Recoding data

class: center, middle, inverse, title-slide

.title[
# Recoding data
]
.subtitle[
## <br><br> Data Science in a Box
]
.author[
### <a href="https://datasciencebox.org/">datasciencebox.org</a>
]

---

layout: true
  
<div class="my-footer">
<span>
<a href="https://datasciencebox.org" target="_blank">datasciencebox.org</a>
</span>
</div>

---

class: middle

# Case study: Religion and income

---

.footnote[
Source: [pewforum.org/religious-landscape-study/income-distribution](https://www.pewforum.org/religious-landscape-study/income-distribution/), Retrieved 14 April, 2020
]

---

## Read data

```r
library(readxl)
rel_inc <- read_excel("data/relig-income.xlsx")
```

.small[

```
## # A tibble: 12 × 6
##   `Religious tradition`         `Less than $30…` `$30,000-$49,9…`
##   <chr>                                    <dbl>            <dbl>
## 1 Buddhist                                  0.36             0.18
## 2 Catholic                                  0.36             0.19
## 3 Evangelical Protestant                    0.35             0.22
## 4 Hindu                                     0.17             0.13
## 5 Historically Black Protestant             0.53             0.22
## 6 Jehovah's Witness                         0.48             0.25
## # … with 6 more rows, and 3 more variables:
## #   `$50,000-$99,999` <dbl>, `$100,000 or more` <dbl>,
## #   `Sample Size` <dbl>
```
]

---

## Rename columns

.midi[

```r
rel_inc %>%
  rename( 
    religion = `Religious tradition`, 
    n = `Sample Size` 
  ) 
```

```
## # A tibble: 12 × 6
##   religion     `Less than $30…` `$30,000-$49,9…` `$50,000-$99,9…`
##   <chr>                   <dbl>            <dbl>            <dbl>
## 1 Buddhist                 0.36             0.18             0.32
## 2 Catholic                 0.36             0.19             0.26
## 3 Evangelical…             0.35             0.22             0.28
## 4 Hindu                    0.17             0.13             0.34
## 5 Historicall…             0.53             0.22             0.17
## 6 Jehovah's W…             0.48             0.25             0.22
## # … with 6 more rows, and 2 more variables:
## #   `$100,000 or more` <dbl>, n <dbl>
```
]

---

.question[
If we want a new variable called `income` with levels such as "Less than $30,000", "$30,000-$49,999", ... etc. which function should we use?
]

```
## # A tibble: 48 × 4
##    religion                   n income            proportion
##    <chr>                  <dbl> <chr>                  <dbl>
##  1 Buddhist                 233 Less than $30,000       0.36
##  2 Buddhist                 233 $30,000-$49,999         0.18
##  3 Buddhist                 233 $50,000-$99,999         0.32
##  4 Buddhist                 233 $100,000 or more        0.13
##  5 Catholic                6137 Less than $30,000       0.36
##  6 Catholic                6137 $30,000-$49,999         0.19
##  7 Catholic                6137 $50,000-$99,999         0.26
##  8 Catholic                6137 $100,000 or more        0.19
##  9 Evangelical Protestant  7462 Less than $30,000       0.35
## 10 Evangelical Protestant  7462 $30,000-$49,999         0.22
## 11 Evangelical Protestant  7462 $50,000-$99,999         0.28
## 12 Evangelical Protestant  7462 $100,000 or more        0.14
## 13 Hindu                    172 Less than $30,000       0.17
## 14 Hindu                    172 $30,000-$49,999         0.13
## 15 Hindu                    172 $50,000-$99,999         0.34
## # … with 33 more rows
```

---

## Pivot longer

.midi[

```r
rel_inc %>%
  rename(
    religion = `Religious tradition`,
    n = `Sample Size`
  ) %>%
  pivot_longer( 
    cols = -c(religion, n),   # all but religion and n 
    names_to = "income",  
    values_to = "proportion" 
  ) 
```

---

## Calculate frequencies

.midi[

```r
rel_inc %>%
  rename(
    religion = `Religious tradition`,
    n = `Sample Size`
  ) %>%
  pivot_longer(
    cols = -c(religion, n), 
    names_to = "income", 
    values_to = "proportion"
  ) %>%
  mutate(frequency = round(proportion * n)) 
```

```
## # A tibble: 48 × 5
##   religion     n income            proportion frequency
##   <chr>    <dbl> <chr>                  <dbl>     <dbl>
## 1 Buddhist   233 Less than $30,000       0.36        84
## 2 Buddhist   233 $30,000-$49,999         0.18        42
## 3 Buddhist   233 $50,000-$99,999         0.32        75
## 4 Buddhist   233 $100,000 or more        0.13        30
## 5 Catholic  6137 Less than $30,000       0.36      2209
## 6 Catholic  6137 $30,000-$49,999         0.19      1166
## # … with 42 more rows
```
]

---

## Save data

```r
rel_inc_long <- rel_inc %>% 
  rename(
    religion = `Religious tradition`,
    n = `Sample Size`
  ) %>%
  pivot_longer(
    cols = -c(religion, n), 
    names_to = "income", 
    values_to = "proportion"
  ) %>%
  mutate(frequency = round(proportion * n))
```

---

## Barplot

```r
ggplot(rel_inc_long, aes(y = religion, x = frequency)) +
  geom_col()
```

---

## Recode religion

.panelset[

.panel[.panel-name[Recode]

```r
rel_inc_long <- rel_inc_long %>%
  mutate(religion = case_when(
    religion == "Evangelical Protestant"           ~ "Ev. Protestant",
    religion == "Historically Black Protestant"    ~ "Hist. Black Protestant",
    religion == 'Unaffiliated (religious "nones")' ~ "Unaffiliated",
    TRUE                                           ~ religion
  ))
```
]

.panel[.panel-name[Plot]
<img src="u2-d13-data-recode_files/figure-html/unnamed-chunk-12-1.png" width="65%" style="display: block; margin: auto;" />
]

]

---

## Reverse religion order

.panelset[

.panel[.panel-name[Recode]

```r
rel_inc_long <- rel_inc_long %>%
  mutate(religion = fct_rev(religion)) 
```
]

.panel[.panel-name[Plot]
<img src="u2-d13-data-recode_files/figure-html/unnamed-chunk-14-1.png" width="65%" style="display: block; margin: auto;" />
]

]

---

## Add income

.panelset[

.panel[.panel-name[Plot]
<img src="u2-d13-data-recode_files/figure-html/rel-income-1.png" width="65%" style="display: block; margin: auto;" />
]

.panel[.panel-name[Code]

```r
ggplot(rel_inc_long, aes(y = religion, x = frequency, fill = income)) + 
  geom_col() 
```
]

]

---

## Fill bars

.panelset[

.panel[.panel-name[Plot]
<img src="u2-d13-data-recode_files/figure-html/rel-income-fill-1.png" width="65%" style="display: block; margin: auto;" />
]

.panel[.panel-name[Code]

```r
ggplot(rel_inc_long, aes(y = religion, x = frequency, fill = income)) +
  geom_col(position = "fill")
```
]

]

---

## Change colors

.panelset[

.panel[.panel-name[Plot]
<img src="u2-d13-data-recode_files/figure-html/rel-income-fill-viridis-1.png" width="65%" style="display: block; margin: auto;" />
]

.panel[.panel-name[Code]

```r
ggplot(rel_inc_long, aes(y = religion, x = frequency, fill = income)) +
  geom_col(position = "fill") +
  scale_fill_viridis_d()
```
]

]

---

## Change theme

.panelset[

.panel[.panel-name[Plot]
<img src="u2-d13-data-recode_files/figure-html/rel-income-fill-viridis-minimal-1.png" width="65%" style="display: block; margin: auto;" />
]

.panel[.panel-name[Code]

```r
ggplot(rel_inc_long, aes(y = religion, x = frequency, fill = income)) +
  geom_col(position = "fill") +
  scale_fill_viridis_d() +
  theme_minimal() 
```
]

]

---

## Move legend to the bottom

.panelset[

.panel[.panel-name[Plot]
<img src="u2-d13-data-recode_files/figure-html/bottom-legend-1.png" width="65%" style="display: block; margin: auto;" />
]

.panel[.panel-name[Code]

```r
ggplot(rel_inc_long, aes(y = religion, x = frequency, fill = income)) +
  geom_col(position = "fill") +
  scale_fill_viridis_d() +
  theme_minimal() +
  theme(legend.position = "bottom")
```
]

]

---

## Legend adjustments

.panelset[

.panel[.panel-name[Plot]
<img src="u2-d13-data-recode_files/figure-html/unnamed-chunk-20-1.png" width="65%" style="display: block; margin: auto;" />
]

.panel[.panel-name[Code]

]

---

## Fix labels

.panelset[

.panel[.panel-name[Plot]
<img src="u2-d13-data-recode_files/figure-html/unnamed-chunk-21-1.png" width="65%" style="display: block; margin: auto;" />
]

.panel[.panel-name[Code]

```r
ggplot(rel_inc_long, aes(y = religion, x = frequency, fill = income)) +
  geom_col(position = "fill") +
  scale_fill_viridis_d() +
  theme_minimal() +
  theme(legend.position = "bottom") +
  guides(fill = guide_legend(nrow = 2, byrow = TRUE)) +
  labs(
    x = "Proportion", y = "", 
    title = "Income distribution by religious group", 
    subtitle = "Source: Pew Research Center, Religious Landscape Study", 
    fill = "Income" 
    )
```
]

]