Import data in R (read.table) - r

I have the following file, which i want to import.
MONTHLY CLIMATOLOGICAL SUMMARY for MAR. 2014
NAME: larissa CITY: STATE:
ELEV: 82 m LAT: 39° 37' 39" N LONG: 22° 23' 55" E
TEMPERATURE (°C), RAIN (mm), WIND SPEED (km/hr)
HEAT COOL AVG
MEAN DEG DEG WIND DOM
DAY TEMP HIGH TIME LOW TIME DAYS DAYS RAIN SPEED HIGH TIME DIR
------------------------------------------------------------------------------------
1 9.7 11.3 15:50 7.6 7:20 8.6 0.0 5.4 1.3 12.9 20:10 NNE
2 11.8 16.9 14:50 9.8 00:00 6.5 0.0 4.2 2.7 24.1 13:30 NE
3 9.3 11.5 14:20 7.9 00:00 9.0 0.0 6.0 0.8 9.7 3:00 N
4 10.7 17.0 15:10 4.7 6:30 7.7 0.0 1.6 0.6 16.1 18:50 SW
5 11.1 18.5 14:40 6.0 7:30 7.3 0.0 0.2 1.1 16.1 18:50 SSW
6 10.9 16.9 13:50 5.1 6:30 7.4 0.0 0.0 1.1 16.1 16:20 ENE
7 11.3 13.8 14:20 10.1 9:00 7.1 0.0 7.0 3.9 25.7 4:20 NNE
8 12.1 16.6 14:00 9.4 8:00 6.2 0.0 2.8 1.8 22.5 22:40 ENE
9 9.0 10.4 13:10 7.6 00:00 9.3 0.0 0.4 1.8 27.4 10:40 NNE
10 7.9 10.1 13:50 6.6 23:50 10.4 0.0 1.0 4.0 24.1 20:20 NE
11 7.8 10.1 14:20 5.4 5:30 10.6 0.0 0.8 1.1 16.1 11:00 N
12 11.3 18.7 15:30 6.8 7:10 7.0 0.0 0.0 1.3 20.9 14:20 SW
13 11.3 19.1 16:00 4.5 7:40 7.1 0.1 0.0 0.6 12.9 13:10 WSW
14 11.7 20.1 15:40 5.1 6:30 6.8 0.2 0.0 0.6 11.3 15:00 WNW
15 12.6 21.1 15:40 5.2 7:10 6.1 0.3 0.0 0.5 9.7 14:10 SSW
16 14.6 22.3 15:40 8.3 7:10 4.4 0.7 0.0 1.1 11.3 10:40 ENE
17 15.0 24.3 15:10 7.1 6:10 4.6 1.3 0.0 1.0 12.9 7:10 ENE
18 16.0 26.9 15:40 7.2 6:40 4.2 1.9 0.0 0.6 11.3 15:00 SSE
19 17.7 28.4 15:10 8.2 6:50 3.3 2.7 0.0 1.8 24.1 23:40 SW
20 16.6 22.5 16:00 11.1 00:00 2.6 0.8 0.0 2.7 24.1 7:50 N
21 13.8 21.9 16:30 6.7 6:20 5.0 0.6 0.0 0.8 16.1 14:50 ENE
22 14.3 24.1 15:40 5.8 5:40 4.9 0.9 0.0 0.5 9.7 13:50 SW
23 16.4 25.7 16:00 9.8 7:40 3.5 1.6 0.0 0.5 9.7 13:30 ESE
24 16.3 24.9 14:50 10.2 6:10 3.2 1.1 0.0 2.4 29.0 16:10 SSW
25 14.1 21.0 15:40 9.2 6:40 4.5 0.3 0.0 3.9 32.2 14:50 SW
26 12.9 19.0 16:20 9.6 6:10 5.4 0.0 1.6 1.0 12.9 12:50 N
27 14.3 19.2 13:50 11.3 2:30 4.1 0.1 0.2 3.2 33.8 14:20 ENE
28 13.1 19.0 15:40 7.4 6:30 5.3 0.0 0.4 1.4 17.7 15:50 SW
29 14.7 21.2 15:10 10.8 5:40 3.9 0.3 0.2 1.3 19.3 11:30 ENE
30 12.6 17.2 15:30 9.2 00:00 5.4 0.0 0.0 2.6 25.7 4:00 ENE
31 13.1 23.0 17:00 5.2 7:30 6.0 0.7 0.0 0.5 8.0 14:50 SW
-------------------------------------------------------------------------------------
12.7 28.4 19 4.5 13 187.4 13.5 31.8 1.6 33.8 27 ENE
Max >= 32.0: 0
Max <= 0.0: 0
Min <= 0.0: 0
Min <= -18.0: 0
Max Rain: 7.01 ON 07/03/14
Days of Rain: 14 (> .2 mm) 5 (> 2 mm) 0 (> 20 mm)
Heat Base: 18.3 Cool Base: 18.3 Method: Integration
By simply trying to use read.table with header=T, dec=".", sep="" as additional arguments, i get this error:
Error in read.table("C:\\blablabla\\file.txt)
more columns than column names
Execution halted
I think the file is not \t separated but rather "". I am also thinking this might be caused by the extra text before the table. Would read.csv make a difference?
Any suggestions? Thanks in advance.


The problem is the fact that there is some additional information listed above the column names. Simply skipping this will solve your issue. For this, you can use the skip parameter which is part of read.csv.
dat = read.csv('/path/to/file.csv', header = TRUE, dev = ".", sep = "", skip = 9)

Related

How can I export THESE 2 matrices in one excel file with proper spacing and formatting in Rstudio

Truck location coordinates>
X[Now] Y[Now]
A 5.4 15.4
B 8.3 9.0
C 6.6 5.2
D 6.5 13.5
E 15.0 1.9
Load location coordinates> print(Bcd)
Pick-up-X Pick-up-Y Drop-off-X Drop-off-Y
1 18.3 0.5 4.0 13.9
2 11.1 0.1 17.1 18.9
3 20.0 8.9 18.4 7.4
4 4.4 18.2 8.6 15.0
5 12.7 2.9 4.0 0.7
6 5.2 10.7 16.9 18.9
7 18.5 19.0 4.8 9.5
8 8.2 17.3 0.6 4.6
9 11.5 0.5 3.4 11.4
10 2.1 11.3 11.4 0.1

Merge 2 tables to create 1 table: Same rows, different columns in R

I have two tables with the same rows, but they each have different columns. Is it possible to merge them together? I am using the kabbleExtra package to output tables.
18-24 25-34 35-44 45-54 55-64 65-74 75+ Total
Democrat 11.8 18.4 14.2 7.1 6.2 4.5 2.1 64.3
Republican 3.1 5.0 4.0 5.4 5.3 3.5 1.7 28.0
Other 2.0 0.9 1.2 2.1 0.7 0.6 0.2 7.7
Total 17.0 24.3 19.4 14.5 12.2 8.6 4.1 100.0
White Latino Asian African-American Other Total
Democrat 25.2 22.4 10.0 2.2 5.2 65.1
Republican 14.4 7.2 2.8 0.4 2.0 26.8
Other 2.5 4.1 0.9 0.0 0.6 8.1
Total 42.2 33.7 13.7 2.6 7.8 100.0
The expected output should look like this:
Ethnicity Age
White Latino Asian African-American Other Total 18-24 25-34 35-44 45-54 55-64 65-74 75+ Total
Democrat 25.2 22.4 10.0 2.2 5.2 65.1 11.8 18.4 14.2 7.1 6.2 4.5 2.1 64.3
Republican 14.4 7.2 2.8 0.4 2.0 26.8 3.1 5.0 4.0 5.4 5.3 3.5 1.7 28.0
Other 2.5 4.1 0.9 0.0 0.6 8.1 2.0 0.9 1.2 2.1 0.7 0.6 0.2 7.7
Total 42.2 33.7 13.7 2.6 7.8 100.0 17.0 24.3 19.4 14.5 12.2 8.6 4.1 100.0

We can merge the tables using dplyr:
> df1 %>% rownames_to_column('party') %>%
+ inner_join(df2 %>% rownames_to_column('party'), by = 'party') %>% column_to_rownames('party')
18-24 25-34 35-44 45-54 55-64 65-74 75+ Total.x White Latino Asian African-American Other Total.y
Democrat 11.8 18.4 14.2 7.1 6.2 4.5 2.1 64.3 25.2 22.4 10.0 2.2 5.2 65.1
Republican 3.1 5.0 4.0 5.4 5.3 3.5 1.7 28.0 14.4 7.2 2.8 0.4 2.0 26.8
Other 2.0 0.9 1.2 2.1 0.7 0.6 0.2 7.7 2.5 4.1 0.9 0.0 0.6 8.1
Total 17.0 24.3 19.4 14.5 12.2 8.6 4.1 100.0 42.2 33.7 13.7 2.6 7.8 100.0
Data used:
> df1
18-24 25-34 35-44 45-54 55-64 65-74 75+ Total
Democrat 11.8 18.4 14.2 7.1 6.2 4.5 2.1 64.3
Republican 3.1 5.0 4.0 5.4 5.3 3.5 1.7 28.0
Other 2.0 0.9 1.2 2.1 0.7 0.6 0.2 7.7
Total 17.0 24.3 19.4 14.5 12.2 8.6 4.1 100.0
> df2
White Latino Asian African-American Other Total
Democrat 25.2 22.4 10.0 2.2 5.2 65.1
Republican 14.4 7.2 2.8 0.4 2.0 26.8
Other 2.5 4.1 0.9 0.0 0.6 8.1
Total 42.2 33.7 13.7 2.6 7.8 100.0
>

estimate phase and amplitude of a seasonal cycle

I have the following data:
CET <- url("http://www.metoffice.gov.uk/hadobs/hadcet/cetml1659on.dat")
cet <- read.table(CET, sep = "", skip = 6, header = TRUE,
fill = TRUE, na.string = c(-99.99, -99.9))
names(cet) <- c(month.abb, "Annual")
cet <- cet[-nrow(cet), ]
rn <- as.numeric(rownames(cet))
Years <- rn[1]:rn[length(rn)]
annCET <- data.frame(Temperature = cet[, ncol(cet)],Year = Years)
cet <- cet[, -ncol(cet)]
cet <- stack(cet)[,2:1]
names(cet) <- c("Month","Temperature")
cet <- transform(cet, Year = (Year <- rep(Years, times = 12)),
nMonth = rep(1:12, each = length(Years)),
Date = as.Date(paste(Year, Month, "15", sep = "-"),format = "%Y-%b-%d"))
cet <- cet[with(cet, order(Date)), ]
idx <- cet$Year > 1900
cet <- cet[idx,]
cet <- cet[,c('Date','Temperature')]
plot(cet, type = 'l')
This demonstrates the monthly temperature cycle from 1900 to 2014 in England, UK.
I would like to evaluate the phase and amplitude of the seasonal cycle of temperature follwowing the methods outlined in this paper. Specifically, they describe that given 12 monthly values (as we have here) we can estimate the yearly component as:
where X(t) represents 12 monthly values of surface temperature, x(t+t0), t = 0.5,...,11.5, are 12 monthly values of the de-meaned monthly temperature, where the factor of two is to account for both positive and negative frequencies.
Then the amplitude and phase of the seasonal cycle can be calculated as
and
They specify, that each year of data, they calculate the yearly (one cycle per year) sinusoidal component using the Fourier transform, as the equation shown above.
I'm a bit stuck on how to generate the time series they demonstrate here. Can anyone please provide some guidance as to how I can reproduce these methods. Note, I also work in matlab - in case anyone has some suggestions as to how this would be achieved in that environment.
Here is a subset of the data.
Date Temperature
1980-01-15 2.3
1980-02-15 5.7
1980-03-15 4.7
1980-04-15 8.8
1980-05-15 11.2
1980-06-15 13.8
1980-07-15 14.7
1980-08-15 15.9
1980-09-15 14.7
1980-10-15 9
1980-11-15 6.6
1980-12-15 5.6
1981-01-15 4.9
1981-02-15 3
1981-03-15 7.9
1981-04-15 7.8
1981-05-15 11.2
1981-06-15 13.2
1981-07-15 15.5
1981-08-15 16.2
1981-09-15 14.5
1981-10-15 8.6
1981-11-15 7.8
1981-12-15 0.3
1982-01-15 2.6
1982-02-15 4.8
1982-03-15 6.1
1982-04-15 8.6
1982-05-15 11.6
1982-06-15 15.5
1982-07-15 16.5
1982-08-15 15.7
1982-09-15 14.2
1982-10-15 10.1
1982-11-15 8
1982-12-15 4.4
1983-01-15 6.7
1983-02-15 1.7
1983-03-15 6.4
1983-04-15 6.8
1983-05-15 10.3
1983-06-15 14.4
1983-07-15 19.5
1983-08-15 17.3
1983-09-15 13.7
1983-10-15 10.5
1983-11-15 7.5
1983-12-15 5.6
1984-01-15 3.8
1984-02-15 3.3
1984-03-15 4.7
1984-04-15 8.1
1984-05-15 9.9
1984-06-15 14.5
1984-07-15 16.9
1984-08-15 17.6
1984-09-15 13.7
1984-10-15 11.1
1984-11-15 8
1984-12-15 5.2
1985-01-15 0.8
1985-02-15 2.1
1985-03-15 4.7
1985-04-15 8.3
1985-05-15 10.9
1985-06-15 12.7
1985-07-15 16.2
1985-08-15 14.6
1985-09-15 14.6
1985-10-15 11
1985-11-15 4.1
1985-12-15 6.3
1986-01-15 3.5
1986-02-15 -1.1
1986-03-15 4.9
1986-04-15 5.8
1986-05-15 11.1
1986-06-15 14.8
1986-07-15 15.9
1986-08-15 13.7
1986-09-15 11.3
1986-10-15 11
1986-11-15 7.8
1986-12-15 6.2
1987-01-15 0.8
1987-02-15 3.6
1987-03-15 4.1
1987-04-15 10.3
1987-05-15 10.1
1987-06-15 12.8
1987-07-15 15.9
1987-08-15 15.6
1987-09-15 13.6
1987-10-15 9.7
1987-11-15 6.5
1987-12-15 5.6
1988-01-15 5.3
1988-02-15 4.9
1988-03-15 6.4
1988-04-15 8.2
1988-05-15 11.9
1988-06-15 14.4
1988-07-15 14.7
1988-08-15 15.2
1988-09-15 13.2
1988-10-15 10.4
1988-11-15 5.2
1988-12-15 7.5
1989-01-15 6.1
1989-02-15 5.9
1989-03-15 7.5
1989-04-15 6.6
1989-05-15 13
1989-06-15 14.6
1989-07-15 18.2
1989-08-15 16.6
1989-09-15 14.7
1989-10-15 11.7
1989-11-15 6.2
1989-12-15 4.9
1990-01-15 6.5
1990-02-15 7.3
1990-03-15 8.3
1990-04-15 8
1990-05-15 12.6
1990-06-15 13.6
1990-07-15 16.9
1990-08-15 18
1990-09-15 13.2
1990-10-15 11.9
1990-11-15 6.9
1990-12-15 4.3
1991-01-15 3.3
1991-02-15 1.5
1991-03-15 7.9
1991-04-15 7.9
1991-05-15 10.8
1991-06-15 12.1
1991-07-15 17.3
1991-08-15 17.1
1991-09-15 14.7
1991-10-15 10.2
1991-11-15 6.8
1991-12-15 4.7
1992-01-15 3.7
1992-02-15 5.4
1992-03-15 7.5
1992-04-15 8.7
1992-05-15 13.6
1992-06-15 15.7
1992-07-15 16.2
1992-08-15 15.3
1992-09-15 13.4
1992-10-15 7.8
1992-11-15 7.4
1992-12-15 3.6
1993-01-15 5.9
1993-02-15 4.6
1993-03-15 6.7
1993-04-15 9.5
1993-05-15 11.4
1993-06-15 15
1993-07-15 15.2
1993-08-15 14.6
1993-09-15 12.4
1993-10-15 8.5
1993-11-15 4.6
1993-12-15 5.5
1994-01-15 5.3
1994-02-15 3.2
1994-03-15 7.7
1994-04-15 8.1
1994-05-15 10.7
1994-06-15 14.5
1994-07-15 18
1994-08-15 16
1994-09-15 12.7
1994-10-15 10.2
1994-11-15 10.1
1994-12-15 6.4
1995-01-15 4.8
1995-02-15 6.5
1995-03-15 5.6
1995-04-15 9.1
1995-05-15 11.6
1995-06-15 14.3
1995-07-15 18.6
1995-08-15 19.2
1995-09-15 13.7
1995-10-15 12.9
1995-11-15 7.7
1995-12-15 2.3
1996-01-15 4.3
1996-02-15 2.5
1996-03-15 4.5
1996-04-15 8.5
1996-05-15 9.1
1996-06-15 14.4
1996-07-15 16.5
1996-08-15 16.5
1996-09-15 13.6
1996-10-15 11.7
1996-11-15 5.9
1996-12-15 2.9
1997-01-15 2.5
1997-02-15 6.7
1997-03-15 8.4
1997-04-15 9
1997-05-15 11.5
1997-06-15 14.1
1997-07-15 16.7
1997-08-15 18.9
1997-09-15 14.2
1997-10-15 10.2
1997-11-15 8.4
1997-12-15 5.8
1998-01-15 5.2
1998-02-15 7.3
1998-03-15 7.9
1998-04-15 7.7
1998-05-15 13.1
1998-06-15 14.2
1998-07-15 15.5
1998-08-15 15.9
1998-09-15 14.9
1998-10-15 10.6
1998-11-15 6.2
1998-12-15 5.5
1999-01-15 5.5
1999-02-15 5.3
1999-03-15 7.4
1999-04-15 9.4
1999-05-15 12.9
1999-06-15 13.9
1999-07-15 17.7
1999-08-15 16.1
1999-09-15 15.6
1999-10-15 10.7
1999-11-15 7.9
1999-12-15 5
2000-01-15 4.9
2000-02-15 6.3
2000-03-15 7.6
2000-04-15 7.8
2000-05-15 12.1
2000-06-15 15.1
2000-07-15 15.5
2000-08-15 16.6
2000-09-15 14.7
2000-10-15 10.3
2000-11-15 7
2000-12-15 5.8
2001-01-15 3.2
2001-02-15 4.4
2001-03-15 5.2
2001-04-15 7.7
2001-05-15 12.6
2001-06-15 14.3
2001-07-15 17.2
2001-08-15 16.8
2001-09-15 13.4
2001-10-15 13.3
2001-11-15 7.5
2001-12-15 3.6
2002-01-15 5.5
2002-02-15 7
2002-03-15 7.6
2002-04-15 9.3
2002-05-15 11.8
2002-06-15 14.4
2002-07-15 16
2002-08-15 17
2002-09-15 14.4
2002-10-15 10.1
2002-11-15 8.5
2002-12-15 5.7
2003-01-15 4.5
2003-02-15 3.9
2003-03-15 7.5
2003-04-15 9.6
2003-05-15 12.1
2003-06-15 16.1
2003-07-15 17.6
2003-08-15 18.3
2003-09-15 14.3
2003-10-15 9.2
2003-11-15 8.1
2003-12-15 4.8
2004-01-15 5.2
2004-02-15 5.4
2004-03-15 6.5
2004-04-15 9.4
2004-05-15 12.1
2004-06-15 15.3
2004-07-15 15.8
2004-08-15 17.6
2004-09-15 14.9
2004-10-15 10.5
2004-11-15 7.7
2004-12-15 5.4
2005-01-15 6
2005-02-15 4.3
2005-03-15 7.2
2005-04-15 8.9
2005-05-15 11.4
2005-06-15 15.5
2005-07-15 16.9
2005-08-15 16.2
2005-09-15 15.2
2005-10-15 13.1
2005-11-15 6.2
2005-12-15 4.4
2006-01-15 4.3
2006-02-15 3.7
2006-03-15 4.9
2006-04-15 8.6
2006-05-15 12.3
2006-06-15 15.9
2006-07-15 19.7
2006-08-15 16.1
2006-09-15 16.8
2006-10-15 13
2006-11-15 8.1
2006-12-15 6.5
2007-01-15 7
2007-02-15 5.8
2007-03-15 7.2
2007-04-15 11.2
2007-05-15 11.9
2007-06-15 15.1
2007-07-15 15.2
2007-08-15 15.4
2007-09-15 13.8
2007-10-15 10.9
2007-11-15 7.3
2007-12-15 4.9
2008-01-15 6.6
2008-02-15 5.4
2008-03-15 6.1
2008-04-15 7.9
2008-05-15 13.4
2008-06-15 13.9
2008-07-15 16.2
2008-08-15 16.2
2008-09-15 13.5
2008-10-15 9.7
2008-11-15 7
2008-12-15 3.5
2009-01-15 3
2009-02-15 4.1
2009-03-15 7
2009-04-15 10
2009-05-15 12.1
2009-06-15 14.8
2009-07-15 16.1
2009-08-15 16.6
2009-09-15 14.2
2009-10-15 11.6
2009-11-15 8.7
2009-12-15 3.1
2010-01-15 1.4
2010-02-15 2.8
2010-03-15 6.1
2010-04-15 8.8
2010-05-15 10.7
2010-06-15 15.2
2010-07-15 17.1
2010-08-15 15.3
2010-09-15 13.8
2010-10-15 10.3
2010-11-15 5.2
2010-12-15 -0.7
2011-01-15 3.7
2011-02-15 6.4
2011-03-15 6.7
2011-04-15 11.8
2011-05-15 12.2
2011-06-15 13.8
2011-07-15 15.2
2011-08-15 15.4
2011-09-15 15.1
2011-10-15 12.6
2011-11-15 9.6
2011-12-15 6
2012-01-15 5.4
2012-02-15 3.8
2012-03-15 8.3
2012-04-15 7.2
2012-05-15 11.7
2012-06-15 13.5
2012-07-15 15.5
2012-08-15 16.6
2012-09-15 13
2012-10-15 9.7
2012-11-15 6.8
2012-12-15 4.8
2013-01-15 3.5
2013-02-15 3.2
2013-03-15 2.7
2013-04-15 7.5
2013-05-15 10.4
2013-06-15 13.6
2013-07-15 18.3
2013-08-15 16.9
2013-09-15 13.7
2013-10-15 12.5
2013-11-15 6.2
2013-12-15 6.3
2014-01-15 5.7
2014-02-15 6.2
2014-03-15 7.6
2014-04-15 10.2
2014-05-15 12.2
2014-06-15 15.1
2014-07-15 17.7
2014-08-15 14.9
2014-09-15 15.1
2014-10-15 12.5
2014-11-15 8.6
2014-12-15 5.2

Literally, the formula for Y can be represented in MATLAB as:
t=0.5:0.5:11.5; %//make sure the step size is indeed 0.5
Y = 1/6.*sum(exp(2*pi*i.*t/12).*X(t0-t); %// add the function for X
phi = atan2(imag(Y)/real(Y)); %// seasonal phase
without knowing the function for X I can't be sure this can indeed be vectorised, or whether you'd have to loop, which can be done like:
t=0.5:0.5:11.5; %//make sure the step size is indeed 0.5
Ytmp(numel(t),1)=0; %// initialise output
for ii = 1:numel(t)
Ytmp(ii,1) = exp(2*pi*i.*t(ii)/12).*X(t0-t(ii));
end
Y = 1/6 * sum(Ytmp)
Just slot in any t0 you want, loop over the codes above and you have your time series.

Reading Consumer Price Index data in R?

I need to read the data from this link http://www.bls.gov/cpi/cpifiles/cpiai.txt into a data frame. Sadly the data is not in a csv file. What is the best way of doing it?
I tried
cpiai <- read.table("http://www.bls.gov/cpi/cpifiles/cpiai.txt")
Thanks!

Sticking to read.table:
path<-"http://www.bls.gov/cpi/cpifiles/cpiai.txt"
cpiai <- read.table(path,header=T,fill=TRUE,skip=16)
>head(cpiai)
Year Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec. Avg. Dec Avg
1 1913 9.8 9.8 9.8 9.8 9.7 9.8 9.9 9.9 10.0 10.0 10.1 10.0 9.9 NA NA
2 1914 10.0 9.9 9.9 9.8 9.9 9.9 10.0 10.2 10.2 10.1 10.2 10.1 10.0 1.0 1.0
3 1915 10.1 10.0 9.9 10.0 10.1 10.1 10.1 10.1 10.1 10.2 10.3 10.3 10.1 2.0 1.0
4 1916 10.4 10.4 10.5 10.6 10.7 10.8 10.8 10.9 11.1 11.3 11.5 11.6 10.9 12.6 7.9
5 1917 11.7 12.0 12.0 12.6 12.8 13.0 12.8 13.0 13.3 13.5 13.5 13.7 12.8 18.1 17.4
6 1918 14.0 14.1 14.0 14.2 14.5 14.7 15.1 15.4 15.7 16.0 16.3 16.5 15.1 20.4 18.0

The answer by J.R. gives you all you need to know, but just to be complete, also consider access via Quandl which curates the data and also gives you a unified (free) API across the millions of series they track.
For this CPI the page is https://www.quandl.com/FRED/CPIAUCSL-Consumer-Price-Index-for-All-Urban-Consumers-All-Items-USA-Inflation and access is as easy as
R> library(Quandl)
R> dat <- Quandl("FRED/CPIAUCSL")
R> head(dat)
Date Value
1 2014-09-01 237.633
2 2014-08-01 237.428
3 2014-07-01 237.909
4 2014-06-01 237.693
5 2014-05-01 237.083
6 2014-04-01 236.254
R>

Subsetting and Looping a Time Series Data in R

I have a dataset of timeseries (30 years). I did a subset for the month and the date I want (shown below in the code). Is there a way to do a loop for each month and the days in those month? Also, is there a way to save the plots automatically, in different folders corresponding to each month? Right now I am doing it manually by changing the month and date which corresponds to dfOct31all <- df [ which(df$Month==10 & df$Day==31), ]in the code below then plotting and saving it. By the way, I'm using RStudio.
Can someone please guide me?
Thanks!
setwd("WDir")
df <- read.csv("Velocity.csv", header = TRUE)
attach(df)
#Day 31
dfOct31all <- df [ which(df$Month==10 & df$Day==31), ]
dfall31Mbs <- dfOct31all[c(-1,-2,-3)]
densities <- lapply(dfall31Mbs, density)
par(mfcol=c(5,5), oma=c(1,1,0,0), mar=c(1,1,1,0), tcl=-0.1, mgp=c(0,0,0))
plot(densities[[1]], col="black",main = "1000mb",xlab=NA,ylab=NA)
plot(densities[[2]], col="black",main="925mb",xlab=NA,ylab=NA)
plot(densities[[3]], col="black",main="850mb",xlab=NA,ylab=NA)
plot(densities[[4]], col="black",main="700mb",xlab=NA,ylab=NA)
plot(densities[[5]], col="black",main="600mb",xlab=NA,ylab=NA)
plot(densities[[6]], col="black",main="500mb",xlab=NA,ylab=NA)
plot(densities[[7]], col ="black",main="400mb",xlab=NA,ylab=NA)
plot(densities[[8]], col="black",main="300mb",xlab=NA,ylab=NA)
plot(densities[[9]], col="black",main="250mb",xlab=NA,ylab=NA)
plot(densities[[10]], col="black",main="200mb",xlab=NA,ylab=NA)
plot(densities[[11]], col= "black",main="150mb",xlab=NA,ylab=NA)
plot(densities[[12]], col= "black",main="100mb",xlab=NA,ylab=NA)
plot(densities[[13]], col = "black",main="70mb",xlab=NA,ylab=NA)
plot(densities[[14]], col="black",main="50mb",xlab=NA,ylab=NA)
plot(densities[[15]], col="black",main="30mb",xlab=NA,ylab=NA)
plot(densities[[16]], col = "black",main="20mb",xlab=NA,ylab=NA)
plot(densities[[17]], col="black",main="10mb",xlab=NA,ylab=NA)
Snippet of data is shown as well
Year Month Day 1000mb 925mb 850mb 700mb 600mb 500mb 400mb 300mb 250mb 200mb 150mb 100mb 70mb 50mb 30mb 20mb 10mb
1984 10 31 6 6.6 7.9 11.5 14.6 17 20.8 25.8 26.4 25.3 24.4 22.7 19.9 19.2 20.4 24.8 30.8
1985 10 31 5.8 7.1 7.7 11.5 14.7 17.3 25.3 32.6 32.9 32.4 27.1 20.9 14.2 9.7 6.4 7.3 7.4
1986 10 31 4.3 6.1 7.7 11.3 18.4 26.3 34.4 44.5 48.9 46.2 34.5 20.4 13.8 13.2 21.7 31 46.4
1987 10 31 2.2 2.9 4 7 9 13.9 19.9 25.8 26.6 23.7 17.3 12 7 3.1 1.7 5.8 14.1
1988 10 31 2.5 2.1 2.3 6.5 6.4 5.1 7.4 12.1 13.4 16.1 16.7 15.2 8.8 5 2.8 6.2 8.9
1989 10 31 3.4 4 4.7 4.4 4.1 4 4.6 4.8 5.9 5.6 10.9 13.9 12.3 10.4 8.1 8 8
1990 10 31 4 4.9 7.5 14.6 19 21.9 25.7 28.3 29.4 29.2 27.3 18 12.6 10.1 9 12 19.9
1991 10 31 2.8 3.2 4 10.8 12.1 11.2 9.9 9.1 9.9 12.8 18 17.5 10.4 6.3 4.2 7.6 11.7
1992 10 31 5.9 6.9 7.9 13.1 17.9 25.2 34.6 47.3 53.3 53 42.4 21.3 11.6 6 4.6 8.5 12.8
1993 10 31 2.3 1.5 0.4 3.6 6.3 10.1 14.3 19.1 21.6 21.8 18.4 13.6 12.3 9.5 6.9 11 18.1
1994 10 31 2 2.2 3.8 11.6 17 19.8 23.6 24.9 25.5 26.2 28.4 25.2 16.7 13.6 9.3 8.3 9.8
1995 10 31 1.5 2 3.4 7.6 9.1 11.2 13.7 17.9 20.3 21.7 21.1 16.7 13 12.1 14.9 21.4 27.3
1996 10 31 1.9 2.4 3.5 8 11.7 17.4 26.4 35.6 33.3 24.6 12.4 4.1 0.5 3.4 7.2 9.4 11.6
1997 10 31 3.7 4.8 7.8 19.2 24.6 29.6 35.6 41 41.8 42 37.9 23.7 11.2 8.6 4.2 3.8 7
1998 10 31 0.7 1.1 0.9 4.8 8.4 11.4 14 25.3 29.7 25.2 15.9 6.6 2.1 1 4.5 8.9 6.1
1999 10 31 1.9 1.6 2.4 10.7 15.3 19 23.2 29 32.4 31.9 28 20.3 10.8 9.4 12 14.5 16.9
2000 10 31 5.1 5.8 6.7 12.8 18.2 23.9 29.9 40.7 42.2 33.7 23.5 12.7 2.6 1.6 3.8 4.7 5.1
2001 10 31 5.7 6.1 7.1 10.1 10.8 14.7 18.3 22.8 22.3 22.2 22 14 9.5 6.6 5.2 6.5 8.6
2002 10 31 1.4 1.6 1.8 9.2 14.5 19.5 24.8 30 30.5 27.6 22.2 13.9 9.1 7.1 8.5 16.1 23.8
2003 10 31 1.5 1.3 0.7 1 3.5 6 11.7 21.5 21.9 22.9 23 20.7 15.8 12.5 14.5 20.1 26
2004 10 31 5.4 5.6 6.9 14.4 23.3 33.3 46.1 60.9 62.1 54.6 42.9 28 17.3 12.3 10.1 13.6 13.3
2005 10 31 1.7 1.3 3 10.3 15.8 19.5 21.1 22.8 24.1 24.5 24.5 20.6 13.5 10.7 10 10.7 10.4
2006 10 31 2.3 1.5 1.7 8.7 12.5 15.9 18.7 20.5 21.8 24.3 29.9 25.3 18.3 12.8 7.7 8.8 12.4
2007 10 31 3.7 2.7 2.3 2.2 2.6 4.2 6.5 11.9 15.9 19.6 17.2 9.5 6.9 5.7 4.9 5.8 11.7
2008 10 31 7.7 10.8 14.3 20.3 23 25.8 27.4 32.1 35.4 34.8 25.8 13.2 7.1 2.9 2.6 3.4 6
2009 10 31 0.5 0.2 2 9.3 13.5 17.6 18.8 20.8 21.4 21.2 18.9 14.2 11.1 6.4 1.9 3 8
2010 10 31 5.6 6.8 8.5 13.4 16.5 20.3 23.8 26.8 31 28.1 24 15.7 9.9 7 4.8 3.9 1.8
2011 10 31 5.9 6.7 5.6 7.9 10.3 11.8 12.5 16.2 19.5 21.4 17.9 13.2 9.6 7.9 8 8.3 10.8
2012 10 31 4.8 6.3 9.4 19.5 24.2 27.2 27.5 27.3 27.7 30.7 27.5 16.7 10 7.6 8 13.8 19.7
2013 10 31 1.4 1.9 3.9 9.1 13.1 17.3 22.9 29.7 30.4 27.3 23.5 18.2 13.1 6.3 4.4 2.4 9.4

I wrote it out for each day rather than doing a loop.

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