Chapter 6: Project Two - SOI and ENSO
In this chapter we attempt to replicate the scientific results generated by several leading climate institutes such as NCAR, NASA and others.
We will use the long term record of sea-level pressure (SLP) measurements to calculate the Southern Oscillation Index (SOI).
The SOI represents the differences in pressure difference between Tahiti and Darwin. The index is representative of the relative intensity and position of the trade winds in the central Pacific, associated atmospheric-oceanic coupling (upwelling strength and thermocline depth) and thus a measure of switches between the two ENSO states of El Nino (warm) and La Nina (cool).
Having a SOI at your disposal is useful as it can be quickly and easily correlated with other changes in the climatic system (i.e. rainfall patterns in Eastern Africa) and be used as a diagnostic tool for severe weather disaster prevention and preparation.
Handling Raw Data
This chapter focuses on some techniques to handle so-called “raw data”. Typically this is data which is not stored in a straightforward manner for direct analysis.
Firstly there is a preliminary phase of re-arranging and subsetting the data into a more usable form, before anaylsis begins.
Lets load in our required data, this time using a slightly different technique:
# read in txt file item by item Tahiti_raw <- scan("tahiti.ascii") Darwin_raw <- scan("darwin.ascii")
Here we have used the scan function - which can directly read in a .dat, .txt or in our case .ascii file. The data now exists as one long numeric vector, of length 1911 items.
Inspect our data (either in a simple text editor like Notepad++) or in R using the head()/tail() command.
We notice the data is arranged so that each line begins with the year of the the data (1866 etc.) and then each successive column is a monthly SLP value.
We require the ordered monthly values (to form one long vector) but the 'counters' of the years are a nuisance.
Subsetting using [] and expressions >, <, = etc.
We could manipulate the data into a matrix and extract only the monthly data but this requires a bit of effort and thought! Instead lets rather solve our problem by subsetting our data using a simple trick.
Notice the SLP values are only double-digits (12, 17 etc), and the years are recorded as 1989…i.e. four-digits.
Therefore lets choose to ONLY select the low values of our “raw data”…
# subset raw data into new object only selecting values of less than 100 # in this case Tahiti_slp <- (Tahiti_raw[Tahiti_raw < 100]) Darwin_slp <- (Darwin_raw[Darwin_raw < 100])
The [] or subset notation of square brackets is used to designate or subset a particular part of a object, whether it be a vector, array or dataframe.
In this case we use the < (less than) symbol to select the part/s of Tahiti_raw where the *values are less than 100.
Alternatively one could also subset data by using a vector selection i.e.[1:25] selecting the first 25 *positions of the object.
Lets check if it worked:
# show the length of an object length(Tahiti_slp)
## [1] 1764
# confirm we have a vector, now shorter str(Tahiti_slp)
## num [1:1764] 10.6 10.9 11.5 11.4 12.5 13.6 13.5 15.5 13.9 13.8 ...
Subsetting using vector expressions
You may have noted that the last few months of our data are missing or assigned NA. Lets just exclude these NA's in this exercise - we will only use the full record up until the end of 2011.
# select subset of data from index positions 1 through 1752 1866-2011, # excludes partially filled data from 2012 Tahiti_slp <- Tahiti_slp[1:1752] Darwin_slp <- Darwin_slp[1:1752]
Now we can get our first term in our Southern Oscillation Index (SOI) equation by a straight-forward subtraction of the SLP's from each other:
# vectors are the same length each element is subtracted from the # corresponding one Pdiff <- Tahiti_slp - Darwin_slp
Time Series Package
Now we need to calculate the average pressure differences between Tahiti and Darwin for each of the months of the year. In essence we need to calculate a climatology - the average differences over an extended period of time.
# freq = 12 for monthly data, start date of ts Pdiff.ts <- ts(Pdiff, frequency = 12)
Using the time.series package included in base R we have taken our vector and transformed it into a time.series object - use str() to check!
Using tapply and several other functions such as cycle and mean we can average our data for each successive month Jan, Feb, Mar, Apr….
# apply mean calculation iteratively tapply(Pdiff.ts, cycle(Pdiff.ts), mean)
## 1 2 3 4 5 6 7 8 9 10 11 12 ## 4.660 4.729 4.005 2.341 1.552 1.222 1.047 1.645 2.465 2.928 3.027 3.829
# aggregate average data into a data.frame Pdiff_avg.df <- aggregate(c(Pdiff.ts), list(month = cycle(Pdiff.ts)), mean)
# extract the data which lies in the un-named column 'x' Pdiff_avg <- Pdiff_avg.df$x
An Alternative Method: Using Matrices
An ALTERNATIVE METHOD in achieving the same outcome would have been to use matrix manipulation techniques:
# calculate length of data coverage in years no_years <- length(Tahiti_slp)/12 # create an empty array with 12 rows and 146 columns to test empty <- array(, dim = c(12, no_years)) # fill array of same dim with Pdiff data filled <- array(Pdiff, dim = c(12, no_years))
The matrix has now been filled with the Pdiff data (columnwise…) i.e the first column is filled with the first 12 months of data, and the second column the second 12 months and so on…
We can use the rowMean function to calculate the average for each required month; Jan, Feb, Mar (i.e each row) across the columns (the 146 year record)…
# rowMeans applied to filled matrix Pdiff_avg.mat <- rowMeans(matrix(filled, nrow = 12, ncol = 146)) # confirm results are the same! :D Pdiff_avg.mat
## [1] 4.660 4.729 4.005 2.341 1.552 1.222 1.047 1.645 2.465 2.928 3.027 ## [12] 3.829
Using tapply
Lets calculate the standard deviation of the Pdiff_avg.
# apply std dev calculation iteratively tapply(Pdiff.ts, cycle(Pdiff.ts), sd)
## 1 2 3 4 5 6 7 8 9 10 11 12 ## 2.223 2.134 2.056 1.470 1.296 1.514 1.622 1.617 1.791 1.676 1.579 1.925
# aggregate std dev data into a data.frame Pdiff_avgsd.df <- aggregate(c(Pdiff.ts), list(month = cycle(Pdiff.ts)), sd)
# extract the data which lies in the un-named column 'x' Pdiff_avgsd <- Pdiff_avgsd.df$x
Above the tapply family of functions were applied again to calculate the standard deviation, in an identical manner as that used used for the calcualtion of the mean pressures.
The final answers were then stored in an appropriately named vector for later use.
Loops: The Advantages of Vectors
The last part of our exercise would traditionally require a loop (and more thinking!). However we can take advantage of the fact that out data is stored in vectors, and these vectors are multiples of each other.
NB: Generally it is advisable to avoid loops when ever possible, as they can be complex to design and slow in execution!! In comparison vector calculations in R are orders of magnitude faster!!
Lets create an empty vector to store our calculation results.
# length of data to be stored - total months len <- no_years * 12 # vector to hold data: of type numeric and length, len Index <- numeric(len)
Now we can proceed with the actual calculation. This is where the power and usefulness of vectors becomes aparrent!! :D
R will evaluate ALL vectors in a continuous manner - we have already seen this before when we subtracted two vectors from each other! In that case each element in one vector was subtracted from the corresponding element in the other vector.
Furthermore shorter vectors will be continuously and successively evaluated ( i.e. 'looped') over and over within a longer vector.
Here is a simple example to illustrate this:
# a 4 element vector long_vec <- c(10, 20, 30, 40) # a 2 element vector short_vec <- c(2, 4) # divide the 'long' vector by the 'short' vector quick_calc <- long_vec/short_vec # show the results quick_calc
## [1] 5 5 15 10
We can apply this principle in our evaluation of the SOI. We need to calculate the index for each data-point in the time series - 1752 data points in total.
We remember our average and standard deviation data are stored in vectors of length 12 - each element representing one month of the year (J,F,M etc.). Within the ensuing calculation these vectors of length 12 will be successively evaluated over and over for each Pdiff value.
# SOI Index Equation Index[] <- 10 * (Pdiff - Pdiff_avg)/Pdiff_avgsd Index
## [1] -5.66711 -0.60328 -3.91821 -4.36159 1.91316 -4.76928 ## [7] -3.98943 5.29032 -2.03857 1.62220 10.59206 -2.22723 ## [13] 1.08165 0.33373 0.45979 5.16308 5.77118 -2.12672 ## [19] 3.40685 4.67192 0.75347 -2.55383 -7.13945 -8.98008 ## [25] -2.06777 -2.94581 -11.70132 1.08108 -13.51894 -14.01823 ## [31] -4.60579 -11.40659 -12.08993 -10.30932 -14.73866 4.00617 ## [37] -17.36497 -2.47730 -3.43177 17.40910 16.57365 11.74672 ## [43] 12.65220 9.61915 0.75347 9.97426 6.79245 2.96727 ## [49] 8.28033 2.67626 -4.89110 2.44175 -1.94487 -6.75119 ## [55] 0.94142 10.23755 -0.36335 -4.34356 -7.77271 -12.61623 ## [61] -10.61621 -1.07179 -15.10643 -3.00093 -3.48808 2.49776 ## [67] 2.17413 5.29032 5.22074 0.42904 1.09304 8.16177 ## [73] 23.12762 11.57787 14.56668 -6.40260 14.25883 18.35311 ## [79] 21.28119 10.23755 29.79071 18.92289 30.22337 24.78416 ## [85] 21.32794 10.64086 14.56668 14.00743 5.77118 -21.94591 ## [91] -12.00207 -3.36734 -20.46605 -14.48535 -2.70657 11.27846 ## [97] 7.83042 -2.94581 -2.94532 6.52375 13.48723 -12.03631 ## [103] -2.14036 9.00075 19.18095 21.90577 5.52591 10.75902 ## [109] 5.58083 -0.60328 -6.83688 9.24509 8.08600 18.35311 ## [115] -5.22214 -0.27532 7.45437 12.36056 -13.47213 -5.86338 ## [121] 9.63009 10.17235 -0.02665 7.88442 7.31439 13.72864 ## [127] -5.83850 12.09277 9.12960 -7.92302 -3.33984 -4.82448 ## [133] -8.36662 -13.72144 -7.80977 -26.81262 -13.51894 -28.55230 ## [139] -10.15300 -7.69617 -16.55719 -15.67850 -12.83886 -14.69403 ## [145] -9.26645 -21.68604 -14.13354 -7.08293 2.68476 -2.78736 ## [151] 14.50127 12.71117 15.83050 10.57083 13.75840 16.47296 ## [157] 10.97984 13.45189 11.64801 10.60576 2.68476 13.06800 ## [163] 20.04848 21.98723 16.94731 14.74686 8.69225 -7.42173 ## [169] 9.18017 6.89281 12.62090 4.48275 12.71562 7.12224 ## [175] 0.94142 13.94798 6.89596 4.60507 6.15918 -3.78558 ## [181] -7.91670 -6.22535 1.43268 0.40074 -3.48808 -4.10864 ## [187] -5.83850 -10.78819 -13.20674 -23.43398 6.15918 5.56452 ## [193] -7.46678 -1.54029 3.86490 1.08108 8.08600 -10.71503 ## [199] -20.01470 -25.01148 -14.32356 1.62220 1.72631 8.16177 ## [205] 5.13091 -8.56788 -23.37599 12.64676 14.25883 3.15840 ## [211] -10.76935 2.19830 -8.73947 4.00850 3.62611 -17.81072 ## [217] -12.41588 -6.69386 7.75646 -11.16493 1.91316 7.78288 ## [223] -3.37308 -5.22255 -5.94743 3.41192 -1.44003 -14.69403 ## [229] -16.01522 0.80224 4.83779 -0.27959 -1.94487 -11.37567 ## [235] -4.60579 -8.93297 -3.71380 -18.06480 -15.37193 4.00617 ## [241] -2.06777 0.33373 2.89201 2.44175 8.08600 3.15840 ## [247] 7.10499 13.94798 12.48005 13.55371 9.32552 12.83681 ## [253] 10.97984 9.70385 8.72935 7.20409 -3.48808 3.81904 ## [259] 3.40685 4.05351 4.66233 5.79823 -5.23964 4.00617 ## [265] -4.31736 -2.00880 -10.24199 -18.64861 -8.88931 -12.69695 ## [271] -15.08385 -7.69617 -9.85629 -13.29220 -13.47213 -4.30503 ## [277] -25.01357 -2.00880 -25.32177 -0.27959 -0.40166 18.35311 ## [283] 1.55778 1.57990 9.12960 3.41192 20.72435 20.62856 ## [289] 18.17852 10.64086 12.62090 7.20409 4.22797 5.80096 ## [295] -3.37308 -2.74893 7.45437 4.00850 1.09304 -1.70778 ## [301] 14.12926 -4.35133 -9.26910 4.48275 0.36995 -0.80544 ## [307] -6.45486 -7.07776 -9.29788 1.02562 -5.23964 -6.90228 ## [313] 1.08165 -10.91041 10.67512 5.84342 11.17242 15.04992 ## [319] 7.72134 5.90872 -0.36335 9.37768 -5.87291 1.40892 ## [325] 7.83042 7.36132 3.86490 9.92542 15.80204 15.04992 ## [331] 16.96669 9.00075 10.80482 6.99138 9.32552 -5.34393 ## [337] 17.72860 9.70385 7.27001 7.88442 -7.34610 1.83712 ## [343] 4.02320 -11.40659 -1.48016 3.41192 -7.13945 0.37002 ## [349] 3.78116 5.95580 -0.51310 8.56475 3.45637 -6.75119 ## [355] -7.68757 -8.93297 -4.83062 -6.13329 -5.87291 -9.49953 ## [361] 4.23107 8.29833 -0.99954 -0.95992 -31.26585 -19.30335 ## [367] -13.85114 -14.49861 -13.20674 -12.69562 -5.87291 -11.57733 ## [373] -11.96596 -7.63087 -15.10643 -14.56660 -17.37696 -0.80544 ## [379] -1.52401 2.19830 0.19506 2.21877 -8.40598 8.68122 ## [385] 5.58083 5.01879 16.99890 8.56475 -1.17326 -1.46608 ## [391] 5.87227 8.38234 8.57119 4.60507 2.35957 2.44782 ## [397] 10.97984 7.36132 11.64801 2.44175 -5.80289 -7.41183 ## [403] -5.83850 -8.93297 -2.03857 6.39480 14.39167 -4.82448 ## [409] -7.46678 -7.16236 -23.37599 -15.24694 -6.57450 20.33503 ## [415] 9.57041 7.76394 -15.44038 -17.46823 -6.50618 -6.90228 ## [421] -1.61785 2.20775 8.72935 3.80242 0.36995 15.71056 ## [427] 13.26855 10.23755 -14.88197 -21.64426 -9.67252 -3.26613 ## [433] 15.02910 -2.94581 10.18868 5.84342 8.85760 2.49776 ## [439] 0.32506 -7.69617 -16.55719 -7.32644 -4.60637 -4.30503 ## [445] -9.71637 -11.37892 15.05312 15.36809 9.62921 -0.14480 ## [451] 4.63956 0.96149 7.45437 5.20165 1.09304 14.39516 ## [457] 11.87967 14.85741 8.24290 25.57311 9.62921 -6.75119 ## [463] -9.53664 0.96149 0.75347 0.42904 -16.63847 0.37002 ## [469] -9.26645 -17.93800 -26.78110 -34.29629 -35.12388 -24.58847 ## [475] -19.39835 -7.69617 -6.50584 -6.13329 -17.90500 -15.73292 ## [481] -4.76728 -8.56788 -3.91821 -7.08293 -7.34610 -2.78736 ## [487] 5.25592 15.18479 16.38891 8.78110 19.45781 1.92837 ## [493] 4.68099 -2.00880 -0.99954 -5.72226 -4.25968 3.81904 ## [499] -0.29129 -7.69617 -0.36335 -3.15041 -3.97311 4.52562 ## [505] -8.36662 7.82982 -3.43177 8.56475 1.91316 -3.44800 ## [511] 12.03584 4.05351 16.94731 7.58795 2.35957 -6.38283 ## [517] -2.51769 -2.94581 -0.51310 -11.16493 2.68476 17.69247 ## [523] 10.18677 9.61915 0.19506 3.41192 6.79245 3.48672 ## [529] 4.23107 14.85741 11.16157 3.80242 1.91316 18.35311 ## [535] 19.43212 10.23755 13.03846 9.37768 18.19127 14.91461 ## [541] 1.53157 1.73925 2.89201 1.08108 -6.57450 -10.71503 ## [547] -12.61842 -10.78819 -8.18107 -12.09905 -8.40598 -3.78558 ## [553] -10.61621 -17.46949 -8.29621 -18.64861 -11.20413 -4.76928 ## [559] -1.52401 -7.69617 -4.27221 -6.72986 1.72631 -10.53843 ## [565] -3.86744 -5.28834 0.45979 -5.72226 -6.57450 -4.10864 ## [571] -2.14036 -7.69617 -9.85629 -9.11617 -11.57232 -8.98008 ## [577] -6.11703 1.27074 8.24290 -14.56660 -8.88931 -16.00015 ## [583] -11.38571 -13.26180 -13.76515 -7.92302 -5.23964 0.37002 ## [589] -25.01357 -5.28834 -19.48443 -12.52560 10.40081 19.67439 ## [595] 22.51390 13.32957 12.48005 9.37768 -14.73866 8.16177 ## [601] 3.78116 -3.41432 -6.35043 -0.27959 6.54279 7.78288 ## [607] 23.74662 15.18479 3.54551 6.39480 9.32552 13.35626 ## [613] 3.78116 10.17235 16.02601 18.08943 21.97488 17.69247 ## [619] 24.97933 33.11851 27.55708 15.34344 20.09108 21.14801 ## [625] 12.32959 15.79442 -2.45888 14.68776 11.17242 -3.44800 ## [631] -14.46749 -3.98574 -7.62266 -4.94014 -0.17350 -9.49953 ## [637] -15.56530 -11.84742 -11.70132 -1.64026 -7.34610 -8.73311 ## [643] -8.92028 -7.69617 -4.83062 -10.30932 -11.57232 -11.05788 ## [649] 0.63173 -2.94581 -4.40465 -0.27959 -1.94487 5.14032 ## [655] 8.33770 4.67192 5.22074 -4.34356 -0.17350 7.64232 ## [661] 10.08000 5.48729 10.18868 -7.76326 4.22797 27.60207 ## [667] 2.79049 -5.84095 3.54551 9.97426 6.79245 6.08397 ## [673] 6.03075 8.29833 4.83779 -3.68126 -5.03129 3.81904 ## [679] 0.94142 -0.89372 4.10392 5.20165 8.05899 9.72012 ## [685] 4.23107 3.14477 7.75646 7.20409 2.68476 -0.14480 ## [691] -11.38571 -17.59063 -13.76515 -5.53671 -12.83886 0.88947 ## [697] -5.66711 0.33373 2.89201 -11.84527 11.94402 5.80096 ## [703] 6.48863 10.85596 6.33755 7.58795 11.22533 3.48672 ## [709] 4.23107 13.92040 12.62090 11.28609 -0.40166 -3.44800 ## [715] -13.23478 -9.55138 -5.94743 -13.29220 -10.30579 -10.01898 ## [721] -6.56695 -15.12696 -11.70132 -5.72226 -1.17326 -5.42992 ## [727] -1.52401 -7.07776 1.31188 4.00850 0.45977 4.00617 ## [733] 3.78116 -0.13478 16.02601 7.20409 7.31439 7.78288 ## [739] 1.55778 -5.84095 -0.36335 -4.34356 -8.40598 6.08397 ## [745] -9.71637 9.70385 11.64801 10.60576 -1.94487 -6.09056 ## [751] 0.32506 9.61915 8.01278 9.97426 1.09304 10.75902 ## [757] 13.67935 17.66845 4.83779 3.80242 -11.20413 1.17648 ## [763] 2.17413 0.34309 -0.92176 7.58795 10.59206 4.52562 ## [769] 10.52992 5.95580 0.94623 -1.64026 1.91316 -4.76928 ## [775] -4.60579 -2.13053 -7.06425 4.60507 1.09304 -3.78558 ## [781] 6.03075 -14.65846 4.35135 7.88442 14.25883 13.72864 ## [787] 8.33770 -3.36734 2.98710 -7.92302 -3.33984 6.60342 ## [793] 1.53157 -2.94581 -5.37754 -18.64861 2.68476 -0.80544 ## [799] -15.08385 -6.45936 -9.85629 -4.34356 -4.60637 1.40892 ## [805] -11.51604 3.61327 -1.48599 3.12208 6.54279 -3.44800 ## [811] 2.17413 -0.89372 2.42869 3.41192 6.15918 6.60342 ## [817] 4.68099 0.33373 0.45979 5.16308 -5.80289 9.76480 ## [823] 2.17413 -21.91946 -6.50584 3.41192 11.22533 -3.78558 ## [829] 5.13091 -4.81983 11.16157 3.12208 -4.25968 -1.46608 ## [835] 0.32506 2.19830 5.22074 7.58795 2.35957 -5.86338 ## [841] -2.51769 -0.60328 0.94623 5.16308 5.77118 -2.12672 ## [847] 3.40685 -7.69617 1.31188 0.42904 -13.47213 -1.18833 ## [853] 7.38050 -5.75685 5.81068 1.08108 -0.40166 2.49776 ## [859] -5.22214 2.81670 0.19506 -3.15041 -3.97311 4.52562 ## [865] 5.58083 3.61327 -2.94532 3.80242 13.48723 15.04992 ## [871] 17.58305 11.47436 7.45437 12.95713 1.09304 12.31736 ## [877] 14.57918 6.42431 9.70223 7.88442 -0.40166 -0.80544 ## [883] 7.72134 -0.89372 -9.85629 -15.08192 -9.03925 -11.05788 ## [889] -0.71802 -5.28834 -9.26910 -7.08293 -13.51894 -16.66079 ## [895] -15.70021 -16.97223 -18.23242 -17.46823 -7.77271 -21.96632 ## [901] -10.16629 -16.06397 -9.75554 -9.12393 -5.80289 -12.03631 ## [907] -20.01470 -18.20904 -8.18107 -19.85453 -9.67252 -10.53843 ## [913] -13.31571 -4.35133 -5.37754 -4.36159 5.77118 6.46160 ## [919] -1.52401 4.05351 7.45437 8.18453 -4.60637 12.31736 ## [925] 7.83042 9.70385 3.37846 11.28609 3.45637 -6.75119 ## [931] 2.17413 7.76394 4.66233 8.78110 2.99284 -10.53843 ## [937] -8.81654 3.14477 4.83779 -4.36159 -0.40166 -3.44800 ## [943] -8.92028 3.43511 1.87029 -8.51959 -7.13945 2.44782 ## [949] 3.78116 5.48729 11.64801 -5.72226 0.36995 6.46160 ## [955] 2.79049 11.47436 7.45437 2.21877 -3.97311 5.04507 ## [961] -3.41753 3.61327 -1.97243 -7.76326 -10.43252 -8.07247 ## [967] -10.15300 -3.98574 -15.44038 -12.09905 -2.07330 -7.42173 ## [973] -5.66711 -4.81983 10.18868 -3.68126 -12.74733 1.83712 ## [979] 8.33770 7.14553 10.24641 -1.95726 8.05899 3.48672 ## [985] -3.86744 -3.41432 -3.91821 2.44175 4.22797 -4.10864 ## [991] 0.32506 -3.98574 -7.62266 5.79823 3.62611 -7.42173 ## [997] -7.91670 1.27074 4.83779 1.08108 -5.03129 -10.05439 ## [1003] -2.14036 -3.98574 1.31188 5.20165 -6.50618 6.08397 ## [1009] 3.78116 16.26293 15.53957 13.32709 8.08600 21.65631 ## [1015] 19.43212 12.09277 5.77915 16.53659 11.22533 21.66746 ## [1021] 11.42976 5.48729 -4.89110 -6.40260 -11.97573 -1.46608 ## [1027] -13.23478 -5.22255 -11.53152 -12.09905 -9.03925 -8.98008 ## [1033] -8.81654 -7.63087 0.94623 -5.72226 8.08600 5.80096 ## [1039] 4.02320 -2.13053 -2.59698 2.81535 -0.17350 -13.65513 ## [1045] 1.08165 -6.69386 -5.37754 -0.27959 -26.63622 -2.12672 ## [1051] -1.52401 -16.35382 -13.20674 -0.76411 -3.33984 -6.38283 ## [1057] 4.23107 -4.81983 -1.48599 5.16308 4.22797 -2.12672 ## [1063] 2.79049 9.61915 1.31188 1.62220 1.72631 11.27846 ## [1069] -5.66711 13.92040 1.91912 -4.36159 11.94402 12.40736 ## [1075] 16.35034 13.94798 13.03846 15.34344 14.39167 7.64232 ## [1081] 9.63009 11.57787 8.24290 8.56475 17.34525 9.76480 ## [1087] 10.80313 10.85596 0.19506 18.32632 1.72631 8.16177 ## [1093] 3.78116 -2.94581 -1.97243 0.40074 -11.97573 -1.46608 ## [1099] 0.94142 -8.31457 -9.85629 -0.76411 -11.57232 -4.82448 ## [1105] -16.91505 -6.69386 -1.48599 1.76141 -9.66092 -0.14480 ## [1111] 2.79049 7.76394 -3.71380 -0.76411 -5.23964 -7.94118 ## [1117] -8.81654 -14.18995 7.75646 2.44175 4.22797 -4.76928 ## [1123] -4.60579 -3.98574 -0.36335 4.00850 10.59206 6.60342 ## [1129] -0.26810 -1.54029 5.32423 7.20409 4.99958 -2.12672 ## [1135] 4.02320 6.52713 6.33755 -0.16753 6.15918 5.56452 ## [1141] -3.41753 5.48729 -19.97088 7.20409 1.91316 -2.12672 ## [1147] 0.94142 -0.89372 0.19506 -4.94014 6.15918 12.31736 ## [1153] 15.02910 -4.81983 -2.45888 -0.27959 12.71562 5.14032 ## [1159] -0.90765 4.67192 4.10392 9.37768 3.62611 -0.14943 ## [1165] 7.38050 2.67626 6.29712 7.20409 2.68476 -9.39375 ## [1171] -2.75672 -2.74893 -6.50584 -14.48535 -9.67252 -13.65513 ## [1177] -4.31736 -2.00880 6.29712 11.28609 0.36995 5.80096 ## [1183] 4.63956 14.56638 12.48005 12.95713 1.72631 -4.82448 ## [1189] -4.76728 1.27074 2.89201 -9.12393 -0.40166 -10.05439 ## [1195] -21.86377 -10.16978 -13.76515 -10.90589 -17.27173 -0.14943 ## [1201] -12.41588 -4.35133 -12.18777 -5.04193 -8.11771 0.51584 ## [1207] -0.90765 4.67192 -2.59698 -2.55383 -0.17350 -5.34393 ## [1213] 12.77951 12.04638 7.27001 -3.00093 -2.71647 4.47968 ## [1219] 0.32506 5.90872 4.66233 -0.76411 -5.23964 -7.42173 ## [1225] 2.88132 8.76683 -2.94532 -2.32059 15.03044 9.76480 ## [1231] 5.87227 0.34309 -3.15539 -1.95726 -3.97311 -0.14943 ## [1237] -13.76563 -7.16236 -0.02665 -7.08293 -5.80289 -0.80544 ## [1243] -7.07121 -3.98574 -10.41470 -11.50247 -0.80677 1.92837 ## [1249] -10.61621 -11.37892 1.43268 -3.68126 2.68476 8.44352 ## [1255] -5.83850 4.05351 11.36323 9.97426 18.19127 16.47296 ## [1261] 1.53157 14.85741 16.99890 18.76976 9.62921 1.83712 ## [1267] 0.94142 14.56638 14.15527 17.13316 6.15918 0.37002 ## [1273] 2.43140 6.89281 1.91912 -4.36159 -25.09301 -10.05439 ## [1279] -18.16563 -8.31457 -14.32356 -10.90589 -3.97311 -14.17458 ## [1285] -3.86744 -14.18995 2.40557 -1.64026 3.45637 9.76480 ## [1291] 5.25592 12.09277 11.92164 6.99138 30.22337 15.43406 ## [1297] 18.62844 15.32592 17.97179 9.24509 11.17242 1.83712 ## [1303] 11.41948 6.52713 10.80482 8.78110 -1.44003 -0.14943 ## [1309] -5.66711 4.55028 10.18868 11.96642 6.54279 12.40736 ## [1315] 20.04848 20.13202 20.85617 17.13316 13.75840 18.03131 ## [1321] 10.08000 12.04638 11.64801 1.08108 2.68476 -0.14480 ## [1327] -11.38571 -11.40659 -12.08993 3.41192 8.05899 -5.34393 ## [1333] -4.76728 8.29833 -8.78265 -7.76326 -9.66092 -14.67887 ## [1339] -14.46749 -11.40659 -8.73947 -12.69562 -14.73866 -12.09678 ## [1345] -3.86744 -25.43409 -5.37754 -6.40260 16.57365 4.47968 ## [1351] 4.63956 2.19830 0.19506 -5.53671 -0.17350 -2.74668 ## [1357] -4.76728 5.95580 -2.94532 -4.36159 4.22797 4.47968 ## [1363] 13.26855 -4.60415 0.75347 -2.55383 -5.23964 -8.98008 ## [1369] 1.98149 0.33373 -7.80977 -10.48460 -2.71647 -3.44800 ## [1375] -2.14036 1.57990 -5.38902 -1.36068 -3.97311 -2.74668 ## [1381] 1.53157 -3.88282 -15.10643 -4.36159 8.85760 11.74672 ## [1387] 7.72134 5.29032 2.98710 -5.53671 1.72631 2.96727 ## [1393] 8.73025 -0.13478 1.43268 -1.64026 -7.34610 -16.00015 ## [1399] -18.78199 -22.53787 -18.23242 -19.85453 -30.57036 -23.52467 ## [1405] -30.41258 -33.86719 -25.32177 -13.88627 5.77118 -2.78736 ## [1411] -8.30393 -0.89372 8.57119 3.41192 -1.44003 -1.70778 ## [1417] 0.18182 5.01879 -5.86399 1.76141 0.36995 -7.41183 ## [1423] 0.32506 2.19830 0.75347 -4.94014 2.99284 -3.26613 ## [1429] -3.86744 8.29833 2.89201 11.96642 3.45637 -8.07247 ## [1435] -2.75672 8.38234 -0.36335 -5.53671 -2.07330 0.37002 ## [1441] 6.48066 -11.37892 0.45979 1.08108 -5.80289 8.44352 ## [1447] 1.55778 -7.07776 -5.38902 5.79823 -14.10539 -15.73292 ## [1453] -7.01687 -13.25294 -15.10643 -20.00928 -20.46338 -16.66079 ## [1459] -16.93292 -13.26180 -10.97311 -5.53671 -0.17350 -6.38283 ## [1465] -2.06777 -5.75685 1.91912 -0.95992 10.40081 -2.12672 ## [1471] 10.18677 14.56638 18.06413 14.15029 19.45781 9.72012 ## [1477] 11.42976 8.29833 5.81068 17.40910 15.03044 5.80096 ## [1483] 8.33770 -5.84095 4.66233 6.99138 -2.70657 -6.90228 ## [1489] -2.06777 -17.93800 -7.80977 -0.27959 14.25883 0.51584 ## [1495] 4.63956 -4.60415 -7.62266 1.62220 -5.87291 -4.30503 ## [1501] 3.78116 -0.13478 -9.75554 -10.48460 -17.37696 -4.76928 ## [1507] -1.52401 -7.07776 -15.99878 -12.69562 -7.77271 -18.84962 ## [1513] -25.01357 -9.97340 -21.91666 -15.24694 1.14155 -10.71503 ## [1519] -8.30393 1.57990 0.19506 -16.87165 -7.77271 -7.42173 ## [1525] -8.81654 -8.56788 -7.80977 -17.28794 -7.34610 -13.35759 ## [1531] -10.76935 -13.26180 -7.62266 -13.29220 -0.80677 -0.14943 ## [1537] -2.51769 -0.13478 -9.75554 -18.64861 -11.97573 -8.73311 ## [1543] -16.93292 -16.35382 -16.55719 -13.88877 -6.50618 -13.65513 ## [1549] -4.76728 -3.41432 2.89201 -11.16493 -8.11771 -1.46608 ## [1555] 3.40685 0.96149 2.42869 -1.36068 0.45977 -6.90228 ## [1561] 6.93058 -0.13478 6.29712 7.20409 1.91316 11.08608 ## [1567] 5.87227 5.90872 5.77915 5.20165 -0.80677 6.60342 ## [1573] 2.88132 12.51488 -7.80977 -9.80427 -21.23499 -19.96399 ## [1579] -10.15300 -18.82744 -14.88197 -17.46823 -14.10539 -11.05788 ## [1585] -23.66382 -19.81202 -25.80821 -20.68961 1.14155 8.44352 ## [1591] 12.03584 10.85596 10.24641 10.57083 11.85859 11.27846 ## [1597] 13.67935 5.95580 7.75646 14.68776 1.14155 -0.14480 ## [1603] 4.63956 1.57990 -0.92176 9.37768 11.85859 11.79791 ## [1609] 4.23107 12.04638 8.24290 12.64676 2.68476 -5.42992 ## [1615] -3.98943 5.29032 8.57119 10.57083 21.35761 5.56452 ## [1621] 6.93058 12.04638 4.35135 -0.27959 -9.66092 0.51584 ## [1627] -3.98943 -7.69617 1.31188 -1.95726 8.05899 -10.01898 ## [1633] 2.43140 6.89281 -5.86399 -3.68126 -14.29054 -6.09056 ## [1639] -7.07121 -13.88021 -6.50584 -5.53671 -5.23964 -12.61623 ## [1645] -2.96761 -8.09937 -6.35043 -4.36159 -6.57450 -10.71503 ## [1651] 2.17413 -1.51213 -1.48016 -1.36068 -3.33984 8.68122 ## [1657] -12.41588 8.29833 -0.02665 -13.88627 11.94402 -13.35759 ## [1663] -7.07121 -5.84095 -3.71380 -1.95726 -7.77271 -9.49953 ## [1669] 1.53157 -29.65064 -0.99954 -10.48460 -13.51894 1.83712 ## [1675] 0.32506 -6.45936 3.54551 11.76398 -2.07330 -2.22723 ## [1681] 12.32959 -1.07179 12.13446 10.60576 -9.66092 -6.09056 ## [1687] -8.30393 -14.49861 -6.50584 -15.08192 1.09304 -4.82448 ## [1693] -7.91670 -2.94581 -1.97243 -4.36159 -4.25968 3.15840 ## [1699] -5.22214 2.19830 1.31188 6.39480 9.95879 13.87571 ## [1705] 13.22943 20.47948 9.21579 5.84342 -3.48808 3.81904 ## [1711] 1.55778 9.00075 12.48005 12.95713 15.65820 11.79791 ## [1717] 7.83042 13.92040 -0.02665 7.20409 -4.25968 -2.12672 ## [1723] 0.94142 -4.60415 2.98710 -14.48535 -7.13945 -8.98008 ## [1729] -10.61621 -15.12696 -9.75554 12.64676 10.40081 1.17648 ## [1735] 18.81576 18.27681 22.53140 17.72974 15.02493 25.82306 ## [1741] 17.72860 21.41649 18.94468 20.81077 2.68476 -0.14480 ## [1747] 9.57041 2.19830 10.24641 6.99138 12.49186 21.66746
# fast plot to check data plot(Index, type = "h", xlim = c(0, 1752), ylim = c(-35, 35))
Custom Plotting
The first full plot of our data has confirmed a viable index has been created but this graphic is not likely to be used in this form in a presentation or paper.
Some additional steps one may take to customize the graph include:
i) subsetting into a smaller time-frame i.e. the last 20 years
ii) adding colour, labels, text and lines
iii) smoothing and equation-fitting
Creating masks using logical vectors
The firts step it to subset the data and also allocate a mask to positive and negative value to be used in our colour plotting scheme.
Nb. When subsetting its always a good idea to replicate the original object/data.
# replicate data Index2 <- Index # locigal expression to show pos/neg split Index2 > 0
## [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE ## [12] FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE ## [23] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE ## [34] FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE ## [45] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE TRUE ## [56] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE ## [67] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE ## [78] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [89] TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE ## [100] TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE ## [111] FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE ## [122] TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE ## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [144] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE ## [155] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [166] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [177] TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE ## [188] FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE ## [199] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE ## [210] TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE ## [221] TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE ## [232] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE ## [243] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [254] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE ## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [276] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE ## [287] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE ## [298] TRUE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE ## [309] FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE ## [320] TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [331] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE ## [342] TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE ## [353] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE ## [364] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [375] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE ## [386] TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE ## [397] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE ## [408] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE ## [419] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE ## [430] FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE ## [441] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE ## [452] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE ## [463] FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE ## [474] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [485] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE ## [496] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE ## [507] FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE ## [518] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [529] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [540] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE ## [551] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [562] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE ## [573] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE ## [584] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE ## [595] TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE ## [606] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [617] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE ## [628] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [639] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE ## [650] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE ## [661] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE ## [672] TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE TRUE TRUE ## [683] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE ## [694] FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE ## [705] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE ## [716] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [727] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE ## [738] TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE ## [749] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [760] TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE ## [771] TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE ## [782] FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE TRUE ## [793] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE ## [804] TRUE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE ## [815] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE ## [826] TRUE TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE ## [837] TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE ## [848] FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE ## [859] FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE ## [870] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [881] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [892] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [903] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [914] FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE ## [925] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE ## [936] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE ## [947] FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE ## [958] TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE ## [969] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE ## [980] TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE ## [991] TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE ## [1002] FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE ## [1013] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE ## [1024] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1035] TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE TRUE ## [1046] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1057] TRUE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE ## [1068] TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE ## [1079] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1090] TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE TRUE FALSE ## [1101] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE ## [1112] TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE ## [1123] FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE ## [1134] FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE ## [1145] TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE ## [1156] FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE ## [1167] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1178] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE ## [1189] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1200] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE ## [1211] FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE ## [1222] FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE ## [1233] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1244] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE ## [1255] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1266] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE ## [1277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE ## [1288] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1299] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE ## [1310] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1321] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE ## [1332] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1343] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE ## [1354] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE ## [1365] TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE ## [1376] TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE ## [1387] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE ## [1398] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1409] TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE ## [1420] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE TRUE ## [1431] TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE ## [1442] FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE ## [1453] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1464] FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE ## [1475] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE ## [1486] TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE ## [1497] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE ## [1508] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE ## [1519] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1530] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE ## [1552] FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE ## [1563] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE ## [1574] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1585] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1596] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE ## [1607] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE ## [1618] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE ## [1629] TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE ## [1640] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1651] TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE ## [1662] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE ## [1673] FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE ## [1684] TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE ## [1695] FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE ## [1706] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1717] TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE ## [1728] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1739] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE ## [1750] TRUE TRUE TRUE
# assign all values in Index2 that are less than zero to the value 0 Index2[Index2 < 0] <- 0 # check assignment head(Index2)
## [1] 0.000 0.000 0.000 0.000 1.913 0.000
What have we achieved?
In technical terms we have masked all negative values in our data set by changing their original values to an arbitrary one - in this case the value 0.
We can see how this changes *the presentation of our data below. Plotting our entire index yields the following:
plot(Index2, type = "h", col = "blue")
Notice 'the gaps' in betweent the 'high-rises' - this where the negative indice values used to to be; now they have assigned to the value zero.
We will repeat this process for the positive values and then we can plot these data as the same time… assigning a different colour to each range of data.
# replicate original data Index3 <- Index # logical expression to show neg/pos split Index < 0
## [1] TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE ## [12] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE ## [23] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE ## [34] TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE ## [45] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE ## [56] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE ## [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE ## [78] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [89] FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE ## [100] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE ## [111] TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE ## [122] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE ## [133] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [144] TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE ## [155] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [166] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [177] FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE ## [188] TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE ## [199] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE ## [210] FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE ## [221] FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE ## [232] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE ## [243] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [254] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE ## [265] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [276] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE ## [287] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE ## [298] FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE ## [309] TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE ## [320] FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [331] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE ## [342] FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE ## [353] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE ## [364] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [375] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE ## [386] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE ## [397] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE ## [408] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE ## [419] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE ## [430] TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE ## [441] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE ## [452] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE ## [463] TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE ## [474] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [485] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE ## [496] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE ## [507] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE ## [518] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [540] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE ## [551] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [562] TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE ## [573] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE ## [584] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE ## [595] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE ## [606] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE ## [628] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [639] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE ## [650] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE ## [661] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE ## [672] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE ## [683] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE ## [694] TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE ## [705] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE ## [716] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [727] TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE ## [738] FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE ## [749] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [760] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE ## [771] FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE ## [782] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE ## [793] FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE ## [804] FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE ## [815] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE ## [826] FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE ## [837] FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE ## [848] TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE ## [859] TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE ## [870] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [881] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [892] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [903] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [914] TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE ## [925] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE ## [936] TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE ## [947] TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE ## [958] FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE ## [969] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE ## [980] FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE ## [991] FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE ## [1002] TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE ## [1013] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE ## [1024] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1035] FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE ## [1046] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1057] FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE ## [1068] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE ## [1079] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1090] FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE TRUE ## [1101] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE ## [1112] FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE ## [1123] TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE ## [1134] TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE ## [1145] FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE TRUE ## [1156] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE ## [1167] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1178] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE ## [1189] TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1200] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE ## [1211] TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE ## [1222] TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE ## [1233] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1244] TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE ## [1255] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1266] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE ## [1277] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE ## [1288] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1299] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE ## [1310] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1321] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE ## [1332] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1343] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE ## [1354] TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE ## [1365] FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE ## [1376] FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE ## [1387] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE ## [1398] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1409] FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE ## [1420] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE ## [1431] FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE ## [1442] TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE TRUE ## [1453] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1464] TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE ## [1475] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE ## [1486] FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE ## [1497] TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE ## [1508] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE ## [1519] TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1530] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1541] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE ## [1552] TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE ## [1563] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE ## [1574] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1585] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1596] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE ## [1607] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE ## [1618] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE ## [1629] FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE ## [1640] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [1651] FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE FALSE ## [1662] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE ## [1673] TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE ## [1684] FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE ## [1695] TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE ## [1706] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1717] FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE ## [1728] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [1739] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE ## [1750] FALSE FALSE FALSE
# assign all values in Index2 that are greater than zero to the value 0 Index3[Index3 > 0] <- 0 # check assignment head(Index3)
## [1] -5.6671 -0.6033 -3.9182 -4.3616 0.0000 -4.7693
plot(Index3, type = "h", col = "red")
Choosing a time-frame
Now that we have two sets of masked values for our data. We can suitably subset them for a shorter time period.
Let's look at data for the last 12 years (2000-2011).
# data of pos values Short_Pos <- Index2[1609:1752] Short_Pos
## [1] 4.2311 12.0464 8.2429 12.6468 2.6848 0.0000 0.0000 5.2903 ## [9] 8.5712 10.5708 21.3576 5.5645 6.9306 12.0464 4.3513 0.0000 ## [17] 0.0000 0.5158 0.0000 0.0000 1.3119 0.0000 8.0590 0.0000 ## [25] 2.4314 6.8928 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 ## [33] 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 ## [41] 0.0000 0.0000 2.1741 0.0000 0.0000 0.0000 0.0000 8.6812 ## [49] 0.0000 8.2983 0.0000 0.0000 11.9440 0.0000 0.0000 0.0000 ## [57] 0.0000 0.0000 0.0000 0.0000 1.5316 0.0000 0.0000 0.0000 ## [65] 0.0000 1.8371 0.3251 0.0000 3.5455 11.7640 0.0000 0.0000 ## [73] 12.3296 0.0000 12.1345 10.6058 0.0000 0.0000 0.0000 0.0000 ## [81] 0.0000 0.0000 1.0930 0.0000 0.0000 0.0000 0.0000 0.0000 ## [89] 0.0000 3.1584 0.0000 2.1983 1.3119 6.3948 9.9588 13.8757 ## [97] 13.2294 20.4795 9.2158 5.8434 0.0000 3.8190 1.5578 9.0007 ## [105] 12.4800 12.9571 15.6582 11.7979 7.8304 13.9204 0.0000 7.2041 ## [113] 0.0000 0.0000 0.9414 0.0000 2.9871 0.0000 0.0000 0.0000 ## [121] 0.0000 0.0000 0.0000 12.6468 10.4008 1.1765 18.8158 18.2768 ## [129] 22.5314 17.7297 15.0249 25.8231 17.7286 21.4165 18.9447 20.8108 ## [137] 2.6848 0.0000 9.5704 2.1983 10.2464 6.9914 12.4919 21.6675
# data of neg values Short_Neg <- Index3[1609:1752] Short_Neg
## [1] 0.00000 0.00000 0.00000 0.00000 0.00000 -5.42992 -3.98943 ## [8] 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 ## [15] 0.00000 -0.27959 -9.66092 0.00000 -3.98943 -7.69617 0.00000 ## [22] -1.95726 0.00000 -10.01898 0.00000 0.00000 -5.86399 -3.68126 ## [29] -14.29054 -6.09056 -7.07121 -13.88021 -6.50584 -5.53671 -5.23964 ## [36] -12.61623 -2.96761 -8.09937 -6.35043 -4.36159 -6.57450 -10.71503 ## [43] 0.00000 -1.51213 -1.48016 -1.36068 -3.33984 0.00000 -12.41588 ## [50] 0.00000 -0.02665 -13.88627 0.00000 -13.35759 -7.07121 -5.84095 ## [57] -3.71380 -1.95726 -7.77271 -9.49953 0.00000 -29.65064 -0.99954 ## [64] -10.48460 -13.51894 0.00000 0.00000 -6.45936 0.00000 0.00000 ## [71] -2.07330 -2.22723 0.00000 -1.07179 0.00000 0.00000 -9.66092 ## [78] -6.09056 -8.30393 -14.49861 -6.50584 -15.08192 0.00000 -4.82448 ## [85] -7.91670 -2.94581 -1.97243 -4.36159 -4.25968 0.00000 -5.22214 ## [92] 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 ## [99] 0.00000 0.00000 -3.48808 0.00000 0.00000 0.00000 0.00000 ## [106] 0.00000 0.00000 0.00000 0.00000 0.00000 -0.02665 0.00000 ## [113] -4.25968 -2.12672 0.00000 -4.60415 0.00000 -14.48535 -7.13945 ## [120] -8.98008 -10.61621 -15.12696 -9.75554 0.00000 0.00000 0.00000 ## [127] 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 ## [134] 0.00000 0.00000 0.00000 0.00000 -0.14480 0.00000 0.00000 ## [141] 0.00000 0.00000 0.00000 0.00000
Overplotting two sets of data using par
Now we can plot our two sets of data using an overplotting technique:
# set axis limits and type of plot plot(Short_Pos, ylim = c(-30, 30), type = "h", lwd = 2.9, col = "blue") # set overplotting to 'on' par(new = T) plot(Short_Neg, ylim = c(-30, 30), type = "h", lwd = 2.9, col = "red")
Cleaning up the plot
Without any alteration our overplotting produces two sets of labels on the y-axis.
Lets clean up the plot, by removing default annotations and adding our own labels.
# set axis limits and type of plot turn off axes and labels for this plot plot(Short_Pos, ylim = c(-32, 32), type = "h", lwd = 2.9, col = "blue", ylab = "", axes = FALSE, ann = F) # set overplotting to 'on' par(new = T) # turn off defualt axes set new ylab set new xlab plot(Short_Neg, ylim = c(-32, 32), type = "h", lwd = 2.9, col = "red", font.lab = 2, axes = FALSE, ylab = "Southern Oscillation Index", xlab = "Years") # custom x axis at beginning of each year axis(1, at = c(0, 13, 25, 37, 49, 61, 73, 85, 97, 109, 121, 133, 145), label = c("2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012"), font = 2) # default y axis with bold horizontal labels axis(2, font = 2, las = 1) # keep overploting 'on' par(new = T) # add zero line across xaxis abline(h = 0, col = "black") # add horizontal dotted lines to plot abline(h = 15, col = "16", lty = 3) abline(h = -15, col = "16", lty = 3) # add some text to plot text(37, -32.5, "Source: http://www.cgd.ucar.edu/cas/catalog/climind/soiAnnual.html", cex = 0.75) # add box around plot box()
# turn overplotting 'off' par(new = F)