Lab2Practice
Elizabeth Click
9/13/2020
Introduction to Data
library(tidyverse)## -- Attaching packages ----------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts -------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(openintro)## Loading required package: airports## Loading required package: cherryblossom## Loading required package: usdataThe Data
The Bureau of Transportation Statistics (BTS) is a statistical agency that is a part of the Research and Innovative Technology Administration (RITA). As its name implies, BTS collects and makes transportation data available, such as the flights data we will be working with in this lab.
First, we’ll view the nycflights data frame. Type the following in your console to load the data:
data(nycflights)View the Names of the Variables
names(nycflights)## [1] "year" "month" "day" "dep_time" "dep_delay" "arr_time"
## [7] "arr_delay" "carrier" "tailnum" "flight" "origin" "dest"
## [13] "air_time" "distance" "hour" "minute"Get the Code Book on NYC Flights
?nycflights## starting httpd help server ... doneLab Report
To record your analysis in a reproducible format, you can adapt the general Lab Report template from the openintro package. Watch the video above to learn how.
Departure Delays
Let’s start by examing the distribution of departure delays of all flights with a histogram
ggplot(data = nycflights, aes(x = dep_delay)) + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
A bin width of 15
ggplot(data = nycflights, aes(x = dep_delay)) + geom_histogram(binwidth = 15)
A bin width of 150
ggplot(data = nycflights, aes(x = dep_delay)) + geom_histogram(binwidth = 150)
Exercise 1
Look carefully at these three histograms. How do they compare? Are features revealed in one that are obscured in another?
** Answer: Smaller binwidths provide more detail - the largest binwidth obscures any useful information.
WHAT DOES THIS PIPING OPERATION DO? Is it naming a new dataset lax_flights as a subset of nycflights by filtering the dest column of LAX(new york flights with los angeles destination); pulls out data in destination column with “LAX” logical = TRUE
lax_flights <- nycflights %>%
filter(dest == "LAX")
ggplot(data = lax_flights, aes(x = dep_delay)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Use binwidth of 10
ggplot(data = lax_flights, aes(x = dep_delay)) +
geom_histogram(bins = 10)
You can also obtain numerical summaries for these flight
lax_flights %>%
summarise(mean_dd = mean(dep_delay),
median_dd = median(dep_delay),
n = n())## # A tibble: 1 x 3
## mean_dd median_dd n
## <dbl> <dbl> <int>
## 1 9.78 -1 1583You can also filter based on multiple criteria. Suppose you are interested in flights headed to San Francisco (SFO) in February:
sfo_feb_flights <- nycflights %>%
filter(dest == "SFO", month == 2)
sfo_feb_flights## # A tibble: 68 x 16
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## <int> <int> <int> <int> <dbl> <int> <dbl> <chr> <chr>
## 1 2013 2 18 1527 57 1903 48 DL N711ZX
## 2 2013 2 3 613 14 1008 38 UA N502UA
## 3 2013 2 15 955 -5 1313 -28 DL N717TW
## 4 2013 2 18 1928 15 2239 -6 UA N24212
## 5 2013 2 24 1340 2 1644 -21 UA N76269
## 6 2013 2 25 1415 -10 1737 -13 UA N532UA
## 7 2013 2 7 1032 1 1352 -10 B6 N627JB
## 8 2013 2 15 1805 20 2122 2 AA N335AA
## 9 2013 2 13 1056 -4 1412 -13 UA N532UA
## 10 2013 2 8 656 -4 1039 -6 DL N710TW
## # ... with 58 more rows, and 7 more variables: flight <int>, origin <chr>,
## # dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>Exercise 2
Use above data frame that includes flights headed to SFO in February,saved as sfo_feb_flights. How many flights meet these criteria? **Answer: 68 flights fit this criteria
dim(sfo_feb_flights)## [1] 68 16Exercise 3
Describe the distribution of the arrival delays of these flights using a histogram and appropriate summary statistics. Hint: The summary statistics you use should depend on the shape of the distribution.
ggplot(sfo_feb_flights, aes(x = dep_delay)) + geom_histogram(bins = 15)
summary(sfo_feb_flights$dep_delay)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -10.0 -5.0 -2.0 10.5 9.0 209.0**Answer: The distribution is skewed to the right
Another useful technique is quickly calculating summary statistics for various groups in your data frame. For example, we can modify the above command using the group_by function to get the same summary stats for each origin airport:
sfo_feb_flights %>%
group_by(origin) %>%
summarize(median_dd = median(dep_delay), mean_dd = mean(dep_delay), iqr_dd = IQR(dep_delay), n_flights = n())## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 5
## origin median_dd mean_dd iqr_dd n_flights
## <chr> <dbl> <dbl> <dbl> <int>
## 1 EWR 0.5 2.5 5.75 8
## 2 JFK -2.5 11.6 15.2 60##Exercise 4 Calculate the median and interquartile range for arr_delays of flights in in the sfo_feb_flights data frame, grouped by carrier. Which carrier has the most variable arrival delays?
sfo_feb_flights %>%
group_by(carrier) %>%
summarise(median_dd = median(dep_delay), iqr_dd = IQR(dep_delay), n_flights = n()) %>%
arrange(desc(iqr_dd))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 5 x 4
## carrier median_dd iqr_dd n_flights
## <chr> <dbl> <dbl> <int>
## 1 AA 13 32.8 10
## 2 VX -3.5 16.8 12
## 3 UA -2 13 21
## 4 DL -3 6.5 19
## 5 B6 -2 3.5 6**Answer: American Airlines has the most variable arrival days because it’s IQR is the largest
##Departure Delays by Month
nycflights %>%
group_by(month) %>%
summarise(mean_dd = mean(dep_delay)) %>%
arrange(desc(mean_dd))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 12 x 2
## month mean_dd
## <int> <dbl>
## 1 7 20.8
## 2 6 20.4
## 3 12 17.4
## 4 4 14.6
## 5 3 13.5
## 6 5 13.3
## 7 8 12.6
## 8 2 10.7
## 9 1 10.2
## 10 9 6.87
## 11 11 6.10
## 12 10 5.88##Exercise 5 Suppose you really dislike departure delays and you want to schedule your travel in a month that minimizes your potential departure delay leaving NYC. One option is to choose the month with the lowest mean departure delay. Another option is to choose the month with the lowest median departure delay. What are the pros and cons of these two choices?
** Answer: because the distribution is skewed right, the mean is going to be much higher than the median, so the more reliable measure would be to use median in this case.
nycflights %>%
group_by(month) %>%
summarise(median_dd = median(dep_delay)) %>%
arrange(desc(median_dd))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 12 x 2
## month median_dd
## <int> <dbl>
## 1 12 1
## 2 6 0
## 3 7 0
## 4 3 -1
## 5 5 -1
## 6 8 -1
## 7 1 -2
## 8 2 -2
## 9 4 -2
## 10 11 -2
## 11 9 -3
## 12 10 -3#On time departure rate for NYC airports Let’s start with classifying each flight as “on time” or “delayed” by creating a new variable with the mutate function.
nycflights <- nycflights %>%
mutate(ot_dep_rate = if_else(dep_delay < 5, "on-time", "delayed"))Now arrange the on-time flights rates in descending order
##{r} ##nycflights %>% ## group_by(origin) %>% ## summarise(ot_dep_rate = sum(dep_type == "on time") / n()) %>% ## arrange(desc(ot_dep_rate))
Excercise 6
If you were selecting an airport simply based on on time departure percentage, which NYC airport would you choose to fly out of?
** Answer: LGA
##{r} ##ggplot(data = nycflights, aes(x = origin, fill = dep_type)) + ## geom_bar() ##
More Practice
Exercise 7
Mutate the data frame so that it includes a new variable that contains the average speed, avg_speed traveled by the plane for each flight (in mph). Hint: Average speed can be calculated as distance divided by number of hours of travel, and note that air_time is given in minutes.
new <- nycflights %>%
mutate(avg_speed = distance/air_time/60)
new## # A tibble: 32,735 x 18
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## <int> <int> <int> <int> <dbl> <int> <dbl> <chr> <chr>
## 1 2013 6 30 940 15 1216 -4 VX N626VA
## 2 2013 5 7 1657 -3 2104 10 DL N3760C
## 3 2013 12 8 859 -1 1238 11 DL N712TW
## 4 2013 5 14 1841 -4 2122 -34 DL N914DL
## 5 2013 7 21 1102 -3 1230 -8 9E N823AY
## 6 2013 1 1 1817 -3 2008 3 AA N3AXAA
## 7 2013 12 9 1259 14 1617 22 WN N218WN
## 8 2013 8 13 1920 85 2032 71 B6 N284JB
## 9 2013 9 26 725 -10 1027 -8 AA N3FSAA
## 10 2013 4 30 1323 62 1549 60 EV N12163
## # ... with 32,725 more rows, and 9 more variables: flight <int>, origin <chr>,
## # dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## # ot_dep_rate <chr>, avg_speed <dbl>Exercise 8
Make a scatterplot of avg_speed vs. distance. Describe the relationship between average speed and distance. Hint: Use geom_point().
ggplot(new, aes(x = distance, y = avg_speed)) + geom_point(xlab = "Distance", ylab = "Average Speed")## Warning: Ignoring unknown parameters: xlab, ylab
Exercise 9
Replicate the following plot. Hint: The data frame plotted only contains flights from American Airlines, Delta Airlines, and United Airlines, and the points are colored by carrier. Once you replicate the plot, determine (roughly) what the cutoff point is for departure delays where you can still expect to get to your destination on time.
plot2 <- nycflights %>%
filter(carrier == "AA" | carrier == "UA" | carrier == "DL") %>%
ggplot(aes(x = dep_delay, y = arr_delay, color = carrier)) +
geom_point()
plot2