R4CSS/V11_Time_and_Trends.Rmd at master · p4css/R4CSS · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
```{r include=FALSE}
knitr::opts_chunk$set(cache = T, warning = F, message = F,
                      class.output = "output", out.width='100%',
                      fig.asp = 0.5, fig.align = 'center')
library(tidyverse)
```

# TIME & TRENDS

## Highlighting: Unemployed Population

This example is referenced from Datacamp's [Introduction to data visualization with ggplot2](https://www.datacamp.com/courses/introduction-to-data-visualization-with-ggplot2)。

### The econimics data

這是一個包含美國經濟時間序列資料的資料集，資料來源為<https://fred.stlouisfed.org/>。`economics`是以「寬」表格方式儲存，而`economics_long` 資料框則以「長」表格方式儲存。每一列之`date`為資料收集的月份。

-   `pce`：個人消費支出，以十億美元為單位，資料來源為 <https://fred.stlouisfed.org/series/PCE>
-   `pop`：總人口數，以千人為單位，資料來源為 <https://fred.stlouisfed.org/series/POP>
-   `psavert`：個人儲蓄率，資料來源為 <https://fred.stlouisfed.org/series/PSAVERT/>
-   `uempmed`：失業中位數持續時間，以週為單位，資料來源為 <https://fred.stlouisfed.org/series/UEMPMED>
-   `unemploy`：失業人數，以千人為單位，資料來源為 <https://fred.stlouisfed.org/series/UNEMPLOY>

```{r}
economics %>% head()
```

### Setting marking area

```{r}
recess <- data.frame(
  begin = c("1969-12-01","1973-11-01","1980-01-01","1981-07-01","1990-07-01","2001-03-01", "2007-12-01"),
  end = c("1970-11-01","1975-03-01","1980-07-01","1982-11-01","1991-03-01","2001-11-01", "2009-07-30"),
  event = c("Fiscal & Monetary\ntightening", "1973 Oil crisis", "Double dip I","Double dip II", "Oil price shock", "Dot-com bubble", "Sub-prime\nmortgage crisis"),
  y =  c(.01415981, 0.02067402, 0.02951190,  0.03419201,  0.02767339, 0.02159662, 0.02520715)
  )

library(lubridate)
recess <- recess %>%
  mutate(begin = ymd(begin),
         end = ymd(end))

economics %>%
  ggplot() +
  aes(x = date, y = unemploy/pop) +
  ggtitle(c("The percentage of unemployed Americans \n increases sharply during recessions")) +
  geom_line() +
  geom_rect(data = recess,
            aes(xmin = begin, xmax = end, ymin = -Inf, ymax = +Inf, fill = "Recession"),
            inherit.aes = FALSE, alpha = 0.2) +
  geom_label(data = recess, aes(x = end, y = y, label=event), size = 3) +
  scale_fill_manual(name = "", values="red", label="Recessions")
```

## Smoothing: Unemployed

-   Smooth by [bin smoothing](http://rafalab.dfci.harvard.edu/dsbook/smoothing.html#bin-smoothing)

```{r}
fit <- with(economics,
            ksmooth(date, unemploy, kernel = "box", bandwidth=210))

economics %>%
  mutate(smooth = fit$y) %>%
  ggplot() + aes(date, unemploy) +
  geom_point(alpha = 5, color = "skyblue") +
  geom_line(aes(date, smooth), color="red") + theme_minimal()
```

### Polls_2008

Second Example comes from [Rafael's online book](http://rafalab.dfci.harvard.edu/dsbook/smoothing.html)

```{r}
library(dslabs)
span <- 7
polls_2008
fit <- with(polls_2008,
            ksmooth(day, margin, kernel = "box", bandwidth = span))

polls_2008 %>%
    mutate(smooth = fit$y) %>%
    ggplot(aes(day, margin)) +
    geom_point(size = 3, alpha = .5, color = "grey") +
    geom_line(aes(day, smooth), color="red") + theme_minimal()
```