In ismayc/chemistr: Facilitates use of R functions and R Markdown files for Chemistry lab reports

library(chemistr)

Standard Curve

# Here is where you input your standards data that you want to graph
Concentration <- c() # This should be the concentrations of your standards.  Use Molarity as your units.  Don't forget 0M.
Absorbance <- c() #Don't forget to include your blank (0M) value.  These should all be within the quantitative range of 0.1-1 Absorbance Units. 
E7_data <- data_frame(Concentration, Absorbance) # Creation of data frame

# Here is where you create your standard curve. To create the scatter plot, go to a prior lab report or the CHEM_ScatterPlot template and copy and paste the code that will 1) create a scatter plot, using the chem_scatter function 2) extract the fit data using the lm function and 3) provide a summary of the fit data using summary.

#Change the code to represent the variables names in your data frame.

#Once you have run this chunk, write a caption after fig.cap = "Insert Caption" in the R chunk heading.  In your caption (sentence format) include the molecule being measured, the wavelength being tested, the size of the cuvette and the details of the fit (slope +/- error and Y-intercept +/- error and R^2).

Sample Data

#Option #1: This is a GOOD picture of your calculation starting at Abs of sample and showing every calculation to final reported values.  Include every calc and every unit.

include_graphics("Name of Figure, ex: Iron.jpg") #To make this work, you must upload the picture into the same folder on RStudio that you saved this file into.  If you choose to type out your calculations, then this code can be deleted.

#Option #2:  You can do all the calculations in R.  If you do this, then you must change echo=FALSE in this header to echo = TRUE.  This will display your code in the final lab report.

#R Calculations for Average, Standard Deviation, 95% Confidence Interval.  Use the Stats help sheet on Moodle for inputting this code (https://moodle.reed.edu/pluginfile.php/268311/mod_resource/content/6/Student%20t_2016.pdf).  Finish with creating a data_frame() of the values you want to put into a nice table, using the chem_table() function, of your stats results (in Word change the table to have the correct sig figs).

Known <- #Input your known concentration/amount that you are comparing your data to.
Sample <- c() #Input the final concentration/amount of analyte in each of your trials
Average <-  #calculate the average concentration in your samples.
s <- #calculate the standard deviation of your trials
n <- #This is the total number of trials in your data set
CI95 <- #Calculate the 95% confidence interval from your trials
Percent_Error <- #Calculate the percent error for your sample.  % Error = 100*Absolute value(Known-Average)/Known
Percent_Relative_Uncertainty <- #Calculate the % relative uncertainty.  % RU = 100*Standard deviation/Average 
Stats <- data.frame(Known, Average, CI95, Percent_Error, Percent_Relative_Uncertainty) #This creates a dataframe to make a table from

chem_table(Stats, Caption = "Input Caption") #This creates a table of the results, but significant figures MUST be changed after knitting into Word.  You will also need to add the units for each column when in Word.

Review the requirements for this experiment: Collect data for a standard curve that shows a linear relationship between absorbance and concentration of analyte. +Curve has at least five data points +Adjusted R^2 value for linear regression line is greater than 0.99 +Caption and labeling are complete Collect data for a sample +At least 3 trials of a sample are run.
+Calculations are easy to follow and correct with correct sig figs and units when necessary. +Accuracy of your experiment - The % error based upon literature or label is less than 20% or the 95% confidence error overlaps with the literature or label value. +Precision of your experiment - The % relative uncertainty between your trials is less than 10%.

Discussion

1. Did you show a linear relationship between concentration of analyte and absorbance? Make sure to directly reference any data in your report that supports your claim.

2. Was your experiment accurate? Make sure to directly reference any data in your report that supports your claim.

3. What is one way that the accuracy of your experiment might have been compromised (regardless of your answer in Q2) even if you followed your instructions exactly as you wrote them. Be specific in how it would influence the accuracy. For example would this issue have caused the average concentration to appear higher or lower than it truly was and why. If it is an accuracy issue, it can only cause it to be one of these. Note: Please do not say experimental or calcuation error or discuss a mistake you could have made.

4. Was your experiment precise? Make sure to directly reference any data in your report that supports your claim.

5. What is one way that you could improve the precision of your experiment. Be specific in how/why this would improve the precision.

ismayc/chemistr documentation built on Feb. 4, 2023, 12:44 p.m.