Changing Variables

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# Changing Variables
## Dr. Mine Dogucu
]

---

``` r
glimpse(arthritis)
```

```
## Rows: 530
## Columns: 5
## $ age         <dbl> 85, 86, 83, 83, 85, 79, 90, 90, 87, 82, 77, 86, 84, 76, 77…
## $ age_gp      <fct> elderly, elderly, elderly, elderly, elderly, elderly, elde…
## $ sex         <fct> female, female, female, female, female, male, female, fema…
## $ yrs_from_dx <dbl> 27, 27, 10, 9, NA, NA, 51, 11, 36, 4, 31, NA, 9, 10, 3, 10…
## $ cdai        <dbl> NA, 23.0, 14.5, NA, NA, NA, NA, 40.0, 6.0, NA, 0.0, NA, NA…
```

**Goal**:

Create a new variable called `age_months` that represents `age` in months.

---

``` r
arthritis %>% 
  mutate(age_months = age*12)
```

```
## # A tibble: 530 × 6
##      age age_gp  sex    yrs_from_dx  cdai age_months
##    <dbl> <fct>   <fct>        <dbl> <dbl>      <dbl>
##  1    85 elderly female          27  NA         1020
##  2    86 elderly female          27  23         1032
##  3    83 elderly female          10  14.5        996
##  4    83 elderly female           9  NA          996
##  5    85 elderly female          NA  NA         1020
##  6    79 elderly male            NA  NA          948
##  7    90 elderly female          51  NA         1080
##  8    90 elderly female          11  40         1080
##  9    87 elderly female          36   6         1044
## 10    82 elderly female           4  NA          984
## # ℹ 520 more rows
```

---

``` r
glimpse(arthritis)
```

---

``` r
arthritis_age_mon <- arthritis %>% 
  mutate(age_months = age*12)
glimpse(arthritis_age_mon)
```

```
## Rows: 530
## Columns: 6
## $ age         <dbl> 85, 86, 83, 83, 85, 79, 90, 90, 87, 82, 77, 86, 84, 76, 77…
## $ age_gp      <fct> elderly, elderly, elderly, elderly, elderly, elderly, elde…
## $ sex         <fct> female, female, female, female, female, male, female, fema…
## $ yrs_from_dx <dbl> 27, 27, 10, 9, NA, NA, 51, 11, 36, 4, 31, NA, 9, 10, 3, 10…
## $ cdai        <dbl> NA, 23.0, 14.5, NA, NA, NA, NA, 40.0, 6.0, NA, 0.0, NA, NA…
## $ age_months  <dbl> 1020, 1032, 996, 996, 1020, 948, 1080, 1080, 1044, 984, 92…
```

---

**Goal**:

Create a new variable called `cdai_level` which reflects the levels in the the data dictionary

- Remission ≤ 2.8
- Low Disease Activity > 2.8 and ≤ 10
- Moderate Disease Activity > 10 and ≤ 22
- High Disease Activity > 22

---

``` r
arthritis %>% 
  mutate(cdai_level = case_when(
    cdai <=2.8 ~ "Remission", 
    cdai > 2.8 & cdai <= 10 ~ "Low",
    cdai > 10 & cdai <=22  ~ "Moderate",
    cdai > 22 ~ "High")) 
```

```
## # A tibble: 530 × 6
##      age age_gp  sex    yrs_from_dx  cdai cdai_level
##    <dbl> <fct>   <fct>        <dbl> <dbl> <chr>     
##  1    85 elderly female          27  NA   <NA>      
##  2    86 elderly female          27  23   High      
##  3    83 elderly female          10  14.5 Moderate  
##  4    83 elderly female           9  NA   <NA>      
##  5    85 elderly female          NA  NA   <NA>      
##  6    79 elderly male            NA  NA   <NA>      
##  7    90 elderly female          51  NA   <NA>      
##  8    90 elderly female          11  40   High      
##  9    87 elderly female          36   6   Low       
## 10    82 elderly female           4  NA   <NA>      
## # ℹ 520 more rows
```

---

We can't really see what we have created

---

We can use pipes with ggplot too!

.left-panel[

``` r
arthritis %>% 
  mutate(cdai_level = case_when(
    cdai <=2.8 ~ "Remission", 
    cdai > 2.8 & cdai <= 10 ~ "Low",
    cdai > 10 & cdai <=22  ~ "Moderate",
    cdai > 22 ~ "High")) %>% 
  ggplot(aes(x = cdai_level)) +
  geom_bar()
```
]

.right-panel[
![](Lab-02d-change-variable_files/figure-html/unnamed-chunk-9-1.png)
]

---

``` r
arthritis <- arthritis %>% 
  mutate(cdai_level = case_when(
    cdai <=2.8 ~ "Remission", 
    cdai > 2.8 & cdai <= 10 ~ "Low",
    cdai > 10 & cdai <=22  ~ "Moderate",
    cdai > 22 ~ "High")) 
```

---

``` r
glimpse(arthritis)
```

```
## Rows: 530
## Columns: 6
## $ age         <dbl> 85, 86, 83, 83, 85, 79, 90, 90, 87, 82, 77, 86, 84, 76, 77…
## $ age_gp      <fct> elderly, elderly, elderly, elderly, elderly, elderly, elde…
## $ sex         <fct> female, female, female, female, female, male, female, fema…
## $ yrs_from_dx <dbl> 27, 27, 10, 9, NA, NA, 51, 11, 36, 4, 31, NA, 9, 10, 3, 10…
## $ cdai        <dbl> NA, 23.0, 14.5, NA, NA, NA, NA, 40.0, 6.0, NA, 0.0, NA, NA…
## $ cdai_level  <chr> NA, "High", "Moderate", NA, NA, NA, NA, "High", "Low", NA,…
```

**Goal**:

Make `cdai_level` a factor and `age` an integer.

---

``` r
arthritis %>% 
  mutate(cdai_level = as.factor(cdai_level),
         age = as.integer(age)) 
```

```
## # A tibble: 530 × 6
##      age age_gp  sex    yrs_from_dx  cdai cdai_level
##    <int> <fct>   <fct>        <dbl> <dbl> <fct>     
##  1    85 elderly female          27  NA   <NA>      
##  2    86 elderly female          27  23   High      
##  3    83 elderly female          10  14.5 Moderate  
##  4    83 elderly female           9  NA   <NA>      
##  5    85 elderly female          NA  NA   <NA>      
##  6    79 elderly male            NA  NA   <NA>      
##  7    90 elderly female          51  NA   <NA>      
##  8    90 elderly female          11  40   High      
##  9    87 elderly female          36   6   Low       
## 10    82 elderly female           4  NA   <NA>      
## # ℹ 520 more rows
```

---

class: middle

`as.factor()` - makes a vector factor  
`as.numeric()` - makes a vector numeric  
`as.integer()` - makes a vector integer  
`as.double()` - makes a vector double  
`as.character()` - makes a vector character