EliasIssaRproject#10
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qtran4
reviewed
Dec 16, 2022
| write.csv(data, file = paste(directory, sub(".txt",".csv", i), sep ="/"), quote=F, row.names=F, sep = ",") | ||
| file.remove(paste(directory,i, sep ="/" )) | ||
| } | ||
| } |
qtran4
reviewed
Dec 16, 2022
| #Stores the data in .csv files as temporary value | ||
| data2 <- read.csv(paste(directory,i, sep = "/")) | ||
| #Remove NAs if choice is removeNA | ||
| if(choice=="removeNA"){ data <- na.omit(data2)} |
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There should be a condition when the user choose not to type either of these.
-0.25
qtran4
reviewed
Dec 16, 2022
| #Total number of rows is equal to the number of screens | ||
| numscreen <- nrow(all) | ||
| #Subsets the data for patients with a single marker present in any of the markers | ||
| infected <- subset(all, marker01 > 0 | marker02 > 0 | marker03 > 0 | marker04 > 0 | marker05 > 0 | marker06 > 0 | marker07 > 0 | marker08 > 0 | marker09 > 0 | marker10 > 0) |
qtran4
reviewed
Dec 16, 2022
| all <- subset(data, age<122) | ||
| #Creates a dataframe with the markers 1,10 and corresponding total marker count | ||
| df <- data.frame(marker = c(seq.int(1,10,by =1)), | ||
| count = c(sum(all$marker01),sum(all$marker02),sum(all$marker03),sum(all$marker04),sum(all$marker05),sum(all$marker06),sum(all$marker07),sum(all$marker08),sum(all$marker09),sum(all$marker10))) |
qtran4
reviewed
Dec 16, 2022
| firstInfection <- function(file){ | ||
| data <- read.csv(file) | ||
| #Subsets the data for patients with a single marker present in any of the markers | ||
| infected <- subset(data, marker01 > 0 | marker02 > 0 | marker03 > 0 | marker04 > 0 | marker05 > 0 | marker06 > 0 | marker07 > 0 | marker08 > 0 | marker09 > 0 | marker10 > 0) |
qtran4
reviewed
Dec 16, 2022
| } | ||
| } | ||
| } | ||
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qtran4
reviewed
Dec 16, 2022
| ggplot() + geom_histogram(data=infected, aes(x=age,color="red"), binwidth = 1) +theme_classic() | ||
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| } | ||
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qtran4
reviewed
Dec 16, 2022
| #Compiles the CSV data for each country into a file allData.csv for future analysis with NAs removed | ||
| compileCSV("countryY", "removeNA") | ||
| compileCSV("countryX", "removeNA") | ||
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qtran4
reviewed
Dec 16, 2022
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| #As country Y is developed the vaccine they would likely develop it for markers 5-10 | ||
| #These markers are present at low levels in country X | ||
| #As such the country Y vaccine would likely not work for country X |
qtran4
reviewed
Dec 16, 2022
| #The first infection for country X is 120 | ||
| firstInfection("countryX/allData.csv") | ||
| #As Country X had infections at day 120, 19 days prior to country Y, the infection likely began in country X | ||
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qtran4
reviewed
Dec 16, 2022
| #Summarizes data for both countries form the file allData.csv | ||
| summarizeData("allData.csv") | ||
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| #The disease appears to infect both sexes fairly equally and appears to mostly infect younger patients under 20 years old |
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Good commenting and efficient code that uses coding concepts that we covered in class – 3.5 points (max is 4)
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