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03a_descriptive.R
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#calculates summary statistics of lead data, plots seasonality
library(readr)
library(lubridate)
library(ggplot2)
imputeDF <- read_csv("data/processed/imputeDF.csv")
imputeDF$month <- month(as.Date(imputeDF$`Date Sampled`))
testedDF <- imputeDF %>% filter(tested)
untestedDF <- imputeDF %>% filter(!tested)
plot(density(testedDF$month))
plotDF <- testedDF %>% group_by(month) %>%
mutate(overOne = ifelse(`2-3 Minute` > 0,1,0)) %>%
summarize(med = median(`2-3 Minute`),
upper = quantile(`2-3 Minute`,.75),
lower = quantile(`2-3 Minute`,.25),
rate = sum(overOne)/n()
)
plotDF$month <- factor(plotDF$month, labels = month.abb)
ggplot(plotDF, aes(x=month,y=med,group=1)) +
geom_line() + geom_ribbon(aes(ymin=lower,ymax=upper),alpha=.2) +
theme_classic()
ggplot(plotDF, aes(x=month,y=rate,group=1)) +
geom_line() +
theme_classic()
#create table of lead concentrations
# Function to calculate the required statistics for a given column
calculate_stats <- function(column) {
n <- nrow(testedDF)
median_val <- median(column)
iqr_val <- IQR(column)
ge1 <- sum(column >= 1) / n
ge5 <- sum(column >= 5) / n
ge15 <- sum(column >= 15) / n
c(Median = paste(median_val, "(", iqr_val, ")", sep = ""),
GE1 = paste(sum(column >= 1), " (", round(ge1 * 100, 2), "%)", sep = ""),
GE5 = paste(sum(column >= 5), " (", round(ge5 * 100, 2), "%)", sep = ""),
GE15 = paste(sum(column >= 15), " (", round(ge15 * 100, 2), "%)", sep = ""))
}
# Creating a table with the required statistics for each column
results <- data.frame(
`1st Draw` = calculate_stats(testedDF$`1st Draw`),
`2-3 Minute` = calculate_stats(testedDF$`2-3 Minute`),
`5 Minute` = calculate_stats(testedDF$`5 Minute`)
)
# Transposing the table to get the desired format
results_table <- t(results)
rownames(results_table) <- c("1st Draw", "2-3 Minute", "5 Minute")
write_csv(as.data.frame(results_table),"data/processed/leadTestConcentrations.csv")
median(testedDF$`1st Draw`)
iqr(testedDF$`1st Draw`)
sum(testedDF$`1st Draw` >= 1)/nrow(testedDF)
sum(testedDF$`1st Draw` >= 5)/nrow(testedDF)
sum(testedDF$`1st Draw` >= 15)/nrow(testedDF)
median(testedDF$`2-3 Minute`)
iqr(testedDF$`2-3 Minute`)
sum(testedDF$`2-3 Minute` >= 1)/nrow(testedDF)
sum(testedDF$`2-3 Minute` >= 5)/nrow(testedDF)
sum(testedDF$`2-3 Minute` >= 15)/nrow(testedDF)
median(testedDF$`5 Minute`)
iqr(testedDF$`5 Minute`)
sum(testedDF$`5 Minute` >= 1)/nrow(testedDF)
sum(testedDF$`5 Minute` >= 5)/nrow(testedDF)
sum(testedDF$`5 Minute` >= 15)/nrow(testedDF)
#tests representativeness of data
allBlocksDF <- imputeDF %>% distinct(blockNum, .keep_all=T)
allBlocksDF$blockNum <- as.character(allBlocksDF$blockNum)
testedBlocksDF <- allBlocksDF %>% filter(tested)
untestedBlocksDF <- allBlocksDF %>% filter(!tested)
untestedDemographics <- getRaceEstimates(untestedDF,tested=F)
testedDemographics <- getRaceEstimates(testedDF,tested=F)
allBlocksDemographics <- getRaceEstimates(allBlocksDF,tested=F)
demoDF <- as.data.frame(cbind(t(as.data.frame(testedDemographics)),
t(as.data.frame(untestedDemographics)))
)
colnames(demoDF) <- c("Tested","Untested")
demoDF$Total <- t(as.data.frame(allBlocksDemographics))
write_csv(demoDF,"data/processed/demoDF.csv")
median(testedDF$blockPopulation)
paste("(", quantile(testedBlocksDF$blockPopulation, probs = 0.25), ",",
quantile(testedBlocksDF$blockPopulation, probs = 0.75),")", sep="")
median(untestedBlocksDF$blockPopulation)
paste("(", quantile(untestedBlocksDF$blockPopulation, probs = 0.25), ",",
quantile(untestedBlocksDF$blockPopulation, probs = 0.75),")", sep="")
median(allBlocksDF$blockPopulation)
paste("(", quantile(allBlocksDF$blockPopulation, probs = 0.25), ",",
quantile(allBlocksDF$blockPopulation, probs = 0.75),")", sep="")
#building characteristics
buildingAge <- read_csv("data/processed/buildingAge.csv")
buildingAge$age <- 2023-buildingAge$year
buildingAge2 <- buildingAge %>%
filter(!is.na(cxy_block_id)) %>%
mutate(blockNum = paste0("17031",cxy_tract_id,cxy_block_id)) %>%
left_join(allBlocksDF %>% select(blockNum,tested)) %>%
group_by(blockNum) %>%
mutate(nBlocks = n()) %>% ungroup() %>%
filter(!is.na(tested))
buildingAge3 <- buildingAge2 %>%
group_by(tested) %>%
summarize(
medianBuildingsPerBlock = median(nBlocks),
percentilesBuildingsPerBlock = paste("(", quantile(nBlocks, probs = 0.25), ",",
quantile(nBlocks, probs = 0.75),")", sep=""),
medianAge = median(age),
percentilesAge = paste("(", quantile(age, probs = 0.25), ",",
quantile(age, probs = 0.75),")", sep="")
)
totalVec <- c(
"Total",
median(buildingAge2$nBlocks),
paste("(", paste(quantile(buildingAge2$nBlocks, probs = c(0.25, 0.75), na.rm = TRUE), collapse=", "), ")", sep=""),
median(buildingAge2$age),
paste("(", paste(quantile(buildingAge2$age, probs = c(0.25, 0.75), na.rm = TRUE), collapse=", "), ")", sep="")
)
buildingDF <- as.data.frame(t(rbind(buildingAge3,totalVec)))
write_csv(buildingDF,"data/processed/buildingAgeStats.csv")
#percentage of tests and blocks with lead > 1ppb
nrow(testedDF %>% filter(overOne_2))/nrow(testedDF)