Output list of variables with significant p-values from several regressions in R - r

I have a dataframe that looks like the following.
consistent admire trust judge
3 3 2 4
5 1 3 6
2 4 5 1
I can run the regressions I need simultaneously using the following code. In the actual dataset, there are many more than 3 variables.
variables <- c("admire", "trust", "judge")
form <- paste("consistent ~ ",variables,"")
model <- form %>%
set_names(variables) %>%
map(~lm(as.formula(.x), data = df))
map(model, summary)
This yields the output for the 3 following regressions.
summary(lm(consistent ~ admire, df))
summary(lm(consistent ~ trust, df))
summary(lm(consistent ~ judge, df))
I would like a list of the variables with significant p-values at p < 0.05. For example, if "admire" was significant and "judge" was significant, the output I am looking for would be something like:
admire, judge
Is there a way to do this that allows me to also run several regressions simultaneously? This question offers a similar answer, but I don't know how to apply it when I have several regressions.
Data:
structure(list(consistent = c(1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1,
0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1,
1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1,
1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,
1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,
1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1,
0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1,
1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0,
0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,
1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0,
1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0,
0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0,
0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,
0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0,
1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0,
1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0), admire = c(7,
3, 1, 1, 3, 5, 5, 6, 7, 1, 4, 2, 5, 3, 3, 1, 3, 1, 2, 1, 5, 5,
3, 1, 5, 3, 5, 4, 5, 1, 6, 1, 6, 2, 1, 4, 1, 1, 3, 2, 1, 5, 1,
7, 1, 4, 1, 4, 2, 2, 4, 2, 4, 1, 5, 5, 1, 2, 6, 6, 1, 1, 3, 5,
5, 1, 5, 7, 2, 4, 5, 1, 4, 4, 3, 5, 6, 1, 5, 2, 1, 5, 6, 2, 3,
3, 5, 6, 1, 4, 4, 6, 4, 4, 4, 6, 5, 4, 1, 2, 5, 4, 2, 4, 6, 1,
3, 7, 4, 4, 3, 2, 7, 5, 3, 2, 1, 2, 2, 5, 7, 3, 5, 4, 6, 2, 2,
4, 4, 5, 5, 1, 5, 6, 1, 2, 4, 7, 1, 4, 5, 4, 2, 4, 1, 4, 3, 4,
7, 5, 6, 3, 1, 1, 7, 1, 6, 4, 1, 1, 2, 1, 1, 6, 3, 1, 4, 4, 7,
2, 1, 5, 3, 3, 7, 4, 5, 1, 3, 7, 5, 4, 1, 1, 1, 5, 2, 1, 1, 4,
1, 5, 4, 5, 1, 4, 4, 4, 7, 1, 1, 2, 5, 2, 4, 2, 4, 6, 4, 2, 6,
5, 6, 7, 4, 4, 5, 1, 5, 7, 1, 7, 2, 7, 3, 6, 2, 5, 7, 3, 5, 4,
1, 4, 1, 5, 1, 1, 6, 6, 7, 3, 4, 1, 6, 4, 1, 6, 7, 5, 4, 2, 6,
5, 5, 4, 1, 2, 6, 1, 5, 3, 1, 1, 1, 7, 7, 3, 5, 1, 5, 1, 7, 2,
5, 4, 2, 1, 4, 1, 1, 5, 5, 4, 5, 2, 4, 5, 5, 1, 4, 4, 1, 3, 4,
2, 7, 6, 6, 4, 3, 6, 1, 6, 1, 1, 4, 7, 7, 1, 3, 1, 4, 2, 2, 6,
1, 2, 1, 1, 1, 4, 2, 5, 4, 1, 4, 2, 5, 5, 2, 1, 6, 1, 2, 3, 4,
1, 7, 2, 2, 4, 5, 1, 6, 2, 5, 1, 5, 6, 2, 5, 1, 1, 7, 4, 5, 6,
1, 4, 5, 2, 4, 4, 6, 4, 4, 2, 6, 1, 1, 2, 6, 1, 3, 5, 5, 3, 7,
5, 6, 4, 3, 4, 7, 5, 4, 2, 1, 5, 7, 2, 6, 3, 1, 2, 4, 3, 5, 4,
1, 6, 1, 3, 1, 1, 1, 4, 3, 3, 1, 1, 1, 6, 4, 1, 1, 1, 1, 4, 1,
6, 4, 4, 4, 4, 1, 5, 2, 4, 5, 4, 4, 3, 3, 6, 7, 3, 2, 4, 2, 5,
1, 4, 5, 4, 1, 2, 4, 1), trust = c(7, 4, 2, 2, 3, 4, 6, 6, 7,
1, 4, 5, 5, 4, 1, 1, 2, 2, 1, 1, 6, 6, 4, 1, 3, 6, 5, 4, 6, 1,
5, 1, 6, 1, 2, 5, 1, 1, 4, 1, 1, 5, 1, 7, 1, 4, 4, 5, 3, 4, 5,
3, 5, 2, 6, 5, 3, 2, 6, 6, 1, 1, 3, 5, 5, 1, 5, 7, 2, 4, 6, 1,
4, 4, 4, 6, 6, 3, 5, 6, 1, 6, 5, 2, 2, 2, 5, 7, 1, 5, 3, 7, 3,
5, 4, 6, 6, 5, 2, 1, 6, 5, 2, 6, 5, 1, 2, 7, 6, 5, 3, 3, 4, 7,
4, 2, 1, 3, 4, 7, 6, 2, 6, 5, 7, 3, 2, 4, 5, 5, 5, 1, 2, 7, 1,
1, 5, 4, 1, 4, 6, 6, 2, 4, 2, 4, 1, 5, 7, 6, 7, 3, 2, 1, 7, 1,
4, 4, 1, 2, 4, 1, 1, 6, 3, 1, 4, 3, 7, 2, 2, 6, 4, 5, 7, 5, 7,
2, 4, 7, 4, 3, 1, 1, 1, 5, 2, 4, 1, 4, 1, 5, 4, 5, 1, 6, 5, 4,
6, 1, 1, 2, 6, 2, 4, 4, 4, 5, 6, 1, 5, 5, 5, 6, 4, 4, 5, 5, 6,
7, 1, 7, 3, 7, 5, 6, 3, 5, 7, 4, 5, 4, 2, 3, 1, 4, 5, 1, 5, 4,
7, 3, 5, 1, 6, 6, 1, 4, 6, 5, 4, 3, 7, 6, 5, 4, 1, 1, 6, 1, 5,
3, 1, 1, 1, 7, 7, 3, 4, 1, 4, 1, 7, 2, 4, 2, 2, 2, 4, 1, 1, 5,
4, 6, 5, 2, 4, 5, 4, 1, 6, 4, 1, 4, 4, 3, 7, 5, 6, 4, 4, 6, 2,
6, 1, 2, 4, 7, 7, 1, 1, 1, 4, 2, 2, 6, 2, 4, 1, 2, 1, 6, 2, 6,
4, 1, 6, 3, 5, 4, 3, 1, 6, 1, 2, 3, 5, 1, 6, 1, 3, 4, 5, 2, 6,
2, 5, 1, 3, 7, 1, 4, 1, 1, 7, 5, 6, 5, 1, 5, 5, 1, 4, 3, 7, 4,
4, 1, 7, 1, 1, 4, 6, 1, 4, 5, 5, 4, 7, 6, 7, 4, 4, 4, 4, 4, 4,
1, 1, 5, 6, 2, 7, 4, 2, 4, 5, 4, 5, 4, 1, 5, 1, 2, 1, 1, 4, 4,
3, 4, 3, 1, 2, 6, 5, 1, 1, 1, 2, 4, 1, 7, 4, 4, 5, 6, 2, 5, 3,
4, 5, 4, 4, 3, 3, 6, 7, 4, 4, 3, 2, 5, 1, 5, 5, 5, 2, 2, 3, 1
), judge = c(1, 5, 6, 3, 6, 3, 4, 5, 4, 1, 3, 2, 3, 2, 4, 3,
4, 2, 5, 4, 3, 3, 4, 4, 7, 5, 4, 4, 1, 3, 6, 2, 3, 2, 5, 2, 3,
4, 2, 4, 4, 3, 4, 4, 1, 4, 1, 2, 3, 1, 2, 2, 3, 5, 3, 5, 5, 3,
1, 4, 4, 2, 5, 4, 3, 1, 5, 4, 4, 5, 2, 2, 2, 7, 3, 3, 1, 1, 5,
3, 3, 1, 2, 5, 2, 3, 5, 4, 3, 4, 3, 2, 1, 3, 4, 4, 5, 5, 3, 2,
2, 3, 2, 4, 1, 1, 4, 2, 2, 3, 3, 2, 4, 4, 6, 1, 7, 4, 2, 3, 4,
1, 2, 4, 4, 5, 2, 1, 3, 2, 2, 1, 1, 7, 2, 3, 5, 5, 1, 2, 2, 5,
6, 5, 1, 1, 1, 4, 1, 5, 4, 3, 6, 1, 4, 1, 3, 4, 6, 1, 2, 4, 3,
3, 4, 7, 1, 3, 1, 2, 2, 3, 2, 3, 5, 3, 4, 2, 6, 3, 1, 1, 1, 1,
4, 2, 2, 4, 4, 5, 4, 2, 1, 6, 7, 5, 2, 2, 4, 5, 6, 1, 5, 2, 4,
5, 5, 2, 2, 3, 4, 5, 2, 2, 4, 1, 3, 4, 4, 4, 2, 3, 1, 4, 4, 3,
2, 3, 1, 4, 2, 4, 4, 1, 5, 4, 4, 4, 4, 6, 1, 3, 5, 7, 2, 6, 1,
5, 7, 5, 4, 2, 3, 6, 3, 1, 1, 2, 2, 5, 5, 2, 5, 4, 4, 5, 4, 4,
3, 7, 4, 4, 4, 2, 5, 3, 6, 5, 4, 4, 4, 6, 4, 5, 5, 1, 5, 2, 6,
4, 4, 1, 1, 4, 6, 1, 7, 1, 5, 2, 5, 4, 2, 3, 2, 6, 3, 2, 2, 1,
1, 5, 4, 1, 1, 4, 1, 5, 1, 4, 3, 2, 3, 4, 1, 6, 1, 2, 1, 3, 5,
5, 2, 1, 3, 4, 2, 4, 5, 4, 6, 3, 4, 6, 7, 6, 2, 4, 6, 2, 4, 5,
1, 4, 1, 3, 2, 4, 1, 6, 4, 3, 1, 3, 4, 5, 1, 6, 1, 5, 1, 3, 3,
1, 3, 4, 2, 4, 1, 1, 2, 2, 2, 3, 1, 6, 5, 4, 1, 7, 5, 6, 5, 2,
3, 5, 4, 3, 4, 5, 7, 1, 5, 2, 5, 1, 3, 4, 3, 5, 1, 4, 2, 3, 4,
1, 7, 5, 5, 2, 1, 2, 5, 6, 5, 5, 3, 1, 3, 1, 4, 1, 5, 2, 3, 5,
6, 4, 4, 3, 2, 4, 1, 3, 4, 3, 4, 4, 1, 5)), row.names = c(NA,
-450L), class = c("tbl_df", "tbl", "data.frame"))


To fit many many simple linear regression models, I recommend Fast pairwise simple linear regression between variables in a data frame. Hmm... looks like I need to collect those functions in an R package...
## suppose your data frame is `df`
## response variable (LHS) in column 1
## independent variable (RHS) in other columns
out <- general_paired_simpleLM(df[1], df[-1])
# LHS RHS alpha beta beta.se beta.tv beta.pv
#1 consistent admire -0.1458455 0.18754326 0.008324192 22.529906 1.040756e-75
#2 consistent trust -0.2211250 0.19565589 0.007721387 25.339475 1.531499e-88
#3 consistent judge 0.3484851 0.04824981 0.014182420 3.402086 7.287372e-04
# sig R2 F.fv F.pv
#1 0.3430602 0.53118295 507.59665 1.040756e-75
#2 0.3212008 0.58902439 642.08902 1.531499e-88
#3 0.4946862 0.02518459 11.57419 7.287372e-04
To get what you want:
with(out, RHS[beta.pv < 0.05])
#[1] "admire" "trust" "judge"

Related

Clean up list output within RStudio when using lapply() or sapply()

I have a function that I am using with lapply().
lapply(x, function(x))
The output looks like this:
[[1]]
[1] "att_admire"
[[2]]
[1] "att_hypocrisy"
[[3]]
[1] "att_annoyed"
[[4]]
[1] ""
[[5]]
[1] "att_respectvalues"
When I use sapply(), it looks like this:
att_admire att_hypocrisy att_annoyed att_judge
"att_admire" "att_hypocrisy" "att_annoyed" ""
att_respectvalues
"att_respectvalues"
I would like the output to look neater, like this. Only the variable names should be included, not the "" or indices.
att_admire, att_hypocrisy, att_annoyed, att_respectvalues
What can I do to modify the code I have? I looked at this question, but it focuses on how to get cleaner results in an output file, whereas I would prefer for the output to look cleaner in RStudio itself.
Below, I have included the function I am using and a sample of the data.
Libraries:
install.packages("lavaan")
library(lavaan)
Main command:
iv <- "consistent"
dv <- "behavior_solarize"
mediators <- c("att_admire", "att_hypocrisy", "att_annoyed", "att_judge", "att_respectvalues")
lapply(mediators, med_overall)
Function (using "lavaan" package):
med_overall <- function(mediator) {
mediation.model <-
paste("
# mediator
",mediator," ~ a * ",iv,"
",dv," ~ b * ",mediator,"
# direct effect
",dv," ~ c * ",iv,"
# indirect effect (a*b)
ab := a*b
# total effect
total := c + (a*b)")
fit <- sem(mediation.model, data = df, meanstructure = TRUE,
se = "boot", bootstrap = 500)
val <- parameterEstimates(fit)
ifelse(val[10,8] < 0.05, mediator, "")}
Sample data:
structure(list(behavior_solarize = c(5, 5, 5, 3, 3, 3, 5, 6,
7, 5, 6, 7, 4, 3, 6, 4, 5, 6, 2, 4, 4, 5, 5, 5, 6, 2, 6, 2, 2,
1, 5, 5, 5, 5, 1, 5, 1, 5, 1, 6, 4, 2, 6, 6, 7, 6, 5, 5, 6, 5,
1, 4, 1, 6, 7, 6, 6, 3, 4, 2, 6, 7, 6, 1, 1, 2, 2, 5, 5, 4, 6,
3, 2, 5, 6, 1, 6, 2, 2, 6, 5, 3, 2, 3, 5, 1, 2, 1, 5, 4, 4, 5,
4, 3, 6, 5, 5, 5, 1, 4, 5, 5, 4, 6, 6, 2, 7, 6, 5, 2, 5, 4, 1,
7, 2, 2, 5, 4, 6, 6, 1, 6, 1, 2, 5, 6, 6, 6, 2, 1, 2, 3, 6, 4,
5, 4, 6, 4, 4, 5, 6, 4, 5, 1, 2, 6, 2, 5, 5, 4, 2, 5, 2, 5, 5,
6, 5, 2, 1, 1, 4, 7, 5, 6, 2, 5, 3, 2, 3, 3, 6, 5, 5, 5, 2, 5,
5, 5, 2, 2, 1, 2, 6, 5, 4, 3, 4, 4, 5, 3, 5, 2, 6, 3, 7, 5, 2,
1, 3, 2, 2, 5, 4, 1, 5, 7, 2, 7, 1, 6, 3, 7, 5, 1, 5, 6, 2, 5,
2, 6, 7, 5, 5, 2, 7, 2, 6, 5, 6, 3, 2, 2, 3, 3, 3, 5, 2, 5, 4,
5, 1, 1, 6, 4, 3, 3, 6, 5, 5, 3, 6, 3, 6, 4, 1, 1, 4, 7, 2, 2,
3, 1, 2, 5, 6, 1, 3, 4, 4, 1, 5, 6, 4, 1, 2, 3, 5, 4, 4, 5, 5,
5, 4, 3, 5, 5, 7, 4, 6, 4, 6, 6, 5, 1, 5, 2, 2, 6, 1, 4, 6, 5,
6, 2, 2, 5, 4, 6, 2, 5, 5, 1, 6, 5, 3, 5, 2, 6, 6, 2, 6, 7, 5,
3, 6, 5, 5, 5, 4, 6, 4, 6, 6, 5, 4, 2, 6, 6, 6, 1, 3, 2, 6, 3,
3, 4, 3, 6, 6, 7, 5, 7, 6, 5, 1, 3, 6, 2, 6, 6, 2, 6, 3, 4, 6,
7, 4, 6, 4, 6, 6, 6, 1, 4, 4, 3, 2, 6, 7, 5, 3, 1, 6, 4, 5, 3,
4, 6, 5, 7, 3, 4, 2, 1, 6, 1, 4, 2, 2, 6, 4, 6, 3, 3, 2, 5, 6,
5, 1, 5, 7, 6, 4, 5, 2, 2, 4, 1, 4, 5, 1, 7, 2, 6, 4, 3, 6, 6,
4, 4, 4, 1, 6, 3, 6, 7, 5, 3, 4, 1, 3, 5, 6, 4, 5, 2, 6, 5, 5,
7, 5, 7, 7, 3, 5, 5, 5, 5, 4, 3, 5, 6, 3, 5, 7, 5, 5, 5, 4, 5,
2, 3, 6, 7, 7, 5, 4, 5, 5, 2, 1, 6, 2, 7, 5, 6, 6, 5, 2, 5, 2,
4, 5, 3, 3, 4, 3, 6, 7, 7, 2, 5, 5, 2, 5, 2, 3, 6, 5, 5, 6, 4,
5, 5, 3, 2, 7, 3, 5, 4, 1, 4, 3, 6, 4, 1, 6, 7, 2, 4, 2, 1, 6,
7, 5, 2, 4, 6, 3, 5, 5, 4, 7, 4, 5, 4, 6, 2, 1, 3, 2, 7, 2, 2,
2, 5, 5, 5, 2, 7, 5, 3, 5, 6, 7, 4, 6, 6, 5, 5, 6, 1, 1, 6, 1,
3, 2, 2, 3, 2, 5, 5, 6, 2, 4, 7, 6, 2, 2, 5, 3, 5, 4, 5, 7, 1,
3, 4, 7, 4, 5, 6, 4, 6, 5, 3, 3, 5, 2, 4, 6, 1, 2, 5, 1, 5, 6,
6, 5, 6, 5, 4, 1, 6, 4, 1, 6, 2, 7, 1, 2, 4, 4, 6, 1, 5, 4, 7,
3, 2, 2, 6, 5, 1, 6, 2, 3, 6, 5, 5, 2, 5, 2, 3, 6, 4, 5, 5, 4,
5, 1, 5, 6, 2, 6, 1, 1, 5, 2, 3, 7, 6, 6, 6, 4, 7, 5, 7, 6, 2,
4, 5, 4, 6, 5, 5, 5, 3, 5, 5, 2, 3, 6), consistent = c(1, 0,
1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0,
1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1,
0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,
0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1,
0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1,
0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0,
1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,
0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1,
0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1,
0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0,
1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1,
1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0,
0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1,
1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0,
0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1,
1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,
1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1,
0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0,
0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0,
0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0), att_admire = c(4,
2, 6, 5, 4, 4, 6, 6, 4, 4, 5, 6, 4, 3, 6, 4, 3, 5, 3, 4, 4, 3,
4, 5, 6, 3, 4, 2, 3, 4, 6, 3, 4, 4, 1, 5, 1, 4, 1, 4, 1, 4, 6,
5, 4, 4, 4, 7, 2, 3, 4, 4, 7, 4, 4, 4, 5, 3, 3, 7, 4, 3, 4, 2,
4, 3, 4, 4, 6, 1, 1, 4, 4, 5, 4, 1, 6, 4, 4, 6, 6, 4, 1, 4, 5,
4, 1, 1, 5, 7, 3, 1, 7, 5, 7, 4, 4, 4, 1, 1, 4, 3, 2, 5, 3, 1,
6, 4, 3, 7, 4, 2, 1, 6, 3, 3, 1, 1, 5, 4, 1, 6, 1, 2, 4, 4, 5,
6, 4, 1, 4, 1, 4, 7, 5, 4, 5, 7, 5, 4, 5, 5, 4, 1, 3, 5, 3, 4,
6, 4, 4, 5, 1, 5, 6, 1, 7, 4, 1, 2, 5, 5, 4, 5, 4, 1, 4, 1, 1,
3, 4, 5, 4, 2, 1, 7, 3, 3, 1, 1, 2, 2, 5, 4, 4, 2, 3, 4, 6, 3,
6, 2, 5, 4, 7, 5, 1, 1, 2, 2, 3, 5, 4, 4, 4, 6, 1, 3, 1, 7, 3,
4, 4, 4, 3, 7, 1, 6, 1, 5, 1, 6, 4, 1, 7, 1, 4, 4, 5, 5, 3, 5,
4, 2, 5, 4, 1, 5, 5, 4, 1, 2, 7, 4, 2, 3, 4, 5, 6, 2, 7, 5, 4,
3, 2, 2, 1, 4, 1, 3, 4, 1, 3, 3, 3, 2, 4, 5, 3, 3, 6, 5, 4, 7,
6, 4, 4, 5, 4, 3, 4, 4, 4, 1, 1, 7, 7, 4, 6, 4, 6, 5, 4, 1, 4,
3, 5, 4, 3, 4, 4, 3, 4, 7, 4, 4, 4, 5, 1, 3, 3, 1, 4, 3, 1, 4,
3, 4, 5, 2, 4, 6, 7, 1, 7, 3, 3, 1, 4, 5, 3, 5, 6, 3, 4, 5, 5,
6, 5, 1, 3, 3, 5, 4, 4, 6, 1, 7, 5, 1, 4, 4, 5, 3, 1, 2, 6, 2,
4, 6, 6, 5, 4, 1, 5, 6, 4, 4, 4, 4, 7, 6, 1, 2, 4, 2, 1, 4, 7,
4, 4, 1, 6, 5, 5, 3, 4, 7, 5, 7, 5, 4, 3, 1, 4, 2, 4, 4, 1, 4,
1, 5, 3, 5, 4, 6, 4, 4, 4, 5, 6, 5, 4, 5, 1, 3, 4, 2, 1, 1, 3,
1, 3, 5, 4, 4, 2, 2, 1, 1, 3, 4, 3, 3, 5, 7, 3, 3, 3, 2, 3, 5,
4, 3, 5, 4, 6, 4, 3, 7, 3, 6, 7, 4, 4, 4, 4, 3, 5, 4, 4, 4, 2,
4, 6, 3, 6, 4, 4, 5, 6, 5, 5, 6, 7, 5, 4, 4, 2, 2, 1, 7, 2, 7,
2, 6, 7, 4, 2, 3, 5, 1, 4, 4, 3, 1, 2, 6, 6, 7, 1, 5, 6, 4, 6,
2, 4, 3, 5, 6, 4, 4, 4, 4, 4, 4, 7, 3, 2, 4, 6, 5, 3, 1, 4, 1,
4, 4, 3, 4, 1, 1, 5, 7, 4, 1, 1, 4, 3, 4, 4, 4, 7, 2, 5, 4, 5,
3, 1, 5, 2, 5, 1, 4, 4, 4, 3, 7, 4, 3, 4, 1, 4, 5, 2, 6, 5, 1,
5, 2, 5, 1, 1, 5, 1, 4, 3, 2, 4, 3, 5, 1, 5, 2, 4, 7, 7, 1, 2,
7, 3, 4, 5, 4, 5, 1, 1, 5, 2, 4, 4, 5, 4, 5, 2, 3, 2, 2, 5, 4,
5, 1, 2, 5, 1, 3, 7, 4, 3, 4, 5, 4, 3, 5, 4, 1, 4, 4, 6, 3, 1,
4, 4, 6, 1, 4, 4, 5, 4, 3, 2, 6, 4, 1, 4, 5, 2, 4, 4, 4, 3, 5,
3, 6, 5, 3, 5, 4, 4, 5, 1, 4, 3, 1, 4, 3, 6, 5, 3, 1, 6, 6, 4,
6, 1, 7, 5, 4, 4, 3, 4, 3, 3, 6, 3, 5, 5, 2, 2, 5, 3, 1, 6),
att_hypocrisy = c(4, 5, 7, 2, 5, 6, 3, 1, 1, 3, 3, 1, 4,
5, 6, 4, 3, 7, 6, 1, 4, 1, 1, 5, 1, 3, 2, 6, 7, 3, 1, 3,
7, 1, 7, 3, 7, 1, 7, 2, 3, 6, 1, 1, 1, 3, 5, 1, 7, 3, 7,
5, 7, 4, 1, 1, 2, 6, 6, 3, 1, 5, 1, 6, 4, 6, 2, 1, 1, 7,
1, 4, 3, 1, 2, 7, 1, 1, 1, 1, 1, 2, 6, 4, 1, 4, 7, 7, 2,
1, 1, 1, 1, 3, 5, 6, 3, 1, 1, 1, 2, 1, 7, 2, 2, 1, 1, 3,
5, 1, 1, 1, 1, 1, 4, 2, 2, 4, 1, 1, 6, 1, 4, 6, 1, 4, 1,
1, 4, 3, 5, 5, 1, 3, 5, 5, 1, 6, 1, 1, 4, 3, 5, 7, 2, 3,
4, 1, 1, 4, 4, 1, 1, 4, 1, 4, 4, 6, 7, 5, 2, 1, 4, 1, 6,
7, 5, 7, 7, 6, 2, 7, 1, 4, 1, 6, 2, 3, 1, 7, 7, 6, 2, 4,
2, 3, 1, 6, 1, 4, 1, 5, 1, 5, 1, 3, 7, 7, 6, 5, 6, 1, 7,
6, 3, 1, 6, 5, 6, 1, 6, 1, 4, 5, 1, 1, 2, 1, 7, 2, 5, 1,
4, 7, 1, 7, 6, 2, 1, 4, 5, 5, 1, 7, 3, 2, 7, 2, 1, 3, 1,
4, 1, 6, 2, 1, 2, 3, 1, 2, 1, 3, 3, 6, 6, 4, 7, 4, 5, 6,
6, 4, 5, 6, 1, 6, 2, 1, 4, 5, 1, 2, 3, 1, 1, 5, 2, 3, 2,
1, 2, 3, 7, 1, 1, 1, 1, 2, 1, 3, 2, 2, 4, 7, 5, 6, 2, 4,
5, 6, 2, 7, 1, 5, 4, 4, 1, 6, 1, 4, 4, 7, 3, 5, 7, 2, 7,
5, 1, 6, 2, 1, 1, 7, 1, 1, 6, 1, 5, 5, 4, 1, 1, 4, 4, 2,
5, 1, 2, 4, 4, 4, 6, 1, 2, 2, 7, 1, 1, 5, 1, 1, 3, 7, 7,
6, 1, 5, 6, 5, 2, 4, 5, 1, 1, 1, 1, 1, 5, 1, 1, 1, 7, 1,
3, 3, 7, 1, 1, 1, 2, 1, 5, 4, 2, 5, 1, 1, 2, 1, 3, 1, 6,
1, 7, 3, 1, 6, 7, 1, 4, 4, 2, 3, 4, 3, 5, 1, 1, 1, 5, 1,
4, 5, 7, 3, 2, 7, 6, 7, 7, 1, 7, 1, 6, 4, 4, 2, 4, 3, 3,
5, 4, 1, 1, 1, 6, 6, 7, 7, 5, 1, 5, 2, 4, 4, 1, 4, 2, 2,
6, 1, 7, 5, 4, 5, 3, 6, 3, 3, 2, 3, 6, 7, 1, 6, 1, 1, 4,
1, 3, 3, 2, 1, 1, 1, 6, 6, 5, 6, 5, 5, 5, 1, 1, 1, 1, 3,
7, 1, 2, 7, 1, 5, 3, 7, 1, 1, 2, 1, 2, 4, 1, 6, 3, 6, 5,
1, 1, 4, 1, 2, 3, 4, 6, 4, 1, 6, 2, 4, 3, 2, 5, 2, 6, 1,
5, 1, 6, 3, 4, 2, 1, 1, 4, 6, 4, 4, 6, 1, 2, 5, 1, 7, 3,
4, 1, 5, 1, 4, 7, 1, 6, 6, 4, 5, 6, 1, 3, 7, 4, 7, 2, 1,
7, 1, 2, 1, 1, 3, 1, 7, 6, 1, 7, 4, 6, 6, 1, 2, 4, 6, 2,
7, 2, 1, 2, 5, 6, 1, 1, 5, 2, 5, 1, 1, 7, 1, 3, 1, 1, 1,
5, 1, 5, 2, 7, 6, 4, 1, 4, 7, 6, 6, 7, 6, 6, 2, 2, 6, 3,
5, 3, 1, 4, 4, 1, 2, 6, 7, 3, 5, 5, 1, 7, 2, 5, 7, 1, 6,
1, 2, 4, 5, 5, 6, 4, 3, 1, 4, 6, 3, 5, 3, 1, 2, 1, 3, 1,
2, 5, 3, 5, 7, 4, 6, 6, 2, 5, 6, 1, 6, 5, 5, 1, 1, 5, 3,
1, 7, 2, 4, 1, 7, 2, 5, 5, 4, 6, 2, 1, 6, 2), att_annoyed = c(5,
5, 4, 3, 3, 2, 3, 1, 1, 2, 3, 1, 4, 4, 4, 4, 3, 6, 5, 1,
4, 3, 1, 4, 1, 3, 2, 6, 3, 3, 3, 4, 5, 5, 7, 2, 4, 3, 2,
2, 3, 4, 1, 1, 1, 1, 4, 1, 5, 5, 4, 4, 7, 4, 5, 1, 2, 5,
3, 1, 1, 5, 2, 6, 2, 5, 5, 1, 1, 7, 1, 3, 4, 2, 1, 7, 1,
5, 1, 3, 2, 3, 5, 2, 1, 1, 1, 5, 5, 1, 1, 1, 1, 3, 1, 3,
1, 2, 5, 1, 1, 2, 5, 2, 4, 1, 1, 3, 4, 1, 1, 5, 1, 1, 2,
5, 2, 3, 2, 1, 6, 1, 1, 3, 1, 3, 1, 1, 5, 2, 3, 3, 1, 2,
3, 3, 1, 1, 4, 1, 2, 2, 1, 7, 7, 2, 6, 2, 1, 4, 4, 3, 1,
4, 2, 6, 2, 2, 7, 5, 2, 1, 4, 1, 6, 5, 5, 7, 6, 5, 2, 4,
1, 7, 7, 2, 2, 4, 5, 6, 6, 5, 2, 5, 2, 2, 1, 5, 2, 5, 2,
2, 1, 5, 1, 2, 7, 7, 6, 5, 6, 2, 4, 1, 2, 1, 5, 1, 7, 1,
4, 1, 4, 5, 1, 2, 3, 1, 6, 3, 1, 1, 2, 4, 1, 6, 1, 1, 1,
5, 5, 1, 3, 3, 3, 5, 6, 1, 1, 4, 1, 6, 1, 4, 1, 1, 2, 1,
1, 2, 1, 3, 2, 4, 6, 1, 4, 4, 4, 4, 5, 4, 5, 3, 2, 4, 3,
1, 1, 5, 1, 1, 3, 1, 1, 4, 3, 3, 2, 1, 2, 3, 7, 1, 1, 1,
1, 2, 1, 4, 1, 2, 4, 7, 3, 5, 2, 1, 5, 5, 2, 1, 1, 1, 6,
4, 5, 3, 3, 5, 5, 7, 2, 5, 6, 1, 6, 4, 1, 5, 2, 1, 1, 7,
1, 2, 4, 1, 3, 3, 5, 1, 1, 1, 1, 2, 2, 1, 2, 4, 7, 5, 7,
3, 3, 2, 7, 1, 1, 2, 1, 1, 6, 2, 7, 5, 1, 5, 1, 6, 2, 1,
2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 7, 1, 1, 5, 2, 1, 1, 1, 3,
7, 4, 3, 2, 5, 1, 1, 1, 1, 5, 1, 2, 1, 4, 1, 1, 3, 3, 1,
7, 4, 2, 4, 4, 4, 5, 5, 1, 1, 7, 2, 2, 3, 7, 3, 5, 6, 2,
7, 1, 1, 1, 1, 3, 4, 1, 2, 1, 1, 3, 1, 3, 1, 1, 1, 2, 4,
6, 3, 3, 1, 2, 4, 2, 2, 1, 3, 3, 1, 4, 1, 1, 1, 5, 3, 3,
5, 2, 5, 2, 1, 6, 3, 1, 5, 1, 1, 4, 2, 1, 3, 2, 1, 1, 4,
3, 1, 5, 5, 4, 6, 5, 1, 1, 1, 1, 2, 7, 1, 4, 6, 3, 1, 2,
7, 2, 1, 1, 1, 2, 4, 2, 4, 5, 2, 3, 1, 1, 2, 1, 3, 1, 3,
1, 2, 1, 4, 2, 1, 3, 1, 6, 1, 2, 4, 1, 1, 5, 5, 4, 5, 1,
1, 2, 3, 1, 1, 6, 2, 2, 3, 1, 4, 2, 4, 2, 4, 2, 4, 4, 1,
1, 3, 2, 5, 4, 2, 1, 7, 4, 7, 3, 1, 7, 2, 2, 1, 1, 3, 1,
7, 4, 1, 5, 1, 5, 3, 3, 5, 3, 5, 2, 1, 5, 1, 1, 1, 2, 1,
1, 2, 3, 1, 3, 1, 6, 1, 1, 5, 1, 1, 1, 1, 5, 4, 6, 3, 2,
1, 2, 6, 6, 3, 4, 4, 6, 2, 5, 2, 1, 5, 7, 4, 1, 4, 1, 2,
3, 4, 6, 4, 4, 1, 7, 2, 1, 6, 1, 4, 1, 1, 3, 5, 1, 4, 5,
1, 1, 4, 2, 2, 4, 1, 1, 2, 1, 3, 1, 2, 7, 1, 2, 5, 2, 6,
6, 2, 5, 1, 1, 5, 3, 5, 1, 1, 5, 1, 1, 6, 1, 4, 1, 4, 6,
6, 5, 3, 5, 2, 2, 5, 6), att_judge = c(3, 5, 4, 4, 4, 1,
5, 3, 3, 3, 5, 1, 4, 3, 5, 4, 4, 6, 4, 2, 4, 5, 2, 4, 2,
5, 2, 4, 3, 4, 5, 7, 3, 7, 6, 3, 1, 3, 1, 4, 3, 2, 1, 4,
1, 1, 3, 7, 4, 2, 1, 1, 7, 1, 1, 4, 2, 4, 2, 1, 2, 1, 4,
4, 6, 2, 2, 1, 1, 1, 5, 2, 4, 5, 4, 6, 6, 6, 1, 3, 3, 4,
4, 4, 2, 4, 1, 1, 3, 2, 1, 2, 4, 4, 1, 3, 3, 5, 3, 1, 1,
2, 5, 5, 5, 5, 4, 5, 4, 4, 4, 1, 1, 3, 5, 6, 1, 1, 1, 4,
2, 1, 1, 3, 4, 4, 2, 4, 2, 5, 5, 1, 2, 4, 6, 2, 3, 1, 3,
1, 4, 2, 2, 1, 4, 3, 7, 4, 2, 4, 1, 2, 1, 4, 5, 4, 3, 2,
2, 4, 4, 1, 4, 2, 3, 2, 5, 6, 3, 1, 4, 2, 1, 5, 7, 3, 2,
5, 2, 1, 3, 2, 3, 4, 2, 4, 1, 4, 4, 4, 3, 2, 4, 1, 4, 3,
1, 1, 6, 3, 4, 3, 4, 2, 5, 4, 4, 1, 4, 4, 2, 3, 5, 3, 1,
4, 1, 2, 2, 4, 1, 4, 4, 7, 1, 1, 2, 3, 5, 6, 2, 1, 3, 2,
3, 3, 4, 3, 1, 4, 1, 4, 4, 2, 2, 2, 3, 2, 1, 4, 1, 4, 4,
3, 2, 1, 1, 4, 4, 1, 2, 4, 5, 2, 2, 1, 3, 1, 4, 4, 1, 2,
3, 1, 1, 2, 3, 5, 4, 2, 4, 4, 4, 1, 1, 1, 1, 2, 5, 5, 4,
4, 4, 7, 5, 2, 2, 3, 4, 3, 4, 1, 4, 1, 7, 4, 4, 1, 2, 5,
5, 1, 3, 2, 4, 1, 1, 1, 5, 4, 2, 1, 1, 1, 1, 1, 4, 1, 3,
3, 1, 1, 1, 1, 4, 4, 4, 4, 5, 4, 7, 2, 6, 5, 3, 2, 1, 2,
2, 3, 5, 2, 3, 2, 2, 2, 3, 5, 1, 6, 3, 2, 1, 3, 3, 1, 4,
1, 1, 5, 4, 3, 5, 1, 1, 3, 2, 2, 4, 4, 3, 1, 5, 1, 2, 3,
2, 1, 3, 1, 3, 4, 5, 1, 4, 6, 3, 4, 1, 1, 4, 5, 3, 3, 2,
4, 2, 2, 3, 4, 3, 5, 4, 3, 1, 3, 2, 2, 4, 2, 1, 1, 1, 4,
5, 4, 2, 2, 1, 4, 3, 1, 4, 3, 2, 1, 3, 3, 1, 3, 4, 1, 3,
3, 3, 4, 4, 5, 3, 4, 1, 5, 7, 1, 4, 2, 3, 3, 3, 2, 3, 5,
4, 1, 3, 2, 4, 1, 4, 4, 2, 3, 4, 4, 1, 5, 3, 3, 3, 2, 1,
6, 4, 7, 1, 2, 5, 1, 6, 1, 5, 4, 5, 2, 4, 1, 1, 5, 3, 4,
3, 4, 4, 4, 3, 2, 2, 2, 1, 2, 3, 3, 2, 4, 1, 4, 1, 4, 3,
1, 2, 4, 3, 5, 5, 2, 1, 4, 6, 4, 4, 3, 4, 1, 7, 1, 4, 2,
3, 6, 5, 4, 3, 2, 3, 3, 1, 1, 2, 2, 5, 6, 1, 7, 5, 5, 4,
5, 5, 1, 5, 6, 3, 1, 1, 1, 4, 3, 2, 4, 3, 3, 1, 5, 1, 4,
4, 3, 4, 3, 3, 2, 3, 2, 4, 7, 4, 4, 2, 5, 3, 4, 5, 2, 3,
1, 1, 1, 4, 5, 1, 5, 1, 6, 5, 4, 4, 1, 2, 4, 4, 4, 4, 5,
4, 2, 6, 3, 4, 2, 4, 2, 7, 2, 1, 4, 1, 6, 4, 3, 4, 4, 2,
1, 7, 5, 3, 5, 1, 1, 3, 3, 3, 4, 2, 1, 3, 1, 4, 4, 4, 4,
4, 3, 4, 3, 2, 5, 4, 5, 1, 1, 4, 4, 3, 1, 7, 4, 3, 1, 7,
6, 4, 5, 1, 4, 5, 7, 2, 4, 4, 5, 1, 5, 5, 7, 4, 4, 4, 5,
1, 1, 5), att_respectvalues = c(5, 4, 4, 3, 4, 5, 4, 5, 7,
4, 5, 7, 4, 4, 5, 4, 5, 6, 4, 5, 3, 4, 5, 4, 6, 4, 5, 4,
3, 4, 4, 3, 5, 4, 1, 5, 2, 4, 2, 5, 5, 2, 7, 5, 7, 5, 4,
7, 2, 3, 4, 6, 7, 6, 4, 4, 6, 5, 4, 7, 6, 6, 5, 3, 2, 5,
5, 5, 6, 4, 5, 4, 5, 6, 4, 4, 5, 4, 7, 6, 6, 4, 4, 5, 7,
5, 4, 2, 4, 7, 4, 6, 7, 4, 5, 3, 4, 5, 3, 6, 6, 4, 3, 5,
2, 5, 7, 6, 3, 7, 6, 4, 1, 4, 4, 3, 4, 4, 7, 5, 4, 6, 4,
4, 6, 4, 6, 2, 4, 5, 6, 4, 4, 5, 3, 6, 6, 6, 4, 6, 6, 5,
5, 4, 3, 5, 4, 4, 7, 4, 4, 5, 4, 3, 6, 4, 5, 6, 3, 4, 4,
5, 4, 5, 5, 4, 5, 1, 3, 2, 5, 5, 7, 3, 5, 7, 5, 4, 2, 4,
5, 3, 5, 4, 5, 4, 4, 4, 7, 4, 6, 4, 7, 4, 5, 6, 4, 7, 3,
3, 3, 6, 7, 5, 4, 7, 2, 7, 1, 4, 3, 6, 4, 4, 7, 7, 3, 6,
3, 6, 4, 5, 3, 3, 7, 3, 7, 4, 6, 3, 4, 6, 4, 4, 5, 5, 5,
6, 6, 5, 4, 4, 7, 4, 3, 4, 6, 6, 6, 5, 7, 4, 5, 5, 3, 4,
1, 4, 4, 3, 5, 4, 4, 6, 5, 4, 4, 4, 4, 4, 7, 5, 4, 7, 5,
4, 3, 4, 4, 5, 5, 7, 4, 7, 4, 4, 7, 5, 6, 3, 5, 5, 7, 7,
4, 4, 4, 6, 5, 3, 4, 3, 4, 6, 4, 4, 4, 6, 6, 5, 4, 7, 5,
6, 4, 4, 4, 6, 6, 3, 5, 5, 7, 1, 6, 6, 3, 4, 4, 4, 4, 7,
4, 4, 4, 5, 5, 5, 4, 4, 2, 4, 7, 5, 5, 6, 1, 6, 6, 7, 4,
7, 3, 5, 4, 3, 6, 4, 6, 6, 6, 6, 5, 3, 7, 4, 4, 5, 4, 5,
7, 5, 3, 7, 4, 5, 4, 6, 7, 7, 3, 4, 5, 6, 6, 3, 7, 7, 5,
7, 4, 7, 4, 4, 4, 4, 6, 4, 3, 4, 4, 5, 3, 5, 6, 4, 4, 4,
4, 4, 5, 5, 6, 5, 5, 4, 5, 5, 3, 4, 7, 7, 4, 6, 7, 4, 4,
4, 7, 3, 4, 4, 4, 4, 5, 7, 4, 4, 3, 6, 4, 6, 4, 5, 5, 4,
4, 6, 5, 7, 3, 6, 7, 4, 4, 4, 4, 2, 5, 4, 5, 4, 5, 5, 7,
5, 5, 4, 4, 5, 5, 5, 5, 7, 7, 4, 4, 6, 2, 3, 4, 6, 4, 4,
5, 6, 4, 6, 4, 6, 5, 2, 4, 6, 3, 1, 5, 6, 6, 7, 3, 5, 6,
5, 6, 5, 4, 5, 7, 4, 6, 5, 5, 5, 7, 3, 7, 4, 3, 2, 5, 4,
4, 4, 5, 1, 6, 7, 4, 5, 6, 2, 7, 7, 4, 3, 4, 5, 4, 2, 4,
5, 7, 5, 5, 5, 5, 4, 1, 3, 4, 7, 4, 4, 4, 4, 3, 7, 4, 4,
5, 1, 5, 7, 4, 6, 6, 4, 6, 4, 5, 4, 1, 7, 4, 7, 4, 4, 4,
4, 6, 2, 5, 5, 4, 7, 6, 4, 4, 5, 7, 4, 5, 5, 5, 7, 2, 4,
3, 3, 4, 5, 7, 6, 3, 4, 3, 1, 5, 4, 5, 3, 4, 5, 4, 4, 5,
5, 4, 5, 6, 6, 2, 5, 6, 4, 6, 2, 7, 4, 4, 4, 4, 6, 1, 5,
4, 7, 4, 5, 4, 7, 5, 4, 6, 5, 3, 4, 7, 3, 4, 5, 5, 6, 6,
5, 5, 3, 4, 4, 4, 2, 3, 4, 5, 4, 7, 5, 3, 5, 7, 6, 4, 7,
7, 7, 4, 7, 4, 4, 7, 5, 5, 6, 2, 6, 5, 4, 2, 4, 5, 4, 5)), row.names = c(NA,
-693L), class = c("tbl_df", "tbl", "data.frame"))

Store the results in an object. Then exclude empty strings (where do they actually come from?) convert toString and cat it.
res <- lapply(mediators, med_overall)
cat(toString(res[res != '']))
# att_admire, att_hypocrisy, att_annoyed, att_respectvalues

Bootstrapping multiple regression error: number of items to replace is not a multiple of replacement length

I want to bootstrap my dataset for multiple regression. Unfortunately I get this error message:
"number of items to replace is not a multiple of replacement length"
I suspect that the factors in my regression formula may be problematic.
What could I do to solve my problem?
My code is as following (I read Andy Field´s Discovering Statistics using R):
BootReg <- function(data, indices, formula) {
d <- data[indices,]
fit <- lm(formula, data=d)
return(coef(fit))
}
bootResults <-boot(statistic = BootReg, formula = TICS_Skala1 ~HSPhoch + HSPhoch*extra.c
+ psy + sex + age.c, data = mod.reg.data, R = 2000)
psy (psychiatric disease), sex and HSPhoch (high sensory-processing sensitivity) are factors. TICS_Skala1, extra.c, age.c are continuos variables.
my sample data:
> dput(head(mod.reg.data, 20))
structure(list(neo_01 = c(3, 4, 3, 0, 4, 4, 3, 2, 3, 1, 4, 2,
3, 3, 1, 2, 3, 4, 0, 2), neo_03 = c(1, 1, 1, 3, 1, 2, 0, 0, 0,
0, 0, 0, 1, 3, 1, 1, 1, 1, 3, 1), neo_04 = c(2, 4, 3, 0, 4, 3,
4, 3, 2, 3, 3, 3, 3, 4, 2, 4, 3, 4, 3, 3), neo_08 = c(3, 0, 1,
2, 3, 3, 4, 3, 2, 1, 2, 4, 0, 3, 1, 1, 3, 1, 3, 1), neo_12 = c(3,
1, 1, 2, 2, 2, 4, 1, 1, 2, 1, 4, 1, 3, 1, 1, 3, 2, 3, 2), neo_13 = c(3,
2, 2, 4, 3, 3, 3, 2, 2, 1, 2, 3, 0, 3, 1, 0, 2, 3, 0, 2), neo_16 = c(3,
1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 3, 0, 2, 0, 0, 0, 0, 2, 1), neo_17 = c(2,
1, 3, 0, 1, 1, 1, 4, 3, 1, 2, 2, 2, 3, 1, 0, 2, 0, 2, 2), neo_18 = c(2,
3, 4, 0, 4, 3, 4, 3, 3, 1, 3, 2, 4, 2, 3, 4, 3, 4, 2, 2), neo_21 = c(3,
0, 1, 2, 1, 2, 1, 1, 1, 1, 1, 3, 0, 4, 1, 0, 0, 0, 4, 1), neo_26 = c(3,
0, 0, 0, 2, 1, 3, 0, 1, 1, 0, 2, 3, 3, 0, 0, 1, 1, 4, 1), neo_27 = c(3,
3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 3, 3, 3, 3, 2, 2), TICS_1 = c(3,
0, 3, 2, 2, 1, 3, 3, 1, 2, 0, 4, 2, 3, 2, 3, 4, 1, 3, 2), TICS_2 = c(3,
1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 4, 3, 1, 1, 1, 2, 1, 2, 1), TICS_3 = c(2,
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 3, 1, 2, 0, 1, 1, 0, 1, 0), TICS_4 = c(2,
0, 2, 0, 1, 2, 1, 3, 0, 0, 0, 4, 1, 2, 1, 2, 1, 1, 2, 2), TICS_5 = c(2,
3, 2, 1, 2, 2, 2, 2, 0, 2, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1), TICS_6 = c(3,
2, 2, 4, 2, 2, 1, 3, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2), TICS_7 = c(3,
3, 2, 2, 2, 2, 0, 3, 1, 2, 1, 4, 2, 0, 2, 1, 4, 1, 0, 1), TICS_8 =c(NA,
NA, NA, NA, NA, NA, NA, NA, 1, 1, 0, 4, 3, 1, 1, 3, 3, 2, 1,
2), TICS_9 = c(NA, NA, NA, NA, NA, NA, NA, NA, 0, 3, 2, 2, 1,
3, 0, 1, 3, 1, 1, 2), TICS_10 = c(2, 2, 0, 0, 2, 3, 0, 2, 1,
1, 2, 2, 1, 0, 0, 1, 1, 2, 2, 1), TICS_11 = c(1, 2, 1, 0, 1,
1, 0, 0, 0, 0, 2, 4, 1, 0, 0, 0, 0, 1, 1, 0), TICS_12 = c(2,
2, 1, 0, 1, 1, 1, 3, 1, 1, 1, 4, 2, 2, 2, 3, 3, 1, 2, 3), TICS_13=
c(1, 1, 3, 0, 2, 3, 2, 1, 1, 2, 1, 2, 2, 3, 2, 2, 1, 2, 2, 2),
TICS_14= c(4, 1, 1, 0, 1, 1, 3, 4, 0, 2, 0, 4, 2, 3, 0, 1, 3, 1, 1,
1), TICS_15= c(3, 1, 1, 3, 0, 2, 0, 2, 0, 2, 1, 2, 0, 1, 1, 1, 0, 0,
0, 1), ICS_16= c(4, 2, 1, 3, 3, 2, 1, 2, 1, 1, 1, 3, 1, 3, 1, 2, 3,
1, 2, 1), TICS_17= c(3, 0, 2, 2, 1, 2, 2, 3, 0, 1, 1, 2, 1, 2, 2, 3,
1, 1, 1, 2), TICS_18= c(3, 0, 1, 2, 0, 1, 1, 0, 0, 1, 0, 4, 2, 2, 0,
0, 1, 0, 2, 0), TICS_19= c(4, 2, 2, 2, 2, 2, 0, 2, 1, 2, 1, 4, 3, 2,
1, 1, 1, 0, 1, 2), TICS_20= c(2, 0, 2, 0, 0, 0, 1, 0, 1, 1, 0, 4, 1,
1, 0, 0, 1, 0, 2, 0), TICS_21= c(2, 1, 1, 0, 2, 3, 0, 1, 0, 1, 3, 2,
2, 1, 2, 1, 1, 1, 3, 0), TICS_22= c(3, 0, 1, 2, 2, 3, 1, 4, 0, 1, 1,
2, 3, 1, 1, 2, 3, 2, 0, 3), TICS_24= c(2, 0, 0, 1, 0, 0, 2, 0, 1, 1,
0, 2, 0, 0, 0, 1, 1, 0, 0, 1), TICS_25= c(4, 0, 1, 2, 2, 2, 4, 2, 1,
1, 0, 3, 0, 2, 0, 1, 2, 1, 2, 1), TICS_26= c(3, 0, 2, 2, 0, 1, 1, 0,
0, 1, 0, 2, 0, 2, 0, 0, 0, 0, 0, 1), TICS_27= c(3,
1, 4, 2, 3, 3, 4, 4, 0, 1, 0, 3, 2, 3, 2, 3, 2, 2, 4, 3), TICS_28=
c(3, 2, 2, 1, 1, 2, 1, 2, 1, 1, 0, 4, 1, 2, 1, 0, 1, 0, 0, 2),
TICS_29= c(2, 0, 1, 0, 2, 2, 1, 0, 1, 0, 0, 4, 1, 1, 0, 1, 0, 0, 1,
1), TICS_30= c(2, 1, 3, 1, 2, 2, 1, 0, 1, 1, 1, 3, 2, 0, 1, 0, 1, 2,
2, 2), TICS_31= c(2, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 3, 2, 1, 0, 0, 1,
0, 2, 1), TICS_32= c(4, 1, 1, 0, 1, 2, 1, 4, 0, 3, 0, 3, 3, 2, 1, 2,
2, 2, 3, 3), TICS_33= c(2,
1, 0, 2, 1, 1, 1, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 1, 1, 1), TICS_34=
c(1, 3, 0, 0, 2, 1, 1, 1, 0, 0, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0),
TICS_35= c(1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 2, 0, 1, 0, 1, 1, 0, 4,
1), TICS_36= c(4, 1, 2, 3, 3, 2, 4, 1, 0, 1, 2, 3, 1, 3, 0, 1, 1, 0,
2, 1), TICS_37= c(1, 1, 2, 0, 2, 3, 3, 0, 1, 2, 1, 2, 1, 0, 2, 2, 1,
1, 2, 1), TICS_38= c(3, 0, 3, 1, 2, 2, 2, 3, 0, 2, 0, 4, 0, 2, 1, 2,
2, 1, 1, 2), TICS_39= c(1, 1, 2, 2, 3, 1, 1, 2, 1, 1, 1, 4, 1, 1, 1,
1, 3, 0, 0, 3), TICS_40= c(2, 0, 2, 0, 3, 2, 1, 2, 0, 0, 0, 3, 2, 2,
0, 1, 2, 0, 0, 1), TICS_41= c(2, 2, 0, 0, 2, 3, 1, 1, 0, 1, 3, 1, 2,
0, 1, 0, 0, 1, 2, 0), TICS_42= c(1, 2, 0, 0, 2, 1, 0, 0, 0, 1, 1, 2,
1, 1, 1, 0, 0, 0, 0, 0), TICS_43= c(4,
1, 1, 2, 2, 3, 3, 3, 0, 2, 1, 4, 3, 2, 1, 1, 3, 1, 2, 3), TICS_44=
c(3, 0, 2, 1, 2, 2, 3, 3, 0, 1, 0, 4, 1, 3, 0, 2, 2, 1, 3, 1),
TICS_45= c(2,
0, 1, 2, 0, 1, 0, 2, 0, 1, 0, 2, 0, 2, 0, 0, 0, 0, 0, 1), TICS_46=
c(2, 1, 0, 1, 2, 2, 1, 0, 0, 3, 1, 4, 3, 1, 1, 0, 1, 1, 2, 1),
TICS_47= c(3,
1, 2, 1, 2, 2, 1, 1, 1, 2, 0, 3, 1, 2, 1, 2, 1, 1, 4, 1), TICS_48=
c(1,
2, 3, 1, 2, 3, 1, 1, 0, 2, 2, 4, 2, 3, 2, 2, 1, 0, 2, 0), TICS_49=
c(1,
3, 2, 2, 1, 2, 2, 1, 0, 1, 1, 4, 3, 0, 1, 2, 4, 1, 0, 3), TICS_50=
c(3,
0, 3, 1, 1, 2, 4, 3, 0, 2, 0, 4, 2, 3, 2, 2, 2, 2, 2, 3), TICS_51=
c(1,
2, 0, 0, 2, 1, 0, 0, 0, 0, 1, 2, 1, 0, 1, 0, 0, 0, 0, 0), TICS_52=
c(2,
1, 3, 0, 1, 1, 1, 1, 0, 1, 0, 2, 0, 3, 0, 0, 0, 0, 0, 1), TICS_53=
c(2,
2, 2, 0, 2, 3, 1, 1, 0, 2, 2, 3, 2, 2, 2, 1, 1, 1, 2, 1), TICS_54=
c(3,
0, 3, 2, 2, 2, 3, 3, 1, 2, 0, 4, 0, 2, 0, 2, 2, 0, 2, 1), TICS_55=
c(2,
0, 0, 1, 0, 1, 2, 0, 0, 1, 0, 4, 0, 1, 0, 1, 1, 0, 2, 0), TICS_56=
c(4,
3, 1, 0, 2, 0, 0, 0, 1, 0, 1, 2, 1, 1, 1, 0, 0, 0, 2, 0), TICS_57=
c(2,
1, 1, 0, 2, 1, 0, 0, 1, 1, 1, 4, 3, 0, 0, 1, 1, 0, 0, 2), HSPS_1 =
c(3,
4, 3, 3, 4, 2, 4, 2, 4, 2, 3, 4, 2, 2, 4, 2, 3, 3, 5, 2), HSPS_2 =
c(4,
4, 3, 5, 5, 3, 2, 4, 5, 5, 3, 4, 3, 4, 4, 2, 4, 3, 4, 3), HSPS_3 =
c(4,
4, 4, 3, 3, 4, 3, 3, 3, 3, 3, 5, 3, 4, 5, 3, 3, 3, 4, 2), HSPS_4 =
c(4,
2, 1, 4, 2, 3, 5, 3, 5, 2, 3, 3, 3, 4, 3, 3, 4, 2, 5, 2), HSPS_5 =
c(2,
2, 2, 4, 3, 3, 3, 1, 4, 3, 3, 4, 3, 2, 4, 3, 4, 3, 5, 1), HSPS_6 =
c(4,
3, 1, 3, 4, 3, 3, 3, 3, 2, 1, 1, 1, 3, 5, 3, 3, 1, 1, 2), HSPS_7 =
c(4,
3, 1, 3, 4, 2, 3, 1, 4, 3, 2, 4, 1, 1, 5, 3, 3, 1, 5, 1), HSPS_8 =
c(4,
3, 5, 5, 4, 5, 5, 3, 4, 4, 3, 3, 2, 4, 4, 3, 4, 3, 3, 3), HSPS_9 =
c(3,
2, 2, 5, 3, 3, 4, 1, 5, 2, 2, 4, 1, 2, 4, 4, 3, 1, 5, 2), HSPS_10=
c(4,
4, 5, 4, 4, 4, 3, 1, 4, 3, 3, 4, 2, 1, 5, 3, 4, 4, 3, 2), HSPS_11=
c(3,
2, 2, 3, 2, 2, 3, 1, 3, 2, 4, 5, 1, 3, 3, 3, 3, 2, 3, 2), HSPS_12=
c(4,
4, 5, 5, 4, 5, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 4, 4, 5, 4), HSPS_13=
c(3,
2, 3, 2, 2, 2, 5, 2, 3, 2, 3, 4, 3, 3, 3, 3, 4, 2, 5, 2), HSPS_14=
c(3,
2, 2, 3, 3, 3, 5, 3, 3, 2, 3, 3, 2, 3, 2, 3, 3, 2, 4, 2), HSPS_15=
c(4,
4, 2, 3, 4, 3, 3, 3, 4, 2, 3, 3, 5, 2, 4, 2, 3, 3, 3, 2), HSPS_16=
c(2,
2, 1, 5, 2, 3, 2, 2, 3, 3, 3, 5, 2, 3, 3, 3, 2, 2, 5, 2), HSPS_17=
c(4,
3, 4, 5, 3, 4, 4, 2, 4, 3, 5, 4, 4, 4, 5, 4, 5, 2, 5, 4), HSPS_18=
c(2,
2, 1, 2, 1, 2, 2, 1, 3, 2, 2, 5, 2, 1, 4, 3, 2, 1, 5, 1), HSPS_19=
c(3,
2, 2, 4, 2, 2, 3, 1, 4, 2, 2, 4, 1, 1, 4, 3, 2, 2, 5, 2), HSPS_20=
c(4,
4, 4, 3, 4, 3, 5, 3, 3, 3, 4, 3, 3, 4, 4, 3, 5, 3, 5, 2), HSPS_21=
c(3,
3, 4, 5, 3, 3, 5, 2, 4, 2, 3, 5, 4, 4, 3, 2, 3, 2, 5, 2), HSPS_22=
c(3,
5, 5, 4, 5, 4, 3, 2, 4, 3, 3, 5, 3, 2, 4, 2, 4, 3, 5, 2), HSPS_23=
c(2,
2, 1, 4, 2, 3, 4, 3, 3, 2, 2, 5, 3, 3, 3, 3, 3, 2, 5, 3), HSPS_24=
c(3,
2, 2, 3, 3, 3, 3, 2, 4, 2, 3, 5, 4, 2, 4, 4, 4, 3, 4, 2), HSPS_25=
c(3,
2, 2, 5, 3, 3, 5, 1, 4, 2, 3, 5, 3, 2, 4, 3, 3, 2, 5, 2), HSPS_26=
c(2,
1, 1, 3, 3, 3, 3, 2, 3, 2, 2, 5, 2, 2, 3, 3, 3, 2, 5, 2), HSPS_27=
c(2,
2, 1, 4, 3, 2, 3, 4, 3, 1, 4, 1, 1, 3, 4, 2, 3, 2, 5, 3), sex =
structure(c(2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L), .Label = c("m", "w", "d"), class = "factor"), Bildung =
structure(c(6L,
5L, 5L, 6L, 6L, 6L, 5L, 6L, 5L, 6L, 6L, 4L, 6L, 5L, 5L, 6L, 6L,
5L, 5L, 6L), .Label = c("kein", "Haupt", "mittlereR", "Fachabi",
"Abi", "Studium"), class = "factor"), job = structure(c(6L, 2L,
2L, 2L, 2L, 6L, 2L, 6L, 5L, 2L, 2L, 1L, 6L, 2L, 2L, 2L, 6L, 2L,
2L, 6L), .Label = c("hausl", "Student", "Azubi", "Suchend", "Rente",
"berufstaetig"), class = "factor"), age = c(23, 24, 21, 70, 25,
29, 22, 25, 57, 24, 25, 30, 31, 20, 28, 27, 26, 21, 24, 53),
VPN = 1:20, consent = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label =
c("ja",
"nein"), class = "factor"), psy = c(0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0), HSPS = c(86, 75,
69, 102, 85, 82, 97, 59, 100, 68, 80, 106, 68, 73, 105, 79,
91, 63, 119, 59), neuro = c(16, 3, 4, 10, 10, 11, 12, 5,
5, 5, 5, 16, 5, 18, 4, 3, 8, 5, 19, 7), extra = c(15, 17,
19, 7, 19, 17, 18, 17, 16, 10, 17, 14, 15, 19, 11, 13, 16,
18, 9, 13), TICS_Skala1 = c(23, 1, 22, 11, 14, 16, 22, 25,
2, 11, 1, 29, 9, 20, 10, 19, 16, 9, 18, 16), TICS_Skala2 = c(14,
12, 11, 9, 11, 10, 4, 10, 5, 8, 5, 24, 13, 5, 6, 6, 14, 2,
1, 13), TICS_Skala3 = c(21, 6, 10, 5, 12, 14, 11, 20, 3,
11, 4, 27, 20, 13, 7, 13, 20, 11, 11, 18), TICS_Skala4 = c(13,
14, 13, 2, 16, 23, 10, 9, 3, 13, 15, 18, 14, 11, 13, 10,
7, 9, 17, 6), TICS_Skala5 = c(12, 2, 6, 5, 3, 5, 8, 3, 4,
6, 0, 18, 3, 7, 1, 6, 6, 1, 13, 3), TICS_Skala6 = c(10, 2,
3, 4, 4, 6, 3, 0, 0, 5, 2, 15, 10, 5, 2, 1, 5, 2, 8, 3),
TICS_Skala7 = c(15, 5, 9, 13, 4, 8, 4, 9, 1, 6, 2, 11, 2,
12, 3, 2, 1, 3, 2, 7), TICS_Skala8 = c(8, 10, 3, 0, 11, 7,
2, 1, 2, 2, 7, 20, 7, 2, 2, 2, 1, 1, 2, 3), TICS_Skala9 = c(12,
3, 4, 8, 8, 6, 9, 5, 2, 6, 5, 11, 3, 11, 1, 5, 9, 3, 7, 5
), TICS_Skala10 = c(32, 5, 18, 16, 19, 18, 21, 16, 5, 17,
7, 39, 12, 24, 3, 15, 20, 6, 25, 14), neuro.c = c(6.08921933085502,
-6.91078066914498, -5.91078066914498, 0.089219330855018,
0.089219330855018, 1.08921933085502, 2.08921933085502,
-4.91078066914498,
-4.91078066914498, -4.91078066914498, -4.91078066914498,
6.08921933085502, -4.91078066914498, 8.08921933085502,
-5.91078066914498,
-6.91078066914498, -1.91078066914498, -4.91078066914498,
9.08921933085502, -2.91078066914498), extra.c = c(5.21003717472119,
7.21003717472119, 9.21003717472119, -2.78996282527881,
9.21003717472119,
7.21003717472119, 8.21003717472119, 7.21003717472119,
6.21003717472119,
0.21003717472119, 7.21003717472119, 4.21003717472119,
5.21003717472119,
9.21003717472119, 1.21003717472119, 3.21003717472119,
6.21003717472119,
8.21003717472119, -0.78996282527881, 3.21003717472119), age.c =
c(-15.4460966542751,
-14.4460966542751, -17.4460966542751, 31.5539033457249,
-13.4460966542751,
-9.4460966542751, -16.4460966542751, -13.4460966542751,
18.5539033457249,
-14.4460966542751, -13.4460966542751, -8.4460966542751,
-7.4460966542751,
-18.4460966542751, -10.4460966542751, -11.4460966542751,
-12.4460966542751, -17.4460966542751, -14.4460966542751,
14.5539033457249), HSP.c = c(-1.92936802973978, -12.9293680297398,
-18.9293680297398, 14.0706319702602, -2.92936802973978,
-5.92936802973978,
9.07063197026022, -28.9293680297398, 12.0706319702602,
-19.9293680297398,
-7.92936802973978, 18.0706319702602, -19.9293680297398,
-14.9293680297398,
17.0706319702602, -8.92936802973978, 3.07063197026022,
-24.9293680297398,
31.0706319702602, -28.9293680297398), HSPhoch = c(1, 0, 0,
1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0)), row.names =
c(NA, 20L), class = "data.frame")

Plotly does not plot a non-quadratic surface

I want to produce a 3D scatterplot and add a surface fitted with a linear regression, using plotly. My data:
structure(list(political_trust = c(1, 6, 7, 5, 0, 2, 1, 3, 5,
0, 2, 5, 5, 6, 6, 3, 3, 2, 5, 8, 3, 7, 3, 4, 5, 4, 5, 0, 0, 4,
6, 1, 0, 4, 0, 5, 5, 6, 7, 3, 5, 4, 5, 2, 4, 4, 7, 6, 7, 5, 4,
6, 7, 5, 7, 3, 3, 3, 2, 5, 2, 7, 3, 2, 7, 2, 3, 0, 7, 5, 7, 3,
0, 7, 2, 6, 3, 8, 7, 2, 2, 5, 0, 1, 6, 3, 6, 5, 1, 3, 4, 4, 5,
3, 3, 0, 2, 4, 9, 6, 3, 3, 2, 3, 4, 5, 8, 0, 4, 1, 5, 0, 4, 0,
5, 6, 3, 2, 7, 5, 4, 3, 8, 3, 4, 0, 3, 6, 7, 7, 2, 3, 5, 5, 5,
0, 3, 2, 1, 7, 5, 0, 4, 0, 2, 7, 3, 0, 8, 3, 2, 4, 5, 5, 3, 2,
3, 8, 6, 5, 6, 7, 0, NA, 7, 7, 2, 0, 3, 4, 7, 2, 1, 2, 0, 0,
4, 3, 3, 6, 6, 1, 4, 0, 4, 0, 0, 7, 6, 4, 4, 6, 5, 4, 3, 3, 0,
NA, 2, 5), political_interest = c(2, 0, 3, 3, 2, 1, 2, 2, 2,
2, 2, 2, 3, 3, 3, 3, 2, 2, 3, 2, 1, 2, 2, 2, 2, 0, 2, 1, 3, 1,
1, 1, 1, 1, 2, 3, 2, 2, 2, 1, 3, 3, 2, 3, 2, 1, 3, 2, 0, 3, 1,
1, 2, 1, 2, 2, 1, 3, 3, 2, 3, 2, 3, 2, 2, 1, 2, 0, 3, 1, 2, 2,
1, 3, 2, 2, 1, 2, 2, 0, 3, 2, 2, 1, 2, 1, 1, 3, 1, 1, 3, 2, 0,
2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 0, 1, 1, 2, 2, 2, 2,
2, 0, 0, 2, 3, 2, 2, 2, 3, 3, 0, 3, 3, 1, 2, 1, 1, 1, 2, 3, 2,
2, 2, 0, 2, 2, 2, 1, 2, 3, 3, 1, 2, 0, 1, 1, 0, 2, 2, 1, 2, 2,
2, 2, 3, 2, 1, 2, 2, 0, 0, 3, 2, 2, 2, 1, 2, 3, 0, 1, 2, 3, 2,
2, 2, 1, 3, 1, 1, 2, 2, 3, 3, 1, 2, 2, 2, 2, 2, 1, 0, 1, 1, 0,
3, 3), education_level = c(0, 2, 1, 5, 5, 0, 4, 4, 0, 0, 3, 2,
3, 4, 0, 4, 4, 4, 4, 3, 0, NA, 4, 0, 4, 3, 4, 1, 5, 2, NA, 0,
0, 4, 3, 3, 5, 3, 4, 0, 4, 4, 0, 4, 5, 4, 2, 2, 0, 5, 3, 0, 4,
1, 5, 4, 0, 4, 4, 5, 5, 4, 4, 4, 5, 2, 3, 2, 4, 0, 4, 0, 5, 4,
4, 4, 4, 4, 4, 2, 4, 5, 3, 4, 3, 0, 4, 4, 4, 3, 4, 4, 0, 3, 4,
2, 3, 3, 0, 4, 4, 4, 5, 4, 0, 4, 4, 4, 0, 3, 1, 4, NA, 4, 0,
1, 2, 4, 0, 2, 1, 4, 4, 4, 3, NA, 5, 2, 1, 0, 0, 4, 3, 3, 4,
3, 0, 3, NA, 4, 0, 0, 4, 5, 4, 5, 2, 2, 0, 3, 4, 3, 1, 3, 2,
3, 5, 0, 4, 5, 0, 5, 2, 0, 3, NA, NA, 2, 4, 3, 4, 3, 2, 2, 4,
4, 3, 0, 4, 0, 4, 4, 3, 0, 4, 4, 3, 5, 0, 3, 0, 4, 3, 0, 3, 3,
3, 4, 5, 1)), row.names = c(NA, -200L), class = "data.frame")
I start by defining a list of relevant variables - this is not necessary but basically a consequence of using the code in a Shiny up:
input <- list()
input$x <- "education_level"
input$y <- "political_trust"
input$z <- "political_interest"
Next, creating the surface data:
# Regressing "political_interest" on "education_level" and "political_trust":
lm <- lm(as.formula(paste0(input$z, " ~ ", input$x, " + ", input$y)), data)
# Defining range of values that outcome will be predicted for
axis_x <- seq(min(data[, input$x], na.rm = T),
max(data[, input$x], na.rm = T), by = 0.2)
axis_y <- seq(min(data[, input$y], na.rm = T),
max(data[, input$y], na.rm = T), by = 0.2)
# Predicting outcome, and getting data into surface format
lm_surface <- expand.grid(x = axis_x, y = axis_y, KEEP.OUT.ATTRS = F)
colnames(lm_surface) <- c(input$x, input$y)
lm_surface <- acast(lm_surface, as.formula(paste0(input$x, " ~ ", input$y)),
value.var = input$z)
Last, plotting this with plotly:
data %>%
filter(!is.na(get(input$z))) %>%
filter(!is.na(get(input$x))) %>%
filter(!is.na(get(input$y))) %>%
plot_ly(., x = ~jitter(get(input$x), factor = 2.5),
y = ~jitter(get(input$y), factor = 2.5),
z = ~jitter(get(input$z), factor = 2.5),
type = "scatter3d", mode = "markers",
marker = list(size = 2, color = "#cccccc")) %>%
add_surface(., z = lm_surface,
x = axis_x,
y = axis_y,
type = "surface")
This gives me the following. As you can see, the surface does not cover the full range of the y-dimension. Note also that the surface plotted is "quadratic" - i.e. same length in x and y - although it should have non-quadratic dimensions.
I can bring plotly to draw larger surface area, e.g. by changing the range of values like below, but it always stays quadratic.
axis_x <- seq(0, 10, by = 0.2)
axis_y <- seq(0, 10, by = 0.2)

Ok, question solved.
It's important which dimension of the surface matrix (lm_surface) is which. Swapping x and y when applying acast fixes the issue:
lm_surface <- acast(lm_surface, as.formula(paste0(input$y, " ~ ", input$x)),
value.var = input$z)

R Multiple T-test: Grouping factor must have 2 variables

I'm trying to compare a control group with an experimental group on a range of variable to show that they are similar (baseline).
I thus need to do multiple t-test (unpaired/ Welch t-test). My data is in a long format with the first variable called "Group" with either a number 1 or a number 2. There are some missing values in some of my other variables but it's pretty random.
So when I run t-test manually using this line of code:
t.test(variable_1 ~ Group,df)
it works.
I then tried to do it all at once using this line of code:
sapply(df[,2:71], function(i) t.test(i ~ df$Group)$p.value)
But I get the following error:
grouping factor must have exactly 2 levels
Could anyone help?
Here is what the structure looks like
structure(list(Group = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 2, 2), EM_Accuracy_Time_Airport = c(3, 3, 0,
1, 1, 2, 2, 1, 1, 3, 3, 2, 2, 2, 1, 3, 1, 3, 1, 1), EM_Accuracy_Place_Airport = c(2,
2, 1, 2, 1, 2, 2, 1, 1, 2, 0, 2, 2, 0, 2, 2, 2, 1, 1, 1), EM_Accuracy_Expl_Airport = c(2,
2, 2, 0, 2, 2, 2, 1, 2, 2, 2, 2, 2, 0, 0, 1, 0, 2, 2, 1), EM_Accuracy_Death_Airport = c(0,
2, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0), EM_Accuracy_Time_Metro = c(3,
1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 2, 1, 3, 1, 1, 2, 1, 3, 3), EM_Accuracy_Death_Metro = c(3,
0, 1, 0, 1, 1, 0, 0, 0, 3, 0, 0, 1, 0, 3, 1, 1, 1, 0, 0), EM_Accuracy_PC_Time_Airpot = c(100,
100, 0, 33.3333333333333, 33.3333333333333, 66.6666666666667,
66.6666666666667, 33.3333333333333, 33.3333333333333, 100, 100,
66.6666666666667, 66.6666666666667, 66.6666666666667, 33.3333333333333,
100, 33.3333333333333, 100, 33.3333333333333, 33.3333333333333
), EM_Accuracy_PC_Place_Airport = c(100, 100, 50, 100, 50, 100,
100, 50, 50, 100, 0, 100, 100, 0, 100, 100, 100, 50, 50, 50),
EM_Accuracy_PC_Expl_Airport = c(100, 100, 100, 0, 100, 100,
100, 50, 100, 100, 100, 100, 100, 0, 0, 50, 0, 100, 100,
50), EM_Accuracy_PC_Death_Airport = c(0, 66.6666666666667,
0, 0, 33.3333333333333, 66.6666666666667, 0, 0, 0, 0, 0,
0, 66.6666666666667, 0, 0, 0, 100, 0, 0, 0), EM_Accuracy_PC_Time_Metro = c(100,
33.3333333333333, 0, 0, 33.3333333333333, 33.3333333333333,
0, 33.3333333333333, 33.3333333333333, 33.3333333333333,
33.3333333333333, 66.6666666666667, 33.3333333333333, 100,
33.3333333333333, 33.3333333333333, 66.6666666666667, 33.3333333333333,
100, 100), EM_Accuracy_PC_Death_Metro = c(100, 0, 33.3333333333333,
0, 33.3333333333333, 33.3333333333333, 0, 0, 0, 100, 0, 0,
33.3333333333333, 0, 100, 33.3333333333333, 33.3333333333333,
33.3333333333333, 0, 0), EM_ACCURACY_PC = c(83.3333333333333,
66.6666666666667, 30.5555555555556, 22.2222222222222, 47.2222222222222,
66.6666666666666, 44.4444444444444, 27.7777777777778, 36.1111111111111,
72.2222222222222, 38.8888888888889, 55.5555555555555, 66.6666666666666,
27.7777777777778, 44.4444444444444, 52.7777777777778, 55.5555555555556,
52.7777777777778, 47.2222222222222, 38.8888888888889), EM_Certainty_Time_Airport = c(3,
1, 1, 1, 2, 2, 1, 1, 2, 3, 3, 2, 2, 2, 4, 2, 3, 3, 2, 2),
EM_Certainty__Place_Airport = c(3, 4, 2, 2, 2, 2, 4, 1, 3,
4, 4, 4, 4, 3, 3, 4, 4, 3, 2, 3), EM_Certainty__Expl_Airport = c(4,
2, 3, 1, 2, 3, 2, 1, 2, 4, 1, 3, 2, 2, 1, 3, 1, 2, 2, 3),
EM_Certainty__Death_Airport = c(1, 1, NA, 1, 2, 1, 3, 1,
2, 3, NA, 3, 2, 1, 2, 1, 1, 1, 4, 4), EM_Certainty__Time_Metro = c(3,
3, 1, 1, 2, 2, 2, 1, 3, 2, 3, 2, 3, 2, 2, 2, 3, 1, 2, 2),
EM_Certainty__Death_Metro = c(2, 1, 1, NA, 2, 1, 1, 1, 2,
1, NA, 3, 2, 1, 1, 1, 1, 1, 1, 4), EM_CERTAINTY = c(2.66666666666667,
2, 1.6, 1.2, 2, 1.83333333333333, 2.16666666666667, 1, 2.33333333333333,
2.83333333333333, 2.75, 2.83333333333333, 2.5, 1.83333333333333,
2.16666666666667, 2.16666666666667, 2.16666666666667, 1.83333333333333,
2.16666666666667, 3), EM_CONFIDENCE = c(5, 5, 1, 2, 2, 4,
5, 2, 3, 4, 5, 5, 3, 3, 4, 4, 3, 2, 3, 2), FBM_CONFIDENCE = c(4,
6, 7, 7, 5, 4, 2, 7, 5, 6, 6, 7, 6, 7, 3, 6, 6, 4, 5, 6),
FBM_Vividness_Time = c(3, 3, 1, 4, 3, 2, 4, 3, 4, 4, 1, 3,
4, 4, 3, 3, 3, 2, 4, 3), FBM_Vividness_How = c(4, 4, 2, 4,
4, 3, 4, 4, 4, 4, 3, 4, 3, 4, 4, 4, 4, 4, 4, 4), FBM_Vividness_Where = c(4,
4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4),
FBM_Vividness_WithWhom = c(4, 4, 3, 4, 3, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4), FBM_Vividness_WereDoing = c(4,
4, 1, 4, 3, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4),
FBM_Vividness_Did_After = c(4, 4, 3, 4, 2, 3, 4, 4, 2, 4,
1, 4, 4, 4, 3, 4, 4, 3, 4, 4), FBM_VIVIDNESS = c(3.83333333333333,
3.83333333333333, 2, 4, 3.16666666666667, 3.33333333333333,
4, 3.83333333333333, 3.66666666666667, 4, 2.33333333333333,
3.83333333333333, 3.83333333333333, 4, 3.66666666666667,
3.83333333333333, 3.83333333333333, 3.5, 4, 3.83333333333333
), FBM_Details_NB_T2 = c(3, 5, 0, 5, 5, 5, 2, 5, 1, 5, 3,
5, 5, 5, 2, 4, 2, 3, 5, 5), P_Novelty_5 = c(5, 6.2, 6.5,
5.6, 4.8, 5.4, 4, 4.2, 4.4, 5.8, 3.4, 5.8, 6, 5.8, 3.8, 6.4,
6.8, 6.6, 7, 3), P_Suprise_emotion = c(6, 6, 6, 6, 4, 5,
1, 7, 1, 5, 4, 5, 7, 7, 6, 4, 7, 7, 2, 5), P_Surprise_Expected = c(1,
3, 5, 2, 4, 3, 6, 2, 2, 1, 6, 4, 3, 1, 5, 1, 1, 1, 5, 4),
P_Surprise_Unbelievable = c(5, 4, 1, 6, 4, 4, 2, 7, 1, 4,
1, 6, 7, 7, 6, 3, 7, 7, 5, 3), `P_Consequence-Importance_5` = c(5.6,
4.8, 3.4, 5, 4.8, 4, 5, 5.4, 3, 5.2, 6.8, 5.4, 4, 4.4, 6,
3.8, 4, 4.8, 5, 5.2), P_Emotional_Intensity_4 = c(5.25, 5.75,
3, 4.75, 4.75, 6, 4, 5.25, 2.5, 5.5, 7, 6.5, 5.75, 6.75,
6.75, 6, 6.25, 6, 5, 2.5), P_Social_Sharing_6 = c(3.66666666666667,
3.83333333333333, 3.4, 3.16666666666667, 3, 3.33333333333333,
3.8, 3.16666666666667, 2.16666666666667, 4.16666666666667,
4, 4.5, 4.5, 4.33333333333333, 4, 3.16666666666667, 3.66666666666667,
4, NA, NA), P_Media_3 = c(4.66666666666667, 4, 3, 2.66666666666667,
2.66666666666667, 2.33333333333333, 3, 2.33333333333333,
2.33333333333333, 3.33333333333333, 4.33333333333333, 5,
4.33333333333333, 5, 4, 2, 3, 3.33333333333333, 2, 1.66666666666667
), P_Ruminations = c(3, NA, 3, 2, 4, NA, 4, 2, 1, 4, 4, 4,
2, 4, 2, 3, 3, 3, 4, 3), P_Novelty_Common_rev = c(6, 7, 7,
7, 4, 6, 4, 7, 2, 6, 3, 7, 7, 7, 3, 6, 7, 7, 7, 3), P_Novelty_Unusual = c(2,
5, 7, 7, 3, 5, 3, 3, 5, 6, 1, 4, 7, 1, 4, 6, 6, 6, 7, 2),
P_Novelty_Special = c(6, 6, NA, 6, 5, 5, 4, 3, 5, 4, 1, 5,
6, 7, 4, 6, 7, 7, 7, 3), P_Novelty_Singular = c(4, 6, 5,
1, 5, 5, 4, 1, 3, 6, 5, 6, 4, 7, 3, 7, 7, 6, 7, 2), P_Novelty_Ordinary_rev = c(7,
7, 7, 7, 7, 6, 5, 7, 7, 7, 7, 7, 6, 7, 5, 7, 7, 7, 7, 5),
P_Consequence = c(6, 7, 5, 4, 5, 4, 5, 3, 5, 5, 7, 5, 5,
2, 6, 6, 1, 4, 6, 3), P_Importance_self = c(4, 3, 3, 4, 4,
3, 5, 6, 1, 5, 7, 5, 3, 3, 5, 2, 2, 4, 5, 3), `P_Importance_friends&family` = c(4,
4, 3, 4, 4, 4, 4, 6, 1, 5, 6, 5, 3, 3, 5, 2, 6, 4, 5, 10),
P_Importance_Belgium = c(7, 5, 3, 7, 6, 5, 6, 7, 3, 7, 7,
7, 5, 7, 7, 5, 6, 7, 6, 6), P_Importance_International = c(7,
5, 3, 6, 5, 4, 5, 5, 5, 4, 7, 5, 4, 7, 7, 4, 5, 5, 3, 4),
P_Emotional_Intensity_Upset = c(4, 5, NA, 3, 3, 5, 3, 5,
2, 5, 7, 5, 5, 6, 7, 6, 6, 5, 5, 3), P_Emotional_Intensity_Indiferent_rev = c(7,
7, 5, 7, 6, 7, 4, 6, 4, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, 4),
P_Emotional_Intensity_Affected = c(6, 6, 3, 5, 5, 6, 5, 6,
2, 5, 7, 7, 5, 7, 7, 6, 6, 6, NA, 2), P_Emotional_Intensity_Shaken = c(4,
5, 1, 4, 5, 6, 4, 4, 2, 5, 7, 7, 6, 7, 6, 5, 6, 6, 5, 1),
P_Rehearsal_Media_TV = c(5, 3, NA, 3, 2, 3, NA, 1, 1, 4,
3, 5, 5, 5, 2, 3, 2, 2, 2, 2), P_Rehearsal_Media_Internet = c(4,
4, 1, 3, 2, 2, 2, 4, 3, 2, 5, 5, 3, 5, 5, 1, 5, 4, 2, 1),
P_Rehearsal_Media_Social_Networks = c(5, 5, 5, 2, 4, 2, 4,
2, 3, 4, 5, 5, 5, 5, 5, 2, 2, 4, 2, 2), P_Social_Sharing_How_Often = c(4,
5, 4, 4, 4, 3, 3, 3, 3, 5, 4, 5, 5, 5, 5, 3, 4, 4, 5, NA),
P_Social_Sharing_With_How_Many_People = c(5, 4, NA, 3, 3,
3, 3, 3, 2, 5, 3, 5, 5, 3, 5, 3, 3, 4, 3, NA), PK_Shops_YN = c(0,
1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1),
PK_Comic = c(0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 1, 0), PK_Hotel = c(0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
0, 0, 1, 1, 0, 0, 0, 0, 0, 0), PK_Decoration_Maelbeek = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1),
PK_Stations_before_after_Maelbeek = c(0, 0.5, 0, 0, 0, 0,
0, 0, 0.5, 1, 0, 0, 0.5, 0.5, 0, 0, 0.5, 0, 0.5, 0), PK_TOTAL_PC = c(0,
50, 0, 40, 40, 40, 20, 0, 10, 60, 20, 40, 90, 70, 20, 0,
30, 20, 70, 40), SI_Attachment_BXL = c(6, 4, 1, 4, 2, 5,
1, 6, 5, 4, 2, 6, 6, 7, 1, 3, 6, 4, 5, 4), SI_Pride_BXL = c(1,
2, 1, 2, 1, 2, 1, 5, 1, 6, 1, 1, 7, 7, 1, 2, 6, 1, 3, 3),
SI_Attachment_Belgium = c(7, 3, 5, 5, 4, 6, 7, 6, 5, 6, 7,
7, 7, 7, 5, 6, 7, 6, 4, 2), SI_Pride_Belgium = c(7, 2, 6,
4, 2, 6, 4, 5, 1, 5, 1, 6, 7, 7, 5, 7, 7, 6, 2, 2), SI_Attachment_EU = c(6,
4, 2, 5, 4, 4, 5, 4, 7, 4, 1, 6, 7, 7, 5, 4, 6, 6, 2, 6),
SI_Pride_EU = c(7, 1, 1, 4, 3, 4, 4, 4, 1, 4, 1, 6, 7, 7,
4, 3, 6, 6, 2, 4)), .Names = c("Group", "EM_Accuracy_Time_Airport",
"EM_Accuracy_Place_Airport", "EM_Accuracy_Expl_Airport", "EM_Accuracy_Death_Airport",
"EM_Accuracy_Time_Metro", "EM_Accuracy_Death_Metro", "EM_Accuracy_PC_Time_Airpot",
"EM_Accuracy_PC_Place_Airport", "EM_Accuracy_PC_Expl_Airport",
"EM_Accuracy_PC_Death_Airport", "EM_Accuracy_PC_Time_Metro",
"EM_Accuracy_PC_Death_Metro", "EM_ACCURACY_PC", "EM_Certainty_Time_Airport",
"EM_Certainty__Place_Airport", "EM_Certainty__Expl_Airport",
"EM_Certainty__Death_Airport", "EM_Certainty__Time_Metro", "EM_Certainty__Death_Metro",
"EM_CERTAINTY", "EM_CONFIDENCE", "FBM_CONFIDENCE", "FBM_Vividness_Time",
"FBM_Vividness_How", "FBM_Vividness_Where", "FBM_Vividness_WithWhom",
"FBM_Vividness_WereDoing", "FBM_Vividness_Did_After", "FBM_VIVIDNESS",
"FBM_Details_NB_T2", "P_Novelty_5", "P_Suprise_emotion", "P_Surprise_Expected",
"P_Surprise_Unbelievable", "P_Consequence-Importance_5", "P_Emotional_Intensity_4",
"P_Social_Sharing_6", "P_Media_3", "P_Ruminations", "P_Novelty_Common_rev",
"P_Novelty_Unusual", "P_Novelty_Special", "P_Novelty_Singular",
"P_Novelty_Ordinary_rev", "P_Consequence", "P_Importance_self",
"P_Importance_friends&family", "P_Importance_Belgium", "P_Importance_International",
"P_Emotional_Intensity_Upset", "P_Emotional_Intensity_Indiferent_rev",
"P_Emotional_Intensity_Affected", "P_Emotional_Intensity_Shaken",
"P_Rehearsal_Media_TV", "P_Rehearsal_Media_Internet", "P_Rehearsal_Media_Social_Networks",
"P_Social_Sharing_How_Often", "P_Social_Sharing_With_How_Many_People",
"PK_Shops_YN", "PK_Comic", "PK_Hotel", "PK_Decoration_Maelbeek",
"PK_Stations_before_after_Maelbeek", "PK_TOTAL_PC", "SI_Attachment_BXL",
"SI_Pride_BXL", "SI_Attachment_Belgium", "SI_Pride_Belgium",
"SI_Attachment_EU", "SI_Pride_EU"), row.names = c(NA, -20L), class = c("tbl_df",
"tbl", "data.frame"))

The error you get means that there's a problem in your dataset, with at least one of your variables.
Here's a process to help you spot problematic variables:
library(tidyverse)
df %>%
group_by(Group) %>% # for each group value
summarise_all(~sum(!is.na(.))) %>% # count non NA values for each variable
gather(var,value,-Group) %>% # reshape
spread(Group, value, sep = "_") %>% # reshape
filter(Group_2 < 2) # get problematic variables
# # A tibble: 5 x 3
# var Group_1 Group_2
# <chr> <int> <int>
# 1 P_Emotional_Intensity_Affected 18 1
# 2 P_Emotional_Intensity_Indiferent_rev 18 1
# 3 P_Social_Sharing_6 18 0
# 4 P_Social_Sharing_How_Often 18 1
# 5 P_Social_Sharing_With_How_Many_People 17 1
0 counts will throw an error about needing two levels in your grouping variables.
1 count will throw an error about needing more observations in one of your groups.
After spotting those you have to treat them accordingly and then your original t.test code should work.

So my problem was just missing data in one variable.
However, if you are looking at doing multiple T-test in a long format: this line of code works:
sapply(df[,2:71], function(i) t.test(i ~ df$Group)$p.value)

R: how to count the number of times two elements have the same ID (perhaps using the outer function)

I have the following three dimensional array:
dput(a)
structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 2, 1, 1, 1, 2, 2,
2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 6, 2, 7, 6, 2, 7, 6, 2, 7, 4, 2, 4, 4, 2, 6, 4, 2, 4, 6, 2,
7, 4, 2, 6, 4, 2, 6, 4, 2, 6, 4, 2, 4, 4, 2, 6, 4, 2, 4, 4, 2,
6, 4, 2, 6, 4, 2, 6, 6, 2, 7, 4, 2, 6, 4, 2, 6, 4, 2, 4, 2, 3,
1, 2, 3, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 3, 7, 2, 3,
7, 2, 3, 7, 2, 3, 7, 2, 3, 7, 2, 3, 7, 2, 3, 7, 2, 3, 7, 2, 3,
7, 2, 3, 7, 1, 2, 5, 2, 3, 7, 1, 2, 4, 2, 3, 7, 2, 3, 7, 2, 3,
7, 2, 3, 7, 2, 3, 7, 2, 3, 7, 2, 3, 7, 2, 6, 3, 2, 6, 3, 2, 6,
3, 2, 6, 3, 2, 6, 3, 2, 6, 3, 2, 6, 3, 2, 6, 3, 2, 6, 3, 2, 6,
3, 1, 1, 1, 2, 6, 3, 1, 5, 5, 2, 6, 3, 2, 6, 3, 2, 6, 3, 2, 6,
3, 2, 6, 3, 2, 6, 3, 2, 6, 3, 3, 3, 2, 3, 3, 2, 3, 3, 2, 3, 13,
2, 3, 13, 2, 3, 5, 2, 3, 5, 2, 15, 17, 2, 15, 17, 2, 15, 17,
2, 3, 5, 2, 15, 17, 2, 3, 13, 2, 15, 17, 2, 15, 17, 2, 3, 13,
2, 3, 5, 2, 15, 17, 2, 15, 17, 2, 3, 5, 2), .Dim = c(3L, 20L,
6L), .Dimnames = list(c("cl.tmp", "cl.tmp", "cl.tmp"), NULL,
NULL))
The dimension of this array (a) is 3x20x6 (after edits).
I wanted to count the proportion of times that a[,i,] matches a[,j,] element-by-element in the matrix. Basically, I wanted to get mean(a[,i,] == a[,j,]) for all i, j, and I would like to do this fast but in R.
It occurred to me that the outer function might be a possibility but I am not sure how to specify the function. Any suggestions, or any other alternative ways?
The output would be a 20x20 symmetric matrix of nonnegative elements with 1 on the diagonals.
The solution given below works (thanks!) but I have one further question (sorry).
I would like to display the coordinates above in a heatmap. I try the following:
n<-dim(a)[2]
xx <- matrix(apply(a[,rep(1:n,n),]==a[,rep(1:n,each=n),],2,sum),nrow=n)/prod(dim(a)[-2])
image(1:20, 1:20, xx, xlab = "", ylab = "")
This gives me the following heatmap.
However, I would like to display (reorder the coordinate) such that I get all the coordinates that have high-values amongst each other together. However, I would not like to bias the results by deciding on the number of groups myself. I tried
hc <- hclust(as.dist(1-xx), method = "single")
but I can not decide how to cut the resulting tree to decide on bunching the coordinates together. Any suggestions? Bascically, in the figure, I would like the coordinate pairs in the top left (and bottom right off-diagonal blocks) to be as low-valued (in this case as red) as possible.
Looking around on SO, I found that there exists a function heatmap which might do this,
heatmap(xx,Colv=T,Rowv=T, scale='none',symm = T)
and I get the following:
which is all right, but I can not figure out how to get rid of the dendrograms on the sides or the axes labels. It does work if I extract out and do the following:
yy <- heatmap(xx,Colv=T,Rowv=T, scale='none',symm = T,keep.dendro=F)
image(1:20, 1:20, xx[yy$rowInd,yy$colInd], xlab = "", ylab = "")
so I guess that is what I will stick with. Here is the result:

Try this:
n<-dim(a)[2]
matrix(apply(a[,rep(1:n,n),]==a[,rep(1:n,each=n),],2,sum),nrow=n)/prod(dim(a)[-2])
It has to be stressed that the memory usage of this method goes with n^2 so you might have trouble to use it with larger arrays.

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