Capstone project for Data Carpentry course.

## 1: Load the data

```{r load_data, message=FALSE}
library(dplyr)
library(ggplot2)
surveys <- read.csv("http://kbroman.org/datacarp/portal_data_joined.csv")
```

There are `r ncol(surveys)` columns and `r nrow(surveys)` rows.

## 2: Boxplots of weight by sex

Boxplots of weight by sex, omitting individuals with missing sex.

```{r boxplots_weight}
surveys %>% select(weight, sex) %>%
    filter(sex != "", !is.na(weight)) %>%
    ggplot() + geom_boxplot(aes(x=sex, y=weight))
```

## 3. Histogram of hindfoot lengths

```{r hist_hindfootlength}
surveys %>% filter(!is.na(hindfoot_length)) %>%
    ggplot() + geom_histogram(aes(x=hindfoot_length), bins=150)
```

## 4. scatterplots of hindfoot length vs weight for 3 species

```{r scatterplots}
surveys %>% filter(species_id %in% c("DM", "DO", "DS")) %>%
    filter(!is.na(weight), !is.na(hindfoot_length)) %>%
    ggplot(aes(x=hindfoot_length, y=weight)) +
    geom_point(aes(color=species_id)) + facet_grid(species_id ~ .)
```

## 5. Table with counts of "DM" by plot_type in 1977.

```{r table_plot_type}
counts1977 <- surveys %>% filter(species_id == "DM", year==1977) %>%
    group_by(plot_type) %>%
    tally() %>%
    select(plot_type, n)
knitr::kable(counts1977)
```


## 6. Plot of counts in "Rodent Exclosure" plots over time

```{r plot_counts_by_year}
counts <- surveys %>% filter(species_id == "DM") %>%
    filter(plot_type=="Rodent Exclosure") %>%
    group_by(year) %>%
    tally()
counts %>% ggplot(aes(x=year, y=n)) + geom_line()
```
