I have a data frame with ratings made by 4 different reviewers; each row is a reviewer pair rating an image.
df <- data.frame(Reviewer1 = c("Name1", "Name2", "Name3", "Name4", "Name2", "Name3", "Name1", "Name3", "Name1", "Name4", "Name1", "Name1", "Name1", "Name2", "Name3", "Name4", "Name2", "Name3", "Name1", "Name2", "Name1", "Name4", "Name1", "Name1", "Name3", "Name2", "Name4", "Name3", "Name1", "Name2", "Name1", "Name3", "Name4", "Name3", "Name2", "Name2", "Name2", "Name3", "Name1", "Name3", "Name3", "Name1", "Name4", "Name2", "Name3", "Name4", "Name4", "Name3", "Name4"),
Rating1 = c("Worst", "Worst", "Best", "Bad", "Good", "Worst", "Best", "Worst", "Best", "Bad", "Worst", "Worst", "Worst", "Good", "Best", "Bad", "Good", "Worst", "Best", "Worst", "Best", "Bad", "Worst", "Worst", "Best", "Worst", "Worst", "Good", "Bad", "Worst", "Good", "Bad", "Worst", "Worst", "Worst", "Good", "Good", "Bad", "Good", "Good", "Bad", "Worst", "Good", "Worst", "Worst", "Worst", "Worst", "Good", "Good"),
Reviewer2 = c("Name3", "Name1", "Name1", "Name1", "Name4", "Name4", "Name2", "Name4", "Name2", "Name2", "Name2", "Name2", "Name3", "Name1", "Name1", "Name1", "Name4", "Name4", "Name2", "Name3", "Name3", "Name2", "Name2", "Name2", "Name1", "Name4", "Name3", "Name1", "Name2", "Name3", "Name3", "Name1", "Name2", "Name4", "Name4", "Name1", "Name4", "Name2", "Name3", "Name4", "Name1", "Name3", "Name2", "Name3", "Name1", "Name2", "Name3", "Name2", "Name3"),
Rating2 = c("Best", "Good", "Worst", "Good", "Best", "Worst", "Best", "Worst", "Worst", "Best", "Worst", "Worst", "Best", "Worst", "Bad", "Worst", "Best", "Worst", "Best", "Worst", "Worst", "Best", "Worst", "Worst", "Best", "Worst", "Worst", "Good", "Bad", "Worst", "Good", "Bad", "Worst", "Worst", "Worst", "Good", "Good", "Bad", "Good", "Good", "Bad", "Worst", "Good", "Bad", "Worst", "Worst", "Worst", "Good", "Worst"))
My end goal is to create contingency tables for Cohen's Kappa analyses of each reviewer pair. For that, I need counts of reviewer pair ratings with the following rules:
- Not include pairs that do not exist (e.g., Reviewer1 Name1 and Reviewer2 Name4; note that Reviewer1 Name4 and Reviewer2 Name1 does exist)
- Reviewer pairings with themselves are still included in the first loop output; ideally, these wouldn't be included
- I'd like to have the reviewer pair names (or i and j integers) in the first and second column in the output in the first loop if possible
Because a given rater can function as Reviewer1 as well as Reviewer2 in a given pair, I also need to sum the data where they functioned as both, e.g., sum the YY count for Name2 as Reviewer1 and Name3 as Reviewer2 with Name3 as Reviewer1 and Name2 as Reviewer2. How to do that?
Thank you so much for your help in advance!
EDIT: I've made some changes to the code below that enable adding the reviewer pairs to the output (last point) and remove the reviewer pairings with themselves (second point) though still ideally they wouldn't be in the first loop output at all.
# Prep for the first loop
plist <- unique(df$Reviewer1) # Get count of Names
pseq <- seq(1, length(plist), by = 1) # Create sequence to use numbers for the loop instead of the reviewer names
pmap <- data.frame(pseq, plist) # Map numbers to names
# Initialize empty lists
NN <- c()
YY <- c()
YN <- c()
NY <- c()
ind <- vector()
pairs <- data.frame()
# Loop over pairs
for(i in pseq) {
for(j in pseq) {
if (i!=j)
ind <- c(i,j)
pairs <- rbind(pairs, ind)
NN[j length(plist)*(i-1)] <- count(df[which(df$Reviewer1==pmap[i,2] & df$Rating1=='Worst' &
df$Reviewer2==pmap[j,2] & df$Rating2=='Worst'), ])
YY[j length(plist)*(i-1)] <- count(df[which(df$Reviewer1==pmap[i,2] & df$Rating1!='Worst' &
df$Reviewer2==pmap[j,2] & df$Rating2!='Worst'), ])
YN[j length(plist)*(i-1)] <- count(df[which(df$Reviewer1==pmap[i,2] & df$Rating1!='Worst' &
df$Reviewer2==pmap[j,2] & df$Rating2=='Worst'), ])
NY[j length(plist)*(i-1)] <- count(df[which(df$Reviewer1==pmap[i,2] & df$Rating1=='Worst' &
df$Reviewer2==pmap[j,2] & df$Rating2!='Worst'), ])
}
}
# Remove the first row as that's Reviewer1 with themselves
NN <- NN[-(1)]
YY <- YY[-(1)]
YN <- YN[-(1)]
NY <- NY[-(1)]
# Put rating lists into one list and convert that to a data frame
resps <- c('YY', 'YN', 'NY', 'NN')
resplist = list(YY, YN, NY, NN)
respdf <- as.data.frame(do.call(cbind, resplist))
colnames(respdf) <- c(resps)
respdf <- cbind(pairs, respdf)
respdf <- respdf[!duplicated(respdf[c('X1', 'X2')]), ] # Remove duplicate rows based on duplicates from the pairs (the duplicates represent 2&2 and 3&3)
# Cohen's Kappa Analyses
# Put data into individual matrices (i.e., contingency tables) and do Kappa analyses, saving the results
kseq <- nrow(respdf)
pabakest = data.frame()
pabakLCI = data.frame()
pabakUCI = data.frame()
kappaest = data.frame()
kappaLCI = data.frame()
kappaUCI = data.frame()
z = data.frame()
p = data.frame()
# Y=all but "Worst" rating and N="Worst" rating
for(i in 1:kseq) {
temp <- as.matrix(respdf[i, 2:5])
tempvec <- unlist(temp)
kappadata <- matrix(tempvec, nrow = 2, byrow = TRUE)
kappa <- epi.kappa(kappadata, method = "cohen", alternative = "greater", conf.level = 0.95)
t1 <- round(kappa[[2]][1],2)
pabakest <- rbind(pabakest,t1)
t2 <- round(kappa[[2]][2],2)
pabakLCI <- rbind(pabakLCI,t2)
t3 <- round(kappa[[2]][3],2)
pabakUCI <- rbind(pabakUCI,t3)
t4 <- round(kappa[[3]][1],2)
kappaest <- rbind(kappaest,t4)
t5 <- round(kappa[[3]][3],2)
kappaLCI <- rbind(kappaLCI,t5)
t6 <- round(kappa[[3]][4],2)
kappaUCI <- rbind(kappaUCI,t6)
t7 <- round(kappa[[4]][1],2)
z <- rbind(z,t7)
t8 <- round(kappa[[4]][2],2)
p <- rbind(p,t8)
}
# Add reviewer pair rows/cols once I have them
kappaoutput <- cbind(pabakest, pabakLCI, pabakUCI, kappaest, kappaLCI, kappaUCI, z, p)
CodePudding user response：
Here is an tidyverse option. I divided the code bit by bit but you can condense further if needed.
First step is to transform all the ratings that are not "Worst" to the same value here "Not_Worst".
library(tidyverse)
df1 = df %>% mutate(across(starts_with('Rating'), ~ case_when(str_detect(., "Worst", negate = TRUE) ~ "Not_Worst", TRUE ~ .)))
> df1
Reviewer1 Rating1 Reviewer2 Rating2
1 Name1 Worst Name3 Not_Worst
2 Name2 Worst Name1 Not_Worst
3 Name3 Not_Worst Name1 Worst
4 Name4 Not_Worst Name1 Not_Worst
5 Name2 Not_Worst Name4 Not_Worst
6 Name3 Worst Name4 Worst
7 Name1 Not_Worst Name2 Not_Worst
8 Name3 Worst Name4 Worst
9 Name1 Not_Worst Name2 Worst
10 Name4 Not_Worst Name2 Not_Worst
Note that with this option you just need to change "Worst" to something else (eg. "Good") to generate the stat based on the new value. You can even wrap all the code in a function to just change this parameter if you wish.
Then, we group_by reviewers and rating to count each pair (r1) :
df2 = df1 %>% group_by(Reviewer1, Reviewer2, Rating1, Rating2) %>% summarise(r1 = n()) %>% ungroup()
> df2
# A tibble: 26 x 5
Reviewer1 Reviewer2 Rating1 Rating2 r1
<chr> <chr> <chr> <chr> <int>
1 Name1 Name2 Not_Worst Not_Worst 3
2 Name1 Name2 Not_Worst Worst 1
3 Name1 Name2 Worst Worst 4
4 Name1 Name3 Not_Worst Not_Worst 2
5 Name1 Name3 Not_Worst Worst 1
6 Name1 Name3 Worst Not_Worst 2
7 Name1 Name3 Worst Worst 1
8 Name2 Name1 Not_Worst Not_Worst 1
9 Name2 Name1 Not_Worst Worst 1
10 Name2 Name1 Worst Not_Worst 1
# ... with 16 more rows
The next step use several dplyr functions to combine the rating as a single column (YY, YN, NY, NN in your code) here called Worst_Worst Worst_Not_Worst,... Then the pivot_wider will moved these new values to columns and fill with the count previously calculated (r1), or 0 if absent. Finally the Worst_Worst column (aka NN) is moved at the end.
df3 = df2 %>% unite(combo_names ,starts_with('Rating'),sep = "_", remove = TRUE) %>%
pivot_wider(names_from = combo_names, values_from = r1, values_fill = list(r1 = 0)) %>%
select(-Worst_Worst, Worst_Worst)
> df3
# A tibble: 11 x 6
Reviewer1 Reviewer2 Not_Worst_Not_Worst Not_Worst_Worst Worst_Not_Worst Worst_Worst
<chr> <chr> <int> <int> <int> <int>
1 Name1 Name2 3 1 0 4
2 Name1 Name3 2 1 2 1
3 Name2 Name1 1 1 1 0
4 Name2 Name3 0 0 1 2
5 Name2 Name4 3 0 0 2
The last change is just to have the data frame ready for the loop by removing the reviewers columns !starts_with('Review')
df4 = df3 %>% select(!starts_with('Review'))
> df4
# A tibble: 11 x 4
Not_Worst_Not_Worst Not_Worst_Worst Worst_Not_Worst Worst_Worst
<int> <int> <int> <int>
1 3 1 0 4
2 2 1 2 1
3 1 1 1 0
4 0 0 1 2
5 3 0 0 2
6 5 1 0 1
7 2 0 0 0
8 1 0 0 4
9 1 1 0 0
10 3 0 0 2
11 0 1 0 2
Now the data are ready for the loop to calculate the kappa stats. You can streamline the loop by creating a empty list() beforehand then each iteration will be added to the list:
kappa_list = list()
for(i in 1:nrow(df4)) {
temp <- matrix(unlist(df4[i, ]), nrow = 2, byrow = TRUE)
kappa_list[[i]] <- epi.kappa(temp, method = "cohen", alternative = "greater", conf.level = 0.95)
}
Now that all the resuts are nested into a list, the last step is to unlist all these and save it as a dataframe, adding the correct column names, then merging to the pair of reviewers:
results = as.data.frame(matrix(unlist(kappa_list), ncol = 11, byrow = TRUE))
names(results) = names(unlist(kappa_list[[1]]))
results = cbind(select(df3, starts_with('Review')), round(results, 2))
> results
Reviewer1 Reviewer2 prop.agree.obs prop.agree.exp pabak.est pabak.lower pabak.upper kappa.est kappa.se kappa.lower kappa.upper z.test.statistic z.p.value
1 Name1 Name2 0.88 0.50 0.75 -0.05 0.99 0.75 0.23 0.29 1.21 3.21 0.00
2 Name1 Name3 0.50 0.50 0.00 -0.76 0.76 0.00 0.41 -0.80 0.80 0.00 0.50
3 Name2 Name1 0.33 0.56 -0.33 -0.98 0.81 -0.50 0.61 -1.70 0.70 -0.82 0.79
4 Name2 Name3 0.67 0.67 0.33 -0.81 0.98 0.00 0.82 -1.60 1.60 0.00 0.50
5 Name2 Name4 1.00 0.52 1.00 -0.04 1.00 1.00 0.00 1.00 1.00 Inf 0.00
6 Name3 Name1 0.86 0.65 0.71 -0.16 0.99 0.59 0.38 -0.16 1.34 1.54 0.06
7 Name3 Name2 1.00 1.00 1.00 -0.68 1.00 NaN NaN NaN NaN NaN NaN
8 Name3 Name4 1.00 0.68 1.00 -0.04 1.00 1.00 0.00 1.00 1.00 Inf 0.00
9 Name4 Name1 0.50 0.50 0.00 -0.97 0.97 0.00 0.71 -1.39 1.39 0.00 0.50
10 Name4 Name2 1.00 0.52 1.00 -0.04 1.00 1.00 0.00 1.00 1.00 Inf 0.00
11 Name4 Name3 0.67 0.67 0.33 -0.81 0.98 0.00 0.82 -1.60 1.60 0.00 0.50