Analysis
Tina Lasisi
August 06, 2022
Last updated: 2022-08-06
Checks: 7 0
Knit directory: HairManikin2022/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 08b8dfb | Ben Zydney | 2022-08-06 | wflow_publish("analysis/analysis.rmd") |
| 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)| wig | wind | radiation | wet_dry | heat_loss | skin_temp | resistance | clo | amb_temp | amb_rh | trial |
|---|---|---|---|---|---|---|---|---|---|---|
| Nude | 0.3 | on | wet | 90.9 | 34 | 34 | 45.8 | 1 | ||
| Nude | 0.3 | on | wet | 86.7 | 34 | 34.1 | 45.8 | 2 | ||
| Nude | 1 | on | wet | 227 | 34 | 34 | 46.3 | 1 | ||
| Nude | 1 | on | wet | 2 | ||||||
| Nude | 2.5 | on | wet | 276 | 34 | 34.2 | 48.2 | 1 | ||
| Nude | 2.5 | on | wet | 272 | 34 | 34.2 | 48.1 | 2 |
1.1 Removing Outlier
# 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
[16] workflowr_1.7.0
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 processx_3.7.0
[16] bit_4.0.4 compiler_4.2.1 git2r_0.30.1
[19] cli_3.3.0 rvest_1.0.2 xml2_1.3.3
[22] labeling_0.4.2 sass_0.4.2 scales_1.2.0
[25] callr_3.7.1 commonmark_1.8.0 digest_0.6.29
[28] rmarkdown_2.14 pkgconfig_2.0.3 htmltools_0.5.3
[31] parallelly_1.32.1 highr_0.9 dbplyr_2.2.1
[34] fastmap_1.1.0 rlang_1.0.4 readxl_1.4.0
[37] rstudioapi_0.13 farver_2.1.1 jquerylib_0.1.4
[40] generics_0.1.3 jsonlite_1.8.0 vroom_1.5.7
[43] googlesheets4_1.0.0 magrittr_2.0.3 Rcpp_1.0.9
[46] munsell_0.5.0 fansi_1.0.3 lifecycle_1.0.1
[49] furrr_0.3.0 stringi_1.7.8 whisker_0.4
[52] yaml_2.3.5 grid_4.2.1 paletteer_1.4.0
[55] parallel_4.2.1 listenv_0.8.0 promises_1.2.0.1
[58] crayon_1.5.1 lattice_0.20-45 haven_2.5.0
[61] splines_4.2.1 hms_1.1.1 ps_1.7.1
[64] pillar_1.8.0 codetools_0.2-18 reprex_2.0.1
[67] glue_1.6.2 evaluate_0.15 getPass_0.2-2
[70] modelr_0.1.8 vctrs_0.4.1 tzdb_0.3.0
[73] httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0
[76] rematch2_2.1.2 future_1.27.0 assertthat_0.2.1
[79] cachem_1.0.6 xfun_0.31 broom_1.0.0
[82] later_1.3.0 googledrive_2.0.0 gargle_1.2.0
[85] globals_0.15.1 ellipsis_0.3.2