Temperature data by room over time

In previous posts I talked about collecting temperature data from different rooms in my house and automating that process using Docker and GitHub Actions. Now it is time to analyze that data to figure out what is happening in the house to make informed decisions about replacing the HVAC units and how I can make the house more comfortable for everyone until then.

During this process I pull the data into R from a DigitalOcean space (like an AWS S3 bucket) using {arrow}, manipulate the data with {dplyr}, {tidyr} and {tsibble}, visualize the data with {timetk}, fit models with {fable} and visualize the models with {coefplot}.

Getting the Data

As discussed in the post about tracking the data, we created a CSV for each day of data in a DigitalOcean space. The CSV tracks room temperatures, thermostat settings, outdoor temperature, etc for every five-minute interval during each day.

In the past, if multiple files had to be read into a single data.frame or tibble the best course of action would have been to combine map_df() from {purrr} with read_csv() or fread() from {readr} and {data.table}, respectively. But as seen in Wes McKinney and Neal Richardson’s talk from the 2020 New York R Conference, using open_data() from {arrow} then collecting the data with dplyr::collect() should be faster. An added bonus is that {arrow} has functionality built in to pull from an S3 bucket, and that includes DigitalOcean spaces.

The first step is to use arrow::S3FileSystem$create() to make a reference to the S3 file system. Several pieces of information are needed for this:

The Access Key can be retrieved at any time, but the Secret Key is only displayed one time, so we must save it somewhere.

This is all the same information used when writing the files to the DigitalOcean space in the first place. We also save it all in environment variables in the .Renviron file to avoid exposing this sensitive information in our code. It is very important to not check this file into git to reduce risk of exposing the private keys.

space <- arrow::S3FileSystem$create(
## S3FileSystem

The space object represents the S3 file system we have access to. Its elements are mostly file system type functions such as cd() ls(), CopyFile() and GetFileInfo() and are accessed via space$function_name(). To see a listing of files in a folder inside a bucket we call space$ls() with the bucket and folder name as the argument, in quotes.

##  [1] "bucket_name/folder_name/2021-01-01.csv"
##  [2] "bucket_name/folder_name/2021-01-02.csv"
##  [3] "bucket_name/folder_name/2021-01-03.csv"
##  [4] "bucket_name/folder_name/2021-01-04.csv"
##  [5] "bucket_name/folder_name/2021-01-05.csv"
##  [6] "bucket_name/folder_name/2021-01-06.csv"
##  [7] "bucket_name/folder_name/2021-01-07.csv"
##  [8] "bucket_name/folder_name/2021-01-08.csv"
##  [9] "bucket_name/folder_name/2021-01-09.csv"
## [10] "bucket_name/folder_name/2021-01-10.csv"
## [11] "bucket_name/folder_name/2021-01-11.csv"
## [12] "bucket_name/folder_name/2021-01-12.csv"
## [13] "bucket_name/folder_name/2021-01-13.csv"
## [14] "bucket_name/folder_name/2021-01-14.csv"
## [15] "bucket_name/folder_name/2021-01-15.csv"
## [16] "bucket_name/folder_name/2021-01-16.csv"
## [17] "bucket_name/folder_name/2021-01-17.csv"
## [18] "bucket_name/folder_name/2021-01-18.csv"
## [19] "bucket_name/folder_name/2021-01-19.csv"
## [20] "bucket_name/folder_name/2021-01-20.csv"
## [21] "bucket_name/folder_name/2021-01-21.csv"
## [22] "bucket_name/folder_name/2021-01-22.csv"
## [23] "bucket_name/folder_name/2021-01-23.csv"
## [24] "bucket_name/folder_name/2021-01-24.csv"
## [25] "bucket_name/folder_name/2021-01-25.csv"
## [26] "bucket_name/folder_name/2021-01-26.csv"
## [27] "bucket_name/folder_name/2021-01-27.csv"
## [28] "bucket_name/folder_name/2021-01-28.csv"
## [29] "bucket_name/folder_name/2021-01-29.csv"
## [30] "bucket_name/folder_name/2021-01-30.csv"
## [31] "bucket_name/folder_name/2021-01-31.csv"
## [32] "bucket_name/folder_name/2021-02-01.csv"
## [33] "bucket_name/folder_name/2021-02-02.csv"
## [34] "bucket_name/folder_name/2021-02-03.csv"
## [35] "bucket_name/folder_name/2021-02-04.csv"
## [36] "bucket_name/folder_name/2021-02-05.csv"
## [37] "bucket_name/folder_name/2021-02-06.csv"
## [38] "bucket_name/folder_name/2021-02-07.csv"
## [39] "bucket_name/folder_name/2021-02-08.csv"
## [40] "bucket_name/folder_name/2021-02-09.csv"
## [41] "bucket_name/folder_name/2021-02-10.csv"
## [42] "bucket_name/folder_name/2021-02-11.csv"
## [43] "bucket_name/folder_name/2021-02-12.csv"
## [44] "bucket_name/folder_name/2021-02-13.csv"
## [45] "bucket_name/folder_name/2021-02-14.csv"
## [46] "bucket_name/folder_name/2021-02-15.csv"
## [47] "bucket_name/folder_name/2021-02-16.csv"
## [48] "bucket_name/folder_name/2021-02-17.csv"
## [49] "bucket_name/folder_name/2021-02-18.csv"
## [50] "bucket_name/folder_name/2021-02-19.csv"
## [51] "bucket_name/folder_name/2021-02-20.csv"
## [52] "bucket_name/folder_name/2021-02-21.csv"
## [53] "bucket_name/folder_name/2021-02-22.csv"
## [54] "bucket_name/folder_name/2021-02-23.csv"

To use open_dataset() we need a path to the folder holding the files. This is built with space$path(). The structure is "bucket_name/folder_name". For security, even the bucket and folder names are stored in environment variables. For this example, the real values have been substituted with "bucket_name" and "folder_name".

files_path <- space$path(glue::glue("{Sys.getenv('DO_SPACE')}/{Sys.getenv('DO_FOLDER')}"))
## SubTreeFileSystem: s3://bucket_name/folder_name/

Now we can call open_dataset() to get access to all the files in that folder. We need to specify format="csv" to let the function know the files are CSVs as opposed to parquet or other formats.

files_path <- space$path(glue::glue("{Sys.getenv('DO_SPACE')}/{Sys.getenv('DO_FOLDER')}"))

temps_dataset <- arrow::open_dataset(files_path, format='csv')
## FileSystemDataset with 66 csv files
## Name: string
## Sensor: string
## date: date32[day]
## time: string
## temperature: double
## occupancy: int64
## Thermostat: int64
## zoneAveTemp: double
## HVACmode: string
## fan: int64
## outdoorTemp: double
## outdoorHumidity: int64
## sky: int64
## wind: int64
## zoneClimate: string
## zoneCoolTemp: double
## zoneHeatTemp: double
## zoneHVACmode: string
## zoneOccupancy: int64

Printing out the temps_dataset object shows the column names along with the type of data stored in each column. {arrow} is pretty great and lets us do column selection and row filtering on the data sitting in files which opens up a whole world of data analysis on data too large to fit in memory. We are simply going to select columns of interest and collect all the data into one data.frame (actually a tibble). Notice we call select() before collect() because this reduces the number of columns being transmitted over the network.

temps_raw %>% head()
## # A tibble: 6 x 16
##   Name  Sensor date       time  temperature   fan zoneAveTemp HVACmode
##   <chr> <chr>  <date>     <chr>       <dbl> <int>       <dbl> <chr>   
## 1 Upst… Bedro… 2021-01-01 00:0…        67.8   300          70 heat    
## 2 Upst… Bedro… 2021-01-01 00:0…        72.4   300          70 heat    
## 3 Upst… Offic… 2021-01-01 00:0…        NA     300          70 heat    
## 4 Upst… Upsta… 2021-01-01 00:0…        63.1   300          70 heat    
## 5 Upst… Offic… 2021-01-01 00:0…        NA     300          70 heat    
## 6 Upst… Offic… 2021-01-01 00:0…        NA     300          70 heat    
## # … with 8 more variables: outdoorTemp <dbl>, outdoorHumidity <int>, sky <int>,
## #   wind <int>, zoneClimate <chr>, zoneHeatTemp <dbl>, zoneCoolTemp <dbl>,
## #   zoneHVACmode <chr>

Preparing the Data

The data are in need of some minor attention. First, the time column is read in as a character instead of time. This is a known issue, so instead we combine it with the date column using tidyr::unite() then convert this combination into a datetime (or timestamp) with lubridate::as_datetime(), which requires us to set a time zone. Then, the sensors named Upstairs Thermostat and Downstairs Thermostat actually represent Bedroom 3 and Dining Room, respectively, so we rename those using case_when() from {dplyr}, leaving all the other sensor names as is. Then we only keep rows where the time is earlier than the present. This is due to a quirk of the Ecobee API where it can return future readings, which happens because we request all of the latest day’s data.

temps <- temps_raw %>%
  tidyr::unite(col='time', date, time, sep=' ', remove=TRUE) %>%
  mutate(time=lubridate::as_datetime(time, tz=Sys.timezone())) %>%
      Sensor == 'Upstairs Thermostat' ~ 'Bedroom 3',
      Sensor == 'Downstairs Thermostat' ~ 'Dining Room',
      TRUE ~ Sensor
  ) %>%
  filter(time <= lubridate::now())

Skip All of This

Since the actual data may contain sensitive information a sanitized version is stored as parquet files on a public DigitalOcean space. This dataset is not as up to date but it will do for those that want to follow along.

publicspace <- arrow::S3FileSystem$create(
  # anonymous means we do not need to provide credentials
  # is crucial, otherwise the function looks for credentials
  # and crashes R if it can't find them
  # the data are stored in the nyc3 region
publicspace$ls('temperaturedata', recursive=TRUE)
## [1] "temperaturedata/2021"                 
## [2] "temperaturedata/2021/1"               
## [3] "temperaturedata/2021/1/part-0.parquet"
## [4] "temperaturedata/2021/2"               
## [5] "temperaturedata/2021/2/part-1.parquet"
publictemp <- arrow::open_dataset(publicspace$path("temperaturedata")) %>%

publictemp %>% head()
## # A tibble: 6 x 15
##   Name  Sensor time                temperature   fan zoneAveTemp HVACmode
##   <chr> <chr>  <dttm>                    <dbl> <int>       <dbl> <chr>   
## 1 Upst… Bedro… 2021-01-01 00:00:00        67.8   300          70 heat    
## 2 Upst… Bedro… 2021-01-01 00:00:00        72.4   300          70 heat    
## 3 Upst… Offic… 2021-01-01 00:00:00        NA     300          70 heat    
## 4 Upst… Bedro… 2021-01-01 00:00:00        63.1   300          70 heat    
## 5 Upst… Offic… 2021-01-01 00:00:00        NA     300          70 heat    
## 6 Upst… Offic… 2021-01-01 00:00:00        NA     300          70 heat    
## # … with 8 more variables: outdoorTemp <dbl>, outdoorHumidity <int>, sky <int>,
## #   wind <int>, zoneClimate <chr>, zoneHeatTemp <dbl>, zoneCoolTemp <dbl>,
## #   zoneHVACmode <chr>

Visualizing with Interactive Plots

The first step in any good analysis is visualizing the data. In the past, I would have recommended {dygraphs} but have since come around to {feasts} from Rob Hyndman’s team and {timetk} from Matt Dancho due to their ease of use, better flexibility with data types and because I personally think they are more visually pleasing.

In order to show the outdoor temperature on the same footing as the sensor temperatures, we select the pertinent columns with the outdoor data from the temps tibble and stack them at the bottom of temps, after accounting for duplicates (Each sensor has an outdoor reading, which is the same across sensors).

all_temps <- dplyr::bind_rows(
  temps %>% 
    dplyr::select(Name, Sensor, time, temperature=outdoorTemp) %>% 
    dplyr::mutate(Sensor='Outdoors', Name='Outdoors') %>% 

Then we use plot_time_series() from {timetk} to make a plot that has a different colored line for each sensor and for the outdoors (with sensors being grouped into facets according to which thermostat they are associated with). Ordinarily the interactive version would be better (.interactive=TRUE below), but for the sake of this post static displays better.

all_temps %>%
  group_by(Name) %>%
    .value=temperature, .color_var=Sensor, .smooth=FALSE,
## Warning: Removed 34776 row(s) containing missing values (geom_path).
Temperature for each room over time

From this we can see a few interesting patterns. Bedroom 1 is consistently higher than the other bedrooms, but lower than the offices which are all on the same thermostat. All the downstairs rooms see their temperatures drop overnight when the thermostat is lowered to conserve energy. Between February 12 and 18 this dip was not as dramatic, due to running an experiment to see how raising the temperature on the downstairs thermostat would affect rooms on the second floor.

Computing Cross Correlations

One room in particular, Bedroom 2, always seemed colder than all the rest of the rooms, so I wanted to figure out why. Was it affected by physically adjacent rooms? Or maybe it was controlled by the downstairs thermostat rather than the upstairs thermostat as presumed.

So the first step was to see which rooms were correlated with each other. But computing correlation with time series data isn’t as simple because we need to account for lagged correlations. This is done with the cross-correlation function (ccf).

In order to compute the ccf, we need to get the data into a time series format. While R has extensive formats, most notably the built-in ts and its extension xts by Jeff Ryan, we are going to use tsibble, from Rob Hyndman and team, which are tibbles with a time component.

The tsibble object can treat multiple time series as individual data and this requires the data to be in long format. For this analysis, we want to treat multiple time series as interconnected, so we need to put the data into wide format using pivot_wider().

wide_temps <- all_temps %>% 
  # we only need these columns
  select(Sensor, time, temperature) %>% 
  # make each time series its own column
  ) %>% 
  # convert into a tsibble
  tsibble::as_tsibble(index=time) %>% 
  # fill down any NAs

## # A tibble: 15,840 x 12
##    time                `Bedroom 2` `Bedroom 1` `Office 1` `Bedroom 3` `Office 3`
##    <dttm>                    <dbl>       <dbl>      <dbl>       <dbl>      <dbl>
##  1 2021-01-01 00:00:00        67.8        72.4         NA        63.1         NA
##  2 2021-01-01 00:05:00        67.8        72.8         NA        62.7         NA
##  3 2021-01-01 00:10:00        67.9        73.3         NA        62.2         NA
##  4 2021-01-01 00:15:00        67.8        73.6         NA        62.2         NA
##  5 2021-01-01 00:20:00        67.7        73.2         NA        62.3         NA
##  6 2021-01-01 00:25:00        67.6        72.7         NA        62.5         NA
##  7 2021-01-01 00:30:00        67.6        72.3         NA        62.7         NA
##  8 2021-01-01 00:35:00        67.6        72.5         NA        62.5         NA
##  9 2021-01-01 00:40:00        67.6        72.7         NA        62.4         NA
## 10 2021-01-01 00:45:00        67.6        72.7         NA        61.9         NA
## # … with 15,830 more rows, and 6 more variables: `Office 2` <dbl>, `Living
## #   Room` <dbl>, Playroom <dbl>, Kitchen <dbl>, `Dining Room` <dbl>,
## #   Outdoors <dbl>

Some rooms’ sensors were added after the others so there are a number of NAs at the beginning of the tracked time.

We compute the ccf using CCF() from the {feasts} package then generate a plot by piping that result into autoplot(). Besides a tsibble, CCF() needs two arguments, the columns whose cross-correlation is being computed.

wide_temps %>%
  CCF(`Living Room`, `Bedroom 2`) %>%
Cross-correlation between Living Room and Bedroom 2

The negative lags are when the first column is a leading indicator of the second column and the positive lags are when the first column is a lagging indicator of the second column. In this case, Living Room seems to follow Bedroom 2 rather than lead.

We want to compute the ccf between Bedroom 2 and every other room. We call CCF() on each column in wide_temps against Bedroom 2, including Bedroom 2 itself, by iterating over the columns of wide_temps with map(). We use a ~ to treat CCF() as a quoted function and wrap .x in double curly braces to take advantage of non-standard evaluation. This creates a list of objects that can be plotted with autoplot(). We make a list of ggplot objects by iterating over the ccf objects using imap(). This allows us to use the name of each element of the list in ggtitle. Lastly, wrap_plots() from {patchwork} is used to show all the plots in one display.


room_ccf <- wide_temps %>% 
  purrr::map(~CCF(wide_temps, {{.x}}, `Bedroom 2`))

# we don't want the CCF with time, outdoor temp or the Bedroom 2 itself
room_ccf$time <- NULL
room_ccf$Outdoors <- NULL
room_ccf$`Bedroom 2` <- NULL

biggest_cor <- room_ccf %>% purrr::map_dbl(~max(.x$ccf)) %>% max()

room_ccf_plots <- purrr::imap(
  ~ autoplot(.x) +
    # show the maximally cross-correlation value
    # so it easier to compare rooms
    geom_hline(yintercept=biggest_cor, color='red', linetype=2) + 
    ggtitle(.y) + 
    labs(x=NULL, y=NULL) + 

wrap_plots(room_ccf_plots, ncol=3)
Cross-correlation between each room and Bedroom 2

This brings up an odd result. Office 1, Office 2, Office 3, Bedroom 1, Bedroom 2 and Bedroom 3 are all controlled by the same thermostat, but Bedroom 2 is only really cross-correlated with Bedroom 3 (and negatively with Office 3). Instead, Bedroom 2 is most correlated with Playroom and Living Room, both of which are controlled by a different thermostat.

So it will be interesting to see which thermostat setting (the set desired temperature) is most cross-correlated with Bedroom 2. At the same time we’ll check how much the outdoor temperature cross-correlates with Bedroom 2 as well.

temp_settings <- all_temps %>% 
  # there is a column for outdoor temperatures so we don't need these rows
  filter(Name != 'Outdoors') %>% 
  select(Name, time, HVACmode, zoneCoolTemp, zoneHeatTemp) %>% 
  # this information is repeated for each sensor, so we only need it once
  distinct() %>% 
  # depending on the setting, the set temperature is in two columns
  mutate(setTemp=if_else(HVACmode == 'heat', zoneHeatTemp, zoneCoolTemp)) %>% 
  tidyr::pivot_wider(id_cols=time, names_from=Name, values_from=setTemp) %>% 
  # get the reading data
  right_join(wide_temps %>% select(time, `Bedroom 2`, Outdoors), by='time') %>% 

## # A tibble: 15,840 x 5
##    time                Upstairs Downstairs `Bedroom 2` Outdoors
##    <dttm>                 <dbl>      <dbl>       <dbl>    <dbl>
##  1 2021-01-01 00:00:00       71         72        67.8     31.3
##  2 2021-01-01 00:05:00       71         72        67.8     31.3
##  3 2021-01-01 00:10:00       71         72        67.9     31.3
##  4 2021-01-01 00:15:00       71         66        67.8     31.3
##  5 2021-01-01 00:20:00       71         66        67.7     31.3
##  6 2021-01-01 00:25:00       71         66        67.6     31.3
##  7 2021-01-01 00:30:00       71         66        67.6     31.2
##  8 2021-01-01 00:35:00       71         66        67.6     31.2
##  9 2021-01-01 00:40:00       71         66        67.6     31.2
## 10 2021-01-01 00:45:00       71         66        67.6     31.2
## # … with 15,830 more rows

Making this tsibble longer allows us to use the built-in grouping and iteration that comes with CCF() and other functions in {fable}.

temp_settings_long <- temp_settings %>% 
    cols=-c(time, `Bedroom 2`), 
    names_to='Thermostat', values_to='Temperature'
room_thermostat_ccf <- temp_settings_long %>% 
  CCF(Temperature, `Bedroom 2`)
room_thermostat_ccf %>% autoplot() + ggthemes::theme_few()
Cross-correlation between Bedroom 2 and thermostat settings and the outdoor temperature

This plot makes a strong argument for Bedroom 2 being more affected by the downstairs thermostat than the upstairs like we presume. But I don’t think the downstairs thermostat is actually controlling the vents in Bedroom 2 because I have looked at the duct work and followed the path (along with a professional HVAC technician) from the furnace to the vent in Bedroom 2. What I think is more likely is that the rooms downstairs get so cold (I lower the temperature overnight to conserve energy), and there is not much insulation between the floors, so the vent in Bedroom 2 can’t pump enough warm air to compensate.

I did try to experiment for a few nights (February 12 and 18) by not lowering the downstairs temperature but the eyeball test didn’t reveal any impact. Perhaps a proper A/B test is called for.

Fitting Time Series Models with {fable}

Going beyond cross-correlations, an ARIMA model with exogenous variables can give an indication if input variables have a significant impact on a time series while accounting for autocorrelation and seasonality. We want to model the temperature in Bedroom 2 while using all the other rooms, the outside temperature and the thermostat settings to see which affected Bedroom 2 the most. We fit a number of different models then compare them using AICc (AIC corrected) to see which fits the best.

To do this we need a tsibble with each of these variables as a column. This requires us to join temp_settings with wide_temps.

all_ts <- wide_temps %>% 
    temp_settings %>% select(time, Upstairs, Downstairs),

Now we use model() from {fable} (technically from {fabletools}) to fit a number of models. We make it run in parallel with plan() from {future}.

plan(multisession, workers=10)



ts_mods <- all_ts %>% 
    mod0=ARIMA(`Bedroom 2`)
    , mod1=ARIMA(`Bedroom 2` ~ `Bedroom 1`)
    , mod2=ARIMA(`Bedroom 2` ~ `Bedroom 3`)
    , mod3=ARIMA(`Bedroom 2` ~ `Bedroom 1` + `Bedroom 3`)
    , mod4=ARIMA(`Bedroom 2` ~ `Living Room`)
    , mod5=ARIMA(`Bedroom 2` ~ Playroom)
    , mod6=ARIMA(`Bedroom 2` ~ `Living Room` + Playroom)
    , mod7=ARIMA(`Bedroom 2` ~ `Living Room` + Playroom + 
                   Kitchen + `Dining Room`
    , mod8=ARIMA(`Bedroom 2` ~ `Bedroom 1` + `Bedroom 3` + 
                   `Living Room` + Playroom + 
                   Kitchen + `Dining Room`
    , mod9=ARIMA(`Bedroom 2` ~ Outdoors)
    , mod10=ARIMA(`Bedroom 2` ~ Upstairs)
    , mod11=ARIMA(`Bedroom 2` ~ Downstairs)
    , mod12=ARIMA(`Bedroom 2` ~ Downstairs + Upstairs)
    , mod13=ARIMA(`Bedroom 2` ~ Outdoors + Downstairs + Upstairs)
    , mod14=ARIMA(`Bedroom 2` ~ Outdoors + 
                    Downstairs + Upstairs + 
                    `Living Room` + Playroom + 
                    Kitchen + `Dining Room`
    , mod15=ARIMA(`Bedroom 2` ~ Outdoors + 
                    Downstairs + Upstairs + 
                    `Bedroom 1` + `Bedroom 3`
    , mod16=ARIMA(`Bedroom 2` ~ Outdoors + 
                    Downstairs + Upstairs + 
                    `Bedroom 1` + `Bedroom 3` +
                    `Living Room` + Playroom + 
                    Kitchen + `Dining Room`
## 99.41 sec elapsed

This results in a tibble of models (termed a mable) with one column per model. We use glance() to see the AICc for each, along with other information.

ts_mods %>% 
  glance() %>% 
## # A tibble: 17 x 8
##    .model sigma2 log_lik     AIC    AICc     BIC ar_roots  ma_roots  
##    <chr>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <list>    <list>    
##  1 mod3   0.0247   6723. -13427. -13427. -13358. <cpl [3]> <cpl [14]>
##  2 mod1   0.0247   6714. -13411. -13411. -13350. <cpl [3]> <cpl [14]>
##  3 mod15  0.0247   6709. -13394. -13394. -13302. <cpl [3]> <cpl [3]> 
##  4 mod4   0.0257   6393. -12768. -12768. -12699. <cpl [3]> <cpl [25]>
##  5 mod2   0.0264   6199. -12381. -12381. -12320. <cpl [2]> <cpl [15]>
##  6 mod9   0.0264   6190. -12365. -12365. -12304. <cpl [2]> <cpl [26]>
##  7 mod10  0.0264   6190. -12365. -12365. -12303. <cpl [2]> <cpl [26]>
##  8 mod0   0.0264   6189. -12365. -12365. -12311. <cpl [2]> <cpl [26]>
##  9 mod13  0.0264   6176. -12331. -12331. -12255. <cpl [2]> <cpl [26]>
## 10 mod12  0.0264   6175. -12331. -12331. -12262. <cpl [2]> <cpl [26]>
## 11 mod11  0.0264   6174. -12331. -12331. -12270. <cpl [2]> <cpl [26]>
## 12 mod6   0.0195   4940.  -9863.  -9863.  -9802. <cpl [5]> <cpl [0]> 
## 13 mod5   0.0195   4932.  -9849.  -9849.  -9796. <cpl [5]> <cpl [0]> 
## 14 mod8   0.0163   4603.  -9184.  -9184.  -9100. <cpl [4]> <cpl [0]> 
## 15 mod16  0.0163   4605.  -9181.  -9181.  -9074. <cpl [4]> <cpl [0]> 
## 16 mod7   0.0171   4364.  -8708.  -8708.  -8631. <cpl [5]> <cpl [0]> 
## 17 mod14  0.0171   4365.  -8704.  -8704.  -8605. <cpl [5]> <cpl [0]>
ts_mods %>% 
  glance() %>% 
  # sort .model according to AICc for better plotting
  mutate(.model=forcats::fct_reorder(.model, .x=AICc, .fun=sort)) %>% 
  # smaller is better for AICc
  # so negate it so that the tallest bar is best
  ggplot(aes(x=.model, y=-AICc)) + 
  geom_col() + 
AICc of various models

The best two models (as judged by AICc) are mod3 and mod1, some of the simpler models. The key thing they have in common is that Bedroom 1 is a predictor, which somewhat contradicts the findings from the cross-correlation function. We can examine mod3 by selecting it out of ts_mods then calling report().

ts_mods %>% 
  select(mod3) %>% 
## Series: Bedroom 2 
## Model: LM w/ ARIMA(3,1,2)(0,0,1)[12] errors 
## Coefficients:
##          ar1      ar2     ar3     ma1     ma2     sma1  `Bedroom 1`
##       0.4497  -0.5184  0.0446  0.0650  0.3160  -0.0049       0.2723
## s.e.  0.0593   0.0471  0.0265  0.0589  0.0274   0.0082       0.0082
##       `Bedroom 3`
##            0.0396
## s.e.       0.0092
## sigma^2 estimated as 0.02469:  log likelihood=6722.64
## AIC=-13427.28   AICc=-13427.27   BIC=-13358.25

This shows that Bedroom 1 and Bedroom 3 are significant predictors with SARIMA(3,1,2)(0,0,1) errors.

The third best model is one of the more complicated models, mod15. Not all the predictors are significant, though we should not judge a model, or the predictors, based on that.

ts_mods %>% 
  select(mod15) %>% 
## Series: Bedroom 2 
## Model: LM w/ ARIMA(3,1,3) errors 
## Coefficients:
##          ar1      ar2      ar3     ma1     ma2     ma3  Outdoors  Downstairs
##       0.1870  -0.4426  -0.0724  0.3288  0.3745  0.0886   -0.0037     -0.0015
## s.e.  0.2351   0.0928   0.1058  0.2352  0.0490  0.0692    0.0026      0.0025
##       Upstairs  `Bedroom 1`  `Bedroom 3`
##         0.0087       0.2718       0.0387
## s.e.    0.0066       0.0081       0.0092
## sigma^2 estimated as 0.02468:  log likelihood=6709.22
## AIC=-13394.43   AICc=-13394.41   BIC=-13302.39

We can visualize this model with {coefplot} after stripping out the time series coefficients.

ts_mods %>% 
    select(mod15) %>% 
    coef() %>% 
    filter(!stringr::str_detect(term, '^ar|ma\\d+$')) %>%
    select(Coefficient=term, Value=estimate, SE=std.error, Model=.model) %>%
        LowOuter=Value - 2*SE, LowInner=Value - 1*SE,
        HighInner=Value + 1*SE, HighOuter=Value + 2*SE
    ) %>%
    arrange(Value) %>%
    mutate(Coefficient=factor(Coefficient, levels=Coefficient)) %>%
Coefficient plot for model 15

What all these top performing models have in common is the inclusion of the other bedrooms as predictors.

While helping me interpret the data, my wife pointed out that all the colder rooms are on one side of the house while the warmer rooms are on the other side. That colder side is the one that gets hit by winds which are rather strong. This made me remember a long conversation I had with a very friendly and knowledgeable sales person at GasTec who said that wind can significantly impact a house’s heat due to it blowing away the thermal envelope. I wanted to test this idea, but while the Ecobee API returns data on wind, the value is always 0, so it doesn’t tell me anything. Hopefully, I can get that fixed.

Running Everything with {targets}

There were many moving parts to the analysis, so I managed it all with {targets}. Each step seen in this post is a separate target in the workflow. There is even a target to build an html report from an R Markdown file. The key is to load objects created by the workflow into the R Markdown document with tar_read() or tar_load(). Then tar_render() will figure out how the R Markdown document figures into the computational graph.


I went over {targets} more thoroughly in the previous post and I recommend Will Landau’s talk at the New York Open Statistical Programming Meetup to learn more.

Reporting with RStudio Connect

In addition to the R Markdown I made a shiny dashboard using the R Markdown flexdashboard format. I host this on RStudio Connect so I can get a live view of the data and examine the models. Besides from being a partner with RStudio, I really love this product and I use it for all of my internal reporting (and side projects) and we have a whole practice around getting companies up to speed with the tool.

What Comes Next?

So what can be done with this information? Based on the ARIMA model the other bedrooms on the same floor are predictive of Bedroom 2, which makes sense since they are controlled by the same thermostat. Bedroom 3 is consistently one of the coldest in the house and shares a wall with Bedroom 2, so maybe that wall could use some insulation. Bedroom 1 on the other hand is the hottest room on that floor, so my idea is to find a way to cool off that room (we sealed the vents with magnetic covers), thus lowering the average temperature on the floor, causing the heat to kick in, raising the temperature everywhere, including Bedroom 2.

The cross-correlation functions also suggest a relationship with Playroom and Living Room, which are some of the colder rooms downstairs and the former is directly underneath Bedroom 2. I’ve already put caulking cord in the gaps around the floorboards in the Playroom and that has noticeably raised the temperature in there, so hopefully that will impact Bedroom 2. We already have storm windows on just about every window and have added thermal curtains to Bedroom 2, Bedroom 3 and the Living Room. My wife and I also discovered air billowing in from around, not in, the fireplace in the Living Room, so if we can close that source of cold air, that room should get warmer then hopefully Bedroom 2 as well. Lastly, we want to insulate between the first floor ceiling and second floor.

While we make these fixes, we’ll keep collecting data and trying new ways to analyze it all. It will be interesting to see how the data change in the summer.

These three posts were intended to show the entire process from data collection to automation (what some people are calling robots) to analysis, and show how data can be used to make actionable decisions. This is one more way I’m improving my life with data.

Related Posts

Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.

Posted in R.

In a recent post I talked about collecting temperature data from different rooms in my house. Using {targets}, I am able to get temperature readings for any given day down to five-minute increments and store that data on a DigitalOcean space.

Since I want to be able to both fit models on the data and track temperatures close to real time I needed this to run on a regular basis, say every 30-60 minutes. I have a machine learning server that Kaz Sakamoto, Michael Beigelmacher and I built from parts we bought and a collection of Raspberry Pis, but I didn’t want to maintain the hardware for this, I wanted something that was handled for me.

Luckily GitHub Actions can run arbitrary code on a schedule and provides free compute for public repositories. So I built a Docker image and GitHub Actions configuration files to make it all happen. This all is based on the templogging repository on GitHub.

Docker Image

To ensure everything works as expected and to prevent package updates from breaking code, I used a combination of {renv} and Docker.

{renv} allows each individual project to have its own set of packages isolated from all other projects’ packages. It also keeps track of package versions so that the project can be restored on other computers, or in a Docker container, in the exact same configuration.

Using {renv} is fairly automatic. When starting a new project run renv::init() to create the isolated package library and start tracking packages. Then packages are installed as usual with install.packages() or renv::install(). Then renv::snapshot() is periodically used to write the specific package versions to the renv.lock file which is saved in the root directory of the project.

Docker is like a lightweight virtual machine that can create a specific environment. For this project I wanted a specific version of R (4.0.3), a handful of system libraries like libxml2-dev (for {igraph}, which is needed for {targets}) and libcurl4-openssl-dev (for {httr}) and all of the packages tracked with {renv}.

The R version is provided by starting with the corresponding r-ver image from the rocker project. The system libraries are installed using apt install.

The R packages are installed using renv::restore() but this requires files from the project to be added to the image in the right order. First, the templogging directory is created to hold everything, then individual files needed for the project and {renv} are added to that directory: templogging.Rproj, .Rprofile and renv.lock. Then the entire renv directory is added as well. After that, the working directory is changed to the templogging directory so that the following commands take place in that location.

Then comes time to install all the packages tracked in renv.lock. This is done with RUN Rscript -e "renv::restore(repos='https://packagemanager.rstudio.com/all/__linux__/focal/latest', confirm=FALSE)". Rscript -e runs R code as if it were executed in an R session. renv::restore() installs all the specific packages. Setting repos='https://packagemanager.rstudio.com/all/__linux__/focal/latest' specifies to use prebuilt binaries from the public RStudio Package Manager (some people need the commercial, private version and if you think this may be you, my company, Lander Analytics can help you get started).

After the packages are installed, the rest of the files in the project are copied into the image with ADD . /templogging. It is important that this step takes place after the packages are installed so that changes to code does not trigger a reinstall of the packages.

This is the complete Dockerfile.

FROM rocker/r-ver:4.0.3

# system libraries
RUN apt update && \
    apt install  -y --no-install-recommends \
    # for igraph
    libxml2-dev \
    libglpk-dev \
    libgmp3-dev \
    # for httr
    libcurl4-openssl-dev \
    libssl-dev && \
    # makes the image smaller
    rm -rf /var/lib/apt/lists/*

# create project directory
RUN mkdir templogging
# add some specific files
ADD ["templogging.Rproj", ".Rprofile", "renv.lock", "/templogging/"]
# add all of the renv folder, except the library which is marked out in .dockerignore
ADD renv/ /templogging/renv

# make sure we are in the project directory so restoring the packages works cleanly
WORKDIR /templogging

# this restores the desired packages (including specific versions)
# it uses the public RStudio package manager to get binary versions of the packages
# this is for faster installation
RUN Rscript -e "renv::restore(repos='https://packagemanager.rstudio.com/all/__linux__/focal/latest', confirm=FALSE)"

# then we add in the rest of the project folder, including  all the code
# we do this separately so that we can change code without having to reinstall all the packages
ADD . /templogging

To make building and running the image easier I made a docker-compose.yml file.

version: '3.4'

    image: jaredlander/templogging
    container_name: tempcheck
      context: .
      dockerfile: ./Dockerfile
      - TZ=${TZ}

So we can now build and upload the image with the following shell code.

docker-compose build
docker push jaredlander/templogging:latest

Now the image is available to be used anywhere. Since this image, and all the code are public, it’s important that nothing personal or private was saved in the code. As can be seen in the docker-compose.yml file or any of the R code in the repo, all potentially sensitive information was stored in environment variables.

The {targets} workflow can be executed inside the Docker container with the following shell command.

docker-compose run templogging R -e "targets::tar_make()"

That will get and write data for the current date. To run this for a specific date, a variable called process_date should be set to that date (assumes the TZ environment variable has been set) and tar_make() should be called with the callr_function argument set to NULL. I’m sure there is a better way to set arguments to make tar_make() of runtime settings, but I haven’t figured it out yet.

docker-compose run templogging R -e "process_date <- as.Date('2021-02-20')" -e "targets::tar_make(callr_function=NULL)"

GitHub Actions

After the Docker image was built, I automated the whole process using GitHub Actions. This requires a yml file inside the .github/workflows folder in the repository.

The first section is on:. This tells GitHub to run this job whenever there is a push to the main or master branch and whenever there is a pull request to these branches. It also runs every 30 minutes thanks to the schedule block that has - cron: '*/30 * * * *'. The */30 means “every thirty” and it’s the first position which means minutes (0-59). The second position would be hours (0-23). The following positions are day of month (1-31), month of year (1-12), day of the week (1-7, with 1 as Monday) and year (1900-3000).

The next section, name:, just provides the name of the workflow.

Then comes the jobs: section. Multiple jobs can be run, but there is only one job called Run-Temp-Logging.

The job runs on (runs-on:) ubuntu-latest as opposed to Windows or macOS.

This is going to use the Docker container so the container: block specifies an image: and environment variables (env:). The environment variables are stored securely in GitHub and are accessed via ${{ secrets.VARIABLE_NAME }}. Since I created this I have been debating if it would be faster to install the packages directly in the virtual machine spun up by the actions runner than to use a Docker image. Given that R package installations can be cached it might work out, but I haven’t tested it yet.

The steps: block runs the {targets} workflow. There can be multiple steps, though in this case there is only one, so each starts with a dash (-). For the step, a name: is given, along with what to run (run: R -e "targets::tar_make()") and the working directory (working-directory: /templogging), each on a separate line in the file. The R -e "targets::tar_make()" command runs in the Docker container with /templogging as the working directory. This has the same result as running docker-compose run templogging R -e "targets::tar_make()" locally. Each time this is run the file in the DigitalOcean space gets written anew.

The entire yml file is below.

            - main
            - master
            - main
            - master
        - cron: '*/30 * * * *'

name: Run-Targets

        runs-on: ubuntu-latest
            image: jaredlander/templogging:latest
                BUCKET_NAME: ${{ secrets.BUCKET_NAME }}
                FOLDER_NAME: ${{ secrets.FOLDER_NAME }}
                AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
                AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
                AWS_DEFAULT_REGION: ${{ secrets.AWS_DEFAULT_REGION }}
                AWS_S3_ENDPOINT: ${{ secrets.AWS_S3_ENDPOINT }}
                AWS_SESSION_TOKEN: ""
                ECOBEE_API_KEY: ${{ secrets.ECOBEE_API_KEY }}
                ECOBEE_REFRESH_TOKEN: ${{ secrets.ECOBEE_REFRESH_TOKEN }}
            - name: Run targets workflow
              run: R -e "targets::tar_make()"
              working-directory: /templogging

With this running every 30 minutes I can pull the data just about any time and have close-enough-to-live insight into my house. But let’s be honest, I really only need to see up to the past few days to make sure everything is all right, so a 30-minute delay is more than good enough.

End of Day

While the job is scheduled to run every thirty minutes, I noticed that it wasn’t exact, so I couldn’t be sure I would capture the last few five-minute intervals of each day. So I made another GitHub Action to pull the prior day’s data at 1:10 AM.

The instructions are mostly the same with just two changes. First, the schedule is - cron: '10 1 * * *' which means on the 10th minute of the first hour. Since the rest of the slots are * the job occurs on every one of them, so it is the 10th minute of the first hour of every day of every month of every year.

The second change is the command given to the Docker container to run the {targets} workflow. As alluded to earlier, the _targets.R file is designed so that if Process_date is assigned a date, the data will be pulled for that date instead of the current date. So the run: portion has the command R -e "Sys.setenv(TZ='${{ secrets.TZ }}')" -e "process_date <- Sys.Date() - 1" -e "targets::tar_make(callr_function=NULL)". This makes sure the timezone environment variable is set in R, process_date gets the value of the prior day’s date and tar_make() is called.

The entire yml file is below.

            - main
            - master
            - main
            - master
        - cron: '10 1 * * *'

name: Run-Targets

        runs-on: ubuntu-latest
            image: jaredlander/templogging:latest
                BUCKET_NAME: ${{ secrets.BUCKET_NAME }}
                FOLDER_NAME: ${{ secrets.FOLDER_NAME }}
                AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
                AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
                AWS_DEFAULT_REGION: ${{ secrets.AWS_DEFAULT_REGION }}
                AWS_S3_ENDPOINT: ${{ secrets.AWS_S3_ENDPOINT }}
                AWS_SESSION_TOKEN: ""
                ECOBEE_API_KEY: ${{ secrets.ECOBEE_API_KEY }}
                ECOBEE_REFRESH_TOKEN: ${{ secrets.ECOBEE_REFRESH_TOKEN }}
            - name: Run targets workflow
              run: R -e "Sys.setenv(TZ='${{ secrets.TZ }}')" -e "process_date <- Sys.Date() - 1" -e "targets::tar_make(callr_function=NULL)"
              working-directory: /templogging

What’s Next?

Now that the data are being collected it’s time to analyze and see what is happening in the house. This is very involved so that will be the subject of the next post.

Related Posts

Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.

Posted in R.

The sixth annual (and first virtual) “New York” R Conference took place August 5-6 & 12-15. Almost 300 attendees, and 30 speakers, plus a stand-up comedian and a whiskey masterclass leader, gathered remotely to explore, share, and inspire ideas.

We had many awesome speakers, many new and some, returning: Dr. Rob J Hyndman (Monash University), Dr. Adam Obeng (Facebook), Ludmila Janda (Amplify), Emily Robinson (Warby Parker), Daniel Chen (Virginia Tech, Lander Analytics), Dr. Jon Krohn (untapt), Dr. Andrew Gelman (Columbia University), David Smith (Microsoft), Laura Gabrysiak (Visa), Brooke Watson (ACLU), Dr. Sebastian Teran Hidalgo (Vroom), Catherine Zhou (Codecademy), Dr. Jacqueline Nolis (Brightloom), Sonia Ang (Microsoft), Emily Dodwell (AT&T Labs Research), Jonah Gabry (Columbia University, Stan Development Team), Wes MckKinney and Dr. Neal Richardson (Ursa Labs), Dr. Thomas Mock (RStudio), Dr. David Robinson, (Heap), Dr. Max Kuhn (RStudio), Dr. Erin LeDell (H2O.ai), Monica Thieu (Columbia University), Camelia Hssaine (Codecademy), and myself and, coming soon, a bonus talk by Heather Nolis (T-Mobile) which will be shared on YouTube as soon as our team is done editing them, along with all the other talks.

Let’s take a look at some of the highlights from the conference:

Andrew Gelman Gave Another 40-Minute Talk (no slides, as always)

Our favorite quotes from Andrew Gelman’s talk, Truly Open Science: From Design and Data Collection to Analysis and Decision Making, which had no slides, as usual:

“Everyone training in statistics becomes a teacher.”

“The most important thing you should take away — put multiple graphs on a page.”

“Honesty and transparency are not enough.”

“Bad science doesn’t make someone a bad person.”

Laura Gabrysiak Shows us We Are Driven By Experience, and not Brand Loyalty…Hope you Folks had a Good Experience!

Laura’s talk on re-Inventing customer engagement with machine learning went through several interesting use cases from her time at Visa. In addition to being a data scientist, she is an active community organizer and the co-founder of R-Ladies Miami.

Adam Obeng Delivered a Talk on Adaptive Experimentation

One of my former students at Columbia University, Adam Obeng, gave a great presentation on his adaptive experimentation. We learned that adaptive experimentation is three things: The name of (1) a family of techniques, (2) Adam’s team at Facebook, and (3) an open source package produced by said team. He went through the applications which are hyper-parameter optimization for ML, experimentation with multiple continuous treatments, and physical experiments or manufacturing.

Dr. Jacqueline Nolis Invited Us to Crash Her Viral Website, Tweet Mashup

Jacqueline asked the crowd to crash her viral website,Tweet Mashup, and gave a great talk on her experience building it back in 2016. Her website that lets you combine the tweets of two different people. After spending a year making it in .NET, when she launched the site it became an immediate sensation. Years later, she was getting more and more frustrated maintaining the F# code and decided to see if I could recreate it in Shiny. Doing so would require having Shiny integrate with the Twitter API in ways that hadn’t been done by anyone before, and pushing the Twitter API beyond normal use cases.

Attendees Participated in Two Virtual Happy Hours Packed with Fun

At the Friday Happy Hour, we had a mathematical standup comedian for the first time in R Conference history. Comic and math major Rachel Lander (no relationship to me!) entertained us with awesome math and stats jokes.

Following the stand up, we had a Whiskey Master Class with our Vibe Sponsor Westland Distillery, and another one on Saturday with Bruichladdich Distillery (hard to pronounce and easy to drink). Attendees and speakers learned and drank together, whether it be their whiskey, matchas, soda or water.

All Proceeds from the A(R)T Auction went to the R Foundation Again

A newer tradition, the A(R)T Auction, took place again! We featured pieces by artists in the R Community, and all proceeds were donated to the R Foundation. The highest-selling piece at auction was Street Cred (2020) by Vivian Peng (Lander Analytics and Los Angeles Mayor’s Office, Innovation Team). The second highest was a piece by Jacqueline Nolis (Brightloom, and Build a Career in Data Science co-author), R Conference speaker, Designed by Allison Horst, artist in residence at RStudio. 

The R-Ladies Group Photo Happened, Even Remotely!

As per tradition, we took an R-Ladies group photo, but, for the first time, remotely– as a screenshot! We would like to note that many more R-Ladies were present in the chat, but just chose not to share video.

Jon Harmon, Edna Mwenda, and Jessica Streeter win Raspberri Pis, Bluetooth Headphones, and Tenkeyless Keyboards for Most Active Tweeting During the Conference

This year’s Twitter Contest, in Malorie’s words, was a “ruthless but noble war.” You can see the NYR 2020 Dashboard here. A custom started that DCR 2018 by our Twitter scorekeeper Malorie Hughes (@data_all_day) has returned every year by popular demand, and now she’s stuck with it forever! Congratulations to our winners!

50+ Conference Attendees Participated in Pre-Conference Workshops Before

For the first time ever, workshops took place over the course of several days to promote work-life balance, and to give attendees the chance to take more than one course. We ran the following seven workshops:

Recreating the In-Person Experience

We recreated as much of the in-person experience as possible with attendee networking sessions, the speaker walk-on songs and fun facts, abundant prizes and giveaways, the Twitter contest, an art auction, and happy hours. In addition to all of this, we mailed conference programs, hex stickers, and other swag to each attendee (in the U.S.), along with discount codes from our Vibe Sponsors, MatchaBar, Westland Distillery and Bruichladdich Distillery.

Thank you, Lander Analytics Team!

Even though it was virtual, there was a lot of work that went into the conference, and I want to thank my amazing team at Lander Analytics along with our producer, Bill Prickett, for making it all come together. 

Looking Forward to D.C. and Dublin
If you attended, we hope you had an incredible experience. If you did not, we hope to see you at the virtual DC R Conference in the fall, and at the first Dublin R Conference and the NYR next year!

Related Posts

Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.

Posted in R.


Earlier this week, my company, Lander Analytics, organized our first public Bayesian short course, taught by Andrew Gelman, Bob Carpenter and Daniel Lee.  Needless to say the class sold out very quickly and left a long wait list.  So we will schedule another public training (exactly when tbd) and will make the same course available for private training.

This was the first time we utilized three instructors (as opposed to a main instructor and assistants which we often use for large classes) and it led to an amazing dynamic.  Bob laid the theoretical foundation for Markov chain Monte Carlo (MCMC), explaining both with math and geometry, and discussed the computational considerations of performing simulation draws.  Daniel led the participants through hands-on examples with Stan, covering everything from how to describe a model, to efficient computation to debugging.  Andrew gave his usual, crowd dazzling performance use previous work as case studies of when and how to use Bayesian methods.

It was an intensive three days of training with an incredible amount of information.  Everyone walked away knowing a lot more about Bayes, MCMC and Stan and eager to try out their new skills, and an autographed copy of Andrew’s book, BDA3.

A big help, as always was Daniel Chen who put in so much effort making the class run smoothly from securing the space, physically moving furniture and running all the technology.

Related Posts

Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.

Posted in R.


Attending this week’s Strata conference it was easy to see quite how prolific the NYC Data Mafia is when it comes to writing.  Some of the found books:

And, of course, my book will be out soon to join them.

Related Posts

Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.

Posted in R.

For the past few weeks Time Out New York‘s Dating columnist, Jamie Bufalino, has been fielding letters discussing the ratio of homosexual to heterosexual questions he answers.  The readers suggested that disproportionate attention is paid to Gay and Lesbian issues compared to the Gay and Lesbian proportion of the general population.      

Jamie rudely called his readers “ass-wipes” and repeatedly told them to “remove your head from your ass.”  He also professed to have “no idea what the percentage is of gay/bi versus straight issues that end up in the column.”  

One question and response:  

Q I see statistics that show NYC to be 6 percent gay, lesbian and bi, and yet in “Get Naked” you feature letters from them almost to the exclusion of heteros. Why the preoccupation with them in your column? It doesn’t seem right or logical. As one of the other 94 percent, I am disappointed and offended weekly.   

A All I can say is: You’ve got your head up your butt. Just in the past month or so, I’ve answered letters from a straight guy with a weird fetish that suddenly stopped delivering the jollies it used to, a straight guy who was juggling a woman from the Ukraine and a woman from Jersey, a woman who had an issue with sticking her finger up her boyfriend’s butt, a 19-year-old woman who was getting pressured to have sex with her boyfriend, and on and on. If, for some reason, you happen to be obsessing over the gay and bi questions and not acknowledging the straight ones, that’s your issue, not mine.  

And another:  

Q I always read your column to see if I can learn something and just for shits and giggles. The one thing that has always bothered me is your preoccupation with gay and bi problems. Gays and lesbians get their own special section of three to four pages!  

A First of all, dude, you sound like one of those total ass-wipes who believes that gay people somehow have all these special privileges that straight people aren’t entitled to. Honestly, I have no idea what the percentage is of gay/bi versus straight issues that end up in the column, because it doesn’t matter. If you removed your head from your ass, you’d realize that so many sexual issues are universal and that you can learn something from all sorts of people who don’t fit into your specific demographic.  

When confronted with the data he once again reffered to a “head lodged up [a] rectum” and suggested the reader was “paranoid.”  

Q As a statistician I am disappointed by your response to a question in the November 4 issue [TONY 788]. The reader wrote, “I see statistics that show NYC to be 6 percent gay, lesbian and bi, and yet in ‘Get Naked’ you feature letters from them almost to the exclusion of heteros. Why the preoccupation with them in your column? … As one of the other 94 percent, I am disappointed and offended weekly.” You responded by citing individual examples of heterosexual questions you’ve fielded, which is not a valid form of proof. I went through about ten months’ worth of “Get Naked” columns on the TONY website and found that approximately 19 percent of the questions were from gay (15 percent) or lesbian (4 percent) readers. Whether or not that percentage is representative of the general population is not my concern. I just feel that Jamie should have his data correct and not write, “You’ve got your head up your butt.”  

A I seriously cannot believe I am still getting letters about this. Okay, Mr. Disappointed Statistician: If you don’t want to come off as someone who has his head lodged up his rectum, it would be an awesome idea not to leap to the defense of some jackass who claims I cater to homo letters “almost to the exclusion of heteros” and then point out that straight issues actually make up a full 81 percent of the subject matter here in “Get Naked.” What I want to know is, why are you even keeping score? Are you really that insecure about the amount of attention heterosexual sex gets in the media? If so, that’s both laughable and sad. This is the last time I’m addressing this, so here’s my final bit of advice to you (and your like-minded brethren): Stop being so paranoid.  

Since Jamie is so rude to his readers and clearly doesn’t have any sense of the data, I thought I’d take a look at the numbers.  Results after the break.   

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Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.

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Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.

Posted in R.

Last week Slice ran a post about a tomato taste test they conducted with Scott Wiener (of Scott’s NYC Pizza Tours), Brooks Jones, Jason Feirman, Nick Sherman and Roberto Caporuscio from Keste.  While the methods used may not be rigorous enough for definitive results, I took the summary data that was in the post and performed some simple analyses.

The first thing to note is that there are only 16 data points, so multiple regression is not an option.  We can all thank the Curse of Dimensionality for that.  So I stuck to simpler methods and visualizations.  If I can get the raw data from Slice, I can get a little more advanced.

For the sake of simplicity I removed the tomatoes from Eataly because their price was such an outlier that it made visualizing the data difficult.  As usual, most of the graphics were made using ggplot2 by Hadley Wickham.  The coefficient plots were made using a little function I wrote.  Here is the code.  Any suggestions for improvement are greatly appreciated, especially if you can help with increasing the left hand margin of the plot.  And as always, all the work was done in R.

The most obvious relationship we want to test is Overall Quality vs. Price.  As can be seen from the scatterplot below with a fitted loess curve, there is not a linear relationship between price and quality.

More after the break. Continue reading

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Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.

Posted in R.