2 Y axes problem

General
, , ,
1

I am trying to create a 2 Y axes graph for a current project that I am working on. Here is my best code so far but it is not perfect. I am trying to rescale the Temp Y axes values so it may look bigger but I have trouble finding a way doing that.

ggplot(Sharks1, aes(x = Date)) +
  geom_line(aes(y = 30 + Depth, color = Min_Max.x)) + 
  geom_line(aes(y = Temp, color = Min_Max.y)) +  
  scale_y_continuous(
    name = "Temperature (°C)",
    breaks = seq(10, 25, by = 5),
    labels = c(10, 15, 20, 25),
    sec.axis = sec_axis(~30 - ., name = "Depth (m)", breaks = seq(0, 30, by = 5),
                        labels = c(0, 5, 10, 15, 20, 25, 30))
    ) +
  labs(color = NULL) +
  theme_minimal()

image
image875×540 9.14 KB

I would appreciate any help possible, and unfortunately it has to be a 2 Y axes line graph

1 Like
2

Could you say more? In what way? And is your data private or proprietary? I ask because it is usually much easier to help if we have access to representative data.

2 Likes
3

image
image875×540 8.17 KB

This is what I mean by bigger, also I have trouble finding a way of how to share the CVS with my data to you right now, but if I will sent to you when I find a way

4

Thanks, Argyris, this is very helpful. In general, depending on the size of the data, there are better ways to share representative data than to share a csv file — could you first tell us the dimensions of the Shark1 table? From the image you shared, it seems it could be easily shared in a reprex.

5

A handy way to supply some sample data is the dput() function. In the case of a large dataset something like dput(head(mydata, 100)) should supply the data we need. Just do dput(mydata) where mydata is your data. Copy the output and paste it here between
```

```

6 
structure(list(Date = structure(c(1696428000, 1696428000, 1696449600, 
1696449600, 1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 
1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 
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1696492800, 1696492800, 1696492800, 1696492800, 1696492800, 1696492800, 
1696492800), tzone = "", class = c("POSIXct", "POSIXt")), Min_Max.x = structure(c(1L, 
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
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1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), levels = c("MinDepth", "MaxDepth"), class = "factor"), 
    Depth = c(-0.75, -5, -4.5, -4.5, -3, -3, -3, -3, -3, -3, 
    -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, 
    -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -4, -4, -4, -4, 
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    -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, 
    -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, 
    -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4
    ), Min_Max.y = structure(c(NA, NA, NA, NA, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L), levels = c("MiTemp", "MaTemp"), class = "factor"), 
    Temp = c(NA, NA, NA, NA, 17.9, 18.1, 17.9, 17.9, 17.9, 17.9, 
    17.9, 17.9, 17.9, 17.9, 17.7, 18.1, 17.7, 17.9, 17.7, 17.7, 
    17.7, 17.7, 17.7, 17.7, 18.1, 18.1, 17.9, 17.9, 18.1, 18.1, 
    17.9, 17.9, 18.1, 18.1, 17.9, 17.9, 17.9, 18.1, 17.9, 17.9, 
    17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.7, 18.1, 17.7, 17.9, 
    17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 18.1, 18.1, 17.9, 17.9, 
    18.1, 18.1, 17.9, 17.9, 18.1, 18.1, 17.9, 17.9, 18.1, 18.3, 
    18.1, 17.9, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 17.7, 18.3, 
    17.7, 17.9, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 18.3, 18.3, 
    17.9, 17.9, 18.3, 18.3, 17.9, 17.9, 18.3, 18.3, 17.9, 17.9
    )), row.names = c(NA, -100L), class = c("tbl_df", "tbl", 
"data.frame"))

So this is my copied data by using the dput(head(mydata, 100))

7

In case you would like to work with a bigger amount of data,

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2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("MinDepth", 
"MaxDepth"), class = "factor"), Depth = c(-0.75, -5, -4.5, -4.5, 
-3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, 
-3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, 
-4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, 
-4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, 
-4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, 
-4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, 
-6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, 
-6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, 
-6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, 
-6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, 
-6, -6, -6, -6, -4, -6, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, 
-5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, 
-5, -5, -5, -5, -5, -5, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, 
-6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, -6, 
-6, -6, -6, -6, -6, -6, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, 
-5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, 
-5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, 
-20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, 
-20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, 
-20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, 
-20, -20, -20, -20.5, -22.5, -20, -20, -20, -20, -20, -20, -20, 
-20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, 
-20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -20, -21, 
-21, -21, -21, -21, -21, -21, -21, -21, -21, -21, -21, -21, -21, 
-21, -21, -21, -21, -21, -21, -21, -21, -21, -21, -21, -21, -21, 
-21, -21, -21, -21, -21, -16, -16, -16, -16, -16, -16, -16, -16, 
-16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, 
-16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, 
-16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, 
-16, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, 
-26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, 
-26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, 
-26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -15.5, -23.5, 
-23, -23, -23, -23, -23, -23, -23, -23, -23, -23, -23, -23, -23, 
-23, -23, -23, -23, -23, -23, -23, -23, -23, -23, -23, -23, -23, 
-23, -23, -23, -23, -23, -23, -26, -26, -26, -26, -26, -26, -26, 
-26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, 
-26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -19, 
-19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, 
-19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, 
-19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, -19, 
-19, -19, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, 
-26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, 
-26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, 
-26, -26, -26, -26, -26, -23.5, -25.5, -18, -18, -18, -18, -18, 
-18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, 
-18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, 
-18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, -18, 
-18, -18, -18, -18, -27, -27, -27, -27, -27, -27, -27, -27, -27, 
-27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, 
-27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, 
-27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, 
-24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, 
-24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, 
-24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, 
-24, -24, -24, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, 
-26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, 
-26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, -26, 
-26, -26, -26, -26, -26, -26, -17, -17, -17, -17, -17, -17, -17, 
-17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, 
-17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, 
-17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, -17, 
-17, -17, -17, -17, -27, -27, -27, -27, -27, -27, -27, -27, -27, 
-27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, 
-27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, 
-27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, -27, 
-27, -27, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, 
-14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, 
-14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, -14, 
-14), Min_Max.y = structure(c(NA, NA, NA, NA, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
NA, NA, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, NA, NA, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, NA, NA, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, NA, NA, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), levels = c("MiTemp", 
"MaTemp"), class = "factor"), Temp = c(NA, NA, NA, NA, 17.9, 
18.1, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.7, 18.1, 
17.7, 17.9, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 18.1, 18.1, 17.9, 
17.9, 18.1, 18.1, 17.9, 17.9, 18.1, 18.1, 17.9, 17.9, 17.9, 18.1, 
17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.7, 18.1, 17.7, 
17.9, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 18.1, 18.1, 17.9, 17.9, 
18.1, 18.1, 17.9, 17.9, 18.1, 18.1, 17.9, 17.9, 18.1, 18.3, 18.1, 
17.9, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 17.7, 18.3, 17.7, 17.9, 
17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 18.3, 18.3, 17.9, 17.9, 18.3, 
18.3, 17.9, 17.9, 18.3, 18.3, 17.9, 17.9, 18.1, 18.3, 18.1, 17.9, 
18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 17.7, 18.3, 17.7, 17.9, 17.7, 
17.7, 17.7, 17.7, 17.7, 17.7, 18.3, 18.3, 17.9, 17.9, 18.3, 18.3, 
17.9, 17.9, 18.3, 18.3, 17.9, 17.9, 17.7, 17.9, 17.7, 17.7, 17.7, 
17.7, 17.7, 17.7, 17.7, 17.7, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 
17.9, 17.9, 17.7, 17.9, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 
17.7, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, NA, NA, 
17.9, 18.1, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.7, 
18.1, 17.7, 17.9, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 18.1, 18.1, 
17.9, 17.9, 18.1, 18.1, 17.9, 17.9, 18.1, 18.1, 17.9, 17.9, 17.9, 
18.1, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.7, 18.1, 
17.7, 17.9, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 18.1, 18.1, 17.9, 
17.9, 18.1, 18.1, 17.9, 17.9, 18.1, 18.1, 17.9, 17.9, 17.5, 17.7, 
17.5, 17.7, 17.5, 15.7, 17.5, 17.5, 17.5, 17.5, 17.5, 17.7, 17.5, 
17.7, 17.5, 15.7, 17.5, 17.5, 17.5, 17.5, 15.5, 17.7, 15.5, 17.7, 
15.5, 15.7, 15.5, 15.5, 15.5, 15.5, 17.7, 17.7, 17.7, 17.7, 15.7, 
15.7, 17.7, 17.7, 17.7, 17.7, 15.7, 15.7, 17.5, 17.7, 17.5, 17.7, 
17.5, 15.7, 17.5, 17.5, 17.5, 17.5, 17.5, 17.7, 17.5, 17.7, 17.5, 
15.7, 17.5, 17.5, 17.5, 17.5, 15.5, 17.7, 15.5, 17.7, 15.5, 15.7, 
15.5, 15.5, 15.5, 15.5, 17.7, 17.7, 17.7, 17.7, 15.7, 15.7, 17.7, 
17.7, 17.7, 17.7, 15.7, 15.7, NA, NA, 14.9, 15.1, 14.9, 15.3, 
14.9, 14.9, 14.9, 14.9, 14.9, 14.9, 15.1, 15.1, 15.1, 15.3, 15.1, 
15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.3, 15.3, 15.1, 15.1, 
15.3, 15.3, 15.1, 15.1, 15.3, 15.3, 14.9, 15.1, 14.9, 15.3, 14.9, 
14.9, 14.9, 14.9, 14.9, 14.9, 15.1, 15.1, 15.1, 15.3, 15.1, 15.1, 
15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.3, 15.3, 15.1, 15.1, 15.3, 
15.3, 15.1, 15.1, 15.3, 15.3, 14.9, 15.1, 14.9, 14.9, 14.9, 14.9, 
14.9, 14.9, 14.9, 14.9, 14.7, 15.1, 14.7, 14.9, 14.7, 14.9, 14.7, 
14.9, 14.7, 14.7, 14.7, 15.1, 14.7, 14.9, 14.7, 14.9, 14.7, 14.9, 
14.7, 14.7, 14.7, 15.1, 14.7, 14.9, 14.7, 14.9, 14.7, 14.9, 14.7, 
14.7, 15.1, 15.1, 14.9, 14.9, 14.9, 14.9, 14.9, 14.9, 14.9, 15.1, 
14.9, 14.9, 14.9, 14.9, 14.9, 14.9, 14.9, 14.9, 14.7, 15.1, 14.7, 
14.9, 14.7, 14.9, 14.7, 14.9, 14.7, 14.7, 14.7, 15.1, 14.7, 14.9, 
14.7, 14.9, 14.7, 14.9, 14.7, 14.7, 14.7, 15.1, 14.7, 14.9, 14.7, 
14.9, 14.7, 14.9, 14.7, 14.7, 15.1, 15.1, 14.9, 14.9, 14.9, 14.9, 
14.9, 14.9, NA, NA, 14.9, 15.1, 14.9, 14.9, 14.9, 14.9, 14.9, 
14.9, 14.9, 14.9, 14.7, 15.1, 14.7, 14.9, 14.7, 14.7, 14.7, 14.7, 
14.7, 14.7, 15.1, 15.1, 14.9, 14.9, 15.1, 15.1, 14.9, 14.9, 15.1, 
15.1, 14.9, 14.9, 14.9, 15.1, 14.9, 14.9, 14.9, 14.9, 14.9, 14.9, 
14.9, 14.9, 14.7, 15.1, 14.7, 14.9, 14.7, 14.7, 14.7, 14.7, 14.7, 
14.7, 15.1, 15.1, 14.9, 14.9, 15.1, 15.1, 14.9, 14.9, 15.1, 15.1, 
14.9, 14.9, 15.7, 15.9, 15.7, 14.9, 15.7, 14.9, 15.7, 15.7, 15.7, 
15.7, 14.7, 15.9, 14.7, 14.9, 14.7, 14.9, 14.7, 14.7, 14.7, 14.7, 
14.7, 15.9, 14.7, 14.9, 14.7, 14.9, 14.7, 14.7, 14.7, 14.7, 15.9, 
15.9, 14.9, 14.9, 14.9, 14.9, 15.9, 15.9, 14.9, 14.9, 14.9, 14.9, 
15.7, 15.9, 15.7, 14.9, 15.7, 14.9, 15.7, 15.7, 15.7, 15.7, 14.7, 
15.9, 14.7, 14.9, 14.7, 14.9, 14.7, 14.7, 14.7, 14.7, 14.7, 15.9, 
14.7, 14.9, 14.7, 14.9, 14.7, 14.7, 14.7, 14.7, 15.9, 15.9, 14.9, 
14.9, 14.9, 14.9, 15.9, 15.9, 14.9, 14.9, 14.9, 14.9, NA, NA, 
14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 14.9, 14.9, 
15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 14.9, 14.9, 15.1, 
14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 14.9, 14.9, 15.1, 14.9, 
15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 14.9, 15.1, 15.1, 15.1, 15.1, 
15.1, 15.1, 15.1, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 
15.1, 14.9, 14.9, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 
14.9, 14.9, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 
14.9, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 14.9, 
15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 14.9, 15.1, 14.9, 
15.1, 14.9, 15.1, 14.9, 14.9, 14.9, 14.9, 14.9, 15.1, 14.9, 15.1, 
14.9, 15.1, 14.9, 14.9, 14.9, 14.9, 14.9, 15.1, 14.9, 15.1, 14.9, 
15.1, 14.9, 14.9, 14.9, 14.9, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 
15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 
15.1, 14.9, 14.9, 14.9, 14.9, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 
14.9, 14.9, 14.9, 14.9, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 
14.9, 14.9, 14.9, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 
15.1, 15.1, 15.1, 15.1, 15.5, 15.7, 15.5, 15.3, 15.5, 15.1, 15.5, 
15.1, 15.5, 15.1, 15.1, 15.7, 15.1, 15.3, 15.1, 15.1, 15.1, 15.1, 
15.1, 15.1, 14.9, 15.7, 14.9, 15.3, 14.9, 15.1, 14.9, 15.1, 14.9, 
15.1, 14.9, 15.7, 14.9, 15.3, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 
14.9, 15.7, 14.9, 15.3, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 15.5, 
15.7, 15.5, 15.3, 15.5, 15.1, 15.5, 15.1, 15.5, 15.1, 15.1, 15.7, 
15.1, 15.3, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 14.9, 15.7, 14.9, 
15.3, 14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 15.7, 14.9, 15.3, 
14.9, 15.1, 14.9, 15.1, 14.9, 15.1, 14.9, 15.7, 14.9, 15.3, 14.9, 
15.1, 14.9, 15.1, 14.9, 15.1, 15.3, 15.5, 15.3, 15.3, 15.3, 15.1, 
15.3, 15.1, 15.3, 15.3, 15.1, 15.5, 15.1, 15.3, 15.1, 15.1, 15.1, 
15.1, 15.1, 15.1, 14.9, 15.5, 14.9, 15.3, 14.9, 15.1, 14.9, 15.1, 
14.9, 14.9, 14.9, 15.5, 14.9, 15.3, 14.9, 15.1, 14.9, 15.1)), row.names = c(NA, 
-950L), class = c("tbl_df", "tbl", "data.frame"))
8

I managed to copy some of my data to here by the dput() function, but it isn't the full amount so the graph might not be the same as the one I provided. I hope it helps

9

Thanks, Argyris. The data only covers a few days, so I would suggest running this code instead :

Sharks1 |> 
  group_by(Date) |> 
  summarise(Temp = mean(Temp, na.rm = T), Depth = mean(Depth, na.rm = T)) |> 
  dput()

and pasting the result here, like you did before.

And if you would like to make the copying and pasting easier, before you run the code above, you could run

sink('shark_temp.txt')

to direct console output to a new file called shark_temp.txt, then run the code above, and then run

sink()

so that R output is redirected to the console. (Very important step!)

Then you can open shark_temp.txt and use Ctrl-A to select all the content to copy, which is much easier than clicking and dragging to capture console output.

14 
structure(list(Date = structure(c(1696428000, 1696449600, 1696471200, 
1696492800, 1696514400, 1696536000, 1696557600, 1696579200, 1696600800, 
1696622400, 1696644000, 1696665600, 1696687200, 1696708800, 1696730400, 
1696752000, 1696773600, 1696795200, 1696816800, 1696838400, 1696860000, 
1696881600, 1696903200, 1696924800, 1696946400, 1696968000, 1696989600, 
1697011200, 1697032800, 1697054400, 1697076000, 1697097600, 1697119200, 
1697140800, 1697162400, 1697184000, 1697205600, 1697227200, 1697248800, 
1697270400, 1697292000, 1697313600, 1697335200, 1697356800, 1697378400, 
1697400000, 1697421600, 1697443200, 1697464800, 1697486400, 1697508000, 
1697529600, 1697551200, 1697572800, 1697637600, 1697745600, 1697788800, 
1697810400, 1697832000, 1697853600, 1697896800, 1697918400, 1697983200, 
1698004800, 1698069600, 1698091200, 1698112800, 1698134400, 1698156000, 
1698177600), tzone = "", class = c("POSIXct", "POSIXt")), Temp = c(NaN, 
NaN, 17.9, 18, 17.8, NaN, 17.9, 16.9333333333333, NaN, 15.1, 
14.85, NaN, 14.9, 15.1333333333333, NaN, 15, 15, 15.16, 15.15, 
15.25, 15.2, NaN, 15.25, 15.15, NaN, 15.4666666666667, 15.25, 
NaN, 15, 15.1333333333333, NaN, 14.85, 15, 15.3333333333333, 
15.7333333333333, 15.45, 15.15, NaN, NaN, NaN, NaN, NaN, 18.8, 
NaN, NaN, 19.6, 19.6, NaN, 20.2, 19.8, NaN, 20.8, 22.8, 19.4, 
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 
NaN, NaN, NaN), Depth = c(-2.875, -4.5, -3.5, -5, -6, -5, -5.5, 
-12.5, -21.5, -20.5, -21, -19.5, -24.5, -22.5, -24.5, -22.5, 
-25, -22, -19.5, -19, -25.5, -22, -21.5, -21.5, -21.5, -17, -16.5, 
-21, -23.5, -24.5, -22.5, -20.5, -21.5, -22, -21, -22.5, -22.5, 
-22, -22.5, -11.25, 0, -23, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -70L))

These are the results

15

Here is a first pass that doesn't address the dual-y-axis question directly, but normalizes the data so that the temperature and depth curves are more comparable. Is this is in the direction of what you were envisioning? Depending on your answer, the path to adding the desired dual y-axis can be addressed later.

assign data to "sharks" table (click to open) 
structure(list(Date = structure(c(1696428000, 1696449600, 1696471200, 
1696492800, 1696514400, 1696536000, 1696557600, 1696579200, 1696600800, 
1696622400, 1696644000, 1696665600, 1696687200, 1696708800, 1696730400, 
1696752000, 1696773600, 1696795200, 1696816800, 1696838400, 1696860000, 
1696881600, 1696903200, 1696924800, 1696946400, 1696968000, 1696989600, 
1697011200, 1697032800, 1697054400, 1697076000, 1697097600, 1697119200, 
1697140800, 1697162400, 1697184000, 1697205600, 1697227200, 1697248800, 
1697270400, 1697292000, 1697313600, 1697335200, 1697356800, 1697378400, 
1697400000, 1697421600, 1697443200, 1697464800, 1697486400, 1697508000, 
1697529600, 1697551200, 1697572800, 1697637600, 1697745600, 1697788800, 
1697810400, 1697832000, 1697853600, 1697896800, 1697918400, 1697983200, 
1698004800, 1698069600, 1698091200, 1698112800, 1698134400, 1698156000, 
1698177600), tzone = "", class = c("POSIXct", "POSIXt")), Temp = c(NaN, 
NaN, 17.9, 18, 17.8, NaN, 17.9, 16.9333333333333, NaN, 15.1, 
14.85, NaN, 14.9, 15.1333333333333, NaN, 15, 15, 15.16, 15.15, 
15.25, 15.2, NaN, 15.25, 15.15, NaN, 15.4666666666667, 15.25, 
NaN, 15, 15.1333333333333, NaN, 14.85, 15, 15.3333333333333, 
15.7333333333333, 15.45, 15.15, NaN, NaN, NaN, NaN, NaN, 18.8, 
NaN, NaN, 19.6, 19.6, NaN, 20.2, 19.8, NaN, 20.8, 22.8, 19.4, 
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 
NaN, NaN, NaN), Depth = c(-2.875, -4.5, -3.5, -5, -6, -5, -5.5, 
-12.5, -21.5, -20.5, -21, -19.5, -24.5, -22.5, -24.5, -22.5, 
-25, -22, -19.5, -19, -25.5, -22, -21.5, -21.5, -21.5, -17, -16.5, 
-21, -23.5, -24.5, -22.5, -20.5, -21.5, -22, -21, -22.5, -22.5, 
-22, -22.5, -11.25, 0, -23, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -70L)) -> sharks

library(tidyverse)
sharks |> 
  pivot_longer(Temp:Depth, names_to = "measure") |> 
  mutate(
    pre_Oct9 = Date < as.Date('2023-10-09'),
    pre_Oct9 = if_else(pre_Oct9, 1, NA_real_)
    ) |> 
  group_by(measure) |>
  mutate(
    pre_Oct9_max = max(value * pre_Oct9, na.rm = T),
    Oct_min = min(value, na.rm = T),
    norm_value = (value - Oct_min) / (pre_Oct9_max - Oct_min)
  ) |>
  ungroup() |> 
  select(-pre_Oct9) |> 
  drop_na() |> 
  ggplot() +
  geom_line(aes(Date, norm_value, color = measure))

Created on 2024-03-31 with reprex v2.0.2

1 Like
16

Yes, this is what I am envisioning for

17

Could you say a little about how the Shark1 table was constructed? I noticed that there are often multiple entries per date-time value — how did you identify the minimum and maximum values of depth and temperature? Was Shark1 constructed from another table?

18

There were 2 tables for the Depth and Temperature data. Each table also had two columns that had the minimum and maximum Depth/Temperature at that point of time. I just mutated the tables by combining the data into a single column for the Depth/Temperature, while I created a different column which indicated the Depth/Temperature was either Minimum or Maximum. I then combined the 2 tables into 1

19

I see, so maybe like this?

library(tidyverse)

tibble(
  date = seq(as.Date("2023-10-01"), by = 1, length.out = 4),
  min = 1:4,
  max = 5:8
) -> depth

depth
#> # A tibble: 4 × 3
#>   date         min   max
#>   <date>     <int> <int>
#> 1 2023-10-01     1     5
#> 2 2023-10-02     2     6
#> 3 2023-10-03     3     7
#> 4 2023-10-04     4     8

tibble(
  date = seq(as.Date("2023-10-01"), by = 1, length.out = 4),
  min = 11:14,
  max = 15:18
) -> temp

temp
#> # A tibble: 4 × 3
#>   date         min   max
#>   <date>     <int> <int>
#> 1 2023-10-01    11    15
#> 2 2023-10-02    12    16
#> 3 2023-10-03    13    17
#> 4 2023-10-04    14    18

If so, it would make plotting easier if the tables depth and temp were combined like this:

depth |> 
  mutate(measure = 'depth') |> 
  bind_rows(
    temp |> 
      mutate(measure = 'temp')
  ) -> depth_plus_temp

depth_plus_temp
#> # A tibble: 8 × 4
#>   date         min   max measure
#>   <date>     <int> <int> <chr>  
#> 1 2023-10-01     1     5 depth  
#> 2 2023-10-02     2     6 depth  
#> 3 2023-10-03     3     7 depth  
#> 4 2023-10-04     4     8 depth  
#> 5 2023-10-01    11    15 temp   
#> 6 2023-10-02    12    16 temp   
#> 7 2023-10-03    13    17 temp   
#> 8 2023-10-04    14    18 temp

and then the depth_plus_temp table were reformatted to be longer

depth_plus_temp |> 
  pivot_longer(min:max, names_to = 'extreme') -> depth_plus_temp_long

depth_plus_temp_long
#> # A tibble: 16 × 4
#>    date       measure extreme value
#>    <date>     <chr>   <chr>   <int>
#>  1 2023-10-01 depth   min         1
#>  2 2023-10-01 depth   max         5
#>  3 2023-10-02 depth   min         2
#>  4 2023-10-02 depth   max         6
#>  5 2023-10-03 depth   min         3
#>  6 2023-10-03 depth   max         7
#>  7 2023-10-04 depth   min         4
#>  8 2023-10-04 depth   max         8
#>  9 2023-10-01 temp    min        11
#> 10 2023-10-01 temp    max        15
#> 11 2023-10-02 temp    min        12
#> 12 2023-10-02 temp    max        16
#> 13 2023-10-03 temp    min        13
#> 14 2023-10-03 temp    max        17
#> 15 2023-10-04 temp    min        14
#> 16 2023-10-04 temp    max        18

Created on 2024-04-01 with reprex v2.0.2

Could you do that with your tables and then post the following output?

depth_plus_temp_long |> 
  group_by(measure) |> 
  slice(1:50) |> 
  ungroup() |> 
  dput()
20 
structure(list(Date = structure(c(1696946400, 1696492800), tzone = "", class = c("POSIXct", 
"POSIXt")), measure = c("depth", "temp"), extreme = c("max", 
"max"), value = c(26.5, 17.7)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -2L))

That's the result

21

Since the Sharks1 table contains hundreds of rows, this code

depth_plus_temp_long |> 
  group_by(measure) |> 
  slice(50) |> 
  ungroup() |> 
  dput()

should have produced a table with 100 rows, so it seems we're not on the same page.

Could you run

[your table] |>
  slice(1:10) |>
  dput()

for each of the temperature and depth tables you used to construct Shark1?

22

Depth:

structure(list(Date = structure(c(1696428000, 1696449600, 1696471200, 
1696492800, 1696514400, 1696536000, 1696557600, 1696579200, 1696600800, 
1696622400), tzone = "", class = c("POSIXct", "POSIXt")), min = c(0.75, 
4.5, 3, 4, 6, 4, 5, 5, 20.5, 20), max = c(5, 4.5, 4, 6, 6, 6, 
6, 20, 22.5, 21)), row.names = c(NA, -10L), class = c("tbl_df", 
"tbl", "data.frame"))

Temp:

structure(list(Date = structure(c(1696471200, 1696471200, 1696471200, 
1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 
1696471200), tzone = "", class = c("POSIXct", "POSIXt")), min = c(17.9, 
17.9, 17.9, 17.9, 17.9, 17.7, 17.7, 17.7, 17.7, 17.7), max = c(18.1, 
17.9, 17.9, 17.9, 17.9, 18.1, 17.9, 17.7, 17.7, 17.7)), row.names = c(NA, 
-10L), class = c("tbl_df", "tbl", "data.frame"), na.action = structure(c(`13` = 13L, 
`14` = 14L, `15` = 15L, `18` = 18L, `19` = 19L, `20` = 20L, `23` = 23L, 
`24` = 24L, `25` = 25L, `38` = 38L, `39` = 39L, `40` = 40L, `43` = 43L, 
`44` = 44L, `45` = 45L, `48` = 48L, `49` = 49L, `50` = 50L, `57` = 57L, 
`58` = 58L, `59` = 59L, `60` = 60L, `62` = 62L, `63` = 63L, `64` = 64L, 
`65` = 65L, `67` = 67L, `68` = 68L, `69` = 69L, `70` = 70L, `72` = 72L, 
`73` = 73L, `74` = 74L, `75` = 75L, `88` = 88L, `89` = 89L, `90` = 90L, 
`93` = 93L, `94` = 94L, `95` = 95L, `98` = 98L, `99` = 99L, `100` = 100L, 
`119` = 119L, `120` = 120L, `124` = 124L, `125` = 125L, `138` = 138L, 
`139` = 139L, `140` = 140L, `143` = 143L, `144` = 144L, `145` = 145L, 
`148` = 148L, `149` = 149L, `150` = 150L, `175` = 175L, `188` = 188L, 
`189` = 189L, `190` = 190L, `193` = 193L, `194` = 194L, `195` = 195L, 
`198` = 198L, `199` = 199L, `200` = 200L, `219` = 219L, `220` = 220L, 
`224` = 224L, `225` = 225L, `250` = 250L, `269` = 269L, `270` = 270L, 
`274` = 274L, `275` = 275L, `325` = 325L, `350` = 350L, `363` = 363L, 
`364` = 364L, `365` = 365L, `368` = 368L, `369` = 369L, `370` = 370L, 
`373` = 373L, `374` = 374L, `375` = 375L, `400` = 400L, `425` = 425L, 
`444` = 444L, `445` = 445L, `449` = 449L, `450` = 450L, `475` = 475L, 
`488` = 488L, `489` = 489L, `490` = 490L, `493` = 493L, `494` = 494L, 
`495` = 495L, `498` = 498L, `499` = 499L, `500` = 500L, `519` = 519L, 
`520` = 520L, `524` = 524L, `525` = 525L, `550` = 550L, `563` = 563L, 
`564` = 564L, `565` = 565L, `568` = 568L, `569` = 569L, `570` = 570L, 
`573` = 573L, `574` = 574L, `575` = 575L, `594` = 594L, `595` = 595L, 
`599` = 599L, `600` = 600L, `619` = 619L, `620` = 620L, `624` = 624L, 
`625` = 625L, `650` = 650L, `675` = 675L, `688` = 688L, `689` = 689L, 
`690` = 690L, `693` = 693L, `694` = 694L, `695` = 695L, `698` = 698L, 
`699` = 699L, `700` = 700L, `713` = 713L, `714` = 714L, `715` = 715L, 
`718` = 718L, `719` = 719L, `720` = 720L, `723` = 723L, `724` = 724L, 
`725` = 725L, `738` = 738L, `739` = 739L, `740` = 740L, `743` = 743L, 
`744` = 744L, `745` = 745L, `748` = 748L, `749` = 749L, `750` = 750L, 
`763` = 763L, `764` = 764L, `765` = 765L, `768` = 768L, `769` = 769L, 
`770` = 770L, `773` = 773L, `774` = 774L, `775` = 775L, `788` = 788L, 
`789` = 789L, `790` = 790L, `793` = 793L, `794` = 794L, `795` = 795L, 
`798` = 798L, `799` = 799L, `800` = 800L, `813` = 813L, `814` = 814L, 
`815` = 815L, `818` = 818L, `819` = 819L, `820` = 820L, `823` = 823L, 
`824` = 824L, `825` = 825L, `838` = 838L, `839` = 839L, `840` = 840L, 
`843` = 843L, `844` = 844L, `845` = 845L, `848` = 848L, `849` = 849L, 
`850` = 850L, `863` = 863L, `864` = 864L, `865` = 865L, `868` = 868L, 
`869` = 869L, `870` = 870L, `873` = 873L, `874` = 874L, `875` = 875L
), class = "omit"))
23

Thanks, Argyris — that helped to spot my typo in the dput() code (!) I'll edit it now, and then you can rerun to produce the 100-row output table:

depth_plus_temp_long |> 
  group_by(measure) |> 
  slice(1:50) |>   # <-- typo was here: said `slice(50)`before
  ungroup() |> 
  dput()
24 
structure(list(Date = structure(c(1696428000, 1696428000, 1696449600, 
1696449600, 1696471200, 1696471200, 1696492800, 1696492800, 1696514400, 
1696514400, 1696536000, 1696536000, 1696557600, 1696557600, 1696579200, 
1696579200, 1696600800, 1696600800, 1696622400, 1696622400, 1696644000, 
1696644000, 1696665600, 1696665600, 1696687200, 1696687200, 1696708800, 
1696708800, 1696730400, 1696730400, 1696752000, 1696752000, 1696773600, 
1696773600, 1696795200, 1696795200, 1696816800, 1696816800, 1696838400, 
1696838400, 1696860000, 1696860000, 1696881600, 1696881600, 1696903200, 
1696903200, 1696924800, 1696924800, 1696946400, 1696946400, 1696471200, 
1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 
1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 
1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 
1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 
1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 
1696471200, 1696492800, 1696492800, 1696492800, 1696492800, 1696492800, 
1696492800, 1696492800, 1696492800, 1696492800, 1696492800, 1696492800, 
1696492800, 1696492800, 1696492800, 1696492800, 1696492800, 1696492800, 
1696492800), tzone = "", class = c("POSIXct", "POSIXt")), measure = c("depth", 
"depth", "depth", "depth", "depth", "depth", "depth", "depth", 
"depth", "depth", "depth", "depth", "depth", "depth", "depth", 
"depth", "depth", "depth", "depth", "depth", "depth", "depth", 
"depth", "depth", "depth", "depth", "depth", "depth", "depth", 
"depth", "depth", "depth", "depth", "depth", "depth", "depth", 
"depth", "depth", "depth", "depth", "depth", "depth", "depth", 
"depth", "depth", "depth", "depth", "depth", "depth", "depth", 
"temp", "temp", "temp", "temp", "temp", "temp", "temp", "temp", 
"temp", "temp", "temp", "temp", "temp", "temp", "temp", "temp", 
"temp", "temp", "temp", "temp", "temp", "temp", "temp", "temp", 
"temp", "temp", "temp", "temp", "temp", "temp", "temp", "temp", 
"temp", "temp", "temp", "temp", "temp", "temp", "temp", "temp", 
"temp", "temp", "temp", "temp", "temp", "temp", "temp", "temp", 
"temp", "temp"), extreme = c("min", "max", "min", "max", "min", 
"max", "min", "max", "min", "max", "min", "max", "min", "max", 
"min", "max", "min", "max", "min", "max", "min", "max", "min", 
"max", "min", "max", "min", "max", "min", "max", "min", "max", 
"min", "max", "min", "max", "min", "max", "min", "max", "min", 
"max", "min", "max", "min", "max", "min", "max", "min", "max", 
"min", "max", "min", "max", "min", "max", "min", "max", "min", 
"max", "min", "max", "min", "max", "min", "max", "min", "max", 
"min", "max", "min", "max", "min", "max", "min", "max", "min", 
"max", "min", "max", "min", "max", "min", "max", "min", "max", 
"min", "max", "min", "max", "min", "max", "min", "max", "min", 
"max", "min", "max", "min", "max"), value = c(0.75, 5, 4.5, 4.5, 
3, 4, 4, 6, 6, 6, 4, 6, 5, 6, 5, 20, 20.5, 22.5, 20, 21, 16, 
26, 15.5, 23.5, 23, 26, 19, 26, 23.5, 25.5, 18, 27, 24, 26, 17, 
27, 14, 25, 12, 26, 25, 26, 17.5, 26.5, 17, 26, 16, 27, 16.5, 
26.5, 17.9, 18.1, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 
17.7, 18.1, 17.7, 17.9, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 18.1, 
18.1, 17.9, 17.9, 18.1, 18.1, 17.9, 17.9, 18.1, 18.1, 17.9, 17.9, 
18.1, 18.3, 18.1, 17.9, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 17.7, 
18.3, 17.7, 17.9, 17.7, 17.7, 17.7, 17.7)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -100L))
Show in New Window
A tibble:677 × 3
Date
<S3: POSIXct>
min
<dbl>
max
<dbl>
2023-10-05 05:00:00	17.9	18.1		
2023-10-05 05:00:00	17.9	17.9		
2023-10-05 05:00:00	17.9	17.9		
2023-10-05 05:00:00	17.9	17.9		
2023-10-05 05:00:00	17.9	17.9		
2023-10-05 05:00:00	17.7	18.1		
2023-10-05 05:00:00	17.7	17.9		
2023-10-05 05:00:00	17.7	17.7		
2023-10-05 05:00:00	17.7	17.7		
2023-10-05 05:00:00	17.7	17.7		
...
1-10 of 677 rows
Show in New Window
structure(list(Date = structure(c(1696428000, 1696449600, 1696471200, 
1696492800, 1696514400, 1696536000, 1696557600, 1696579200, 1696600800, 
1696622400), tzone = "", class = c("POSIXct", "POSIXt")), min = c(0.75, 
4.5, 3, 4, 6, 4, 5, 5, 20.5, 20), max = c(5, 4.5, 4, 6, 6, 6, 
6, 20, 22.5, 21)), row.names = c(NA, -10L), class = c("tbl_df", 
"tbl", "data.frame"))
Show in New Window
structure(list(Date = structure(c(1696471200, 1696471200, 1696471200, 
1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 1696471200, 
1696471200), tzone = "", class = c("POSIXct", "POSIXt")), min = c(17.9, 
17.9, 17.9, 17.9, 17.9, 17.7, 17.7, 17.7, 17.7, 17.7), max = c(18.1, 
17.9, 17.9, 17.9, 17.9, 18.1, 17.9, 17.7, 17.7, 17.7)), row.names = c(NA, 
-10L), class = c("tbl_df", "tbl", "data.frame"), na.action = structure(c(`13` = 13L, 
`14` = 14L, `15` = 15L, `18` = 18L, `19` = 19L, `20` = 20L, `23` = 23L, 
`24` = 24L, `25` = 25L, `38` = 38L, `39` = 39L, `40` = 40L, `43` = 43L, 
`44` = 44L, `45` = 45L, `48` = 48L, `49` = 49L, `50` = 50L, `57` = 57L, 
`58` = 58L, `59` = 59L, `60` = 60L, `62` = 62L, `63` = 63L, `64` = 64L, 
`65` = 65L, `67` = 67L, `68` = 68L, `69` = 69L, `70` = 70L, `72` = 72L, 
`73` = 73L, `74` = 74L, `75` = 75L, `88` = 88L, `89` = 89L, `90` = 90L, 
`93` = 93L, `94` = 94L, `95` = 95L, `98` = 98L, `99` = 99L, `100` = 100L, 
`119` = 119L, `120` = 120L, `124` = 124L, `125` = 125L, `138` = 138L, 
`139` = 139L, `140` = 140L, `143` = 143L, `144` = 144L, `145` = 145L, 
`148` = 148L, `149` = 149L, `150` = 150L, `175` = 175L, `188` = 188L, 
`189` = 189L, `190` = 190L, `193` = 193L, `194` = 194L, `195` = 195L, 
`198` = 198L, `199` = 199L, `200` = 200L, `219` = 219L, `220` = 220L, 
`224` = 224L, `225` = 225L, `250` = 250L, `269` = 269L, `270` = 270L, 
`274` = 274L, `275` = 275L, `325` = 325L, `350` = 350L, `363` = 363L, 
`364` = 364L, `365` = 365L, `368` = 368L, `369` = 369L, `370` = 370L, 
`373` = 373L, `374` = 374L, `375` = 375L, `400` = 400L, `425` = 425L, 
`444` = 444L, `445` = 445L, `449` = 449L, `450` = 450L, `475` = 475L, 
`488` = 488L, `489` = 489L, `490` = 490L, `493` = 493L, `494` = 494L, 
`495` = 495L, `498` = 498L, `499` = 499L, `500` = 500L, `519` = 519L, 
`520` = 520L, `524` = 524L, `525` = 525L, `550` = 550L, `563` = 563L, 
`564` = 564L, `565` = 565L, `568` = 568L, `569` = 569L, `570` = 570L, 
`573` = 573L, `574` = 574L, `575` = 575L, `594` = 594L, `595` = 595L, 
`599` = 599L, `600` = 600L, `619` = 619L, `620` = 620L, `624` = 624L, 
`625` = 625L, `650` = 650L, `675` = 675L, `688` = 688L, `689` = 689L, 
`690` = 690L, `693` = 693L, `694` = 694L, `695` = 695L, `698` = 698L, 
`699` = 699L, `700` = 700L, `713` = 713L, `714` = 714L, `715` = 715L, 
`718` = 718L, `719` = 719L, `720` = 720L, `723` = 723L, `724` = 724L, 
`725` = 725L, `738` = 738L, `739` = 739L, `740` = 740L, `743` = 743L, 
`744` = 744L, `745` = 745L, `748` = 748L, `749` = 749L, `750` = 750L, 
`763` = 763L, `764` = 764L, `765` = 765L, `768` = 768L, `769` = 769L, 
`770` = 770L, `773` = 773L, `774` = 774L, `775` = 775L, `788` = 788L, 
`789` = 789L, `790` = 790L, `793` = 793L, `794` = 794L, `795` = 795L, 
`798` = 798L, `799` = 799L, `800` = 800L, `813` = 813L, `814` = 814L, 
`815` = 815L, `818` = 818L, `819` = 819L, `820` = 820L, `823` = 823L, 
`824` = 824L, `825` = 825L, `838` = 838L, `839` = 839L, `840` = 840L, 
`843` = 843L, `844` = 844L, `845` = 845L, `848` = 848L, `849` = 849L, 
`850` = 850L, `863` = 863L, `864` = 864L, `865` = 865L, `868` = 868L, 
`869` = 869L, `870` = 870L, `873` = 873L, `874` = 874L, `875` = 875L
), class = "omit"))

Here are the results