Week 3: Data transformation

# Week 3: Data transformation
## PUBPOL 750 Data Analysis for Public Policy I
### Justin Savoie
### MPP-DS McMaster
### 2022-05-27

---

# Offset in weeks
### Week 3 in content becomes week 4 in calendar
### Week 4 in content becomes week 5 in calendar etc.
### HW1 due June 3; Project 1 due July 8
### Project 2: no change, due August 12

---

# Summary Week 2 Data Viz

---

```r
ggplot(mpg) +
  geom_point(aes(x=displ,y=hwy, color=class))
```

![](Slides_files/figure-html/unnamed-chunk-1-1.png)

---

```r
ggplot(mpg) +
  geom_point(aes(x=displ,y=hwy, color=factor(year))) +
  facet_wrap(~class) +
  coord_polar()
```

![](Slides_files/figure-html/unnamed-chunk-2-1.png)

---

```r
library(maps)
world <- map_data("world")
world$type <- "No"
world$type <- ifelse(world$region %in% c("China","France","Russia","UK","USA"),"Perm.",world$type)
world$type <- ifelse(world$region %in% c("Albania","Brazil","Gabon","Ghana","India","Ireland","Kenya","Mexico","Norway","United Arab Emirates"),"Non.perm",world$type)

ggplot() +
  geom_polygon(data = world, aes(x=long, y = lat, group = group, fill=type)) + 
  labs(fill="UN Security Council") + coord_fixed(1.3) + theme(legend.position = "top", plot.margin = margin(-5,1,1.5,1.2, "cm"))
```

---
class: inverse, center, middle

# Basics

---

### Objects

```r
x <- c(1,2,3)
x
```

```
## [1] 1 2 3
```

```r
x <- "my name is justin"
x
```

```
## [1] "my name is justin"
```

```r
z <- sum(c(1,2,3))*2
z
```

```
## [1] 12
```

```r
x1 <- c(2,4,6)
x2 <- c(4,5,1)
x12 <- x1+x2
x12
```

```
## [1] 6 9 7
```

---

### Functions

```r
my_vector <- c(1,2,3,4,5)
mean(my_vector)
```

```
## [1] 3
```

```r
rnorm(10,mean=100,sd=25)
```

```
##  [1]  90.10275  96.39162 107.58930  98.40490  78.81469 143.16369 116.23539
##  [8] 103.00779 127.63424 124.21096
```

```r
seq(from=0,to=10,by=2)
```

```
## [1]  0  2  4  6  8 10
```

```r
seq(0,10,2)
```

```
## [1]  0  2  4  6  8 10
```
---
class: inverse, center, middle

# Data transformation

---

### Five key verbs

`filter()`
`arrange()`
`select()`
`mutate()`
`summarise()`

can be used with 
`groub_by()`

which applies the verbs, but "by group"

---

### Let's look at the flights data frame

```r
library(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
## # … 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>
```

---

### filter()

```r
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
## # … 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>
```
---
### filter()

```r
filtered_flights <- filter(flights,month==1,day==1)
filtered_flights
```

```r
filter(flights,arr_time>2330)
```

```
## # A tibble: 6,196 × 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     1843           1835         8     2339           2346
##  2  2013     1     1     1846           1855        -9     2336           2355
##  3  2013     1     1     1952           1930        22     2358           2207
##  4  2013     1     1     1959           1930        29     2331           2306
##  5  2013     1     1     2021           2025        -4     2351           2341
##  6  2013     1     1     2025           2030        -5     2334           2348
##  7  2013     1     1     2025           2028        -3     2358           2351
##  8  2013     1     1     2030           2035        -5     2354           2342
##  9  2013     1     1     2031           2030         1     2344           2335
## 10  2013     1     1     2035           2030         5     2337              5
## # … with 6,186 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()

```r
filter(flights,arr_time %in% c(2200,2300,2400))
```

```
## # A tibble: 794 × 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     1912           1915        -3     2200           2219
##  2  2013     1     1     2209           2155        14     2400           2337
##  3  2013     1     2     1918           1925        -7     2200           2205
##  4  2013     1     3     1857           1900        -3     2200           2301
##  5  2013     1     3     1941           1946        -5     2200           2154
##  6  2013     1     3     1946           1930        16     2300           2247
##  7  2013     1     4     1859           1900        -1     2200           2238
##  8  2013     1     5     2116           2130       -14     2400             18
##  9  2013     1     6     1950           2000       -10     2200           2145
## 10  2013     1     6     2048           2040         8     2200           2149
## # … with 784 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()

```r
filter(flights,is.na(arr_time))
```

```
## # A tibble: 8,713 × 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     2016           1930        46       NA           2220
##  2  2013     1     1       NA           1630        NA       NA           1815
##  3  2013     1     1       NA           1935        NA       NA           2240
##  4  2013     1     1       NA           1500        NA       NA           1825
##  5  2013     1     1       NA            600        NA       NA            901
##  6  2013     1     2     2041           2045        -4       NA           2359
##  7  2013     1     2     2145           2129        16       NA             33
##  8  2013     1     2       NA           1540        NA       NA           1747
##  9  2013     1     2       NA           1620        NA       NA           1746
## 10  2013     1     2       NA           1355        NA       NA           1459
## # … with 8,703 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>
```
---

### arrange()

```r
head(arrange(flights,arr_time))
```

```
## # A tibble: 6 × 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     2     2130           2130         0        1             18
## 2  2013     1    11     2157           2000       117        1           2208
## 3  2013     1    11     2253           2249         4        1           2357
## 4  2013     1    14     2122           2130        -8        1              2
## 5  2013     1    14     2246           2250        -4        1              7
## 6  2013     1    15     2304           2245        19        1           2357
## # … 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>
```
---

```r
head(arrange(flights,desc(arr_time)))
```

```
## # A tibble: 6 × 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     2209           2155        14     2400           2337
## 2  2013     1     5     2116           2130       -14     2400             18
## 3  2013     1    13     2243           2129        74     2400           2224
## 4  2013     1    16     2138           2107        31     2400           2322
## 5  2013     1    17     2256           2249         7     2400           2357
## 6  2013     1    22     2212           2055        77     2400           2250
## # … 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>
```
---

```r
head(arrange(flights,month,desc(day)))
```

```
## # A tibble: 6 × 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    31        1           2100       181      124           2225
## 2  2013     1    31        4           2359         5      455            444
## 3  2013     1    31        7           2359         8      453            437
## 4  2013     1    31       12           2250        82      132              7
## 5  2013     1    31       26           2154       152      328             50
## 6  2013     1    31       34           2159       155      135           2315
## # … 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>
```

---

### select()

```r
select(flights,month,day)  
```

```
## # A tibble: 336,776 × 2
##    month   day
##    <int> <int>
##  1     1     1
##  2     1     1
##  3     1     1
##  4     1     1
##  5     1     1
##  6     1     1
##  7     1     1
##  8     1     1
##  9     1     1
## 10     1     1
## # … with 336,766 more rows
```
---

### select()

```r
select(flights,-month,-day)  
```

```
## # A tibble: 336,776 × 17
##     year dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay
##    <int>    <int>          <int>     <dbl>    <int>          <int>     <dbl>
##  1  2013      517            515         2      830            819        11
##  2  2013      533            529         4      850            830        20
##  3  2013      542            540         2      923            850        33
##  4  2013      544            545        -1     1004           1022       -18
##  5  2013      554            600        -6      812            837       -25
##  6  2013      554            558        -4      740            728        12
##  7  2013      555            600        -5      913            854        19
##  8  2013      557            600        -3      709            723       -14
##  9  2013      557            600        -3      838            846        -8
## 10  2013      558            600        -2      753            745         8
## # … with 336,766 more rows, and 10 more variables: carrier <chr>, flight <int>,
## #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## #   hour <dbl>, minute <dbl>, time_hour <dttm>
```
---

### select()

```r
select(flights,contains('time'))
```

```
## # A tibble: 336,776 × 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
```
---

### mutate()

```r
mutate(mtcars,one=1)
```

```
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb one
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4   1
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4   1
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1   1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1   1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2   1
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1   1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4   1
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2   1
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2   1
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4   1
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4   1
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3   1
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3   1
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3   1
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4   1
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4   1
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4   1
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1   1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2   1
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1   1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1   1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2   1
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2   1
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4   1
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2   1
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1   1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2   1
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2   1
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4   1
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6   1
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8   1
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2   1
```
---
### mutate()

```r
mutate(mtcars,hp2=hp^2)
```

```
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb    hp2
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4  12100
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4  12100
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1   8649
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1  12100
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2  30625
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1  11025
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4  60025
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2   3844
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2   9025
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4  15129
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4  15129
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3  32400
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3  32400
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3  32400
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4  42025
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4  46225
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4  52900
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1   4356
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2   2704
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1   4225
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1   9409
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2  22500
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2  22500
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4  60025
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2  30625
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1   4356
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2   8281
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2  12769
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4  69696
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6  30625
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 112225
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  11881
```
---
### mutate()

```r
mutate(mtcars,hpbycyl=hp/cyl)
```

```
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
##                      hpbycyl
## Mazda RX4           18.33333
## Mazda RX4 Wag       18.33333
## Datsun 710          23.25000
## Hornet 4 Drive      18.33333
## Hornet Sportabout   21.87500
## Valiant             17.50000
## Duster 360          30.62500
## Merc 240D           15.50000
## Merc 230            23.75000
## Merc 280            20.50000
## Merc 280C           20.50000
## Merc 450SE          22.50000
## Merc 450SL          22.50000
## Merc 450SLC         22.50000
## Cadillac Fleetwood  25.62500
## Lincoln Continental 26.87500
## Chrysler Imperial   28.75000
## Fiat 128            16.50000
## Honda Civic         13.00000
## Toyota Corolla      16.25000
## Toyota Corona       24.25000
## Dodge Challenger    18.75000
## AMC Javelin         18.75000
## Camaro Z28          30.62500
## Pontiac Firebird    21.87500
## Fiat X1-9           16.50000
## Porsche 914-2       22.75000
## Lotus Europa        28.25000
## Ford Pantera L      33.00000
## Ferrari Dino        29.16667
## Maserati Bora       41.87500
## Volvo 142E          27.25000
```
---
### group_by() with mutate()

```r
mtcars_grouped <- group_by(mtcars,cyl)
mutate(mtcars_grouped,avg_mpg_cyl=mean(mpg))
```

```
## # A tibble: 32 × 12
## # Groups:   cyl [3]
##      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb avg_mpg_cyl
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>       <dbl>
##  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4        19.7
##  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4        19.7
##  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1        26.7
##  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1        19.7
##  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2        15.1
##  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1        19.7
##  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4        15.1
##  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2        26.7
##  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2        26.7
## 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4        19.7
## # … with 22 more rows
```
---
### group_by() with summarise()

```r
mtcars_grouped <- group_by(mtcars,cyl)
summarise(mtcars_grouped,avg_mpg_cyl=mean(mpg))
```

```
## # A tibble: 3 × 2
##     cyl avg_mpg_cyl
##   <dbl>       <dbl>
## 1     4        26.7
## 2     6        19.7
## 3     8        15.1
```

---
class: inverse, center, middle

# Homework 1

---

# Exercices

### 5.2.4 5.3.1 5.4.1 5.5.2 5.6.7 5.7.1