Although the object exists, R cannot find it. Here's the code:
# Setting up the RStudio environment
#~~~~~~~~~~~
# Libraries
#~~~~~~~~~~
library(dplyr)
library(ggplot2)
library(tidyr)
#~~~~~~~~~~~
# Functions
#~~~~~~~~~~
rescale_01 <-function(x) (x-min(x))/(max(x)-min(x)) -1/2
z_stand<-function(x) (x-mean(x))/sd(x)
ExpectedBrix <- function(x) (x*0.21084778699754 + 4.28455310831511)
#~~~~~~~~~~~~
# Thresholds
#~~~~~~~~~~~
Thresh.Brix.min <- 15
Thresh.Brix.max <- 30
Thresh.Pol.min <- 50
Thresh.Pol.max <- 105
ExpectedBrix.delta <- 1
Thresh.Fibre.min <- 4
Thresh.Fibre.max <- 25
Thresh.Fibre.delta <- .25
Thresh.Ash.min <- 0
Thresh.Ash.max <- 8
# Import the Fibre data into a data table called “Lab_Fibre_Data”
Lab_Fibre_Data <- read.table("Lab_Fibre_Weights.csv", header=TRUE, sep=",", dec =".")
# Calculate Percentage Fibre Variables using direct assignment
# Name the resulting variable “Fibre1” and add this new variable to the “Lab_Fibre_Data” data table, using direct assignment
# Fibre% = 100 * (InitialSampleCanWeight – FinalSampleCanWeight) / SampleWeight
Lab_Fibre_Data$Fibre1 <- 100 * (Lab_Fibre_Data$InitialSampleCanWeight_1 - Lab_Fibre_Data$FinalSampleCanWeight_1) / Lab_Fibre_Data$SampleWeight_1
# Calculate Percentage Fibre Variables using the mutate() function from the dplyr package, call it “Fibre2”
# Add “Fibre2” as a new variable to the “Lab_Fibre_Data” data table
Lab_Fibre_Data <- Lab_Fibre_Data %>% mutate(Fibre2 = 100 *(InitialSampleCanWeight_2 - FinalSampleCanWeight_2) / SampleWeight_2)
# Use the filter() function to remove samples (rows) that contain a missing value
Lab_Fibre_Filtered <- Lab_Fibre_Data %>% filter(InitialSampleCanWeight_1 >0 &
InitialSampleCanWeight_2 >0 &
FinalSampleCanWeight_1 >0 &
FinalSampleCanWeight_2 >0 &
SampleWeight_1 >0 &
SampleWeight_2 >0)
# UPDATE code in table 5 to include maximum fibre difference limit as an additional filtering criteria
# Absolute difference between the two estimates (computed variables “Fibre1” and “Fibre2”) = or > 0.25 units
# UPDATE your code in table 5 to include this maximum fibre difference limit as an additional filtering criteria
Lab_Fibre_Filtered <- Lab_Fibre_Filtered %>% filter(!(abs(Fibre1 - Fibre2) >= 0.25))
# Calculate the final fibre estimates by averaging the replicate fibre measurements, “Fibre1” and “Fibre2”
# Use mutate() to calculate the average fibre and add it as a new variable named “Fibre” to the Lab_Fibre_Filtered data table
Lab_Fibre_Filtered <- Lab_Fibre_Filtered %>% mutate(Fibre = (Fibre1 + Fibre2)/2)
# Use a PIPE to sequentially filter the measurements in Lab_Fibre_Filtered to remove the out-of-range fibre values
# Keeping only the rows of the data table for which “Fibre” is greater than Thresh.Fibre.min
# Keeping only the resulting rows for which “Fibre” is less than Thresh.Fibre.max. Save the resulting data into the Lab_Fibre_Filtered table
Lab_Fibre_Filtered <- Lab_Fibre_Filtered %>% filter(Fibre > Thresh.Fibre.min & Fibre < Thresh.Fibre.max)
# Use select() to save the LabID and Fibre variables (1st and last columns) from Lab_Fibre_Filtered to a new data table called Lab_Fibre
Lab_Fibre <- Lab_Fibre_Filtered %>% select(LabID,Fibre)
# Import the Ash data into a data table called “Lab_Ash_Data”
Lab_Ash_Data <- read.table("Lab_Ash_Weights.csv", header=TRUE, sep=",", dec =".")
# Calculate Ash Variables
# Use a PIPE to filter out the missing values using filter()
# Sequentially calculate “InitialWeight”, “FinalWeight” and “Ash” as new variables using mutate()
# TinWeight (column 2), InitialSampleInTinWeight (column 3), FinalSampleInTinWeight (column 4)
# Ash = 100*FinalWeight/InitialWeight
# InitialWeight=InitialSampleInTinWeight-TinWeight, FinalWeight=FinalSampleInTinWeight-TinWeight
Lab_Ash_Calculated$Ash <- Lab_Ash_Data %>%
filter(InitialSampleInTinWeight ==0 &
FinalSampleInTinWeight ==0) &
mutate(InitialWeight = InitialSampleInTinWeight) &
mutate(FinalWeight = FinalSampleInTinWeight) &
mutate(Ash = 100 *(FinalWeight/InitialWeight))
Error in mutate(InitialWeight = InitialSampleInTinWeight) :
object 'InitialSampleInTinWeight' not found
# Filtering Ash Variables
# Threshold values for ash are: Thresh.Ash.min and Thresh.Ash.max.
# Update pipe to filter out any out-of-range Ash values. Add the additional filter at the end of the pipe.
Lab_Ash_Calculated <- Lab_Ash_Calculated %>% filter(Ash > Thresh.Ash.min & Ash < Thresh.Ash.max)
Error in eval(lhs, parent, parent) :
object 'Lab_Ash_Calculated' not found
# Summarising Ash Variables
# Use a PIPE with the grouped_by() and summarize() functions to produce a data table called Lab_Ash
# Grouped by LabID and summarises variable Ash by taking its grouped mean values
# Resulting table, Lab_Ash, must then have two variables, LabID and Ash, where LabID now contains unique values (no replicates)
Lab_Ash<-Lab_Ash_Calculated %>% grouped_by(LabID) %>% summarize(Ash=mean(Ash))
Error in eval(lhs, parent, parent) :
object 'Lab_Ash_Calculated' not found
# Import the Pol and Brix data into a data table called “Lab_PB_Data”
Lab_PB_Data <- read.table("Lab_Pol_Brix.csv", header=TRUE, sep=",", dec =".")
# Use the mutate() function to add to the data table Lab_PB_Data a new variable, PredBrix, which uses the ExpectedBrix function with Pol as input
Lab_PB_Data <-mutate(Lab_PB_Data,PredBrix=ExpectedBrix(Pol))
# Use ggplot() to visually display a scatter (i.e. point) plot of variables Brix and Pol.
# Colour code the points according to if the absolute difference between the measured Brix (variable Brix, 3rd column) and the predicted Brix (variable PredBrix, 4th column) is greater than one.
geom_point<-ggplot(data=Lab_PB_Data, mapping=aes(x=Brix, y=Pol)+geom_point(aes(col=abs(PredBrix-Brix)<1)))
Error: Mapping should be created with `aes() or `aes_()`.
Here's the file output for Lab_Ash_Data which contains 'InitialSampleInTinWeight':
dput(Lab_Ash_Data)
structure(list(LabID = c(27L, 27L, 448L, 448L, 1041L, 1041L,
1059L, 1059L, 1183L, 1183L, 2080L, 2080L, 2205L, 2205L, 2812L,
2812L, 3288L, 3288L, 3916L, 3916L, 4047L, 4047L, 4148L, 4148L,
4208L, 4208L, 4223L, 4223L, 4783L, 4783L, 4851L, 4851L, 5141L,
5141L, 5359L, 5359L, 5521L, 5521L, 5531L, 5531L, 5687L, 5687L,
5769L, 5769L, 6205L, 6205L, 6607L, 6821L, 6821L, 6919L, 6919L,
6941L, 6941L, 7024L, 7024L, 7076L, 7076L, 7237L, 7237L, 7512L,
7512L, 7632L, 7632L, 8180L, 8180L, 8980L, 8980L, 9688L, 9688L,
9796L, 9796L, 9980L, 9980L, 10022L, 10022L, 10541L, 10541L, 10638L,
10638L, 10798L, 10798L, 10874L, 10874L, 11671L, 11671L, 12718L,
12718L, 12962L, 12962L, 13038L, 13038L, 13297L, 13297L, 13429L,
13429L, 13501L, 13501L, 13892L, 13892L, 14117L, 14117L, 14501L,
14501L, 14554L, 14554L, 15129L, 15129L, 15213L, 15213L, 15218L,
15218L, 15237L, 15237L, 15325L, 15325L, 16145L, 16145L, 16223L,
16223L, 16602L, 16602L, 16842L, 16842L, 17169L, 17169L, 17468L,
17468L, 17938L, 17938L, 18100L, 18100L, 18818L, 18818L, 20482L,
20482L, 20483L, 20483L, 20688L, 20688L, 21011L, 21011L, 21784L,
21784L, 22631L, 22631L, 23077L, 23077L, 23445L, 23445L, 23769L,
23769L, 23955L, 23955L, 24513L, 24513L, 24717L, 24717L, 24816L,
24816L, 24854L, 24854L, 25084L, 25084L, 25160L, 25160L, 25209L,
25209L, 25683L, 25683L, 25704L, 25704L, 26551L, 26551L, 26609L,
26609L, 26696L, 26696L, 27151L, 27151L, 27181L, 27181L, 27866L,
27866L, 27994L, 27994L, 28318L, 28318L, 28607L, 28607L, 28616L,
28616L, 28939L, 28939L, 29318L, 29318L, 29799L, 29799L, 29825L,
29825L, 30256L, 30256L, 30464L, 30464L, 30569L, 30569L, 31156L,
31156L, 31478L, 31478L, 31709L, 31709L, 31854L, 31854L, 31929L,
31929L, 32159L, 32159L, 32327L, 32327L, 32753L, 32753L, 33088L,
33088L, 33545L, 33545L, 33697L, 33697L, 33876L, 33876L, 34803L,
34803L, 35039L, 35039L, 35048L, 35048L, 35852L, 35852L, 35862L,
35862L, 36045L, 36045L, 37320L, 37320L, 37819L, 37819L, 38297L,
38297L, 38923L, 38923L, 39011L, 39011L, 39174L, 39174L, 39293L,
39293L, 39437L, 39437L, 40066L, 40066L, 40331L, 40331L, 40413L,
40413L, 40417L, 40417L, 40430L, 40430L, 40497L, 40497L, 40501L,
40501L, 41029L, 41029L, 41541L, 41541L, 41580L, 41580L, 41596L,
41596L, 42168L, 42168L, 42224L, 42224L, 42241L, 42241L, 43030L,
43030L, 43449L, 43449L, 43843L, 43843L, 44123L, 44123L, 44461L,
44461L, 44670L, 44670L, 45456L, 45456L, 45604L, 45604L, 45794L,
45794L, 46009L, 46009L, 46361L, 46361L, 46476L, 46476L, 46945L,
46945L, 46947L, 46947L, 47225L, 47225L, 47446L, 47446L, 47744L,
47744L, 47862L, 47862L, 48422L, 48422L, 48498L, 48498L, 48893L,
48893L, 49038L, 49038L, 49159L, 49159L, 49856L, 49856L, 49951L,
49951L, 50473L, 50473L, 50763L, 50763L, 50858L, 50858L, 51416L,
51416L, 51464L, 51464L, 51672L, 51672L, 51705L, 51705L, 51898L,
51898L, 51930L, 51930L, 52174L, 52174L, 52174L, 52174L, 52244L,
52244L, 52483L, 52483L, 52585L, 52585L, 53247L, 53247L, 53734L,
53734L, 54062L, 54062L, 54718L, 54718L, 54804L, 54856L, 54856L,
55019L, 55019L, 55055L, 55055L, 55492L, 55492L, 55772L, 55772L,
55858L, 55858L, 56706L, 56706L, 57235L, 57235L, 57244L, 57244L,
57448L, 57448L, 57687L, 57687L, 57879L, 57879L, 58295L, 58295L,
58594L, 58594L, 58947L, 58947L, 59331L, 59331L, 59337L, 59337L,
59459L, 59459L, 60268L, 60268L, 60337L, 60337L, 60693L, 60693L,
60789L, 60789L, 61538L, 61538L, 61622L, 61622L, 62078L, 62078L,
62234L, 62234L, 62282L, 62282L, 62543L, 62543L, 62653L, 62653L,
62881L, 62881L, 63089L, 63089L, 63249L, 63249L, 63443L, 63443L,
63531L, 63531L, 63725L, 63725L, 63730L, 63730L, 63767L, 63767L,
63818L, 63818L, 63969L, 63969L, 64334L, 64334L, 64354L, 64354L
), TinWeight = c(425.2, 426.84, 428.77, 440.68, 426, 427.18,
428.53, 441.57, 426.65, 426.61, 429.56, 442.14, 426.09, 426.67,
429.41, 440.7, 426.79, 427.52, 428.85, 440.44, 426.41, 428.06,
427.57, 441.45, 444.88, 435.29, 425.65, 428.22, 428.23, 441.57,
425.29, 426.51, 426.87, 426.74, 427.87, 440.56, 428.07, 441.85,
425.73, 427.28, 425.87, 426.49, 426.85, 427.17, 428.27, 442.31,
425.33, 426.66, 428.03, 441.55, 426.11, 427.09, 429.1, 441.66,
425.14, 428.11, 425.77, 427.29, 424.99, 426.93, 427.83, 440.86,
426.33, 426.34, 427.68, 440.78, 425.18, 428.17, 428.05, 440.58,
444.63, 435.44, 426.08, 426.79, 428.89, 441.43, 425.85, 427.43,
426.27, 427.73, 426.6, 427.36, 428.41, 440.41, 425.96, 426.95,
427.34, 440.21, 428.87, 440.97, 428.55, 441.6, 425.96, 427.18,
443.82, 434.28, 426.01, 428.43, 425.62, 427.14, 428.71, 440.17,
425.26, 426.76, 428.94, 441.41, 425.8, 427.01, 426.22, 428.43,
444.31, 434.57, 428.63, 439.83, 426.55, 426.94, 425.44, 428.51,
428.4, 440.05, 425.31, 427.36, 426.59, 426.88, 425.09, 428.26,
427.64, 440.7, 426.41, 428.28, 428.48, 440.13, 444.25, 435.51,
440.66, 428.23, 427.67, 440.57, 440.75, 428.5, 426.19, 427.69,
441.06, 429.11, 428.37, 440.08, 424.79, 426.84, 441.27, 427.49,
443.85, 436.65, 440.73, 428.09, 424.77, 427.04, 440.29, 428.14,
440.28, 428.39, 424.54, 427.23, 435.28, 444.07, 424.99, 426.93,
444.31, 436.1, 441.04, 428.3, 424.88, 427.11, 428.45, 441.4,
444.54, 435.99, 424.82, 427.66, 426.51, 427.31, 427.49, 441.27,
443.7, 436.38, 425.99, 427.72, 427.61, 441.07, 444.11, 435.13,
424.62, 426.77, 427.58, 440.46, 444.23, 435.81, 426.1, 426.86,
427.65, 440.89, 426.51, 426.92, 429.04, 440.44, 425.38, 427.84,
429.28, 441.12, 425.22, 426.42, 425.18, 427.79, 427.53, 440.76,
443.5, 436.53, 424.69, 427.78, 427.67, 440.66, 443.79, 436.05,
427.13, 428.73, 441.97, 444.71, 436.44, 425.12, 427.51, 427.65,
441.43, 423.89, 427.71, 428.07, 440.97, 444, 436.42, 426.03,
427.45, 427.74, 441.69, 424.81, 426.2, 428.47, 441.56, 443.99,
434.86, 425.14, 427.85, 429.17, 441.6, 426.46, 428.06, 429.32,
442.05, 444.61, 436.24, 425.17, 426.84, 428.81, 440.69, 424.53,
426.57, 426.89, 439.99, 425.14, 426.32, 427.65, 441.32, 424.47,
426.82, 425.9, 425.53, 425.9, 426.38, 427.82, 440.96, 425.66,
427.56, 428.75, 439.74, 424.9, 426.83, 426.99, 440.96, 425.23,
426.81, 427.22, 440.66, 425.01, 427.39, 427.85, 440.23, 425.77,
426.36, 427.72, 441.2, 425.37, 425.73, 424.42, 426.31, 425.32,
427.06, 426.27, 426.65, 426.97, 441.35, 424.97, 426.24, 425.89,
425.81, 427.76, 441.08, 424.32, 426.28, 426.17, 426.97, 424.46,
426.2, 424.65, 426.31, 428.6, 441.28, 425.56, 425.8, 428.71,
440.26, 425.3, 425.78, 428.31, 440.6, 425.87, 426.52, 426.12,
426.56, 425.17, 425.99, 426.34, 427.62, 425.29, 426.89, 425.42,
427.83, 424.45, 426.32, 427.68, 440.1, 425.89, 427.06, 427.48,
440.8, 424.96, 425.81, 424.65, 426.93, 428.87, 441.69, 425.83,
426.28, 428.1, 441.47, 425.81, 425.84, 427.65, 440.24, 424.82,
426.1, 427.16, 440.69, 434.57, 443.23, 443.89, 434.67, 444.69,
434.18, 444.98, 436.03, 444.99, 435.8, 426.73, 439.99, 444.51,
434.38, 436.1, 444.02, 445.07, 435.06, 428.23, 439.5, 444.3,
435.01, 444.34, 434.73, 441.11, 426.78, 434.74, 444.1, 445.06,
434.43, 427.53, 439.86, 444.81, 435.32, 444.69, 434.73, 435.93,
443.28, 435.81, 445.13, 443.9, 435.01, 427.79, 440.92, 426, 425.64,
444.85, 435.34, 444.1, 434.63, 427.84, 439.9, 444.08, 434.79,
435.72, 444.92, 440.07, 427.42, 435.48, 443.92, 441.33, 427.16,
435.17, 443.89, 440.3, 427.26, 440.81, 427.3, 441.47, 427.61,
440.03, 443.3, 435.87, 444.15, 434.57, 443.33, 434.88, 428.36,
441.2, 443.57, 435.66, 427.95, 440, 435.78, 445.24), InitialSampleInTinWeight = c(638.22,
642.11, 565.92, 569.51, 581.44, 611.96, 578.72, 600.77, 545.29,
560.29, 586.57, 600.18, 586.04, 569.28, 563.82, 590.07, 553.66,
556.59, 550.61, 561.4, 544.3, 564.17, 550.53, 575.93, 580.88,
563.4, 685.21, 688.04, 559.48, 571.26, 569.51, 574.62, 719.53,
706.97, 566.89, 580.83, 557.41, 579.87, 570.41, 581.58, 558.99,
574.82, 589.3, 591.81, 567.09, 577.42, 738.82, 729.83, 691.63,
683.86, 568.45, 577.91, 559.24, 572.2, 579.41, 574.78, 672.66,
647.58, 626.78, 639.1, 661.73, 656.12, 678.42, 695.25, 698.92,
730.69, 687.49, 665.05, 730.12, 639.99, 670.37, 657.29, 544.5,
550.5, 662.71, 663.94, 647.71, 661.18, 652.44, 652.8, 543.19,
556.97, 562.56, 585.57, 570.23, 543.37, 542.86, 559.78, 543.34,
561, 695.66, 731.11, 720.02, 768.93, 588.05, 575.73, 704.02,
691.27, 690.23, 0, 560.56, 579.22, 544.84, 544.52, 561.99, 576.41,
714.08, 750.39, 728.17, 757.84, 592.2, 582.1, 565.34, 577.42,
589.68, 574.35, 757.63, 779.88, 593.82, 596.65, 586.08, 565.63,
571.83, 559.83, 566.52, 581.69, 575.81, 593.37, 575.83, 583.11,
615.66, 612.14, 602.15, 599.01, 645.15, 631.51, 621.89, 617.63,
628.1, 623.56, 613.88, 603.51, 645.28, 636.31, 600.93, 598.67,
488.39, 497.8, 636.59, 606.56, 677.71, 646.05, 640.03, 634.21,
600.17, 604.88, 617.13, 597.61, 636.41, 606.89, 581.25, 572.06,
619.05, 622.12, 594.42, 593.55, 576.37, 604.95, 670.56, 659.04,
586.26, 502.79, 594.11, 600.52, 614.1, 600.54, 598.85, 617.69,
608.61, 611.29, 601.78, 608.88, 644.3, 640.64, 627.84, 620.5,
646.76, 645.4, 632.93, 647.62, 643.69, 635.79, 646.47, 679.38,
690.52, 650.24, 644.37, 660.07, 631.86, 645.24, 629.21, 612.85,
627.65, 663.79, 640.18, 604.68, 557.06, 630.42, 647.59, 645.01,
635.49, 633.74, 604.23, 640.98, 661.38, 627.3, 606.15, 609.65,
625.54, 622.91, 656.05, 629.24, 630.19, 608.45, 633.79, 657.43,
657.38, 652.77, 642.89, 626.66, 629.94, 633.11, 613.7, 629.03,
646.17, 651.35, 646.45, 618.42, 615.41, 627.49, 634.88, 635.83,
626.07, 652.18, 637.34, 684.17, 651.79, 620.82, 611.84, 633.16,
640.96, 638.09, 663.71, 621.4, 640.66, 651.81, 674.47, 636.3,
635.98, 620.75, 633.87, 539.67, 539.63, 545.41, 563.37, 578.68,
579.66, 539.55, 554.1, 558.11, 577.09, 540.54, 550.61, 572.59,
559.27, 541.37, 565.94, 554.41, 541.7, 558.14, 571.25, 548.57,
548.89, 559.17, 562.69, 563.53, 580.35, 542.78, 546.17, 553.29,
595.25, 585.52, 591.29, 544.94, 546.91, 600.05, 647, 564.16,
594.76, 565.25, 576.33, 575.69, 582.02, 580.79, 546.22, 563.46,
585.17, 550.92, 555.5, 537.78, 546.15, 574.06, 580.78, 553.55,
547.66, 560.81, 562.06, 565.43, 566.04, 561.87, 559.88, 562.03,
570.73, 586.18, 561.16, 577.69, 616.91, 557.01, 559.21, 563.18,
569.45, 564.42, 553.72, 590.67, 580.39, 544.52, 549.2, 553.98,
563.65, 546.35, 545.6, 546.83, 550.2, 567.64, 564.01, 560.29,
568.8, 533.4, 550.37, 563.4, 593.05, 554.96, 546.41, 547.79,
549.64, 548.39, 556.51, 561.4, 560.36, 566.26, 573.83, 537.88,
549.29, 564.59, 584.6, 557.12, 573.82, 552.99, 559.26, 598.12,
602.64, 586.44, 628.51, 556.02, 528.22, 580.23, 581.04, 566.83,
558.73, 604.72, 571.39, 579.43, 564.96, 566.37, 612.86, 582.13,
576.24, 541.36, 564.82, 575, 559.83, 567.31, 555.46, 592.02,
594.63, 575.1, 571.98, 572.28, 595.24, 539.64, 545.42, 580.68,
564.63, 586.02, 600.5, 570.37, 581.67, 543.96, 575.78, 604.49,
587.91, 607.72, 577.29, 591.82, 564.47, 596.59, 571.47, 561.16,
565.56, 588.88, 575.84, 589.57, 578.83, 570.9, 588.3, 558.56,
549.05, 558.41, 568.06, 552.11, 532.45, 602.02, 587.33, 566.71,
582.83, 541.03, 527.63, 570.58, 535.16, 542.56, 555.25, 552.66,
551.39, 546.94, 560.34, 560.66, 555.42, 567.07, 573.76, 554.58,
544.47, 549.91, 563.3, 564.33), FinalSampleInTinWeight = c(430.46,
431.58, 434.09, 447.14, 432.01, 433.87, 431.16, 444.08, 429.47,
430.89, 432.81, 446.08, 429.21, 429.91, 430, 442.92, 427.78,
429.2, 431.29, 443.78, 427.72, 428.93, 430.78, 442.78, 445.42,
437.98, 427.64, 430.58, 431.56, 443.86, 433.02, 433.8, 431.36,
432.44, 434.49, 447.74, 430.71, 444.62, 427.68, 429.69, 428.41,
430.43, 530.16, 431.79, 430.88, 443.54, 438.44, 440.33, 432.63,
444.61, 429.49, 431.1, 430.11, 442.76, 429.21, 431.24, 432.97,
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