_1", "other_party_2", etc.), I just need these columns to be filled with data from parties that have not been considered in the variables labeled "Party A", "Party B", etc.
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How to reshape conjoint data from wide to long?
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Below I share 50 observations from my real data.
> dput(cjdata_wide) structure(list(ID = 1:50, org1_Effeciency_conj_1 = > c(3L, 2L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, > 2L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, > 3L, 2L, 3L, 2L, 3L, 2L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 2L, 2L, 1L ), > org1_Oppenes_conj_1 = c(3L, 3L, 1L, 3L, 1L, 3L, 2L, 3L, 2L, 3L, 1L, > 1L, 1L, 2L, 3L, 2L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, > 1L, 2L, 2L, 3L, 2L, 2L, 1L, 3L, 1L, 3L, 2L, 2L, 1L, 2L, 3L, 3L, 3L, > 3L, 3L, 2L, 3L, 1L), org1_Inclusion_conj_1 = c(2L, 1L, 1L, 2L, 2L, > 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L), org1_Leader_conj_1 = > c(5L, 6L, 3L, 6L, 1L, 4L, 2L, 6L, 1L, 6L, 1L, 2L, 2L, 6L, 3L, 2L, 6L, > 3L, 5L, 6L, 3L, 1L, 4L, 3L, 5L, 5L, 2L, 1L, 4L, 1L, 3L, 4L, 2L, 3L, > 5L, 2L, 1L, 3L, 3L, 2L, 1L, 4L, 1L, 5L, 2L, 6L, 1L, 4L, 2L, 3L), > org1_Gain_conj_1 = c(4L, 4L, 1L, 3L, 3L, 8L, 3L, 2L, 6L, 5L, 1L, 6L, > 3L, 8L, 1L, 3L, 6L, 2L, 2L, 5L, 5L, 3L, 4L, 8L, 6L, 4L, 5L, 6L, 6L, > 8L, 4L, 4L, 5L, 7L, 6L, 7L, 3L, 7L, 8L, 2L, 6L, 4L, 6L, 4L, 8L, 4L, > 6L, 4L, 3L, 6L), org1_System_conj_1 = c(5L, 4L, 5L, 1L, 4L, 4L, 5L, > 1L, 2L, 2L, 4L, 3L, 1L, 4L, 4L, 2L, 3L, 3L, 2L, 4L, 3L, 1L, 4L, 3L, > 1L, 1L, 5L, 3L, 1L, 3L, 5L, 4L, 5L, 3L, 2L, 4L, 1L, 2L, 3L, 4L, 1L, > 1L, 3L, 5L, 5L, 5L, 1L, 1L, 5L, 3L), org2_Effeciency_conj_1 = c(2L, > 1L, 3L, 2L, 1L, 3L, 1L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 1L, > 2L, 2L, 2L, 3L, 1L, 3L, 1L, 3L, 2L, 1L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, > 1L, 1L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L), > org2_Oppenes_conj_1 = c(1L, 1L, 3L, 1L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, > 3L, 2L, 1L, 1L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 1L, > 2L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, > 2L, 1L, 1L, 1L, 3L), org2_Inclusion_conj_1 = c(1L, 2L, 2L, 1L, 1L, > 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, > 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, > 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L), org2_Leader_conj_1 = > c(4L, 5L, 6L, 3L, 2L, 5L, 1L, 3L, 6L, 2L, 4L, 6L, 6L, 5L, 6L, 4L, 1L, > 2L, 4L, 2L, 4L, 6L, 5L, 6L, 4L, 1L, 3L, 5L, 3L, 5L, 6L, 1L, 6L, 4L, > 1L, 3L, 4L, 2L, 1L, 3L, 4L, 3L, 5L, 2L, 4L, 4L, 3L, 3L, 4L, 2L), > org2_Gain_conj_1 = c(5L, 1L, 6L, 5L, 8L, 6L, 4L, 3L, 8L, 8L, 7L, 7L, > 7L, 5L, 7L, 7L, 2L, 6L, 7L, 7L, 6L, 8L, 3L, 1L, 8L, 2L, 6L, 2L, 5L, > 6L, 7L, 1L, 7L, 2L, 2L, 5L, 8L, 6L, 2L, 7L, 8L, 7L, 1L, 8L, 4L, 3L, > 4L, 7L, 7L, 7L), org2_System_conj_1 = c(3L, 3L, 3L, 4L, 3L, 3L, 3L, > 5L, 4L, 4L, 1L, 4L, 3L, 1L, 5L, 5L, 5L, 4L, 3L, 3L, 4L, 4L, 1L, 5L, > 5L, 3L, 4L, 2L, 5L, 2L, 2L, 5L, 3L, 4L, 3L, 5L, 5L, 5L, 5L, 2L, 3L, > 4L, 2L, 1L, 3L, 3L, 2L, 4L, 4L, 2L), org1_Effeciency_conj_2 = c(2L, > 1L, 2L, 3L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 2L, 3L, 3L, 2L, 3L, > 3L, 1L, 2L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, > 1L, 1L, 3L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 2L, 1L, 2L, 3L, 3L), > org1_Oppenes_conj_2 = c(1L, 3L, 2L, 1L, 2L, 3L, 3L, 2L, 1L, 3L, 3L, > 2L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 2L, 1L, 3L, 2L, > 3L, 3L, 3L, 3L, 2L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, > 1L, 3L, 3L, 2L, 3L), org1_Inclusion_conj_2 = c(2L, 1L, 1L, 2L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, > 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, > 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L), org1_Leader_conj_2 = > c(3L, 3L, 2L, 2L, 5L, 5L, 6L, 2L, 2L, 1L, 6L, 5L, 2L, 1L, 2L, 4L, 5L, > 4L, 3L, 6L, 4L, 1L, 5L, 3L, 1L, 5L, 5L, 4L, 6L, 6L, 5L, 6L, 5L, 4L, > 4L, 6L, 3L, 4L, 6L, 2L, 4L, 4L, 1L, 4L, 4L, 3L, 3L, 1L, 4L, 4L), > org1_Gain_conj_2 = c(3L, 1L, 7L, 7L, 2L, 1L, 8L, 1L, 2L, 7L, 5L, 4L, > 4L, 3L, 6L, 3L, 1L, 1L, 8L, 3L, 4L, 3L, 3L, 5L, 4L, 3L, 4L, 8L, 6L, > 8L, 3L, 1L, 8L, 5L, 6L, 3L, 3L, 6L, 7L, 1L, 3L, 6L, 5L, 7L, 6L, 6L, > 3L, 4L, 2L, 6L), org1_System_conj_2 = c(5L, 1L, 5L, 1L, 4L, 3L, 3L, > 4L, 2L, 1L, 5L, 3L, 5L, 3L, 4L, 2L, 2L, 3L, 4L, 1L, 1L, 4L, 3L, 4L, > 3L, 2L, 1L, 1L, 4L, 5L, 2L, 3L, 5L, 3L, 5L, 2L, 4L, 2L, 1L, 5L, 5L, > 1L, 2L, 2L, 5L, 2L, 4L, 3L, 2L, 3L), org2_Effeciency_conj_2 = c(3L, > 3L, 1L, 2L, 2L, 1L, 3L, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 3L, 1L, > 2L, 3L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, > 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L), > org2_Oppenes_conj_2 = c(2L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, > 3L, 3L, 1L, 2L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 1L, 3L, 1L, 3L, > 2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, > 2L, 2L, 1L, 1L, 2L), org2_Inclusion_conj_2 = c(1L, 2L, 2L, 1L, 2L, > 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, > 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, > 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L), org2_Leader_conj_2 = > c(6L, 6L, 1L, 4L, 1L, 4L, 4L, 1L, 4L, 4L, 1L, 3L, 5L, 2L, 1L, 5L, 4L, > 6L, 4L, 2L, 3L, 3L, 1L, 4L, 2L, 2L, 6L, 6L, 1L, 5L, 4L, 4L, 1L, 3L, > 3L, 4L, 5L, 5L, 3L, 3L, 6L, 3L, 2L, 5L, 2L, 6L, 4L, 2L, 5L, 1L), > org2_Gain_conj_2 = c(8L, 5L, 3L, 6L, 8L, 2L, 2L, 2L, 7L, 6L, 4L, 1L, > 6L, 7L, 2L, 1L, 2L, 2L, 3L, 2L, 5L, 5L, 4L, 2L, 7L, 2L, 7L, 4L, 7L, > 1L, 2L, 5L, 1L, 2L, 7L, 1L, 6L, 2L, 8L, 7L, 7L, 1L, 6L, 3L, 3L, 2L, > 5L, 3L, 4L, 2L), org2_System_conj_2 = c(1L, 5L, 3L, 4L, 5L, 1L, 4L, > 3L, 4L, 4L, 4L, 5L, 2L, 2L, 1L, 3L, 4L, 4L, 5L, 2L, 5L, 1L, 2L, 1L, > 2L, 3L, 3L, 4L, 1L, 3L, 3L, 5L, 4L, 5L, 1L, 5L, 5L, 5L, 4L, 3L, 2L, > 4L, 4L, 3L, 3L, 4L, 3L, 1L, 1L, 2L), org1_Effeciency_conj_3 = c(1L, > 3L, 3L, 1L, 2L, 3L, 3L, 1L, 2L, 3L, 1L, 3L, 3L, 3L, 2L, 3L, 2L, 1L, > 1L, 2L, 2L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 2L, 1L, 2L, 3L, 3L, 1L, 3L, > 3L, 2L, 1L, 1L, 1L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 1L), > org1_Oppenes_conj_3 = c(2L, 3L, 3L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 3L, > 2L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, > 3L, 1L, 2L, 3L, 2L, 1L, 3L, 1L, 3L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, > 3L, 2L, 3L, 3L, 3L), org1_Inclusion_conj_3 = c(1L, 1L, 1L, 2L, 1L, > 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, > 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, > 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L), org1_Leader_conj_3 = > c(3L, 1L, 5L, 6L, 3L, 2L, 2L, 6L, 4L, 3L, 3L, 2L, 2L, 1L, 2L, 3L, 5L, > 6L, 4L, 1L, 2L, 4L, 5L, 1L, 2L, 2L, 2L, 6L, 4L, 6L, 4L, 6L, 1L, 1L, > 3L, 5L, 4L, 1L, 3L, 6L, 2L, 6L, 6L, 1L, 2L, 2L, 6L, 2L, 6L, 5L), > org1_Gain_conj_3 = c(2L, 7L, 2L, 4L, 6L, 7L, 2L, 4L, 1L, 5L, 5L, 7L, > 5L, 7L, 7L, 3L, 2L, 6L, 2L, 5L, 6L, 6L, 7L, 3L, 5L, 6L, 3L, 8L, 1L, > 2L, 8L, 5L, 2L, 8L, 5L, 6L, 5L, 2L, 5L, 3L, 3L, 2L, 4L, 2L, 4L, 5L, > 7L, 6L, 2L, 7L), org1_System_conj_3 = c(5L, 5L, 1L, 1L, 4L, 3L, 1L, > 1L, 2L, 5L, 1L, 5L, 2L, 1L, 5L, 4L, 1L, 1L, 3L, 4L, 5L, 1L, 5L, 3L, > 3L, 5L, 1L, 3L, 2L, 5L, 2L, 1L, 5L, 1L, 3L, 2L, 5L, 5L, 2L, 1L, 3L, > 2L, 2L, 4L, 4L, 4L, 2L, 3L, 5L, 4L), org2_Effeciency_conj_3 = c(2L, > 1L, 2L, 2L, 1L, 2L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, > 3L, 1L, 3L, 1L, 1L, 2L, 2L, 1L, 3L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, > 2L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L), > org2_Oppenes_conj_3 = c(1L, 1L, 1L, 2L, 3L, 3L, 2L, 1L, 3L, 3L, 1L, > 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 3L, > 1L, 2L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, > 2L, 1L, 1L, 2L, 1L), org2_Inclusion_conj_3 = c(2L, 2L, 2L, 1L, 2L, > 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, > 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, > 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L), org2_Leader_conj_3 = > c(1L, 5L, 2L, 1L, 2L, 4L, 4L, 1L, 2L, 4L, 5L, 5L, 5L, 4L, 3L, 4L, 6L, > 3L, 2L, 2L, 5L, 2L, 2L, 5L, 5L, 3L, 5L, 3L, 3L, 1L, 5L, 5L, 2L, 2L, > 2L, 2L, 1L, 6L, 1L, 5L, 1L, 5L, 1L, 2L, 6L, 6L, 4L, 3L, 2L, 6L), > org2_Gain_conj_3 = c(1L, 8L, 3L, 5L, 2L, 6L, 3L, 2L, 7L, 1L, 2L, 2L, > 8L, 1L, 2L, 6L, 1L, 8L, 6L, 3L, 7L, 4L, 5L, 2L, 6L, 8L, 2L, 7L, 6L, > 8L, 5L, 7L, 3L, 6L, 1L, 8L, 4L, 3L, 7L, 5L, 8L, 8L, 3L, 6L, 3L, 4L, > 5L, 4L, 4L, 5L), org2_System_conj_3 = c(4L, 1L, 4L, 3L, 3L, 5L, 3L, > 3L, 4L, 2L, 3L, 1L, 1L, 5L, 2L, 3L, 3L, 2L, 5L, 3L, 1L, 2L, 3L, 5L, > 1L, 4L, 5L, 2L, 3L, 2L, 3L, 2L, 4L, 3L, 5L, 3L, 1L, 1L, 3L, 2L, 4L, > 5L, 5L, 3L, 1L, 1L, 4L, 1L, 4L, 5L), org1_Effeciency_conj_4 = c(1L, > 1L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 3L, > 3L, 1L, 1L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 2L, > 3L, 3L, 2L, 3L, 1L, 2L, 2L, 3L, 2L, 1L, 1L, 3L, 3L, 1L, 3L), > org1_Oppenes_conj_4 = c(2L, 1L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 1L, 1L, > 1L, 3L, 1L, 3L, 2L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, > 1L, 1L, 2L, 3L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 3L, 1L, 3L, 3L, 1L, 3L, > 3L, 3L, 2L, 3L, 2L), org1_Inclusion_conj_4 = c(2L, 2L, 1L, 2L, 2L, > 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, > 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, > 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L), org1_Leader_conj_4 = > c(4L, 6L, 5L, 1L, 2L, 1L, 1L, 3L, 3L, 6L, 2L, 5L, 6L, 6L, 6L, 2L, 3L, > 3L, 4L, 4L, 4L, 1L, 5L, 5L, 2L, 6L, 2L, 5L, 4L, 4L, 2L, 5L, 6L, 5L, > 1L, 4L, 4L, 3L, 4L, 2L, 3L, 2L, 5L, 1L, 3L, 6L, 2L, 6L, 4L, 1L), > org1_Gain_conj_4 = c(3L, 1L, 2L, 3L, 4L, 7L, 2L, 7L, 4L, 1L, 6L, 3L, > 5L, 8L, 3L, 7L, 8L, 1L, 3L, 6L, 7L, 1L, 1L, 1L, 1L, 3L, 4L, 3L, 1L, > 8L, 3L, 2L, 1L, 7L, 2L, 4L, 4L, 1L, 6L, 8L, 6L, 3L, 7L, 3L, 8L, 7L, > 3L, 1L, 3L, 3L), org1_System_conj_4 = c(5L, 1L, 2L, 3L, 2L, 5L, 5L, > 2L, 3L, 5L, 3L, 4L, 5L, 2L, 4L, 2L, 3L, 2L, 4L, 4L, 1L, 1L, 4L, 3L, > 2L, 4L, 3L, 1L, 5L, 5L, 2L, 4L, 5L, 4L, 3L, 3L, 1L, 5L, 4L, 1L, 2L, > 3L, 5L, 5L, 3L, 2L, 5L, 2L, 3L, 3L), org2_Effeciency_conj_4 = c(3L, > 3L, 3L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 1L, > 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 3L, 2L, 2L, 1L, 3L, 1L, 3L, > 2L, 2L, 3L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 3L, 2L, 2L, 3L, 1L), > org2_Oppenes_conj_4 = c(1L, 3L, 1L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, > 3L, 2L, 2L, 2L, 1L, 3L, 1L, 3L, 2L, 2L, 1L, 1L, 3L, 3L, 2L, 1L, 3L, > 3L, 2L, 3L, 1L, 3L, 3L, 2L, 1L, 3L, 1L, 3L, 1L, 2L, 2L, 1L, 2L, 1L, > 1L, 2L, 3L, 1L, 1L), org2_Inclusion_conj_4 = c(1L, 1L, 2L, 1L, 1L, > 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, > 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, > 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L), org2_Leader_conj_4 = > c(1L, 5L, 2L, 6L, 6L, 6L, 2L, 1L, 2L, 4L, 5L, 3L, 4L, 4L, 2L, 1L, 6L, > 1L, 1L, 2L, 6L, 3L, 1L, 4L, 4L, 3L, 3L, 4L, 6L, 5L, 3L, 2L, 3L, 6L, > 6L, 5L, 2L, 6L, 3L, 5L, 5L, 1L, 6L, 5L, 4L, 5L, 1L, 2L, 2L, 6L), > org2_Gain_conj_4 = c(5L, 8L, 1L, 2L, 7L, 2L, 7L, 8L, 2L, 6L, 7L, 7L, > 7L, 5L, 8L, 4L, 6L, 6L, 6L, 4L, 6L, 6L, 7L, 2L, 5L, 6L, 6L, 1L, 8L, > 5L, 2L, 5L, 6L, 3L, 3L, 7L, 7L, 8L, 4L, 7L, 5L, 2L, 2L, 7L, 6L, 4L, > 7L, 4L, 4L, 1L), org2_System_conj_4 = c(2L, 3L, 3L, 2L, 4L, 4L, 4L, > 4L, 1L, 4L, 1L, 2L, 4L, 5L, 2L, 3L, 5L, 1L, 1L, 1L, 5L, 4L, 2L, 2L, > 3L, 2L, 1L, 4L, 3L, 4L, 5L, 3L, 1L, 3L, 2L, 4L, 4L, 1L, 3L, 3L, 4L, > 5L, 4L, 4L, 1L, 1L, 3L, 5L, 5L, 1L), CHOICE_conj1 = c(2L, 2L, 1L, 2L, > 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, > 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, > 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L ), RATING_conj1_org1 = > c(1L, 3L, 6L, 5L, 3L, 1L, 5L, 2L, 0L, 7L, 6L, 8L, 5L, 10L, 8L, 10L, > 1L, 6L, 5L, 8L, 2L, 7L, 0L, 6L, 8L, 0L, 4L, 2L, 8L, 6L, 7L, 7L, 7L, > 2L, 3L, 8L, 6L, 7L, 2L, 7L, 3L, 8L, 5L, 7L, 8L, 6L, 6L, 10L, 3L, 9L), > RATING_conj1_org2 = c(7L, 6L, 4L, 7L, 7L, 1L, 6L, 6L, 0L, 3L, 2L, 0L, > 0L, 9L, 5L, 3L, 1L, 6L, 8L, 5L, 2L, 2L, 0L, 4L, 5L, 0L, 6L, 8L, 3L, > 5L, 6L, 6L, 5L, 8L, 3L, 8L, 3L, 1L, 5L, 9L, 7L, 3L, 7L, 6L, 6L, 4L, > 4L, 0L, 6L, 7L), CHOICE_conj2 = c(1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, > 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, > 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, > 2L, 1L, 1L, 2L, 1L, 1L, 2L), RATING_conj2_org1 = c(5L, 4L, 4L, 4L, > 5L, 1L, 5L, 7L, 0L, 3L, 5L, 6L, 5L, 9L, 5L, 3L, 1L, 4L, 4L, 8L, 3L, > 7L, 0L, 9L, 9L, 1L, 3L, 2L, 3L, 5L, 6L, 4L, 5L, 8L, 3L, 7L, 6L, 1L, > 7L, 0L, 7L, 6L, 6L, 8L, 9L, 7L, 5L, 10L, 7L, 7L), RATING_conj2_org2 = > c(0L, 2L, 7L, 4L, 8L, 1L, 7L, 8L, 0L, 3L, 6L, 0L, 0L, 7L, 8L, 10L, > 0L, 3L, 6L, 8L, 2L, 5L, 0L, 4L, 5L, 2L, 5L, 5L, 7L, 5L, 5L, 7L, 1L, > 2L, 3L, 8L, 3L, 7L, 3L, 6L, 2L, 8L, 8L, 8L, 7L, 6L, 6L, 5L, 5L, 9L), > CHOICE_conj3 = c(2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, > 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, > 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, > 2L, 1L, 1L), RATING_conj3_org1 = c(4L, 6L, 4L, 6L, 7L, 1L, 6L, 3L, > 0L, 6L, 2L, 7L, 0L, 9L, 5L, 3L, 1L, 3L, 4L, 7L, 1L, 8L, 0L, 5L, 5L, > 1L, 5L, 2L, 8L, 5L, 5L, 5L, 3L, 8L, 2L, 4L, 5L, 7L, 8L, 6L, 7L, 6L, > 4L, 9L, 7L, 5L, 4L, 2L, 8L, 9L), RATING_conj3_org2 = c(7L, 4L, 6L, > 5L, 6L, 1L, 3L, 7L, 0L, 3L, 2L, 3L, 3L, 6L, 5L, 10L, 0L, 3L, 4L, 10L, > 0L, 4L, 0L, 7L, 5L, 2L, 3L, 2L, 3L, 5L, 8L, 2L, 7L, 2L, 7L, 5L, 3L, > 3L, 0L, 0L, 2L, 6L, 7L, 8L, 5L, 2L, 8L, 10L, 6L, 8L), CHOICE_conj4 = > c(2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, > 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, > 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L), > RATING_conj4_org1 = c(4L, 5L, 8L, 6L, 4L, 1L, 8L, 3L, 0L, 7L, 5L, 5L, > 2L, 8L, 7L, 10L, 1L, 5L, 5L, 10L, 1L, 3L, 0L, 6L, 7L, 1L, 2L, 5L, 7L, > 8L, 7L, 3L, 6L, 2L, 2L, 8L, 5L, 5L, 4L, 5L, 3L, 7L, 3L, 8L, 8L, 6L, > 2L, 10L, 7L, 7L), RATING_conj4_org2 = c(6L, 4L, 4L, 4L, 5L, 1L, 6L, > 7L, 0L, 3L, 6L, 2L, 0L, 5L, 5L, 3L, 0L, 3L, 4L, 9L, 4L, 8L, 0L, 5L, > 6L, 2L, 8L, 3L, 2L, 5L, 5L, 7L, 2L, 6L, 7L, 8L, 3L, 3L, 1L, 5L, 7L, > 10L, 7L, 10L, 5L, 5L, 7L, 5L, 5L, 8L), Q7 = c(0L, 0L, 8L, 9L, 6L, > 10L, 2L, 2L, 6L, 8L, 0L, 0L, 5L, 2L, 7L, 7L, 3L, 0L, 0L, 5L, 6L, 4L, > 7L, 2L, 977L, 0L, 6L, 3L, 2L, 4L, 7L, 8L, 2L, 1L, 9L, 8L, 10L, 6L, > 0L, 9L, 5L, 0L, 3L, 0L, 0L, 0L, 2L, 5L, 977L, 2L), Q8 = c(1L, 1L, 2L, > 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 977L, 1L, 2L, 2L, 1L, 3L, 1L, 1L, > 3L, 1L, 3L, 1L, 2L, 1L, 977L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, > 3L, 3L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 977L, 1L), Q9 = c(4L, 8L, > 1L, 0L, 4L, 0L, 8L, 7L, 0L, 0L, 10L, 10L, 0L, 4L, 0L, 10L, 4L, 5L, > 10L, 8L, 2L, 9L, 0L, 5L, 2L, 0L, 5L, 4L, 4L, 8L, 0L, 0L, 5L, 6L, 2L, > 0L, 0L, 0L, 7L, 4L, 5L, 5L, 6L, 10L, 7L, 4L, 6L, 0L, 977L, 7L), Q10 = > c(8L, 10L, 7L, 5L, 7L, 2L, 7L, 8L, 0L, 2L, 10L, 10L, 0L, 10L, 2L, > 10L, 8L, 8L, 10L, 8L, 7L, 10L, 5L, 7L, 4L, 0L, 7L, 7L, 10L, 10L, 4L, > 2L, 5L, 9L, 5L, 6L, 2L, 4L, 10L, 3L, 5L, 7L, 9L, 10L, 10L, 10L, 8L, > 977L, 977L, 10L), Q11 = c(10L, 9L, 1L, 4L, 5L, 0L, 5L, 6L, 1L, 3L, > 9L, 10L, 0L, 10L, 7L, 7L, 5L, 7L, 10L, 10L, 9L, 7L, 0L, 8L, 7L, 0L, > 7L, 7L, 8L, 10L, 5L, 2L, 2L, 10L, 5L, 1L, 2L, 4L, 6L, 4L, 7L, 10L, > 6L, 8L, 8L, 6L, 8L, 6L, 977L, 10L), Q12 = c(0L, 0L, 0L, 5L, 1L, 10L, > 2L, 0L, 0L, 2L, 0L, 0L, 5L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, > 0L, 0L, 10L, 3L, 0L, 0L, 977L, 10L, 7L, 0L, 0L, 5L, 8L, 2L, 0L, 966L, > 7L, 977L, 0L, 0L, 0L, 0L, 0L, 0L, 977L, 977L, 0L), Q13 = c(2L, 2L, > 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 977L, 2L), Q14 = > c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, > 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, > 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), Q1 = > c(2L, 2L, 8L, 6L, 5L, 1L, 7L, 3L, 7L, 4L, 1L, 6L, 4L, 1L, 5L, 10L, > 5L, 4L, 3L, 7L, 2L, 5L, 3L, 5L, 977L, 0L, 5L, 4L, 4L, 7L, 5L, 3L, 8L, > 3L, 3L, 0L, 5L, 6L, 3L, 4L, 0L, 3L, 3L, 2L, 7L, 4L, 2L, 7L, 4L, 7L), > Q2 = c(1L, 1L, 1L, 977L, 1L, 3L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 1L, 3L, > 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 977L, 2L, 3L, 3L, 1L, 1L, 3L, 2L, > 1L, 1L, 3L, 3L, 2L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 977L, 1L, 3L, 977L, > 977L, 1L), gender = c(1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, > 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, > 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, > 1L, 2L, 2L, 1L), profile_age = c(5L, 2L, 5L, 5L, 3L, 5L, 2L, 5L, 3L, > 5L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 2L, 5L, 5L, 5L, 5L, 2L, 5L, 5L, > 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L ), educ = c(6L, 5L, 2L, 5L, 6L, 6L, 4L, 6L, > 3L, 5L, 4L, 5L, 6L, 4L, 4L, 6L, 6L, 6L, 3L, 6L, 5L, 6L, 5L, 5L, 3L, > 4L, 6L, 6L, 5L, 3L, 3L, 4L, 3L, 6L, 3L, 5L, 5L, 6L, 3L, 5L, 3L, 3L, > 3L, 3L, 4L, 5L, 5L, 4L, 2L, 3L)), class = "data.frame", row.names = > c(NA, > -50L))What I have done so far is this:
library(cregg) str(long <- cj_tidy(cjdata_wide, profile_variables = c("All the profile variables"), task_variables = c("CHOICE AND RATING VARIABLES HERE"), id = ~ id)) stopifnot(nrow(long) == nrow(data)*4*2But I'm keep getting errors. I have tried to follow the example given by the
creggpackage - but with no success. Any help is much appreciated! I am open to all possible ways, be it so through cregg package ortidyrfor instance. -
How to plot sjPlots from a nested tibble?
I create some models like this using a nested tidyr dataframe:
set.seed(1) library(tidyr) library(dplyr) library(sjPlot) library(tibble) library(purrr) fits <- tribble(~group, ~colA, ~colB, ~colC, sample(c("group1", "group2"), 10, replace = T), 0, sample(10, replace = T), sample(10, replace = T), sample(c("group1", "group2"), 10, replace = T), 1, sample(10, replace = T), sample(10, replace = T)) %>% unnest(cols = c(colB, colC)) %>% nest(data=-group) %>% mutate(fit= map(data, ~glm(formula = colA ~ colB + colC, data = .x, family="binomial"))) %>% dplyr::select(group, fit) %>% tibble::column_to_rownames("group")I would like to use this data to create some quick marginal effects plots with
sjPlot::plot_modelslike thisplot_models(as.list(fits), type = "pred", terms = c("colB", "colA", "colC"))Unfortunately, I get the error
Error in if (fam.info$is_linear) tf <- NULL else tf <- "exp" : argument is of length zero In addition: Warning message: Could not access model information.I've played around a bit with the nesting of the data but I've been unable to get it into a format that
sjPlot::plot_modelswill accept.What I was expecting to get is a "Forest plot of multiple regression models" as described in the help file. Ultimately, the goal is to plot the marginal effects of regression models by group, which I was hoping the plot_models will do (please correct me if I'm wrong).