vendo-os-dados.Rmd

---
title: "Vendo os dados"
author: "Nazareno Andrade e João Arthur B. Monteiro"
date: "12 de dezembro de 2015"
output: html_document
---

```{r}
Sys.setenv(PARSE_APPLICATION_ID = "bck5MqFK8Xm5LK6TBuaSec2LRaVsz9Bff4cV55W2")
Sys.setenv(PARSE_API_KEY = "BpdV0CtpaK5awBu3ADvr5gf5wo9h6xsJ0VLUSPTX")
#Sys.setenv(PARSE_API_KEY = "1ZLSbeuSgyKRUv3F2GDybZ99Wzr4Ow4y9ZvGxOXI")
```


```{r}
require(rparse)
require(jsonlite)
require(dplyr)
require(reshape)
require(ggplot2)
library(zoo)
```

```{r}
q <- parse_query("Estrela", limit = 1000)
q2 <- parse_query("Estrela", skip = 1000)
q3 <- parse_query("Estrela", skip = 2000)
q <- rbind(q, parse_query("Estrela", skip = 3000))
q <- rbind(q, q2, q3)
q$acceleration = q$acceleration * 10

d <- melt(q[,c(1,2,3,5,7)])
#d <- melt(q[,c(1,2,3,5,7)], id = c("starid", "acceleration"))
d <- d %>% 
  group_by(starid, variable) %>% 
  mutate(index = 1:n()) %>% ungroup()

d.n <- d %>% 
  group_by(starid) %>% 
  summarise(n = n()) %>% 
  filter(n > 300)

d <- d %>% 
  filter(starid %in% d.n$starid) %>% 
  group_by(starid, variable) %>% 
  mutate(value_s=rollmean(value, k = 3, na.pad=TRUE, align="right"))

toplot <- filter(d, !(starid %in% c("", "Teste1", "Teste2")))
toplot <- filter(d, (starid %in% c("Deitadosafari")))
#toplot <- d
ggplot(data = toplot, 
       aes(index, value_s, colour = variable)) + 
  geom_line(size = 1.5) + 
  scale_colour_manual(values = c("black", "red", "green", "blue")) + 
  facet_grid(variable ~ starid)

```

================
Estrelas que brilham, brilham, BRILHAM! 

```{r}
q <- parse_query("EstrelaQueBrilha", limit = 1000)
q <- rbind(q, parse_query("EstrelaQueBrilha", skip = 1000))
# q <- rbind(q, q2, q3)
q$v = q$v * 1

d <- melt(q[,c(3, 6, 7, 8, 9)])
#d <- melt(q[,c(1,2,3,5,7)], id = c("starid", "acceleration"))
d <- d %>% 
  group_by(starid, variable) %>% 
  mutate(index = 1:n()) %>% ungroup()

d.n <- d %>% 
  group_by(starid) %>% 
  summarise(n = n()) %>% 
  filter(n > 300)

d <- d %>% 
  filter(starid %in% d.n$starid) %>% 
  group_by(starid, variable) %>% 
  mutate(value_s=rollmean(value, k = 3, na.pad=TRUE, align="right"))

#toplot <- filter(d, !(starid %in% c("", "Teste1", "Teste2")))
#toplot <- filter(d, (starid %in% c("Deitadosafari")))
toplot <- d
ggplot(data = toplot, 
       aes(index, value, colour = variable)) + 
  geom_line(size = 1.5) + 
  facet_grid(variable ~ starid)

```

```{r}
require("rgl")
jeff = filter(q, starid == "Naza001")
plot3d(jeff$x, jeff$y, jeff$z)

```

```{r}
jeff = filter(q, starid %in% c("Naza001", "Jeff1", "Allan1", "Matheus1"))
pca <- prcomp(data = jeff, ~ x + y + z)
plot(pca)
scores = as.data.frame(pca$x)

# plot of observations
ggplot(data = scores, aes(x = PC1, y = PC2, label = rownames(scores))) +
  geom_point(colour = "tomato", alpha = 0.8, size = 4) 
```


==================

```{r}
estrelinhas <- fromJSON(txt = "data-example/estrelinhas.json")[['results']]
options(digits.secs=2)
estrelinhas$time <- as.POSIXct(
  strptime(
    gsub(pattern="T", 
         replacement=" ", 
         gsub(pattern="Z",replacement="",estrelinhas$createdAt)), format="%Y-%m-%d %H:%M:%OS"))
```


```{r}
helder <- filter(estrelinhas, starid == "Jeff1")

roll_k = 80
helder2 <- data.frame(x = rollmean(helder[,5], roll_k), 
                      y = rollmean(helder[,6], roll_k), 
                      z = rollmean(helder[,7], roll_k))

helder2$x <- helder2$x - helder2$x[1]
helder2$y <- helder2$y - helder2$y[1]
helder2$z <- helder2$z - helder2$z[1]

d <- filter(melt(helder2), value < 100)
ggplot(data = d, aes(x = 1:NROW(d), y = value, colour = variable)) + 
  geom_line() + 
  facet_grid(. ~ variable)

require("rgl")
plot3d(helder2$x, helder2$y, helder2$z)
lines3d(helder2$x, helder2$y, helder2$z,col=2,lwd=2)
       
```