steps_to_photos.Rmd

---
title: "Photos per steps"
output: html_notebook
---

```{r}
setwd('C:/Users/vale/Google Drive/apple_health_export/')
library(tidyverse)
library(brms)
library(brmstools)
```

```{r}
data <- read_csv('steps_per_day.csv')
```

```{r}
data %>% 
  filter(steps > 0) %>%
  filter(!is.na(photos)) ->
  data
```

```{r}
data %>% gather('key', 'value', -creationDate) %>%
  ggplot(aes(x = creationDate, y = value)) +
  geom_bar(stat = 'identity') + 
  facet_grid('key ~ .', scales='free_y') + 
  theme_minimal() +
  labs(x = 'Date', y = 'Count', title='Daily steps and photos taken', subtitle = 'Sep 2017 - Mar 2019')

ggsave('days_and_photos.png')
```



```{r}
data %>% ggplot(aes(x = steps, y = photos)) +
  geom_point() + 
  scale_y_log10() +
  scale_x_log10() + 
  theme_minimal()
```

```{r}
fit1 <- brm('photos ~ log(steps)', family = poisson(), data = data)
```


```{r}
fit1
```

```{r}
marginal_effects(fit1, method = 'predict')
```

```{r}
fit1.pred <- predict(fit1)
```

```{r}
jdata <- bind_cols(data, as_tibble(fit1.pred))
```


```{r}
ggplot(data = jdata, aes(x = steps, y = photos)) +
  geom_point() + 
  geom_ribbon(aes(ymin = Q2.5, ymax = Q97.5), alpha=0.1, fill='red') + 
  geom_line(color='red', size=0.5, aes(x = steps, y = Estimate)) +
  scale_x_log10() +
  scale_y_log10() +
  theme_minimal() +
  labs(x = 'Steps taken', y = 'Photos taken', title = 'Poisson regression', subtitle = '~ 50 more photos for every 10,000 steps')

ggsave('steps_to_photos_regression.png')
```