Term Project: Draft
Erik Nome, Mike Gardner, Samuel Evans
12/4/19
- Import data
- Description of the data and definition of variables
- Visualize data
- Correlation and regression analysis
- Share interesting stories you found from the data
- Hide the messages, but display the code and its results on the webpage.
- List names of all group members (both first and last name) at the top of the webpage.
- Use the correct slug.
library(tidyverse)
library(lubridate)
ufo_sightings <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-06-25/ufo_sightings.csv") %>%
# Extract year from date_time, and calculate decade from year
mutate(year = year(parse_date_time(date_time, 'mdy_hm')),
decade = as.factor(year %/% 10 * 10))It would be interesting to see why there was a huge hike in UFO sightings since late 1990’s. Any news articles?
ufo_sightings %>%
count(decade, sort = TRUE) %>%
ggplot(aes(decade, n)) +
geom_col() +
scale_y_continuous(labels = scales::comma) +
labs(title = "UFO Sightings by Decade",
y = "number of sightings",
x = NULL)
The number of sightings is higher in populous states (e.g., California, Texas and New York). What do they mean? Are these states more popular among aliens?
# the code from the textbook: 6.2.2 Data by US state
library(choroplethr)
library(openintro) # for abbr2state()
data(continental_us_states)
# Create region and value columns to pass to state_choropleth() to create map
ufo_map <-
ufo_sightings %>%
count(state, sort = TRUE) %>%
mutate(region = tolower(abbr2state(state))) %>%
rename(value = n) %>%
filter(!is.na(region))
# Pass to state_choropleth() to create map
state_choropleth(ufo_map,
num_colors=9,
zoom = continental_us_states) +
scale_fill_brewer(palette="YlOrBr") +
labs(title = "UFO Sightings in the United States",
caption = "source: THE NATIONAL UFO REPORTING CENTER",
fill = "Nunmber of Sightings") 
Import data
Hint: You can choose any data you like but can’t take one that is already taken by other groups.
ufo_sightings <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-06-25/ufo_sightings.csv")Description of the data and definition of variables
The data collected is roughly 80,000 observations of UFO sightings around the world. The variables of the data are date sighting occurred, city or area of sighting, state/region of sighting, country of sighting, UFO shape, length of encounter in seconds, described length of encounter, description of encounter, date documented, latitude, and longitude.
Visualize data
Hint: Create at least two plots.
library(ggplot2)
data(ufo_sightings)
ggplot(ufo_sightings,
aes(x = country,
y = encounter_length)) +
geom_point()
library(dplyr)
plotdata <- filter(ufo_sightings)
ggplot(plotdata,
aes(x = encounter_length,
y = latitude)) +
geom_line()
library(dplyr)
plotdata <- filter(ufo_sightings)
ggplot(plotdata,
aes(x = encounter_length,
y = longitude)) +
geom_line()
Correlation and regression analysis
data(ufo_sightings)
df <- dplyr :: select_if(ufo_sightings, is.numeric)
r <- cor(df, use = "complete.obs")
round(r,2)
## encounter_length latitude longitude
## encounter_length 1.00 0.00 0.01
## latitude 0.00 1.00 -0.39
## longitude 0.01 -0.39 1.00houses_lm <- lm(encounter_length ~ country,
data = ufo_sightings)
summary(houses_lm)
##
## Call:
## lm(formula = encounter_length ~ country, data = ufo_sightings)
##
## Residuals:
## Min 1Q Median 3Q Max
## -66061 -5785 -5620 -5200 97769939
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3806 27002 0.141 0.8879
## countryca 25053 29323 0.854 0.3929
## countryde 20450 66820 0.306 0.7596
## countrygb 62255 30578 2.036 0.0418 *
## countryus 1994 27113 0.074 0.9414
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 626300 on 70655 degrees of freedom
## (9672 observations deleted due to missingness)
## Multiple R-squared: 0.000291, Adjusted R-squared: 0.0002344
## F-statistic: 5.142 on 4 and 70655 DF, p-value: 0.0003864