The series of === are just making a line.

The three “back ticks” () must be followed by curly brackets “{”, and then “r” to tell the computer that you are using R code. This line is then closed off by another curly bracket “}”.

Anything before three more back ticks “”“ are then considered R code (a script).

I'm reading in the bike lanes here.

```
# readin is just a 'label' for this code chunk code chunk is just a 'chunk'
# of code, where this code usually does just one thing, aka a module
# comments are still # here you can do all your reading in there
data.dir <- "~/Dropbox/WinterR_2014/Lectures/data"
### let's say we loaded some packages
library(stringr)
library(plyr)
fname <- file.path(data.dir, "Bike_Lanes.csv")
## file.path takes a directory and makes a full name with a full file path
bike = read.csv(fname, as.is = TRUE)
getwd()
```

```
## [1] "/Users/andrew.jaffe/Dropbox/WinterR_2014"
```

You can write your introduction here.

Bike lanes are in Baltimore. People like them. Why are they so long?

Let's look at some plots of bike length. Let's say we wanted to look at what affects bike length.

Note we made the subsection by using three "hashes” (pound signs): ###.

```
no.missyear <- bike[bike$dateInstalled != 0, ]
plot(no.missyear$dateInstalled, no.missyear$length)
```

```
no.missyear$dateInstalled = factor(no.missyear$dateInstalled)
boxplot(no.missyear$length ~ no.missyear$dateInstalled, main = "Boxplots of Bike Lenght by Year",
xlab = "Year", ylab = "Bike Length")
```

What does it look like if we took the log (base 10) of the bike length:

```
no.missyear$log.length <- log10(no.missyear$length)
### see here that if you specify the data argument, you don't need to do the $
boxplot(log.length ~ dateInstalled, data = no.missyear, main = "Boxplots of Bike Lenght by Year",
xlab = "Year", ylab = "Bike Length")
```

I want my boxplots colored, so I set the `col`

argument.

```
boxplot(log.length ~ dateInstalled, data = no.missyear, main = "Boxplots of Bike Lenght by Year",
xlab = "Year", ylab = "Bike Length", col = "red")
```

As we can see, 2006 had a much higher bike length. What about for the type of bike path?

```
### type is a character, but when R sees a 'character' in a 'formula', then it
### automatically converts it to factor a formula is something that has a y ~
### x, which says I want to plot y against x or if it were a model you would
### do y ~ x, which meant regress against y
boxplot(log.length ~ type, data = no.missyear, main = "Boxplots of Bike Lenght by Year",
xlab = "Year", ylab = "Bike Length")
```

What if we want to extract means by each type?

Let's show a few ways:

```
### tapply takes in vector 1, then does a function by vector 2, and then you
### tell what that function is
tapply(no.missyear$log.length, no.missyear$type, mean)
```

```
## BIKE LANE CONTRAFLOW SHARED BUS BIKE SHARROW
## 2.331 2.087 2.363 2.256
## SIDEPATH SIGNED ROUTE
## 2.782 2.264
```

```
## aggregate
aggregate(x = no.missyear$log.length, by = list(no.missyear$type), FUN = mean)
```

```
## Group.1 x
## 1 BIKE LANE 2.331
## 2 CONTRAFLOW 2.087
## 3 SHARED BUS BIKE 2.363
## 4 SHARROW 2.256
## 5 SIDEPATH 2.782
## 6 SIGNED ROUTE 2.264
```

```
### now let's specify the data argument and use a 'formula' - much easier to
### read and more 'intuitive'
aggregate(log.length ~ type, data = no.missyear, FUN = mean)
```

```
## type log.length
## 1 BIKE LANE 2.331
## 2 CONTRAFLOW 2.087
## 3 SHARED BUS BIKE 2.363
## 4 SHARROW 2.256
## 5 SIDEPATH 2.782
## 6 SIGNED ROUTE 2.264
```

```
## ddply is from the plyr package takes in a data frame, (the first d refers
## to data.frame) splits it up by some variables (let's say type) then we'll
## use summarise to summarize whatever we want then returns a data.frame (the
## second d) - hence why it's ddply if we wanted to do it on a 'list' thne
## return data.frame, it'd be ldply
ddply(no.missyear, .(type), summarise, mean = mean(log.length))
```

```
## type mean
## 1 BIKE LANE 2.331
## 2 CONTRAFLOW 2.087
## 3 SHARED BUS BIKE 2.363
## 4 SHARROW 2.256
## 5 SIDEPATH 2.782
## 6 SIGNED ROUTE 2.264
```

`ddply`

(and other functions in the `plyr`

package) is cool because you can do multiple functions really easy.

Let's show a what if we wanted to go over `type`

and `dateInstalled`

:

```
### For going over 2 variables, we need to do it over a 'list' of vectors
tapply(no.missyear$log.length, list(no.missyear$type, no.missyear$dateInstalled),
mean)
```

```
## 2006 2007 2008 2009 2010 2011 2012 2013
## BIKE LANE 3.046 2.351 2.366 2.381 2.307 2.242 2.362 2.408
## CONTRAFLOW NA NA NA NA 2.087 NA NA NA
## SHARED BUS BIKE NA NA NA 2.351 2.404 NA NA NA
## SHARROW NA 2.301 2.221 2.692 2.247 NA 2.236 NA
## SIDEPATH NA NA 2.625 NA 2.774 3.267 NA NA
## SIGNED ROUTE NA 2.288 NA NA 2.239 2.210 NA NA
```

```
tapply(no.missyear$log.length, list(no.missyear$type, no.missyear$dateInstalled),
mean, na.rm = TRUE)
```

```
## 2006 2007 2008 2009 2010 2011 2012 2013
## BIKE LANE 3.046 2.351 2.366 2.381 2.307 2.242 2.362 2.408
## CONTRAFLOW NA NA NA NA 2.087 NA NA NA
## SHARED BUS BIKE NA NA NA 2.351 2.404 NA NA NA
## SHARROW NA 2.301 2.221 2.692 2.247 NA 2.236 NA
## SIDEPATH NA NA 2.625 NA 2.774 3.267 NA NA
## SIGNED ROUTE NA 2.288 NA NA 2.239 2.210 NA NA
```

```
## aggregate - looks better
aggregate(log.length ~ type + dateInstalled, data = no.missyear, FUN = mean)
```

```
## type dateInstalled log.length
## 1 BIKE LANE 2006 3.046
## 2 BIKE LANE 2007 2.351
## 3 SHARROW 2007 2.301
## 4 SIGNED ROUTE 2007 2.288
## 5 BIKE LANE 2008 2.366
## 6 SHARROW 2008 2.221
## 7 SIDEPATH 2008 2.625
## 8 BIKE LANE 2009 2.381
## 9 SHARED BUS BIKE 2009 2.351
## 10 SHARROW 2009 2.692
## 11 BIKE LANE 2010 2.307
## 12 CONTRAFLOW 2010 2.087
## 13 SHARED BUS BIKE 2010 2.404
## 14 SHARROW 2010 2.247
## 15 SIDEPATH 2010 2.774
## 16 SIGNED ROUTE 2010 2.239
## 17 BIKE LANE 2011 2.242
## 18 SIDEPATH 2011 3.267
## 19 SIGNED ROUTE 2011 2.210
## 20 BIKE LANE 2012 2.362
## 21 SHARROW 2012 2.236
## 22 BIKE LANE 2013 2.408
```

```
## ddply is from the plyr package
ddply(no.missyear, .(type, dateInstalled), summarise, mean = mean(log.length),
median = median(log.length), Mode = mode(log.length), Std.Dev = sd(log.length))
```

```
## type dateInstalled mean median Mode Std.Dev
## 1 BIKE LANE 2006 3.046 3.046 numeric 0.47974
## 2 BIKE LANE 2007 2.351 2.444 numeric 0.40662
## 3 BIKE LANE 2008 2.366 2.355 numeric 0.38916
## 4 BIKE LANE 2009 2.381 2.311 numeric 0.49447
## 5 BIKE LANE 2010 2.307 2.328 numeric 0.32076
## 6 BIKE LANE 2011 2.242 2.235 numeric 0.33398
## 7 BIKE LANE 2012 2.362 2.324 numeric 0.28528
## 8 BIKE LANE 2013 2.408 2.505 numeric 0.24041
## 9 CONTRAFLOW 2010 2.087 2.142 numeric 0.25655
## 10 SHARED BUS BIKE 2009 2.351 2.464 numeric 0.30610
## 11 SHARED BUS BIKE 2010 2.404 2.587 numeric 0.27380
## 12 SHARROW 2007 2.301 2.364 numeric 0.42193
## 13 SHARROW 2008 2.221 2.238 numeric 0.32664
## 14 SHARROW 2009 2.692 2.708 numeric 0.06945
## 15 SHARROW 2010 2.247 2.298 numeric 0.35905
## 16 SHARROW 2012 2.236 2.339 numeric 0.42924
## 17 SIDEPATH 2008 2.625 2.787 numeric 0.29583
## 18 SIDEPATH 2010 2.774 2.774 numeric 0.33480
## 19 SIDEPATH 2011 3.267 3.267 numeric NA
## 20 SIGNED ROUTE 2007 2.288 2.332 numeric 0.41825
## 21 SIGNED ROUTE 2010 2.239 2.256 numeric 0.39201
## 22 SIGNED ROUTE 2011 2.210 2.208 numeric 0.20880
```

OK let's do an linear model

```
### type is a character, but when R sees a 'character' in a 'formula', then it
### automatically converts it to factor a formula is something that has a y ~
### x, which says I want to plot y against x or if it were a model you would
### do y ~ x, which meant regress against y
mod.type = lm(log.length ~ type, data = no.missyear)
mod.yr = lm(log.length ~ factor(dateInstalled), data = no.missyear)
mod.yrtype = lm(log.length ~ type + factor(dateInstalled), data = no.missyear)
summary(mod.type)
```

```
##
## Call:
## lm(formula = log.length ~ type, data = no.missyear)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5150 -0.1906 0.0292 0.2322 1.3102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3306 0.0149 156.70 < 2e-16 ***
## typeCONTRAFLOW -0.2434 0.1029 -2.37 0.01813 *
## typeSHARED BUS BIKE 0.0324 0.0606 0.53 0.59319
## typeSHARROW -0.0742 0.0213 -3.48 0.00051 ***
## typeSIDEPATH 0.4512 0.1506 3.00 0.00277 **
## typeSIGNED ROUTE -0.0669 0.0273 -2.45 0.01430 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.367 on 1499 degrees of freedom
## Multiple R-squared: 0.0196, Adjusted R-squared: 0.0163
## F-statistic: 5.98 on 5 and 1499 DF, p-value: 1.74e-05
```

That's rather UGLY, so let's use a package called `xtable`

and then make this model into an `xtable`

object and then print it out nicely.

```
### DON'T DO THIS. YOU SHOULD ALWAYS DO library() statements in the FIRST
### code chunk. this is just to show you the logic of a report/analysis.
require(xtable)
```

```
## Loading required package: xtable
```

```
# smod <- summary(mod.yr)
xtab <- xtable(mod.yr)
```

Well `xtable`

can make html tables, so let's print this. We must tell R that the results is actually an html output, so we say the results should be embedded in the html “asis” (aka just print out whatever R spits out).

```
print.xtable(xtab, type = "html")
```

Estimate | Std. Error | t value | Pr(> |t|) | |
---|---|---|---|---|

(Intercept) | 3.0463 | 0.2600 | 11.71 | 0.0000 |

factor(dateInstalled)2007 | -0.7332 | 0.2608 | -2.81 | 0.0050 |

factor(dateInstalled)2008 | -0.7808 | 0.2613 | -2.99 | 0.0029 |

factor(dateInstalled)2009 | -0.6394 | 0.2631 | -2.43 | 0.0152 |

factor(dateInstalled)2010 | -0.7791 | 0.2605 | -2.99 | 0.0028 |

factor(dateInstalled)2011 | -0.8022 | 0.2626 | -3.05 | 0.0023 |

factor(dateInstalled)2012 | -0.7152 | 0.2625 | -2.72 | 0.0065 |

factor(dateInstalled)2013 | -0.6380 | 0.2849 | -2.24 | 0.0253 |

OK, that's pretty good, but let's say we have all three models. Another package called `stargazer`

can put models together easily and pritn them out. So `xtable`

is really good when you are trying to print out a table (in html, otherwise make the table and use `write.csv`

to get it in Excel and then format) really quickly and in a report. But it doesn't work so well with *many* models together. So let's use stargazer. Again, you need to use `install.packages("stargazer")`

if you don't have function.

```
require(stargazer)
```

```
## Loading required package: stargazer
```

```
## Warning: there is no package called 'stargazer'
```

OK, so what's the difference here? First off, we said results are “markup”, so that it will not try to reformat the output. Also, I didn't want those # for comments, so I just made comment an empty string “”.

```
stargazer(mod.yr, mod.type, mod.yrtype, type = "text")
```

```
Error: could not find function "stargazer"
```

Let's say I want to get data INTO my text. Like there are N number of bike lanes with a date installed that isn't zero. There are 1505 bike lanes with a date installed after 2006. So you use one backtick and then you say “r” to tell that it's R code. And then you run R code that gets evaulated and then returns the value. Let's say you want to compute a bunch of things:

```
### let's get number of bike lanes installed by year
n.lanes = ddply(no.missyear, .(dateInstalled), nrow)
names(n.lanes) <- c("date", "nlanes")
n2009 <- n.lanes$nlanes[n.lanes$date == 2009]
n2010 <- n.lanes$nlanes[n.lanes$date == 2010]
getwd()
```

```
## [1] "/Users/andrew.jaffe/Dropbox/WinterR_2014"
```

Now I can just say there are 86 lanes in 2009 and 625 in 2010.

```
fname <- file.path(data.dir, "Charm_City_Circulator_Ridership.csv")
## file.path takes a directory and makes a full name with a full file path
charm = read.csv(fname, as.is = TRUE)
library(chron)
```

```
## Error: there is no package called 'chron'
```

```
days = levels(weekdays(1, abbreviate = FALSE))
```

```
## Error: no applicable method for 'weekdays' applied to an object of class
## "c('double', 'numeric')"
```

```
charm$day <- factor(charm$day, levels = days)
```

```
## Error: object 'days' not found
```

```
charm$date <- as.Date(charm$date, format = "%m/%d/%Y")
cn <- colnames(charm)
daily <- charm[, c("day", "date", "daily")]
```

```
charm$daily <- NULL
require(reshape2)
```

```
## Loading required package: reshape2
```

```
long.charm <- melt(charm, id.vars = c("day", "date"))
long.charm$type <- "Boardings"
long.charm$type[grepl("Alightings", long.charm$variable)] <- "Alightings"
long.charm$type[grepl("Average", long.charm$variable)] <- "Average"
long.charm$line <- "orange"
long.charm$line[grepl("purple", long.charm$variable)] <- "purple"
long.charm$line[grepl("green", long.charm$variable)] <- "green"
long.charm$line[grepl("banner", long.charm$variable)] <- "banner"
long.charm$variable <- NULL
long.charm$line <- factor(long.charm$line, levels = c("orange", "purple", "green",
"banner"))
head(long.charm)
```

```
## day date value type line
## 1 Monday 2010-01-11 877 Boardings orange
## 2 Tuesday 2010-01-12 777 Boardings orange
## 3 Wednesday 2010-01-13 1203 Boardings orange
## 4 Thursday 2010-01-14 1194 Boardings orange
## 5 Friday 2010-01-15 1645 Boardings orange
## 6 Saturday 2010-01-16 1457 Boardings orange
```

```
### NOW R has a column of day, the date, a 'value', the type of value and the
### circulator line that corresponds to it value is now either the Alightings,
### Boardings, or Average from the charm dataset
```

Let's do some plotting now!

```
require(ggplot2)
```

```
## Loading required package: ggplot2
```

```
### let's make a 'ggplot' the format is ggplot(dataframe, aes(x=COLNAME,
### y=COLNAME)) where COLNAME are colnames of the dataframe you can also set
### color to a different factor other options in AES (fill, alpha level -which
### is the 'transparency' of points)
g <- ggplot(long.charm, aes(x = date, y = value, color = line))
### let's change the colors to what we want- doing this manually, not letting
### it choose for me
g <- g + scale_color_manual(values = c("orange", "purple", "green", "blue"))
### plotting points
g + geom_point()
```

```
## Warning: Removed 5328 rows containing missing values (geom_point).
```

```
### Let's make Lines!
g + geom_line()
```

```
## Warning: Removed 5043 rows containing missing values (geom_path).
```

```
### let's make a new plot of poitns
gpoint <- g + geom_point()
### let's plot the value by the type of value - boardings/average, etc
gpoint + facet_wrap(~type)
```

```
## Warning: Removed 1814 rows containing missing values (geom_point).
## Warning: Removed 1700 rows containing missing values (geom_point).
## Warning: Removed 1814 rows containing missing values (geom_point).
```

OK let's turn off some warnings - making `warning=FALSE`

as an option.

```
## let's compare vertically
gpoint + facet_wrap(~type, ncol = 1)
```

```
gfacet = g + facet_wrap(~type, ncol = 1)
## let's smooth this - get a rough estimate of what's going on
gfacet + geom_smooth(se = FALSE)
```

```
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
```