See Part I to get started with the necessary data and R packages.
The practical reality
A good rule of thumb (and one that we follow) is to keep your data in long format whenever you’re doing any data manipulation or processing and save wide format for displaying it.
Of course, there are exceptions and sometimes you have a reason not to do your processing in long format, for instance when a function requires a wide data frame.
That means that in reality, you’ll often find yourself wanting to switch between long and wide format. Fortunately, Fortunately, using the tidyr package, it is very simple to convert from long to wide format and back again.
Pivoting from long to wide
We’ve already seen an example of pivoting from long to wide format. Let’s explore that in a little more detail.
We use tidyr::pivot_wider to go from long to wide.
The most important arguments to the function are id_cols, names_from and values_from, and they each specify a column in our long dataframe.
- The
id_colscolumn specifies the unique identifier of each observation in our wide data frame. - The unique values in the
names_fromcolumn become the column names in the wide data frame. - The values in the
values_fromcolumn gets populated into the cells of the wide data frame.
In our example:
- We want to index our wide dataframe by date, so we specify
id_cols = date - We want the tickers to form columns in the wide dataframe, so we specify
names_from = ticker - We want to populate our wide dataframe with returns values, so we specify
values_from = returns
Here’s what that looks like:
dailyindex_df %>%
pivot_wider(id_cols = date, names_from = ticker, values_from = returns) %>%
kable() %>%
kable_styling(position = ‘center’) %>%
scroll_box(width = ‘800px’, height = ‘300px’)
| date | EQ_US | EQ_NONUS_DEV | EQ_EMER | TN_US | TB_US | BOND_EMER | GOLD |
|---|---|---|---|---|---|---|---|
| 2020-03-02 | 0.0460048 | 0.0111621 | 0.0114468 | -0.0008475 | -0.0073579 | 0.0051502 | 0.0179376 |
| 2020-03-03 | -0.0280740 | 0.0097820 | 0.0106093 | 0.0093299 | 0.0154987 | 0.0093937 | 0.0311450 |
| 2020-03-04 | 0.0422302 | 0.0062777 | 0.0097196 | -0.0016807 | -0.0099536 | 0.0059222 | -0.0008743 |
| 2020-03-05 | -0.0336854 | -0.0014521 | 0.0014743 | 0.0050505 | 0.0227882 | -0.0067283 | 0.0151949 |
| 2020-03-06 | -0.0170369 | -0.0232132 | -0.0262282 | 0.0041876 | 0.0498034 | -0.0076207 | 0.0026644 |
| 2020-03-09 | -0.0761665 | 0.0000000 | 0.0000000 | 0.0041701 | 0.0262172 | 0.0000000 | 0.0018757 |
| 2020-03-10 | 0.0494037 | 0.0000000 | 0.0000000 | -0.0091362 | -0.0486618 | -0.0477816 | -0.0091271 |
| 2020-03-11 | -0.0487713 | 0.0000000 | 0.0000000 | -0.0025147 | -0.0108696 | -0.0206093 | -0.0107857 |
| 2020-03-12 | -0.0949084 | 0.0000000 | 0.0000000 | 0.0008403 | -0.0155139 | 0.0000000 | -0.0317549 |
| 2020-03-13 | 0.0931900 | 0.0000000 | 0.0000000 | -0.0058774 | -0.0216678 | -0.0439158 | -0.0462765 |
| 2020-03-16 | -0.1197921 | 0.0000000 | 0.0000000 | 0.0126689 | 0.0510067 | -0.0325359 | -0.0203396 |
| 2020-03-17 | 0.0597801 | 0.0000000 | 0.0000000 | -0.0133445 | -0.0587484 | -0.0197824 | 0.0265681 |
| 2020-03-18 | -0.0517360 | 0.0000000 | 0.0000000 | -0.0067625 | -0.0434193 | -0.0544904 | -0.0311081 |
| 2020-03-19 | 0.0047010 | 0.0000000 | 0.0000000 | 0.0017021 | 0.0007092 | -0.0234792 | 0.0011498 |
| 2020-03-20 | -0.0431907 | 0.0000000 | 0.0000000 | 0.0144435 | 0.0652020 | 0.0142077 | 0.0039756 |
| 2020-03-23 | -0.0292942 | -0.2532532 | 0.0000000 | 0.0083752 | 0.0419162 | -0.0215517 | 0.0568462 |
| 2020-03-24 | 0.0939740 | 0.0000000 | 0.0000000 | -0.0041528 | -0.0051086 | 0.0121145 | 0.0575354 |
| 2020-03-25 | 0.0115569 | 0.0000000 | 0.0000000 | 0.0008340 | -0.0064185 | 0.0315560 | -0.0174002 |
| 2020-03-26 | 0.0624207 | 0.0000000 | 0.0000000 | 0.0016667 | 0.0064599 | 0.0295359 | 0.0159455 |
| 2020-03-27 | -0.0336616 | 0.0000000 | 0.0000000 | 0.0049917 | 0.0237484 | -0.0081967 | -0.0037858 |
| 2020-03-30 | 0.0336403 | 0.0000000 | 0.0000000 | -0.0008278 | -0.0050157 | -0.0144628 | -0.0065711 |
| 2020-03-31 | -0.0159221 | 0.0000000 | 0.0000000 | -0.0016570 | -0.0144928 | 0.0115304 | -0.0283711 |
| 2020-04-01 | -0.0441380 | 0.0000000 | 0.0000000 | 0.0008299 | 0.0147059 | -0.0124352 | -0.0032808 |
| 2020-04-02 | 0.0230223 | 0.0000000 | 0.0000000 | 0.0008292 | 0.0056711 | 0.0000000 | 0.0291310 |
Could that be any easier?
Actually, yes!
id_cols defaults to any column or columns that aren’t specified by the names_from and values_from arguments. So in our case, we could actually not even bother with the id_cols argument:
| date | EQ_US | EQ_NONUS_DEV | EQ_EMER | TN_US | TB_US | BOND_EMER | GOLD |
|---|---|---|---|---|---|---|---|
| 2020-03-02 | 0.0460048 | 0.0111621 | 0.0114468 | -0.0008475 | -0.0073579 | 0.0051502 | 0.0179376 |
| 2020-03-03 | -0.0280740 | 0.0097820 | 0.0106093 | 0.0093299 | 0.0154987 | 0.0093937 | 0.0311450 |
| 2020-03-04 | 0.0422302 | 0.0062777 | 0.0097196 | -0.0016807 | -0.0099536 | 0.0059222 | -0.0008743 |
| 2020-03-05 | -0.0336854 | -0.0014521 | 0.0014743 | 0.0050505 | 0.0227882 | -0.0067283 | 0.0151949 |
| 2020-03-06 | -0.0170369 | -0.0232132 | -0.0262282 | 0.0041876 | 0.0498034 | -0.0076207 | 0.0026644 |
| 2020-03-09 | -0.0761665 | 0.0000000 | 0.0000000 | 0.0041701 | 0.0262172 | 0.0000000 | 0.0018757 |
| 2020-03-10 | 0.0494037 | 0.0000000 | 0.0000000 | -0.0091362 | -0.0486618 | -0.0477816 | -0.0091271 |
| 2020-03-11 | -0.0487713 | 0.0000000 | 0.0000000 | -0.0025147 | -0.0108696 | -0.0206093 | -0.0107857 |
| 2020-03-12 | -0.0949084 | 0.0000000 | 0.0000000 | 0.0008403 | -0.0155139 | 0.0000000 | -0.0317549 |
| 2020-03-13 | 0.0931900 | 0.0000000 | 0.0000000 | -0.0058774 | -0.0216678 | -0.0439158 | -0.0462765 |
| 2020-03-16 | -0.1197921 | 0.0000000 | 0.0000000 | 0.0126689 | 0.0510067 | -0.0325359 | -0.0203396 |
| 2020-03-17 | 0.0597801 | 0.0000000 | 0.0000000 | -0.0133445 | -0.0587484 | -0.0197824 | 0.0265681 |
| 2020-03-18 | -0.0517360 | 0.0000000 | 0.0000000 | -0.0067625 | -0.0434193 | -0.0544904 | -0.0311081 |
| 2020-03-19 | 0.0047010 | 0.0000000 | 0.0000000 | 0.0017021 | 0.0007092 | -0.0234792 | 0.0011498 |
| 2020-03-20 | -0.0431907 | 0.0000000 | 0.0000000 | 0.0144435 | 0.0652020 | 0.0142077 | 0.0039756 |
| 2020-03-23 | -0.0292942 | -0.2532532 | 0.0000000 | 0.0083752 | 0.0419162 | -0.0215517 | 0.0568462 |
| 2020-03-24 | 0.0939740 | 0.0000000 | 0.0000000 | -0.0041528 | -0.0051086 | 0.0121145 | 0.0575354 |
| 2020-03-25 | 0.0115569 | 0.0000000 | 0.0000000 | 0.0008340 | -0.0064185 | 0.0315560 | -0.0174002 |
| 2020-03-26 | 0.0624207 | 0.0000000 | 0.0000000 | 0.0016667 | 0.0064599 | 0.0295359 | 0.0159455 |
| 2020-03-27 | -0.0336616 | 0.0000000 | 0.0000000 | 0.0049917 | 0.0237484 | -0.0081967 | -0.0037858 |
| 2020-03-30 | 0.0336403 | 0.0000000 | 0.0000000 | -0.0008278 | -0.0050157 | -0.0144628 | -0.0065711 |
| 2020-03-31 | -0.0159221 | 0.0000000 | 0.0000000 | -0.0016570 | -0.0144928 | 0.0115304 | -0.0283711 |
| 2020-04-01 | -0.0441380 | 0.0000000 | 0.0000000 | 0.0008299 | 0.0147059 | -0.0124352 | -0.0032808 |
| 2020-04-02 | 0.0230223 | 0.0000000 | 0.0000000 | 0.0008292 | 0.0056711 | 0.0000000 | 0.0291310 |
Same result as above. Brilliant.
Stay tuned for the next installment in which Kris will discuss Pivoting from wide to long.
Visit Robot Wealth website to read the full article and watch the instructional video: https://robotwealth.com/working-with-tidy-financial-data-in-tidyr/
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