R Writing to data frame from inside for-loop - r

Question

Brand new to R programming so please forgive me if I'm using wrong terminologies.
I'm trying to insert/append values to a data frame from inside a for-loop.
I can get the right values if I just print() them, but when I try to put it inside the data frame, I get mostly NA's. If I run this code it prints out the values I want.
output <- data.frame()
for (i in seq_along(Reasons)){
assign(paste(Reasons[i]), sum(ER$Reason == paste(Reasons[i])))
Tot <- get(paste(Reasons[i]))
assign(paste(Reasons[i],'ER',sep="_"), sum(grepl("ER|Er", ER$Disposition) & ER$Reason == paste(Reasons[i])))
Er <- get(paste(Reasons[i],'ER',sep="_"))
assign(paste(Reasons[i],'adm',sep="_"), sum(grepl("Admi|admi|ADMI|ADmi", ER$Disposition) & ER$Reason == paste(Reasons[i])))
Adm <- get(paste(Reasons[i],'adm',sep="_"))
assign(paste(Reasons[i],'admrate',sep="_"), sprintf("%.0f%%", (sum(grepl("Admi|admi|ADMI|ADmi", ER$Disposition) & ER$Reason == paste(Reasons[i])))/(sum(ER$Reason == paste(Reasons[i])))*100))
Rate <- get(paste(Reasons[i],'admrate',sep="_"))
print(c(Er,Adm,Tot,Rate))
#clear variables just created
rm(list=ls(pattern=Reasons[i]))
rm(Tot,Er,Adm,Rate)
}
[1] "7" "13" "20" "65%"
[1] "4" "8" "12" "67%"
[1] "12" "12" "24" "50%"
[1] "23" "7" "30" "23%"
[1] "7" "1" "8" "12%"
[1] "3" "1" "4" "25%"
[1] "3" "0" "3" "0%"
[1] "6" "5" "11" "45%"
[1] "2" "9" "11" "82%"
[1] "2" "4" "6" "67%"
[1] "10" "4" "14" "29%"
[1] "5" "0" "5" "0%"
[1] "10" "4" "14" "29%"
[1] "0" "3" "3" "100%"
[1] "7" "3" "10" "30%"
[1] "0" "4" "4" "100%"
But when I use
output <- rbind(output, c(Er, Adm, Tot, Rate))
Instead of
print(c(Er,Adm,Tot,Rate))
I get the first row of values (7, 13, 20, 65%), then all NA's except the "7" in rows 5 and 15... What am I doing wrong?
Thank you in advance

Answer 1

As I don't know what your data look like I cannot reproduce your error. If I understand it correctly, for each value in Reasons you want to find (a) the total number of observations, (b) the number of observations with the string "Er" in the variable Disposition, (c) the number of observations with the string "Admi" in the variable Disposition and (d) the percentage of observations with the string "Admi" in the variable Disposition. If that is the case then you don't have to use assign and get to do this.
Here is a simpler way to do it (although it's not the best way to do it, see below):
## Here I just generated some data that might look like the data
## you are dealing with:
Reasons <- LETTERS[1:10]
ER <- data.frame(Reason = LETTERS[sample.int(10,100, replace = TRUE)],
Disposition = c("ER", "Admi", "SomethingElse")[sample.int(3,100, replace = TRUE)])
output <- data.frame()
for (i in seq(along = Reasons)){
Tot <- sum(ER$Reason ==Reasons[i])
Er <- sum(grepl("ER|Er", ER$Disposition) & (ER$Reason ==Reasons[i]))
Adm <- sum(grepl("Admi|admi|ADMI|ADmi", ER$Disposition) & (ER$Reason ==Reasons[i]))
Rate <- paste(round(Adm/Tot*100), "%")
output <- rbind(output, c(Er, Adm, Tot, Rate))
}
> output
X.4. X.3. X.10. X.30...
1 4 3 10 30 %
2 2 3 6 50 %
3 2 1 6 17 %
4 5 2 14 14 %
5 3 5 11 45 %
6 2 4 11 36 %
7 3 6 14 43 %
8 2 2 5 40 %
9 1 7 11 64 %
10 4 4 12 33 %
Dynamically appending rows to a data frame or matrix is generally not a very good idea as it is quite memory intensive. If you know the dimensions of your matrix beforehand (as you do) you should initialize it with the right size and then fill the entries inside your loop:
## Initialize data:
output <- matrix(nrow = length(Reasons), ncol = 4)
for (i in seq(along = Reasons)){
Tot <- sum(ER$Reason ==Reasons[i])
Er <- sum(grepl("ER|Er", ER$Disposition) & (ER$Reason ==Reasons[i]))
Adm <- sum(grepl("Admi|admi|ADMI|ADmi", ER$Disposition) & (ER$Reason ==Reasons[i]))
Rate <- paste(round(Adm/Tot*100), "%")
output[i,] <- c(Er, Adm, Tot, Rate)
}
There are, however, even simpler ways to do this kind of evaluation. You could e.g. use the dplyr package, where you can group the data by a variable (the different Values of ER$Reason in your case) and the evaluate the values you need:
## Load the package 'dplyr'
library(dplyr)
## Group the variable and evaluate:
output <- ER %>% group_by(Reason) %>%
dplyr::summarise(Er = sum(grepl("ER|Er", Disposition)),
Adm = sum(grepl("Admi|admi|ADMI|ADmi", Disposition)),
Tot = n(),
Rate = paste(round(Adm/Tot*100), "%"))
> output
# A tibble: 10 × 5
Reason Er Adm Tot Rate
<chr> <int> <int> <int> <chr>
1 A 4 3 10 30 %
2 B 2 3 6 50 %
3 C 2 1 6 17 %
4 D 5 2 14 14 %
5 E 3 5 11 45 %
6 F 2 4 11 36 %
7 G 3 6 14 43 %
8 H 2 2 5 40 %
9 I 1 7 11 64 %
10 J 4 4 12 33 %