Ex. 1.2: Data Exploration

class: center, middle, inverse, title-slide

# Ex. 1.2: Data Exploration
## Using summarize()

---

count: false

.panel1-the_chunk-auto[

```r
*wage_data
```
]
 
.panel2-the_chunk-auto[

```
# A tibble: 180,084 x 10
   Education Sex   Occupation   Age Earnings MaritalStatus Race  FamilySize
   <chr>     <chr> <chr>      <dbl>    <dbl> <chr>         <chr>      <dbl>
 1 Bachelors M     40: Offic…    49   220000 Married       White          5
 2 Some Col… F     53: Never…    51        0 Married       White          5
 3 Less tha… F     39: Retai…    20     8000 Never Married White          5
 4 Less tha… M     8: Comput…    16     4000 Never Married White          5
 5 Less tha… F     53: Never…    80        0 Widowed       White          5
 6 Less tha… M     32: Chefs…    27    17350 Never Married Black          2
 7 Less tha… M     33: Food …    24    12000 Never Married Hisp…          2
 8 Bachelors M     31: Anima…    62    25480 Never Married White          1
 9 Less tha… F     53: Never…    70        0 Widowed       White          1
10 Bachelors F     41: Farmi…    53     6000 Married       White          6
# … with 180,074 more rows, and 2 more variables: FamilyMakeup <chr>,
#   Age_squared <dbl>
```
]

---
count: false

.panel1-the_chunk-auto[

```r
wage_data %>%
* filter(Occupation != "53: Never Worked" &
*          Earnings > 0)
```
]
 
.panel2-the_chunk-auto[

```
# A tibble: 84,631 x 10
   Education Sex   Occupation   Age Earnings MaritalStatus Race  FamilySize
   <chr>     <chr> <chr>      <dbl>    <dbl> <chr>         <chr>      <dbl>
 1 Bachelors M     40: Offic…    49   220000 Married       White          5
 2 Less tha… F     39: Retai…    20     8000 Never Married White          5
 3 Less tha… M     8: Comput…    16     4000 Never Married White          5
 4 Less tha… M     32: Chefs…    27    17350 Never Married Black          2
 5 Less tha… M     33: Food …    24    12000 Never Married Hisp…          2
 6 Bachelors M     31: Anima…    62    25480 Never Married White          1
 7 Bachelors F     41: Farmi…    53     6000 Married       White          6
 8 Bachelors M     8: Comput…    52    70200 Married       Asian          6
 9 Less tha… F     41: Farmi…    16    10520 Never Married Asian          6
10 Some Col… F     30: Publi…    31    46000 Married       White          4
# … with 84,621 more rows, and 2 more variables: FamilyMakeup <chr>,
#   Age_squared <dbl>
```
]

---
count: false

.panel1-the_chunk-auto[

```r
wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
* summarise(mean(Earnings))
```
]
 
.panel2-the_chunk-auto[

```
# A tibble: 1 x 1
  `mean(Earnings)`
             <dbl>
1           52670.
```
]

---
count: false

.panel1-the_chunk-auto[

```r
wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
  summarise(mean(Earnings)) %>%
* as.numeric()
```
]
 
.panel2-the_chunk-auto[

```
[1] 52669.56
```
]

---
count: false

.panel1-the_chunk-auto[

```r
wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
  summarise(mean(Earnings)) %>%
  as.numeric() ->
*mean_earnings
```
]
 
.panel2-the_chunk-auto[

]

---
count: false

.panel1-the_chunk-auto[

```r
wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
  summarise(mean(Earnings)) %>%
  as.numeric() ->
mean_earnings

#printing saved object
*mean_earnings
```
]
 
.panel2-the_chunk-auto[

```
[1] 52669.56
```
]

---
count: false

.panel1-the_chunk-auto[

```r
wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
  summarise(mean(Earnings)) %>%
  as.numeric() ->
mean_earnings

#printing saved object
mean_earnings

*wage_data
```
]
 
.panel2-the_chunk-auto[

```
[1] 52669.56
```

---
count: false

.panel1-the_chunk-auto[

```r
wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
  summarise(mean(Earnings)) %>%
  as.numeric() ->
mean_earnings

#printing saved object
mean_earnings

wage_data %>%
* filter(Occupation != "53: Never Worked" &
*          Earnings > 0)
```
]
 
.panel2-the_chunk-auto[

```
[1] 52669.56
```

---
count: false

.panel1-the_chunk-auto[

```r
wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
  summarise(mean(Earnings)) %>%
  as.numeric() ->
mean_earnings

#printing saved object
mean_earnings

wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
* group_by(Sex)
```
]
 
.panel2-the_chunk-auto[

```
[1] 52669.56
```

```
# A tibble: 84,631 x 10
# Groups:   Sex [2]
   Education Sex   Occupation   Age Earnings MaritalStatus Race  FamilySize
   <chr>     <chr> <chr>      <dbl>    <dbl> <chr>         <chr>      <dbl>
 1 Bachelors M     40: Offic…    49   220000 Married       White          5
 2 Less tha… F     39: Retai…    20     8000 Never Married White          5
 3 Less tha… M     8: Comput…    16     4000 Never Married White          5
 4 Less tha… M     32: Chefs…    27    17350 Never Married Black          2
 5 Less tha… M     33: Food …    24    12000 Never Married Hisp…          2
 6 Bachelors M     31: Anima…    62    25480 Never Married White          1
 7 Bachelors F     41: Farmi…    53     6000 Married       White          6
 8 Bachelors M     8: Comput…    52    70200 Married       Asian          6
 9 Less tha… F     41: Farmi…    16    10520 Never Married Asian          6
10 Some Col… F     30: Publi…    31    46000 Married       White          4
# … with 84,621 more rows, and 2 more variables: FamilyMakeup <chr>,
#   Age_squared <dbl>
```
]

---
count: false

.panel1-the_chunk-auto[

```r
wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
  summarise(mean(Earnings)) %>%
  as.numeric() ->
mean_earnings

#printing saved object
mean_earnings

wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
  group_by(Sex) %>%
* summarise(mean = mean(Earnings),
*           count = n())
```
]
 
.panel2-the_chunk-auto[

```
[1] 52669.56
```

```
# A tibble: 2 x 3
  Sex     mean count
  <chr>  <dbl> <int>
1 F     42501. 41023
2 M     62235. 43608
```
]

---
count: false

.panel1-the_chunk-auto[

```r
wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
  summarise(mean(Earnings)) %>%
  as.numeric() ->
mean_earnings

#printing saved object
mean_earnings

wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
  group_by(Sex) %>%
  summarise(mean = mean(Earnings),
            count = n()) ->
*mean_earnings_by_sex
```
]
 
.panel2-the_chunk-auto[

```
[1] 52669.56
```
]

---
count: false

.panel1-the_chunk-auto[

```r
wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
  summarise(mean(Earnings)) %>%
  as.numeric() ->
mean_earnings

#printing saved object
mean_earnings

wage_data %>%
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
  group_by(Sex) %>%
  summarise(mean = mean(Earnings),
            count = n()) ->
mean_earnings_by_sex

*mean_earnings_by_sex
```
]
 
.panel2-the_chunk-auto[

```
[1] 52669.56
```

```
# A tibble: 2 x 3
  Sex     mean count
  <chr>  <dbl> <int>
1 F     42501. 41023
2 M     62235. 43608
```
]

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