adding day4 lectures

discussion-data-sources/examples.txt

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+Here are some examples of data sources relevant to students in population health science:
+
+    Demographic and health surveys, such as the National Health and Nutrition Examination Survey (NHANES) and the Behavioral Risk Factor Surveillance System (BRFSS)
+
+    Health data from government agencies, such as the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO)
+
+    Economic and social data from organizations such as the World Bank and the United Nations
+
+    Environmental data from sources such as the Environmental Protection Agency (EPA) and the National Oceanic and Atmospheric Administration (NOAA)
+
+    Geospatial data from sources such as the US Census Bureau and the National Geographic Society
+
+    Clinical data from electronic health records (EHRs) and clinical trials.

factor_datetime_code/data/hobo_cleaning.R

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+### Author: AHz
+### Date: 01/08/2023
+### Written in: R version 4.2.1
+### Purpose: Load and clean CO2/temp data
+
+# set working directory to file location
+source_file_loc <- dirname(rstudioapi::getActiveDocumentContext()$path)
+setwd(source_file_loc)
+
+library(pacman)
+p_load(tidyverse)
+p_load(janitor)
+p_load(lubridate)
+
+
+###############################################################################
+# 1. FUNCTIONS TO CLEAN RAW DATA #########################################
+###############################################################################
+
+#clean HOBO data (without CO2 logger)
+clean_hobo_dat <- function(dat){
+  dat %>% 
+    clean_names() %>% 
+    rename_all(~str_remove_all(., "_lgr.*")) %>% 
+    rename_with(~"date_time", contains("date_time")) %>% 
+    select(date_time:rh_percent) %>%
+    mutate(date_time = mdy_hms(date_time)) %>% 
+    pivot_longer(names_to = "metric", values_to = "result", temp_f:rh_percent) #%>%
+    # mutate(metric = factor(metric, levels = c("temp_f", "rh_percent"),
+    #                        labels = c("Temperature (F)", "Relative Humidity (%)")))
+
+}
+
+#clean HOBO CO2 logger data
+clean_hobo_co2 <- function(dat){
+  dat %>% 
+    clean_names() %>% 
+    rename_all(~str_remove_all(., "_lgr.*")) %>% 
+    rename_with(~"date_time", contains("date_time")) %>% 
+    select(date_time:co2_ppm) %>% 
+    mutate(date_time = mdy_hms(date_time)) %>% 
+    pivot_longer(names_to = "metric", values_to = "result", temp_f:co2_ppm) %>%
+    filter(metric == "co2_ppm") #%>%
+    # mutate(metric = factor(metric, levels = c("co2_ppm"),
+    #                        labels = c("CO2 (ppm)")))
+}
+
+
+###############################################################################
+# 2. LOAD AND CLEAN DATA   #########################################
+###############################################################################
+
+hobo <- read_csv("day4/lecture2-factors-and-datetimes/data/hobo_g2_2023-01-09.csv", skip = 1) %>% 
+  clean_hobo_dat() %>% 
+  filter(date_time %within% interval(start = ymd_hms("2023-01-09 13:15:00"), 
+                                     end = ymd_hms("2023-01-09 18:00:00")))
+
+hobo_co2 <- read_csv("day4/lecture2-factors-and-datetimes/data/hobo_co2_g2_2023-01-09.csv", skip = 1) %>% 
+  clean_hobo_co2() %>% 
+  filter(date_time %within% interval(start = ymd_hms("2023-01-09 13:15:00"), 
+                                     end = ymd_hms("2023-01-09 18:00:00")))
+
+
+hobo_g2 <- bind_rows(hobo, hobo_co2)
+
+
+write_csv(hobo_g2, "day4/lecture2-factors-and-datetimes/data/hobo_g2.csv")