I want to make any 0 values in my data frame have a positive number so that my model will work.
However, when I try to replace all zero values, I also replace the zeros that are in strings belonging to much larger numbers such as 10, 20, 30, 40... 100, 1000 etc..
How do I specify that I only want to replace those values which are actually zero, and not just any string which contains the number zero?
Thanks!
Here's the code:
total<- read.csv("total.csv")
total.rm <- na.omit(total)
#removing NAs/NAN
total.rm$mediansp[which(is.nan(total.rm$mediansp))] = NA
total.rm$mediansp[which(total.rm$mediansp==Inf)] = NA
total.rm$connections[which(is.nan(total.rm$connections))] = NA
total.rm$connections[which(total.rm$connections==Inf)] = NA
#make all 0 values positive
total.rm$mediansp <- gsub("0", "0.00001", total.rm$mediansp)
total.rm$connections <- gsub("0", "0.00001", total.rm$connections)
#remove zeros varaibles
total.rm$mediansp <- gsub("NA", "0", total.rm$mediansp)
total.rm$connections <- gsub("NA", "0", total.rm$connections)
total.rm$mediansp <- gsub("0", "0.01", total.rm$mediansp)
total.rm$connections <- gsub("0", "0.01", total.rm$connections)
#convert character variables to numeric variables
total.rm$mediansp <- as.numeric(total.rm$mediansp)
total.rm$connections <- as.numeric(total.rm$connections)
#plot maps with fitted values and with residuals
sc.lm <- lm (log(mediansp) ~ log(connections), total.rm, na.action="na.exclude")
total.rm$fitted.s <- predict(sc.lm, total.rm) - mean(predict(sc.lm, total.rm))
total.rm$residuals <- residuals(sc.lm)
Here's the structure:
data.frame': 133537 obs. of 19 variables:
$ pcd : Factor w/ 1736958 levels "AB101AA","AB101AB",..:
$ pcdstatus : Factor w/ 5 levels "Insufficient Data",..: 5 5 5 5 5 5 5 5 5 5 ...
$ mbps2 : num 0 0 0 0 1 0 1 1 0 0 ...
$ averagesp : chr "16" "19.3" "14.1" "14.9" ...
$ mediansp : chr "16.2" "20" "18.7" "16.8" ...
$ maxsp : chr "23.8" "24" "20.2" "19.7" ...
$ nga : num 0 0 0 1 0 1 1 1 1 1 ...
$ connections : chr "54" "14" "98" "43" ...
$ oslaua : Factor w/ 407 levels "","95A","95B",..: 326 326 326 326 326 326 326
$ x : int 540194 540194 540300 539958 540311 539894 540311 540379 540310
$ y : int 169201 169201 169607 169584 168997 169713 168997 168749 168879
$ ctry : Factor w/ 4 levels "E92000001","N92000002",..: 1 1 1 1 1 1 1 1 1 1
$ hro2 : Factor w/ 13 levels "","E12000001",..: 8 8 8 8 8 8 8 8 8 8 ...
$ soa2 : Factor w/ 7197 levels "","E02000001",..: 145 145 135 135 145 135 145
$ urindew : int 5 5 5 5 5 5 5 5 5 5 ...
$ averagesp.lt : num 2.77 2.96 2.65 2.7 2.05 ...
$ mediansp.lt : num 2.79 3 2.93 2.82 2.09 ...
$ maxsp.lt : num 3.17 3.18 3.01 2.98 2.68 ...
$ connections.lt: num 3.99 2.64 4.58 3.76 3.22 ...