1
00:00:04,490 --> 00:00:06,910
In this video, we'll
create a heat map

2
00:00:06,910 --> 00:00:09,690
on a map of the United States.

3
00:00:09,690 --> 00:00:13,760
We'll be using the data
set murders.csv, which

4
00:00:13,760 --> 00:00:16,460
is data provided
by the FBI giving

5
00:00:16,460 --> 00:00:21,080
the total number of murders
in the United States by state.

6
00:00:21,080 --> 00:00:23,720
Let's start by reading
in our data set.

7
00:00:23,720 --> 00:00:31,020
We'll call it murders, and
we'll use the read.csv function

8
00:00:31,020 --> 00:00:33,420
to read in the data
file murders.csv.

9
00:00:36,610 --> 00:00:39,470
Let's take a look at the
structure of this data

10
00:00:39,470 --> 00:00:42,600
using the str function.

11
00:00:42,600 --> 00:00:46,110
We have 51 observations
for the 50 states

12
00:00:46,110 --> 00:00:50,460
plus Washington, DC, and
six different variables:

13
00:00:50,460 --> 00:00:54,490
the name of the state, the
population, the population

14
00:00:54,490 --> 00:00:58,040
density, the number
of murders, the number

15
00:00:58,040 --> 00:01:03,230
of murders that used guns,
and the rate of gun ownership.

16
00:01:03,230 --> 00:01:06,060
A map of the United
States is included

17
00:01:06,060 --> 00:01:12,720
in R. Let's load the map
and call it statesMap.

18
00:01:12,720 --> 00:01:18,360
We can do so using
the map_data function,

19
00:01:18,360 --> 00:01:24,390
where the only argument
is "state" in quotes.

20
00:01:24,390 --> 00:01:27,020
Let's see what this looks like
by typing in str(statesMap).

21
00:01:32,930 --> 00:01:35,250
This is just a data
frame summarizing

22
00:01:35,250 --> 00:01:37,830
how to draw the United States.

23
00:01:37,830 --> 00:01:43,470
To plot the map, we'll use the
polygons geometry of ggplot.

24
00:01:43,470 --> 00:01:49,039
So type ggplot, and then in
parentheses, our data frame

25
00:01:49,039 --> 00:01:55,000
is statesMap, and then
our aesthetic is x = long,

26
00:01:55,000 --> 00:01:59,750
the longitude variable
in statesMap, y = lat,

27
00:01:59,750 --> 00:02:04,660
the latitude variable,
and then group = group.

28
00:02:04,660 --> 00:02:06,590
This is the variable
defining how

29
00:02:06,590 --> 00:02:10,780
to draw the United States
into groups by state.

30
00:02:10,780 --> 00:02:13,470
Then close both
parentheses here,

31
00:02:13,470 --> 00:02:19,780
and we'll add geom_polygon
where our arguments here will be

32
00:02:19,780 --> 00:02:25,410
fill="white"-- we'll just
fill all states in white--

33
00:02:25,410 --> 00:02:31,950
and color="black" to
outline the states in black.

34
00:02:31,950 --> 00:02:34,110
Now in your R
graphics window, you

35
00:02:34,110 --> 00:02:37,790
should see a map of
the United States.

36
00:02:37,790 --> 00:02:40,500
Before we can plot
our data on this map,

37
00:02:40,500 --> 00:02:42,630
we need to make sure
that the state names are

38
00:02:42,630 --> 00:02:45,630
the same in the
murders data frame

39
00:02:45,630 --> 00:02:48,820
and in the statesMap data frame.

40
00:02:48,820 --> 00:02:51,470
In the murders data
frame, our state names

41
00:02:51,470 --> 00:02:53,490
are in the State
variable, and they

42
00:02:53,490 --> 00:02:55,510
start with a capital letter.

43
00:02:55,510 --> 00:02:58,540
But in the statesMap data
frame, our state names

44
00:02:58,540 --> 00:03:02,550
are in the region variable,
and they're all lowercase.

45
00:03:02,550 --> 00:03:07,020
So let's create a new variable
called region in our murders

46
00:03:07,020 --> 00:03:11,050
data frame to match the state
name variable in the statesMap

47
00:03:11,050 --> 00:03:12,580
data frame.

48
00:03:12,580 --> 00:03:18,380
So we'll add to our murders
data frame the variable region,

49
00:03:18,380 --> 00:03:22,000
which will be equal to
the lowercase version--

50
00:03:22,000 --> 00:03:25,400
using the tolower function that
we used in the text analytics

51
00:03:25,400 --> 00:03:29,320
lectures-- and the argument
will be murders$State.

52
00:03:34,820 --> 00:03:37,140
This will just convert
the State variable

53
00:03:37,140 --> 00:03:39,940
to all lowercase
letters and store it

54
00:03:39,940 --> 00:03:43,220
as a new variable called region.

55
00:03:43,220 --> 00:03:47,070
Now we can join the statesMap
data frame with the murders

56
00:03:47,070 --> 00:03:50,460
data frame by using the
merge function, which

57
00:03:50,460 --> 00:03:54,970
matches rows of a data frame
based on a shared identifier.

58
00:03:54,970 --> 00:03:57,320
We just defined the
variable region,

59
00:03:57,320 --> 00:04:00,140
which exists in
both data frames.

60
00:04:00,140 --> 00:04:06,580
So we'll call our new
data frame murderMap,

61
00:04:06,580 --> 00:04:08,790
and we'll use the
merge function,

62
00:04:08,790 --> 00:04:14,430
where the first argument is our
first data frame, statesMap,

63
00:04:14,430 --> 00:04:19,040
the second argument is our
second data frame, murders,

64
00:04:19,040 --> 00:04:23,410
and the third argument
is by="region".

65
00:04:23,410 --> 00:04:27,570
This is the identifier
to use to merge the rows.

66
00:04:27,570 --> 00:04:29,160
Let's take a look
at the data frame

67
00:04:29,160 --> 00:04:31,860
we just created using
the str function.

68
00:04:34,580 --> 00:04:36,740
We have the same
number of observations

69
00:04:36,740 --> 00:04:39,490
here that we had in the
statesMap data frame,

70
00:04:39,490 --> 00:04:42,750
but now we have both the
variables from the statesMap

71
00:04:42,750 --> 00:04:46,100
data frame and the variables
from the murders data

72
00:04:46,100 --> 00:04:50,540
frame, which were matched up
based on the region variable.

73
00:04:50,540 --> 00:04:53,180
So now, let's plot
the number of murders

74
00:04:53,180 --> 00:04:55,720
on our map of the United States.

75
00:04:55,720 --> 00:04:59,870
We'll again use the ggplot
function, but this time,

76
00:04:59,870 --> 00:05:06,020
our data frame is murderMap,
and in our aesthetic we want

77
00:05:06,020 --> 00:05:14,540
to again say x=long,
y=lat, and group=group,

78
00:05:14,540 --> 00:05:16,750
but we'll add one more
argument this time,

79
00:05:16,750 --> 00:05:22,370
which is fill=Murders so that
the states will be colored

80
00:05:22,370 --> 00:05:25,030
according to the
Murders variable.

81
00:05:25,030 --> 00:05:30,840
Then we need to add the
polygon geometry where the only

82
00:05:30,840 --> 00:05:33,909
argument here will
be color="black"

83
00:05:33,909 --> 00:05:37,880
to outline the states
in black, like before.

84
00:05:37,880 --> 00:05:46,790
And lastly, we'll add
scale_fill_gradient where

85
00:05:46,790 --> 00:05:56,450
the arguments here, we'll put
low="black" and high="red"

86
00:05:56,450 --> 00:06:00,980
to make our color scheme
range from black to red,

87
00:06:00,980 --> 00:06:08,350
and then guide="legend" to
make sure we get a legend

88
00:06:08,350 --> 00:06:10,220
on our plot.

89
00:06:10,220 --> 00:06:14,400
If you hit Enter and look
at your graphics window now,

90
00:06:14,400 --> 00:06:16,060
you should see that
each of the states

91
00:06:16,060 --> 00:06:19,480
is colored by the number
of murders in that state.

92
00:06:19,480 --> 00:06:23,010
States with a larger number
of murders are more red.

93
00:06:23,010 --> 00:06:25,440
So it looks like
California and Texas

94
00:06:25,440 --> 00:06:27,750
have the largest
number of murders.

95
00:06:27,750 --> 00:06:31,470
But is that just because they're
the most populous states?

96
00:06:31,470 --> 00:06:34,390
Let's create a map of the
population of each state

97
00:06:34,390 --> 00:06:35,750
to check.

98
00:06:35,750 --> 00:06:39,780
So back in the R Console,
hit the Up arrow, and then,

99
00:06:39,780 --> 00:06:47,740
instead of fill=Murders, we want
to put fill=Population to color

100
00:06:47,740 --> 00:06:51,820
each state according to
the Population variable.

101
00:06:51,820 --> 00:06:53,320
If you look at the
graphics window,

102
00:06:53,320 --> 00:06:55,930
we have a population
map here which

103
00:06:55,930 --> 00:06:59,340
looks exactly the same
as our murders map.

104
00:06:59,340 --> 00:07:01,990
So we need to plot the
murder rate instead

105
00:07:01,990 --> 00:07:04,860
of the number of murders
to make sure we're not just

106
00:07:04,860 --> 00:07:07,470
plotting a population map.

107
00:07:07,470 --> 00:07:09,600
So in our R Console,
let's create

108
00:07:09,600 --> 00:07:12,130
a new variable for
the murder rate.

109
00:07:12,130 --> 00:07:17,580
So in our murderMap data frame,
we'll create the MurderRate

110
00:07:17,580 --> 00:07:23,550
variable, which is equal
to murderMap$Murders--

111
00:07:23,550 --> 00:07:32,760
the number of murders-- divided
by murderMap$Population times

112
00:07:32,760 --> 00:07:34,940
100,000.

113
00:07:34,940 --> 00:07:37,159
So we've created a
new variable that's

114
00:07:37,159 --> 00:07:41,480
the number of murders
per 100,000 population.

115
00:07:41,480 --> 00:07:45,590
Now let's redo our plot with
the fill equal to MurderRate.

116
00:07:45,590 --> 00:07:49,640
So hit the Up arrow twice to get
back to the plotting command,

117
00:07:49,640 --> 00:07:53,750
and instead of fill=Population,
this time we'll put

118
00:07:53,750 --> 00:07:54,420
fill=MurderRate.

119
00:08:00,310 --> 00:08:02,660
If you look at your
graphics window now,

120
00:08:02,660 --> 00:08:06,320
you should see that the plot
is surprisingly maroon-looking.

121
00:08:06,320 --> 00:08:08,530
There aren't really
any red states.

122
00:08:08,530 --> 00:08:09,770
Why?

123
00:08:09,770 --> 00:08:12,270
It turns out that
Washington, DC is

124
00:08:12,270 --> 00:08:15,210
an outlier with a
very high murder rate,

125
00:08:15,210 --> 00:08:17,510
but it's such a small
region on the map

126
00:08:17,510 --> 00:08:19,610
that we can't even see it.

127
00:08:19,610 --> 00:08:23,360
So let's redo our plot,
removing any observations

128
00:08:23,360 --> 00:08:25,620
with murder rates
above 10, which

129
00:08:25,620 --> 00:08:29,160
we know will only
exclude Washington, DC.

130
00:08:29,160 --> 00:08:31,720
Keep in mind that when
interpreting and explaining

131
00:08:31,720 --> 00:08:34,159
the resulting plot,
you should always

132
00:08:34,159 --> 00:08:36,610
note what you did to
create it: removed

133
00:08:36,610 --> 00:08:40,210
Washington, DC from the data.

134
00:08:40,210 --> 00:08:44,740
So in your R Console, hit the
Up arrow again, and this time,

135
00:08:44,740 --> 00:08:55,670
after guide="legend", we'll type
limits=c(0,10) and hit Enter.

136
00:08:55,670 --> 00:08:58,520
Now if you look back at
your graphics window,

137
00:08:58,520 --> 00:09:01,930
you can see a range
of colors on the map.

138
00:09:01,930 --> 00:09:04,860
In this video, we saw how
we can make a heat map

139
00:09:04,860 --> 00:09:07,340
on a map of the
United States, which

140
00:09:07,340 --> 00:09:10,340
is very useful for organizations
like the World Health

141
00:09:10,340 --> 00:09:14,520
Organization or government
entities who want to show data

142
00:09:14,520 --> 00:09:18,510
to the public organized
by state or country.

143
00:09:18,510 --> 00:09:20,780
In the next video,
we'll conclude

144
00:09:20,780 --> 00:09:24,740
by discussing the analytics
edge of predictive policing.