Instructions

location - the name of the town, township, or regional area in Maine n_wells_tested - the number of wells tested percent_wells_above_guideline - percentage of wells that tested above the maximum exposure guideline median - mg/L for flouride, ug/L for arsenic percentile_95 - the 95th percentile readings in mg/L or ug/L maximum - the maximum readings in mg/L or ug/L Prepare a report by editing assign02.Rmd, that has an interesting narrative that focuses on a subset of the data you find interesting that includes both arsenic and fluoride data. Your report should be uploaded to RPubs, and you should copy the link to your RPubs report and paste in the text submission box below. You are required to join the data.

you must create a data frame or tibble that joins both arsenic and fluoride by location. (20 points) at least one table showing relevant data that is not so long that it overwhelms the report (consider using the head command). (10 points) at least one chart. For at least one of your charts, the code that created it must not be displayed. (10 points) a narrative discussing what you find interesting along with any issues you might have had preparing the data (10 points) published on RPubs (40 points) clickable link posted in Brightspace to your RPubs report (10 points)

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     3.3

Then 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      71

Join 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.

head(chemicals)
## # 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.

# The code below displays the top 10 cities with the most arsenic and flouride combined
topTen <- chemicals %>% slice_max(percent_wells_above_guideline.x+percent_wells_above_guideline.y, n = 10) %>% select(location,percent_wells_above_guideline.x,percent_wells_above_guideline.y)

Edit this part to discuss how you selected your interesting subset.

I started by adding up arsenic and floride levels, then I sliced the top ten

Display the first few rows of your interesting subset in the code chunk below.

head(topTen)
## # A tibble: 6 x 3
##   location   percent_wells_above_guideline.x percent_wells_above_guideline.y
##   <chr>                                <dbl>                           <dbl>
## 1 Otis                                  30                              39.6
## 2 Manchester                             3.3                            58.9
## 3 Surry                                 18.3                            40.3
## 4 Monmouth                               3.1                            49.5
## 5 Blue Hill                              9.6                            42.7
## 6 Mercer                                15.6                            36.4

Visualize your subset

In the code chunk below, create a ggplot visualization of your subset that is fairly simple for a viewer to comprehend.

library(ggplot2)
# Stacked barplot with multiple groups
ggplot(data=topTen, aes(x=factor(location), y=percent_wells_above_guideline.y, fill=percent_wells_above_guideline.x)) +geom_bar(stat="identity")

Once you are done, knit, publish, and then submit your link to your published RPubs document in Brightspace.