exploratory-data-analysis.Rmd

# Exploratory data analysis {#explore}

For this chapter you'll need the following files, which are available for download [here](https://github.com/jacobkap/crimebythenumbers/tree/master/data): ucr2017.rda and offenses_known_yearly_1960_2020.rds.

When you first start working on new data it is important to spend some time getting familiar with the data. This includes understanding how many rows and columns it has, what each row means (is each row an offender? a victim? crime in a city over a day/month/year?, etc.), and what columns it has. **Basically you want to know if the data is capable of answering the question you are asking.**

While not a comprehensive list, the following is a good start for exploratory data analysis of new data sets.

+ What are the units (what does each row represent?)?
+ What variables are available?
+ What time period does it cover?
+ Are there outliers? How many?
+ Are there missing values? How many?

For the first part of this lesson we will use a data set of FBI Uniform Crime Reporting (UCR) data for 2017. This data includes every agency that reported their data for all 12 months of the year. In this part of the chapter we will look at some summary statistics for the variables we are interested in and make some basic graphs to visualize the data. 

First, we need to load the data. Make sure your working directory is set to the folder where the data is.

```{r}
load("data/ucr2017.rda")
```

The function `head()` will print out the first 6 rows of every column in the data. Since we only have 9 columns, we will use this function. Be careful when you have many columns (100+) as printing all of them out makes it difficult to read.

```{r}
head(ucr2017)
```

From these results it appears that each row is a single agency's annual data for 2017, and the columns show the number of crimes for four crime categories included. 

Finally, we can run `names()` to print out every column name. We can already see every name from `head()`, but this is useful when we have many columns and don't want to use `head()`.

```{r}
names(ucr2017)
```

## Summary and Table

An important function in understanding the data you have is `summary()` which, as discussed in Section \@ref(first-steps-to-exploring-data), provides summary statistics on the numeric columns you have. Let's take a look at the results before seeing how to do something similar for categorical columns.

```{r}
summary(ucr2017)
```

The `table()` function returns every unique value in a category **and** how often that value appears. Unlike `summary()` we can't just put the entire data set into the (), we need to specify a single column. To specify a column you use the dollar sign notation, which is `data$column`. For most functions we use to examine the data as a whole, such as `head()`, you can do the same for a specific column. 

```{r}
head(ucr2017$agency_name)
```

There are only two columns in our data with categorical values that we can use - *year* and *state*, so let's use `table()` on both of them. The columns *ori* and *agency_name* are also categorical but as each row of data has a unique ORI and name, running `table()` on those columns would not be helpful.

```{r}
table(ucr2017$year)
```

We can see that every year in our data is 2017, as expected based on the data name. *year* is a numerical column so why can we use `table()` on it? R doesn't differentiate between numbers and characters when seeing how often each value appears. If we ran `table()` on the column "actual_murder" it would tell us how many times each unique value in the column appeared in the data. That wouldn't be very useful as we don't really care how many times an agency has, for example, 7 murders. As numeric variables often have many more unique values than character variables, it also leads to many values being printed, making it harder to understand. For columns where the number of categories is important to us, such as years, states, neighborhoods, we should use `table()`. 

```{r}
table(ucr2017$state)
```

This shows us how many times each state is present in the data. States with a larger population tend to appear more often; this makes sense as those states have more agencies to report. Right now the results are in alphabetical order, but when knowing how frequently something appears, we usually want it ordered by frequency. We can use the `sort()` function to order the results from `table()`. Just put the entire `table()` function inside of the () in `sort()`.

```{r}
sort(table(ucr2017$state))
```

And if we want to sort it in decreasing order of frequency, we can use the parameter `decreasing` in `sort()` and set it to TRUE. A parameter is just an option used in an R function to change the way the function is used or what output it gives. Almost all functions have these parameters, and they are useful if you don't want to use the default setting in the function. This parameter, `decreasing`, changes the `sort()` output to print from largest to smallest. By default this parameter is set to FALSE, and here we say it is equal to TRUE.

```{r}
sort(table(ucr2017$state), decreasing = TRUE)
```

## Graphing

We often want to make quick plots of our data to get a visual understanding of the data. We will learn a different - and in my opinion a superior - way to make graphs in Chapters \@ref(graphing-intro) and \@ref(ois-graphs), but for now let's use the function `plot()`. The `plot()` function is built into R so we don't need to use any packages for it.

Let's make a few scatterplots showing the relationship between two variables. With `plot()` the syntax (how you write the code) is `plot(x_axis_variable, y_axis_variable)`. So all we need to do is give it the variable for the x- and y-axis. Each dot will represent a single agency (a single row in our data).

```{r}
plot(ucr2017$actual_murder,
     ucr2017$actual_robbery_total)
```

Above we are telling R to plot the number of murders on the x-axis and the number of robberies on the y-axis. This shows the relationship between a city's number of murders and number of robberies. We can see that there is a relationship where more murders is correlated with more robberies. However, there are a huge number of agencies in the bottom-left corner that have very few murders or robberies. This makes sense as - as we see in the `summary()` above - most agencies are small, with the median population under 5,000 people. 

To try to avoid that clump of small agencies at the bottom, let's make a new data set of only agencies with a population over 1 million. We will use the `filter()` function from the `dplyr` package that was introduced in Chapter \@ref(subsetting-intro). For `filter()`, we need to first include our data set name, which is ucr2017, and then say our conditional statement. Our conditional statement is that rows in the "population" column have a value of over 1 million. For the `dplyr` functions we don't put our column name in quotes. 

And we'll assign our results to a new object called ucr2017_big_cities Since we're using the `dplyr` package we need to tell R that we want to use it by using `library(dplyr)`.

```{r}
library(dplyr)
ucr2017_big_cities <- filter(ucr2017, population > 1000000) 
```

Now we have 18 agencies with a population of over 1 million people. 

Now we can do the same graph as above but using this new data set.

```{r}
plot(ucr2017_big_cities$actual_murder, 
     ucr2017_big_cities$actual_robbery_total)
```

The problem is somewhat solved. There is still a small clumping of agencies with few robberies or murders, but the issue is much better. And interestingly the trend is similar with this small subset of data as with all agencies included.

To make our graph look better, we can add labels for the axes and a title (there are many options for changing the appearance of this graph, we will just use these three).

+ xlab - X-axis label
+ ylab - Y-axis label
+ main - Graph title

Like all parameters, we add them in the () of `plot()` and separate each parameter by a comma. Since we are adding text to write in the plot, all of these parameter inputs must be in quotes.

```{r}
plot(ucr2017_big_cities$actual_murder,
     ucr2017_big_cities$actual_robbery_total,
     xlab = "Murders",
     ylab = "Robberies",
     main = "Relationship between murder and robbery")
```

## Aggregating (summaries of groups) {#aggregate}

Right now we have the number of crimes in each agency. For many policy analyses we'd be looking at the effect on the state as a whole, rather than at the agency-level. If we wanted to do this in our data, we would need to aggregate up to the state level. Aggregating data means that we group values at some higher level than they currently are (e.g. from agency to state, from day to month, from city street to city neighborhood) and then do some mathematical operation of our choosing (in our case usually sum) to that group. 

In Section \@ref(subset-colorado-data) we started to see if marijuana legalization affected murder in Colorado. We subsetted the data to only include agencies in Colorado from 2011-2017. Now we can continue to answer the question by aggregating to the state-level to see the total number of murders per year.

Let's think about how our data are and how we would (theoretically, before we write any code) find that out. 

Our data has a single row for each agency, and we have a column indicating the year the agency reported. So how would we find out how many murders happened in Colorado for each year? Well, first we take all the agencies in 2011 (the first year we're looking at) and add up the murders for all agencies that reported that year. Then take all the rows in 2012 and add up their murders. And so on for all the years. 

To do this in R, we'll be using two new functions from the `dplyr` package: `group_by()` and `summarize()`. 

These functions do the aggregation process in two steps. First we use `group_by()` to tell R which columns we want to group our data by - these are the higher level of aggregation columns so in our case will be the year of data. Then we need to sum up the number of murders each year. We do this using `summarize()`, and we'll specify in the function that we want to sum up the data, rather than use some other math operation on it like finding the average number of murders each year. 

First, let's load back in the data and then repeat the subsetting code we did in Chapter \@ref(subset-colorado-data) to keep only data for Colorado from 2011 through 2017. We'll also include the "actual_robbery_total" column that we excluded in Chapter \@ref(subset-colorado-data) so we can see how easy it is to aggregate multiple columns at once using this method.

```{r }
ucr <- readRDS("data/offenses_known_yearly_1960_2020.rds")
colorado <- filter(ucr, state == "colorado", 
                   year %in% 2011:2017)
colorado <- select(colorado, actual_murder, actual_robbery_total, 
                   state, year, population, ori, agency_name)
```

First we must group the data by using the `group_by()` function. Here we're just grouping the data by year, but we could group it by multiple columns if we want by adding a comma and then the next column we want. Following other `dplyr` function syntax, we first input the data set name and then the column name - neither of which need to be in quotes.

```{r}
colorado <- group_by(colorado, year)
```

Now we can summarize the data using the `summarize()` function. As with other `dplyr` functions the first input is the data set name. Then we choose our math function (sum, mean, median, etc.) and just apply that function on the column we want. So in our case we want the sum of murders so we use `sum()` and include the column we want to aggregate inside of `sum()`'s parentheses. 

```{r}
summarize(colorado, sum(actual_murder))
```

If we want to aggregate another column we just add a comma after our initial column and add another math operation function and the column we want. Here we're also using `sum()`, but we could use different math operations if we want - they don't need to be the same.

```{r}
summarize(colorado, sum(actual_murder), 
          sum(actual_robbery_total))
```

We could even do different math operations on the same column and we'd get multiple columns from it. Let's add another column showing the average number of robberies as an example.

```{r}
summarize(colorado, sum(actual_murder),
          sum(actual_robbery_total),
          mean(actual_robbery_total))
```

By default `summarize()` calls the columns it makes using what we include in the parentheses. Since we said "sum(actual_murder)", to get the sum of the murder column, it names that new column "sum(actual_murder)". Usually we'll want to name the columns ourselves. We can do this by assigning the summarized column to a name using "name = " before it. For example, we could write "murders = sum(actual_murder)" and it will name that column "murders" instead of "sum(actual_murder)". Like other things in `dplyr` functions, we don't need to put quotes around our new column name. We'll assign this final summarized data to an object called "colorado_agg" so we can use it to make graphs. And to be able to create crime rates per population, we'll also find the sum of the population for each year.

```{r}
colorado_agg <- summarize(colorado,
                          murders    = sum(actual_murder),
                          robberies  = sum(actual_robbery_total),
                          population = sum(population))
colorado_agg
```

Now we can see that the total number of murders increased over time. So can we conclude that marijuana legalization increases murder? No, all this analysis shows is that the years following marijuana legalization, murders increased in Colorado. But that can be due to many reasons other than marijuana. For a proper analysis you'd need a comparison state that is similar to Colorado prior to legalization (and that didn't legalize marijuana) and see if their murders changes following Colorado's legalization.

To control for population, we'll standardize our murder data by creating a murder rate per 100,000 people. We can do this by dividing the murder column by the population column and then multiplying by 100,000. Let's do that and assign the result into a new column called "murder_rate".

```{r}
colorado_agg$murder_rate <- colorado_agg$murders / 
  colorado_agg$population * 100000
```

If we also wanted a robbery rate we'd do the same with the robberies column.

```{r}
colorado_agg$robbery_rate <- colorado_agg$robberies / 
  colorado_agg$population * 100000
```

The `dplyr` package has a helpful function that can do this too, and allows us to do it while writing less code. The `mutate()` function lets us create or alter columns in our data. Like other `dplyr` functions we start by including our data set in the parentheses, and then we can follow standard assignment (covered in Section \@ref(assignment)) though we must use `=` here and not `<-`. A benefit of using `mutate()` is that we don't have to write out our data set name each time. So we'd write `murder_rate = murders / population * 100000`. And if we wanted to make two (or more) columns at the same time we just add a comma after our first assignment and then do the next assignment. 

```{r}
mutate(colorado_agg,
       murder_rate  = murders / population * 100000,
       robbery_rate = robberies / population * 100000)
```


Now let's make a plot of this data showing the murder rate over time. With time-series graphs we want the time variable to be on the x-axis and the numeric variable that we are measuring to be on the y-axis.

```{r}
plot(x = colorado_agg$year,
     y = colorado_agg$murder_rate)
```

By default `plot()` makes a scatterplot. If we set the parameter `type` to "l" it will be a **l**ine plot. 

```{r}
plot(x = colorado_agg$year,
     y = colorado_agg$murder_rate,
     type = "l")
```

We can add some labels and a title to make this graph easier to read.

```{r}
plot(x = colorado_agg$year,
     y = colorado_agg$murder_rate,
     type = "l",
     xlab = "Year",
     ylab = "Murders per 100k Population",
     main = "Murder Rate in Colorado, 2011-2017")
```

## Pipes in `dplyr` {#dplyr-pipes}

To end this chapter we'll talk about something called a pipe that is a very useful and powerful part of `dplyr`. 

Think about the math equation 1 + 2 + 3 + 4. Here we know that we add 1 and 2 together, and then add the result to 3 and then add the result of that to 4. This is much simpler to write than splitting everything up and summing each value together in a different line. In terms of R, we have so far been doing things as if we could only add two numbers together and then need a separate line to add the third (and another line to add the fourth) number. For example, below are the two lines of code we used to subset the data to just the right state and years we wanted, and the columns we wanted. We did this in two separate lines. In our math example, we did 1 + 2. And then found the answer, and separately did 3 + 3. And then again found the answer and did 6 + 4.

```{r}
colorado <- filter(ucr, state == "colorado",
                   year %in% 2011:2017)
colorado <- select(colorado,
                   actual_murder, 
                   actual_robbery_total,
                   state, 
                   year,
                   population,
                   ori,
                   agency_name)
head(colorado)
```

With `dplyr` we actually do have a way to chain together functions; to do the programming equivalent of 1 + 2 + 3 + 4 all at once.^[Pipes are technically from the `magrittr` package, but we'll just be using pipes in the context of using functions from `dplyr` or other tidyverse packages.] We do this through what is called a pipe, which allows us to take the result of one function and immediately put it into another function without having to save the initial result or start a new line of code. To use a pipe we put the following code after the end of a function: `%>%`.

These three characters, `%>%`, are the pipe, and they must be written exactly like this. The pipe is itself actually a function, but it is a special type of function we won't go into detail about. Personally I don't think this really looks like a pipe at all, but it is called a pipe so that's the terminology I'll be using. How a pipe technically works is that it takes the output of the initial function (which is usually a tibble, which is the tidyverse's modified version of a data.frame) and puts it automatically in the first input in the next function. This won't work for all functions but nearly all functions from the tidyverse collection of packages have a data set as the first input so it will work here. The benefit is that we don't need to keep saving the output from functions or specifying which data set to include in each function.

As an example, we'll rewrite the previous code using a pipe. We start with our data.frame, which is normally the first thing we put in any `dplyr` function, and then immediately have a pipe `%>%` into a `dplyr` function, which here is `filter()`. Now we don't need to say what the data set is because it takes the last thing that was piped into the function, which in our case is the entire data.frame ucr. After our `filter()` is done we have another pipe and go into `select()`. Now `select()` will use as its first input whatever is outputted from the `filter()`. So the input to `select()` will be the subsetted data output from `filter()`. We can have as many pipes as we wish, and chain many different `dplyr` functions together, but we just use two functions here so we'll end after our `select()` function. 

```{r}
colorado <- ucr %>% filter(state == "colorado",
                           year %in% 2011:2017) %>% 
  select(actual_murder,
         actual_robbery_total,
         state, year, population, 
         ori, agency_name)
```

If we check results using `head()`, we can see that this code is exactly the same as not using pipes.

```{r}
head(colorado)
```


The normal way to write code using pipes is to have a new line after the pipe and after each comma in `filter()` and `select()`. This doesn't change how the code works at all, but it is easier to read now because it has less code bunched together in a single line.

```{r}
colorado <- ucr %>%
  filter(state == "colorado",
         year %in% 2011:2017) %>%
  select(actual_murder, 
         actual_robbery_total,
         state, 
         year,
         population,
         ori, 
         agency_name)
```