ggplot solutions

Author: Ludwigm6

14-ggplot.Rmd

@@ -198,3 +198,128 @@ Create the following plots:
     3.1 a horizontal line indicating critical oxygen concentrations of 4 mg/l
     3.2 color the line based on the temperature values. Use an appropriate color scale.
 
+
+## Solution
+
+<details>
+<summary>Click for Answer</summary>
+
+
+
+
+### Boxplots of the Aranae Species per crop type.
+
+```{r}
+library(ggplot2)
+
+cropspecies = read.csv("data/crop_species.csv")
+
+ggplot(data = cropspecies,
+       mapping = aes(x = Croptype,
+                     y = AraSpec,
+                     fill = Croptype))+
+    geom_boxplot()+
+    scale_fill_manual(values = c("red", "blue", "green", "yellow", "black"))
+
+
+## Define colors beforehand
+sc = c("KE" = "#66CD00",
+       "ZR" = "#FFB90F",
+       "SM" = "#FF7F00",
+       "WR" = "yellow",
+       "WW" = "black")
+
+
+ggplot(data = cropspecies,
+       mapping = aes(x = Croptype,
+                     y = AraSpec,
+                     fill = Croptype))+
+    geom_boxplot()+
+    scale_fill_manual(values = sc)
+
+```
+
+
+
+
+
+
+### Plot the Carabidae Individuums vs. the Carabidae Species and color the points based on the crop on the field.
+
+
+```{r}
+ggplot(cropspecies, aes(x = CaraInd,
+                        y = CaraSpec))+
+    geom_point()+
+    geom_smooth(method = "lm")+
+    geom_point(aes(color = Croptype))
+```
+
+
+### Create a map of the trees in Münster + Heatmap
+
+
+```{r}
+trees = read.csv("data/muenster_trees.csv")
+
+
+ggplot(trees, aes(x = X, y = Y))+
+    geom_point(alpha = 0.5)
+
+ggplot(trees, aes(x = X, y = Y))+
+    geom_bin_2d(bins = 40)
+
+ggplot(trees, aes(x = X, y = Y))+
+    geom_hex()+
+    theme_bw()
+```
+
+
+
+
+### A bar plot (geom_col) of the Top 10 districts of Münster with the most trees.
+
+```{r}
+districts = table(trees$district)
+districts = as.data.frame(districts)
+
+
+districts = districts[order(districts$Freq, decreasing = TRUE),]
+districts_t10 = districts[seq(10),]
+
+ggplot(districts_t10, aes(y = reorder(Var1, Freq), x = Freq))+
+    geom_col()
+```
+
+
+
+
+
+
+
+### A time series of the oxygen content in the Aasee
+
+
+```{r}
+library(lubridate)
+
+aasee = read.csv("data/2021-07_aasee.csv")
+aasee$Datum = lubridate::ymd_hm(aasee$Datum)
+
+
+ggplot(aasee, aes(x = Datum, y = Sauerstoffgehalt, color = Wassertemperatur))+
+    geom_line()+
+    geom_abline(slope = 0, intercept = 1, color = "red")+
+    scale_x_datetime(name = "Datum")+
+    annotate(geom = "text",
+             label = "Text",
+             x = lubridate::ymd_hm("2021-07-01 02:00"), y = 4)+
+    scale_color_gradientn(colors = viridis::inferno(50))
+```
+
+
+
+
+
+</details>
+

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@@ -6,7 +6,7 @@
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@@ -277,20 +239,6 @@ <h1>Page not found</h1>
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