~/code/R/

Quarterly Data

read.table(text = "
Country Time Value 
1 USA 1999-Q1 292929 
2 USA 1999-Q2 392023 
3 USA 1999-Q3 9392992") %>%
  mutate(Time = as.yearqtr(Time, format = "%Y-Q%q")) %>%
  ggplot + geom_line(aes(Time, Value, col = Country)) +
  scale_x_yearqtr(format = "%Y-Q%q")

New ones

kings <- scan("http://robjhyndman.com/tsdldata/misc/kings.dat", skip=3)
kingstimeseries <- ts(kings)
kingstimeseries
# Time Series:
# Start = 1 
# End = 42 
# Frequency = 1 
#  [1] 60 43 67 50 56 42 50 65 68 43 65 34 47 34 49 41 13 35 53 56 16 43 69 59 48
# [26] 59 86 55 68 51 33 49 67 77 81 67 71 81 68 70 77 56
plot.ts(kingstimeseries)

kingstimeseriesSMA3 <- SMA(kingstimeseries, n = 3)
plot.ts(kingstimeseriesSMA3)

souvenir <- scan("http://robjhyndman.com/tsdldata/data/fancy.dat")
souvenirtimeseries <- ts(souvenir, frequency=12, start=c(1987, 1))
souvenirtimeseries
#            Jan       Feb       Mar       Apr       May       Jun       Jul
# 1987   1664.81   2397.53   2840.71   3547.29   3752.96   3714.74   4349.61
# 1988   2499.81   5198.24   7225.14   4806.03   5900.88   4951.34   6179.12
# 1989   4717.02   5702.63   9957.58   5304.78   6492.43   6630.80   7349.62
# 1990   5921.10   5814.58  12421.25   6369.77   7609.12   7224.75   8121.22
# 1991   4826.64   6470.23   9638.77   8821.17   8722.37  10209.48  11276.55
# 1992   7615.03   9849.69  14558.40  11587.33   9332.56  13082.09  16732.78
# 1993  10243.24  11266.88  21826.84  17357.33  15997.79  18601.53  26155.15
#            Aug       Sep       Oct       Nov       Dec
# 1987   3566.34   5021.82   6423.48   7600.60  19756.21
# 1988   4752.15   5496.43   5835.10  12600.08  28541.72
# 1989   8176.62   8573.17   9690.50  15151.84  34061.01
# 1990   7979.25   8093.06   8476.70  17914.66  30114.41
# 1991  12552.22  11637.39  13606.89  21822.11  45060.69
# 1992  19888.61  23933.38  25391.35  36024.80  80721.71
# 1993  28586.52  30505.41  30821.33  46634.38 104660.67

Complete dataset Example

There is an example here. We start from the following dataset of VATs:

taux_tva %>%
  print_table_long()
date taux_normal taux_reduit
2014-01-01 0.200 0.055
2012-01-01 0.196 0.055
2000-04-01 0.196 0.055
1995-08-01 0.206 0.055
1993-01-01 0.186 0.055
1991-07-29 0.186 0.055
1990-09-13 0.186 0.055
1990-01-01 0.186 0.055
1989-09-08 0.186 0.055
1989-01-01 0.186 0.055
1987-09-17 0.186 0.055
1986-07-01 0.186 0.055
1982-07-01 0.186 0.055
1977-01-01 0.176 0.070
1972-01-01 0.200 0.070
1970-01-01 0.230 0.075
1968-12-01 0.190 0.070
1955-07-01 0.195 0.100
1954-07-01 0.165 0.075

If we plot this, it does not make any sense:

taux_tva %>%
  gather(variable, value, -date) %>%
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  theme(legend.position = c(0.65, 0.6),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 50, 1),
                     labels = percent_format(accuracy = 1)) + 
  ylab("Tax Rate (%)") + xlab("")

taux_tva %>%
  complete(date = seq.Date(min(date), max(date), by = "day")) %>%
  fill(taux_normal, taux_reduit) %>%
  head(10) %>%
  print_table_long()
date taux_normal taux_reduit
1954-07-01 0.165 0.075
1954-07-02 0.165 0.075
1954-07-03 0.165 0.075
1954-07-04 0.165 0.075
1954-07-05 0.165 0.075
1954-07-06 0.165 0.075
1954-07-07 0.165 0.075
1954-07-08 0.165 0.075
1954-07-09 0.165 0.075
1954-07-10 0.165 0.075 
taux_tva %>%
  complete(date = seq.Date(min(date), max(date), by = "day")) %>%
  fill(taux_normal, taux_reduit) %>%
  gather(variable, value, -date) %>%
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  theme(legend.position = c(0.65, 0.6),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 50, 1),
                     labels = percent_format(accuracy = 1)) + 
  ylab("Tax Rate (%)") + xlab("")