Data Analysis
Setup
Let’s load some important packages.
library(tidyverse)## ── Attaching packages ───────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.2.1 ✔ purrr 0.3.2
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 0.8.3 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0## ── Conflicts ──────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(ggplot2)Reading the Data
First we read in the data.
dat <- read_csv("SPEC_2014.csv.gz")
dat## # A tibble: 1,519,790 x 26
## State.Code County.Code Site.Num Parameter.Code POC Latitude Longitude
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 01 073 0023 88102 5 33.6 -86.8
## 2 01 073 0023 88102 5 33.6 -86.8
## 3 01 073 0023 88102 5 33.6 -86.8
## 4 01 073 0023 88102 5 33.6 -86.8
## 5 01 073 0023 88102 5 33.6 -86.8
## 6 01 073 0023 88102 5 33.6 -86.8
## 7 01 073 0023 88102 5 33.6 -86.8
## 8 01 073 0023 88102 5 33.6 -86.8
## 9 01 073 0023 88102 5 33.6 -86.8
## 10 01 073 0023 88102 5 33.6 -86.8
## # … with 1.52e+06 more rows, and 19 more variables: Datum <chr>,
## # Parameter.Name <chr>, Sample.Duration <chr>, Pollutant.Standard <lgl>,
## # Date.Local <date>, Units.of.Measure <chr>, Event.Type <chr>,
## # Observation.Count <dbl>, Observation.Percent <dbl>,
## # Sample.Value <dbl>, X1st.Max.Value <dbl>, X1st.Max.Hour <dbl>,
## # AQI <lgl>, Method.Code <dbl>, State.Name <chr>, County.Name <chr>,
## # City.Name <chr>, CBSA.Name <chr>, Date.of.Last.Change <date>This dataset has 1519790 rows and 26 columns.
The dataset has the following variables.
names(dat)## [1] "State.Code" "County.Code" "Site.Num"
## [4] "Parameter.Code" "POC" "Latitude"
## [7] "Longitude" "Datum" "Parameter.Name"
## [10] "Sample.Duration" "Pollutant.Standard" "Date.Local"
## [13] "Units.of.Measure" "Event.Type" "Observation.Count"
## [16] "Observation.Percent" "Sample.Value" "X1st.Max.Value"
## [19] "X1st.Max.Hour" "AQI" "Method.Code"
## [22] "State.Name" "County.Name" "City.Name"
## [25] "CBSA.Name" "Date.of.Last.Change"How many pollutants are there in the dataset?
unique(dat$Parameter.Name)## [1] "Antimony PM2.5 LC"
## [2] "Arsenic PM2.5 LC"
## [3] "Aluminum PM2.5 LC"
## [4] "Barium PM2.5 LC"
## [5] "Bromine PM2.5 LC"
## [6] "Cadmium PM2.5 LC"
## [7] "Calcium PM2.5 LC"
## [8] "Chromium PM2.5 LC"
## [9] "Cobalt PM2.5 LC"
## [10] "Copper PM2.5 LC"
## [11] "Chlorine PM2.5 LC"
## [12] "Cerium PM2.5 LC"
## [13] "Cesium PM2.5 LC"
## [14] "Iron PM2.5 LC"
## [15] "Lead PM2.5 LC"
## [16] "Indium PM2.5 LC"
## [17] "Manganese PM2.5 LC"
## [18] "Nickel PM2.5 LC"
## [19] "Magnesium PM2.5 LC"
## [20] "Phosphorus PM2.5 LC"
## [21] "Selenium PM2.5 LC"
## [22] "Tin PM2.5 LC"
## [23] "Titanium PM2.5 LC"
## [24] "Vanadium PM2.5 LC"
## [25] "Silicon PM2.5 LC"
## [26] "Silver PM2.5 LC"
## [27] "Zinc PM2.5 LC"
## [28] "Strontium PM2.5 LC"
## [29] "Sulfur PM2.5 LC"
## [30] "Rubidium PM2.5 LC"
## [31] "Potassium PM2.5 LC"
## [32] "Sodium PM2.5 LC"
## [33] "Zirconium PM2.5 LC"
## [34] "Chloride PM2.5 LC"
## [35] "Ammonium Ion PM2.5 LC"
## [36] "Sodium Ion Pm2.5 LC"
## [37] "Potassium Ion PM2.5 LC"
## [38] "Total Nitrate PM2.5 LC"
## [39] "OC PM2.5 LC TOR"
## [40] "EC PM2.5 LC TOR"
## [41] "OC1 PM2.5 LC"
## [42] "OC2 PM2.5 LC"
## [43] "OC3 PM2.5 LC"
## [44] "OC4 PM2.5 LC"
## [45] "OP PM2.5 LC TOR"
## [46] "EC1 PM2.5 LC"
## [47] "EC2 PM2.5 LC"
## [48] "EC3 PM2.5 LC"
## [49] "OC CSN_Rev Unadjusted PM2.5 LC TOT"
## [50] "EC CSN_Rev Unadjusted PM2.5 LC TOT"
## [51] "OC CSN_Rev Unadjusted PM2.5 LC TOR"
## [52] "OC1 CSN_Rev Unadjusted PM2.5 LC"
## [53] "OC2 CSN_Rev Unadjusted PM2.5 LC"
## [54] "OC3 CSN_Rev Unadjusted PM2.5 LC"
## [55] "OC4 CSN_Rev Unadjusted PM2.5 LC"
## [56] "OP CSN_Rev Unadjusted PM2.5 LC TOR"
## [57] "EC CSN_Rev Unadjusted PM2.5 LC TOR"
## [58] "EC1 CSN_Rev Unadjusted PM2.5 LC"
## [59] "EC2 CSN_Rev Unadjusted PM2.5 LC"
## [60] "EC3 CSN_Rev Unadjusted PM2.5 LC"
## [61] "OP CSN_Rev Unadjusted PM2.5 LC TOT"
## [62] "Sulfate PM2.5 LC"
## [63] "Europium PM2.5 LC"
## [64] "Gallium PM2.5 LC"
## [65] "Hafnium PM2.5 LC"
## [66] "Iridium PM2.5 LC"
## [67] "Molybdenum PM2.5 LC"
## [68] "Mercury PM2.5 LC"
## [69] "Gold PM2.5 LC"
## [70] "Lanthanum PM2.5 LC"
## [71] "Niobium PM2.5 LC"
## [72] "Samarium PM2.5 LC"
## [73] "Scandium PM2.5 LC"
## [74] "Tantalum PM2.5 LC"
## [75] "Terbium PM2.5 LC"
## [76] "Uranium PM2.5 LC"
## [77] "Yttrium PM2.5 LC"
## [78] "Tungsten PM2.5 LC"
## [79] "Total Carbon PM2.5 LC TOT"
## [80] "OP PM2.5 LC TOT"
## [81] "OC CSN Unadjusted PM2.5 LC TOT"
## [82] "EC CSN PM2.5 LC TOT"
## [83] "Optical EC PM2.5 LC TOT"
## [84] "Black Carbon PM2.5 at 880 nm"
## [85] "UV Carbon PM2.5 at 370 nm"
## [86] "Non-volatile Nitrate PM2.5 LC"The data were collected over the following range of dates.
range(dat$Date.Local)## [1] "2014-01-01" "2014-12-31"Summary statistics
Here are the data for Baltimore County.
balt <- filter(dat, State.Code == "24" &
County.Code == "005" &
Parameter.Name == "Sulfate PM2.5 LC") %>%
select(Date.Local, Sample.Value)
balt## # A tibble: 111 x 2
## Date.Local Sample.Value
## <date> <dbl>
## 1 2014-01-05 2.15
## 2 2014-01-08 1.16
## 3 2014-01-11 1.04
## 4 2014-01-14 0.851
## 5 2014-01-17 5.45
## 6 2014-01-23 2.53
## 7 2014-01-26 1.23
## 8 2014-01-29 1.78
## 9 2014-02-04 2.54
## 10 2014-02-10 6.42
## # … with 101 more rowsHere’s a plot of the Sulfate Baltimore County data.
balt %>%
ggplot(aes(Date.Local, Sample.Value)) +
geom_point()