Assignment 2
Evan Libby
install.packages(‘readr’) ## Instructions
Refer to the detailed instructions for this assignment in Brightspace.
Data Import
Don’t alter the three code chunks in this section. First we read in the two data sets and deleting missing values.
library(tidyverse)
fluoride <- read_csv("http://jamessuleiman.com/teaching/datasets/fluoride.csv")
fluoride <- fluoride %>% drop_na()
arsenic <- read_csv("http://jamessuleiman.com/teaching/datasets/arsenic.csv")
arsenic <- arsenic %>% drop_na()Next we display the first few rows of fluoride.
head(fluoride)## # A tibble: 6 x 6
## location n_wells_tested percent_wells_above_gui… median percentile_95 maximum
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Otis 60 30 1.13 3.2 3.6
## 2 Dedham 102 22.5 0.94 3.27 7
## 3 Denmark 46 19.6 0.45 3.15 3.9
## 4 Surry 175 18.3 0.8 3.52 6.9
## 5 Prospect 57 17.5 0.785 2.5 2.7
## 6 Eastbrook 31 16.1 1.29 2.44 3.3Then we display the first few rows of arsenic.
head(arsenic)## # A tibble: 6 x 6
## location n_wells_tested percent_wells_above_g… median percentile_95 maximum
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Manchester 275 58.9 14 93 200
## 2 Gorham 467 50.1 10.5 130 460
## 3 Columbia 42 50 9.8 65.9 200
## 4 Monmouth 277 49.5 10 110 368
## 5 Eliot 73 49.3 9.7 41.4 45
## 6 Columbia F… 25 48 8.1 53.8 71Join data
In the code chunk below, create a new tibble called chemicals that joins fluoride and arsenic. You probably want to do an inner join but the join type is up to you.
chemicals <- fluoride %>% inner_join(arsenic, by = "location")The next code chunk displays the head of your newly created chemicals tibble. Take a look to verify that your join looks ok.
## # A tibble: 6 x 11
## location n_wells_tested.x percent_wells_a… median.x percentile_95.x maximum.x
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Otis 60 30 1.13 3.2 3.6
## 2 Dedham 102 22.5 0.94 3.27 7
## 3 Denmark 46 19.6 0.45 3.15 3.9
## 4 Surry 175 18.3 0.8 3.52 6.9
## 5 Prospect 57 17.5 0.785 2.5 2.7
## 6 Eastbro… 31 16.1 1.29 2.44 3.3
## # … with 5 more variables: n_wells_tested.y <dbl>,
## # percent_wells_above_guideline.y <dbl>, median.y <dbl>,
## # percentile_95.y <dbl>, maximum.y <dbl>Intersting subset
In the code chunk below create an interesting subset of the data. You’ll likely find an interesting subset by filtering for locations that have high or low levels of arsenic, flouride, or both.
interesting <- chemicals %>% select(location, percent_wells_above_guideline.y, percent_wells_above_guideline.x) %>%
filter( percent_wells_above_guideline.y >= 40)
interesting1 <- interesting %>% rename(Flouride = percent_wells_above_guideline.x, Arsenic = percent_wells_above_guideline.y, Location = location) %>%
arrange(desc(Arsenic))
interesting1## # A tibble: 16 x 3
## Location Arsenic Flouride
## <chr> <dbl> <dbl>
## 1 Manchester 58.9 3.3
## 2 Gorham 50.1 0
## 3 Columbia 50 1.9
## 4 Monmouth 49.5 3.1
## 5 Eliot 49.3 0
## 6 Columbia Falls 48 0
## 7 Winthrop 44.8 3.1
## 8 Hallowell 44.6 0
## 9 Buxton 43.4 1
## 10 Blue Hill 42.7 9.6
## 11 Litchfield 42 1.9
## 12 Hollis 41.4 3.5
## 13 Orland 40.7 8.6
## 14 Surry 40.3 18.3
## 15 Mariaville 40 7.5
## 16 Danforth 40 0Edit this part to discuss how you selected your interesting subset.
Given the data I had, I wanted to see how and if levels of Arsenic were a determining factor in levels of Fluoride. I was hoping, but failed to prove that bad water generally really bad. This project was a little harder than I thought as I had gotten everything correct, only to get to the graphing section, where I had to rethink my data in order to create an easy to read graph. I ended up not being able to figure out how to incorporate both fluoride and arsenic data on one graph despite trying different coding techniques for hours. I also had issues as you can see with a tibble displaying in the ggplot section. In order to sort/manage my data I created a few new variables which some how resulted in a tibble of my data displaying.
Display the first few rows of your interesting subset in the code chunk below.
## # A tibble: 5 x 3
## Location Arsenic Flouride
## <chr> <dbl> <dbl>
## 1 Manchester 58.9 3.3
## 2 Gorham 50.1 0
## 3 Columbia 50 1.9
## 4 Monmouth 49.5 3.1
## 5 Eliot 49.3 0Visualize your subset
In the code chunk below, create a ggplot visualization of your subset that is fairly simple for a viewer to comprehend.
## # A tibble: 16 x 3
## Location Arsenic Flouride
## <chr> <dbl> <dbl>
## 1 Manchester 58.9 3.3
## 2 Gorham 50.1 0
## 3 Columbia 50 1.9
## 4 Monmouth 49.5 3.1
## 5 Eliot 49.3 0
## 6 Columbia Falls 48 0
## 7 Winthrop 44.8 3.1
## 8 Hallowell 44.6 0
## 9 Buxton 43.4 1
## 10 Blue Hill 42.7 9.6
## 11 Litchfield 42 1.9
## 12 Hollis 41.4 3.5
## 13 Orland 40.7 8.6
## 14 Surry 40.3 18.3
## 15 Mariaville 40 7.5
## 16 Danforth 40 0
Once you are done, knit, publish, and then submit your link to your published RPubs document in Brightspace.