library(tidyverse)## ── Attaching packages ──────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.2.1 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.3
## ✓ tidyr 1.0.0 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0## ── Conflicts ─────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# runs the tables
table1## # A tibble: 6 x 4
## country year cases population
## <chr> <int> <int> <int>
## 1 Afghanistan 1999 745 19987071
## 2 Afghanistan 2000 2666 20595360
## 3 Brazil 1999 37737 172006362
## 4 Brazil 2000 80488 174504898
## 5 China 1999 212258 1272915272
## 6 China 2000 213766 1280428583table2## # A tibble: 12 x 4
## country year type count
## <chr> <int> <chr> <int>
## 1 Afghanistan 1999 cases 745
## 2 Afghanistan 1999 population 19987071
## 3 Afghanistan 2000 cases 2666
## 4 Afghanistan 2000 population 20595360
## 5 Brazil 1999 cases 37737
## 6 Brazil 1999 population 172006362
## 7 Brazil 2000 cases 80488
## 8 Brazil 2000 population 174504898
## 9 China 1999 cases 212258
## 10 China 1999 population 1272915272
## 11 China 2000 cases 213766
## 12 China 2000 population 1280428583table3## # A tibble: 6 x 3
## country year rate
## * <chr> <int> <chr>
## 1 Afghanistan 1999 745/19987071
## 2 Afghanistan 2000 2666/20595360
## 3 Brazil 1999 37737/172006362
## 4 Brazil 2000 80488/174504898
## 5 China 1999 212258/1272915272
## 6 China 2000 213766/1280428583table4a ## # A tibble: 3 x 3
## country `1999` `2000`
## * <chr> <int> <int>
## 1 Afghanistan 745 2666
## 2 Brazil 37737 80488
## 3 China 212258 213766table4b ## # A tibble: 3 x 3
## country `1999` `2000`
## * <chr> <int> <int>
## 1 Afghanistan 19987071 20595360
## 2 Brazil 172006362 174504898
## 3 China 1272915272 1280428583Dataset Tidy: Each variable must have its own column Each observation must have its own row Each value must have its own cell
# Compute rate per 10,000
table1 %>%
mutate(rate = cases / population * 10000)## # A tibble: 6 x 5
## country year cases population rate
## <chr> <int> <int> <int> <dbl>
## 1 Afghanistan 1999 745 19987071 0.373
## 2 Afghanistan 2000 2666 20595360 1.29
## 3 Brazil 1999 37737 172006362 2.19
## 4 Brazil 2000 80488 174504898 4.61
## 5 China 1999 212258 1272915272 1.67
## 6 China 2000 213766 1280428583 1.67# Compute cases per year
table1 %>%
count(year, wt = cases)## # A tibble: 2 x 2
## year n
## <int> <int>
## 1 1999 250740
## 2 2000 296920#1 Using prose, describe how the variables and observations are organised in each of the sample tables.
#Columns are their own variables, meaning year only has year or country only has a country.
table1## # A tibble: 6 x 4
## country year cases population
## <chr> <int> <int> <int>
## 1 Afghanistan 1999 745 19987071
## 2 Afghanistan 2000 2666 20595360
## 3 Brazil 1999 37737 172006362
## 4 Brazil 2000 80488 174504898
## 5 China 1999 212258 1272915272
## 6 China 2000 213766 1280428583# Combined columns, such as cases and population in the type column.
table2## # A tibble: 12 x 4
## country year type count
## <chr> <int> <chr> <int>
## 1 Afghanistan 1999 cases 745
## 2 Afghanistan 1999 population 19987071
## 3 Afghanistan 2000 cases 2666
## 4 Afghanistan 2000 population 20595360
## 5 Brazil 1999 cases 37737
## 6 Brazil 1999 population 172006362
## 7 Brazil 2000 cases 80488
## 8 Brazil 2000 population 174504898
## 9 China 1999 cases 212258
## 10 China 1999 population 1272915272
## 11 China 2000 cases 213766
## 12 China 2000 population 1280428583# Rate is a combine column of separated by a /.
table3## # A tibble: 6 x 3
## country year rate
## * <chr> <int> <chr>
## 1 Afghanistan 1999 745/19987071
## 2 Afghanistan 2000 2666/20595360
## 3 Brazil 1999 37737/172006362
## 4 Brazil 2000 80488/174504898
## 5 China 1999 212258/1272915272
## 6 China 2000 213766/1280428583# Contains the cases, with the the years separated into separate columns.
table4a ## # A tibble: 3 x 3
## country `1999` `2000`
## * <chr> <int> <int>
## 1 Afghanistan 745 2666
## 2 Brazil 37737 80488
## 3 China 212258 213766# Contains the population.
table4b ## # A tibble: 3 x 3
## country `1999` `2000`
## * <chr> <int> <int>
## 1 Afghanistan 19987071 20595360
## 2 Brazil 172006362 174504898
## 3 China 1272915272 1280428583#2 Compute the rate for table2, and table 4a+table 4b.
#1 Extract the bumber of TB case per country per year.
#2 Extract the matching population per country per year.
#3 Divide the cases by population and mulitply by 1000
#4 Sore back in the appropraite place
# Which representation is easiest to work with? Which is hardest? Why?
#Table2
cases <- table2 %>%
filter(type == "cases")
populations <- table2 %>%
filter(type == "population")
merged <- cbind(cases, populations)
merged$rates= (cases$count / populations$count)*1000
merged## country year type count country year type count
## 1 Afghanistan 1999 cases 745 Afghanistan 1999 population 19987071
## 2 Afghanistan 2000 cases 2666 Afghanistan 2000 population 20595360
## 3 Brazil 1999 cases 37737 Brazil 1999 population 172006362
## 4 Brazil 2000 cases 80488 Brazil 2000 population 174504898
## 5 China 1999 cases 212258 China 1999 population 1272915272
## 6 China 2000 cases 213766 China 2000 population 1280428583
## rates
## 1 0.0372741
## 2 0.1294466
## 3 0.2193930
## 4 0.4612363
## 5 0.1667495
## 6 0.1669488#Tables4
rates_99 <- (table4a$`1999` / table4b$`1999`)*1000
rates_00 <- (table4a$`2000`/ table4b$`2000`)*1000
new <- table4a['country']
new$rates_00 = rates_00
new$rates_99 = rates_99
new## # A tibble: 3 x 3
## country rates_00 rates_99
## <chr> <dbl> <dbl>
## 1 Afghanistan 0.129 0.0373
## 2 Brazil 0.461 0.219
## 3 China 0.167 0.167#Table 4a and 4b were easier to work with, whereas Table 2 had to be separated and filter before doing any of the calculations.A common problem is a dataset where some of the column names are not names of variables, but values of a variable.
pivot_longer() makes wide tables narrower and longer
table4a## # A tibble: 3 x 3
## country `1999` `2000`
## * <chr> <int> <int>
## 1 Afghanistan 745 2666
## 2 Brazil 37737 80488
## 3 China 212258 213766# Changes 1999 to year and 2000 to cases
table4a %>%
pivot_longer(c(`1999`, `2000`), names_to = "year", values_to = "cases")## # A tibble: 6 x 3
## country year cases
## <chr> <chr> <int>
## 1 Afghanistan 1999 745
## 2 Afghanistan 2000 2666
## 3 Brazil 1999 37737
## 4 Brazil 2000 80488
## 5 China 1999 212258
## 6 China 2000 213766table4b## # A tibble: 3 x 3
## country `1999` `2000`
## * <chr> <int> <int>
## 1 Afghanistan 19987071 20595360
## 2 Brazil 172006362 174504898
## 3 China 1272915272 1280428583# Tidying table 4b by using pivot_longer
table4b %>%
pivot_longer(c(`1999`, `2000`), names_to = "year", values_to = "population")## # A tibble: 6 x 3
## country year population
## <chr> <chr> <int>
## 1 Afghanistan 1999 19987071
## 2 Afghanistan 2000 20595360
## 3 Brazil 1999 172006362
## 4 Brazil 2000 174504898
## 5 China 1999 1272915272
## 6 China 2000 1280428583# Combines the tidied table4a and table4b
tidy4a <- table4a %>%
pivot_longer(c(`1999`, `2000`), names_to = "year", values_to = "cases")
tidy4b <- table4b %>%
pivot_longer(c(`1999`, `2000`), names_to = "year", values_to = "population")
left_join(tidy4a, tidy4b)## Joining, by = c("country", "year")## # A tibble: 6 x 4
## country year cases population
## <chr> <chr> <int> <int>
## 1 Afghanistan 1999 745 19987071
## 2 Afghanistan 2000 2666 20595360
## 3 Brazil 1999 37737 172006362
## 4 Brazil 2000 80488 174504898
## 5 China 1999 212258 1272915272
## 6 China 2000 213766 1280428583pivot_wider() is the opposite of pivot_longer(). You use it when an observation is scattered across multiple rows.
pivot_wider() makes long tables shorter and wider.
table2## # A tibble: 12 x 4
## country year type count
## <chr> <int> <chr> <int>
## 1 Afghanistan 1999 cases 745
## 2 Afghanistan 1999 population 19987071
## 3 Afghanistan 2000 cases 2666
## 4 Afghanistan 2000 population 20595360
## 5 Brazil 1999 cases 37737
## 6 Brazil 1999 population 172006362
## 7 Brazil 2000 cases 80488
## 8 Brazil 2000 population 174504898
## 9 China 1999 cases 212258
## 10 China 1999 population 1272915272
## 11 China 2000 cases 213766
## 12 China 2000 population 1280428583# separates type into two columns of case and population with the count as the values
table2 %>%
pivot_wider(names_from = type, values_from = count)## # A tibble: 6 x 4
## country year cases population
## <chr> <int> <int> <int>
## 1 Afghanistan 1999 745 19987071
## 2 Afghanistan 2000 2666 20595360
## 3 Brazil 1999 37737 172006362
## 4 Brazil 2000 80488 174504898
## 5 China 1999 212258 1272915272
## 6 China 2000 213766 1280428583#1 Why are pivot_longer and pivot_wider not perfectly symmetrical?
stocks <- tibble(
year = c(2015, 2015, 2016, 2016),
half = c( 1, 2, 1, 2),
return = c(1.88, 0.59, 0.92, 0.17)
)
stocks %>%
pivot_wider(names_from = year, values_from = return) %>%
pivot_longer(`2015`:`2016`, names_to = "year", values_to = "return")## # A tibble: 4 x 3
## half year return
## <dbl> <chr> <dbl>
## 1 1 2015 1.88
## 2 1 2016 0.92
## 3 2 2015 0.59
## 4 2 2016 0.17# Since the shape of the original tibble has changed and the orignal placement of columns and values. Pivot_wider() creates each year as it's own column instead of a double value column.#2 Why does this code fail?
#table4a %>%
# pivot_longer(c(1999, 2000), names_to = "year", values_to = "cases")
# Fails because we tried to use column names as integers#3 Why would happen if you widen this table? Why? How could you add a new column to uniquely identify each value?
people <- tribble(
~name, ~names, ~values,
#-----------------|--------|------
"Phillip Woods", "age", 45,
"Phillip Woods", "height", 186,
"Phillip Woods", "age", 50,
"Jessica Cordero", "age", 37,
"Jessica Cordero", "height", 156
)
# Receive an error when trying to widen the table, due to the duplication of names. To avoid the error, I would create columns for age and one for height.
people %>%
pivot_wider(names_from = names, values_from = values)## Warning: Values in `values` are not uniquely identified; output will contain list-cols.
## * Use `values_fn = list(values = list)` to suppress this warning.
## * Use `values_fn = list(values = length)` to identify where the duplicates arise
## * Use `values_fn = list(values = summary_fun)` to summarise duplicates## # A tibble: 2 x 3
## name age height
## <chr> <list<dbl>> <list<dbl>>
## 1 Phillip Woods [2] [1]
## 2 Jessica Cordero [1] [1]#4 Tidy the simple tibble below. Do you need to make it wider or longer? What are the variables?
preg <- tribble(
~pregnant, ~male, ~female,
"yes", NA, 10,
"no", 20, 12
)
preg%>%
pivot_longer(c('male','female'), names_to="sex",values_to=)## # A tibble: 4 x 3
## pregnant sex value
## <chr> <chr> <dbl>
## 1 yes male NA
## 2 yes female 10
## 3 no male 20
## 4 no female 12separate() pulls apart one column into multiple columns, by splitting wherever a separator character appears.
table3## # A tibble: 6 x 3
## country year rate
## * <chr> <int> <chr>
## 1 Afghanistan 1999 745/19987071
## 2 Afghanistan 2000 2666/20595360
## 3 Brazil 1999 37737/172006362
## 4 Brazil 2000 80488/174504898
## 5 China 1999 212258/1272915272
## 6 China 2000 213766/1280428583# separates rate into cases and population at the forward slash
table3 %>%
separate(rate, into = c("cases", "population"))## # A tibble: 6 x 4
## country year cases population
## <chr> <int> <chr> <chr>
## 1 Afghanistan 1999 745 19987071
## 2 Afghanistan 2000 2666 20595360
## 3 Brazil 1999 37737 172006362
## 4 Brazil 2000 80488 174504898
## 5 China 1999 212258 1272915272
## 6 China 2000 213766 1280428583# could also write as:
#table3 %>%
#separate(rate, into = c("cases", "population"), sep = "/")table3 %>%
separate(rate, into = c("cases", "population"), convert = TRUE)## # A tibble: 6 x 4
## country year cases population
## <chr> <int> <int> <int>
## 1 Afghanistan 1999 745 19987071
## 2 Afghanistan 2000 2666 20595360
## 3 Brazil 1999 37737 172006362
## 4 Brazil 2000 80488 174504898
## 5 China 1999 212258 1272915272
## 6 China 2000 213766 1280428583table3 %>%
separate(year, into = c("century", "year"), sep = 2)## # A tibble: 6 x 4
## country century year rate
## <chr> <chr> <chr> <chr>
## 1 Afghanistan 19 99 745/19987071
## 2 Afghanistan 20 00 2666/20595360
## 3 Brazil 19 99 37737/172006362
## 4 Brazil 20 00 80488/174504898
## 5 China 19 99 212258/1272915272
## 6 China 20 00 213766/1280428583unite() is the inverse of separate(): it combines multiple columns into a single column
table5 %>%
unite(new, century, year)## # A tibble: 6 x 3
## country new rate
## <chr> <chr> <chr>
## 1 Afghanistan 19_99 745/19987071
## 2 Afghanistan 20_00 2666/20595360
## 3 Brazil 19_99 37737/172006362
## 4 Brazil 20_00 80488/174504898
## 5 China 19_99 212258/1272915272
## 6 China 20_00 213766/1280428583table5 %>%
unite(new, century, year, sep = "")## # A tibble: 6 x 3
## country new rate
## <chr> <chr> <chr>
## 1 Afghanistan 1999 745/19987071
## 2 Afghanistan 2000 2666/20595360
## 3 Brazil 1999 37737/172006362
## 4 Brazil 2000 80488/174504898
## 5 China 1999 212258/1272915272
## 6 China 2000 213766/1280428583#1 What do the extra and fill arguments do in separate()? Experiment with the various options for the following two toy datasets.
# The extra argument 'g'. The extra tells what to do if there are too many
tibble(x = c("a,b,c", "d,e,f,g", "h,i,j")) %>%
separate(x, c("one", "two", "three"), extra = "drop")## # A tibble: 3 x 3
## one two three
## <chr> <chr> <chr>
## 1 a b c
## 2 d e f
## 3 h i jtibble(x = c("a,b,c", "d,e,f,g", "h,i,j")) %>%
separate(x, c("one", "two", "three"), extra = "merge")## # A tibble: 3 x 3
## one two three
## <chr> <chr> <chr>
## 1 a b c
## 2 d e f,g
## 3 h i j# The fill argument 'f'. The fill argument tells it what to do if there is something missing.
tibble(x = c("a,b,c", "d,e", "f,g,i")) %>%
separate(x, c("one", "two", "three"))## Warning: Expected 3 pieces. Missing pieces filled with `NA` in 1 rows [2].## # A tibble: 3 x 3
## one two three
## <chr> <chr> <chr>
## 1 a b c
## 2 d e <NA>
## 3 f g i#2 Both unite() and separate() have a remove argument. What does it do? Why would you set it to FALSE?
#The remove argument removes/discards the columns used to either unite or separate. Setting it to FALSE, keeps the original columns.#3 Compare and contrast separate() and extract(). Why are there three variations of separation (position, separator, groups), but only one unite?
# Separate() splits a column into multiple columns.
# Extract() splits a column into multuple columns as well.
# Separate and Extract both start with a single column broken into several columns.
# Unite takes multiple columns converts it into a single column.# Missing two values
stocks <- tibble(
year = c(2015, 2015, 2015, 2015, 2016, 2016, 2016),
qtr = c( 1, 2, 3, 4, 2, 3, 4),
return = c(1.88, 0.59, 0.35, NA, 0.92, 0.17, 2.66)
)stocks %>%
pivot_wider(names_from = year, values_from = return)## # A tibble: 4 x 3
## qtr `2015` `2016`
## <dbl> <dbl> <dbl>
## 1 1 1.88 NA
## 2 2 0.59 0.92
## 3 3 0.35 0.17
## 4 4 NA 2.66stocks %>%
pivot_wider(names_from = year, values_from = return) %>%
pivot_longer(
cols = c(`2015`, `2016`),
names_to = "year",
values_to = "return",
values_drop_na = TRUE
)## # A tibble: 6 x 3
## qtr year return
## <dbl> <chr> <dbl>
## 1 1 2015 1.88
## 2 2 2015 0.59
## 3 2 2016 0.92
## 4 3 2015 0.35
## 5 3 2016 0.17
## 6 4 2016 2.66stocks %>%
complete(year, qtr)## # A tibble: 8 x 3
## year qtr return
## <dbl> <dbl> <dbl>
## 1 2015 1 1.88
## 2 2015 2 0.59
## 3 2015 3 0.35
## 4 2015 4 NA
## 5 2016 1 NA
## 6 2016 2 0.92
## 7 2016 3 0.17
## 8 2016 4 2.66treatment <- tribble(
~ person, ~ treatment, ~response,
"Derrick Whitmore", 1, 7,
NA, 2, 10,
NA, 3, 9,
"Katherine Burke", 1, 4
)# Fill takes a set of columns where you want missing values to be replaced by the most recent non-missing value (sometimes called last observation carried forward).
treatment %>%
fill(person)## # A tibble: 4 x 3
## person treatment response
## <chr> <dbl> <dbl>
## 1 Derrick Whitmore 1 7
## 2 Derrick Whitmore 2 10
## 3 Derrick Whitmore 3 9
## 4 Katherine Burke 1 4#1 Compare and contrast fill arguments to pivot_wider() and complete().
treatment <- tribble(
~ person, ~ treatment, ~response,
"Derrick Whitmore", 1, 7,
NA, 2, 10,
NA, 3, 9,
"Katherine Burke", 1, 4
)
# Carries Derrick Whitmore to fill in the NA in person
treatment %>%
fill(person)## # A tibble: 4 x 3
## person treatment response
## <chr> <dbl> <dbl>
## 1 Derrick Whitmore 1 7
## 2 Derrick Whitmore 2 10
## 3 Derrick Whitmore 3 9
## 4 Katherine Burke 1 4# Creates an error there is missing data from the person column. Must do pivot_longer() as well to drop the NA values.
#pivot_wider(names_from = person, values_from = response)
# Finds unique values and creates several NA in person
treatment %>%
complete(treatment, response)## # A tibble: 12 x 3
## treatment response person
## <dbl> <dbl> <chr>
## 1 1 4 Katherine Burke
## 2 1 7 Derrick Whitmore
## 3 1 9 <NA>
## 4 1 10 <NA>
## 5 2 4 <NA>
## 6 2 7 <NA>
## 7 2 9 <NA>
## 8 2 10 <NA>
## 9 3 4 <NA>
## 10 3 7 <NA>
## 11 3 9 <NA>
## 12 3 10 <NA>#2 What does the direction argument to fill() do?
# The direction tells it in which direction it should take the last data to fill in the blanks with.
treatment <- tribble(
~ person, ~ treatment, ~response,
"Derrick Whitmore", 1, 7,
NA, 2, 10,
NA, 3, 9,
"Katherine Burke", 1, 4
)
# Carries Katherine Burke to fill in the NA
fill(treatment, person, .direction = "up")## # A tibble: 4 x 3
## person treatment response
## <chr> <dbl> <dbl>
## 1 Derrick Whitmore 1 7
## 2 Katherine Burke 2 10
## 3 Katherine Burke 3 9
## 4 Katherine Burke 1 4who## # A tibble: 7,240 x 60
## country iso2 iso3 year new_sp_m014 new_sp_m1524 new_sp_m2534 new_sp_m3544
## <chr> <chr> <chr> <int> <int> <int> <int> <int>
## 1 Afghan… AF AFG 1980 NA NA NA NA
## 2 Afghan… AF AFG 1981 NA NA NA NA
## 3 Afghan… AF AFG 1982 NA NA NA NA
## 4 Afghan… AF AFG 1983 NA NA NA NA
## 5 Afghan… AF AFG 1984 NA NA NA NA
## 6 Afghan… AF AFG 1985 NA NA NA NA
## 7 Afghan… AF AFG 1986 NA NA NA NA
## 8 Afghan… AF AFG 1987 NA NA NA NA
## 9 Afghan… AF AFG 1988 NA NA NA NA
## 10 Afghan… AF AFG 1989 NA NA NA NA
## # … with 7,230 more rows, and 52 more variables: new_sp_m4554 <int>,
## # new_sp_m5564 <int>, new_sp_m65 <int>, new_sp_f014 <int>,
## # new_sp_f1524 <int>, new_sp_f2534 <int>, new_sp_f3544 <int>,
## # new_sp_f4554 <int>, new_sp_f5564 <int>, new_sp_f65 <int>,
## # new_sn_m014 <int>, new_sn_m1524 <int>, new_sn_m2534 <int>,
## # new_sn_m3544 <int>, new_sn_m4554 <int>, new_sn_m5564 <int>,
## # new_sn_m65 <int>, new_sn_f014 <int>, new_sn_f1524 <int>,
## # new_sn_f2534 <int>, new_sn_f3544 <int>, new_sn_f4554 <int>,
## # new_sn_f5564 <int>, new_sn_f65 <int>, new_ep_m014 <int>,
## # new_ep_m1524 <int>, new_ep_m2534 <int>, new_ep_m3544 <int>,
## # new_ep_m4554 <int>, new_ep_m5564 <int>, new_ep_m65 <int>,
## # new_ep_f014 <int>, new_ep_f1524 <int>, new_ep_f2534 <int>,
## # new_ep_f3544 <int>, new_ep_f4554 <int>, new_ep_f5564 <int>,
## # new_ep_f65 <int>, newrel_m014 <int>, newrel_m1524 <int>,
## # newrel_m2534 <int>, newrel_m3544 <int>, newrel_m4554 <int>,
## # newrel_m5564 <int>, newrel_m65 <int>, newrel_f014 <int>,
## # newrel_f1524 <int>, newrel_f2534 <int>, newrel_f3544 <int>,
## # newrel_f4554 <int>, newrel_f5564 <int>, newrel_f65 <int># Focus on the values that are present and create new colum names.
who1 <- who %>%
pivot_longer(
cols = new_sp_m014:newrel_f65,
names_to = "key",
values_to = "cases",
values_drop_na = TRUE
)
who1## # A tibble: 76,046 x 6
## country iso2 iso3 year key cases
## <chr> <chr> <chr> <int> <chr> <int>
## 1 Afghanistan AF AFG 1997 new_sp_m014 0
## 2 Afghanistan AF AFG 1997 new_sp_m1524 10
## 3 Afghanistan AF AFG 1997 new_sp_m2534 6
## 4 Afghanistan AF AFG 1997 new_sp_m3544 3
## 5 Afghanistan AF AFG 1997 new_sp_m4554 5
## 6 Afghanistan AF AFG 1997 new_sp_m5564 2
## 7 Afghanistan AF AFG 1997 new_sp_m65 0
## 8 Afghanistan AF AFG 1997 new_sp_f014 5
## 9 Afghanistan AF AFG 1997 new_sp_f1524 38
## 10 Afghanistan AF AFG 1997 new_sp_f2534 36
## # … with 76,036 more rows# Structure of the values
who1 %>%
count(key)## # A tibble: 56 x 2
## key n
## <chr> <int>
## 1 new_ep_f014 1032
## 2 new_ep_f1524 1021
## 3 new_ep_f2534 1021
## 4 new_ep_f3544 1021
## 5 new_ep_f4554 1017
## 6 new_ep_f5564 1017
## 7 new_ep_f65 1014
## 8 new_ep_m014 1038
## 9 new_ep_m1524 1026
## 10 new_ep_m2534 1020
## # … with 46 more rows# Fixes variable names that are inconsistent
who2 <- who1 %>%
mutate(names_from = stringr::str_replace(key, "newrel", "new_rel"))
who2## # A tibble: 76,046 x 7
## country iso2 iso3 year key cases names_from
## <chr> <chr> <chr> <int> <chr> <int> <chr>
## 1 Afghanistan AF AFG 1997 new_sp_m014 0 new_sp_m014
## 2 Afghanistan AF AFG 1997 new_sp_m1524 10 new_sp_m1524
## 3 Afghanistan AF AFG 1997 new_sp_m2534 6 new_sp_m2534
## 4 Afghanistan AF AFG 1997 new_sp_m3544 3 new_sp_m3544
## 5 Afghanistan AF AFG 1997 new_sp_m4554 5 new_sp_m4554
## 6 Afghanistan AF AFG 1997 new_sp_m5564 2 new_sp_m5564
## 7 Afghanistan AF AFG 1997 new_sp_m65 0 new_sp_m65
## 8 Afghanistan AF AFG 1997 new_sp_f014 5 new_sp_f014
## 9 Afghanistan AF AFG 1997 new_sp_f1524 38 new_sp_f1524
## 10 Afghanistan AF AFG 1997 new_sp_f2534 36 new_sp_f2534
## # … with 76,036 more rows# Separates at each underscore
who3 <- who2 %>%
separate(key, c("new", "type", "sexage"), sep = "_")## Warning: Expected 3 pieces. Missing pieces filled with `NA` in 2580 rows [243,
## 244, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 903,
## 904, 905, 906, ...].who3## # A tibble: 76,046 x 9
## country iso2 iso3 year new type sexage cases names_from
## <chr> <chr> <chr> <int> <chr> <chr> <chr> <int> <chr>
## 1 Afghanistan AF AFG 1997 new sp m014 0 new_sp_m014
## 2 Afghanistan AF AFG 1997 new sp m1524 10 new_sp_m1524
## 3 Afghanistan AF AFG 1997 new sp m2534 6 new_sp_m2534
## 4 Afghanistan AF AFG 1997 new sp m3544 3 new_sp_m3544
## 5 Afghanistan AF AFG 1997 new sp m4554 5 new_sp_m4554
## 6 Afghanistan AF AFG 1997 new sp m5564 2 new_sp_m5564
## 7 Afghanistan AF AFG 1997 new sp m65 0 new_sp_m65
## 8 Afghanistan AF AFG 1997 new sp f014 5 new_sp_f014
## 9 Afghanistan AF AFG 1997 new sp f1524 38 new_sp_f1524
## 10 Afghanistan AF AFG 1997 new sp f2534 36 new_sp_f2534
## # … with 76,036 more rowswho3 %>%
count(new)## # A tibble: 2 x 2
## new n
## <chr> <int>
## 1 new 73466
## 2 newrel 2580# Drops columns
who4 <- who3 %>%
select(-new, -iso2, -iso3)# Separates sexage into sex and age
who5 <- who4 %>%
separate(sexage, c("sex", "age"), sep = 1)
who5## # A tibble: 76,046 x 7
## country year type sex age cases names_from
## <chr> <int> <chr> <chr> <chr> <int> <chr>
## 1 Afghanistan 1997 sp m 014 0 new_sp_m014
## 2 Afghanistan 1997 sp m 1524 10 new_sp_m1524
## 3 Afghanistan 1997 sp m 2534 6 new_sp_m2534
## 4 Afghanistan 1997 sp m 3544 3 new_sp_m3544
## 5 Afghanistan 1997 sp m 4554 5 new_sp_m4554
## 6 Afghanistan 1997 sp m 5564 2 new_sp_m5564
## 7 Afghanistan 1997 sp m 65 0 new_sp_m65
## 8 Afghanistan 1997 sp f 014 5 new_sp_f014
## 9 Afghanistan 1997 sp f 1524 38 new_sp_f1524
## 10 Afghanistan 1997 sp f 2534 36 new_sp_f2534
## # … with 76,036 more rows# TIDY DATASET
who %>%
pivot_longer(
cols = new_sp_m014:newrel_f65,
names_to = "key",
values_to = "cases",
values_drop_na = TRUE
) %>%
mutate(
key = stringr::str_replace(key, "newrel", "new_rel")
) %>%
separate(key, c("new", "var", "sexage")) %>%
select(-new, -iso2, -iso3) %>%
separate(sexage, c("sex", "age"), sep = 1)## # A tibble: 76,046 x 6
## country year var sex age cases
## <chr> <int> <chr> <chr> <chr> <int>
## 1 Afghanistan 1997 sp m 014 0
## 2 Afghanistan 1997 sp m 1524 10
## 3 Afghanistan 1997 sp m 2534 6
## 4 Afghanistan 1997 sp m 3544 3
## 5 Afghanistan 1997 sp m 4554 5
## 6 Afghanistan 1997 sp m 5564 2
## 7 Afghanistan 1997 sp m 65 0
## 8 Afghanistan 1997 sp f 014 5
## 9 Afghanistan 1997 sp f 1524 38
## 10 Afghanistan 1997 sp f 2534 36
## # … with 76,036 more rows#1 In this case study I set values_drop_na = TRUE just to make it easier to check that we had the correct values. Is this reasonable? Think about how missing values are represented in this dataset. Are there implicit missing values? What's the difference between an NA and 0?
# values_drop_na = TRUE may be reasonable depending on no cases of TB were represented. If there are no 0s in the dataset, then NA may represent no cases of TB. Therefore dropping the NA values skews the data. 0 is an integer while NA means a blank in the dataset.#2 What happens if you neglect the mutate() step?
#(mutate(names_from = stringr::str_replace(key, "newrel", "new_rel")))
# The mutate steps replaces the variable name with new_rel for consistency purposes and to avoid following errors. It allows for the separation at the '_' in the following step to avoid multiple columns.#3 I claimed that iso2 and iso3 were redundant with country. Confirm this claim.
select(who3, country, iso2, iso3) %>%
distinct() %>%
group_by(country) %>%
filter(n() > 1)## # A tibble: 0 x 3
## # Groups: country [0]
## # … with 3 variables: country <chr>, iso2 <chr>, iso3 <chr>#4 For each country, year, and sex compute the total number of cases of TB.
who5 %>%
group_by(country, year, sex) %>%
summarise(cases = sum(cases))## # A tibble: 6,729 x 4
## # Groups: country, year [3,484]
## country year sex cases
## <chr> <int> <chr> <int>
## 1 Afghanistan 1997 f 102
## 2 Afghanistan 1997 m 26
## 3 Afghanistan 1998 f 1207
## 4 Afghanistan 1998 m 571
## 5 Afghanistan 1999 f 517
## 6 Afghanistan 1999 m 228
## 7 Afghanistan 2000 f 1751
## 8 Afghanistan 2000 m 915
## 9 Afghanistan 2001 f 3062
## 10 Afghanistan 2001 m 1577
## # … with 6,719 more rowsTwo main reasons to use other data structures: Alternative representations may have substantial performance or space advantages. Specialised fields have evolved their own conventions for storing data that may be quite different to the conventions of tidy data.
library(nycflights13)
library(tidyverse)nycflights13::flights## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>Allows to subset observations based on their values First argument: name of data frame Second argument +: expressions that filter the data frame
filter(flights, month == 1, day == 1)## # A tibble: 842 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 832 more rows, and 11 more variables: arr_delay <dbl>, carrier <chr>,
## # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
## # distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>The arrow saves the results
jan1 <- filter(flights, month == 1, day == 1)The arrow and parentheses saves AND prints the results
(dec25 <- filter(flights, month == 12, day == 25))## # A tibble: 719 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 12 25 456 500 -4 649 651
## 2 2013 12 25 524 515 9 805 814
## 3 2013 12 25 542 540 2 832 850
## 4 2013 12 25 546 550 -4 1022 1027
## 5 2013 12 25 556 600 -4 730 745
## 6 2013 12 25 557 600 -3 743 752
## 7 2013 12 25 557 600 -3 818 831
## 8 2013 12 25 559 600 -1 855 856
## 9 2013 12 25 559 600 -1 849 855
## 10 2013 12 25 600 600 0 850 846
## # … with 709 more rows, and 11 more variables: arr_delay <dbl>, carrier <chr>,
## # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
## # distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>R provides the standard suite: >, >=, <, <=, != (not equal), and == (equal)
# Finds all flights departed in November or December
filter(flights, month == 11 | month == 12)## # A tibble: 55,403 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 11 1 5 2359 6 352 345
## 2 2013 11 1 35 2250 105 123 2356
## 3 2013 11 1 455 500 -5 641 651
## 4 2013 11 1 539 545 -6 856 827
## 5 2013 11 1 542 545 -3 831 855
## 6 2013 11 1 549 600 -11 912 923
## 7 2013 11 1 550 600 -10 705 659
## 8 2013 11 1 554 600 -6 659 701
## 9 2013 11 1 554 600 -6 826 827
## 10 2013 11 1 554 600 -6 749 751
## # … with 55,393 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm># Shorthand for the code above
nov_dec <- filter(flights, month %in% c(11, 12))# Simplifed complicated subsetting by rem
filter(flights, !(arr_delay > 120 | dep_delay > 120))## # A tibble: 316,050 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 316,040 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>filter(flights, arr_delay <= 120, dep_delay <= 120)## # A tibble: 316,050 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 316,040 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>NA represents an unknown value
df <- tibble(x = c(1, NA, 3))
filter(df, x > 1)## # A tibble: 1 x 1
## x
## <dbl>
## 1 3#this showcases both the FALSE and NA values explicitly
filter(df, is.na(x) | x > 1)## # A tibble: 2 x 1
## x
## <dbl>
## 1 NA
## 2 3# 1.1 Had an arrival delay of two or more hours
filter(flights, arr_delay >= 120)## # A tibble: 10,200 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 811 630 101 1047 830
## 2 2013 1 1 848 1835 853 1001 1950
## 3 2013 1 1 957 733 144 1056 853
## 4 2013 1 1 1114 900 134 1447 1222
## 5 2013 1 1 1505 1310 115 1638 1431
## 6 2013 1 1 1525 1340 105 1831 1626
## 7 2013 1 1 1549 1445 64 1912 1656
## 8 2013 1 1 1558 1359 119 1718 1515
## 9 2013 1 1 1732 1630 62 2028 1825
## 10 2013 1 1 1803 1620 103 2008 1750
## # … with 10,190 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm># 1.2 Flew to Houston (IAH to HOU)
filter(flights, origin == "IAH", dest == "HOU")## # A tibble: 0 x 19
## # … with 19 variables: year <int>, month <int>, day <int>, dep_time <int>,
## # sched_dep_time <int>, dep_delay <dbl>, arr_time <int>,
## # sched_arr_time <int>, arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm># 1.3 Were operated by United, American, or Delta
filter(flights, carrier %in% c("UA","AA","DL"))## # A tibble: 139,504 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 554 600 -6 812 837
## 5 2013 1 1 554 558 -4 740 728
## 6 2013 1 1 558 600 -2 753 745
## 7 2013 1 1 558 600 -2 924 917
## 8 2013 1 1 558 600 -2 923 937
## 9 2013 1 1 559 600 -1 941 910
## 10 2013 1 1 559 600 -1 854 902
## # … with 139,494 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm># 1.4 Departed in Summer (July(7), August(8), September(9))
filter(flights, month %in% 7:9)## # A tibble: 86,326 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 7 1 1 2029 212 236 2359
## 2 2013 7 1 2 2359 3 344 344
## 3 2013 7 1 29 2245 104 151 1
## 4 2013 7 1 43 2130 193 322 14
## 5 2013 7 1 44 2150 174 300 100
## 6 2013 7 1 46 2051 235 304 2358
## 7 2013 7 1 48 2001 287 308 2305
## 8 2013 7 1 58 2155 183 335 43
## 9 2013 7 1 100 2146 194 327 30
## 10 2013 7 1 100 2245 135 337 135
## # … with 86,316 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm># 1.5 Arrived more than 2 hours late, but didn't leave late
filter(flights, arr_delay > 120, dep_delay <= 0)## # A tibble: 29 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 27 1419 1420 -1 1754 1550
## 2 2013 10 7 1350 1350 0 1736 1526
## 3 2013 10 7 1357 1359 -2 1858 1654
## 4 2013 10 16 657 700 -3 1258 1056
## 5 2013 11 1 658 700 -2 1329 1015
## 6 2013 3 18 1844 1847 -3 39 2219
## 7 2013 4 17 1635 1640 -5 2049 1845
## 8 2013 4 18 558 600 -2 1149 850
## 9 2013 4 18 655 700 -5 1213 950
## 10 2013 5 22 1827 1830 -3 2217 2010
## # … with 19 more rows, and 11 more variables: arr_delay <dbl>, carrier <chr>,
## # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
## # distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm># 1.6 Were delayed by at least an hour, but made up over 30 mins in flight
filter(flights, dep_delay >= 60, dep_delay - arr_delay > 30)## # A tibble: 1,844 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 2205 1720 285 46 2040
## 2 2013 1 1 2326 2130 116 131 18
## 3 2013 1 3 1503 1221 162 1803 1555
## 4 2013 1 3 1839 1700 99 2056 1950
## 5 2013 1 3 1850 1745 65 2148 2120
## 6 2013 1 3 1941 1759 102 2246 2139
## 7 2013 1 3 1950 1845 65 2228 2227
## 8 2013 1 3 2015 1915 60 2135 2111
## 9 2013 1 3 2257 2000 177 45 2224
## 10 2013 1 4 1917 1700 137 2135 1950
## # … with 1,834 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm># 1.7 Departed between midnight and 6am (inclusive)
filter(flights, dep_time >= 2400 | dep_time <= 600)## # A tibble: 9,373 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 9,363 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm># 2. Another usful dplyr filtering helper is between(). What does it do? Can you use it simplify the code needed to answer the previous challenge?
#The between() function finds the flights that occur between the variables, such as the summer months in problem 1.4.
filter(flights, between(month, 7,9))## # A tibble: 86,326 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 7 1 1 2029 212 236 2359
## 2 2013 7 1 2 2359 3 344 344
## 3 2013 7 1 29 2245 104 151 1
## 4 2013 7 1 43 2130 193 322 14
## 5 2013 7 1 44 2150 174 300 100
## 6 2013 7 1 46 2051 235 304 2358
## 7 2013 7 1 48 2001 287 308 2305
## 8 2013 7 1 58 2155 183 335 43
## 9 2013 7 1 100 2146 194 327 30
## 10 2013 7 1 100 2245 135 337 135
## # … with 86,316 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>#3 How many flights have a missing dep_time? What other variables are missing? What might these rows represent?
#The arrival time, depart time, air time, and depart delay are all missing. This could imply the flight had been cancelled.
filter(flights, is.na(dep_time))## # A tibble: 8,255 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 NA 1630 NA NA 1815
## 2 2013 1 1 NA 1935 NA NA 2240
## 3 2013 1 1 NA 1500 NA NA 1825
## 4 2013 1 1 NA 600 NA NA 901
## 5 2013 1 2 NA 1540 NA NA 1747
## 6 2013 1 2 NA 1620 NA NA 1746
## 7 2013 1 2 NA 1355 NA NA 1459
## 8 2013 1 2 NA 1420 NA NA 1644
## 9 2013 1 2 NA 1321 NA NA 1536
## 10 2013 1 2 NA 1545 NA NA 1910
## # … with 8,245 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>#4 Why is NA ^ 0 not missing? Why is NA | TRUE not missing? Why is FALSE & NA not missing? Can you figure out the general rule?
NA ^ 0## [1] 1# Equals 1 always
NA | TRUE## [1] TRUE# True is always true
NA & FALSE## [1] FALSE# False is always false
#NA * 0 is not equal to 0, because the NA represents infinity and is undefined.Arrange() is similiar to filter(); but changes the order
arrange(flights, year, month, day)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>#orders columns in descending order
arrange(flights, desc(dep_delay))## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 9 641 900 1301 1242 1530
## 2 2013 6 15 1432 1935 1137 1607 2120
## 3 2013 1 10 1121 1635 1126 1239 1810
## 4 2013 9 20 1139 1845 1014 1457 2210
## 5 2013 7 22 845 1600 1005 1044 1815
## 6 2013 4 10 1100 1900 960 1342 2211
## 7 2013 3 17 2321 810 911 135 1020
## 8 2013 6 27 959 1900 899 1236 2226
## 9 2013 7 22 2257 759 898 121 1026
## 10 2013 12 5 756 1700 896 1058 2020
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>#missing values are sorted at the end
df <- tibble(x = c(5, 2, NA))
arrange(df, x)## # A tibble: 3 x 1
## x
## <dbl>
## 1 2
## 2 5
## 3 NAarrange(df, desc(x))## # A tibble: 3 x 1
## x
## <dbl>
## 1 5
## 2 2
## 3 NA#1 How could you use arrange() to sort all missing values to the start?
arrange(flights, desc(is.na(dep_time)), dep_time)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 NA 1630 NA NA 1815
## 2 2013 1 1 NA 1935 NA NA 2240
## 3 2013 1 1 NA 1500 NA NA 1825
## 4 2013 1 1 NA 600 NA NA 901
## 5 2013 1 2 NA 1540 NA NA 1747
## 6 2013 1 2 NA 1620 NA NA 1746
## 7 2013 1 2 NA 1355 NA NA 1459
## 8 2013 1 2 NA 1420 NA NA 1644
## 9 2013 1 2 NA 1321 NA NA 1536
## 10 2013 1 2 NA 1545 NA NA 1910
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>#2 Sort flights to find the most delayed flights. Find the flights that left earliest.
#most delayed
arrange(flights, desc(dep_delay))## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 9 641 900 1301 1242 1530
## 2 2013 6 15 1432 1935 1137 1607 2120
## 3 2013 1 10 1121 1635 1126 1239 1810
## 4 2013 9 20 1139 1845 1014 1457 2210
## 5 2013 7 22 845 1600 1005 1044 1815
## 6 2013 4 10 1100 1900 960 1342 2211
## 7 2013 3 17 2321 810 911 135 1020
## 8 2013 6 27 959 1900 899 1236 2226
## 9 2013 7 22 2257 759 898 121 1026
## 10 2013 12 5 756 1700 896 1058 2020
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>#left earliest
arrange(flights, dep_delay)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 12 7 2040 2123 -43 40 2352
## 2 2013 2 3 2022 2055 -33 2240 2338
## 3 2013 11 10 1408 1440 -32 1549 1559
## 4 2013 1 11 1900 1930 -30 2233 2243
## 5 2013 1 29 1703 1730 -27 1947 1957
## 6 2013 8 9 729 755 -26 1002 955
## 7 2013 10 23 1907 1932 -25 2143 2143
## 8 2013 3 30 2030 2055 -25 2213 2250
## 9 2013 3 2 1431 1455 -24 1601 1631
## 10 2013 5 5 934 958 -24 1225 1309
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>#3 Sort flights to find the fastest(highest speed) flights.
arrange(flights, air_time)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 16 1355 1315 40 1442 1411
## 2 2013 4 13 537 527 10 622 628
## 3 2013 12 6 922 851 31 1021 954
## 4 2013 2 3 2153 2129 24 2247 2224
## 5 2013 2 5 1303 1315 -12 1342 1411
## 6 2013 2 12 2123 2130 -7 2211 2225
## 7 2013 3 2 1450 1500 -10 1547 1608
## 8 2013 3 8 2026 1935 51 2131 2056
## 9 2013 3 18 1456 1329 87 1533 1426
## 10 2013 3 19 2226 2145 41 2305 2246
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>#4 Which flights travelled the farthest? Which travlled the shortest?
# travelled the farthest
arrange(flights, desc(distance))## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 857 900 -3 1516 1530
## 2 2013 1 2 909 900 9 1525 1530
## 3 2013 1 3 914 900 14 1504 1530
## 4 2013 1 4 900 900 0 1516 1530
## 5 2013 1 5 858 900 -2 1519 1530
## 6 2013 1 6 1019 900 79 1558 1530
## 7 2013 1 7 1042 900 102 1620 1530
## 8 2013 1 8 901 900 1 1504 1530
## 9 2013 1 9 641 900 1301 1242 1530
## 10 2013 1 10 859 900 -1 1449 1530
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm># travelled the shortest
arrange(flights, distance)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 7 27 NA 106 NA NA 245
## 2 2013 1 3 2127 2129 -2 2222 2224
## 3 2013 1 4 1240 1200 40 1333 1306
## 4 2013 1 4 1829 1615 134 1937 1721
## 5 2013 1 4 2128 2129 -1 2218 2224
## 6 2013 1 5 1155 1200 -5 1241 1306
## 7 2013 1 6 2125 2129 -4 2224 2224
## 8 2013 1 7 2124 2129 -5 2212 2224
## 9 2013 1 8 2127 2130 -3 2304 2225
## 10 2013 1 9 2126 2129 -3 2217 2224
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>Select() allows you to zoom in on useful subset using operations based on the names of the variables
select(flights, year, month, day)## # A tibble: 336,776 x 3
## year month day
## <int> <int> <int>
## 1 2013 1 1
## 2 2013 1 1
## 3 2013 1 1
## 4 2013 1 1
## 5 2013 1 1
## 6 2013 1 1
## 7 2013 1 1
## 8 2013 1 1
## 9 2013 1 1
## 10 2013 1 1
## # … with 336,766 more rowsselect(flights, year:day)## # A tibble: 336,776 x 3
## year month day
## <int> <int> <int>
## 1 2013 1 1
## 2 2013 1 1
## 3 2013 1 1
## 4 2013 1 1
## 5 2013 1 1
## 6 2013 1 1
## 7 2013 1 1
## 8 2013 1 1
## 9 2013 1 1
## 10 2013 1 1
## # … with 336,766 more rowsselect(flights, -(year:day))## # A tibble: 336,776 x 16
## dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
## <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 517 515 2 830 819 11 UA
## 2 533 529 4 850 830 20 UA
## 3 542 540 2 923 850 33 AA
## 4 544 545 -1 1004 1022 -18 B6
## 5 554 600 -6 812 837 -25 DL
## 6 554 558 -4 740 728 12 UA
## 7 555 600 -5 913 854 19 B6
## 8 557 600 -3 709 723 -14 EV
## 9 557 600 -3 838 846 -8 B6
## 10 558 600 -2 753 745 8 AA
## # … with 336,766 more rows, and 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>starts_with(“abc”): matches names that begin with “abc”.
ends_with(“xyz”): matches names that end with “xyz”.
contains(“ijk”): matches names that contain “ijk”.
matches(“(.)\1”): selects variables that match a regular expression. This one matches any variables that contain repeated characters. You’ll learn more about regular expressions in strings.
num_range(“x”, 1:3): matches x1, x2 and x3.
# renames the values
rename(flights, tail_num = tailnum)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tail_num <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm># moves to the start of the data
select(flights, time_hour, air_time, everything())## # A tibble: 336,776 x 19
## time_hour air_time year month day dep_time sched_dep_time
## <dttm> <dbl> <int> <int> <int> <int> <int>
## 1 2013-01-01 05:00:00 227 2013 1 1 517 515
## 2 2013-01-01 05:00:00 227 2013 1 1 533 529
## 3 2013-01-01 05:00:00 160 2013 1 1 542 540
## 4 2013-01-01 05:00:00 183 2013 1 1 544 545
## 5 2013-01-01 06:00:00 116 2013 1 1 554 600
## 6 2013-01-01 05:00:00 150 2013 1 1 554 558
## 7 2013-01-01 06:00:00 158 2013 1 1 555 600
## 8 2013-01-01 06:00:00 53 2013 1 1 557 600
## 9 2013-01-01 06:00:00 140 2013 1 1 557 600
## 10 2013-01-01 06:00:00 138 2013 1 1 558 600
## # … with 336,766 more rows, and 12 more variables: dep_delay <dbl>,
## # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,
## # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, distance <dbl>,
## # hour <dbl>, minute <dbl>#1 Brainstorm as many ways as possible to select dep_time, dep_delay, arr_time, and arr_delay from flights.
select(flights, dep_time, dep_delay, arr_time, arr_delay)## # A tibble: 336,776 x 4
## dep_time dep_delay arr_time arr_delay
## <int> <dbl> <int> <dbl>
## 1 517 2 830 11
## 2 533 4 850 20
## 3 542 2 923 33
## 4 544 -1 1004 -18
## 5 554 -6 812 -25
## 6 554 -4 740 12
## 7 555 -5 913 19
## 8 557 -3 709 -14
## 9 557 -3 838 -8
## 10 558 -2 753 8
## # … with 336,766 more rows#2 What happens if you include the name of a variable multiple times in a select() call?
select(flights, year, month, month)## # A tibble: 336,776 x 2
## year month
## <int> <int>
## 1 2013 1
## 2 2013 1
## 3 2013 1
## 4 2013 1
## 5 2013 1
## 6 2013 1
## 7 2013 1
## 8 2013 1
## 9 2013 1
## 10 2013 1
## # … with 336,766 more rows# it ignores the duplication and only incldues the variable once#3 What does the one_of() function do? Why might it be helpful in conjunction with this vector?
vars <- c("year", "month", "day", "dep_delay", "arr_delay")
select(flights, one_of(vars))## # A tibble: 336,776 x 5
## year month day dep_delay arr_delay
## <int> <int> <int> <dbl> <dbl>
## 1 2013 1 1 2 11
## 2 2013 1 1 4 20
## 3 2013 1 1 2 33
## 4 2013 1 1 -1 -18
## 5 2013 1 1 -6 -25
## 6 2013 1 1 -4 12
## 7 2013 1 1 -5 19
## 8 2013 1 1 -3 -14
## 9 2013 1 1 -3 -8
## 10 2013 1 1 -2 8
## # … with 336,766 more rows#this only works if vars is not a column name in the flights data#4 Does the result of running the following code surpise you? How do the select helpers deal wit case by default? How can you change that default?
select(flights, contains("TIME"))## # A tibble: 336,776 x 6
## dep_time sched_dep_time arr_time sched_arr_time air_time time_hour
## <int> <int> <int> <int> <dbl> <dttm>
## 1 517 515 830 819 227 2013-01-01 05:00:00
## 2 533 529 850 830 227 2013-01-01 05:00:00
## 3 542 540 923 850 160 2013-01-01 05:00:00
## 4 544 545 1004 1022 183 2013-01-01 05:00:00
## 5 554 600 812 837 116 2013-01-01 06:00:00
## 6 554 558 740 728 150 2013-01-01 05:00:00
## 7 555 600 913 854 158 2013-01-01 06:00:00
## 8 557 600 709 723 53 2013-01-01 06:00:00
## 9 557 600 838 846 140 2013-01-01 06:00:00
## 10 558 600 753 745 138 2013-01-01 06:00:00
## # … with 336,766 more rows# it is ingores case
select(flights, contains("TIME", ignore.case = FALSE))## # A tibble: 336,776 x 0# changes the default behaviorMutate(): Adds new columns that are functions of existing columns; adds columns to the end of your dataset
flights_sml <- select(flights,
year:day,
ends_with("delay"),
distance,
air_time
)
mutate(flights_sml,
gain = dep_delay - arr_delay,
speed = distance / air_time * 60
)## # A tibble: 336,776 x 9
## year month day dep_delay arr_delay distance air_time gain speed
## <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2013 1 1 2 11 1400 227 -9 370.
## 2 2013 1 1 4 20 1416 227 -16 374.
## 3 2013 1 1 2 33 1089 160 -31 408.
## 4 2013 1 1 -1 -18 1576 183 17 517.
## 5 2013 1 1 -6 -25 762 116 19 394.
## 6 2013 1 1 -4 12 719 150 -16 288.
## 7 2013 1 1 -5 19 1065 158 -24 404.
## 8 2013 1 1 -3 -14 229 53 11 259.
## 9 2013 1 1 -3 -8 944 140 5 405.
## 10 2013 1 1 -2 8 733 138 -10 319.
## # … with 336,766 more rows# can refer to the columns that were just created
mutate(flights_sml,
gain = dep_delay - arr_delay,
hours = air_time / 60,
gain_per_hour = gain / hours
)## # A tibble: 336,776 x 10
## year month day dep_delay arr_delay distance air_time gain hours
## <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2013 1 1 2 11 1400 227 -9 3.78
## 2 2013 1 1 4 20 1416 227 -16 3.78
## 3 2013 1 1 2 33 1089 160 -31 2.67
## 4 2013 1 1 -1 -18 1576 183 17 3.05
## 5 2013 1 1 -6 -25 762 116 19 1.93
## 6 2013 1 1 -4 12 719 150 -16 2.5
## 7 2013 1 1 -5 19 1065 158 -24 2.63
## 8 2013 1 1 -3 -14 229 53 11 0.883
## 9 2013 1 1 -3 -8 944 140 5 2.33
## 10 2013 1 1 -2 8 733 138 -10 2.3
## # … with 336,766 more rows, and 1 more variable: gain_per_hour <dbl># only want to keep the new variables, use the transmute()
transmute(flights,
gain = dep_delay - arr_delay,
hours = air_time / 60,
gain_per_hour = gain / hours
)## # A tibble: 336,776 x 3
## gain hours gain_per_hour
## <dbl> <dbl> <dbl>
## 1 -9 3.78 -2.38
## 2 -16 3.78 -4.23
## 3 -31 2.67 -11.6
## 4 17 3.05 5.57
## 5 19 1.93 9.83
## 6 -16 2.5 -6.4
## 7 -24 2.63 -9.11
## 8 11 0.883 12.5
## 9 5 2.33 2.14
## 10 -10 2.3 -4.35
## # … with 336,766 more rows# example
transmute(flights,
dep_time,
hour = dep_time %/% 100,
minute = dep_time %% 100
)## # A tibble: 336,776 x 3
## dep_time hour minute
## <int> <dbl> <dbl>
## 1 517 5 17
## 2 533 5 33
## 3 542 5 42
## 4 544 5 44
## 5 554 5 54
## 6 554 5 54
## 7 555 5 55
## 8 557 5 57
## 9 557 5 57
## 10 558 5 58
## # … with 336,766 more rows#1 Currently dep_time and sched_dep_time are convenient to look at, but hard to compute with because they're not really continuous numbers. Convert them to a more convenient represenation of number of minutes since midnight.
#divide dep_time by 100 to get hours since midnight, then mulitply by 60 to get minutes. Get the remainder of the dep_time and add the reaminding minutes. The 1440 represents midnight by converting it to 0.
flights_times <- mutate(flights,dep_time_mins = (dep_time %/% 100 * 60 + dep_time %% 100) %% 1440,sched_dep_time_mins = (sched_dep_time %/% 100 * 60 + sched_dep_time %% 100) %% 1440
)
# view columns
select(
flights_times, dep_time, dep_time_mins, sched_dep_time,
sched_dep_time_mins
)## # A tibble: 336,776 x 4
## dep_time dep_time_mins sched_dep_time sched_dep_time_mins
## <int> <dbl> <int> <dbl>
## 1 517 317 515 315
## 2 533 333 529 329
## 3 542 342 540 340
## 4 544 344 545 345
## 5 554 354 600 360
## 6 554 354 558 358
## 7 555 355 600 360
## 8 557 357 600 360
## 9 557 357 600 360
## 10 558 358 600 360
## # … with 336,766 more rows#2 Compare air_time and arr_time - dep_time. What do you expect to see? What do you see? What do you need to do to fix it?
# I would think that air_time = arr_time-dep_time.
select(flights, air_time, arr_time, dep_time)## # A tibble: 336,776 x 3
## air_time arr_time dep_time
## <dbl> <int> <int>
## 1 227 830 517
## 2 227 850 533
## 3 160 923 542
## 4 183 1004 544
## 5 116 812 554
## 6 150 740 554
## 7 158 913 555
## 8 53 709 557
## 9 140 838 557
## 10 138 753 558
## # … with 336,766 more rows# The arr_time and dep_time need to be converted to minutes from midnight like #1 to get a true comparison.#3 Compare dep_time, sched_dep_time, and dep_delay. How would you expect those three numbers to be related?
# I would expect the dep_delay = dep_time - sched_dep_time
select(flights, dep_time, sched_dep_time, dep_delay)## # A tibble: 336,776 x 3
## dep_time sched_dep_time dep_delay
## <int> <int> <dbl>
## 1 517 515 2
## 2 533 529 4
## 3 542 540 2
## 4 544 545 -1
## 5 554 600 -6
## 6 554 558 -4
## 7 555 600 -5
## 8 557 600 -3
## 9 557 600 -3
## 10 558 600 -2
## # … with 336,766 more rows# To get a true comparison, then the numbers would need to be converted to minutes from midnight. #4 Find the 10 most delayed flights using a ranking function. How do you want to handle ties? Carefully read the documentation for min_rank().
flights %>%
filter(min_rank(-(dep_delay)) %in% 1:10)## # A tibble: 10 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 9 641 900 1301 1242 1530
## 2 2013 1 10 1121 1635 1126 1239 1810
## 3 2013 12 5 756 1700 896 1058 2020
## 4 2013 3 17 2321 810 911 135 1020
## 5 2013 4 10 1100 1900 960 1342 2211
## 6 2013 6 15 1432 1935 1137 1607 2120
## 7 2013 6 27 959 1900 899 1236 2226
## 8 2013 7 22 845 1600 1005 1044 1815
## 9 2013 7 22 2257 759 898 121 1026
## 10 2013 9 20 1139 1845 1014 1457 2210
## # … with 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>#4 What does 1:3 + 1:10 return? Why?
1:3 + 1:10## Warning in 1:3 + 1:10: longer object length is not a multiple of shorter object
## length## [1] 2 4 6 5 7 9 8 10 12 11# Returns a warning; could indicate a bug#6 What trignometric function does R provide?
#sin(), cos(), tan()# Collapses data frame to a single row
summarise(flights, delay = mean(dep_delay, na.rm = TRUE))## # A tibble: 1 x 1
## delay
## <dbl>
## 1 12.6# Summarise() is more useful when paired with group_by()
by_day <- group_by (flights, year, month, day)
summarise(by_day, delay = mean(dep_delay, na.rm = TRUE))## # A tibble: 365 x 4
## # Groups: year, month [12]
## year month day delay
## <int> <int> <int> <dbl>
## 1 2013 1 1 11.5
## 2 2013 1 2 13.9
## 3 2013 1 3 11.0
## 4 2013 1 4 8.95
## 5 2013 1 5 5.73
## 6 2013 1 6 7.15
## 7 2013 1 7 5.42
## 8 2013 1 8 2.55
## 9 2013 1 9 2.28
## 10 2013 1 10 2.84
## # … with 355 more rowsby_dest <- group_by(flights, dest)
delay <- summarise(by_dest,
count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE)
)
delay <- filter(delay, count > 20, dest != "HNL")# Same problem using the pipe %>%
# Focuses on the transformations, not what's being transformed
delays <- flights %>%
group_by(dest) %>%
summarise(
count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE)
) %>%
filter(count > 20, dest != "HNL")# The na.rm produces a lot of missing values
flights %>%
group_by(year, month, day) %>%
summarise(mean = mean(dep_delay))## # A tibble: 365 x 4
## # Groups: year, month [12]
## year month day mean
## <int> <int> <int> <dbl>
## 1 2013 1 1 NA
## 2 2013 1 2 NA
## 3 2013 1 3 NA
## 4 2013 1 4 NA
## 5 2013 1 5 NA
## 6 2013 1 6 NA
## 7 2013 1 7 NA
## 8 2013 1 8 NA
## 9 2013 1 9 NA
## 10 2013 1 10 NA
## # … with 355 more rows# Removes the missing values
flights %>%
group_by(year, month, day) %>%
summarise(mean = mean(dep_delay, na.rm = TRUE))## # A tibble: 365 x 4
## # Groups: year, month [12]
## year month day mean
## <int> <int> <int> <dbl>
## 1 2013 1 1 11.5
## 2 2013 1 2 13.9
## 3 2013 1 3 11.0
## 4 2013 1 4 8.95
## 5 2013 1 5 5.73
## 6 2013 1 6 7.15
## 7 2013 1 7 5.42
## 8 2013 1 8 2.55
## 9 2013 1 9 2.28
## 10 2013 1 10 2.84
## # … with 355 more rows# Takes into account of the cancelled flights
not_cancelled <- flights %>%
filter(!is.na(dep_delay), !is.na(arr_delay))
not_cancelled %>%
group_by(year, month, day) %>%
summarise(mean = mean(dep_delay))## # A tibble: 365 x 4
## # Groups: year, month [12]
## year month day mean
## <int> <int> <int> <dbl>
## 1 2013 1 1 11.4
## 2 2013 1 2 13.7
## 3 2013 1 3 10.9
## 4 2013 1 4 8.97
## 5 2013 1 5 5.73
## 6 2013 1 6 7.15
## 7 2013 1 7 5.42
## 8 2013 1 8 2.56
## 9 2013 1 9 2.30
## 10 2013 1 10 2.84
## # … with 355 more rowsGood idea to include: count(n()) or (sum(!is.na(x))) to avoid conclusions based on small amounts of data
delays <- not_cancelled %>%
group_by(tailnum) %>%
summarise(
delay = mean(arr_delay)
)delays <- not_cancelled %>%
group_by(tailnum) %>%
summarise(
delay = mean(arr_delay, na.rm = TRUE),
n = n()
)not_cancelled %>%
group_by(year, month, day) %>%
summarise(
avg_delay1 = mean(arr_delay),
avg_delay2 = mean(arr_delay[arr_delay > 0]) # the average positive delay
)## # A tibble: 365 x 5
## # Groups: year, month [12]
## year month day avg_delay1 avg_delay2
## <int> <int> <int> <dbl> <dbl>
## 1 2013 1 1 12.7 32.5
## 2 2013 1 2 12.7 32.0
## 3 2013 1 3 5.73 27.7
## 4 2013 1 4 -1.93 28.3
## 5 2013 1 5 -1.53 22.6
## 6 2013 1 6 4.24 24.4
## 7 2013 1 7 -4.95 27.8
## 8 2013 1 8 -3.23 20.8
## 9 2013 1 9 -0.264 25.6
## 10 2013 1 10 -5.90 27.3
## # … with 355 more rowsnot_cancelled %>%
group_by(dest) %>%
summarise(distance_sd = sd(distance)) %>%
arrange(desc(distance_sd))## # A tibble: 104 x 2
## dest distance_sd
## <chr> <dbl>
## 1 EGE 10.5
## 2 SAN 10.4
## 3 SFO 10.2
## 4 HNL 10.0
## 5 SEA 9.98
## 6 LAS 9.91
## 7 PDX 9.87
## 8 PHX 9.86
## 9 LAX 9.66
## 10 IND 9.46
## # … with 94 more rowsnot_cancelled %>%
group_by(year, month, day) %>%
summarise(
first = min(dep_time),
last = max(dep_time)
)## # A tibble: 365 x 5
## # Groups: year, month [12]
## year month day first last
## <int> <int> <int> <int> <int>
## 1 2013 1 1 517 2356
## 2 2013 1 2 42 2354
## 3 2013 1 3 32 2349
## 4 2013 1 4 25 2358
## 5 2013 1 5 14 2357
## 6 2013 1 6 16 2355
## 7 2013 1 7 49 2359
## 8 2013 1 8 454 2351
## 9 2013 1 9 2 2252
## 10 2013 1 10 3 2320
## # … with 355 more rowsnot_cancelled %>%
group_by(year, month, day) %>%
summarise(
first_dep = first(dep_time),
last_dep = last(dep_time)
)## # A tibble: 365 x 5
## # Groups: year, month [12]
## year month day first_dep last_dep
## <int> <int> <int> <int> <int>
## 1 2013 1 1 517 2356
## 2 2013 1 2 42 2354
## 3 2013 1 3 32 2349
## 4 2013 1 4 25 2358
## 5 2013 1 5 14 2357
## 6 2013 1 6 16 2355
## 7 2013 1 7 49 2359
## 8 2013 1 8 454 2351
## 9 2013 1 9 2 2252
## 10 2013 1 10 3 2320
## # … with 355 more rows# Complementary to measures of position
not_cancelled %>%
group_by(year, month, day) %>%
mutate(r = min_rank(desc(dep_time))) %>%
filter(r %in% range(r))## # A tibble: 770 x 20
## # Groups: year, month, day [365]
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 2356 2359 -3 425 437
## 3 2013 1 2 42 2359 43 518 442
## 4 2013 1 2 2354 2359 -5 413 437
## 5 2013 1 3 32 2359 33 504 442
## 6 2013 1 3 2349 2359 -10 434 445
## 7 2013 1 4 25 2359 26 505 442
## 8 2013 1 4 2358 2359 -1 429 437
## 9 2013 1 4 2358 2359 -1 436 445
## 10 2013 1 5 14 2359 15 503 445
## # … with 760 more rows, and 12 more variables: arr_delay <dbl>, carrier <chr>,
## # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
## # distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>, r <int>not_cancelled %>%
group_by(dest) %>%
summarise(carriers = n_distinct(carrier)) %>%
arrange(desc(carriers))## # A tibble: 104 x 2
## dest carriers
## <chr> <int>
## 1 ATL 7
## 2 BOS 7
## 3 CLT 7
## 4 ORD 7
## 5 TPA 7
## 6 AUS 6
## 7 DCA 6
## 8 DTW 6
## 9 IAD 6
## 10 MSP 6
## # … with 94 more rowsnot_cancelled %>%
count(dest)## # A tibble: 104 x 2
## dest n
## <chr> <int>
## 1 ABQ 254
## 2 ACK 264
## 3 ALB 418
## 4 ANC 8
## 5 ATL 16837
## 6 AUS 2411
## 7 AVL 261
## 8 BDL 412
## 9 BGR 358
## 10 BHM 269
## # … with 94 more rows# Total number of miles a plane flew
not_cancelled %>%
count(tailnum, wt = distance)## # A tibble: 4,037 x 2
## tailnum n
## <chr> <dbl>
## 1 D942DN 3418
## 2 N0EGMQ 239143
## 3 N10156 109664
## 4 N102UW 25722
## 5 N103US 24619
## 6 N104UW 24616
## 7 N10575 139903
## 8 N105UW 23618
## 9 N107US 21677
## 10 N108UW 32070
## # … with 4,027 more rows# How many flights left before 5am? (these usually indicate delayed
# flights from the previous day)
not_cancelled %>%
group_by(year, month, day) %>%
summarise(n_early = sum(dep_time < 500))## # A tibble: 365 x 4
## # Groups: year, month [12]
## year month day n_early
## <int> <int> <int> <int>
## 1 2013 1 1 0
## 2 2013 1 2 3
## 3 2013 1 3 4
## 4 2013 1 4 3
## 5 2013 1 5 3
## 6 2013 1 6 2
## 7 2013 1 7 2
## 8 2013 1 8 1
## 9 2013 1 9 3
## 10 2013 1 10 3
## # … with 355 more rows# What proportion of flights are delayed by more than an hour?
not_cancelled %>%
group_by(year, month, day) %>%
summarise(hour_prop = mean(arr_delay > 60))## # A tibble: 365 x 4
## # Groups: year, month [12]
## year month day hour_prop
## <int> <int> <int> <dbl>
## 1 2013 1 1 0.0722
## 2 2013 1 2 0.0851
## 3 2013 1 3 0.0567
## 4 2013 1 4 0.0396
## 5 2013 1 5 0.0349
## 6 2013 1 6 0.0470
## 7 2013 1 7 0.0333
## 8 2013 1 8 0.0213
## 9 2013 1 9 0.0202
## 10 2013 1 10 0.0183
## # … with 355 more rowsdaily <- group_by(flights, year, month, day)
(per_day <- summarise(daily, flights = n()))## # A tibble: 365 x 4
## # Groups: year, month [12]
## year month day flights
## <int> <int> <int> <int>
## 1 2013 1 1 842
## 2 2013 1 2 943
## 3 2013 1 3 914
## 4 2013 1 4 915
## 5 2013 1 5 720
## 6 2013 1 6 832
## 7 2013 1 7 933
## 8 2013 1 8 899
## 9 2013 1 9 902
## 10 2013 1 10 932
## # … with 355 more rows(per_month <- summarise(per_day, flights = sum(flights)))## # A tibble: 12 x 3
## # Groups: year [1]
## year month flights
## <int> <int> <int>
## 1 2013 1 27004
## 2 2013 2 24951
## 3 2013 3 28834
## 4 2013 4 28330
## 5 2013 5 28796
## 6 2013 6 28243
## 7 2013 7 29425
## 8 2013 8 29327
## 9 2013 9 27574
## 10 2013 10 28889
## 11 2013 11 27268
## 12 2013 12 28135(per_year <- summarise(per_month, flights = sum(flights)))## # A tibble: 1 x 2
## year flights
## <int> <int>
## 1 2013 336776daily %>%
ungroup() %>% # no longer grouped by date
summarise(flights = n()) # all flights## # A tibble: 1 x 1
## flights
## <int>
## 1 336776#1 Brainstorm at least 5 different ways to assess the typical dealy characteristics of a group of flights.
delay_char <-
flights %>%
group_by(flight) %>%
summarise(n = n(),
fif_early = mean(arr_delay == -15, na.rm = TRUE),
fif_late = mean(arr_delay == 15, na.rm = TRUE),
ten_always = mean(arr_delay == 10, na.rm = TRUE),
thirty_early = mean(arr_delay == -30, na.rm = TRUE),
thirty_late = mean(arr_delay == 30, na.rm = TRUE),
per_on_time = mean(arr_delay == 0, na.rm = TRUE),
two_hours = mean(arr_delay > 120, na.rm = TRUE))
#A flight is 15 minutes early 50% of the time, and 15 minutes late 50% of the time.
delay_char %>%
filter(fif_early == 0.5, fif_late == 0.5)## # A tibble: 0 x 9
## # … with 9 variables: flight <int>, n <int>, fif_early <dbl>, fif_late <dbl>,
## # ten_always <dbl>, thirty_early <dbl>, thirty_late <dbl>, per_on_time <dbl>,
## # two_hours <dbl>#A flight is always 10 minutes late.
delay_char %>%
filter(ten_always == 1)## # A tibble: 5 x 9
## flight n fif_early fif_late ten_always thirty_early thirty_late
## <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2254 1 0 0 1 0 0
## 2 3656 1 0 0 1 0 0
## 3 3785 2 0 0 1 0 0
## 4 3880 1 0 0 1 0 0
## 5 5854 1 0 0 1 0 0
## # … with 2 more variables: per_on_time <dbl>, two_hours <dbl>#A flight is 30 minutes early 50% of the time, and 30 minutes late 50% of the time.
delay_char %>%
filter(thirty_early == 0.5 & thirty_late == 0.5)## # A tibble: 0 x 9
## # … with 9 variables: flight <int>, n <int>, fif_early <dbl>, fif_late <dbl>,
## # ten_always <dbl>, thirty_early <dbl>, thirty_late <dbl>, per_on_time <dbl>,
## # two_hours <dbl>#99% of the time a flight is on time. 1% of the time it’s 2 hours late.
delay_char %>%
filter(per_on_time == 0.99 & two_hours == 0.01)## # A tibble: 0 x 9
## # … with 9 variables: flight <int>, n <int>, fif_early <dbl>, fif_late <dbl>,
## # ten_always <dbl>, thirty_early <dbl>, thirty_late <dbl>, per_on_time <dbl>,
## # two_hours <dbl># Which is more important: arrival delay or departure delay?
# Arrival delay is mort important because it can affect the following flights/connector flights. #2 Come up with another approach that will give you the same output as not_cancelled %>% count(dest) and not_cancelled %>% count(tailnum, wt = distance) (without using count()).
not_cancelled %>% count(dest)## # A tibble: 104 x 2
## dest n
## <chr> <int>
## 1 ABQ 254
## 2 ACK 264
## 3 ALB 418
## 4 ANC 8
## 5 ATL 16837
## 6 AUS 2411
## 7 AVL 261
## 8 BDL 412
## 9 BGR 358
## 10 BHM 269
## # … with 94 more rowsnot_cancelled %>%
group_by(dest) %>%
summarise(n = length(dest))## # A tibble: 104 x 2
## dest n
## <chr> <int>
## 1 ABQ 254
## 2 ACK 264
## 3 ALB 418
## 4 ANC 8
## 5 ATL 16837
## 6 AUS 2411
## 7 AVL 261
## 8 BDL 412
## 9 BGR 358
## 10 BHM 269
## # … with 94 more rows#3 Our defintion of cancelled flights (is.na(dep_delay)) | is.na(arr_delay)) is slightly suboptimal. Why? Which is the most important column?
#The most important column is the arr_delay. The definition is suboptimal because if a flight never left, then it was cancelled.#4 Look at the number of cancelled flights per day. Is there a pattern? Is the proportion of cancelled flights related to the average delay?
flights%>%
group_by(day) %>%
summarise(cancelled = mean(is.na(dep_delay)))## # A tibble: 31 x 2
## day cancelled
## <int> <dbl>
## 1 1 0.0223
## 2 2 0.0231
## 3 3 0.00972
## 4 4 0.00741
## 5 5 0.0208
## 6 6 0.0268
## 7 7 0.0289
## 8 8 0.0817
## 9 9 0.0546
## 10 10 0.0477
## # … with 21 more rows# I'm not sure what the pattern is.#5 Which carrier has the worst delays? Challenge: Can you disentangle the effects of bad airports vs. bad carriers? Why/why not?
flights %>%
group_by(carrier) %>%
summarise(arr_delay = mean(arr_delay, na.rm = TRUE)) %>%
arrange(desc(arr_delay))## # A tibble: 16 x 2
## carrier arr_delay
## <chr> <dbl>
## 1 F9 21.9
## 2 FL 20.1
## 3 EV 15.8
## 4 YV 15.6
## 5 OO 11.9
## 6 MQ 10.8
## 7 WN 9.65
## 8 B6 9.46
## 9 9E 7.38
## 10 UA 3.56
## 11 US 2.13
## 12 VX 1.76
## 13 DL 1.64
## 14 AA 0.364
## 15 HA -6.92
## 16 AS -9.93# I'm confused by the challenge question. F9 is the worst carrier. Would you look at the airports that it goes to?#6 What does the sort argument to count() do. When might you use it?
#Sorts the results in the order of 'n'; Sorts based on the countflights_sml %>%
group_by(year, month, day) %>%
filter(rank(desc(arr_delay)) < 10)## # A tibble: 3,306 x 7
## # Groups: year, month, day [365]
## year month day dep_delay arr_delay distance air_time
## <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 2013 1 1 853 851 184 41
## 2 2013 1 1 290 338 1134 213
## 3 2013 1 1 260 263 266 46
## 4 2013 1 1 157 174 213 60
## 5 2013 1 1 216 222 708 121
## 6 2013 1 1 255 250 589 115
## 7 2013 1 1 285 246 1085 146
## 8 2013 1 1 192 191 199 44
## 9 2013 1 1 379 456 1092 222
## 10 2013 1 2 224 207 550 94
## # … with 3,296 more rowspopular_dests <- flights %>%
group_by(dest) %>%
filter(n() > 365)
popular_dests## # A tibble: 332,577 x 19
## # Groups: dest [77]
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 332,567 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>popular_dests %>%
filter(arr_delay > 0) %>%
mutate(prop_delay = arr_delay / sum(arr_delay)) %>%
select(year:day, dest, arr_delay, prop_delay)## # A tibble: 131,106 x 6
## # Groups: dest [77]
## year month day dest arr_delay prop_delay
## <int> <int> <int> <chr> <dbl> <dbl>
## 1 2013 1 1 IAH 11 0.000111
## 2 2013 1 1 IAH 20 0.000201
## 3 2013 1 1 MIA 33 0.000235
## 4 2013 1 1 ORD 12 0.0000424
## 5 2013 1 1 FLL 19 0.0000938
## 6 2013 1 1 ORD 8 0.0000283
## 7 2013 1 1 LAX 7 0.0000344
## 8 2013 1 1 DFW 31 0.000282
## 9 2013 1 1 ATL 12 0.0000400
## 10 2013 1 1 DTW 16 0.000116
## # … with 131,096 more rows#1 Refer back to the lists of useful mutate and filtering functions. Describe how each operation changes when you combine it with grouping.
# See #### 5.5.1 Useful Creation Functions for notes#2 Which plane(tailnum) has the worst on-time record?
flights %>%
filter(!is.na(arr_delay)) %>%
group_by(tailnum) %>%
summarise(prop_time = sum(arr_delay <= 30)/n(),
mean_arr = mean(arr_delay, na.rm = T),
fl = n()) %>%
arrange(desc(prop_time))## # A tibble: 4,037 x 4
## tailnum prop_time mean_arr fl
## <chr> <dbl> <dbl> <int>
## 1 N103US 1 -6.93 46
## 2 N1200K 1 -9.38 21
## 3 N121DE 1 15 2
## 4 N137DL 1 -5 1
## 5 N143DA 1 24 1
## 6 N14628 1 -6 1
## 7 N14629 1 -16.2 4
## 8 N1607B 1 -16 3
## 9 N1608 1 -11.3 3
## 10 N1610D 1 -14.5 2
## # … with 4,027 more rows#3 What time of day should you fly if you want to avoid delays as much as possible?
# Grouped by hour to get the time of day
flights %>%
group_by(hour) %>%
summarise(arr_delay = mean(arr_delay, na.rm = TRUE)) %>%
arrange(arr_delay)## # A tibble: 20 x 2
## hour arr_delay
## <dbl> <dbl>
## 1 7 -5.30
## 2 5 -4.80
## 3 6 -3.38
## 4 9 -1.45
## 5 8 -1.11
## 6 10 0.954
## 7 11 1.48
## 8 12 3.49
## 9 13 6.54
## 10 14 9.20
## 11 23 11.8
## 12 15 12.3
## 13 16 12.6
## 14 18 14.8
## 15 22 16.0
## 16 17 16.0
## 17 19 16.7
## 18 20 16.7
## 19 21 18.4
## 20 1 NaN# Morning flights have less delays#4 For each destination, compute the total minutes of delay. For each flight, compute the proportion of the delay for its destination.
flights %>%
group_by(dest) %>%
filter(!is.na(dep_delay)) %>%
summarise(total=sum(dep_delay))## # A tibble: 104 x 2
## dest total
## <chr> <dbl>
## 1 ABQ 3490
## 2 ACK 1711
## 3 ALB 9897
## 4 ANC 103
## 5 ATL 211391
## 6 AUS 31496
## 7 AVL 2154
## 8 BDL 7301
## 9 BGR 7011
## 10 BHM 8077
## # … with 94 more rowsflights%>%
group_by(dest, tailnum)%>%
filter(!is.na(dep_delay)) %>%
summarise(avg=mean(dep_delay))## # A tibble: 44,218 x 3
## # Groups: dest [104]
## dest tailnum avg
## <chr> <chr> <dbl>
## 1 ABQ N503JB 10
## 2 ABQ N504JB 9.5
## 3 ABQ N505JB 0
## 4 ABQ N506JB 15
## 5 ABQ N507JB 3
## 6 ABQ N508JB -3.5
## 7 ABQ N509JB 4.5
## 8 ABQ N510JB 7.5
## 9 ABQ N517JB -2
## 10 ABQ N519JB 53
## # … with 44,208 more rows#5 Delays are typically temporally correlated: even once the problem that caused the initial delay has been resolved, later flights are delayed to allow earlier flights to leave. Using lag(), explore how the delay of a flight is related to the delay of the immediately preceding flight.
flights%>%
group_by(origin)%>%
arrange(origin,month,day,dep_time)%>%
mutate(dep_delay_lag = lag(dep_delay)) %>%
filter(!is.na(dep_delay), !is.na(dep_delay_lag))## # A tibble: 327,649 x 20
## # Groups: origin [3]
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 554 558 -4 740 728
## 2 2013 1 1 555 600 -5 913 854
## 3 2013 1 1 558 600 -2 923 937
## 4 2013 1 1 559 600 -1 854 902
## 5 2013 1 1 601 600 1 844 850
## 6 2013 1 1 606 610 -4 858 910
## 7 2013 1 1 607 607 0 858 915
## 8 2013 1 1 608 600 8 807 735
## 9 2013 1 1 615 615 0 833 842
## 10 2013 1 1 622 630 -8 1017 1014
## # … with 327,639 more rows, and 12 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>,
## # dep_delay_lag <dbl>#6 Look at each destination. Can you find flights that are suspiciously fast? Compute the air time of a flight relative to the shortest flight to that destination. Which flights were most delayed in the air?
flights %>%
group_by(dest) %>%
mutate(shortest = air_time - min(air_time, na.rm = TRUE)) %>%
top_n(1, air_time) %>%
arrange(desc(air_time)) %>%
select(tailnum, sched_dep_time, sched_arr_time, shortest)## Warning in min(air_time, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Adding missing grouping variables: `dest`## # A tibble: 112 x 5
## # Groups: dest [104]
## dest tailnum sched_dep_time sched_arr_time shortest
## <chr> <chr> <int> <int> <dbl>
## 1 HNL N77066 1335 1836 133
## 2 SFO N703TW 1730 2110 195
## 3 LAX N178DN 1815 2146 165
## 4 ANC N572UA 1615 1953 46
## 5 SAN N794JB 1620 1934 134
## 6 SNA N16709 1819 2137 131
## 7 BUR N624JB 1730 2046 110
## 8 LAS N852UA 1729 2013 143
## 9 SJC N632JB 1830 2205 91
## 10 SEA N17245 1727 2040 119
## # … with 102 more rows#7 Find all the destinations that are flown by at least two carriers. Use that information to rank the carriers.
flights %>%
group_by(dest) %>%
filter(n_distinct(carrier) >= 2) %>%
group_by(carrier) %>%
summarise(n = n_distinct(dest)) %>%
arrange(desc(n))## # A tibble: 16 x 2
## carrier n
## <chr> <int>
## 1 EV 51
## 2 9E 48
## 3 UA 42
## 4 DL 39
## 5 B6 35
## 6 AA 19
## 7 MQ 19
## 8 WN 10
## 9 OO 5
## 10 US 5
## 11 VX 4
## 12 YV 3
## 13 FL 2
## 14 AS 1
## 15 F9 1
## 16 HA 1#8 For each plan, count the number of flights before the first delay of greater than 1 hour.
flights %>%
filter(!is.na(dep_delay)) %>%
group_by(tailnum) %>%
mutate(total = sum(dep_delay > 60))## # A tibble: 328,521 x 20
## # Groups: tailnum [4,037]
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 328,511 more rows, and 12 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>,
## # total <int>