exam_rates from the following
vectors:name=c("Marie", "Gianni", "Silvia", "Laura","Mariachiara", "Simone", "Sarah", "Francesca", "Matteo")
id=c(8452, "AE12", 6732, "AS54", "GF49", 9328, "DS34", 3476, 7628)
pointsTest1=c(12, 15, 14, 5, 18, 3, 10, 7, 16)
pointsTest2=c(10, 9, 10, 7, 10, 3, 9, 8, 1)
pointsTest3=c(1, 2, 2, 2, 2, 1, 1, 0, 0)exam_rates=cbind.data.frame(name, id, pointsTest1, pointsTest2, pointsTest3)score containing sum of
pointsTest1, pointsTest2 and
pointsTest3 columns and a column average with
the mean of these 3 columns. Hint: see functionalities of
rowSums(), colSums(), rowMeans()
and colMeans().exam_rates$score=rowSums(exam_rates[, c("pointsTest1", "pointsTest2", "pointsTest3")])
exam_rates$average=rowMeans(exam_rates[, c("pointsTest1", "pointsTest2", "pointsTest3")])passed containing
TRUE if the score is >17 and
FALSE otherwise. Hint: remember the theoretical part on
logical vectors.exam_rates$passed=exam_rates$score>17table( ) function.table(exam_rates$score)##
## 7 14 15 17 20 23 26 30
## 1 1 1 1 1 1 2 1exam_rates2 by selecting from
the previous dataframe only people that passed the exam and columns
name,pointsTest1,pointsTest2 and
pointsTest3exam_rates2=subset(exam_rates, passed)[, c("name","pointsTest1","pointsTest2", "pointsTest3" )]exam_rates2.
Inspect the structure of the dataframe. What happened?exam_rates2=reshape2::melt(exam_rates2)## Using name as id variablesvec by extracting value
column from the dataset exam_rates2. Then evaluate sum,
mean, standard deviation, summary and quantiles of the vector. Hint: use
sum( ), mean( ), sd( ),
summary() and quantile() functions. Take some
time to inspect resultsvec<-exam_rates2$value
sum(vec)## [1] 125mean(vec)## [1] 8.333333sd(vec)## [1] 5.498918summary(vec)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 10.000 8.333 11.000 18.000quantile(vec)## 0% 25% 50% 75% 100%
## 1 2 10 11 18collection that contains
exam_rates, exam_rates2, vec and
the quantiles of vec.collection=list(exam_rates, exam_rates2, vec, quantile(vec))names(collection)=c("Full", "Manipulated", "vector", "quantile")student_marks by binding following
vectors as rows.Marie=c(28, 29, 30, 27, 30)
Gianni=c(18, 19, 21, 26, 28)
Silvia=c(23, 24, 26, 30, 22)
Laura=c(30,29, 30, 28, 29)
Mariachiara=c(29, 30, 28, 27, 25)
Simone=c(24, 29, 30, 22, 19)
Sarah=c(26, 28, 25, 29, 30)
Francesca=c(18, 26, 28, 19, 26)
Matteo=c(26, 28, 30, 30, 28)student_marks=rbind(Marie, Gianni, Silvia, Laura, Mariachiara, Simone, Sarah, Francesca, Matteo)CourseA,
CourseB, CourseC, CourseD,
CourseEcolnames(student_marks)=c("CourseA", "CourseB", "CourseC", "CounseD", "CourseE")collection list and name it.
Hint: remember how to concatenate lists; lists can also contain one
single elementstudent_marks=list(student_marks=student_marks)
collection=c(collection,student_marks)age=c(21, 24, 22, NA, NA, 20, 23, NA, NA)
names(age)=c("Marie", "Gianni", "Silvia", "Laura", "Mariachiara", "Simone", "Sarah", "Francesca", "Matteo")mean() function. Try in both ways!grep or grepl functions extract ages
of people with “ia” in their names.names(age[is.na(age)])## [1] "Laura" "Mariachiara" "Francesca" "Matteo"names(age[!is.na(age)])## [1] "Marie" "Gianni" "Silvia" "Simone" "Sarah"mean(age[!is.na(age)])## [1] 22mean(age, na.rm = T)## [1] 22age[is.na(age)]<-mean(age, na.rm = T)
age[grep("ia", names(age))]## Gianni Silvia Mariachiara
## 24 22 22age[grepl("ia", names(age))]## Gianni Silvia Mariachiara
## 24 22 22info=cbind.data.frame(name=c("Marie", "Gianni", "Silvia", "Laura", "Mariachiara", "Simone", "Sarah", "Francesca", "Matteo"),
goes_to_gym=c(TRUE, TRUE, FALSE, NA, FALSE, NA, FALSE, TRUE, TRUE),
times_gym= c(3, NA,0, NA,0,NA, 0, NA, 5 ),
likes=c("ski", "snowboard", "pilates", "football", "tennis", "basketball", "tennis", NA, NA))goes_to_gymtimes_gymlikessubset function and conditions into
[ ]info[is.na(info$goes_to_gym),]## name goes_to_gym times_gym likes
## 4 Laura NA NA football
## 6 Simone NA NA basketballinfo[is.na(info$times_gym),]## name goes_to_gym times_gym likes
## 2 Gianni TRUE NA snowboard
## 4 Laura NA NA football
## 6 Simone NA NA basketball
## 8 Francesca TRUE NA <NA>info[is.na(info$likes),]## name goes_to_gym times_gym likes
## 8 Francesca TRUE NA <NA>
## 9 Matteo TRUE 5 <NA>subset(info, !is.na(goes_to_gym) & !is.na(times_gym) & !is.na(likes))## name goes_to_gym times_gym likes
## 1 Marie TRUE 3 ski
## 3 Silvia FALSE 0 pilates
## 5 Mariachiara FALSE 0 tennis
## 7 Sarah FALSE 0 tennisinfo[!is.na(info$goes_to_gym) & !is.na(info$times_gym) & !is.na(info$likes), ]## name goes_to_gym times_gym likes
## 1 Marie TRUE 3 ski
## 3 Silvia FALSE 0 pilates
## 5 Mariachiara FALSE 0 tennis
## 7 Sarah FALSE 0 tennisinfo that contains the following elements
(l, hour,station and
mt ). Be sure to assign names to the elements.l=cbind.data.frame(Date=c("03-04", "03-05", "03-06", "03-07", "03-08", "03-09", "03-10", "03-11", "03-12"),
Temp=c(12,5,18, 20, 12, 15, 17, 15, 19))
hour=c(12, 4, 14, 11, 13, 16, 12.30, 20, 13.30 )
station=c(rep("Saluzzo(CN)", 2), rep("Montecatini(PT)", 3),"Pescia(PT)", rep("Milano(MI)", 3))
mt=rbind(c(13,12,35), c(2.6, 6.2, 9), seq(30, 50, 6.7))info=list(l, hour, station, mt)
names(info)=c("l", "hour", "station", "mt")Without extracting elements from the list modify them with the following steps (you cannot use the original objects but only those in the list):
Assign to hour vector names by using values in the
Temp column of the dataframe l
Transform station in a factor vector fixing levels
that correspond to the alphabetical order of the cities
Add a column in l that contains values in
station
Assign to hour an attribute station
containing character values of station. Notice that I said
character, not factor
Order station vector
Add to the list another vector New containing
Temp values extracted matching station and the
new column you added to the data frame (keep only the first
element)
Evaluate if in the Temp column there are values in
the range 19:40
Extract the indexes of the elements that are equal to 15 in
hour
Create a new logical vector by evaluating in station
the cities that are not in the “PT” province. Hint: explore the
paramenters of the function you will use. Then add this vector to the
list
How many elements does your list contain?
Create a new matrix mt2 of the same dimensions as
mt that contains mt values if they are minor
than 10 and mt values +1 if they are major or equal to 10.
Add this to the list.
Using only a combination of [[ ]] and
[ ] extract the second element of the fifth element of the
list
Extract the second column of the mt matrix from the
list
Extract all elements, except the second, of the list
Extract the data frame contained into the list and assign it to a
variable data_frame
Subset data_frame by keeping only data for the
stations “Milano(MI)” and “Saluzzo(CN)” and reassign it to the variable
data_frame
Concatenate data_frame with the following dataframe
data2 and reassign the result to
data_frame
data2=cbind.data.frame(Date=c("03-07", "03-08", "03-09", "03-10", "03-11", "03-12", "03-04", "03-05", "03-06", "03-09", "03-10", "03-11", "03-12"),
Temp=c(11, 12, 14, 9, 8, 13, 14, 15, 6, 10, 18,14, 13),
station=c(rep("Milano(MI)", 6), rep("Saluzzo(CN)",7)))table() evaluate how many times a certain
Temp is retrieved in each citydcast expand the data frame using as reference
columns Date and station (reassign to
data_frame)rownames values in the column
DateDate from the dataframestr() and
typeof()names(info$hour)=info$l$Temp
info$station=factor(info$station, levels=c("Milano(MI)","Montecatini(PT)","Pescia(PT)","Saluzzo(CN)"))
info$l$station=info$station
attr(info$hour, "station")=as.character(info$station)
info$station=info$station[order(info$station)]
info$New=info$l$Temp[match(info$station, info$l$station)] # notice that you can add directly an element using $
info$l$Temp%in%19:40## [1] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUEwhich(info$hour==15)## named integer(0)v=info$station[grep("(PT)", info$station, invert = T)]
info=c(info, list(v))
length(info)## [1] 6mt2=info$mt+(info$mt>=10)
info$mt2=mt2
info[[5]][2]## [1] 17info$mt[,2]## [1] 12.0 6.2 36.7info[-2]## $l
## Date Temp station
## 1 03-04 12 Saluzzo(CN)
## 2 03-05 5 Saluzzo(CN)
## 3 03-06 18 Montecatini(PT)
## 4 03-07 20 Montecatini(PT)
## 5 03-08 12 Montecatini(PT)
## 6 03-09 15 Pescia(PT)
## 7 03-10 17 Milano(MI)
## 8 03-11 15 Milano(MI)
## 9 03-12 19 Milano(MI)
##
## $station
## [1] Milano(MI) Milano(MI) Milano(MI) Montecatini(PT)
## [5] Montecatini(PT) Montecatini(PT) Pescia(PT) Saluzzo(CN)
## [9] Saluzzo(CN)
## Levels: Milano(MI) Montecatini(PT) Pescia(PT) Saluzzo(CN)
##
## $mt
## [,1] [,2] [,3]
## [1,] 13.0 12.0 35.0
## [2,] 2.6 6.2 9.0
## [3,] 30.0 36.7 43.4
##
## $New
## [1] 17 17 17 18 18 18 15 12 12
##
## [[5]]
## [1] Milano(MI) Milano(MI) Milano(MI) Saluzzo(CN) Saluzzo(CN)
## Levels: Milano(MI) Montecatini(PT) Pescia(PT) Saluzzo(CN)
##
## $mt2
## [,1] [,2] [,3]
## [1,] 14.0 13.0 36.0
## [2,] 2.6 6.2 9.0
## [3,] 31.0 37.7 44.4data_frame=info$l
data_frame=subset(data_frame, station%in%c("Milano(MI)","Saluzzo(CN)"))
data_frame=rbind.data.frame(data_frame, data2)
table(data_frame$Temp, data_frame$station)##
## Milano(MI) Montecatini(PT) Pescia(PT) Saluzzo(CN)
## 5 0 0 0 1
## 6 0 0 0 1
## 8 1 0 0 0
## 9 1 0 0 0
## 10 0 0 0 1
## 11 1 0 0 0
## 12 1 0 0 1
## 13 1 0 0 1
## 14 1 0 0 2
## 15 1 0 0 1
## 17 1 0 0 0
## 18 0 0 0 1
## 19 1 0 0 0data_frame=reshape2::dcast(data_frame, Date~station, value.var = "Temp")## Aggregation function missing: defaulting to lengthrownames(data_frame)=data_frame$Date
data_frame$Date<-NULL
data_frame=as.matrix(data_frame)
rownames(data_frame)[data_frame[,1]==data_frame[,2]]## [1] "03-09"data_frame[data_frame==13]="a"
data_frame>10## Milano(MI) Saluzzo(CN)
## 03-04 FALSE TRUE
## 03-05 FALSE TRUE
## 03-06 FALSE FALSE
## 03-07 FALSE FALSE
## 03-08 FALSE FALSE
## 03-09 FALSE FALSE
## 03-10 TRUE FALSE
## 03-11 TRUE FALSE
## 03-12 TRUE FALSECreate a dataframe df1 using the vectors
samples, genes and
expression_counts (binding by columns) and an other
dataframe df2 using vectors FOXA1,
MYC, AR, ENSG00000281133,
FAM138A (binding by rows). Assign the following
colnames to df2: gene_name,
gene_type, gene_id.
Then:
df1 colums gene_id and
gene_type by taking the information in
df2protein coding genes
from df1cases by grepping from df1
the string “MOD” and keeping only unique valuescontrols by grepping from
df1 the string “NORM” and keeping only unique valuescases
vectorcontrols
vectorsamples by concatenating
cases and controlsannotation to df1 in which
you have to put “case” and “control” values by matching the column
samples in df1 with values in the vector
samplessamples=c(rep("MOD01",5),
rep("NORM01",5),
rep("NORM02", 5),
rep("MOD02", 5),
rep("MOD03", 5),
rep("NORM03", 5) )
genes=rep(c("FOXA1","AR","MYC","ENSG00000281133","FAM138A"), 6)
expression_counts=rnorm(30, 25, 4)
FOXA1=c("FOXA1", "protein_coding","ENSG00000129514.8")
AR=c("AR","protein_coding","ENSG00000169083.18")
MYC=c("MYC","protein_coding","ENSG00000136997.21")
ENSG00000281133=c("ENSG00000281133","pseudogene" ,"ENSG00000281133.1")
FAM138A=c("FAM138A", "lncRNA","ENSG00000237613.2")df1=cbind.data.frame(samples,genes,expression_counts )
df2=rbind.data.frame(FOXA1, AR, MYC, ENSG00000281133, FAM138A)
colnames(df2)=c("gene_name", "gene_type", "gene_id")
df1=cbind.data.frame(df1, df2[match(df1$genes, df2$gene_name), c("gene_type", "gene_id")])
unique(df1$genes[which(df1$gene_type=="protein_coding")])## [1] "FOXA1" "AR" "MYC"cases=unique(df1$samples[grep("MOD", df1$samples)])
controls=unique(df1$samples[grep("NORM", df1$samples)])
names(cases)=rep("case",3)
names(controls)=rep("control",3)
samples=c(cases, controls)
df1$annotation<-NA
df1$annotation=names(samples)[match(df1$samples, samples)]
df1## samples genes expression_counts gene_type gene_id
## 1 MOD01 FOXA1 23.58046 protein_coding ENSG00000129514.8
## 2 MOD01 AR 28.77553 protein_coding ENSG00000169083.18
## 3 MOD01 MYC 24.71959 protein_coding ENSG00000136997.21
## 4 MOD01 ENSG00000281133 22.62500 pseudogene ENSG00000281133.1
## 5 MOD01 FAM138A 21.64108 lncRNA ENSG00000237613.2
## 1.1 NORM01 FOXA1 22.12389 protein_coding ENSG00000129514.8
## 2.1 NORM01 AR 25.29696 protein_coding ENSG00000169083.18
## 3.1 NORM01 MYC 25.98398 protein_coding ENSG00000136997.21
## 4.1 NORM01 ENSG00000281133 23.70522 pseudogene ENSG00000281133.1
## 5.1 NORM01 FAM138A 21.00481 lncRNA ENSG00000237613.2
## 1.2 NORM02 FOXA1 24.56363 protein_coding ENSG00000129514.8
## 2.2 NORM02 AR 26.04510 protein_coding ENSG00000169083.18
## 3.2 NORM02 MYC 30.83418 protein_coding ENSG00000136997.21
## 4.2 NORM02 ENSG00000281133 23.57142 pseudogene ENSG00000281133.1
## 5.2 NORM02 FAM138A 20.00303 lncRNA ENSG00000237613.2
## 1.3 MOD02 FOXA1 25.23526 protein_coding ENSG00000129514.8
## 2.3 MOD02 AR 29.66635 protein_coding ENSG00000169083.18
## 3.3 MOD02 MYC 28.14392 protein_coding ENSG00000136997.21
## 4.3 MOD02 ENSG00000281133 26.38283 pseudogene ENSG00000281133.1
## 5.3 MOD02 FAM138A 19.50276 lncRNA ENSG00000237613.2
## 1.4 MOD03 FOXA1 34.50365 protein_coding ENSG00000129514.8
## 2.4 MOD03 AR 29.67558 protein_coding ENSG00000169083.18
## 3.4 MOD03 MYC 27.90093 protein_coding ENSG00000136997.21
## 4.4 MOD03 ENSG00000281133 22.27610 pseudogene ENSG00000281133.1
## 5.4 MOD03 FAM138A 36.03155 lncRNA ENSG00000237613.2
## 1.5 NORM03 FOXA1 22.78865 protein_coding ENSG00000129514.8
## 2.5 NORM03 AR 27.10168 protein_coding ENSG00000169083.18
## 3.5 NORM03 MYC 26.59054 protein_coding ENSG00000136997.21
## 4.5 NORM03 ENSG00000281133 25.06675 pseudogene ENSG00000281133.1
## 5.5 NORM03 FAM138A 21.61097 lncRNA ENSG00000237613.2
## annotation
## 1 case
## 2 case
## 3 case
## 4 case
## 5 case
## 1.1 control
## 2.1 control
## 3.1 control
## 4.1 control
## 5.1 control
## 1.2 control
## 2.2 control
## 3.2 control
## 4.2 control
## 5.2 control
## 1.3 case
## 2.3 case
## 3.3 case
## 4.3 case
## 5.3 case
## 1.4 case
## 2.4 case
## 3.4 case
## 4.4 case
## 5.4 case
## 1.5 control
## 2.5 control
## 3.5 control
## 4.5 control
## 5.5 controlget_info that takes as input a number
and a dataframe and return as output:expression_counts of
df1 (previous exercise) that are lower than that
numberget_info<-function(n,df){
if(n<10){cat("LOW")
}else if(n>40){cat("HIGH")
}else {
out=sum(df$expression_counts<n)
return(out)}
}
get_info(8,df1)## LOWget_info(42,df1)## HIGHget_info(28,df1)## [1] 23gene_name = c("FOXA1","SRSF1","MYC","PTBP1","AR"). Then,
use a for loop to iterate over the elements in the vector.
For each element, print “Present” if it is found in the
genes column of df1, or “Not Present”
otherwise. (HINT: try to use "\n" to write each output in a
new line)for loop to iterate over the elements in the
gene_name vector. If an element is not found in the
genes column of df1, skip to the next element.
Otherwise, select the rows corresponding to that gene and display the
average of the expression_counts column.total<-0. Use a for
loop to iterate over the elements in the expression_counts
column of df1. At each iteration, add the new value to
total. The loop should terminate once total
exceeds 150. Display the final value of total.gene_name = c("FOXA1","SRSF1","MYC","PTBP1","AR")
for(gene in gene_name){
if(gene %in% df1$genes){
cat("Present \n")
} else { cat("Not.Present \n")}
}## Present
## Not.Present
## Present
## Not.Present
## Presentfor(gene in gene_name){
if(! gene %in% df1$genes){
next
} else {
m<-mean(subset(df1,genes==gene)$expression_counts)
cat(m)
cat("\n")}
}## 25.46593
## 27.36219
## 27.7602total<-0
for (counts in df1$expression_counts){
if(total>150){break
}else {
total<-total+counts
}
}
total## [1] 168.7625g:
NaN (use the helper in R
Studio).NA, NaN or infinite values.g=list(Value=c(NaN,32, NA,39, Inf, -Inf, 8.9, 4 ),
Mat=matrix(c(1:9, NA, NaN, 989:103, Inf, NA, 10^7, 9^5, 6*7, 5/3, 6+2, 5-7), ncol=6, byrow = T),
Df=cbind.data.frame(place=factor(c("Garden", "House", "Square", NA)), N=c(NA, 5, 7, NaN))
)# first solution (long)
g$Mat[!is.na(g$Mat[,1]) & !is.na(g$Mat[,2]) & !is.na(g$Mat[,3]) & !is.na(g$Mat[,4]) & !is.na(g$Mat[,5]) & !is.na(g$Mat[,6]),]## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1 2 3 4.000000 5 6
## [2,] 988 987 986 985.000000 984 983
## [3,] 982 981 980 979.000000 978 977
## [4,] 976 975 974 973.000000 972 971
## [5,] 970 969 968 967.000000 966 965
## [6,] 964 963 962 961.000000 960 959
## [7,] 958 957 956 955.000000 954 953
## [8,] 952 951 950 949.000000 948 947
## [9,] 946 945 944 943.000000 942 941
## [10,] 940 939 938 937.000000 936 935
## [11,] 934 933 932 931.000000 930 929
## [12,] 928 927 926 925.000000 924 923
## [13,] 922 921 920 919.000000 918 917
## [14,] 916 915 914 913.000000 912 911
## [15,] 910 909 908 907.000000 906 905
## [16,] 904 903 902 901.000000 900 899
## [17,] 898 897 896 895.000000 894 893
## [18,] 892 891 890 889.000000 888 887
## [19,] 886 885 884 883.000000 882 881
## [20,] 880 879 878 877.000000 876 875
## [21,] 874 873 872 871.000000 870 869
## [22,] 868 867 866 865.000000 864 863
## [23,] 862 861 860 859.000000 858 857
## [24,] 856 855 854 853.000000 852 851
## [25,] 850 849 848 847.000000 846 845
## [26,] 844 843 842 841.000000 840 839
## [27,] 838 837 836 835.000000 834 833
## [28,] 832 831 830 829.000000 828 827
## [29,] 826 825 824 823.000000 822 821
## [30,] 820 819 818 817.000000 816 815
## [31,] 814 813 812 811.000000 810 809
## [32,] 808 807 806 805.000000 804 803
## [33,] 802 801 800 799.000000 798 797
## [34,] 796 795 794 793.000000 792 791
## [35,] 790 789 788 787.000000 786 785
## [36,] 784 783 782 781.000000 780 779
## [37,] 778 777 776 775.000000 774 773
## [38,] 772 771 770 769.000000 768 767
## [39,] 766 765 764 763.000000 762 761
## [40,] 760 759 758 757.000000 756 755
## [41,] 754 753 752 751.000000 750 749
## [42,] 748 747 746 745.000000 744 743
## [43,] 742 741 740 739.000000 738 737
## [44,] 736 735 734 733.000000 732 731
## [45,] 730 729 728 727.000000 726 725
## [46,] 724 723 722 721.000000 720 719
## [47,] 718 717 716 715.000000 714 713
## [48,] 712 711 710 709.000000 708 707
## [49,] 706 705 704 703.000000 702 701
## [50,] 700 699 698 697.000000 696 695
## [51,] 694 693 692 691.000000 690 689
## [52,] 688 687 686 685.000000 684 683
## [53,] 682 681 680 679.000000 678 677
## [54,] 676 675 674 673.000000 672 671
## [55,] 670 669 668 667.000000 666 665
## [56,] 664 663 662 661.000000 660 659
## [57,] 658 657 656 655.000000 654 653
## [58,] 652 651 650 649.000000 648 647
## [59,] 646 645 644 643.000000 642 641
## [60,] 640 639 638 637.000000 636 635
## [61,] 634 633 632 631.000000 630 629
## [62,] 628 627 626 625.000000 624 623
## [63,] 622 621 620 619.000000 618 617
## [64,] 616 615 614 613.000000 612 611
## [65,] 610 609 608 607.000000 606 605
## [66,] 604 603 602 601.000000 600 599
## [67,] 598 597 596 595.000000 594 593
## [68,] 592 591 590 589.000000 588 587
## [69,] 586 585 584 583.000000 582 581
## [70,] 580 579 578 577.000000 576 575
## [71,] 574 573 572 571.000000 570 569
## [72,] 568 567 566 565.000000 564 563
## [73,] 562 561 560 559.000000 558 557
## [74,] 556 555 554 553.000000 552 551
## [75,] 550 549 548 547.000000 546 545
## [76,] 544 543 542 541.000000 540 539
## [77,] 538 537 536 535.000000 534 533
## [78,] 532 531 530 529.000000 528 527
## [79,] 526 525 524 523.000000 522 521
## [80,] 520 519 518 517.000000 516 515
## [81,] 514 513 512 511.000000 510 509
## [82,] 508 507 506 505.000000 504 503
## [83,] 502 501 500 499.000000 498 497
## [84,] 496 495 494 493.000000 492 491
## [85,] 490 489 488 487.000000 486 485
## [86,] 484 483 482 481.000000 480 479
## [87,] 478 477 476 475.000000 474 473
## [88,] 472 471 470 469.000000 468 467
## [89,] 466 465 464 463.000000 462 461
## [90,] 460 459 458 457.000000 456 455
## [91,] 454 453 452 451.000000 450 449
## [92,] 448 447 446 445.000000 444 443
## [93,] 442 441 440 439.000000 438 437
## [94,] 436 435 434 433.000000 432 431
## [95,] 430 429 428 427.000000 426 425
## [96,] 424 423 422 421.000000 420 419
## [97,] 418 417 416 415.000000 414 413
## [98,] 412 411 410 409.000000 408 407
## [99,] 406 405 404 403.000000 402 401
## [100,] 400 399 398 397.000000 396 395
## [101,] 394 393 392 391.000000 390 389
## [102,] 388 387 386 385.000000 384 383
## [103,] 382 381 380 379.000000 378 377
## [104,] 376 375 374 373.000000 372 371
## [105,] 370 369 368 367.000000 366 365
## [106,] 364 363 362 361.000000 360 359
## [107,] 358 357 356 355.000000 354 353
## [108,] 352 351 350 349.000000 348 347
## [109,] 346 345 344 343.000000 342 341
## [110,] 340 339 338 337.000000 336 335
## [111,] 334 333 332 331.000000 330 329
## [112,] 328 327 326 325.000000 324 323
## [113,] 322 321 320 319.000000 318 317
## [114,] 316 315 314 313.000000 312 311
## [115,] 310 309 308 307.000000 306 305
## [116,] 304 303 302 301.000000 300 299
## [117,] 298 297 296 295.000000 294 293
## [118,] 292 291 290 289.000000 288 287
## [119,] 286 285 284 283.000000 282 281
## [120,] 280 279 278 277.000000 276 275
## [121,] 274 273 272 271.000000 270 269
## [122,] 268 267 266 265.000000 264 263
## [123,] 262 261 260 259.000000 258 257
## [124,] 256 255 254 253.000000 252 251
## [125,] 250 249 248 247.000000 246 245
## [126,] 244 243 242 241.000000 240 239
## [127,] 238 237 236 235.000000 234 233
## [128,] 232 231 230 229.000000 228 227
## [129,] 226 225 224 223.000000 222 221
## [130,] 220 219 218 217.000000 216 215
## [131,] 214 213 212 211.000000 210 209
## [132,] 208 207 206 205.000000 204 203
## [133,] 202 201 200 199.000000 198 197
## [134,] 196 195 194 193.000000 192 191
## [135,] 190 189 188 187.000000 186 185
## [136,] 184 183 182 181.000000 180 179
## [137,] 178 177 176 175.000000 174 173
## [138,] 172 171 170 169.000000 168 167
## [139,] 166 165 164 163.000000 162 161
## [140,] 160 159 158 157.000000 156 155
## [141,] 154 153 152 151.000000 150 149
## [142,] 148 147 146 145.000000 144 143
## [143,] 142 141 140 139.000000 138 137
## [144,] 136 135 134 133.000000 132 131
## [145,] 130 129 128 127.000000 126 125
## [146,] 124 123 122 121.000000 120 119
## [147,] 118 117 116 115.000000 114 113
## [148,] 112 111 110 109.000000 108 107
## [149,] 10000000 59049 42 1.666667 8 -2# second solution (short)
g$Mat[rowSums(is.na(g$Mat))==0,]## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1 2 3 4.000000 5 6
## [2,] 988 987 986 985.000000 984 983
## [3,] 982 981 980 979.000000 978 977
## [4,] 976 975 974 973.000000 972 971
## [5,] 970 969 968 967.000000 966 965
## [6,] 964 963 962 961.000000 960 959
## [7,] 958 957 956 955.000000 954 953
## [8,] 952 951 950 949.000000 948 947
## [9,] 946 945 944 943.000000 942 941
## [10,] 940 939 938 937.000000 936 935
## [11,] 934 933 932 931.000000 930 929
## [12,] 928 927 926 925.000000 924 923
## [13,] 922 921 920 919.000000 918 917
## [14,] 916 915 914 913.000000 912 911
## [15,] 910 909 908 907.000000 906 905
## [16,] 904 903 902 901.000000 900 899
## [17,] 898 897 896 895.000000 894 893
## [18,] 892 891 890 889.000000 888 887
## [19,] 886 885 884 883.000000 882 881
## [20,] 880 879 878 877.000000 876 875
## [21,] 874 873 872 871.000000 870 869
## [22,] 868 867 866 865.000000 864 863
## [23,] 862 861 860 859.000000 858 857
## [24,] 856 855 854 853.000000 852 851
## [25,] 850 849 848 847.000000 846 845
## [26,] 844 843 842 841.000000 840 839
## [27,] 838 837 836 835.000000 834 833
## [28,] 832 831 830 829.000000 828 827
## [29,] 826 825 824 823.000000 822 821
## [30,] 820 819 818 817.000000 816 815
## [31,] 814 813 812 811.000000 810 809
## [32,] 808 807 806 805.000000 804 803
## [33,] 802 801 800 799.000000 798 797
## [34,] 796 795 794 793.000000 792 791
## [35,] 790 789 788 787.000000 786 785
## [36,] 784 783 782 781.000000 780 779
## [37,] 778 777 776 775.000000 774 773
## [38,] 772 771 770 769.000000 768 767
## [39,] 766 765 764 763.000000 762 761
## [40,] 760 759 758 757.000000 756 755
## [41,] 754 753 752 751.000000 750 749
## [42,] 748 747 746 745.000000 744 743
## [43,] 742 741 740 739.000000 738 737
## [44,] 736 735 734 733.000000 732 731
## [45,] 730 729 728 727.000000 726 725
## [46,] 724 723 722 721.000000 720 719
## [47,] 718 717 716 715.000000 714 713
## [48,] 712 711 710 709.000000 708 707
## [49,] 706 705 704 703.000000 702 701
## [50,] 700 699 698 697.000000 696 695
## [51,] 694 693 692 691.000000 690 689
## [52,] 688 687 686 685.000000 684 683
## [53,] 682 681 680 679.000000 678 677
## [54,] 676 675 674 673.000000 672 671
## [55,] 670 669 668 667.000000 666 665
## [56,] 664 663 662 661.000000 660 659
## [57,] 658 657 656 655.000000 654 653
## [58,] 652 651 650 649.000000 648 647
## [59,] 646 645 644 643.000000 642 641
## [60,] 640 639 638 637.000000 636 635
## [61,] 634 633 632 631.000000 630 629
## [62,] 628 627 626 625.000000 624 623
## [63,] 622 621 620 619.000000 618 617
## [64,] 616 615 614 613.000000 612 611
## [65,] 610 609 608 607.000000 606 605
## [66,] 604 603 602 601.000000 600 599
## [67,] 598 597 596 595.000000 594 593
## [68,] 592 591 590 589.000000 588 587
## [69,] 586 585 584 583.000000 582 581
## [70,] 580 579 578 577.000000 576 575
## [71,] 574 573 572 571.000000 570 569
## [72,] 568 567 566 565.000000 564 563
## [73,] 562 561 560 559.000000 558 557
## [74,] 556 555 554 553.000000 552 551
## [75,] 550 549 548 547.000000 546 545
## [76,] 544 543 542 541.000000 540 539
## [77,] 538 537 536 535.000000 534 533
## [78,] 532 531 530 529.000000 528 527
## [79,] 526 525 524 523.000000 522 521
## [80,] 520 519 518 517.000000 516 515
## [81,] 514 513 512 511.000000 510 509
## [82,] 508 507 506 505.000000 504 503
## [83,] 502 501 500 499.000000 498 497
## [84,] 496 495 494 493.000000 492 491
## [85,] 490 489 488 487.000000 486 485
## [86,] 484 483 482 481.000000 480 479
## [87,] 478 477 476 475.000000 474 473
## [88,] 472 471 470 469.000000 468 467
## [89,] 466 465 464 463.000000 462 461
## [90,] 460 459 458 457.000000 456 455
## [91,] 454 453 452 451.000000 450 449
## [92,] 448 447 446 445.000000 444 443
## [93,] 442 441 440 439.000000 438 437
## [94,] 436 435 434 433.000000 432 431
## [95,] 430 429 428 427.000000 426 425
## [96,] 424 423 422 421.000000 420 419
## [97,] 418 417 416 415.000000 414 413
## [98,] 412 411 410 409.000000 408 407
## [99,] 406 405 404 403.000000 402 401
## [100,] 400 399 398 397.000000 396 395
## [101,] 394 393 392 391.000000 390 389
## [102,] 388 387 386 385.000000 384 383
## [103,] 382 381 380 379.000000 378 377
## [104,] 376 375 374 373.000000 372 371
## [105,] 370 369 368 367.000000 366 365
## [106,] 364 363 362 361.000000 360 359
## [107,] 358 357 356 355.000000 354 353
## [108,] 352 351 350 349.000000 348 347
## [109,] 346 345 344 343.000000 342 341
## [110,] 340 339 338 337.000000 336 335
## [111,] 334 333 332 331.000000 330 329
## [112,] 328 327 326 325.000000 324 323
## [113,] 322 321 320 319.000000 318 317
## [114,] 316 315 314 313.000000 312 311
## [115,] 310 309 308 307.000000 306 305
## [116,] 304 303 302 301.000000 300 299
## [117,] 298 297 296 295.000000 294 293
## [118,] 292 291 290 289.000000 288 287
## [119,] 286 285 284 283.000000 282 281
## [120,] 280 279 278 277.000000 276 275
## [121,] 274 273 272 271.000000 270 269
## [122,] 268 267 266 265.000000 264 263
## [123,] 262 261 260 259.000000 258 257
## [124,] 256 255 254 253.000000 252 251
## [125,] 250 249 248 247.000000 246 245
## [126,] 244 243 242 241.000000 240 239
## [127,] 238 237 236 235.000000 234 233
## [128,] 232 231 230 229.000000 228 227
## [129,] 226 225 224 223.000000 222 221
## [130,] 220 219 218 217.000000 216 215
## [131,] 214 213 212 211.000000 210 209
## [132,] 208 207 206 205.000000 204 203
## [133,] 202 201 200 199.000000 198 197
## [134,] 196 195 194 193.000000 192 191
## [135,] 190 189 188 187.000000 186 185
## [136,] 184 183 182 181.000000 180 179
## [137,] 178 177 176 175.000000 174 173
## [138,] 172 171 170 169.000000 168 167
## [139,] 166 165 164 163.000000 162 161
## [140,] 160 159 158 157.000000 156 155
## [141,] 154 153 152 151.000000 150 149
## [142,] 148 147 146 145.000000 144 143
## [143,] 142 141 140 139.000000 138 137
## [144,] 136 135 134 133.000000 132 131
## [145,] 130 129 128 127.000000 126 125
## [146,] 124 123 122 121.000000 120 119
## [147,] 118 117 116 115.000000 114 113
## [148,] 112 111 110 109.000000 108 107
## [149,] 10000000 59049 42 1.666667 8 -2colSums(g$Mat, na.rm=T) # Notice that in this case not only NA are removed, but also NaN## [1] 10080964.00 139867.00 80714.00 80517.67 Inf 81108.00rowSums(g$Mat, na.rm=T)## [1] 21 1013 5913 5877 5841 5805 5769 5733
## [9] 5697 5661 5625 5589 5553 5517 5481 5445
## [17] 5409 5373 5337 5301 5265 5229 5193 5157
## [25] 5121 5085 5049 5013 4977 4941 4905 4869
## [33] 4833 4797 4761 4725 4689 4653 4617 4581
## [41] 4545 4509 4473 4437 4401 4365 4329 4293
## [49] 4257 4221 4185 4149 4113 4077 4041 4005
## [57] 3969 3933 3897 3861 3825 3789 3753 3717
## [65] 3681 3645 3609 3573 3537 3501 3465 3429
## [73] 3393 3357 3321 3285 3249 3213 3177 3141
## [81] 3105 3069 3033 2997 2961 2925 2889 2853
## [89] 2817 2781 2745 2709 2673 2637 2601 2565
## [97] 2529 2493 2457 2421 2385 2349 2313 2277
## [105] 2241 2205 2169 2133 2097 2061 2025 1989
## [113] 1953 1917 1881 1845 1809 1773 1737 1701
## [121] 1665 1629 1593 1557 1521 1485 1449 1413
## [129] 1377 1341 1305 1269 1233 1197 1161 1125
## [137] 1089 1053 1017 981 945 909 873 837
## [145] 801 765 729 693 657 Inf 10059099colMeans(g$Mat, na.rm=T)## [1] 66761.3510 926.2715 534.5298 536.7844 Inf 540.7200rowMeans(g$Mat, na.rm=T) ## [1] 3.50 253.25 985.50 979.50 973.50 967.50
## [7] 961.50 955.50 949.50 943.50 937.50 931.50
## [13] 925.50 919.50 913.50 907.50 901.50 895.50
## [19] 889.50 883.50 877.50 871.50 865.50 859.50
## [25] 853.50 847.50 841.50 835.50 829.50 823.50
## [31] 817.50 811.50 805.50 799.50 793.50 787.50
## [37] 781.50 775.50 769.50 763.50 757.50 751.50
## [43] 745.50 739.50 733.50 727.50 721.50 715.50
## [49] 709.50 703.50 697.50 691.50 685.50 679.50
## [55] 673.50 667.50 661.50 655.50 649.50 643.50
## [61] 637.50 631.50 625.50 619.50 613.50 607.50
## [67] 601.50 595.50 589.50 583.50 577.50 571.50
## [73] 565.50 559.50 553.50 547.50 541.50 535.50
## [79] 529.50 523.50 517.50 511.50 505.50 499.50
## [85] 493.50 487.50 481.50 475.50 469.50 463.50
## [91] 457.50 451.50 445.50 439.50 433.50 427.50
## [97] 421.50 415.50 409.50 403.50 397.50 391.50
## [103] 385.50 379.50 373.50 367.50 361.50 355.50
## [109] 349.50 343.50 337.50 331.50 325.50 319.50
## [115] 313.50 307.50 301.50 295.50 289.50 283.50
## [121] 277.50 271.50 265.50 259.50 253.50 247.50
## [127] 241.50 235.50 229.50 223.50 217.50 211.50
## [133] 205.50 199.50 193.50 187.50 181.50 175.50
## [139] 169.50 163.50 157.50 151.50 145.50 139.50
## [145] 133.50 127.50 121.50 115.50 109.50 Inf
## [151] 1676516.44sum(unique(is.na(g$Mat[,5]) | is.nan(g$Mat[,5]) | is.infinite(g$Mat[,5]))) # There is at least 1 row in which one of theese conditions happens, so I remove the column## [1] 1g$Mat=g$Mat[,-5]
g$Mat## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 2 3 4.000000 6
## [2,] 7 8 9 NA 989
## [3,] 988 987 986 985.000000 983
## [4,] 982 981 980 979.000000 977
## [5,] 976 975 974 973.000000 971
## [6,] 970 969 968 967.000000 965
## [7,] 964 963 962 961.000000 959
## [8,] 958 957 956 955.000000 953
## [9,] 952 951 950 949.000000 947
## [10,] 946 945 944 943.000000 941
## [11,] 940 939 938 937.000000 935
## [12,] 934 933 932 931.000000 929
## [13,] 928 927 926 925.000000 923
## [14,] 922 921 920 919.000000 917
## [15,] 916 915 914 913.000000 911
## [16,] 910 909 908 907.000000 905
## [17,] 904 903 902 901.000000 899
## [18,] 898 897 896 895.000000 893
## [19,] 892 891 890 889.000000 887
## [20,] 886 885 884 883.000000 881
## [21,] 880 879 878 877.000000 875
## [22,] 874 873 872 871.000000 869
## [23,] 868 867 866 865.000000 863
## [24,] 862 861 860 859.000000 857
## [25,] 856 855 854 853.000000 851
## [26,] 850 849 848 847.000000 845
## [27,] 844 843 842 841.000000 839
## [28,] 838 837 836 835.000000 833
## [29,] 832 831 830 829.000000 827
## [30,] 826 825 824 823.000000 821
## [31,] 820 819 818 817.000000 815
## [32,] 814 813 812 811.000000 809
## [33,] 808 807 806 805.000000 803
## [34,] 802 801 800 799.000000 797
## [35,] 796 795 794 793.000000 791
## [36,] 790 789 788 787.000000 785
## [37,] 784 783 782 781.000000 779
## [38,] 778 777 776 775.000000 773
## [39,] 772 771 770 769.000000 767
## [40,] 766 765 764 763.000000 761
## [41,] 760 759 758 757.000000 755
## [42,] 754 753 752 751.000000 749
## [43,] 748 747 746 745.000000 743
## [44,] 742 741 740 739.000000 737
## [45,] 736 735 734 733.000000 731
## [46,] 730 729 728 727.000000 725
## [47,] 724 723 722 721.000000 719
## [48,] 718 717 716 715.000000 713
## [49,] 712 711 710 709.000000 707
## [50,] 706 705 704 703.000000 701
## [51,] 700 699 698 697.000000 695
## [52,] 694 693 692 691.000000 689
## [53,] 688 687 686 685.000000 683
## [54,] 682 681 680 679.000000 677
## [55,] 676 675 674 673.000000 671
## [56,] 670 669 668 667.000000 665
## [57,] 664 663 662 661.000000 659
## [58,] 658 657 656 655.000000 653
## [59,] 652 651 650 649.000000 647
## [60,] 646 645 644 643.000000 641
## [61,] 640 639 638 637.000000 635
## [62,] 634 633 632 631.000000 629
## [63,] 628 627 626 625.000000 623
## [64,] 622 621 620 619.000000 617
## [65,] 616 615 614 613.000000 611
## [66,] 610 609 608 607.000000 605
## [67,] 604 603 602 601.000000 599
## [68,] 598 597 596 595.000000 593
## [69,] 592 591 590 589.000000 587
## [70,] 586 585 584 583.000000 581
## [71,] 580 579 578 577.000000 575
## [72,] 574 573 572 571.000000 569
## [73,] 568 567 566 565.000000 563
## [74,] 562 561 560 559.000000 557
## [75,] 556 555 554 553.000000 551
## [76,] 550 549 548 547.000000 545
## [77,] 544 543 542 541.000000 539
## [78,] 538 537 536 535.000000 533
## [79,] 532 531 530 529.000000 527
## [80,] 526 525 524 523.000000 521
## [81,] 520 519 518 517.000000 515
## [82,] 514 513 512 511.000000 509
## [83,] 508 507 506 505.000000 503
## [84,] 502 501 500 499.000000 497
## [85,] 496 495 494 493.000000 491
## [86,] 490 489 488 487.000000 485
## [87,] 484 483 482 481.000000 479
## [88,] 478 477 476 475.000000 473
## [89,] 472 471 470 469.000000 467
## [90,] 466 465 464 463.000000 461
## [91,] 460 459 458 457.000000 455
## [92,] 454 453 452 451.000000 449
## [93,] 448 447 446 445.000000 443
## [94,] 442 441 440 439.000000 437
## [95,] 436 435 434 433.000000 431
## [96,] 430 429 428 427.000000 425
## [97,] 424 423 422 421.000000 419
## [98,] 418 417 416 415.000000 413
## [99,] 412 411 410 409.000000 407
## [100,] 406 405 404 403.000000 401
## [101,] 400 399 398 397.000000 395
## [102,] 394 393 392 391.000000 389
## [103,] 388 387 386 385.000000 383
## [104,] 382 381 380 379.000000 377
## [105,] 376 375 374 373.000000 371
## [106,] 370 369 368 367.000000 365
## [107,] 364 363 362 361.000000 359
## [108,] 358 357 356 355.000000 353
## [109,] 352 351 350 349.000000 347
## [110,] 346 345 344 343.000000 341
## [111,] 340 339 338 337.000000 335
## [112,] 334 333 332 331.000000 329
## [113,] 328 327 326 325.000000 323
## [114,] 322 321 320 319.000000 317
## [115,] 316 315 314 313.000000 311
## [116,] 310 309 308 307.000000 305
## [117,] 304 303 302 301.000000 299
## [118,] 298 297 296 295.000000 293
## [119,] 292 291 290 289.000000 287
## [120,] 286 285 284 283.000000 281
## [121,] 280 279 278 277.000000 275
## [122,] 274 273 272 271.000000 269
## [123,] 268 267 266 265.000000 263
## [124,] 262 261 260 259.000000 257
## [125,] 256 255 254 253.000000 251
## [126,] 250 249 248 247.000000 245
## [127,] 244 243 242 241.000000 239
## [128,] 238 237 236 235.000000 233
## [129,] 232 231 230 229.000000 227
## [130,] 226 225 224 223.000000 221
## [131,] 220 219 218 217.000000 215
## [132,] 214 213 212 211.000000 209
## [133,] 208 207 206 205.000000 203
## [134,] 202 201 200 199.000000 197
## [135,] 196 195 194 193.000000 191
## [136,] 190 189 188 187.000000 185
## [137,] 184 183 182 181.000000 179
## [138,] 178 177 176 175.000000 173
## [139,] 172 171 170 169.000000 167
## [140,] 166 165 164 163.000000 161
## [141,] 160 159 158 157.000000 155
## [142,] 154 153 152 151.000000 149
## [143,] 148 147 146 145.000000 143
## [144,] 142 141 140 139.000000 137
## [145,] 136 135 134 133.000000 131
## [146,] 130 129 128 127.000000 125
## [147,] 124 123 122 121.000000 119
## [148,] 118 117 116 115.000000 113
## [149,] 112 111 110 109.000000 107
## [150,] 106 105 104 103.000000 NA
## [151,] 10000000 59049 42 1.666667 -2g$Df=subset(g$Df, !is.na(place) & !is.na(N))
g$Df## place N
## 2 House 5
## 3 Square 7which(is.infinite(g$Value))## [1] 5 6