How to Use Multiple filter() functions with Boolean logic

class: center, middle, inverse, title-slide

# How to Use Multiple <em>filter()</em> functions with Boolean logic
## Selecting specific rows with multiple criteria
### Peter Higgins
### 2021-01-10

---

### How to Use Multiple Filters to Pick Out a Subset of Rows

#### Combining Logical Statements

Format: <br>

`dataset %>% 
  filter(var1 > value & var2 > value)`

You can combine statements with the following:
- & (AND) (age >60 & fam_hx < 1)
- | (OR)  (age >65 | fam_hx == 1)
- ! (NOT) (age >= 65 & fam_hx ! = 1)
- XOR (exclusive OR - only if exactly one of the two is true
filter(xor(age >= 65, fam_hx == 1)

**R**emember that filte**R** is for selecting **R**ows because it ends with an **R**.

Let's look at some examples!
---

count: false
 
Example 1/4: Filter Rows with Multiple Criteria:  Age >65 and Tumor Volume > 1
.panel1-filter1-auto[

```r
# how many rows when you start
*nrow(prostate)
```
]
 
.panel2-filter1-auto[

```
[1] 316
```
]

---
count: false
 
Example 1/4: Filter Rows with Multiple Criteria:  Age >65 and Tumor Volume > 1
.panel1-filter1-auto[

```r
# how many rows when you start
nrow(prostate)

*prostate
```
]
 
.panel2-filter1-auto[

```
[1] 316
```

```
# A tibble: 316 x 20
   rbc_age_group median_rbc_age   age    aa fam_hx p_vol t_vol t_stage  b_gs
           <dbl>          <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>   <dbl> <dbl>
 1             3             25  72.1     0      0  54       3       1     3
 2             3             25  73.6     0      0  43.2     3       2     2
 3             3             25  67.5     0      0 103.      1       1     3
 4             2             15  65.8     0      0  46       1       1     1
 5             2             15  63.2     0      0  60       2       1     2
 6             3             25  65.4     0      0  45.9     2       1     1
 7             3             25  65.5     1      0  42.6     2       1     1
 8             1             10  67.1     0      0  40.7     3       1     1
 9             1             10  63.9     0      0  45       2       1     1
10             2             15  63       1      0  67.6     2       1     2
# … with 306 more rows, and 11 more variables: bn <dbl>, organ_confined <dbl>,
#   preop_psa <dbl>, preop_therapy <dbl>, units <dbl>, s_gs <dbl>,
#   any_adj_therapy <dbl>, adj_rad_therapy <dbl>, recurrence <dbl>,
#   censor <dbl>, time_to_recurrence <dbl>
```
]

---
count: false
 
Example 1/4: Filter Rows with Multiple Criteria:  Age >65 and Tumor Volume > 1
.panel1-filter1-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and t_vol change w/filter
* select(age, t_vol, recurrence)
```
]
 
.panel2-filter1-auto[

```
[1] 316
```

```
# A tibble: 316 x 3
     age t_vol recurrence
   <dbl> <dbl>      <dbl>
 1  72.1     3          1
 2  73.6     3          1
 3  67.5     1          0
 4  65.8     1          0
 5  63.2     2          0
 6  65.4     2          0
 7  65.5     2          0
 8  67.1     3          1
 9  63.9     2          0
10  63       2          0
# … with 306 more rows
```
]

---
count: false
 
Example 1/4: Filter Rows with Multiple Criteria:  Age >65 and Tumor Volume > 1
.panel1-filter1-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and t_vol change w/filter
  select(age, t_vol, recurrence) %>%
* filter(age > 65 & t_vol>1)
```
]
 
.panel2-filter1-auto[

```
[1] 316
```

```
# A tibble: 72 x 3
     age t_vol recurrence
   <dbl> <dbl>      <dbl>
 1  72.1     3          1
 2  73.6     3          1
 3  65.4     2          0
 4  65.5     2          0
 5  67.1     3          1
 6  73.3     2          0
 7  67.6     2          0
 8  65.2     2          0
 9  67.1     3          0
10  69.8     2          0
# … with 62 more rows
```
]

---
count: false
 
Example 1/4: Filter Rows with Multiple Criteria:  Age >65 and Tumor Volume > 1
.panel1-filter1-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and t_vol change w/filter
  select(age, t_vol, recurrence) %>%
  filter(age > 65 & t_vol>1)

# see how many rows now
# check age and t_vol
# Format:
*# filter(logic test 1 & logic test 2)        <br>  # filter(logic test 1 & logic test 2)        <br>
```
]
 
.panel2-filter1-auto[

```
[1] 316
```

---
count: false
 
Example 1/4: Filter Rows with Multiple Criteria:  Age >65 and Tumor Volume > 1
.panel1-filter1-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and t_vol change w/filter
  select(age, t_vol, recurrence) %>%
  filter(age > 65 & t_vol>1)

# see how many rows now
# check age and t_vol
# Format:
# filter(logic test 1 & logic test 2)        <br>  # filter(logic test 1 & logic test 2)        <br>
```
]
 
.panel2-filter1-auto[

```
[1] 316
```

---

count: false
 
Example 2/4: Filter Rows with OR Criteria - **watch** the symbol for OR
.panel1-filter2-auto[

```r
# how many rows when you start
*nrow(prostate)
```
]
 
.panel2-filter2-auto[

```
[1] 316
```
]

---
count: false
 
Example 2/4: Filter Rows with OR Criteria - **watch** the symbol for OR
.panel1-filter2-auto[

```r
# how many rows when you start
nrow(prostate)

*prostate
```
]
 
.panel2-filter2-auto[

```
[1] 316
```

---
count: false
 
Example 2/4: Filter Rows with OR Criteria - **watch** the symbol for OR
.panel1-filter2-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
* select(age, aa, t_vol)
```
]
 
.panel2-filter2-auto[

```
[1] 316
```

```
# A tibble: 316 x 3
     age    aa t_vol
   <dbl> <dbl> <dbl>
 1  72.1     0     3
 2  73.6     0     3
 3  67.5     0     1
 4  65.8     0     1
 5  63.2     0     2
 6  65.4     0     2
 7  65.5     1     2
 8  67.1     0     3
 9  63.9     0     2
10  63       1     2
# … with 306 more rows
```
]

---
count: false
 
Example 2/4: Filter Rows with OR Criteria - **watch** the symbol for OR
.panel1-filter2-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
  select(age, aa, t_vol) %>%
* filter(age > 69 | t_vol > 1)
```
]
 
.panel2-filter2-auto[

```
[1] 316
```

```
# A tibble: 256 x 3
     age    aa t_vol
   <dbl> <dbl> <dbl>
 1  72.1     0     3
 2  73.6     0     3
 3  63.2     0     2
 4  65.4     0     2
 5  65.5     1     2
 6  67.1     0     3
 7  63.9     0     2
 8  63       1     2
 9  58.5     0     3
10  56.2     0     2
# … with 246 more rows
```
]

---
count: false
 
Example 2/4: Filter Rows with OR Criteria - **watch** the symbol for OR
.panel1-filter2-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
  select(age, aa, t_vol) %>%
  filter(age > 69 | t_vol > 1) %>%
* arrange(desc(age))
```
]
 
.panel2-filter2-auto[

```
[1] 316
```

```
# A tibble: 256 x 3
     age    aa t_vol
   <dbl> <dbl> <dbl>
 1  79       0     2
 2  78.3     0     1
 3  76.9     0     1
 4  76.3     0    NA
 5  74.9     0     3
 6  74.8     0     2
 7  74.6     0     1
 8  74.4     0     3
 9  74.2     0     2
10  73.9     1     2
# … with 246 more rows
```
]

---
count: false
 
Example 2/4: Filter Rows with OR Criteria - **watch** the symbol for OR
.panel1-filter2-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
  select(age, aa, t_vol) %>%
  filter(age > 69 | t_vol > 1) %>%
  arrange(desc(age))

# see how many rows now
# watch age and aa status
# Format:
*# filter(logic test 1 | logic test 2)        <br>  # filter(logic test 1 | logic test 2)        <br>
```
]
 
.panel2-filter2-auto[

```
[1] 316
```

---
count: false
 
Example 2/4: Filter Rows with OR Criteria - **watch** the symbol for OR
.panel1-filter2-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
  select(age, aa, t_vol) %>%
  filter(age > 69 | t_vol > 1) %>%
  arrange(desc(age))

# see how many rows now
# watch age and aa status
# Format:
# filter(logic test 1 | logic test 2)        <br>  # filter(logic test 1 | logic test 2)        <br>
```
]
 
.panel2-filter2-auto[

```
[1] 316
```

---

count: false
 
Example 3/4: Filter Rows with XOR Criteria - **Watch** the Syntax!<br> xor = Exclusive OR - only one OR the other statement is TRUE, not both.
.panel1-filter3-auto[

```r
# how many rows when you start
*nrow(prostate)
```
]
 
.panel2-filter3-auto[

```
[1] 316
```
]

---
count: false
 
Example 3/4: Filter Rows with XOR Criteria - **Watch** the Syntax!<br> xor = Exclusive OR - only one OR the other statement is TRUE, not both.
.panel1-filter3-auto[

```r
# how many rows when you start
nrow(prostate)

*prostate
```
]
 
.panel2-filter3-auto[

```
[1] 316
```

---
count: false
 
Example 3/4: Filter Rows with XOR Criteria - **Watch** the Syntax!<br> xor = Exclusive OR - only one OR the other statement is TRUE, not both.
.panel1-filter3-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
* select(age, aa, t_vol)
```
]
 
.panel2-filter3-auto[

```
[1] 316
```

---
count: false
 
Example 3/4: Filter Rows with XOR Criteria - **Watch** the Syntax!<br> xor = Exclusive OR - only one OR the other statement is TRUE, not both.
.panel1-filter3-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
  select(age, aa, t_vol) %>%
* filter(xor(age > 69, t_vol > 1))
```
]
 
.panel2-filter3-auto[

```
[1] 316
```

```
# A tibble: 224 x 3
     age    aa t_vol
   <dbl> <dbl> <dbl>
 1  63.2     0     2
 2  65.4     0     2
 3  65.5     1     2
 4  67.1     0     3
 5  63.9     0     2
 6  63       1     2
 7  58.5     0     3
 8  56.2     0     2
 9  59.7     1     2
10  67.6     0     2
# … with 214 more rows
```
]

---
count: false
 
Example 3/4: Filter Rows with XOR Criteria - **Watch** the Syntax!<br> xor = Exclusive OR - only one OR the other statement is TRUE, not both.
.panel1-filter3-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
  select(age, aa, t_vol) %>%
  filter(xor(age > 69, t_vol > 1)) %>%
* arrange(desc(age))
```
]
 
.panel2-filter3-auto[

```
[1] 316
```

```
# A tibble: 224 x 3
     age    aa t_vol
   <dbl> <dbl> <dbl>
 1  78.3     0     1
 2  76.9     0     1
 3  74.6     0     1
 4  71.9     0     1
 5  71.4     0     1
 6  71.2     0     1
 7  71       0     1
 8  70.6     1     1
 9  69.7     0     1
10  69       1     2
# … with 214 more rows
```
]

---
count: false
 
Example 3/4: Filter Rows with XOR Criteria - **Watch** the Syntax!<br> xor = Exclusive OR - only one OR the other statement is TRUE, not both.
.panel1-filter3-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
  select(age, aa, t_vol) %>%
  filter(xor(age > 69, t_vol > 1)) %>%
  arrange(desc(age))

# see how many rows now
# watch age and aa status
# Format:
*# filter(xor(logic test 1, logic test 2))        <br>  # filter(xor(logic test 1, logic test 2))        <br>
```
]
 
.panel2-filter3-auto[

```
[1] 316
```

---
count: false
 
Example 3/4: Filter Rows with XOR Criteria - **Watch** the Syntax!<br> xor = Exclusive OR - only one OR the other statement is TRUE, not both.
.panel1-filter3-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
  select(age, aa, t_vol) %>%
  filter(xor(age > 69, t_vol > 1)) %>%
  arrange(desc(age))

# see how many rows now
# watch age and aa status
# Format:
# filter(xor(logic test 1, logic test 2))        <br>  # filter(xor(logic test 1, logic test 2))        <br>
```
]
 
.panel2-filter3-auto[

```
[1] 316
```

---

count: false
 
Example 4/4: Filter Rows with Age >64, NOT African-American
.panel1-filter4-auto[

```r
# how many rows when you start
*nrow(prostate)
```
]
 
.panel2-filter4-auto[

```
[1] 316
```
]

---
count: false
 
Example 4/4: Filter Rows with Age >64, NOT African-American
.panel1-filter4-auto[

```r
# how many rows when you start
nrow(prostate)

*prostate
```
]
 
.panel2-filter4-auto[

```
[1] 316
```

---
count: false
 
Example 4/4: Filter Rows with Age >64, NOT African-American
.panel1-filter4-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
* select(age, aa, recurrence)
```
]
 
.panel2-filter4-auto[

```
[1] 316
```

```
# A tibble: 316 x 3
     age    aa recurrence
   <dbl> <dbl>      <dbl>
 1  72.1     0          1
 2  73.6     0          1
 3  67.5     0          0
 4  65.8     0          0
 5  63.2     0          0
 6  65.4     0          0
 7  65.5     1          0
 8  67.1     0          1
 9  63.9     0          0
10  63       1          0
# … with 306 more rows
```
]

---
count: false
 
Example 4/4: Filter Rows with Age >64, NOT African-American
.panel1-filter4-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
  select(age, aa, recurrence) %>%
* filter(age > 64 & aa != 1)
```
]
 
.panel2-filter4-auto[

```
[1] 316
```

```
# A tibble: 103 x 3
     age    aa recurrence
   <dbl> <dbl>      <dbl>
 1  72.1     0          1
 2  73.6     0          1
 3  67.5     0          0
 4  65.8     0          0
 5  65.4     0          0
 6  67.1     0          1
 7  73.3     0          0
 8  67.6     0          0
 9  65.2     0          0
10  67.1     0          0
# … with 93 more rows
```
]

---
count: false
 
Example 4/4: Filter Rows with Age >64, NOT African-American
.panel1-filter4-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
  select(age, aa, recurrence) %>%
  filter(age > 64 & aa != 1) %>%
* arrange(desc(age))
```
]
 
.panel2-filter4-auto[

```
[1] 316
```

```
# A tibble: 103 x 3
     age    aa recurrence
   <dbl> <dbl>      <dbl>
 1  79       0          1
 2  78.3     0          1
 3  76.9     0          0
 4  76.3     0          1
 5  74.9     0          0
 6  74.8     0          0
 7  74.6     0          0
 8  74.4     0          0
 9  74.2     0          0
10  73.6     0          1
# … with 93 more rows
```
]

---
count: false
 
Example 4/4: Filter Rows with Age >64, NOT African-American
.panel1-filter4-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
  select(age, aa, recurrence) %>%
  filter(age > 64 & aa != 1) %>%
  arrange(desc(age))

# see how many rows now
# watch age and aa status
# Format:
*# filter(logic test 1 & logic test 2)         <br>  # filter(logic test 1 & logic test 2)         <br>
```
]
 
.panel2-filter4-auto[

```
[1] 316
```

---
count: false
 
Example 4/4: Filter Rows with Age >64, NOT African-American
.panel1-filter4-auto[

```r
# how many rows when you start
nrow(prostate)

prostate %>%
# selected 3 columns
# see how age and aa change w/filter
  select(age, aa, recurrence) %>%
  filter(age > 64 & aa != 1) %>%
  arrange(desc(age))

# see how many rows now
# watch age and aa status
# Format:
# filter(logic test 1 & logic test 2)         <br>  # filter(logic test 1 & logic test 2)         <br>
```
]
 
.panel2-filter4-auto[

```
[1] 316
```

---
class: inverse, center

# End of This Flipbook

## On to The Coding Exercises!