How to Use the filter() function for numeric values

class: center, middle, inverse, title-slide

# How to Use the <em>filter()</em> function for numeric values
## Selecting specific rows
### Peter Higgins
### 2020-12-31

---

### How to Use Filter to Pick Out a Subset of Rows

#### We will Start with Numeric Variables

Format: <br>

`dataset %>% 
  filter(var > value)` # you can use <=, ==, <, >, >=, != etc.

What is in the parentheses **must** evaluate to a logical vector, <br>
e.g, a string of TRUE or FALSE values, one for each row in your dataset.

You can Remember that filte**R** is for selecting **R**ows because it ends with an **R**.

Remember that **testing for equality** in a logic test requires 2 = =,
not just one =. This is a **very** common syntax error in R code.

You can also use *near* to get numbers in a range

Let's look at some examples!
---

count: false
 
Example 1/6: filter prostate data for cases with cancer recurrence == 1
.panel1-filter1-auto[

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

```
[1] 316
```
]

---
count: false
 
Example 1/6: filter prostate data for cases with cancer recurrence == 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 1/6: filter prostate data for cases with cancer recurrence == 1
.panel1-filter1-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
* select(age, aa, fam_hx, recurrence)
```
]
 
.panel2-filter1-auto[

```
[1] 316
```

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

---
count: false
 
Example 1/6: filter prostate data for cases with cancer recurrence == 1
.panel1-filter1-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, aa, fam_hx, recurrence) %>%
* filter(recurrence == 1)
```
]
 
.panel2-filter1-auto[

```
[1] 316
```

```
# A tibble: 54 x 4
     age    aa fam_hx recurrence
   <dbl> <dbl>  <dbl>      <dbl>
 1  72.1     0      0          1
 2  73.6     0      0          1
 3  67.1     0      0          1
 4  64.4     0      0          1
 5  69       1      0          1
 6  63.4     1      0          1
 7  70.4     1      0          1
 8  65.3     1      0          1
 9  60.8     0      0          1
10  72.1     0      1          1
# … with 44 more rows
```
]

---
count: false
 
Example 1/6: filter prostate data for cases with cancer recurrence == 1
.panel1-filter1-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, aa, fam_hx, recurrence) %>%
  filter(recurrence == 1)

# see how many rows x cols now
#
# Format:
*# filter(variable [comparison] value)        <br>  # filter(variable [comparison] value)        <br>
```
]
 
.panel2-filter1-auto[

```
[1] 316
```

---
count: false
 
Example 1/6: filter prostate data for cases with cancer recurrence == 1
.panel1-filter1-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, aa, fam_hx, recurrence) %>%
  filter(recurrence == 1)

# see how many rows x cols now
#
# Format:
# filter(variable [comparison] value)        <br>  # filter(variable [comparison] value)        <br>
```
]
 
.panel2-filter1-auto[

```
[1] 316
```

---

count: false
 
Example 2/6: Filter Preoperative PSA for Values > 20
.panel1-filter2-auto[

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

```
[1] 316
```
]

---
count: false
 
Example 2/6: Filter Preoperative PSA for Values > 20
.panel1-filter2-auto[

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

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

```
[1] 316
```

---
count: false
 
Example 2/6: Filter Preoperative PSA for Values > 20
.panel1-filter2-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
* select(age, recurrence, preop_psa)
```
]
 
.panel2-filter2-auto[

```
[1] 316
```

```
# A tibble: 316 x 3
     age recurrence preop_psa
   <dbl>      <dbl>     <dbl>
 1  72.1          1     14.1 
 2  73.6          1     10.5 
 3  67.5          0      6.98
 4  65.8          0      4.4 
 5  63.2          0     21.4 
 6  65.4          0      5.1 
 7  65.5          0      6.03
 8  67.1          1      8.7 
 9  63.9          0      3.83
10  63            0      7.98
# … with 306 more rows
```
]

---
count: false
 
Example 2/6: Filter Preoperative PSA for Values > 20
.panel1-filter2-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
* filter(preop_psa > 20)
```
]
 
.panel2-filter2-auto[

```
[1] 316
```

```
# A tibble: 17 x 3
     age recurrence preop_psa
   <dbl>      <dbl>     <dbl>
 1  63.2          0      21.4
 2  73.3          0      30.6
 3  67.9          0      29  
 4  68            0      20.7
 5  49.9          0      29.7
 6  51.1          1      24.9
 7  68.9          1      27  
 8  65.8          0      22.9
 9  60.1          0      22.3
10  50.8          1      37  
11  66.1          1      26.1
12  50.9          0      26.2
13  71.9          0      27.7
14  56.5          1      40.1
15  53            0      39  
16  59.6          0      31.0
17  51.9          1      27.2
```
]

---
count: false
 
Example 2/6: Filter Preoperative PSA for Values > 20
.panel1-filter2-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
  filter(preop_psa > 20)

# see how many rows now
#
# Format:
*# filter(variable [comparison] value)        <br>  # filter(variable [comparison] value)        <br>
```
]
 
.panel2-filter2-auto[

```
[1] 316
```

---
count: false
 
Example 2/6: Filter Preoperative PSA for Values > 20
.panel1-filter2-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
  filter(preop_psa > 20)

# see how many rows now
#
# Format:
# filter(variable [comparison] value)        <br>  # filter(variable [comparison] value)        <br>
```
]
 
.panel2-filter2-auto[

```
[1] 316
```

---

count: false
 
Example 3/6: Filter Preoperative PSA for Values very close to 10
.panel1-filter3-auto[

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

```
[1] 316
```
]

---
count: false
 
Example 3/6: Filter Preoperative PSA for Values very close to 10
.panel1-filter3-auto[

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

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

```
[1] 316
```

---
count: false
 
Example 3/6: Filter Preoperative PSA for Values very close to 10
.panel1-filter3-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
* select(age, recurrence, preop_psa)
```
]
 
.panel2-filter3-auto[

```
[1] 316
```

---
count: false
 
Example 3/6: Filter Preoperative PSA for Values very close to 10
.panel1-filter3-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
* filter(near(preop_psa, 10))
```
]
 
.panel2-filter3-auto[

```
[1] 316
```

```
# A tibble: 2 x 3
    age recurrence preop_psa
  <dbl>      <dbl>     <dbl>
1  55.8          0        10
2  57.1          0        10
```
]

---
count: false
 
Example 3/6: Filter Preoperative PSA for Values very close to 10
.panel1-filter3-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
  filter(near(preop_psa, 10))

# see how many rows now
#
# Format:
*# filter(variable [comparison] value)        <br>  # filter(variable [comparison] value)        <br>
```
]
 
.panel2-filter3-auto[

```
[1] 316
```

```
# A tibble: 2 x 3
    age recurrence preop_psa
  <dbl>      <dbl>     <dbl>
1  55.8          0        10
2  57.1          0        10
```
]

---
count: false
 
Example 3/6: Filter Preoperative PSA for Values very close to 10
.panel1-filter3-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
  filter(near(preop_psa, 10))

# see how many rows now
#
# Format:
# filter(variable [comparison] value)        <br>  # filter(variable [comparison] value)        <br>
```
]
 
.panel2-filter3-auto[

```
[1] 316
```

```
# A tibble: 2 x 3
    age recurrence preop_psa
  <dbl>      <dbl>     <dbl>
1  55.8          0        10
2  57.1          0        10
```
]

---

count: false
 
Example 4/6: Filter Preoperative PSA for Values within 1.5 units of 17
.panel1-filter4-auto[

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

```
[1] 316
```
]

---
count: false
 
Example 4/6: Filter Preoperative PSA for Values within 1.5 units of 17
.panel1-filter4-auto[

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

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

```
[1] 316
```

---
count: false
 
Example 4/6: Filter Preoperative PSA for Values within 1.5 units of 17
.panel1-filter4-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
* select(age, recurrence, preop_psa)
```
]
 
.panel2-filter4-auto[

```
[1] 316
```

---
count: false
 
Example 4/6: Filter Preoperative PSA for Values within 1.5 units of 17
.panel1-filter4-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
* filter(near(preop_psa, 17, tol = 1.5))
```
]
 
.panel2-filter4-auto[

```
[1] 316
```

```
# A tibble: 6 x 3
    age recurrence preop_psa
  <dbl>      <dbl>     <dbl>
1  63.4          1      15.7
2  65            0      15.7
3  57.5          0      15.9
4  58.8          1      15.6
5  63.9          0      18  
6  70.6          0      17  
```
]

---
count: false
 
Example 4/6: Filter Preoperative PSA for Values within 1.5 units of 17
.panel1-filter4-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
  filter(near(preop_psa, 17, tol = 1.5))

# see how many rows now
#
# Format:
*# filter(variable [comparison] value)        <br>  # filter(variable [comparison] value)        <br>
```
]
 
.panel2-filter4-auto[

```
[1] 316
```

---
count: false
 
Example 4/6: Filter Preoperative PSA for Values within 1.5 units of 17
.panel1-filter4-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
  filter(near(preop_psa, 17, tol = 1.5))

# see how many rows now
#
# Format:
# filter(variable [comparison] value)        <br>  # filter(variable [comparison] value)        <br>
```
]
 
.panel2-filter4-auto[

```
[1] 316
```

---

count: false
 
Example 5/6: Filter Preoperative PSA for Values between 10 and 13
.panel1-filter5-auto[

```r
# how many rows when you start
*nrow(prostate)
```
]
 
.panel2-filter5-auto[

```
[1] 316
```
]

---
count: false
 
Example 5/6: Filter Preoperative PSA for Values between 10 and 13
.panel1-filter5-auto[

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

*prostate
```
]
 
.panel2-filter5-auto[

```
[1] 316
```

---
count: false
 
Example 5/6: Filter Preoperative PSA for Values between 10 and 13
.panel1-filter5-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
* select(age, recurrence, preop_psa)
```
]
 
.panel2-filter5-auto[

```
[1] 316
```

---
count: false
 
Example 5/6: Filter Preoperative PSA for Values between 10 and 13
.panel1-filter5-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
* filter(between(preop_psa, 10, 13))
```
]
 
.panel2-filter5-auto[

```
[1] 316
```

```
# A tibble: 24 x 3
     age recurrence preop_psa
   <dbl>      <dbl>     <dbl>
 1  73.6          1      10.5
 2  59.7          0      10.4
 3  72.1          1      10.1
 4  55.8          0      10  
 5  57.1          0      10  
 6  70.6          0      11.9
 7  55.6          0      11.9
 8  71.8          1      11.9
 9  50.6          0      11.1
10  63.9          1      10.1
# … with 14 more rows
```
]

---
count: false
 
Example 5/6: Filter Preoperative PSA for Values between 10 and 13
.panel1-filter5-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
  filter(between(preop_psa, 10, 13))

# see how many rows now
#
# Format:
*# filter(variable [comparison] value)        <br>  # filter(variable [comparison] value)        <br>
```
]
 
.panel2-filter5-auto[

```
[1] 316
```

---
count: false
 
Example 5/6: Filter Preoperative PSA for Values between 10 and 13
.panel1-filter5-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
  filter(between(preop_psa, 10, 13))

# see how many rows now
#
# Format:
# filter(variable [comparison] value)        <br>  # filter(variable [comparison] value)        <br>
```
]
 
.panel2-filter5-auto[

```
[1] 316
```

---

count: false
 
Example 6/6: Filter Preoperative PSA for Values of 10 or 13 or 17
.panel1-filter5-auto[

```r
# how many rows when you start
*nrow(prostate)
```
]
 
.panel2-filter5-auto[

```
[1] 316
```
]

---
count: false
 
Example 6/6: Filter Preoperative PSA for Values of 10 or 13 or 17
.panel1-filter5-auto[

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

*prostate
```
]
 
.panel2-filter5-auto[

```
[1] 316
```

---
count: false
 
Example 6/6: Filter Preoperative PSA for Values of 10 or 13 or 17
.panel1-filter5-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
* select(age, recurrence, preop_psa)
```
]
 
.panel2-filter5-auto[

```
[1] 316
```

---
count: false
 
Example 6/6: Filter Preoperative PSA for Values of 10 or 13 or 17
.panel1-filter5-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
* filter(between(preop_psa, 10, 13))
```
]
 
.panel2-filter5-auto[

```
[1] 316
```

---
count: false
 
Example 6/6: Filter Preoperative PSA for Values of 10 or 13 or 17
.panel1-filter5-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
  filter(between(preop_psa, 10, 13))

# see how many rows now
#
# Format:
*# filter(variable [comparison] value)        <br>  # filter(variable [comparison] value)        <br>
```
]
 
.panel2-filter5-auto[

```
[1] 316
```

---
count: false
 
Example 6/6: Filter Preoperative PSA for Values of 10 or 13 or 17
.panel1-filter5-auto[

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

prostate %>%
# check next line: some recur, some do not
# selected 4 columns
  select(age, recurrence, preop_psa) %>%
  filter(between(preop_psa, 10, 13))

# see how many rows now
#
# Format:
# filter(variable [comparison] value)        <br>  # filter(variable [comparison] value)        <br>
```
]
 
.panel2-filter5-auto[

```
[1] 316
```

---
class: inverse, center

# End of This Flipbook

## On to The Coding Exercises!