Ex. 1.1: Data Exploration

class: center, middle, inverse, title-slide

# Ex. 1.1: Data Exploration
## Tailoring Your Data with tidyverse

---

count: false

.panel1-the_chunk-auto[

```r
*read_csv("wage_data.csv")
```
]
 
.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
read_csv("wage_data.csv") ->
* wage_data
```
]
 
.panel2-the_chunk-auto[

]

---
count: false

.panel1-the_chunk-auto[

```r
read_csv("wage_data.csv") ->
  wage_data

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

---
count: false

.panel1-the_chunk-auto[

```r
read_csv("wage_data.csv") ->
  wage_data

wage_data %>%
## filter out anyone who has never
## worked and anyone who reported no earnings
* 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
read_csv("wage_data.csv") ->
  wage_data

```
# A tibble: 84,631 x 11
   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 3 more variables: FamilyMakeup <chr>,
#   Age_squared <dbl>, Earnings_per_member <dbl>
```
]

---
count: false

.panel1-the_chunk-auto[

```r
read_csv("wage_data.csv") ->
  wage_data

wage_data %>%
## filter out anyone who has never
## worked and anyone who reported no earnings
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
## create a new variable showing earnings
## per family member
  mutate(Earnings_per_member = Earnings / FamilySize) %>%
## keep specific columns
* select(Earnings, Earnings_per_member, Sex, Age, Education)
```
]
 
.panel2-the_chunk-auto[

```
# A tibble: 84,631 x 5
   Earnings Earnings_per_member Sex     Age Education              
      <dbl>               <dbl> <chr> <dbl> <chr>                  
 1   220000              44000  M        49 Bachelors              
 2     8000               1600  F        20 Less than HS           
 3     4000                800  M        16 Less than HS           
 4    17350               8675  M        27 Less than HS           
 5    12000               6000  M        24 Less than HS           
 6    25480              25480  M        62 Bachelors              
 7     6000               1000  F        53 Bachelors              
 8    70200              11700  M        52 Bachelors              
 9    10520               1753. F        16 Less than HS           
10    46000              11500  F        31 Some College/Associates
# … with 84,621 more rows
```
]

---
count: false

.panel1-the_chunk-auto[

```r
read_csv("wage_data.csv") ->
  wage_data

wage_data %>%
## filter out anyone who has never
## worked and anyone who reported no earnings
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
## create a new variable showing earnings
## per family member
  mutate(Earnings_per_member = Earnings / FamilySize) %>%
## keep specific columns
  select(Earnings, Earnings_per_member, Sex, Age, Education) %>%
* filter(Earnings< 150000)
```
]
 
.panel2-the_chunk-auto[

```
# A tibble: 80,635 x 5
   Earnings Earnings_per_member Sex     Age Education              
      <dbl>               <dbl> <chr> <dbl> <chr>                  
 1     8000               1600  F        20 Less than HS           
 2     4000                800  M        16 Less than HS           
 3    17350               8675  M        27 Less than HS           
 4    12000               6000  M        24 Less than HS           
 5    25480              25480  M        62 Bachelors              
 6     6000               1000  F        53 Bachelors              
 7    70200              11700  M        52 Bachelors              
 8    10520               1753. F        16 Less than HS           
 9    46000              11500  F        31 Some College/Associates
10    40000              40000  M        37 Less than HS           
# … with 80,625 more rows
```
]

---
count: false

.panel1-the_chunk-auto[

```r
read_csv("wage_data.csv") ->
  wage_data

wage_data %>%
## filter out anyone who has never
## worked and anyone who reported no earnings
  filter(Occupation != "53: Never Worked" &
           Earnings > 0) %>%
## create a new variable showing earnings
## per family member
  mutate(Earnings_per_member = Earnings / FamilySize) %>%
## keep specific columns
  select(Earnings, Earnings_per_member, Sex, Age, Education) %>%
  filter(Earnings< 150000) ->
*employed_under_150K
```
]
 
.panel2-the_chunk-auto[

]

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