R for Data Science

Ch5: Workflow: basics

import data

flights

## # A tibble: 336,776 × 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
## # ℹ 336,766 more rows
## # ℹ 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>

nycflights13::flights

## # A tibble: 336,776 × 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
## # ℹ 336,766 more rows
## # ℹ 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 rows

filter(flights, month == 1, day == 1)

## # A tibble: 842 × 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
## # ℹ 832 more rows
## # ℹ 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, month == 1 & day == 1)

## # A tibble: 842 × 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
## # ℹ 832 more rows
## # ℹ 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, month == 1 | day == 1)

## # A tibble: 37,198 × 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
## # ℹ 37,188 more rows
## # ℹ 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, month %in% c(11, 12))

## # A tibble: 55,403 × 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
## # ℹ 55,393 more rows
## # ℹ 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>

Arrange rows

arrange(flights, desc(month), desc(day))

## # A tibble: 336,776 × 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    31       13           2359        14      439            437
##  2  2013    12    31       18           2359        19      449            444
##  3  2013    12    31       26           2245       101      129           2353
##  4  2013    12    31      459            500        -1      655            651
##  5  2013    12    31      514            515        -1      814            812
##  6  2013    12    31      549            551        -2      925            900
##  7  2013    12    31      550            600       -10      725            745
##  8  2013    12    31      552            600        -8      811            826
##  9  2013    12    31      553            600        -7      741            754
## 10  2013    12    31      554            550         4     1024           1027
## # ℹ 336,766 more rows
## # ℹ 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 columns

select(flights, year:dep_time)

## # A tibble: 336,776 × 4
##     year month   day dep_time
##    <int> <int> <int>    <int>
##  1  2013     1     1      517
##  2  2013     1     1      533
##  3  2013     1     1      542
##  4  2013     1     1      544
##  5  2013     1     1      554
##  6  2013     1     1      554
##  7  2013     1     1      555
##  8  2013     1     1      557
##  9  2013     1     1      557
## 10  2013     1     1      558
## # ℹ 336,766 more rows

select(flights, year, month, day, dep_time)

## # A tibble: 336,776 × 4
##     year month   day dep_time
##    <int> <int> <int>    <int>
##  1  2013     1     1      517
##  2  2013     1     1      533
##  3  2013     1     1      542
##  4  2013     1     1      544
##  5  2013     1     1      554
##  6  2013     1     1      554
##  7  2013     1     1      555
##  8  2013     1     1      557
##  9  2013     1     1      557
## 10  2013     1     1      558
## # ℹ 336,766 more rows

select(flights, year, month, day, dep_time, dep_delay)

## # A tibble: 336,776 × 5
##     year month   day dep_time dep_delay
##    <int> <int> <int>    <int>     <dbl>
##  1  2013     1     1      517         2
##  2  2013     1     1      533         4
##  3  2013     1     1      542         2
##  4  2013     1     1      544        -1
##  5  2013     1     1      554        -6
##  6  2013     1     1      554        -4
##  7  2013     1     1      555        -5
##  8  2013     1     1      557        -3
##  9  2013     1     1      557        -3
## 10  2013     1     1      558        -2
## # ℹ 336,766 more rows

select(flights, year, month, day, starts_with("dep"))

## # A tibble: 336,776 × 5
##     year month   day dep_time dep_delay
##    <int> <int> <int>    <int>     <dbl>
##  1  2013     1     1      517         2
##  2  2013     1     1      533         4
##  3  2013     1     1      542         2
##  4  2013     1     1      544        -1
##  5  2013     1     1      554        -6
##  6  2013     1     1      554        -4
##  7  2013     1     1      555        -5
##  8  2013     1     1      557        -3
##  9  2013     1     1      557        -3
## 10  2013     1     1      558        -2
## # ℹ 336,766 more rows

select(flights, year, month, day, contains("time"))

## # A tibble: 336,776 × 9
##     year month   day dep_time sched_dep_time arr_time sched_arr_time air_time
##    <int> <int> <int>    <int>          <int>    <int>          <int>    <dbl>
##  1  2013     1     1      517            515      830            819      227
##  2  2013     1     1      533            529      850            830      227
##  3  2013     1     1      542            540      923            850      160
##  4  2013     1     1      544            545     1004           1022      183
##  5  2013     1     1      554            600      812            837      116
##  6  2013     1     1      554            558      740            728      150
##  7  2013     1     1      555            600      913            854      158
##  8  2013     1     1      557            600      709            723       53
##  9  2013     1     1      557            600      838            846      140
## 10  2013     1     1      558            600      753            745      138
## # ℹ 336,766 more rows
## # ℹ 1 more variable: time_hour <dttm>

select(flights, year, month, day, ends_with("time"))

## # A tibble: 336,776 × 8
##     year month   day dep_time sched_dep_time arr_time sched_arr_time air_time
##    <int> <int> <int>    <int>          <int>    <int>          <int>    <dbl>
##  1  2013     1     1      517            515      830            819      227
##  2  2013     1     1      533            529      850            830      227
##  3  2013     1     1      542            540      923            850      160
##  4  2013     1     1      544            545     1004           1022      183
##  5  2013     1     1      554            600      812            837      116
##  6  2013     1     1      554            558      740            728      150
##  7  2013     1     1      555            600      913            854      158
##  8  2013     1     1      557            600      709            723       53
##  9  2013     1     1      557            600      838            846      140
## 10  2013     1     1      558            600      753            745      138
## # ℹ 336,766 more rows

select(flights, year, month, day, contains("time"), everything())

## # A tibble: 336,776 × 19
##     year month   day dep_time sched_dep_time arr_time sched_arr_time air_time
##    <int> <int> <int>    <int>          <int>    <int>          <int>    <dbl>
##  1  2013     1     1      517            515      830            819      227
##  2  2013     1     1      533            529      850            830      227
##  3  2013     1     1      542            540      923            850      160
##  4  2013     1     1      544            545     1004           1022      183
##  5  2013     1     1      554            600      812            837      116
##  6  2013     1     1      554            558      740            728      150
##  7  2013     1     1      555            600      913            854      158
##  8  2013     1     1      557            600      709            723       53
##  9  2013     1     1      557            600      838            846      140
## 10  2013     1     1      558            600      753            745      138
## # ℹ 336,766 more rows
## # ℹ 11 more variables: time_hour <dttm>, dep_delay <dbl>, arr_delay <dbl>,
## #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## #   distance <dbl>, hour <dbl>, minute <dbl>

mutate(flights,
       gain = dep_delay - arr_delay) %>%
    
    # select, year, month, day, and gain
    select(year:day, gain)

## # A tibble: 336,776 × 4
##     year month   day  gain
##    <int> <int> <int> <dbl>
##  1  2013     1     1    -9
##  2  2013     1     1   -16
##  3  2013     1     1   -31
##  4  2013     1     1    17
##  5  2013     1     1    19
##  6  2013     1     1   -16
##  7  2013     1     1   -24
##  8  2013     1     1    11
##  9  2013     1     1     5
## 10  2013     1     1   -10
## # ℹ 336,766 more rows

#just keep gain
mutate(flights,
       gain = dep_delay - arr_delay) %>%
    
    # select, year, month, day, and gain
    select(gain)

## # A tibble: 336,776 × 1
##     gain
##    <dbl>
##  1    -9
##  2   -16
##  3   -31
##  4    17
##  5    19
##  6   -16
##  7   -24
##  8    11
##  9     5
## 10   -10
## # ℹ 336,766 more rows

#alternative using transmute()
transmute(flights,
          gain = dep_delay - arr_delay)

## # A tibble: 336,776 × 1
##     gain
##    <dbl>
##  1    -9
##  2   -16
##  3   -31
##  4    17
##  5    19
##  6   -16
##  7   -24
##  8    11
##  9     5
## 10   -10
## # ℹ 336,766 more rows

#lag()
select(flights, dep_time) %>%

   mutate(dep_time_lag1 = lag(dep_time))

## # A tibble: 336,776 × 2
##    dep_time dep_time_lag1
##       <int>         <int>
##  1      517            NA
##  2      533           517
##  3      542           533
##  4      544           542
##  5      554           544
##  6      554           554
##  7      555           554
##  8      557           555
##  9      557           557
## 10      558           557
## # ℹ 336,766 more rows

#cumsum()
select(flights, minute) %>%
    mutate(minute_cumsum = cumsum(minute))

## # A tibble: 336,776 × 2
##    minute minute_cumsum
##     <dbl>         <dbl>
##  1     15            15
##  2     29            44
##  3     40            84
##  4     45           129
##  5      0           129
##  6     58           187
##  7      0           187
##  8      0           187
##  9      0           187
## 10      0           187
## # ℹ 336,766 more rows

1000 / 10

## [1] 100

x <- 1000 / 10
x

## [1] 100

What’s in a name?

Calling functions

Use of TAB

seq(from = 1, to = 10, by = 1)

##  [1]  1  2  3  4  5  6  7  8  9 10

continuation character, +

seq(from = 1, to = 10)

##  [1]  1  2  3  4  5  6  7  8  9 10

Printing to screen

y <- seq(from = 1, to = 10)
y

##  [1]  1  2  3  4  5  6  7  8  9 10