# Chapter 5 Data transformation

Author: yueyanw
Reviewer: chunjiw at 11/3/2017

``````library(tidyverse)
library(nycflights13)``````

## 5.2

### 5.2.4 Exercises

1. Find all flights that
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
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
##  1  2013     1     1      811            630       101     1047
##  2  2013     1     1      848           1835       853     1001
##  3  2013     1     1      957            733       144     1056
##  4  2013     1     1     1114            900       134     1447
##  5  2013     1     1     1505           1310       115     1638
##  6  2013     1     1     1525           1340       105     1831
##  7  2013     1     1     1549           1445        64     1912
##  8  2013     1     1     1558           1359       119     1718
##  9  2013     1     1     1732           1630        62     2028
## 10  2013     1     1     1803           1620       103     2008
## # ... with 10,190 more rows, and 12 more variables: 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>``````
2. Flew to Houston (`IAH` or `HOU`)

``filter(flights, dest %in% c('IAH', 'HOU'))``
``````## # A tibble: 9,313 x 19
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
##  1  2013     1     1      517            515         2      830
##  2  2013     1     1      533            529         4      850
##  3  2013     1     1      623            627        -4      933
##  4  2013     1     1      728            732        -4     1041
##  5  2013     1     1      739            739         0     1104
##  6  2013     1     1      908            908         0     1228
##  7  2013     1     1     1028           1026         2     1350
##  8  2013     1     1     1044           1045        -1     1352
##  9  2013     1     1     1114            900       134     1447
## 10  2013     1     1     1205           1200         5     1503
## # ... with 9,303 more rows, and 12 more variables: 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>``````
3. Were operated by United, American, or Delta
4. Departed in summer (July, August, and September)
5. Arrived more than two hours late, but didn’t leave late
6. Were delayed by at least an hour, but made up over 30 minutes in flight
7. Departed between midnight and 6am (inclusive)

## 5.3

### 5.3.1 Exercises

1. How could you use `arrange()` to sort all missing values to the start? (Hint: use `is.na()`).
``````df <- tibble(x = c(5, 2, NA))
arrange(df, !is.na(x), x)``````
``````## # A tibble: 3 x 1
##       x
##   <dbl>
## 1    NA
## 2     2
## 3     5``````

## 5.6

### 5.6.7 Exercises

``````not_cancelled <- flights %>%
filter(!is.na(dep_delay), !is.na(arr_delay))
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``````
``not_cancelled %>% group_by(tailnum) %>% summarize(n = sum(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``````