Massachusetts Bay Transportation Authority
The Massachusetts Bay Transportation Authority (“MBTA” or just “the T” for short) manages America’s oldest subway, as well as Greater Boston’s commuter rail, ferry, and bus systems.
We will clean the data. The dataset is stored as an Excel spreadsheet called mbta.xlsx.
The first row is a title, so we’ll need to skip the first row.
# Load readxl
library(readxl)
# Import mbta.xlsx and skip first row: mbta
mbta <- read_excel("mbta.xlsx", skip = 1)Exploring the dataset
The first step when cleaning a dataset is to explore it a bit. Pay particular attention to how the rows and columns are organized and to the locations of missing values.
# View the structure of mbta
str(mbta)Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 11 obs. of 60 variables:
$ ...1 : num 1 2 3 4 5 6 7 8 9 10 ...
$ mode : chr "All Modes by Qtr" "Boat" "Bus" "Commuter Rail" ...
$ 2007-01: chr "NA" "4" "335.819" "142.2" ...
$ 2007-02: chr "NA" "3.6" "338.675" "138.5" ...
$ 2007-03: num 1188 40 340 138 459 ...
$ 2007-04: chr "NA" "4.3" "352.162" "139.5" ...
$ 2007-05: chr "NA" "4.9" "354.367" "139" ...
$ 2007-06: num 1246 5.8 350.5 143 477 ...
$ 2007-07: chr "NA" "6.521" "357.519" "142.391" ...
$ 2007-08: chr "NA" "6.572" "355.479" "142.364" ...
$ 2007-09: num 1256.57 5.47 372.6 143.05 499.57 ...
$ 2007-10: chr "NA" "5.145" "368.847" "146.542" ...
$ 2007-11: chr "NA" "3.763" "330.826" "145.089" ...
$ 2007-12: num 1216.89 2.98 312.92 141.59 448.27 ...
$ 2008-01: chr "NA" "3.175" "340.324" "142.145" ...
$ 2008-02: chr "NA" "3.111" "352.905" "142.607" ...
$ 2008-03: num 1253.52 3.51 361.15 137.45 494.05 ...
$ 2008-04: chr "NA" "4.164" "368.189" "140.389" ...
$ 2008-05: chr "NA" "4.015" "363.903" "142.585" ...
$ 2008-06: num 1314.82 5.19 362.96 142.06 518.35 ...
$ 2008-07: chr "NA" "6.016" "370.921" "145.731" ...
$ 2008-08: chr "NA" "5.8" "361.057" "144.565" ...
$ 2008-09: num 1307.04 4.59 389.54 141.91 517.32 ...
$ 2008-10: chr "NA" "4.285" "357.974" "151.957" ...
$ 2008-11: chr "NA" "3.488" "345.423" "152.952" ...
$ 2008-12: num 1232.65 3.01 325.77 140.81 446.74 ...
$ 2009-01: chr "NA" "3.014" "338.532" "141.448" ...
$ 2009-02: chr "NA" "3.196" "360.412" "143.529" ...
$ 2009-03: num 1209.79 3.33 353.69 142.89 467.22 ...
$ 2009-04: chr "NA" "4.049" "359.38" "142.34" ...
$ 2009-05: chr "NA" "4.119" "354.75" "144.225" ...
$ 2009-06: num 1233.1 4.9 347.9 142 473.1 ...
$ 2009-07: chr "NA" "6.444" "339.477" "137.691" ...
$ 2009-08: chr "NA" "5.903" "332.661" "139.158" ...
$ 2009-09: num 1230.5 4.7 374.3 139.1 500.4 ...
$ 2009-10: chr "NA" "4.212" "385.868" "137.104" ...
$ 2009-11: chr "NA" "3.576" "366.98" "129.343" ...
$ 2009-12: num 1207.85 3.11 332.39 126.07 440.93 ...
$ 2010-01: chr "NA" "3.207" "362.226" "130.91" ...
$ 2010-02: chr "NA" "3.195" "361.138" "131.918" ...
$ 2010-03: num 1208.86 3.48 373.44 131.25 483.4 ...
$ 2010-04: chr "NA" "4.452" "378.611" "131.722" ...
$ 2010-05: chr "NA" "4.415" "380.171" "128.8" ...
$ 2010-06: num 1244.41 5.41 363.27 129.14 490.26 ...
$ 2010-07: chr "NA" "6.513" "353.04" "122.935" ...
$ 2010-08: chr "NA" "6.269" "343.688" "129.732" ...
$ 2010-09: num 1225.5 4.7 381.6 132.9 521.1 ...
$ 2010-10: chr "NA" "4.402" "384.987" "131.033" ...
$ 2010-11: chr "NA" "3.731" "367.955" "130.889" ...
$ 2010-12: num 1216.26 3.16 326.34 121.42 450.43 ...
$ 2011-01: chr "NA" "3.14" "334.958" "128.396" ...
$ 2011-02: chr "NA" "3.284" "346.234" "125.463" ...
$ 2011-03: num 1223.45 3.67 380.4 134.37 516.73 ...
$ 2011-04: chr "NA" "4.251" "380.446" "134.169" ...
$ 2011-05: chr "NA" "4.431" "385.289" "136.14" ...
$ 2011-06: num 1302.41 5.47 376.32 135.58 529.53 ...
$ 2011-07: chr "NA" "6.581" "361.585" "132.41" ...
$ 2011-08: chr "NA" "6.733" "353.793" "130.616" ...
$ 2011-09: num 1291 5 388 137 550 ...
$ 2011-10: chr "NA" "4.484" "398.456" "128.72" ...# View the first 6 rows of mbta
head(mbta)
# View a summary of mbta
summary(mbta) ...1 mode 2007-01 2007-02 2007-03
Min. : 1.0 Length:11 Length:11 Length:11 Min. : 0.114
1st Qu.: 3.5 Class :character Class :character Class :character 1st Qu.: 9.278
Median : 6.0 Mode :character Mode :character Mode :character Median : 137.700
Mean : 6.0 Mean : 330.293
3rd Qu.: 8.5 3rd Qu.: 399.225
Max. :11.0 Max. :1204.725
2007-04 2007-05 2007-06 2007-07 2007-08
Length:11 Length:11 Min. : 0.096 Length:11 Length:11
Class :character Class :character 1st Qu.: 5.700 Class :character Class :character
Mode :character Mode :character Median : 143.000 Mode :character Mode :character
Mean : 339.846
3rd Qu.: 413.788
Max. :1246.129
2007-09 2007-10 2007-11 2007-12 2008-01
Min. : -0.007 Length:11 Length:11 Min. : -0.060 Length:11
1st Qu.: 5.539 Class :character Class :character 1st Qu.: 4.385 Class :character
Median : 143.051 Mode :character Mode :character Median : 141.585 Mode :character
Mean : 352.554 Mean : 321.588
3rd Qu.: 436.082 3rd Qu.: 380.594
Max. :1310.764 Max. :1216.890
2008-02 2008-03 2008-04 2008-05 2008-06
Length:11 Min. : 0.058 Length:11 Length:11 Min. : 0.060
Class :character 1st Qu.: 5.170 Class :character Class :character 1st Qu.: 5.742
Mode :character Median : 137.453 Mode :character Mode :character Median : 142.057
Mean : 345.604 Mean : 359.667
3rd Qu.: 427.601 3rd Qu.: 440.656
Max. :1274.031 Max. :1320.728
2008-07 2008-08 2008-09 2008-10 2008-11
Length:11 Length:11 Min. : 0.021 Length:11 Length:11
Class :character Class :character 1st Qu.: 5.691 Class :character Class :character
Mode :character Mode :character Median : 141.907 Mode :character Mode :character
Mean : 362.099
3rd Qu.: 453.430
Max. :1338.015
2008-12 2009-01 2009-02 2009-03 2009-04
Min. : -0.015 Length:11 Length:11 Min. : -0.050 Length:11
1st Qu.: 4.689 Class :character Class :character 1st Qu.: 5.003 Class :character
Median : 140.810 Mode :character Mode :character Median : 142.893 Mode :character
Mean : 319.882 Mean : 330.142
3rd Qu.: 386.255 3rd Qu.: 410.455
Max. :1232.655 Max. :1210.912
2009-05 2009-06 2009-07 2009-08 2009-09
Length:11 Min. : -0.079 Length:11 Length:11 Min. : -0.035
Class :character 1st Qu.: 5.845 Class :character Class :character 1st Qu.: 5.693
Mode :character Median : 142.006 Mode :character Mode :character Median : 139.087
Mean : 333.194 Mean : 346.687
3rd Qu.: 410.482 3rd Qu.: 437.332
Max. :1233.085 Max. :1291.564
2009-10 2009-11 2009-12 2010-01 2010-02
Length:11 Length:11 Min. : -0.022 Length:11 Length:11
Class :character Class :character 1st Qu.: 4.784 Class :character Class :character
Mode :character Mode :character Median : 126.066 Mode :character Mode :character
Mean : 312.962
3rd Qu.: 386.659
Max. :1207.845
2010-03 2010-04 2010-05 2010-06 2010-07
Min. : 0.012 Length:11 Length:11 Min. : 0.008 Length:11
1st Qu.: 5.274 Class :character Class :character 1st Qu.: 6.436 Class :character
Median : 131.252 Mode :character Mode :character Median : 129.144 Mode :character
Mean : 332.726 Mean : 335.964
3rd Qu.: 428.420 3rd Qu.: 426.769
Max. :1225.556 Max. :1244.409
2010-08 2010-09 2010-10 2010-11 2010-12
Length:11 Min. : 0.001 Length:11 Length:11 Min. : -0.004
Class :character 1st Qu.: 5.567 Class :character Class :character 1st Qu.: 4.466
Mode :character Median : 132.892 Mode :character Mode :character Median : 121.422
Mean : 346.524 Mean : 312.917
3rd Qu.: 451.361 3rd Qu.: 388.385
Max. :1293.117 Max. :1216.262
2011-01 2011-02 2011-03 2011-04 2011-05
Length:11 Length:11 Min. : 0.05 Length:11 Length:11
Class :character Class :character 1st Qu.: 6.03 Class :character Class :character
Mode :character Mode :character Median : 134.37 Mode :character Mode :character
Mean : 345.17
3rd Qu.: 448.56
Max. :1286.66
2011-06 2011-07 2011-08 2011-09 2011-10
Min. : 0.054 Length:11 Length:11 Min. : 0.043 Length:11
1st Qu.: 6.926 Class :character Class :character 1st Qu.: 6.660 Class :character
Median : 135.581 Mode :character Mode :character Median : 136.901 Mode :character
Mean : 353.331 Mean : 362.555
3rd Qu.: 452.923 3rd Qu.: 469.204
Max. :1302.414 Max. :1348.754 Missing values
It appears that the data are organized with observations stored as columns rather than as rows.
First, though, we can address the missing data. All of the NA values are stored in the All Modes by Qtr row. This row really belongs in a different data frame; it is a quarterly average of weekday MBTA ridership. Since this dataset tracks monthly average ridership, we’ll remove that row.
Similarly, the 7th row (Pct Chg / Yr) and the 11th row (TOTAL) are not really observations as much as they are analysis. We can remove them as well.
The first column also needs to be removed because it’s just listing the row numbers.
In cas
# Remove rows 1, 7, and 11 of mbta: mbta2
mbta2 <- mbta[-c(1, 7, 11),]
# Remove the first column of mbta2: mbta3
mbta3 <- mbta2[,-1]Collecting columns with gather()
The different modes of transportation (commuter rail, bus, subway, ferry, …) are variables, providing information about each month’s average ridership. The months themselves are observations. You can tell which is which because as you go through time, the month changes, but the modes of transport offered by the T do not.
The first step is to use the gather() function from the tidyr package, which will gather columns into key-value pairs.
# Load tidyr
library(tidyr)Registered S3 method overwritten by 'dplyr':
method from
print.rowwise_df # Gather columns of mbta3: mbta4
mbta4 <- gather(mbta3, month, thou_riders, -mode)
# View the head of mbta4
head(mbta4)The dataset is long now – notice that the first column still stores variable names, but now, months have their own column instead of being headers. Also notice the data type of each column.
We’ll put variables where they belong (as column names). But first, we can change the average weekday ridership column, thou_riders, into numeric values rather than character strings. That way, you’ll be able to do things like compare values and do math.
# Coerce thou_riders to numeric
mbta4$thou_riders <- as.numeric(mbta4$thou_riders)Variables into columns with spread()
We can now get variables into columns.
We’ll use the tidyr function spread() to make them into columns containing average weekday ridership for the given month and mode of transport.
# Spread the contents of mbta4: mbta5
mbta5 <- spread(mbta4, mode, thou_riders)
# View the head of mbta5
head(mbta5)Splitting a column
There’s only one small problem: if you want to look at ridership on the T during every January (for example), the month and year are together in the same column, which makes it a little tricky.
We’ll separate the month column into distinct month and year columns to make life easier.
# Split month column into month and year: mbta6
mbta6 <- separate(mbta5, month, c("year", "month"), sep = "-")
# View the head of mbta6
head(mbta6)#Visualizing data
It’s a good idea to screen the data for any obvious mistakes and/or outliers.
We can plot a histogram.
# View a summary of mbta6
summary(mbta6) year month Boat Bus Commuter Rail Heavy Rail
Length:58 Length:58 Min. : 2.985 Min. :312.9 Min. :121.4 Min. :435.3
Class :character Class :character 1st Qu.: 3.494 1st Qu.:345.6 1st Qu.:131.4 1st Qu.:471.1
Mode :character Mode :character Median : 4.293 Median :359.9 Median :138.8 Median :487.3
Mean : 5.068 Mean :358.6 Mean :137.4 Mean :489.3
3rd Qu.: 5.356 3rd Qu.:372.2 3rd Qu.:142.4 3rd Qu.:511.3
Max. :40.000 Max. :398.5 Max. :153.0 Max. :554.9
Light Rail Private Bus RIDE Trackless Trolley
Min. :194.4 Min. :2.213 Min. :4.900 Min. : 5.777
1st Qu.:220.6 1st Qu.:2.641 1st Qu.:5.965 1st Qu.:11.679
Median :231.9 Median :2.820 Median :6.615 Median :12.598
Mean :233.0 Mean :3.352 Mean :6.604 Mean :12.125
3rd Qu.:244.5 3rd Qu.:4.167 3rd Qu.:7.149 3rd Qu.:13.320
Max. :271.1 Max. :4.878 Max. :8.598 Max. :15.109 # Generate a histogram of Boat column
hist(mbta6$Boat)
Outliers
That’s quite an interesting histogram – every value clustered around 4 and one loner out around 40.
Think for a minute about that Boat histogram. Every month, average weekday commuter boat ridership was on either side of four thousand. Then, one month it jumped to 40 thousand without warning?
Unless the Olympics were happening in Boston that month (they weren’t), this value is certainly an error. We can assume that whoever was entering the data that month accidentally typed 40 instead of 4.
Because it’s an error, we don’t want this value influencing your analysis.
# Find the row number of the incorrect value: i
i <- which(mbta6$Boat>30)
# Replace the incorrect value with 4
mbta6$Boat[i] <- 4
# Generate a histogram of Boat column
hist(mbta6$Boat)
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