Analysis
Tina Lasisi
August 11, 2022
Last updated: 2022-08-11
Checks: 6 1
Knit directory: HairManikin2022/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 60ac80e | Ben Zydney | 2022-08-11 | Merge branch ‘main’ of https://github.com/tinalasisi/HairManikin2022 |
| Rmd | 3a1d68c | Ben Zydney | 2022-08-11 | Updated analysis folder. |
| html | 3a1d68c | Ben Zydney | 2022-08-11 | Updated analysis folder. |
| Rmd | 78fb2fe | Tina Lasisi | 2022-08-11 | Updated plots + libraries |
| html | 6e70659 | Ben Zydney | 2022-08-06 | Updated Website |
| Rmd | 9db041e | Ben Zydney | 2022-08-06 | Reworked hair analysis calculations. |
| Rmd | dd0e722 | Tina Lasisi | 2022-03-13 | edited df creation |
| html | dd0e722 | Tina Lasisi | 2022-03-13 | edited df creation |
| Rmd | 210cee3 | Tina Lasisi | 2022-03-10 | updated figure size |
| html | 210cee3 | Tina Lasisi | 2022-03-10 | updated figure size |
| Rmd | bf62a07 | Tina Lasisi | 2022-03-10 | Updating analyses + figures |
| html | bf62a07 | Tina Lasisi | 2022-03-10 | Updating analyses + figures |
| html | 1a1f7bc | Tina Lasisi | 2022-03-07 | Build site. |
| Rmd | 7478e4c | Tina Lasisi | 2022-03-07 | updating analyses with models |
| html | c0ce5d2 | Tina Lasisi | 2022-03-06 | Build site. |
| Rmd | 4041aec | Tina Lasisi | 2022-03-06 | wflow_publish(files = "analysis/*", all = TRUE, republish = TRUE, |
| html | 4041aec | Tina Lasisi | 2022-03-06 | wflow_publish(files = "analysis/*", all = TRUE, republish = TRUE, |
| Rmd | a796ceb | Ginawsy | 2022-02-21 | updated sum_manikin_df variable |
| Rmd | 520dcfc | GitHub | 2022-02-16 | Update analysis.Rmd |
| Rmd | 9c5b0d8 | Tina Lasisi | 2022-02-15 | Update analysis + figures |
| html | 9c5b0d8 | Tina Lasisi | 2022-02-15 | Update analysis + figures |
| Rmd | e9fa430 | GitHub | 2022-01-30 | Update analysis.Rmd |
| html | bb36720 | Tina Lasisi | 2022-01-19 | Build site. |
| Rmd | 05389ae | Tina Lasisi | 2022-01-19 | Added figures + analysis placeholders |
| html | 05389ae | Tina Lasisi | 2022-01-19 | Added figures + analysis placeholders |
| Rmd | bb99f1d | Tina Lasisi | 2022-01-08 | Update analysis.rmd + add data |
| html | bb99f1d | Tina Lasisi | 2022-01-08 | Update analysis.rmd + add data |
| Rmd | 0a50ef7 | Tina Lasisi | 2022-01-07 | Adding main analysis file |
1 Preparing Data
First, we import the data and label the variables.
# Preview data
head(df_wetdry)| wind | wig | wet_dry | heat_loss | skin_temp | resistance | clo | amb_temp | amb_rh | radiation | trial |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.3 | Nude | wet | 90.9 | 34 | 4.36e-08 | 2.81e-07 | 34 | 45.8 | on | 1 |
| 0.3 | Nude | wet | 86.7 | 34 | -0.00121 | -0.00782 | 34.1 | 45.8 | on | 2 |
| 1 | Nude | wet | 227 | 34 | 0.000119 | 0.000767 | 34 | 46.3 | on | 1 |
| 2.5 | Nude | wet | 276 | 34 | -0.00082 | -0.00529 | 34.3 | 48.2 | on | 1 |
| 2.5 | Nude | wet | 272 | 34 | -0.000804 | -0.00519 | 34.2 | 48.1 | on | 2 |
| 0.3 | Straight | wet | 30 | 34 | -0.00422 | -0.0272 | 34.1 | 45.4 | on | 1 |
1.1 Removing Outlier
It was noticed that the 2nd trial conducted with wet, tightly curled hair, 2.5 m/s wind speed, and radiation on, had more heat loss than any of the trials with radiation off. With the understanding that radiation should always decrease heat loss, we elected to remove that data point.
# Remove specific entry
df_wetdry <- df_wetdry %>% filter(!(wig == "Tightly\nCurled" & wind == 2.5 & radiation == "on" & wet_dry == "wet" & trial == "1"))
2 Calculating Thermal Resistance
\[I_t = \frac{T_{Skin} - T_{Air}}{H_{Dry}}\]
df_wetdry['dry_heat_resistance'] <- (df_wetdry['skin_temp'] - df_wetdry['amb_temp']) / df_wetdry['heat_loss']
# For the dry data, leave this blank
df_wetdry <- df_wetdry %>% mutate(dry_heat_resistance = ifelse(wet_dry == 'wet', NaN, dry_heat_resistance))
3 Calculating Net Solar Influx
\[I_{Dry} = H_{Dry} - H_{Dry}^{Solar}\] \[I_{Evap} = H_{Evap} - H_{Evap}^{Solar}\]
# Average all trials with the same characteristics
df_averaged_trials <- df_wetdry %>%
group_by(wig, wind, radiation, wet_dry) %>%
drop_na(heat_loss) %>%
summarise(heat_loss = mean(heat_loss))
# Pivot the dataframe to incldue radiation on and off as part of same event
df_radiation_split <- df_averaged_trials %>%
pivot_wider(names_from = c(radiation), values_from = c(heat_loss)) %>%
rename(heat_loss_off = off) %>%
rename(heat_loss_on = on)
# Calculate the net influx
df_net_influx_plots <- df_radiation_split %>%
group_by(wig, wind) %>%
summarise(wet_dry = wet_dry,
net_influx = heat_loss_off - heat_loss_on)
df_net_influx <- df_net_influx_plots %>% spread(wet_dry, net_influx)
4 Adjusting Heat Losses to 30 Degrees Celsius
4.1 Dry Heat Loss
\[H_{Dry}^{30^\circ C} = \frac{35 -30}{I_t}\]
# Their calculation
df_wetdry['heat_30'] = (35 - 30) / df_wetdry['dry_heat_resistance']
# What I would expect
#df_wetdry['heat_30'] = (df_wetdry['skin_temp'] - 30) / df_wetdry['dry_heat_resistance']
# Recreate the radiation split dataframe to include heat_30
df_averaged_trials <- df_wetdry %>%
group_by(wig, wind, radiation, wet_dry) %>%
drop_na(heat_loss) %>%
summarise(heat_loss = mean(heat_loss),
heat_30 = mean(heat_30))
df_radiation_split <- df_averaged_trials %>%
pivot_wider(names_from = c(radiation), values_from = c(heat_loss, heat_30))4.2 Dry and Wet Heat Losses With Solar Radiation
\[H_{Dry}^{30^{\circ} C,\:Solar} = H_{Dry}^{30^{\circ} C} - I_{Dry}\] \[H_{Wet}^{30^{\circ} C,\:Solar} = H_{Evap + Dry}^{30^{\circ} C} = H_{Evap} + I_{Dry} + H_{Dry}^{30^{\circ} C,\:Solar}\]
dry_heat_30 = df_radiation_split[df_radiation_split$wet_dry == 'dry',]
heat_evap = df_radiation_split[df_radiation_split$wet_dry == 'wet',]
df_adjusted_solar <- data.frame(
dry_heat_loss <- dry_heat_30$heat_30_off - df_net_influx$dry,
wind <- dry_heat_30$wind,
wig <- dry_heat_30$wig
) %>% rename('dry_heat_loss' = 'dry_heat_loss....dry_heat_30.heat_30_off...df_net_influx.dry') %>%
rename('wind' = 'wind....dry_heat_30.wind') %>%
rename('wig' = 'wig....dry_heat_30.wig')
df_adjusted_solar['wet_heat_loss'] <- + heat_evap$heat_loss_on + df_net_influx$dry + df_adjusted_solar$dry_heat_loss
df_adjusted_solar_plots <-df_adjusted_solar %>%
pivot_longer(cols = c('dry_heat_loss', 'wet_heat_loss'), names_to = 'wet_dry', values_to = 'heat_loss')
5 Calculating Evaporative Potential
\[H_{Max}^{30^{\circ} C,\:Solar} = H_{Wet}^{30^{\circ} C,\:Solar} - H_{Dry}^{30^{\circ} C,\:Solar}\]
df_evaporative_potential <- df_adjusted_solar$wet_heat_loss - df_adjusted_solar$dry_heat_loss6 Calculating Sweat Requirements
\[Sweat_{Max} = \frac{H_{Max}^{30^{\circ} C,\:Solar} * 3600}{2430}\]
\[ IF \; H_{Dry}^{30^{\circ} C,\:Solar} < 0, \; Sweat_{Zero} = -\frac{H_{Dry}^{30^{\circ} C,\:Solar} * 3600}{2430} \\ ELSE, \; Sweat_{Zero} = 0\]
# Create a new df with the sweat requirements
df_sweat_requirements <- data.frame(
sweat_max <- df_evaporative_potential * 3600 / 2430,
sweat_zero <- -3600 / 2430 * df_adjusted_solar['dry_heat_loss'],
wig <- df_adjusted_solar$wig,
wind <- df_adjusted_solar$wind
)
#Rename columns
colnames(df_sweat_requirements) <- c('sweat_max', 'sweat_zero')
#Replace all values less than 0 with 0 per formula
df_sweat_requirements['sweat_zero'][df_sweat_requirements['sweat_zero'] < 0] <- 0
R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] huxtable_5.5.0 broom.mixed_0.2.9.4 patchwork_1.1.1
[4] gridExtra_2.3 fs_1.5.2 knitr_1.39
[7] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9
[10] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
[13] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] nlme_3.1-157 bit64_4.0.5 lubridate_1.8.0
[4] httr_1.4.3 rprojroot_2.0.3 tools_4.2.1
[7] backports_1.4.1 bslib_0.4.0 utf8_1.2.2
[10] R6_2.5.1 DBI_1.1.3 colorspace_2.0-3
[13] withr_2.5.0 tidyselect_1.1.2 bit_4.0.4
[16] compiler_4.2.1 git2r_0.30.1 cli_3.3.0
[19] rvest_1.0.2 xml2_1.3.3 labeling_0.4.2
[22] sass_0.4.2 scales_1.2.0 commonmark_1.8.0
[25] digest_0.6.29 rmarkdown_2.14 pkgconfig_2.0.3
[28] htmltools_0.5.3 parallelly_1.32.1 highr_0.9
[31] dbplyr_2.2.1 fastmap_1.1.0 rlang_1.0.4
[34] readxl_1.4.0 rstudioapi_0.13 farver_2.1.1
[37] jquerylib_0.1.4 generics_0.1.3 jsonlite_1.8.0
[40] vroom_1.5.7 googlesheets4_1.0.0 magrittr_2.0.3
[43] Rcpp_1.0.9 munsell_0.5.0 fansi_1.0.3
[46] lifecycle_1.0.1 furrr_0.3.0 stringi_1.7.8
[49] whisker_0.4 yaml_2.3.5 grid_4.2.1
[52] paletteer_1.4.0 parallel_4.2.1 listenv_0.8.0
[55] promises_1.2.0.1 crayon_1.5.1 lattice_0.20-45
[58] haven_2.5.0 splines_4.2.1 hms_1.1.1
[61] pillar_1.8.0 codetools_0.2-18 reprex_2.0.1
[64] glue_1.6.2 evaluate_0.15 modelr_0.1.8
[67] vctrs_0.4.1 tzdb_0.3.0 httpuv_1.6.5
[70] cellranger_1.1.0 gtable_0.3.0 rematch2_2.1.2
[73] future_1.27.0 assertthat_0.2.1 cachem_1.0.6
[76] xfun_0.31 broom_1.0.0 later_1.3.0
[79] googledrive_2.0.0 gargle_1.2.0 workflowr_1.7.0
[82] globals_0.15.1 ellipsis_0.3.2