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< section id="introduction" class="level1">
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Introduction
Time series analysis is a powerful tool in the hands of a data scientist or analyst. It allows us to uncover patterns, trends, and insights hidden within temporal data. In this blog post, we’ll explore how to create a time series in R using the base R function ts().
The ts() Function
The ts() function in R is a fundamental tool for handling time series data. It takes four main arguments:
- data: A vector or matrix of time series values.
- start: The time of the first observation.
- end: The time of the last observation.
- frequency: The number of observations per unit of time.
Let’s dive into a practical example. Suppose we have a vector sales representing monthly sales data for a year. We can create a time series object using the ts() function as follows:
# Sample data sales <- c(120, 150, 200, 180, 250, 300, 280, 320, 400, 350, 300, 380) # Creating a time series sales_ts <- ts( data = sales, start = c(2022, 1), end = c(2022, 12), frequency = 12 ) # Display the time series print(sales_ts)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2022 120 150 200 180 250 300 280 320 400 350 300 380
plot(sales_ts)
This example creates a monthly time series for sales data throughout the year 2022.
< section id="try-it-out" class="level1">Try it out!
Creating time series in R is a hands-on task. I encourage you to pick a dataset relevant to your interests or work and follow the same steps. You can use various time frequencies (e.g., daily, weekly, monthly) depending on your data.
< section id="bonus-using-convert_to_ts-from-tidydensity" class="level1">Bonus: Using convert_to_ts() from TidyDensity
Now, let’s explore a bonus section discussing the convert_to_ts() function from TidyDensity. This function is designed to convert data in a data frame or tibble into a time series format. It works seamlessly with data generated from tidy_distribution functions.
convert_to_ts() Function Details
The convert_to_ts() function takes the following arguments:
- .data: A data frame or tibble to be converted into a time series format.
- .return_ts: A logical value indicating whether to return the time series data. Default is TRUE.
- .pivot_longer: A logical value indicating whether to pivot the data into long format. Default is FALSE.
How It Works
- The function checks if the input is a data frame or tibble; otherwise, it raises an error.
- It verifies if the data comes from a tidy_distribution function; otherwise, it raises an error.
- The data is then converted into a time series format, grouping it by “sim_number” and transforming the “y” column into a time series.
Example Usage
library(TidyDensity)
# Assuming you have a tidy data frame 'tidy_data'
tidy_time_series <- convert_to_ts(.data = tidy_normal(),
.return_ts = TRUE,
.pivot_longer = FALSE)
# Display the result
head(tidy_time_series)
y [1,] -2.1617445 [2,] 0.7630891 [3,] 0.1951564 [4,] 1.0558584 [5,] -1.5169866 [6,] -1.4532770
plot(tidy_time_series)
multiple_simulations_series <- convert_to_ts(.data = tidy_normal(.num_sims = 10),
.return_ts = TRUE,
.pivot_longer = TRUE)
head(multiple_simulations_series)
1 2 3 4 5 6
[1,] 0.6591429 1.0850380 -1.41562870 -0.59330831 -0.2680326 0.6516654
[2,] -1.7456947 -0.5792555 0.95670979 0.35232047 1.3702818 -1.0709930
[3,] 0.2665711 -2.1701118 2.18141262 0.25480605 1.5762242 -0.8022482
[4,] 0.3128563 0.4328502 0.55082256 0.06628991 0.7984409 0.3048087
[5,] 0.6763225 -0.3997367 -0.09709908 1.13736623 1.0121689 0.3383476
[6,] -0.1086352 1.3522350 -1.00235321 0.14722832 1.3395307 -0.1026343
7 8 9 10
[1,] -0.1113009 1.6959992 -1.1897814 -0.2290430
[2,] -0.7512943 -0.6969146 1.1334643 0.7554655
[3,] 1.0782559 0.5296079 -1.0057891 1.1089107
[4,] -1.8030557 1.5021519 0.7094383 -1.0848102
[5,] -0.5539205 0.7127801 -1.3130555 -0.6742046
[6,] -0.7625295 -1.1712384 0.8147821 0.8036737
plot(multiple_simulations_series)
convert_to_ts(.data = tidy_normal(.num_sims = 10),
.return_ts = FALSE,
.pivot_longer = FALSE)
# A tibble: 50 × 10
`1` `2` `3` `4` `5` `6` `7` `8` `9` `10`
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.34 0.300 0.475 -3.07 1.35 1.18 0.403 -0.104 -0.347 -0.590
2 0.153 -0.563 -1.28 -0.0429 0.354 1.24 -0.184 0.850 0.338 -0.700
3 -0.448 0.160 -1.22 0.600 -0.532 -0.357 -0.759 0.659 0.691 -0.778
4 0.0384 -1.38 -0.918 1.12 -1.09 0.949 0.0276 0.456 0.510 1.10
5 -0.0148 -0.663 0.401 0.00200 -0.790 -1.98 0.714 0.613 0.658 1.02
6 0.528 0.164 -0.104 -0.977 -0.889 -0.589 1.39 0.916 0.496 0.326
7 1.08 -1.21 0.116 0.685 1.22 0.132 0.608 -0.322 -1.06 -0.624
8 -0.620 1.45 0.666 -1.39 -1.20 -0.175 0.0665 1.21 1.95 -0.237
9 -0.143 -1.93 -0.683 0.603 -1.24 0.623 -0.564 -0.417 0.0639 1.34
10 0.125 0.869 -1.47 0.953 0.608 -1.80 0.272 1.16 1.17 -1.09
# ℹ 40 more rows
convert_to_ts(.data = tidy_normal(.num_sims = 10),
.pivot_longer = TRUE)
Time Series:
Start = 1
End = 50
Frequency = 1
1 2 3 4 5 6
1 -0.655132170 1.124119928 -1.68015013 0.27119005 -1.34222309 -0.85029821
2 -0.563753358 0.148496529 -0.08420323 0.52425342 -0.22838128 0.38009238
3 0.162642987 0.944409541 -0.31995660 1.62739294 0.04586743 -1.00996184
4 -1.946332513 1.693491637 -0.76559306 2.58711915 -0.67796334 0.73113802
5 0.009260305 0.563304432 -0.90409541 -0.74206033 1.18933630 1.19979835
6 0.422154075 -0.827368569 0.38440082 1.58106342 0.53744939 -0.01699670
7 0.672312916 1.917586521 0.18758750 0.52812847 0.04987565 0.42471806
8 -0.099748356 0.548332963 -0.44544054 -0.74466540 0.27741779 0.03754982
9 0.599374087 2.416047894 0.13083797 -0.71925129 1.46397148 0.21928699
10 2.137103506 1.012679620 0.10946528 -1.21309250 -0.40298312 -0.06149162
11 -0.584328794 -0.977150553 1.98763118 0.80100807 1.59761439 0.96962954
12 -0.977410854 0.153227900 -0.89588458 -0.04738268 -0.99671233 -0.79875286
13 0.348559098 -0.139254894 0.17014759 -0.41635428 1.16343543 0.45536856
14 2.050755613 0.379570270 -1.57943509 -0.49670553 -0.49108776 -0.81654431
15 -0.536403083 0.815378564 0.44909230 -0.17645908 2.14187118 0.85912010
16 0.154173100 -0.009842145 -0.06548799 1.36411289 -0.26842334 1.69185208
17 0.526008171 -1.021411558 -0.01515875 -0.39923678 -1.91505446 1.47060381
18 -0.382628412 1.554043999 -0.99083886 0.03813862 -0.12122244 0.78097490
19 0.068027520 1.559917873 1.56624132 -0.72556821 0.04364763 -1.08713622
20 -0.375312760 0.182453162 -0.29118413 0.63647087 1.14593859 0.33562580
21 0.527199674 0.514358677 -0.85199804 -0.65959289 1.34991539 -0.88580797
22 0.606739583 -0.349281940 -0.42159565 -0.29988202 0.86897866 0.83607508
23 -0.750044523 -0.721098218 0.81759595 1.81533167 1.67514533 0.49723656
24 -0.167121348 1.050352702 1.70669358 -2.78197517 0.70872919 -1.19627981
25 -0.442427162 -0.009639385 0.66120474 0.07333735 0.39692306 2.72030582
26 -0.405109397 -0.463937639 2.92697938 0.64263562 -0.59588190 1.27812303
27 0.554957737 1.508452736 -0.67384362 -1.14296113 0.25859866 -0.09516432
28 -0.167333762 -0.154519359 1.29634414 2.05276305 1.22046958 -0.20261165
29 -0.426751358 0.299516899 -0.10270470 0.45218446 -1.01077778 0.41069225
30 -0.915369502 -1.134302489 -0.45195412 -0.02372924 -0.87979497 -2.22429752
31 0.398618595 -0.246544276 0.63197257 0.04685569 0.46524825 -0.41017315
32 0.001083079 -0.530058643 0.82139589 0.39120899 0.63881495 0.63570075
33 -0.502176823 -0.642359996 0.42880920 -0.44803379 -0.01992917 0.38896456
34 0.276462923 -1.042478900 1.14313112 -1.55697201 0.52390061 0.07794736
35 0.923405153 -0.237080174 0.96970857 0.86964379 -0.91567940 -0.63612591
36 -0.400504232 0.217544505 -0.63904106 0.91258477 -0.01156312 -0.41156245
37 0.458010625 -1.456299801 -0.95303905 -1.01123779 -0.04321204 -0.84060963
38 -0.202664022 -0.729410616 1.10544600 -1.54460728 0.48443033 -0.67777269
39 0.041503245 -0.610875862 -0.45167645 -0.47658183 -2.46133081 0.26514433
40 -0.121966128 -0.265657879 -0.40754380 0.41665215 -3.23015392 -0.11733447
41 2.275054279 -0.140453274 1.01738854 1.08335318 -0.72963796 -0.07750213
42 0.305032329 -0.912897889 -0.54486760 -0.06350812 0.35661866 -0.89575613
43 -0.097368038 0.879480923 0.42349178 0.90800105 1.22592750 -1.93884437
44 -0.053250772 0.590070911 -0.04377062 0.38001642 -0.56422962 -1.55652310
45 -0.631346970 0.510970484 1.03953655 -0.52313314 0.66643930 -0.50328900
46 -0.573835910 0.662271162 -0.94615866 -1.09348403 1.51469795 0.02769026
47 2.056373176 -1.438372766 0.64932666 -1.17330573 0.41932438 1.53009528
48 -2.353078785 0.487698963 -0.81844578 -0.92462341 0.27244456 0.42617475
49 -3.231722103 -1.271841203 0.24348256 -1.36611307 -0.97603663 -1.95217754
50 0.753719680 -0.366101153 -0.01044950 -1.59566595 0.08057617 1.11583833
7 8 9 10
1 0.171342136 -1.143037486 0.798526236 -0.244110488
2 -0.940732995 -0.098286237 -0.002464059 -1.329350897
3 -0.694053671 -0.861938231 -0.981141759 -0.016408426
4 0.715770296 0.891107430 -1.631236257 -0.812448587
5 -1.487322100 1.513993297 1.034899433 -1.562309837
6 -2.299581079 -1.372057771 1.141053483 -0.523175745
7 -1.551226431 0.584053401 -0.124530500 1.386795935
8 0.199088420 0.176433940 0.896122531 0.150326444
9 -2.610686399 1.619626479 -0.304107194 1.999026100
10 0.993024200 -1.717659646 -0.936505161 0.249643134
11 0.242493969 -1.104745018 2.139557395 1.308248416
12 1.438262730 -0.371852512 -0.367182295 -1.589296525
13 -0.149204186 -1.054119573 -0.465127766 0.423034528
14 1.199604760 -0.295676868 1.818224237 1.651671457
15 0.682116022 1.589055554 0.940553190 0.044546697
16 -0.023887103 -0.544176304 0.078750649 -1.618718807
17 0.783402254 -0.024077038 1.530981707 0.610937582
18 0.840292783 -0.781554633 0.177714516 -0.059345413
19 -1.313595307 -1.101811653 0.057190918 0.067426355
20 0.005232829 0.145444788 1.066697084 1.068481723
21 -0.554820885 0.380379950 -0.162190910 1.185489015
22 -0.861222004 0.030283953 0.908438632 -0.231394452
23 1.157935009 0.063995477 2.361496504 -0.396326692
24 -0.897071507 0.369621973 -0.266053668 -0.131590687
25 0.035629927 -0.084923255 0.003248558 -0.368614537
26 0.509364566 -1.832084693 0.890542325 0.888462980
27 1.207424021 -2.671878721 0.063299112 -0.878590418
28 0.211237171 1.535283026 0.759650387 0.549046140
29 -0.595276048 -2.514556134 0.445083701 -0.769968392
30 1.348793576 0.004755218 -0.301946343 -2.037938159
31 0.361619164 -1.340745382 -0.706048393 -0.003291719
32 0.014851985 -0.249794267 0.741063865 -0.398728564
33 -1.172677388 -0.193834398 1.018583201 -0.351067819
34 -0.572769045 -2.072442096 0.577545791 1.284331483
35 0.443800268 -0.108977727 1.866110069 -0.020469667
36 0.926425998 -0.687618149 1.224365387 -0.096690188
37 -0.460173605 -0.302608648 0.671541153 -2.696710002
38 0.277085477 0.335125232 -0.754473314 0.619338071
39 1.279310040 -0.842097806 -0.275860802 -0.768216600
40 0.015055026 0.835779589 -0.535925622 -0.990428811
41 0.690052418 1.488830535 0.318262300 -0.265301715
42 -2.342151157 -0.587400371 1.794438099 1.190162522
43 -1.284973383 0.976120498 -0.678730423 0.895248035
44 -1.857641265 -0.484324204 1.312931115 1.671816010
45 -0.828061584 -1.461679865 1.175113675 0.392315093
46 -0.124694812 0.800465295 -0.328118006 0.170025963
47 0.698593283 0.676449924 1.963221359 -0.477702054
48 0.118048192 0.257227889 -0.600914093 0.908605679
49 -0.101844218 0.458018251 0.177924006 -0.469079298
50 0.249644640 -1.684424651 0.620835209 1.330859032
This example showcases how to leverage TidyDensity’s functionality to convert tidy data into a time series format effortlessly. At this point in time though, the parameters of the ts() function are not utilized, meaning you cannot also pass in a start, end or frequency, but that will be added in the future.
In conclusion, mastering the ts() function in base R and exploring additional tools like convert_to_ts() opens up new avenues for time series analysis. So, roll up your sleeves, experiment with your data, and unlock the insights hidden in the temporal dimension. Happy coding!
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