Using mudata objects

The mudata2 package is designed to be used as little as possible. That is, if you need use data that is currently in mudata format, the functions in this package are designed to let you spend as little time as possible reading, subsetting, and inspecting your data. The steps are generally as follows:

In this vignette we will use the ns_climate dataset within the mudata2 package, which is a collection of monthly climate observations from Nova Scotia (Canada), sourced from Environment Canada using the rclimateca package.

Reading an object

The ns_climate object is already an object in R, but if it wasn’t, you would need to use read_mudata() to read it in. If you’re curious what a mudata object looks like on disk, you could try using write_mudata() to find out. I tend to prefer writing to a directory rather than a JSON or ZIP file, but you can take your pick.

# write to directory
write_mudata(ns_climate, "ns_climate.mudata")
# write to ZIP
write_mudata(ns_climate, "ns_climate.mudata.zip")
# write to JSON
write_mudata(ns_climate, "ns_climate.mudata.json")

Then, you can read in the object using read_mudata():

# read from directory
read_mudata("ns_climate.mudata")
# read from ZIP
read_mudata("ns_climate.mudata.zip")
# read from JSON
read_mudata("ns_climate.mudata.json")

Inspecting an object

The three main ways to quickly inspect a mudata object are print() and summary(). The print() function is what you get when you type the name of the object at the prompt, and gives a short summary of the object. The output suggests a couple of other ways to inspect the object, including distinct_locations(), which returns a character vector of location identifiers, and distinct_params(), which returns a character vector of parameter identifiers.

print(ns_climate)

## A mudata object aligned along "date"
##   distinct_datasets():  "ecclimate_monthly"
##   distinct_locations(): "ANNAPOLIS ROYAL 6289", "BADDECK 6297" ... and 13 more
##   distinct_params():    "dir_of_max_gust", "extr_max_temp" ... and 9 more
##   src_tbls():           "data", "locations" ... and 3 more
## 
## tbl_data() %>% head():
## # A tibble: 6 × 7
##   dataset           location          param       date       value flag  flag_…¹
##   <chr>             <chr>             <chr>       <date>     <dbl> <chr> <chr>  
## 1 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-01-01    NA M     Missing
## 2 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-02-01    NA M     Missing
## 3 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-03-01    NA M     Missing
## 4 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-04-01    NA M     Missing
## 5 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-05-01    NA M     Missing
## 6 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-06-01    NA M     Missing
## # … with abbreviated variable name ¹flag_text

The summary() function provides some numeric summaries by dataset, location, and parameter if the value column of the data table is numeric (if it isn’t, it provides counts instead).

summary(ns_climate)

## # A tibble: 137 × 7
##    param           location             dataset      mean_…¹ sd_va…²     n  n_NA
##    <chr>           <chr>                <chr>          <dbl>   <dbl> <int> <int>
##  1 dir_of_max_gust SABLE ISLAND 6454    ecclimate_m…    19.8   10.2    299     0
##  2 extr_max_temp   ANNAPOLIS ROYAL 6289 ecclimate_m…    19.9    7.24   995    28
##  3 extr_max_temp   BADDECK 6297         ecclimate_m…    18.9    8.58   901    43
##  4 extr_max_temp   BEAVERBANK 6301      ecclimate_m…    17.2   10.4     24    17
##  5 extr_max_temp   COLLEGEVILLE 6329    ecclimate_m…    20.3    8.54  1061    34
##  6 extr_max_temp   DIGBY 6338           ecclimate_m…    19.0    6.92   624    20
##  7 extr_max_temp   KENTVILLE CDA 6375   ecclimate_m…    21.0    8.27  1002     3
##  8 extr_max_temp   MAHONE BAY 6396      ecclimate_m…    20.8    8.35   108    11
##  9 extr_max_temp   MOUNT UNIACKE 6413   ecclimate_m…    19.7    8.21   972    30
## 10 extr_max_temp   NAPPAN CDA 6414      ecclimate_m…    19.3    8.04  1121    19
## # … with 127 more rows, and abbreviated variable names ¹mean_value, ²sd_value

Inspecting metadata

You can have a look at the embedded documentation using tbl_params(), and tbl_locations(), which contain any additional information about parameters and locations for which data are available. The identifiers (i.e., param and location columns) of these can be used to subset the object using select_*() functions; the tables themselves can be used to subset the object using the filter_*() functions.

# extract the parameters table
ns_climate %>% tbl_params()

## # A tibble: 11 × 4
##    dataset           param              label                      unit    
##    <chr>             <chr>              <chr>                      <chr>   
##  1 ecclimate_monthly mean_max_temp      Mean Max Temp (C)          C       
##  2 ecclimate_monthly mean_min_temp      Mean Min Temp (C)          C       
##  3 ecclimate_monthly mean_temp          Mean Temp (C)              C       
##  4 ecclimate_monthly extr_max_temp      Extr Max Temp (C)          C       
##  5 ecclimate_monthly extr_min_temp      Extr Min Temp (C)          C       
##  6 ecclimate_monthly total_rain         Total Rain (mm)            mm      
##  7 ecclimate_monthly total_snow         Total Snow (cm)            cm      
##  8 ecclimate_monthly total_precip       Total Precip (mm)          mm      
##  9 ecclimate_monthly snow_grnd_last_day Snow Grnd Last Day (cm)    cm      
## 10 ecclimate_monthly dir_of_max_gust    Dir of Max Gust (10's deg) 10's deg
## 11 ecclimate_monthly spd_of_max_gust    Spd of Max Gust (km/h)     km/h

# exract the locations table
ns_climate %>% tbl_locations()

## # A tibble: 15 × 19
##    dataset    locat…¹ name  provi…² clima…³ stati…⁴ wmo_id tc_id latit…⁵ longi…⁶
##    <chr>      <chr>   <chr> <chr>   <chr>     <int>  <int> <chr>   <dbl>   <dbl>
##  1 ecclimate… ANNAPO… ANNA… NOVA S… 8200100    6289     NA ""       44.8   -65.5
##  2 ecclimate… BADDEC… BADD… NOVA S… 8200300    6297     NA ""       46.1   -60.8
##  3 ecclimate… BEAVER… BEAV… NOVA S… 8200550    6301     NA ""       44.9   -63.7
##  4 ecclimate… COLLEG… COLL… NOVA S… 8201000    6329     NA ""       45.5   -62.0
##  5 ecclimate… DIGBY … DIGBY NOVA S… 8201600    6338     NA ""       44.6   -65.8
##  6 ecclimate… KENTVI… KENT… NOVA S… 8202800    6375     NA ""       45.1   -64.5
##  7 ecclimate… MAHONE… MAHO… NOVA S… 8203300    6396     NA ""       44.5   -64.4
##  8 ecclimate… MOUNT … MOUN… NOVA S… 8203600    6413     NA ""       44.9   -63.8
##  9 ecclimate… NAPPAN… NAPP… NOVA S… 8203700    6414     NA ""       45.8   -64.2
## 10 ecclimate… PARRSB… PARR… NOVA S… 8204400    6428     NA ""       45.4   -64.3
## 11 ecclimate… PORT H… PORT… NOVA S… 8204480    6441     NA ""       45.6   -61.4
## 12 ecclimate… SABLE … SABL… NOVA S… 8204700    6454  71600 "ESA"    43.9   -60.0
## 13 ecclimate… ST MAR… ST M… NOVA S… 8204800    6456     NA ""       44.7   -63.9
## 14 ecclimate… SPRING… SPRI… NOVA S… 8205200    6473     NA ""       44.7   -64.8
## 15 ecclimate… UPPER … UPPE… NOVA S… 8206200    6495     NA ""       45.2   -63  
## # … with 9 more variables: elevation <dbl>, first_year <int>, last_year <int>,
## #   hly_first_year <int>, hly_last_year <int>, dly_first_year <int>,
## #   dly_last_year <int>, mly_first_year <int>, mly_last_year <int>, and
## #   abbreviated variable names ¹location, ²province, ³climate_id, ⁴station_id,
## #   ⁵latitude, ⁶longitude

Subsetting an object

You can subset mudata objects using select_params() and select_locations(), which use dplyr-like selection syntax to quickly subset mudata objects using the identifiers from distinct_locations() and distinct_params() (respectively).

# find out which parameters are available
ns_climate %>% distinct_params()

##  [1] "dir_of_max_gust"    "extr_max_temp"      "extr_min_temp"     
##  [4] "mean_max_temp"      "mean_min_temp"      "mean_temp"         
##  [7] "snow_grnd_last_day" "spd_of_max_gust"    "total_precip"      
## [10] "total_rain"         "total_snow"

# subset by parameter
ns_climate %>% select_params(mean_temp, total_precip)

## A mudata object aligned along "date"
##   distinct_datasets():  "ecclimate_monthly"
##   distinct_locations(): "ANNAPOLIS ROYAL 6289", "BADDECK 6297" ... and 13 more
##   distinct_params():    "mean_temp", "total_precip"
##   src_tbls():           "data", "locations" ... and 3 more
## 
## tbl_data() %>% head():
## # A tibble: 6 × 7
##   dataset           location          param     date       value flag  flag_text
##   <chr>             <chr>             <chr>     <date>     <dbl> <chr> <chr>    
## 1 ecclimate_monthly SABLE ISLAND 6454 mean_temp 1897-01-01    NA M     Missing  
## 2 ecclimate_monthly SABLE ISLAND 6454 mean_temp 1897-02-01    NA M     Missing  
## 3 ecclimate_monthly SABLE ISLAND 6454 mean_temp 1897-03-01    NA M     Missing  
## 4 ecclimate_monthly SABLE ISLAND 6454 mean_temp 1897-04-01    NA M     Missing  
## 5 ecclimate_monthly SABLE ISLAND 6454 mean_temp 1897-05-01    NA M     Missing  
## 6 ecclimate_monthly SABLE ISLAND 6454 mean_temp 1897-06-01    NA M     Missing

You can also use the dplyr select helpers to select related params/locations…

ns_climate %>% select_params(contains("temp"))

## A mudata object aligned along "date"
##   distinct_datasets():  "ecclimate_monthly"
##   distinct_locations(): "ANNAPOLIS ROYAL 6289", "BADDECK 6297" ... and 13 more
##   distinct_params():    "extr_max_temp", "extr_min_temp" ... and 3 more
##   src_tbls():           "data", "locations" ... and 3 more
## 
## tbl_data() %>% head():
## # A tibble: 6 × 7
##   dataset           location          param       date       value flag  flag_…¹
##   <chr>             <chr>             <chr>       <date>     <dbl> <chr> <chr>  
## 1 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-01-01    NA M     Missing
## 2 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-02-01    NA M     Missing
## 3 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-03-01    NA M     Missing
## 4 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-04-01    NA M     Missing
## 5 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-05-01    NA M     Missing
## 6 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-06-01    NA M     Missing
## # … with abbreviated variable name ¹flag_text

…and rename params/locations on the fly.

ns_climate %>% select_locations(Kentville = starts_with("KENT"))

## A mudata object aligned along "date"
##   distinct_datasets():  "ecclimate_monthly"
##   distinct_locations(): "Kentville"
##   distinct_params():    "extr_max_temp", "extr_min_temp" ... and 7 more
##   src_tbls():           "data", "locations" ... and 3 more
## 
## tbl_data() %>% head():
## # A tibble: 6 × 7
##   dataset           location  param         date       value flag  flag_text
##   <chr>             <chr>     <chr>         <date>     <dbl> <chr> <chr>    
## 1 ecclimate_monthly Kentville mean_max_temp 1913-01-01  NA   M     Missing  
## 2 ecclimate_monthly Kentville mean_max_temp 1913-02-01  NA   M     Missing  
## 3 ecclimate_monthly Kentville mean_max_temp 1913-03-01  NA   M     Missing  
## 4 ecclimate_monthly Kentville mean_max_temp 1913-04-01   9.7 <NA>  <NA>     
## 5 ecclimate_monthly Kentville mean_max_temp 1913-05-01  12.5 <NA>  <NA>     
## 6 ecclimate_monthly Kentville mean_max_temp 1913-06-01  19.9 <NA>  <NA>

To select params/locations based on the tbl_params() and tbl_locations() tables, you can use the filter_*() functions (note that last_year is a column in tbl_locations(), and unit is a column in tbl_params()):

# only use locations whose last data point was after 2000
ns_climate %>%
  filter_locations(last_year > 2000)

## A mudata object aligned along "date"
##   distinct_datasets():  "ecclimate_monthly"
##   distinct_locations(): "ANNAPOLIS ROYAL 6289", "COLLEGEVILLE 6329" ... and 7 more
##   distinct_params():    "dir_of_max_gust", "extr_max_temp" ... and 9 more
##   src_tbls():           "data", "locations" ... and 3 more
## 
## tbl_data() %>% head():
## # A tibble: 6 × 7
##   dataset           location          param       date       value flag  flag_…¹
##   <chr>             <chr>             <chr>       <date>     <dbl> <chr> <chr>  
## 1 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-01-01    NA M     Missing
## 2 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-02-01    NA M     Missing
## 3 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-03-01    NA M     Missing
## 4 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-04-01    NA M     Missing
## 5 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-05-01    NA M     Missing
## 6 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-06-01    NA M     Missing
## # … with abbreviated variable name ¹flag_text

# use only params measured in mm
ns_climate %>%
  filter_params(unit == "mm")

## A mudata object aligned along "date"
##   distinct_datasets():  "ecclimate_monthly"
##   distinct_locations(): "ANNAPOLIS ROYAL 6289", "BADDECK 6297" ... and 13 more
##   distinct_params():    "total_precip", "total_rain"
##   src_tbls():           "data", "locations" ... and 3 more
## 
## tbl_data() %>% head():
## # A tibble: 6 × 7
##   dataset           location          param      date       value flag  flag_t…¹
##   <chr>             <chr>             <chr>      <date>     <dbl> <chr> <chr>   
## 1 ecclimate_monthly SABLE ISLAND 6454 total_rain 1891-01-01  NA   M     Missing 
## 2 ecclimate_monthly SABLE ISLAND 6454 total_rain 1891-02-01  40.4 <NA>  <NA>    
## 3 ecclimate_monthly SABLE ISLAND 6454 total_rain 1891-03-01  32   <NA>  <NA>    
## 4 ecclimate_monthly SABLE ISLAND 6454 total_rain 1891-04-01 132.  <NA>  <NA>    
## 5 ecclimate_monthly SABLE ISLAND 6454 total_rain 1891-05-01  44.7 <NA>  <NA>    
## 6 ecclimate_monthly SABLE ISLAND 6454 total_rain 1891-06-01 106.  <NA>  <NA>    
## # … with abbreviated variable name ¹flag_text

Similarly, we can subset parameters, locations, and the data table all at once using filter_data().

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

# extract only June temperature from the data table
ns_climate %>%
  filter_data(month(date) == 6)

## A mudata object aligned along "date"
##   distinct_datasets():  "ecclimate_monthly"
##   distinct_locations(): "ANNAPOLIS ROYAL 6289", "BADDECK 6297" ... and 13 more
##   distinct_params():    "dir_of_max_gust", "extr_max_temp" ... and 9 more
##   src_tbls():           "data", "locations" ... and 3 more
## 
## tbl_data() %>% head():
## # A tibble: 6 × 7
##   dataset           location          param       date       value flag  flag_…¹
##   <chr>             <chr>             <chr>       <date>     <dbl> <chr> <chr>  
## 1 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1897-06-01  NA   M     Missing
## 2 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1898-06-01  13.4 <NA>  <NA>   
## 3 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1899-06-01  14.4 <NA>  <NA>   
## 4 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1900-06-01  14.6 <NA>  <NA>   
## 5 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1901-06-01  15.3 <NA>  <NA>   
## 6 ecclimate_monthly SABLE ISLAND 6454 mean_max_t… 1902-06-01  13.6 <NA>  <NA>   
## # … with abbreviated variable name ¹flag_text

Extracting data

The data is stored in the data table (i.e., tbl_data()) in parameter-long form (that is, one row per measurement rather than one row per observation). This has advantages in that information about each measurement can be stored next to the value (e.g., standard deviation, notes, etc.), however it is rarely the form required for analysis. To extract data in parameter-long form, you can use tbl_data():

ns_climate %>% tbl_data()

## # A tibble: 115,541 × 7
##    dataset           location          param      date       value flag  flag_…¹
##    <chr>             <chr>             <chr>      <date>     <dbl> <chr> <chr>  
##  1 ecclimate_monthly SABLE ISLAND 6454 mean_max_… 1897-01-01  NA   M     Missing
##  2 ecclimate_monthly SABLE ISLAND 6454 mean_max_… 1897-02-01  NA   M     Missing
##  3 ecclimate_monthly SABLE ISLAND 6454 mean_max_… 1897-03-01  NA   M     Missing
##  4 ecclimate_monthly SABLE ISLAND 6454 mean_max_… 1897-04-01  NA   M     Missing
##  5 ecclimate_monthly SABLE ISLAND 6454 mean_max_… 1897-05-01  NA   M     Missing
##  6 ecclimate_monthly SABLE ISLAND 6454 mean_max_… 1897-06-01  NA   M     Missing
##  7 ecclimate_monthly SABLE ISLAND 6454 mean_max_… 1897-07-01  NA   M     Missing
##  8 ecclimate_monthly SABLE ISLAND 6454 mean_max_… 1897-08-01  NA   M     Missing
##  9 ecclimate_monthly SABLE ISLAND 6454 mean_max_… 1897-09-01  NA   M     Missing
## 10 ecclimate_monthly SABLE ISLAND 6454 mean_max_… 1897-10-01  12.2 <NA>  <NA>   
## # … with 115,531 more rows, and abbreviated variable name ¹flag_text

To extract data in a more standard parameter-wide form, you can use tbl_data_wide():

ns_climate %>% tbl_data_wide()

## # A tibble: 14,311 × 14
##    dataset    locat…¹ date       dir_o…² extr_…³ extr_…⁴ mean_…⁵ mean_…⁶ mean_…⁷
##    <chr>      <chr>   <date>       <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 ecclimate… ANNAPO… 1914-01-01      NA    NA      NA      NA      NA      NA  
##  2 ecclimate… ANNAPO… 1914-02-01      NA    NA      NA      NA      NA      NA  
##  3 ecclimate… ANNAPO… 1914-03-01      NA    NA      NA      NA      NA      NA  
##  4 ecclimate… ANNAPO… 1914-04-01      NA    19.4   -11.1     8.2    -3.1     2.6
##  5 ecclimate… ANNAPO… 1914-05-01      NA    30      -3.9    15.8     3.8     9.8
##  6 ecclimate… ANNAPO… 1914-06-01      NA    26.7    -1.7    19.8     7.2    13.5
##  7 ecclimate… ANNAPO… 1914-07-01      NA    30       3.9    22.3    10.2    16.3
##  8 ecclimate… ANNAPO… 1914-08-01      NA    NA      NA      NA      NA      NA  
##  9 ecclimate… ANNAPO… 1914-09-01      NA    NA      NA      NA      NA      NA  
## 10 ecclimate… ANNAPO… 1914-10-01      NA    NA      NA      NA      NA      NA  
## # … with 14,301 more rows, 5 more variables: snow_grnd_last_day <dbl>,
## #   spd_of_max_gust <dbl>, total_precip <dbl>, total_rain <dbl>,
## #   total_snow <dbl>, and abbreviated variable names ¹location,
## #   ²dir_of_max_gust, ³extr_max_temp, ⁴extr_min_temp, ⁵mean_max_temp,
## #   ⁶mean_min_temp, ⁷mean_temp

The tbl_data_wide() function isn’t limited to parameter-wide data - data can be anything-wide (Edzer Pebesma has a great discussion on this). Using tbl_data_wide() is identical to using tbl_data() and tidyr::spread(), with context-specific defaults.

ns_climate %>%
  select_params(mean_temp) %>%
  filter_data(year(date) == 1960) %>%
  tbl_data_wide(key = location)

## # A tibble: 12 × 16
##    dataset      param date       BADDE…¹ COLLE…² DIGBY…³ KENTV…⁴ MAHON…⁵ MOUNT…⁶
##    <chr>        <chr> <date>       <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 ecclimate_m… mean… 1960-01-01    -3.8    -6      -2.6    -5.1    -5.7    -6.7
##  2 ecclimate_m… mean… 1960-02-01    -1.2    -2.5     0.3    -1.2    -2.3    -3.1
##  3 ecclimate_m… mean… 1960-03-01    -1.3    -3.1     0      -1.8    -2.3    -3.5
##  4 ecclimate_m… mean… 1960-04-01     3       2.1     6.5     4.8     4.7     3.1
##  5 ecclimate_m… mean… 1960-05-01    11.7    10.9    12.8    13.1    11.5    11.6
##  6 ecclimate_m… mean… 1960-06-01    14.4    14.7    16.4    17.2    16.2    15  
##  7 ecclimate_m… mean… 1960-07-01    17.1    18      18.9    19.4    18.2    17.2
##  8 ecclimate_m… mean… 1960-08-01    NA      18.5    18.6    19.6    18.9    18  
##  9 ecclimate_m… mean… 1960-09-01    15.2    14      14.8    15.1    15      13.1
## 10 ecclimate_m… mean… 1960-10-01     8.7     6.9     9.1     8.1     7.2     6.6
## 11 ecclimate_m… mean… 1960-11-01     4.6     3.2     6.7     4.9     4.1     3  
## 12 ecclimate_m… mean… 1960-12-01    -0.8    -3.5    -0.4    -2.4    NA      -4.3
## # … with 7 more variables: `NAPPAN CDA 6414` <dbl>, `PARRSBORO 6428` <dbl>,
## #   `PORT HASTINGS 6441` <dbl>, `SABLE ISLAND 6454` <dbl>,
## #   `SPRINGFIELD 6473` <dbl>, `ST MARGARET'S BAY 6456` <dbl>,
## #   `UPPER STEWIACKE 6495` <dbl>, and abbreviated variable names
## #   ¹`BADDECK 6297`, ²`COLLEGEVILLE 6329`, ³`DIGBY 6338`,
## #   ⁴`KENTVILLE CDA 6375`, ⁵`MAHONE BAY 6396`, ⁶`MOUNT UNIACKE 6413`

Putting it all together

Using the pipe (%>%), we can string all the steps together concisely:

temp_1960 <- ns_climate %>%
  # pick parameters
  select_params(contains("temp")) %>%
  # pick locations
  select_locations(
    `Sable Island` = starts_with("SABLE"),
    `Kentville` = starts_with("KENT"),
    `Badeck` = starts_with("BADD")
  ) %>%
  # filter data table
  filter_data(year(date) == 1960) %>%
  # extract data in wide format
  tbl_data_wide()

temp_1960

## # A tibble: 36 × 8
##    dataset           location date       extr_…¹ extr_…² mean_…³ mean_…⁴ mean_…⁵
##    <chr>             <chr>    <date>       <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 ecclimate_monthly Badeck   1960-01-01     8.9   -16.7    -0.6    -6.9    -3.8
##  2 ecclimate_monthly Badeck   1960-02-01     6.1   -13.3     1.7    -4.1    -1.2
##  3 ecclimate_monthly Badeck   1960-03-01     7.2    -9.4     0.9    -3.4    -1.3
##  4 ecclimate_monthly Badeck   1960-04-01    16.7    -7.8     6.1    -0.2     3  
##  5 ecclimate_monthly Badeck   1960-05-01    26.7     2.2    17.2     6.2    11.7
##  6 ecclimate_monthly Badeck   1960-06-01    30.6     0      19.6     9.2    14.4
##  7 ecclimate_monthly Badeck   1960-07-01    28.3     8.9    22.6    11.6    17.1
##  8 ecclimate_monthly Badeck   1960-08-01    33.3     8.9    24.3    NA      NA  
##  9 ecclimate_monthly Badeck   1960-09-01    25.6     4.4    19.8    10.6    15.2
## 10 ecclimate_monthly Badeck   1960-10-01    18.3    -0.6    12.3     5       8.7
## # … with 26 more rows, and abbreviated variable names ¹extr_max_temp,
## #   ²extr_min_temp, ³mean_max_temp, ⁴mean_min_temp, ⁵mean_temp

We can then use this data with ggplot2 to lead us to the conclusion that three locations in the same province had more or less the same monthly temperature characteristics in 1960.

library(ggplot2)
ggplot(
  temp_1960,
  aes(
    x = date,
    y = mean_temp,
    ymin = extr_min_temp,
    ymax = extr_max_temp,
    col = location,
    fill = location
  )
) +
  geom_ribbon(alpha = 0.2, col = NA) +
  geom_line()