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I have a dataset that collected temperature on 10 different sensors each minute per three months.

I have cleaned the dataframe omitting the 'NA's and excluding other variables like air temperature, relative humidity etc... as I just want to analyse the temperature distribution across the days (see pic). I'm using R studio, version 4.2.2 "innocent and trusting"

The problem is that I have no clue how to plot this, never encountered continuous data before.

Thank you.

I have tried the hist() to obtain a gauss distribution and the most common ggplot basic functions, but I'd prefer not to share my confusion to don't confuse also who reads.

date    time    Temperature1    Temperature2    Temperature3    Temperature4    Temperature5    Temperature6    
2/02/2023   3:06:00 PM  37.7    34.6    34.28   37.02   34.51   35.54   40.2    36.51   35.02   34.54
2/02/2023   3:07:00 PM  NA  34.31   34.12   36.22   34.42   35.49   39.39   36.58   34.95   34.75
2/02/2023   3:08:00 PM  35.9    34.49   34.16   36.8    34.57   35.58   41.24   37.02   35.18   35.33
2/02/2023   3:09:00 PM  35.69   34.66   34.18   37.16   34.57   35.15   38.68   36.31   35.58   35.45
2/02/2023   3:13:00 PM  35.4    34.36   33.9    35.7    34.05   34.63   37.74   35.72   35.08   37.52
2/02/2023   3:14:00 PM  35.25   34.66   34.08   36.86   34.11   34.58   38.22   35.64   35.77   38.91
2/02/2023   3:15:00 PM  35.63   35.29   34.09   37.96   34.85   35.58   41.01   35.93   36.11   39.73
2/02/2023   3:16:00 PM  35.95   35.47   34.25   37.17   34.64   35.04   39.51   35.93   35.67   39.31
2/02/2023   3:17:00 PM  35.94   34.87   34.05   36.51   34.17   34.51   39.38   35.9    35.72   38.87
2/02/2023   3:20:00 PM  35.16   33.99   33.83   35.85   33.97   34.76   38.26   35.36   34.89   37.82
2/02/2023   3:21:00 PM  NA  34.22   33.77   36.99   34.34   35.37   40.31   35.86   35.31   38.51
2/02/2023   3:22:00 PM  35.31   34.28   33.74   36.34   34.03   34.76   37.72   35.43   35.26   38.61
Mark
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user22027606
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    We cannot read data into R from images. Please [make this question reproducible](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) by including a small representative dataset in a plain text format - for example the output from `dput(cleaned_data)`, if that is not too large. And it does help to see code, and any error messages. – neilfws Aug 10 '23 at 01:22
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    I would typically do some variation of `library(tidyverse); df |> pivot_longer(-c(date, time)) |> mutate(date = lubridate::mdy(date)) |> ggplot(aes(date, value, color = name)) + geom_line()`, or if that got too spaghetti-plotty, use facets to show each series with the others as background. – Jon Spring Aug 10 '23 at 01:46

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