diff --git a/submit.qmd b/submit.qmd
index d4b907f..da558b6 100644
--- a/submit.qmd
+++ b/submit.qmd
@@ -1,28 +1,165 @@
 ---
-title: ""
-author: ""
-format:
-  html:
-    embed-resources: true
+title: "Lab 4_v2"
+author: "Karisa Ke"
+format: html
+editor: visual
+embed-resources: true
 ---
 
-## Quarto
+## 1. Read in the data
 
-Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see <https://quarto.org>.
+```{r}
+
+
+if (!file.exists("met_all.gz"))
+  download.file(
+    url = "https://raw.githubusercontent.com/USCbiostats/data-science-data/master/02_met/met_all.gz",
+    destfile = "met_all.gz",
+    method   = "libcurl",
+    timeout  = 60
+    )
+met <- data.table::fread("met_all.gz")
+```
+
+## 2. Prepare the data
+
+-   Remove temperatures less than -17C
+
+```{r}
+met <- met[temp>-17]
+```
+
+-   Make sure there are no missing data in the key variables coded as 9999, 999, etc
+
+```{r}
+met[met$elev==9999.0] <- NA
+```
+
+```{r}
+summary(met$dew.point)
+```
+
+-   Generate a date variable using the functions `as.Date()`
+
+```{r}
+met$date <- as.Date(paste(met$year, met$month, met$day, sep = "-"))
+```
+
+```{r}
+met$week = week(met$date)
+```
+
+```{r}
+met =filter(met, week < 33 & day < 8)
+```
+
+```{r}
+met
+```
+
+```{r}
+met_avg <- met[,.(temp=mean(temp,na.rm=TRUE), rh=mean(rh,na.rm=TRUE), wind.sp=mean(wind.sp,na.rm=TRUE), vis.dist=mean(vis.dist,na.rm=TRUE), dew.point = mean(dew.point, na.rm=TRUE), lat=mean(lat), lon=mean(lon), elev=mean(elev,na.rm=TRUE)), by="USAFID"]
+```
+
+```{r}
+met_avg$region <- ifelse(met_avg$lon > -98 & met_avg$lat >39.71, "NE", ifelse(met_avg$lon > -98 & met_avg$lat < 39.71, "SE", ifelse(met_avg$lon < -98 & met_avg$lat >39.71, "NW", "SW")))
+```
 
-## Running Code
+```{r}
+met_avg$elev_cat <- ifelse(met_avg$elev> 252, "high", "low")
+```
+
+```{r}
+table(met_avg$region)
+```
+
+## 3. **Use `geom_violin` to examine the wind speed and dew point by region**
+
+```{r}
+met_avg %>%
+  filter(!(region %in% NA)) %>%
+ggplot()+
+  geom_violin(mapping = aes(y=wind.sp, x=1)) +
+  facet_wrap(~region, nrow=2)
+```
 
-When you click the **Render** button a document will be generated that includes both content and the output of embedded code. You can embed code like this:
+```{r}
+met_avg %>%
+  filter(!(region %in% NA)) %>%
+ggplot()+
+  geom_violin(mapping = aes(y=dew.point, x=1)) +
+  facet_wrap(~region, nrow=2)
+```
+
+## 4. **Use `geom_jitter` with `stat_smooth` to examine the association between dew point and wind speed by region**
 
 ```{r}
-1 + 1
+met_avg %>%
+filter(!(region %in% NA)) %>%
+  ggplot(mapping = aes(x=dew.point, y=wind.sp, color=region))+
+  geom_jitter() + 
+  stat_smooth(method=lm)
 ```
 
-You can add options to executable code like this
+## 5. **Use `geom_bar` to create barplots of the weather stations by elevation category colored by region**
 
 ```{r}
-#| echo: false
-2 * 2
+met_avg %>%
+filter(!(region %in% NA)) %>%
+  ggplot()+
+  geom_bar(mapping=aes(x=elev_cat,fill=region), position = "dodge")+
+  scale_fill_brewer(palette = "PuOr")+
+  labs(title="Number of weather stations by elevation category and region", x="Elevation Category", y= "Count")+
+  theme_bw()
 ```
 
-The `echo: false` option disables the printing of code (only output is displayed).
+## 6. **Use `stat_summary` to examine mean dew point and wind speed by region with standard deviation error bars**
+
+```{r}
+met_avg %>%
+filter(!(region %in% NA)) %>%
+  ggplot(mapping=aes(x=region, y=dew.point)) +
+  stat_summary(fun.data="mean_sdl", geom="errorbar") +
+  stat_summary(fun.data="mean_sdl")Ye1
+```
+
+```{r}
+met_avg %>%
+filter(!(region %in% NA)) %>%
+  ggplot(mapping=aes(x=region, y=wind.sp)) +
+  stat_summary(fun.data="mean_sdl", geom="errorbar") +
+  stat_summary(fun.data="mean_sdl")
+```
+
+```{r}
+
+```
+
+## 7. **Make a map showing the spatial trend in relative humidity in the US**
+
+```{r}
+library(leaflet)
+met_avg2<-met_avg[!is.na(rh)]
+
+tops <- met_avg2[rank(-rh) <= 10]
+
+rh_pal = colorNumeric(c('red','yellow','blue'), domain=met_avg2$rh)
+leaflet(met_avg2) %>%
+  addProviderTiles('OpenStreetMap') %>%
+  addCircles(lat=~lat, lng=~lon, color=~rh_pal(rh), label=~paste0(round(rh,2), ' rh'), opacity=1,fillOpacity=1, radius=500) %>%
+  addMarkers(lat=~lat, lng=~lon, label=~paste0(round(rh,2), ' rh'), data = tops) %>%
+  addLegend('bottomleft',pal=rh_pal, values=met_avg2$rh, title="Relative Humidity", opacity=1)
+```
+
+## 8. Use a ggplot extension
+
+```{r}
+install.packages("ggforce")
+```
+
+```{r}
+library(ggforce)
+ggplot(met_avg, aes(wind.sp, dew.point, colour = region)) +
+  geom_point() +
+  facet_zoom(x = Species == "versicolor")
+```