Saving outputs as dataframe in loop - r

Question

Here I have tried to test my data for presence of acf and select a trend test method for daily climate station data ordered into multiple columns. The code intends to work as such:
find monthly average
test the acf
Select trend test method
save result of trend test for each month in excel.
so far i have managed to write a code that does for each month. But, I encountered a problem saving the result as dataframe and export into excel(Error in Result[k, ] <- Outcome : incorrect number of subscripts on matrix). Can any one help me? I have attached a sample data and code I have written.
Data:
structure(list(Date = structure(c(5479, 5480, 5481, 5482, 5483,
5484, 5485, 5486, 5487, 5488, 5489, 5490, 5491, 5492, 5493, 5494,
5495, 5496, 5497, 5498, 5499, 5500, 5501, 5502, 5503, 5504, 5505,
5506, 5507, 5508, 5509, 5510, 5511, 5512, 5513, 5514), class = "Date"),
Adaba = c(6.7, 7.6, 4.9, 6.2, 7.8, 3.1, 4.5, 4.9, 4.2, 5.8,
6.7, 6.1, 5.7, 5.8, 6.4, 5.3, 5.1, 7.6, 7.1, 5.8, 6.7, 6.5,
8.9, 7.6, 7.6, 11.3, 9.5, 11.3, 7.8, 7.6, 6.7, 7.1, 7.6,
7.5, 6.7, 6.5), Bedessa = c(15.1, 14.1, 10.8, 9.9, 10.7,
10.7, 12.4, 13.5, 13, 11.4, 12.9, 13, 13.6, 13, 10.8, 11.9,
13, 10.8, 9.7, 10.8, 9.2, 8.7, 9.2, 10.9, 9.7, 8.8, 12, 10.8,
11.4, 10.3, 10.8, 14.1, 13.5, 13, 14.1, 15.5), Beletu = c(15.3,
14.9, 15.1, 15.7, 15.5, 15.3, 14.8, 15.3, 15.5, 15.2, 14.7,
15.8, 15.9, 14.6, 13.7, 15.2, 15.3, 15.7, 16.2, 15, 15.4,
12.5, 12.6, 12.9, 13.4, 13.2, 11.5, 11.6, 11.7, 12.5, 12.6,
12.6, 12.7, 12, 10.7, 11.8)), row.names = c(NA, 36L), class = "data.frame")`enter code here`
code:
Wabi <- read.csv("Tmin_17.csv",TRUE,",")
# makes the file as data frame
class(Wabi)
# this the package to identify the date type
library(xlsx)
library(lubridate)
library(dplyr)
library(modifiedmk)
# to make new columns of month and year from the original data
Wabi$Date <- as.Date(Wabi$Date, format("%m/%d/%Y"))
# add the package lubridate
Wabi$month <- lubridate::month(Wabi$Date)
Wabi$year <- lubridate::year(Wabi$Date)
# to view the change in the original data
head(Wabi)
N=34
Result <- matrix(nrow = 100,ncol = 2)
# this function is written to sum monthly values
for (k in 1:192){
for(j in 2:17) {
colnum <- colnames(Wabi[j])
Wabi_mon <- Wabi%>%group_by(year,month)%>%summarise_at(.vars = colnum,.funs = mean)
for (i in 1:12)
{
test = acf((Wabi_mon %>% group_by(month) %>% filter(month == i))[3],lag.max = 1)
Trendtest1 <- as.data.frame(mmky(as.data.frame((Wabi_mon %>% group_by(month) %>% filter(month == i))[3])[,]))
Trendtest2 <- as.data.frame(mkttest(as.data.frame((Wabi_mon %>% group_by(month) %>% filter(month == i))[3])[,]))
if (abs(test$acf[2])>abs(((-1-1.96*(N-1)^0.5))/N-1))
Outcome <- Trendtest1
else
Outcome <- Trendtest2
Result[k,] <- Outcome
}
}
}
Result <- data.frame(Result)
class(Result)
write.xlsx(Result,file = "tmin_trend.xlsx",sheetName = "Sheet1")