m = rbind(c(1, 12, 8, 6), c(4, 10, 2, 9), c(11, 3, 5, 7)) m # Reconstruct the matrix n = matrix(c(1,12,8,6,4,10,2,9,11,3,5,7), nrow=3, ncol=4, byrow=TRUE) n all.equal(m,n) # Print the element in the 3rd-row and 4th column n[3,4] # Print the 2nd column n[,2] # Print all but the 3rd row n[-3,]
library('ISDSWorkshop') library('dplyr') data(GI) write.csv(GI, file="GI.csv", row.names=FALSE) # In case the file isn't already there GI = read.csv("GI.csv") GI$ageC = cut(GI$age, c(-Inf, 5, 18, 45 ,60, Inf))
# Create icd9code cuts = c(0, 140, 240, 280, 290, 320, 360, 390, 460, 520, 580, 630, 680, 710, 740, 760, 780, 800, 1000, Inf) GI$icd9code = cut(GI$icd9, cuts, right=FALSE) # Find the icd9code that is most numerous # There are many ways to do this table(GI$icd9code) # Eliminate zeros GI$icd9code = factor(GI$icd9code) table(GI$icd9code)
library('dplyr')
# Aggregate the GI data set by gender, ageC, and icd9code (the ones created in the last activity). GI %>% group_by(gender, ageC, icd9code) %>% summarize(total = n())
Construct a histogram and boxplot for age at facility 37 using ggplot2.
library('ggplot2')
# Construct a histogram for age at facility 37. ggplot(GI %>% filter(facility == 37), aes(x = age)) + geom_histogram(binwidth = 1) # Construct a boxplot for age at facility 37. ggplot(GI %>% filter(facility == 37), aes(x = 1, y = age)) + geom_boxplot()
Construct a bar chart for the zipcode at facility 37 using ggplot2
# Construct a bar chart for the zipcode at facility 37. ggplot(GI %>% filter(facility == 37), aes(x = trunc(zipcode/100))) + geom_bar()
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