library('ggplot2') library('dplyr') library('ISDSWorkshop') workshop(launch_index = FALSE) # to make sure scripts and csv files are available
# Read in csv files GI = read.csv("GI.csv") icd9df = read.csv("icd9.csv")
# Mutate data.frame GI <- GI %>% mutate( date = as.Date(date), weekC = cut(date, breaks="weeks"), week = as.numeric(weekC), facility = as.factor(facility), icd9class = factor(cut(icd9, breaks = icd9df$code_cutpoint, labels = icd9df$classification[-nrow(icd9df)], right = TRUE)), ageC = cut(age, breaks = c(-Inf, 5, 18, 45 ,60, Inf)), zip3 = trunc(zipcode/100))
# Create weekD variable in GI data set GI$weekD = as.Date(GI$weekC) # could have used mutate str(GI$weekD)
# Construct a data set aggregated by week and age category GI_wa <- GI %>% group_by(week, ageC) %>% summarize(count = n()) # Construct a plot to look at weekly GI cases by age category ggplot(GI_wa, aes(x = week, y = count, color = ageC)) + geom_point() ggplot(GI_wa, aes(x = week, y = count, shape = ageC)) + geom_point() ggplot(GI_wa, aes(x = week, y = count, shape = ageC, color = ageC)) + geom_point()
Construct a plot of weekly GI counts by zip3 and ageC.
# Construct data set GI_za <- GI %>% group_by(week, zip3, ageC) %>% summarize(count = n()) # Construct plot of weekly GI counts by zip3 and ageC ggplot(GI_za, aes(x = week, y = count)) + geom_point() + facet_grid(ageC ~ zip3)
Construct a plot for those in zipcode 206xx in 2008.
# Filter the data to zipcode 206xx in 2008 zip206_w <- GI %>% mutate(year = as.numeric(format(date, "%Y"))) %>% filter(zip3 == 206, year == 2008) %>% group_by(week) %>% summarize(count = n()) # Construct the plot of weekly GI counts in zipcode 206xx. ggplot(zip206_w, aes(x = week, y = count)) + geom_point()
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