diff --git a/Assignment02/README.Rmd b/Assignment02/README.Rmd
index 703a68a..f4a666a 100644
--- a/Assignment02/README.Rmd
+++ b/Assignment02/README.Rmd
@@ -1,6 +1,8 @@
---
title: "Assignment02"
-output: html_document
+output:
+ html_document: default
+ github_document: default
---
```{r setup, include=FALSE}
@@ -20,7 +22,7 @@ chs_individual <- data.table::fread("chs_individual.csv")
chs_regional <- data.table::fread("chs_regional.csv")
# merge two data frames by location
-total <- merge(chs_individual,dchs_regional,by="townname")
+total <- merge(chs_individual, chs_regional,by="townname")
dim(total)
head(total)
tail(total)
@@ -34,36 +36,40 @@ nrow(total)
```{r na-replacing}
total1 <- total
for(i in 1:ncol(total)) {
- total1[ , i][is.na(total1[ , i])] <- mean(total1[ , i], na.rm = TRUE)
+ total1[[i]][is.na(total1[[i]])] <- mean(total1[[i]], na.rm = TRUE)
}
total1
```
```{r obesity_level}
total1<-data.table(total1)
-ifelse(bmi < 14, obesity_level <- "underweight BMI")
-fifelse(bmi<22, obesity_level <- "normal BMI")
-fifelse(bmi<24, obesity_level <- "overweight BMI")
-fifelse(bmi<24, obesity_level <- "obese BMI")
+total1[, obesity_level := fifelse(bmi < 14, "underweight BMI", NA_character_)]
+total1[is.na(obesity_level), obesity_level := fifelse(bmi<22, "normal BMI", NA_character_)]
+total1[is.na(obesity_level), obesity_level := fifelse(bmi<24, "overweight BMI", NA_character_)]
+total1[is.na(obesity_level), obesity_level := fifelse(bmi<24, "obese BMI", NA_character_)]
-summary(total1$obesity_level)
+total1[, .(min = min(bmi), max = max(bmi), n = .N), by = obesity_level]
```
```{r smoke_gas_exposure}
-total1<-data.table(total1)
-ifelse(smoke==1 & gasstove==1, smoke_gas_exposure <- "smoke_gas")
-fifelse(smoke==1 & gasstove==0, smoke_gas_exposure <- "smoke_nongas")
-fifelse(smoke==0 & gasstove==1, smoke_gas_exposure <- "nonsmoke_gas")
-fifelse(smoke==0 & gasstove==0, smoke_gas_exposure <- "nonsmoke_nongas")
+total1[, smoke_gas_exposure := fifelse(smoke==1 & gasstove==1, "smoke_gas", NA_character_)]
+total1[is.na(smoke_gas_exposure), smoke_gas_exposure := fifelse(smoke==1 & gasstove==0, "smoke_nongas", NA_character_)]
+total1[is.na(smoke_gas_exposure), smoke_gas_exposure := fifelse(smoke==0 & gasstove==1, "nonsmoke_gas", NA_character_)]
+total1[is.na(smoke_gas_exposure), smoke_gas_exposure := fifelse(smoke==0 & gasstove==0, "nonsmoke_nongas", NA_character_)]
-summary(total1$obesity_level)
+total1[, table(smoke_gas_exposure)]
```
```{r summary}
-total1 %>% tbl_summary(by = townname) %>% add_p()
-total1 %>% tbl_summary(by = sex) %>% add_p()
-total1 %>% tbl_summary(by = obesity_level) %>% add_p()
-total1 %>% tbl_summary(by = smoke_gas_exposure) %>% add_p()
+total1[, .(
+ "Mean fev" = mean(fev, na.rm = TRUE),
+ "Sd fev" = sd(fev, na.rm = TRUE),
+ "Prop Asthma" = mean(asthma, na.rm = TRUE),
+ "Sd Asthma" = sd(asthma, na.rm = TRUE)
+ ),
+ by = townname]
+
+# You figure out the rest
```
```{r Facet plot}
@@ -74,42 +80,50 @@ ggplot(data = total1, mapping = aes(x = bmi, y = fev, fill = townname)) +
```{r}
ggplot(total1) +
- geom_histogram(mapping = aes(x = fev, fill=obesity_level, col=green))
+ # Setting the fill color argument to be viridis discrete
+ scale_fill_viridis_d() +
+ geom_histogram(mapping = aes(x = fev, fill=obesity_level))
-ggplot(total1) +
- geom_histogram(mapping = aes(x = fev, fill=smoke_gas_exposure, col=blue))
+# You figure out the other
+# ggplot(total1) +
+# geom_histogram(mapping = aes(x = fev, fill=smoke_gas_exposure, col=blue))
```
```{r Barchart}
-total1[!is.na(bmi) & bmi != "clear"] %>%
- ggplot() +
- geom_bar(mapping = aes(x = bmi, fill = smoke_gas_exposure))+
+ggplot(total1) +
+ geom_bar(mapping = aes(x = obesity_level, fill = smoke_gas_exposure))+
scale_fill_viridis_d()
```
```{r Statistical summary graphs}
-l <- total1[!is.na(fev) & fev != "clear"] %>%
- ggplot() +
+ggplot(total1) +
stat_summary(mapping = aes(x = fev, y = obesity_level),
fun.min = min,
fun.max = max,
fun = median)
-l <- total1[!is.na(fev) & fev != "clear"] %>%
- ggplot() +
- stat_summary(mapping = aes(x = fev, y = smoke_gas_exposure),
- fun.min = min,
- fun.max = max,
- fun = median)
+# l <- total1[!is.na(fev) & fev != "clear"] %>%
+# ggplot() +
+# stat_summary(mapping = aes(x = fev, y = smoke_gas_exposure),
+# fun.min = min,
+# fun.max = max,
+# fun = median)
```
-```{r leaflet map}
-total2 <- (unique(total[,c("lat","lon")]))
-dim(total2)
+```{r leaflet map, eval = knitr::is_html_output(excludes = "gfm")}
+total2 <- unique(total1[,.(townname, lon, lat, pm25_mass)])
+
+pal <- colorNumeric(c("green", "yellow", "red"), domain = range(total2$pm25_mass))
leaflet(total2) %>%
- addProviderTiles('CHS communities') %>%
- addCircles(lat = ~lat, lng = ~lon, opacity = 1, fillOpacity = 1, radius = 400)
+ addTiles() %>%
+ addCircles(
+ lat = ~lat, lng = ~lon, opacity = 1, fillOpacity = .5,
+ radius = ~pm25_mass*500,
+ stroke = 2,
+ color = ~ pal(pm25_mass),
+ fillColor = ~ pal(pm25_mass)) %>%
+ addLegend(pal = pal, values = ~pm25_mass, title = "PM2.5 Mass")
```
```{r examine whether PM2.5 mass is associated with FEV}
diff --git a/Assignment02/README.html b/Assignment02/README.html
new file mode 100644
index 0000000..0ae31a3
--- /dev/null
+++ b/Assignment02/README.html
@@ -0,0 +1,364 @@
+
+
+
+
+
+
+Assignment02
+library (data.table)library (tidyverse)library (ggplot2)library (leaflet)library (gtsummary)<- data.table:: fread ("chs_individual.csv" )<- data.table:: fread ("chs_regional.csv" )# merge two data frames by location <- merge (chs_individual, chs_regional,by= "townname" )dim (total)## [1] 1200 49## townname sid male race hispanic agepft height weight bmi asthma
+## 1: Alpine 835 0 W 0 10.099932 143 69 15.33749 0
+## 2: Alpine 838 0 O 1 9.486653 133 62 15.93183 0
+## 3: Alpine 839 0 M 1 10.053388 142 86 19.38649 0
+## 4: Alpine 840 0 W 0 9.965777 146 78 16.63283 0
+## 5: Alpine 841 1 W 1 10.548939 150 78 15.75758 0
+## 6: Alpine 842 1 M 1 9.489391 139 65 15.29189 0
+## active_asthma father_asthma mother_asthma wheeze hayfever allergy
+## 1: 0 0 0 0 0 1
+## 2: 0 0 0 0 0 0
+## 3: 0 0 1 1 1 1
+## 4: 0 0 0 0 0 0
+## 5: 0 0 0 0 0 0
+## 6: 0 0 0 1 0 0
+## educ_parent smoke pets gasstove fev fvc mmef pm25_mass
+## 1: 3 0 1 0 2529.276 2826.316 3406.579 8.74
+## 2: 4 NA 1 0 1737.793 1963.545 2133.110 8.74
+## 3: 3 1 1 0 2121.711 2326.974 2835.197 8.74
+## 4: NA NA 0 NA 2466.791 2638.221 3466.464 8.74
+## 5: 5 0 1 0 2251.505 2594.649 2445.151 8.74
+## 6: 1 1 1 0 2188.716 2423.934 2524.599 8.74
+## pm25_so4 pm25_no3 pm25_nh4 pm25_oc pm25_ec pm25_om pm10_oc pm10_ec pm10_tc
+## 1: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49 3.75
+## 2: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49 3.75
+## 3: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49 3.75
+## 4: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49 3.75
+## 5: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49 3.75
+## 6: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49 3.75
+## formic acetic hcl hno3 o3_max o3106 o3_24 no2 pm10 no_24hr pm2_5_fr
+## 1: 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48 10.28
+## 2: 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48 10.28
+## 3: 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48 10.28
+## 4: 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48 10.28
+## 5: 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48 10.28
+## 6: 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48 10.28
+## iacid oacid total_acids lon lat
+## 1: 2.39 3.52 5.5 -116.7664 32.83505
+## 2: 2.39 3.52 5.5 -116.7664 32.83505
+## 3: 2.39 3.52 5.5 -116.7664 32.83505
+## 4: 2.39 3.52 5.5 -116.7664 32.83505
+## 5: 2.39 3.52 5.5 -116.7664 32.83505
+## 6: 2.39 3.52 5.5 -116.7664 32.83505## townname sid male race hispanic agepft height weight bmi asthma
+## 1: Upland 1866 0 O 1 9.806982 139 60 14.11559 0
+## 2: Upland 1867 0 M 1 9.618070 140 71 16.46568 0
+## 3: Upland 2031 1 W 0 9.798768 135 83 20.70084 0
+## 4: Upland 2032 1 W 0 9.549624 137 59 14.28855 0
+## 5: Upland 2033 0 M 0 10.121834 130 67 18.02044 0
+## 6: Upland 2053 0 W 0 NA NA NA NA 0
+## active_asthma father_asthma mother_asthma wheeze hayfever allergy
+## 1: 0 NA 0 0 NA NA
+## 2: 0 1 0 0 0 0
+## 3: 0 0 0 1 0 1
+## 4: 0 0 1 1 1 1
+## 5: 1 0 0 1 1 0
+## 6: 0 0 0 0 0 0
+## educ_parent smoke pets gasstove fev fvc mmef pm25_mass
+## 1: 3 0 1 0 1691.275 1928.859 1890.604 22.46
+## 2: 3 0 1 0 1733.338 1993.040 2072.643 22.46
+## 3: 3 0 1 1 2034.177 2505.535 1814.075 22.46
+## 4: 3 0 1 1 2077.703 2275.338 2706.081 22.46
+## 5: 3 0 1 1 1929.866 2122.148 2558.054 22.46
+## 6: 3 0 1 0 NA NA NA 22.46
+## pm25_so4 pm25_no3 pm25_nh4 pm25_oc pm25_ec pm25_om pm10_oc pm10_ec pm10_tc
+## 1: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22 9.54
+## 2: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22 9.54
+## 3: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22 9.54
+## 4: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22 9.54
+## 5: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22 9.54
+## 6: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22 9.54
+## formic acetic hcl hno3 o3_max o3106 o3_24 no2 pm10 no_24hr pm2_5_fr iacid
+## 1: 2.67 4.73 0.46 4.03 63.83 46.5 22.2 37.97 40.8 18.48 27.73 4.49
+## 2: 2.67 4.73 0.46 4.03 63.83 46.5 22.2 37.97 40.8 18.48 27.73 4.49
+## 3: 2.67 4.73 0.46 4.03 63.83 46.5 22.2 37.97 40.8 18.48 27.73 4.49
+## 4: 2.67 4.73 0.46 4.03 63.83 46.5 22.2 37.97 40.8 18.48 27.73 4.49
+## 5: 2.67 4.73 0.46 4.03 63.83 46.5 22.2 37.97 40.8 18.48 27.73 4.49
+## 6: 2.67 4.73 0.46 4.03 63.83 46.5 22.2 37.97 40.8 18.48 27.73 4.49
+## oacid total_acids lon lat
+## 1: 7.4 11.43 -117.6484 34.09751
+## 2: 7.4 11.43 -117.6484 34.09751
+## 3: 7.4 11.43 -117.6484 34.09751
+## 4: 7.4 11.43 -117.6484 34.09751
+## 5: 7.4 11.43 -117.6484 34.09751
+## 6: 7.4 11.43 -117.6484 34.09751## Classes 'data.table' and 'data.frame': 1200 obs. of 49 variables:
+## $ townname : chr "Alpine" "Alpine" "Alpine" "Alpine" ...
+## $ sid : int 835 838 839 840 841 842 843 844 847 849 ...
+## $ male : int 0 0 0 0 1 1 1 1 1 1 ...
+## $ race : chr "W" "O" "M" "W" ...
+## $ hispanic : int 0 1 1 0 1 1 0 1 0 0 ...
+## $ agepft : num 10.1 9.49 10.05 9.97 10.55 ...
+## $ height : int 143 133 142 146 150 139 149 143 137 147 ...
+## $ weight : int 69 62 86 78 78 65 98 65 69 112 ...
+## $ bmi : num 15.3 15.9 19.4 16.6 15.8 ...
+## $ asthma : int 0 0 0 0 0 0 0 NA 0 0 ...
+## $ active_asthma: int 0 0 0 0 0 0 0 0 0 0 ...
+## $ father_asthma: int 0 0 0 0 0 0 0 NA 0 1 ...
+## $ mother_asthma: int 0 0 1 0 0 0 0 NA 0 0 ...
+## $ wheeze : int 0 0 1 0 0 1 1 NA 0 1 ...
+## $ hayfever : int 0 0 1 0 0 0 0 NA 0 0 ...
+## $ allergy : int 1 0 1 0 0 0 1 NA 0 1 ...
+## $ educ_parent : int 3 4 3 NA 5 1 3 NA 5 3 ...
+## $ smoke : int 0 NA 1 NA 0 1 0 NA 0 0 ...
+## $ pets : int 1 1 1 0 1 1 1 0 1 1 ...
+## $ gasstove : int 0 0 0 NA 0 0 1 NA 1 0 ...
+## $ fev : num 2529 1738 2122 2467 2252 ...
+## $ fvc : num 2826 1964 2327 2638 2595 ...
+## $ mmef : num 3407 2133 2835 3466 2445 ...
+## $ pm25_mass : num 8.74 8.74 8.74 8.74 8.74 8.74 8.74 8.74 8.74 8.74 ...
+## $ pm25_so4 : num 1.73 1.73 1.73 1.73 1.73 1.73 1.73 1.73 1.73 1.73 ...
+## $ pm25_no3 : num 1.59 1.59 1.59 1.59 1.59 1.59 1.59 1.59 1.59 1.59 ...
+## $ pm25_nh4 : num 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 ...
+## $ pm25_oc : num 2.54 2.54 2.54 2.54 2.54 2.54 2.54 2.54 2.54 2.54 ...
+## $ pm25_ec : num 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 ...
+## $ pm25_om : num 3.04 3.04 3.04 3.04 3.04 3.04 3.04 3.04 3.04 3.04 ...
+## $ pm10_oc : num 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 ...
+## $ pm10_ec : num 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.49 ...
+## $ pm10_tc : num 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 ...
+## $ formic : num 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 ...
+## $ acetic : num 2.49 2.49 2.49 2.49 2.49 2.49 2.49 2.49 2.49 2.49 ...
+## $ hcl : num 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 ...
+## $ hno3 : num 1.98 1.98 1.98 1.98 1.98 1.98 1.98 1.98 1.98 1.98 ...
+## $ o3_max : num 65.8 65.8 65.8 65.8 65.8 ...
+## $ o3106 : num 55 55 55 55 55 ...
+## $ o3_24 : num 41.2 41.2 41.2 41.2 41.2 ...
+## $ no2 : num 12.2 12.2 12.2 12.2 12.2 ...
+## $ pm10 : num 24.7 24.7 24.7 24.7 24.7 ...
+## $ no_24hr : num 2.48 2.48 2.48 2.48 2.48 2.48 2.48 2.48 2.48 2.48 ...
+## $ pm2_5_fr : num 10.3 10.3 10.3 10.3 10.3 ...
+## $ iacid : num 2.39 2.39 2.39 2.39 2.39 2.39 2.39 2.39 2.39 2.39 ...
+## $ oacid : num 3.52 3.52 3.52 3.52 3.52 3.52 3.52 3.52 3.52 3.52 ...
+## $ total_acids : num 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 ...
+## $ lon : num -117 -117 -117 -117 -117 ...
+## $ lat : num 32.8 32.8 32.8 32.8 32.8 ...
+## - attr(*, ".internal.selfref")=<externalptr>
+## - attr(*, "sorted")= chr "townname"## [1] 1200## [1] 12## [1] 1200<- total for (i in 1 : ncol (total)) { is.na (total1[[i]])] <- mean (total1[[i]], na.rm = TRUE )## Warning in mean.default(total1[[i]], na.rm = TRUE): argument is not numeric or
+## logical: returning NA
+
+## Warning in mean.default(total1[[i]], na.rm = TRUE): argument is not numeric or
+## logical: returning NA## townname sid male race hispanic agepft height weight bmi
+## 1: Alpine 835 0 W 0 10.099932 143.0000 69.00000 15.33749
+## 2: Alpine 838 0 O 1 9.486653 133.0000 62.00000 15.93183
+## 3: Alpine 839 0 M 1 10.053388 142.0000 86.00000 19.38649
+## 4: Alpine 840 0 W 0 9.965777 146.0000 78.00000 16.63283
+## 5: Alpine 841 1 W 1 10.548939 150.0000 78.00000 15.75758
+## ---
+## 1196: Upland 1867 0 M 1 9.618070 140.0000 71.00000 16.46568
+## 1197: Upland 2031 1 W 0 9.798768 135.0000 83.00000 20.70084
+## 1198: Upland 2032 1 W 0 9.549624 137.0000 59.00000 14.28855
+## 1199: Upland 2033 0 M 0 10.121834 130.0000 67.00000 18.02044
+## 1200: Upland 2053 0 W 0 9.923704 138.9595 79.33123 18.50483
+## asthma active_asthma father_asthma mother_asthma wheeze hayfever allergy
+## 1: 0 0 0 0 0 0 1
+## 2: 0 0 0 0 0 0 0
+## 3: 0 0 0 1 1 1 1
+## 4: 0 0 0 0 0 0 0
+## 5: 0 0 0 0 0 0 0
+## ---
+## 1196: 0 0 1 0 0 0 0
+## 1197: 0 0 0 0 1 0 1
+## 1198: 0 0 0 1 1 1 1
+## 1199: 0 1 0 0 1 1 0
+## 1200: 0 0 0 0 0 0 0
+## educ_parent smoke pets gasstove fev fvc mmef pm25_mass
+## 1: 3.000000 0.0000000 1 0.000000 2529.276 2826.316 3406.579 8.74
+## 2: 4.000000 0.1637931 1 0.000000 1737.793 1963.545 2133.110 8.74
+## 3: 3.000000 1.0000000 1 0.000000 2121.711 2326.974 2835.197 8.74
+## 4: 2.796655 0.1637931 0 0.781491 2466.791 2638.221 3466.464 8.74
+## 5: 5.000000 0.0000000 1 0.000000 2251.505 2594.649 2445.151 8.74
+## ---
+## 1196: 3.000000 0.0000000 1 0.000000 1733.338 1993.040 2072.643 22.46
+## 1197: 3.000000 0.0000000 1 1.000000 2034.177 2505.535 1814.075 22.46
+## 1198: 3.000000 0.0000000 1 1.000000 2077.703 2275.338 2706.081 22.46
+## 1199: 3.000000 0.0000000 1 1.000000 1929.866 2122.148 2558.054 22.46
+## 1200: 3.000000 0.0000000 1 0.000000 2031.265 2323.915 2398.768 22.46
+## pm25_so4 pm25_no3 pm25_nh4 pm25_oc pm25_ec pm25_om pm10_oc pm10_ec
+## 1: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49
+## 2: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49
+## 3: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49
+## 4: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49
+## 5: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49
+## ---
+## 1196: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22
+## 1197: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22
+## 1198: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22
+## 1199: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22
+## 1200: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22
+## pm10_tc formic acetic hcl hno3 o3_max o3106 o3_24 no2 pm10 no_24hr
+## 1: 3.75 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48
+## 2: 3.75 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48
+## 3: 3.75 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48
+## 4: 3.75 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48
+## 5: 3.75 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48
+## ---
+## 1196: 9.54 2.67 4.73 0.46 4.03 63.83 46.50 22.20 37.97 40.80 18.48
+## 1197: 9.54 2.67 4.73 0.46 4.03 63.83 46.50 22.20 37.97 40.80 18.48
+## 1198: 9.54 2.67 4.73 0.46 4.03 63.83 46.50 22.20 37.97 40.80 18.48
+## 1199: 9.54 2.67 4.73 0.46 4.03 63.83 46.50 22.20 37.97 40.80 18.48
+## 1200: 9.54 2.67 4.73 0.46 4.03 63.83 46.50 22.20 37.97 40.80 18.48
+## pm2_5_fr iacid oacid total_acids lon lat
+## 1: 10.28 2.39 3.52 5.50 -116.7664 32.83505
+## 2: 10.28 2.39 3.52 5.50 -116.7664 32.83505
+## 3: 10.28 2.39 3.52 5.50 -116.7664 32.83505
+## 4: 10.28 2.39 3.52 5.50 -116.7664 32.83505
+## 5: 10.28 2.39 3.52 5.50 -116.7664 32.83505
+## ---
+## 1196: 27.73 4.49 7.40 11.43 -117.6484 34.09751
+## 1197: 27.73 4.49 7.40 11.43 -117.6484 34.09751
+## 1198: 27.73 4.49 7.40 11.43 -117.6484 34.09751
+## 1199: 27.73 4.49 7.40 11.43 -117.6484 34.09751
+## 1200: 27.73 4.49 7.40 11.43 -117.6484 34.09751<- data.table (total1): = fifelse (bmi < 14 , "underweight BMI" , NA_character_ )]is.na (obesity_level), obesity_level : = fifelse (bmi< 22 , "normal BMI" , NA_character_ )]is.na (obesity_level), obesity_level : = fifelse (bmi< 24 , "overweight BMI" , NA_character_ )]is.na (obesity_level), obesity_level : = fifelse (bmi< 24 , "obese BMI" , NA_character_ )]min = min (bmi), max = max (bmi), n = .N), by = obesity_level]## obesity_level min max n
+## 1: normal BMI 14.00380 21.96387 975
+## 2: overweight BMI 22.02353 23.99650 87
+## 3: <NA> 24.00647 41.26613 103
+## 4: underweight BMI 11.29640 13.98601 35: = fifelse (smoke== 1 & gasstove== 1 , "smoke_gas" , NA_character_ )]is.na (smoke_gas_exposure), smoke_gas_exposure : = fifelse (smoke== 1 & gasstove== 0 , "smoke_nongas" , NA_character_ )]is.na (smoke_gas_exposure), smoke_gas_exposure : = fifelse (smoke== 0 & gasstove== 1 , "nonsmoke_gas" , NA_character_ )]is.na (smoke_gas_exposure), smoke_gas_exposure : = fifelse (smoke== 0 & gasstove== 0 , "nonsmoke_nongas" , NA_character_ )]table (smoke_gas_exposure)]## smoke_gas_exposure
+## nonsmoke_gas nonsmoke_nongas smoke_gas smoke_nongas
+## 739 214 151 36"Mean fev" = mean (fev, na.rm = TRUE ),"Sd fev" = sd (fev, na.rm = TRUE ),"Prop Asthma" = mean (asthma, na.rm = TRUE ),"Sd Asthma" = sd (asthma, na.rm = TRUE )= townname]## townname Mean fev Sd fev Prop Asthma Sd Asthma
+## 1: Alpine 2086.126 290.8511 0.1143884 0.3139150
+## 2: Atascadero 2076.968 323.5605 0.2529256 0.4339816
+## 3: Lake Elsinore 2039.020 302.8403 0.1273139 0.3254539
+## 4: Lake Gregory 2085.021 319.0736 0.1514628 0.3585507
+## 5: Lancaster 2005.996 316.1541 0.1643884 0.3673800
+## 6: Lompoc 2037.600 350.3531 0.1143884 0.3139150
+## 7: Long Beach 1987.212 318.9554 0.1358512 0.3369506
+## 8: Mira Loma 1987.984 324.7413 0.1573139 0.3572089
+## 9: Riverside 1990.267 276.5614 0.1100000 0.3144660
+## 10: San Dimas 2027.875 318.7055 0.1714628 0.3771430
+## 11: Santa Maria 2023.511 311.1821 0.1343884 0.3372223
+## 12: Upland 2027.603 342.4664 0.1214628 0.3263833# You figure out the rest ggplot (data = total1, mapping = aes (x = bmi, y = fev, fill = townname)) + geom_point () + geom_smooth (method = "lm" , se = FALSE ) + labs (title = "BMI vs FEV" , xlab = "BMI" , y = "FEV" )## `geom_smooth()` using formula 'y ~ x'
+ggplot (total1) + # Setting the fill color argument to be viridis discrete scale_fill_viridis_d () + geom_histogram (mapping = aes (x = fev, fill= obesity_level))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+# You figure out the other # ggplot(total1) + # geom_histogram(mapping = aes(x = fev, fill=smoke_gas_exposure, col=blue)) ggplot (total1) + geom_bar (mapping = aes (x = obesity_level, fill = smoke_gas_exposure))+ scale_fill_viridis_d ()
+ggplot (total1) + stat_summary (mapping = aes (x = fev, y = obesity_level),fun.min = min,fun.max = max,fun = median)
+# l <- total1[!is.na(fev) & fev != "clear"] %>% # ggplot() + # stat_summary(mapping = aes(x = fev, y = smoke_gas_exposure), # fun.min = min, # fun.max = max, # fun = median) <- unique (total1[,.(townname, lon, lat, pm25_mass)])<- colorNumeric (c ("green" , "yellow" , "red" ), domain = range (total2$ pm25_mass))leaflet (total2) %>% addTiles () %>% addCircles (lat = ~ lat, lng = ~ lon, opacity = 1 , fillOpacity = .5 ,radius = ~ pm25_mass* 500 ,stroke = 2 ,color = ~ pal (pm25_mass),fillColor = ~ pal (pm25_mass)) %>% addLegend (pal = pal, values = ~ pm25_mass, title = "PM2.5 Mass" )<- lm (fev ~ pm25_mass, data = total1)plot (pch= 19 , cex= 0.5 , main = "FEV and PM2.5 mass" , xlab = "PM2.5 mass" , ylab = "FEV" )## NULLabline (mod, lwd= 2 , col= "red" )
+
+
+
diff --git a/Assignment02/README.md b/Assignment02/README.md
new file mode 100644
index 0000000..f03b781
--- /dev/null
+++ b/Assignment02/README.md
@@ -0,0 +1,424 @@
+Assignment02
+================
+
+``` r
+library(data.table)
+library(tidyverse)
+library(ggplot2)
+library(leaflet)
+library(gtsummary)
+```
+
+``` r
+chs_individual <- data.table::fread("chs_individual.csv")
+chs_regional <- data.table::fread("chs_regional.csv")
+
+# merge two data frames by location
+total <- merge(chs_individual, chs_regional,by="townname")
+dim(total)
+```
+
+ ## [1] 1200 49
+
+``` r
+head(total)
+```
+
+ ## townname sid male race hispanic agepft height weight bmi asthma
+ ## 1: Alpine 835 0 W 0 10.099932 143 69 15.33749 0
+ ## 2: Alpine 838 0 O 1 9.486653 133 62 15.93183 0
+ ## 3: Alpine 839 0 M 1 10.053388 142 86 19.38649 0
+ ## 4: Alpine 840 0 W 0 9.965777 146 78 16.63283 0
+ ## 5: Alpine 841 1 W 1 10.548939 150 78 15.75758 0
+ ## 6: Alpine 842 1 M 1 9.489391 139 65 15.29189 0
+ ## active_asthma father_asthma mother_asthma wheeze hayfever allergy
+ ## 1: 0 0 0 0 0 1
+ ## 2: 0 0 0 0 0 0
+ ## 3: 0 0 1 1 1 1
+ ## 4: 0 0 0 0 0 0
+ ## 5: 0 0 0 0 0 0
+ ## 6: 0 0 0 1 0 0
+ ## educ_parent smoke pets gasstove fev fvc mmef pm25_mass
+ ## 1: 3 0 1 0 2529.276 2826.316 3406.579 8.74
+ ## 2: 4 NA 1 0 1737.793 1963.545 2133.110 8.74
+ ## 3: 3 1 1 0 2121.711 2326.974 2835.197 8.74
+ ## 4: NA NA 0 NA 2466.791 2638.221 3466.464 8.74
+ ## 5: 5 0 1 0 2251.505 2594.649 2445.151 8.74
+ ## 6: 1 1 1 0 2188.716 2423.934 2524.599 8.74
+ ## pm25_so4 pm25_no3 pm25_nh4 pm25_oc pm25_ec pm25_om pm10_oc pm10_ec pm10_tc
+ ## 1: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49 3.75
+ ## 2: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49 3.75
+ ## 3: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49 3.75
+ ## 4: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49 3.75
+ ## 5: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49 3.75
+ ## 6: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49 3.75
+ ## formic acetic hcl hno3 o3_max o3106 o3_24 no2 pm10 no_24hr pm2_5_fr
+ ## 1: 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48 10.28
+ ## 2: 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48 10.28
+ ## 3: 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48 10.28
+ ## 4: 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48 10.28
+ ## 5: 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48 10.28
+ ## 6: 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48 10.28
+ ## iacid oacid total_acids lon lat
+ ## 1: 2.39 3.52 5.5 -116.7664 32.83505
+ ## 2: 2.39 3.52 5.5 -116.7664 32.83505
+ ## 3: 2.39 3.52 5.5 -116.7664 32.83505
+ ## 4: 2.39 3.52 5.5 -116.7664 32.83505
+ ## 5: 2.39 3.52 5.5 -116.7664 32.83505
+ ## 6: 2.39 3.52 5.5 -116.7664 32.83505
+
+``` r
+tail(total)
+```
+
+ ## townname sid male race hispanic agepft height weight bmi asthma
+ ## 1: Upland 1866 0 O 1 9.806982 139 60 14.11559 0
+ ## 2: Upland 1867 0 M 1 9.618070 140 71 16.46568 0
+ ## 3: Upland 2031 1 W 0 9.798768 135 83 20.70084 0
+ ## 4: Upland 2032 1 W 0 9.549624 137 59 14.28855 0
+ ## 5: Upland 2033 0 M 0 10.121834 130 67 18.02044 0
+ ## 6: Upland 2053 0 W 0 NA NA NA NA 0
+ ## active_asthma father_asthma mother_asthma wheeze hayfever allergy
+ ## 1: 0 NA 0 0 NA NA
+ ## 2: 0 1 0 0 0 0
+ ## 3: 0 0 0 1 0 1
+ ## 4: 0 0 1 1 1 1
+ ## 5: 1 0 0 1 1 0
+ ## 6: 0 0 0 0 0 0
+ ## educ_parent smoke pets gasstove fev fvc mmef pm25_mass
+ ## 1: 3 0 1 0 1691.275 1928.859 1890.604 22.46
+ ## 2: 3 0 1 0 1733.338 1993.040 2072.643 22.46
+ ## 3: 3 0 1 1 2034.177 2505.535 1814.075 22.46
+ ## 4: 3 0 1 1 2077.703 2275.338 2706.081 22.46
+ ## 5: 3 0 1 1 1929.866 2122.148 2558.054 22.46
+ ## 6: 3 0 1 0 NA NA NA 22.46
+ ## pm25_so4 pm25_no3 pm25_nh4 pm25_oc pm25_ec pm25_om pm10_oc pm10_ec pm10_tc
+ ## 1: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22 9.54
+ ## 2: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22 9.54
+ ## 3: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22 9.54
+ ## 4: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22 9.54
+ ## 5: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22 9.54
+ ## 6: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22 9.54
+ ## formic acetic hcl hno3 o3_max o3106 o3_24 no2 pm10 no_24hr pm2_5_fr iacid
+ ## 1: 2.67 4.73 0.46 4.03 63.83 46.5 22.2 37.97 40.8 18.48 27.73 4.49
+ ## 2: 2.67 4.73 0.46 4.03 63.83 46.5 22.2 37.97 40.8 18.48 27.73 4.49
+ ## 3: 2.67 4.73 0.46 4.03 63.83 46.5 22.2 37.97 40.8 18.48 27.73 4.49
+ ## 4: 2.67 4.73 0.46 4.03 63.83 46.5 22.2 37.97 40.8 18.48 27.73 4.49
+ ## 5: 2.67 4.73 0.46 4.03 63.83 46.5 22.2 37.97 40.8 18.48 27.73 4.49
+ ## 6: 2.67 4.73 0.46 4.03 63.83 46.5 22.2 37.97 40.8 18.48 27.73 4.49
+ ## oacid total_acids lon lat
+ ## 1: 7.4 11.43 -117.6484 34.09751
+ ## 2: 7.4 11.43 -117.6484 34.09751
+ ## 3: 7.4 11.43 -117.6484 34.09751
+ ## 4: 7.4 11.43 -117.6484 34.09751
+ ## 5: 7.4 11.43 -117.6484 34.09751
+ ## 6: 7.4 11.43 -117.6484 34.09751
+
+``` r
+str(total)
+```
+
+ ## Classes 'data.table' and 'data.frame': 1200 obs. of 49 variables:
+ ## $ townname : chr "Alpine" "Alpine" "Alpine" "Alpine" ...
+ ## $ sid : int 835 838 839 840 841 842 843 844 847 849 ...
+ ## $ male : int 0 0 0 0 1 1 1 1 1 1 ...
+ ## $ race : chr "W" "O" "M" "W" ...
+ ## $ hispanic : int 0 1 1 0 1 1 0 1 0 0 ...
+ ## $ agepft : num 10.1 9.49 10.05 9.97 10.55 ...
+ ## $ height : int 143 133 142 146 150 139 149 143 137 147 ...
+ ## $ weight : int 69 62 86 78 78 65 98 65 69 112 ...
+ ## $ bmi : num 15.3 15.9 19.4 16.6 15.8 ...
+ ## $ asthma : int 0 0 0 0 0 0 0 NA 0 0 ...
+ ## $ active_asthma: int 0 0 0 0 0 0 0 0 0 0 ...
+ ## $ father_asthma: int 0 0 0 0 0 0 0 NA 0 1 ...
+ ## $ mother_asthma: int 0 0 1 0 0 0 0 NA 0 0 ...
+ ## $ wheeze : int 0 0 1 0 0 1 1 NA 0 1 ...
+ ## $ hayfever : int 0 0 1 0 0 0 0 NA 0 0 ...
+ ## $ allergy : int 1 0 1 0 0 0 1 NA 0 1 ...
+ ## $ educ_parent : int 3 4 3 NA 5 1 3 NA 5 3 ...
+ ## $ smoke : int 0 NA 1 NA 0 1 0 NA 0 0 ...
+ ## $ pets : int 1 1 1 0 1 1 1 0 1 1 ...
+ ## $ gasstove : int 0 0 0 NA 0 0 1 NA 1 0 ...
+ ## $ fev : num 2529 1738 2122 2467 2252 ...
+ ## $ fvc : num 2826 1964 2327 2638 2595 ...
+ ## $ mmef : num 3407 2133 2835 3466 2445 ...
+ ## $ pm25_mass : num 8.74 8.74 8.74 8.74 8.74 8.74 8.74 8.74 8.74 8.74 ...
+ ## $ pm25_so4 : num 1.73 1.73 1.73 1.73 1.73 1.73 1.73 1.73 1.73 1.73 ...
+ ## $ pm25_no3 : num 1.59 1.59 1.59 1.59 1.59 1.59 1.59 1.59 1.59 1.59 ...
+ ## $ pm25_nh4 : num 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 ...
+ ## $ pm25_oc : num 2.54 2.54 2.54 2.54 2.54 2.54 2.54 2.54 2.54 2.54 ...
+ ## $ pm25_ec : num 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 ...
+ ## $ pm25_om : num 3.04 3.04 3.04 3.04 3.04 3.04 3.04 3.04 3.04 3.04 ...
+ ## $ pm10_oc : num 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 ...
+ ## $ pm10_ec : num 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.49 ...
+ ## $ pm10_tc : num 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 3.75 ...
+ ## $ formic : num 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 ...
+ ## $ acetic : num 2.49 2.49 2.49 2.49 2.49 2.49 2.49 2.49 2.49 2.49 ...
+ ## $ hcl : num 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 ...
+ ## $ hno3 : num 1.98 1.98 1.98 1.98 1.98 1.98 1.98 1.98 1.98 1.98 ...
+ ## $ o3_max : num 65.8 65.8 65.8 65.8 65.8 ...
+ ## $ o3106 : num 55 55 55 55 55 ...
+ ## $ o3_24 : num 41.2 41.2 41.2 41.2 41.2 ...
+ ## $ no2 : num 12.2 12.2 12.2 12.2 12.2 ...
+ ## $ pm10 : num 24.7 24.7 24.7 24.7 24.7 ...
+ ## $ no_24hr : num 2.48 2.48 2.48 2.48 2.48 2.48 2.48 2.48 2.48 2.48 ...
+ ## $ pm2_5_fr : num 10.3 10.3 10.3 10.3 10.3 ...
+ ## $ iacid : num 2.39 2.39 2.39 2.39 2.39 2.39 2.39 2.39 2.39 2.39 ...
+ ## $ oacid : num 3.52 3.52 3.52 3.52 3.52 3.52 3.52 3.52 3.52 3.52 ...
+ ## $ total_acids : num 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 ...
+ ## $ lon : num -117 -117 -117 -117 -117 ...
+ ## $ lat : num 32.8 32.8 32.8 32.8 32.8 ...
+ ## - attr(*, ".internal.selfref")=
+ ## - attr(*, "sorted")= chr "townname"
+
+``` r
+nrow(chs_individual)
+```
+
+ ## [1] 1200
+
+``` r
+nrow(chs_regional)
+```
+
+ ## [1] 12
+
+``` r
+nrow(total)
+```
+
+ ## [1] 1200
+
+``` r
+total1 <- total
+for(i in 1:ncol(total)) {
+ total1[[i]][is.na(total1[[i]])] <- mean(total1[[i]], na.rm = TRUE)
+}
+```
+
+ ## Warning in mean.default(total1[[i]], na.rm = TRUE): argument is not numeric or
+ ## logical: returning NA
+
+ ## Warning in mean.default(total1[[i]], na.rm = TRUE): argument is not numeric or
+ ## logical: returning NA
+
+``` r
+total1
+```
+
+ ## townname sid male race hispanic agepft height weight bmi
+ ## 1: Alpine 835 0 W 0 10.099932 143.0000 69.00000 15.33749
+ ## 2: Alpine 838 0 O 1 9.486653 133.0000 62.00000 15.93183
+ ## 3: Alpine 839 0 M 1 10.053388 142.0000 86.00000 19.38649
+ ## 4: Alpine 840 0 W 0 9.965777 146.0000 78.00000 16.63283
+ ## 5: Alpine 841 1 W 1 10.548939 150.0000 78.00000 15.75758
+ ## ---
+ ## 1196: Upland 1867 0 M 1 9.618070 140.0000 71.00000 16.46568
+ ## 1197: Upland 2031 1 W 0 9.798768 135.0000 83.00000 20.70084
+ ## 1198: Upland 2032 1 W 0 9.549624 137.0000 59.00000 14.28855
+ ## 1199: Upland 2033 0 M 0 10.121834 130.0000 67.00000 18.02044
+ ## 1200: Upland 2053 0 W 0 9.923704 138.9595 79.33123 18.50483
+ ## asthma active_asthma father_asthma mother_asthma wheeze hayfever allergy
+ ## 1: 0 0 0 0 0 0 1
+ ## 2: 0 0 0 0 0 0 0
+ ## 3: 0 0 0 1 1 1 1
+ ## 4: 0 0 0 0 0 0 0
+ ## 5: 0 0 0 0 0 0 0
+ ## ---
+ ## 1196: 0 0 1 0 0 0 0
+ ## 1197: 0 0 0 0 1 0 1
+ ## 1198: 0 0 0 1 1 1 1
+ ## 1199: 0 1 0 0 1 1 0
+ ## 1200: 0 0 0 0 0 0 0
+ ## educ_parent smoke pets gasstove fev fvc mmef pm25_mass
+ ## 1: 3.000000 0.0000000 1 0.000000 2529.276 2826.316 3406.579 8.74
+ ## 2: 4.000000 0.1637931 1 0.000000 1737.793 1963.545 2133.110 8.74
+ ## 3: 3.000000 1.0000000 1 0.000000 2121.711 2326.974 2835.197 8.74
+ ## 4: 2.796655 0.1637931 0 0.781491 2466.791 2638.221 3466.464 8.74
+ ## 5: 5.000000 0.0000000 1 0.000000 2251.505 2594.649 2445.151 8.74
+ ## ---
+ ## 1196: 3.000000 0.0000000 1 0.000000 1733.338 1993.040 2072.643 22.46
+ ## 1197: 3.000000 0.0000000 1 1.000000 2034.177 2505.535 1814.075 22.46
+ ## 1198: 3.000000 0.0000000 1 1.000000 2077.703 2275.338 2706.081 22.46
+ ## 1199: 3.000000 0.0000000 1 1.000000 1929.866 2122.148 2558.054 22.46
+ ## 1200: 3.000000 0.0000000 1 0.000000 2031.265 2323.915 2398.768 22.46
+ ## pm25_so4 pm25_no3 pm25_nh4 pm25_oc pm25_ec pm25_om pm10_oc pm10_ec
+ ## 1: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49
+ ## 2: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49
+ ## 3: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49
+ ## 4: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49
+ ## 5: 1.73 1.59 0.88 2.54 0.48 3.04 3.25 0.49
+ ## ---
+ ## 1196: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22
+ ## 1197: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22
+ ## 1198: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22
+ ## 1199: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22
+ ## 1200: 2.65 7.75 2.96 6.49 1.19 7.79 8.32 1.22
+ ## pm10_tc formic acetic hcl hno3 o3_max o3106 o3_24 no2 pm10 no_24hr
+ ## 1: 3.75 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48
+ ## 2: 3.75 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48
+ ## 3: 3.75 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48
+ ## 4: 3.75 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48
+ ## 5: 3.75 1.03 2.49 0.41 1.98 65.82 55.05 41.23 12.18 24.73 2.48
+ ## ---
+ ## 1196: 9.54 2.67 4.73 0.46 4.03 63.83 46.50 22.20 37.97 40.80 18.48
+ ## 1197: 9.54 2.67 4.73 0.46 4.03 63.83 46.50 22.20 37.97 40.80 18.48
+ ## 1198: 9.54 2.67 4.73 0.46 4.03 63.83 46.50 22.20 37.97 40.80 18.48
+ ## 1199: 9.54 2.67 4.73 0.46 4.03 63.83 46.50 22.20 37.97 40.80 18.48
+ ## 1200: 9.54 2.67 4.73 0.46 4.03 63.83 46.50 22.20 37.97 40.80 18.48
+ ## pm2_5_fr iacid oacid total_acids lon lat
+ ## 1: 10.28 2.39 3.52 5.50 -116.7664 32.83505
+ ## 2: 10.28 2.39 3.52 5.50 -116.7664 32.83505
+ ## 3: 10.28 2.39 3.52 5.50 -116.7664 32.83505
+ ## 4: 10.28 2.39 3.52 5.50 -116.7664 32.83505
+ ## 5: 10.28 2.39 3.52 5.50 -116.7664 32.83505
+ ## ---
+ ## 1196: 27.73 4.49 7.40 11.43 -117.6484 34.09751
+ ## 1197: 27.73 4.49 7.40 11.43 -117.6484 34.09751
+ ## 1198: 27.73 4.49 7.40 11.43 -117.6484 34.09751
+ ## 1199: 27.73 4.49 7.40 11.43 -117.6484 34.09751
+ ## 1200: 27.73 4.49 7.40 11.43 -117.6484 34.09751
+
+``` r
+total1<-data.table(total1)
+total1[, obesity_level := fifelse(bmi < 14, "underweight BMI", NA_character_)]
+total1[is.na(obesity_level), obesity_level := fifelse(bmi<22, "normal BMI", NA_character_)]
+total1[is.na(obesity_level), obesity_level := fifelse(bmi<24, "overweight BMI", NA_character_)]
+total1[is.na(obesity_level), obesity_level := fifelse(bmi<24, "obese BMI", NA_character_)]
+
+total1[, .(min = min(bmi), max = max(bmi), n = .N), by = obesity_level]
+```
+
+ ## obesity_level min max n
+ ## 1: normal BMI 14.00380 21.96387 975
+ ## 2: overweight BMI 22.02353 23.99650 87
+ ## 3: 24.00647 41.26613 103
+ ## 4: underweight BMI 11.29640 13.98601 35
+
+``` r
+total1[, smoke_gas_exposure := fifelse(smoke==1 & gasstove==1, "smoke_gas", NA_character_)]
+total1[is.na(smoke_gas_exposure), smoke_gas_exposure := fifelse(smoke==1 & gasstove==0, "smoke_nongas", NA_character_)]
+total1[is.na(smoke_gas_exposure), smoke_gas_exposure := fifelse(smoke==0 & gasstove==1, "nonsmoke_gas", NA_character_)]
+total1[is.na(smoke_gas_exposure), smoke_gas_exposure := fifelse(smoke==0 & gasstove==0, "nonsmoke_nongas", NA_character_)]
+
+total1[, table(smoke_gas_exposure)]
+```
+
+ ## smoke_gas_exposure
+ ## nonsmoke_gas nonsmoke_nongas smoke_gas smoke_nongas
+ ## 739 214 151 36
+
+``` r
+total1[, .(
+ "Mean fev" = mean(fev, na.rm = TRUE),
+ "Sd fev" = sd(fev, na.rm = TRUE),
+ "Prop Asthma" = mean(asthma, na.rm = TRUE),
+ "Sd Asthma" = sd(asthma, na.rm = TRUE)
+ ),
+ by = townname]
+```
+
+ ## townname Mean fev Sd fev Prop Asthma Sd Asthma
+ ## 1: Alpine 2086.126 290.8511 0.1143884 0.3139150
+ ## 2: Atascadero 2076.968 323.5605 0.2529256 0.4339816
+ ## 3: Lake Elsinore 2039.020 302.8403 0.1273139 0.3254539
+ ## 4: Lake Gregory 2085.021 319.0736 0.1514628 0.3585507
+ ## 5: Lancaster 2005.996 316.1541 0.1643884 0.3673800
+ ## 6: Lompoc 2037.600 350.3531 0.1143884 0.3139150
+ ## 7: Long Beach 1987.212 318.9554 0.1358512 0.3369506
+ ## 8: Mira Loma 1987.984 324.7413 0.1573139 0.3572089
+ ## 9: Riverside 1990.267 276.5614 0.1100000 0.3144660
+ ## 10: San Dimas 2027.875 318.7055 0.1714628 0.3771430
+ ## 11: Santa Maria 2023.511 311.1821 0.1343884 0.3372223
+ ## 12: Upland 2027.603 342.4664 0.1214628 0.3263833
+
+``` r
+# You figure out the rest
+```
+
+``` r
+ggplot(data = total1, mapping = aes(x = bmi, y = fev, fill = townname)) +
+ geom_point() + geom_smooth(method = "lm", se = FALSE) +
+ labs(title = "BMI vs FEV", xlab = "BMI", y = "FEV")
+```
+
+ ## `geom_smooth()` using formula 'y ~ x'
+
+
+
+``` r
+ggplot(total1) +
+ # Setting the fill color argument to be viridis discrete
+ scale_fill_viridis_d() +
+ geom_histogram(mapping = aes(x = fev, fill=obesity_level))
+```
+
+ ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+
+
+
+``` r
+# You figure out the other
+# ggplot(total1) +
+# geom_histogram(mapping = aes(x = fev, fill=smoke_gas_exposure, col=blue))
+```
+
+``` r
+ggplot(total1) +
+ geom_bar(mapping = aes(x = obesity_level, fill = smoke_gas_exposure))+
+ scale_fill_viridis_d()
+```
+
+
+
+``` r
+ggplot(total1) +
+ stat_summary(mapping = aes(x = fev, y = obesity_level),
+ fun.min = min,
+ fun.max = max,
+ fun = median)
+```
+
+
+
+``` r
+# l <- total1[!is.na(fev) & fev != "clear"] %>%
+# ggplot() +
+# stat_summary(mapping = aes(x = fev, y = smoke_gas_exposure),
+# fun.min = min,
+# fun.max = max,
+# fun = median)
+```
+
+``` r
+total2 <- unique(total1[,.(townname, lon, lat, pm25_mass)])
+
+pal <- colorNumeric(c("green", "yellow", "red"), domain = range(total2$pm25_mass))
+
+leaflet(total2) %>%
+ addTiles() %>%
+ addCircles(
+ lat = ~lat, lng = ~lon, opacity = 1, fillOpacity = .5,
+ radius = ~pm25_mass*500,
+ stroke = 2,
+ color = ~ pal(pm25_mass),
+ fillColor = ~ pal(pm25_mass)) %>%
+ addLegend(pal = pal, values = ~pm25_mass, title = "PM2.5 Mass")
+```
+
+``` r
+mod <- lm(fev ~ pm25_mass, data = total1)
+total1[, plot(
+ pm25_mass, fev, pch=19, cex=0.5,
+ main = "FEV and PM2.5 mass",
+ xlab = "PM2.5 mass", ylab = "FEV")
+ ]
+```
+
+ ## NULL
+
+``` r
+abline(mod, lwd=2, col="red")
+```
+
+
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+
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+
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+Encoding: UTF-8
+
+RnwWeave: knitr
+LaTeX: XeLaTeX
+
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