Assignment 1: Haley White

fluoride<-read.csv(url("http://jamessuleiman.com/teaching/datasets/fluoride.csv"),
                    stringsAsFactors = FALSE)
arsenic<-read.csv(url("http://jamessuleiman.com/teaching/datasets/arsenic.csv"),
                    stringsAsFactors = FALSE)
arsenic_noNA<-arsenic %>%
  filter(!is.na(percent_wells_above_guideline)) 
fluoride_noNA<-fluoride %>%
  filter(!is.na(percent_wells_above_guideline))
new_arsenic<-arsenic_noNA %>%
  select(location, percent_wells_above_guideline)

new_fluoride<-fluoride_noNA %>%
  select(location, percent_wells_above_guideline)

Tibble

ilo_data<-new_arsenic %>%
  inner_join(new_fluoride, by=c("location"))

ilo_data$percentage<-ilo_data$percent_wells_above_guideline.x + ilo_data$percent_wells_above_guideline.y
merged_data <- ilo_data %>% select(location, percentage) %>% top_n(5) %>%
  arrange(desc(percentage)) 
## Selecting by percentage
as_tibble(merged_data)
## # A tibble: 5 x 2
##   location   percentage
##   <chr>           <dbl>
## 1 Otis             69.6
## 2 Manchester       62.2
## 3 Surry            58.6
## 4 Monmouth         52.6
## 5 Blue Hill        52.3

Table

library(pander)
panderOptions('round',2)
set.caption("Maine Towns with Highest Percentage of Wells Above Arsenic and Fluoride Levels")
pander(merged_data)
Maine Towns with Highest Percentage of Wells Above Arsenic and Fluoride Levels
location percentage
Otis 69.6
Manchester 62.2
Surry 58.6
Monmouth 52.6
Blue Hill 52.3

Chart

Narrative

  • Things I found interesting:
    • Over half of the wells in the top 5 towns (featured on chart) in Maine are above the levels of arsenic or fluoride.
    • Joining the data and creating the tibble was difficult, but I found it easy to turn the tibble into a table and chart.
  • Issues I had:
    • Coming up with a narrative from so much data was overwhelming.
    • Figuring out the code for how to effectively join the data was difficult.