edits from Lindy

Bio110 - Week 1 Lab.Rmd

@@ -153,7 +153,7 @@ We can add additional information to the plot, including specific x and y axis l
 
 ```{r}
 #if you want to adjust the axis limits (adjust as needed for your data), add labels, and a title
-plot(y1~x, xlim=c(0,120), ylim=c(0,140), main="My heart beats", xlab="time in seconds", ylab = "number of heartbeats")
+plot(y1~x, xlim=c(0,120), ylim=c(0,140), main="My Title", xlab="time in seconds", ylab = "number of heartbeats")
 
 
 ```
@@ -165,9 +165,9 @@ Next we want to add a best fit trendline to determine heartrate.
 
 ```{r}
 # in order to run the abline function below, you must first run the plot function:
-plot(y1~x, xlim=c(0,120), ylim=c(0,140), main="My heart beats", xlab="time in seconds", ylab = "number of heartbeats")
+plot(y1~x, xlim=c(0,120), ylim=c(0,140), main="My Title", xlab="time in seconds", ylab = "number of heartbeats")
 
-#to add the line, setting the y interect at zero, and get its slope, fit a linear model to the data and store the results in a variable (named "model" here).
+#to add the line, setting the y intersect at zero, and get its slope, fit a linear model to the data and store the results in a variable (named "model" here).
 model<-lm(formula=y1~0+x)
 #add a best-fit line to the graph
 abline (reg=model)
@@ -190,7 +190,7 @@ Now we can use the "matplot" function to plot multiple columns or sets of data:
 
 ```{r}
 
-matplot(x=x, y=cbind(y1,y2), type="p", col=c("red", "green"), pch=c(16,16), main="Experimental vs. control heart rate", xlab="time in seconds", ylab="heartbeats")
+matplot(x=x, y=cbind(y1,y2), type="p", col=c("red", "green"), pch=c(16,16), main="Mt Title", xlab="time in seconds", ylab="heartbeats")
 
 ```
 
@@ -206,7 +206,7 @@ par(cex.axis=0.6)
 
 #more on graphical parameters here: https://www.rdocumentation.org/packages/graphics/versions/3.6.2/topics/par 
 
-matplot(x=x, y=cbind(y1,y2), type="p", col=c("red", "green"), pch=c(16,16), main="My Title", xlab="time in seconds", ylab="control beats-red and experimental beats-green", ylim=c(0,140))
+matplot(x=x, y=cbind(y1,y2), type="p", col=c("red", "green"), pch=c(16,16), main="My Title", xlab="time in seconds", ylab="heartbeats", ylim=c(0,140))
 
 #note: you may have to adjust the ylim values to ensure all your data displays - for example, if you have a data point at 150, you'll want to increase your ylim 2nd value so it's 150 or greater
 
@@ -222,7 +222,7 @@ abline(model2, col="green")
 model2
 
 #to add a legend, first need coordinates of the legend, c(“labels”), lty determines symbols with 1 = lines, lwd determines width of lines, colors 
-legend(x=0, y=140, legend=c("experiemental heartrate", "control heartrate"), lty=c(1,1), lwd=c(2.5, 2.5), col=c("green", "red") )
+legend(x=0, y=140, legend=c("experimental heartrate", "control heartrate"), lty=c(1,1), lwd=c(2.5, 2.5), col=c("green", "red") )
 
 #note - you may have to adjust the x and y values of the legend if they don't fit in the plot. 
 

README.md

@@ -15,7 +15,7 @@ Link to this page: http://bit.ly/bio110-lab1
 
 in console (lower left-hand corner):
 * library(usethis)
-* use_course("https://github.com/watzek/bio110-week1/archive/0.5.zip")
+* use_course("https://github.com/watzek/bio110-week1/archive/0.6.zip")
 
 After running the command above, you'll be prompted whether to download to your current directory. Go ahead and answer in the affirmative (you'll be presented with variations on "yes"). Once the course files download, you'll be prompted as to whether to delete the zip file. Again, answer in the affirmative. Once you do, click "Save", and your window will refresh to a new R session.