Is there a way in R to specify which column in my data is groups and which is blocks in order to do a Friedman's test? I am comparing results to SPSS

I will give a sample below of how my data is organized but every time I run Friedman’s using frieman.test(y =, groups = , blocks= ) it gives me an error that my data is not from an unreplicated complete block design despite the fact that it is.

library(readr)

sample_data$treatment <- as.factor(sample_data$treatment) #setting treatment as categorical independent variable

sample_data$day <- as.factor(sample_data$day) #setting day as categorical independent variable

summary(sample_data)

friedman3 <- friedman.test(y = sample_data$score, groups = sample_data$treatment, blocks = sample_data$day)

summary(friedman3)

the code above gives me the error I described earlier.

However when I convert the csv data to a matrix, Friedman’s works but the answer seems wrong as SPSS gives a different result for the degrees of freedom.

sample_data$treatment <- as.factor(sample_data$treatment) #converting to categorical independent variable
sample_data$day <- as.factor(sample_data$day)  #converting to categorical independent variable
data = as.matrix(sample_data)
friedman.test(data)
friedman2 <- friedman.test(y = data$score, groups = data$treatment, blocks = data$day)
summary(friedman2)

Any idea what I am doing incorrectly?

I am aware that Friedman’s gives me chi-square but I am also wondering how can I get the test statistic instead of the chi-square value.

I am using Rstudio and I am new to R. And I want to know how to specify groups as treatment and day as blocks.

>Solution :

We could summarise the data by taking the mean of the ‘score’ and then use that summarised data in friedman.test

sample_data1 <- aggregate(score ~ ., sample_data, FUN = mean)
friedman.test(sample_data1$score, groups = sample_data1$treatment, 
    blocks = sample_data1$day)