library(ggplot2)
library(ggthemes)
d <- read.csv(header=TRUE, "~/sort-perf")
d2 <- d[d$test == "set-model", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Set Model")+xlab("Model size")+ylab("Time")
d2 <- d[d$test == "append-half", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append half")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append half")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append half")+xlab("Model size")+ylab("Changes")
d2 <- d[d$test == "append-10th", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 10th")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 10th")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 10th")+xlab("Model size")+ylab("Changes")
d2 <- d[d$test == "append-100th", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 100th")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 100th")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 100th")+xlab("Model size")+ylab("Changes")
d2 <- d[d$test == "append-1", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 1")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 1")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 1")+xlab("Model size")+ylab("Changes")
d2 <- d[d$test == "append-2", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 2")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 2")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 2")+xlab("Model size")+ylab("Changes")
d2 <- d[d$test == "append-10", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 10")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 10")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 10")+xlab("Model size")+ylab("Changes")
d2 <- d[d$test == "remove-half", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove half")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove half")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove half")+xlab("Model size")+ylab("Changes")
d2 <- d[d$test == "remove-10th", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10th")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10th")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10th")+xlab("Model size")+ylab("Changes")
d2 <- d[d$test == "remove-100th", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 100th")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 100th")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 100th")+xlab("Model size")+ylab("Changes")
d2 <- d[d$test == "remove-10th", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10th")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10th")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10th")+xlab("Model size")+ylab("Changes")
d2 <- d[d$test == "remove-1", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 1")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 1")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 1")+xlab("Model size")+ylab("Changes")
d2 <- d[d$test == "remove-2", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 2")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 2")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 2")+xlab("Model size")+ylab("Changes")
d2 <- d[d$test == "remove-10", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10")+xlab("Model size")+ylab("Time")
ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10")+xlab("Model size")+ylab("Comparisons")
ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10")+xlab("Model size")+ylab("Changes")
---
title: "R Notebook"
output:
  html_notebook: default
  pdf_document: default
---


```{r}
library(ggplot2)
library(ggthemes)
d <- read.csv(header=TRUE, "~/sort-perf")
```

```{r}
d2 <- d[d$test == "set-model", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Set Model")+xlab("Model size")+ylab("Time")
```

```{r}
d2 <- d[d$test == "append-half", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append half")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append half")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append half")+xlab("Model size")+ylab("Changes")

```

```{r}
d2 <- d[d$test == "append-10th", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 10th")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 10th")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 10th")+xlab("Model size")+ylab("Changes")
```

```{r}
d2 <- d[d$test == "append-100th", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 100th")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 100th")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 100th")+xlab("Model size")+ylab("Changes")

```

```{r}
d2 <- d[d$test == "append-1", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 1")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 1")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 1")+xlab("Model size")+ylab("Changes")

```
```{r}
d2 <- d[d$test == "append-2", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 2")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 2")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 2")+xlab("Model size")+ylab("Changes")

```

```{r}
d2 <- d[d$test == "append-10", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 10")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 10")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Append 10")+xlab("Model size")+ylab("Changes")

```

```{r}
d2 <- d[d$test == "remove-half", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove half")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove half")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove half")+xlab("Model size")+ylab("Changes")

```

```{r}
d2 <- d[d$test == "remove-10th", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10th")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10th")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10th")+xlab("Model size")+ylab("Changes")
```

```{r}
d2 <- d[d$test == "remove-100th", ]

ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 100th")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 100th")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 100th")+xlab("Model size")+ylab("Changes")
```
```{r}
d2 <- d[d$test == "remove-10th", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10th")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10th")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10th")+xlab("Model size")+ylab("Changes")
```
```{r}
d2 <- d[d$test == "remove-1", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 1")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 1")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 1")+xlab("Model size")+ylab("Changes")

```
```{r}
d2 <- d[d$test == "remove-2", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 2")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 2")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 2")+xlab("Model size")+ylab("Changes")

```
```{r}
d2 <- d[d$test == "remove-10", ]
ggplot(d2,aes(x=model.size,y=time,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10")+xlab("Model size")+ylab("Time")

ggplot(d2,aes(x=model.size,y=comparisons,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10")+xlab("Model size")+ylab("Comparisons")

ggplot(d2,aes(x=model.size,y=changes,group=model,color=model))+geom_line()+scale_x_continuous(trans="log2")+ggtitle("Remove 10")+xlab("Model size")+ylab("Changes")

```