1
00:00:04,580 --> 00:00:07,280
In this video, we'll
add the hour of the day

2
00:00:07,280 --> 00:00:09,640
to our line plot,
and then create

3
00:00:09,640 --> 00:00:13,310
an alternative visualization
using a heat map.

4
00:00:13,310 --> 00:00:17,870
We can do this by creating a
line for each day of the week

5
00:00:17,870 --> 00:00:21,570
and making the x-axis
the hour of the day.

6
00:00:21,570 --> 00:00:23,860
We first need to
create a counts table

7
00:00:23,860 --> 00:00:26,070
for the weekday, and hour.

8
00:00:26,070 --> 00:00:30,500
So we'll use the table function
and give as the first variable,

9
00:00:30,500 --> 00:00:33,170
the Weekday variable
in our data frame.

10
00:00:33,170 --> 00:00:37,650
and as the second variable,
the Hour variable.

11
00:00:37,650 --> 00:00:41,640
This table gives, for each
day of the week and each hour,

12
00:00:41,640 --> 00:00:45,080
the total number of motor
vehicle thefts that occurred.

13
00:00:45,080 --> 00:00:48,650
For example, on
Friday at 4 AM, there

14
00:00:48,650 --> 00:00:53,300
were 473 motor vehicle
thefts, whereas on Saturday

15
00:00:53,300 --> 00:00:58,860
at midnight, there were
2,050 motor vehicle thefts.

16
00:00:58,860 --> 00:01:01,260
Let's save this
table to a data frame

17
00:01:01,260 --> 00:01:03,960
so that we can use it
in our visualizations.

18
00:01:03,960 --> 00:01:11,490
We'll call it DayHourCounts and
use the as.data.frame function,

19
00:01:11,490 --> 00:01:16,140
run on our table, where the
first variable is the Weekday

20
00:01:16,140 --> 00:01:18,430
and the second
variable is the Hour.

21
00:01:21,289 --> 00:01:23,300
Let's take a look at the
structure of the data

22
00:01:23,300 --> 00:01:24,220
frame we just created.

23
00:01:28,220 --> 00:01:32,360
We can see that we have
168 observations-- one

24
00:01:32,360 --> 00:01:35,259
for each day of the
week and hour pair,

25
00:01:35,259 --> 00:01:37,490
and three different variables.

26
00:01:37,490 --> 00:01:40,930
The first variable, Var1,
gives the day of the week.

27
00:01:40,930 --> 00:01:45,229
The second variable, Var2,
gives the hour of the day.

28
00:01:45,229 --> 00:01:48,620
And the third variable,
Freq for frequency,

29
00:01:48,620 --> 00:01:51,100
gives the total crime count.

30
00:01:51,100 --> 00:01:54,440
Let's convert the
second variable, Var2,

31
00:01:54,440 --> 00:01:57,009
to actual numbers
and call it Hour,

32
00:01:57,009 --> 00:01:58,759
since this is the
hour of the day,

33
00:01:58,759 --> 00:02:01,430
and it makes sense
that it's numerical.

34
00:02:01,430 --> 00:02:07,520
So we'll add a new variable to
our data frame called Hour =

35
00:02:07,520 --> 00:02:09,400
as.numeric(as.character(DayHourCounts$Var2)).

36
00:02:21,910 --> 00:02:27,920
This is how we convert a factor
variable to a numeric variable.

37
00:02:27,920 --> 00:02:30,410
Now we're ready to
create our plot.

38
00:02:30,410 --> 00:02:33,840
We just need to change
the group to Var1,

39
00:02:33,840 --> 00:02:35,620
which is the day of the week.

40
00:02:35,620 --> 00:02:38,510
So we'll use the
ggplot function where

41
00:02:38,510 --> 00:02:43,860
our data frame is DayHourCounts,
and then in our aesthetic,

42
00:02:43,860 --> 00:02:47,420
we want the x-axis
to be Hour this time,

43
00:02:47,420 --> 00:02:53,620
the y-axis to be Freq, and
then in the geom_line option,

44
00:02:53,620 --> 00:02:56,100
like we used in
the previous video,

45
00:02:56,100 --> 00:03:01,740
we want the aesthetic to
have the group equal to Var1,

46
00:03:01,740 --> 00:03:03,790
which is the day of the week.

47
00:03:03,790 --> 00:03:04,970
Go ahead and hit Enter.

48
00:03:04,970 --> 00:03:09,570
You should see a new plot show
up in the graphics window.

49
00:03:09,570 --> 00:03:13,860
It has seven lines, one
for each day of the week.

50
00:03:13,860 --> 00:03:15,670
While this is
interesting, we can't

51
00:03:15,670 --> 00:03:18,280
tell which line is which
day, so let's change

52
00:03:18,280 --> 00:03:20,570
the colors of the
lines to correspond

53
00:03:20,570 --> 00:03:22,410
to the days of the week.

54
00:03:22,410 --> 00:03:26,320
To do that, just scroll
up in your R console,

55
00:03:26,320 --> 00:03:32,630
and after group =
Var1, add color = Var1.

56
00:03:32,630 --> 00:03:34,370
This will make the
colors of the lines

57
00:03:34,370 --> 00:03:37,050
correspond to the
day of the week.

58
00:03:37,050 --> 00:03:40,040
After that parenthesis,
go ahead and type comma,

59
00:03:40,040 --> 00:03:41,820
and then size = 2.

60
00:03:41,820 --> 00:03:43,410
We'll make our lines
a little thicker.

61
00:03:46,000 --> 00:03:49,579
Now in our plot, each line
is colored corresponding

62
00:03:49,579 --> 00:03:51,600
to the day of the week.

63
00:03:51,600 --> 00:03:54,290
This helps us see that
on Saturday and Sunday,

64
00:03:54,290 --> 00:03:57,790
for example, the green
and the teal lines,

65
00:03:57,790 --> 00:04:01,570
there's less motor vehicle
thefts in the morning.

66
00:04:01,570 --> 00:04:04,630
While we can get some
information from this plot,

67
00:04:04,630 --> 00:04:06,870
it's still quite
hard to interpret.

68
00:04:06,870 --> 00:04:09,020
Seven lines is a lot.

69
00:04:09,020 --> 00:04:14,170
Let's instead visualize the same
information with a heat map.

70
00:04:14,170 --> 00:04:16,519
To make a heat map,
we'll use our data

71
00:04:16,519 --> 00:04:19,230
in our data frame DayHourCounts.

72
00:04:19,230 --> 00:04:23,050
First, though, we need to
fix the order of the days

73
00:04:23,050 --> 00:04:26,140
so that they'll show up
in chronological order

74
00:04:26,140 --> 00:04:28,240
instead of in
alphabetical order.

75
00:04:28,240 --> 00:04:31,620
We'll do the same thing we
did in the previous video.

76
00:04:31,620 --> 00:04:37,110
So for DayHourCounts$Var1,
which is the day of the week,

77
00:04:37,110 --> 00:04:41,090
we're going to use the factor
function where the first

78
00:04:41,090 --> 00:04:46,850
argument is our variable,
DayHourCounts$Var1,

79
00:04:46,850 --> 00:04:51,210
the second argument
is ordered = TRUE,

80
00:04:51,210 --> 00:04:54,000
and the third argument is
the order we want the days

81
00:04:54,000 --> 00:04:55,760
of the week to show up in.

82
00:04:55,760 --> 00:05:00,660
So we'll set levels,
equals, and then c,

83
00:05:00,660 --> 00:05:02,800
and then list your
days of the week.

84
00:05:02,800 --> 00:05:06,440
Let's put the weekdays first
and the weekends at the end.

85
00:05:06,440 --> 00:05:11,290
So we'll start with Monday,
and then Tuesday, then

86
00:05:11,290 --> 00:05:22,600
Wednesday, then Thursday,
Friday, Saturday and Sunday.

87
00:05:26,450 --> 00:05:28,490
Now let's make our heat map.

88
00:05:28,490 --> 00:05:32,280
We'll use the ggplot
function like we always do,

89
00:05:32,280 --> 00:05:36,570
and give our data frame
name, DayHourCounts.

90
00:05:36,570 --> 00:05:39,980
Then in our aesthetic,
we want the x-axis

91
00:05:39,980 --> 00:05:43,409
to be the hour of the
day, and the y-axis

92
00:05:43,409 --> 00:05:46,860
to be the day of the
week, which is Var1.

93
00:05:46,860 --> 00:05:48,680
Then we're going
to add geom_tile.

94
00:05:51,230 --> 00:05:54,210
This is the function we
use to make a heat map.

95
00:05:54,210 --> 00:05:57,040
And then in the
aesthetic for our tiles,

96
00:05:57,040 --> 00:06:00,930
we want the fill to
be equal to Freq.

97
00:06:00,930 --> 00:06:05,000
This will define the colors of
the rectangles in our heat map

98
00:06:05,000 --> 00:06:08,850
to correspond to
the total crime.

99
00:06:08,850 --> 00:06:12,530
You should see a heat map pop
up in your graphics window.

100
00:06:12,530 --> 00:06:14,500
So how do we read this?

101
00:06:14,500 --> 00:06:17,440
For each hour and
each day of the week,

102
00:06:17,440 --> 00:06:20,150
we have a rectangle
in our heat map.

103
00:06:20,150 --> 00:06:23,450
The color of that rectangle
indicates the frequency,

104
00:06:23,450 --> 00:06:26,420
or the number of crimes
that occur in that hour

105
00:06:26,420 --> 00:06:28,070
and on that day.

106
00:06:28,070 --> 00:06:31,120
Our legend tells us
that lighter colors

107
00:06:31,120 --> 00:06:33,680
correspond to more crime.

108
00:06:33,680 --> 00:06:36,250
So we can see that
a lot of crime

109
00:06:36,250 --> 00:06:41,720
happens around midnight,
particularly on the weekends.

110
00:06:41,720 --> 00:06:45,090
We can change the
label on the legend,

111
00:06:45,090 --> 00:06:49,510
and get rid of the y label to
make our plot a little nicer.

112
00:06:49,510 --> 00:06:52,930
We can do this by just scrolling
up to our previous command

113
00:06:52,930 --> 00:06:56,659
in our R console and then
adding scale_fill_gradient.

114
00:07:02,180 --> 00:07:04,960
This defines properties
of the legend,

115
00:07:04,960 --> 00:07:11,400
and we want name =
"Total MV Thefts",

116
00:07:11,400 --> 00:07:12,930
for total motor vehicle thefts.

117
00:07:15,930 --> 00:07:17,820
Then let's add, in the
theme(axis.title.y =

118
00:07:17,820 --> 00:07:18,530
element_blank()).

119
00:07:28,200 --> 00:07:29,660
This is what you
can do if you want

120
00:07:29,660 --> 00:07:33,100
to get rid of one
of the axis labels.

121
00:07:33,100 --> 00:07:35,090
Go ahead and hit Enter.

122
00:07:35,090 --> 00:07:37,130
And now on our heat
map, the legend

123
00:07:37,130 --> 00:07:43,010
is titled "Total MV Thefts"
and the y-axis label is gone.

124
00:07:43,010 --> 00:07:45,880
We can also change
the color scheme.

125
00:07:45,880 --> 00:07:49,360
We can do this by scrolling
up in our R console,

126
00:07:49,360 --> 00:07:52,490
and going to that
scale_fill_gradient function,

127
00:07:52,490 --> 00:07:55,610
the one that defines
properties of our legend,

128
00:07:55,610 --> 00:08:00,890
and after name =
"Total MV Thefts",

129
00:08:00,890 --> 00:08:08,230
low = "white", high = "red".

130
00:08:08,230 --> 00:08:10,510
We'll make lower
values correspond

131
00:08:10,510 --> 00:08:13,020
to white colors
and higher values

132
00:08:13,020 --> 00:08:15,060
correspond to red colors.

133
00:08:15,060 --> 00:08:18,350
If you hit enter, a
new plot should show up

134
00:08:18,350 --> 00:08:20,610
with different colors.

135
00:08:20,610 --> 00:08:23,340
This is a common color
scheme in policing.

136
00:08:23,340 --> 00:08:27,950
It shows the hot spots, or the
places with more crime, in red.

137
00:08:27,950 --> 00:08:31,480
So now the most crime is
shown by the red spots

138
00:08:31,480 --> 00:08:35,280
and the least crime is
shown by the lighter areas.

139
00:08:35,280 --> 00:08:38,570
It looks like Friday night
is a pretty common time

140
00:08:38,570 --> 00:08:40,220
for motor vehicle thefts.

141
00:08:40,220 --> 00:08:43,200
We saw something that we didn't
really see in the heat map

142
00:08:43,200 --> 00:08:44,750
before.

143
00:08:44,750 --> 00:08:48,010
It's often useful to change
the color scheme depending

144
00:08:48,010 --> 00:08:50,610
on whether you want high
values or low values

145
00:08:50,610 --> 00:08:55,340
to pop out, and the feeling
you want the plot to portray.

146
00:08:55,340 --> 00:08:59,710
In this video, we've seen how to
create some new types of plots.

147
00:08:59,710 --> 00:09:02,940
In the next video, we'll
see how to add data

148
00:09:02,940 --> 00:09:05,470
to geographical maps.