1. From the data you have chosen, select a variable that you are interested in
  2. Use pastecs::stat.desc to describe the variable. Include a few sentences about what the variable is and what it’s measuring.
  3. Remove NA’s if needed using dplyr:filter (or anything similar)
  4. Provide a histogram of the variable (as shown in this lesson)
  5. transform the variable using the log transformation or square root transformation (whatever is more appropriate) using dplyr::mutate or something similar
  6. provide a histogram of the transformed variable
  7. submit via rpubs on CANVAS
#Loaded data (adjusted skip since headers off)
raw_data <-read_excel("2025-County-Health-Rankings-Texas-Data-v3.xlsx", sheet = "Select Measure Data", skip = 2) 
## New names:
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QUESTION 1 PERCENTAGE ANNUAL MAMMOGRAM SCREENING AMONG TEXAS FEMALE MEDICARE ENROLLEES 65-74

Focusing on older adults (65+), highlighting preventive care disparities in Texas counties. Independent variable:“Mammo”(it represents the % of female Medicare enrollees 65-74 who receive annual mammogram screening). This variable assesses preventive breast cancer screening access and preventive utilization among older women in Medicare.

older_data <- raw_data[, c(3,106)] 

colnames(older_data) <- c("County", "Mammo") 

older_data[,2:2] <- lapply(older_data[,2:2], as.numeric) 

older_data <- na.omit(older_data)

QUESTION 2 PASTECS

pastecs::stat.desc(older_data$Mammo) # % with Annual Mammogram
##      nbr.val     nbr.null       nbr.na          min          max        range 
##  250.0000000    0.0000000    0.0000000   14.0000000   55.0000000   41.0000000 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 8637.0000000   35.5000000   34.5480000    0.5045961    0.9938207   63.6543133 
##      std.dev     coef.var 
##    7.9783653    0.2309357

The above information indicates moderate variation in screening rates. A slight left skew does suggest a few Texas counties have low preventive utilization when it comes to annual mammogram screenings.

QUESTION 3 - Remove NAs

# Removed NAs + bad values 

older_data$Mammo <- as.numeric(as.character(older_data$Mammo)) 

# Dropped rows with any NA 

older_data <- older_data[complete.cases(older_data[,2:2]), ] 

nrow(older_data)  # 250 good rows now (one county name was missing and four counties had no information on % with annual mammogram screening)
## [1] 250

QUESTION 4 - Histogram

hist(older_data$Mammo)

QUESTION 5 - Transform the Variable

older_data<-older_data %>% mutate(Mammo_transformed=sqrt(Mammo))

QUESTION 6 - Histogram of the Transformed Variable

hist(older_data$Mammo_transformed)