Homework1_Vilcek_Fall21
VICTOR LEON
10/12/2021
Read in the data:
crusio1 <- read.csv("https://raw.githubusercontent.com/fredlapolla/VilcekR_fall21/main/Crusio1.csv")
- Use ifelse to create a new variable of high and normal task times on day one dichotomizing with falling in the upper quartile as a threshold for high levels
#considering that upper quantile is 75% or up
crusio1[,86] <- ifelse(crusio1$task_time_d1>quantile(crusio1$task_time_d1, na.rm = TRUE)[4], "high", "low" )
crusio1[,86]<-factor(crusio1[,86])
summary(crusio1[,86])## high low NA's
## 226 681 16
- Subset to only have females (sex == “f”)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionfemales <- filter(crusio1, sex == 'f')
head(females)## strain sex id bw center_time center_dist periphery_time periphery_dist
## 1 BXD1 f 138 22.7 34 4084 66 6989
## 2 BXD1 f 139 21.1 44 6172 56 6581
## 3 BXD100 f 1602 24.1 27 2341 73 3299
## 4 BXD100 f 1603 22.4 19 1521 81 3839
## 5 BXD100 f 2115 23.2 13 1642 87 7469
## 6 BXD100 f 2116 25.0 17 1715 83 3699
## periphery_dist_pct activity lean rear jump defec groom_freq groom_dur
## 1 63.12 11073 56 13 0 0 11 28.00
## 2 51.60 12753 50 74 0 3 12 78.00
## 3 58.49 5640 28 17 0 8 19 151.40
## 4 71.62 5360 61 31 0 13 12 102.70
## 5 81.98 9111 94 61 0 2 6 29.04
## 6 68.32 5414 42 34 0 6 13 136.30
## groom_bout task_time_d1 task_time_d2 task_time_d3 task_time_d4 task_time_d5
## 1 2.545 791 625 363 222 162
## 2 6.500 389 117 363 414 363
## 3 7.966 620 620 485 375 887
## 4 8.560 673 316 334 365 196
## 5 4.840 898 527 277 182 265
## 6 10.490 1211 307 454 263 141
## num_arms_d1 num_arms_d2 num_arms_d3 num_arms_d4 num_arms_d5 num_arms_adj_d3
## 1 5 6 5 7 7 -0.3
## 2 7 7 5 6 6 -0.3
## 3 6 4 6 5 4 0.7
## 4 5 6 6 6 6 0.7
## 5 4 5 7 6 6 1.7
## 6 5 6 4 5 7 -1.3
## num_arms_adj_d4 num_arms_adj_d5 num_arms_adj_d3_d5 errors_d1 errors_d2
## 1 1.7 1.7 1.03300 7 15
## 2 0.7 0.7 0.36670 11 2
## 3 -0.3 -1.3 -0.30000 6 14
## 4 0.7 0.7 0.70000 12 3
## 5 0.7 0.7 1.03300 21 12
## 6 -0.3 1.7 0.03333 30 3
## errors_d3 errors_d4 errors_d5 errors_d3_d5 visit_time_d3 visit_time_d4
## 1 6 5 3 14 25.93 17.08
## 2 8 16 9 33 22.69 17.25
## 3 7 7 7 21 32.33 25.00
## 4 9 11 3 23 19.65 19.21
## 5 4 4 9 17 23.08 15.17
## 6 8 6 1 15 28.38 18.79
## visit_time_d5 visit_time_d3_d5 latency_d1 latency_d2 attack_d1 attack_d2
## 1 14.73 19.24 NA NA NA NA
## 2 21.35 20.43 NA NA NA NA
## 3 59.13 38.82 NA NA NA NA
## 4 17.82 18.89 NA NA NA NA
## 5 15.59 17.95 NA NA NA NA
## 6 15.67 20.94 NA NA NA NA
## attack_combine brain_wt brain_wt_pct hippocampus_L hippocampus_R iipmf_L
## 1 NA 0.46 2.026 1677741 1625291 34125.0
## 2 NA 0.45 2.133 NA NA NA
## 3 NA 0.44 1.826 NA NA NA
## 4 NA 0.41 1.830 1373309 1515029 16794.2
## 5 NA 0.41 1.767 NA NA NA
## 6 NA 0.45 1.800 1795644 1608368 29970.0
## iipmf_R iipmf_pct_L iipmf_pct_R iipmf_pct_mean hilus_L hilus_R hilus_pct_L
## 1 32936.8 2.034 2.027 2.030 184197.2 156521.8 10.980
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 28613.8 1.223 1.889 1.556 86149.6 107296.0 6.273
## 5 NA NA NA NA NA NA NA
## 6 42677.0 1.669 2.653 2.161 138301.6 106120.6 7.702
## hilus_pct_R hilus_pct_mean supra_L supra_R supra_pct_L supra_pct_R
## 1 9.630 10.300 195271.2 182789.8 11.640 11.250
## 2 NA NA NA NA NA NA
## 3 NA NA NA NA NA NA
## 4 7.082 6.678 117051.4 144061.8 8.523 9.509
## 5 NA NA NA NA NA NA
## 6 6.598 7.150 158641.6 160599.8 8.835 9.985
## supra_pct_mean pyr_L pyr_R pyr_pct_L pyr_pct_R pyr_pct_mean oriens_L
## 1 11.440 189534 221690.8 11.30 13.64 12.47 550027.2
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 9.016 244054 244510.8 17.77 16.14 16.96 460611.4
## 5 NA NA NA NA NA NA NA
## 6 9.410 273505 228972.0 15.23 14.24 14.73 631311.2
## oriens_R oriens_pct_L oriens_pct_R oriens_pct_mean rad_L rad_R
## 1 515438.6 32.78 31.71 32.25 439035.6 441760.8
## 2 NA NA NA NA NA NA
## 3 NA NA NA NA NA NA
## 4 499582.4 33.54 32.98 33.26 386880.2 424987.2
## 5 NA NA NA NA NA NA
## 6 610758.2 35.16 37.97 36.57 474278.0 421802.2
## rad_pct_L rad_pct_R rad_pct_mean lacun_L lacun_R lacun_pct_L lacun_pct_R
## 1 26.17 27.18 26.67 119675.6 107088.8 7.133 6.589
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 28.17 28.05 28.11 78562.4 94590.6 5.721 6.243
## 5 NA NA NA NA NA NA NA
## 6 26.41 26.23 26.32 119606.6 80115.2 6.661 4.981
## lacun_pct_mean V86
## 1 6.861 high
## 2 NA low
## 3 NA low
## 4 5.982 high
## 5 NA high
## 6 5.821 high
- Create a subset of males and find the median body weight
males <- filter(crusio1, sex == 'm' )
median(males$bw, na.rm = T)## [1] 26.5
- Try to create a summary of the mean bw by factoring on if the mice attacked one another or not on either day.
crusio1$attack_combine <- factor(crusio1$attack_combine)
attacking <- filter(crusio1, attack_combine == 1)
nonattacking <- filter(crusio1, attack_combine == 0)
summary(attacking$bw, na.rm = T)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 20.40 24.70 26.00 26.66 27.95 41.80summary(nonattacking$bw, na.rm = T)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 20.20 24.40 26.60 26.75 28.38 42.50 3mean(attacking$bw, na.rm = T)## [1] 26.6631mean(nonattacking$bw, na.rm = T)## [1] 26.74628How does R treat blank values?
What command can be used to identify where NAs are located in a column?
# by typing is.na()How can you use indexing to create a subset without any NAs?
# it can be used utilizing the negation of is.na(),
#which is !is.na() and utilize the brackets and write it
# in the "row" position inside the brackets after the
# indicating the column of interest, for example in
#bw in crusio1 , it would be written as
clean <- crusio1[!is.na(crusio1$bw),]
is.na(clean$bw)## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEHow could you delete a column of data, for example, the 5th
# by placing - and column number inside the brakes in the second postion
#for example
#data[,-5]What command would let you find a set of text in the cells of a column?
# We can use the grep function
# if you want to find and replace , you would use gsubRename the bw variable to body_wt
crusio1 <- rename(crusio1, body_wt=bw)
names(crusio1)## [1] "strain" "sex" "id"
## [4] "body_wt" "center_time" "center_dist"
## [7] "periphery_time" "periphery_dist" "periphery_dist_pct"
## [10] "activity" "lean" "rear"
## [13] "jump" "defec" "groom_freq"
## [16] "groom_dur" "groom_bout" "task_time_d1"
## [19] "task_time_d2" "task_time_d3" "task_time_d4"
## [22] "task_time_d5" "num_arms_d1" "num_arms_d2"
## [25] "num_arms_d3" "num_arms_d4" "num_arms_d5"
## [28] "num_arms_adj_d3" "num_arms_adj_d4" "num_arms_adj_d5"
## [31] "num_arms_adj_d3_d5" "errors_d1" "errors_d2"
## [34] "errors_d3" "errors_d4" "errors_d5"
## [37] "errors_d3_d5" "visit_time_d3" "visit_time_d4"
## [40] "visit_time_d5" "visit_time_d3_d5" "latency_d1"
## [43] "latency_d2" "attack_d1" "attack_d2"
## [46] "attack_combine" "brain_wt" "brain_wt_pct"
## [49] "hippocampus_L" "hippocampus_R" "iipmf_L"
## [52] "iipmf_R" "iipmf_pct_L" "iipmf_pct_R"
## [55] "iipmf_pct_mean" "hilus_L" "hilus_R"
## [58] "hilus_pct_L" "hilus_pct_R" "hilus_pct_mean"
## [61] "supra_L" "supra_R" "supra_pct_L"
## [64] "supra_pct_R" "supra_pct_mean" "pyr_L"
## [67] "pyr_R" "pyr_pct_L" "pyr_pct_R"
## [70] "pyr_pct_mean" "oriens_L" "oriens_R"
## [73] "oriens_pct_L" "oriens_pct_R" "oriens_pct_mean"
## [76] "rad_L" "rad_R" "rad_pct_L"
## [79] "rad_pct_R" "rad_pct_mean" "lacun_L"
## [82] "lacun_R" "lacun_pct_L" "lacun_pct_R"
## [85] "lacun_pct_mean" "V86"Explore the Mutate command and create a ratio of errors on day 1 divided by task time on day 1
crusio1 <- crusio1 %>% mutate(errors_d1/task_time_d1)
crusio1$`errors_d1/task_time_d1`## [1] 0.0088495575 0.0282776350 0.0198598131 0.0179924242 0.0096774194
## [6] 0.0178306092 0.0233853007 0.0247729149 0.0070312500 0.0230061350
## [11] 0.0132450331 0.0076687117 0.0149532710 0.0181268882 0.0173724213
## [16] 0.0231481481 0.0232172471 0.0367412141 0.0270270270 0.0372526193
## [21] 0.0369127517 0.0141752577 0.0160142349 0.0136612022 0.0347826087
## [26] 0.0129198966 0.0272727273 0.0263157895 0.0062656642 0.0270270270
## [31] 0.0465116279 0.0443037975 0.0396475771 0.0320699708 0.0165289256
## [36] 0.0117647059 0.0107296137 0.0299401198 0.0358422939 0.0326530612
## [41] 0.0136054422 0.0372023810 0.0128440367 0.0202312139 0.0223880597
## [46] 0.0439814815 0.0486111111 0.0206718346 0.0217391304 0.0224215247
## [51] 0.0393162393 0.0480769231 0.0571992110 0.0417910448 0.0430107527
## [56] 0.0420168067 0.0102669405 0.0221518987 0.0348258706 0.0022222222
## [61] 0.0027643400 0.0253699789 0.0074503311 0.0045248869 0.0180000000
## [66] 0.0157894737 0.0067453626 0.0118750000 0.0038461538 0.0022222222
## [71] 0.0000000000 0.0007867821 0.0071556351 0.0108695652 0.0069182390
## [76] 0.0033333333 0.0077777778 0.0130505710 0.0255102041 0.0386221294
## [81] 0.0219941349 0.0227743271 0.0249520154 0.0058685446 0.0184804928
## [86] 0.0182370821 0.0243902439 0.0055555556 0.0354969574 0.0210918114
## [91] 0.0252707581 0.0079113924 0.0139165010 0.0115942029 0.0000000000
## [96] 0.0100000000 0.0048231511 0.0039123631 0.0323741007 0.0350109409
## [101] 0.0290697674 0.0280112045 0.0127226463 0.0413333333 0.0201149425
## [106] 0.0392156863 0.0195121951 0.0375586854 0.0376569038 0.0398406375
## [111] 0.0264026403 0.0344827586 0.0302267003 0.0261780105 0.0335365854
## [116] 0.0435643564 0.0089418778 0.0298850575 0.0273348519 0.0231481481
## [121] 0.0155440415 0.0088888889 0.0112994350 0.0123076923 0.0202247191
## [126] 0.0186046512 0.0189701897 0.0514018692 0.0201511335 0.0173160173
## [131] 0.0102301790 0.0388059701 0.0231884058 0.0103626943 0.0175953079
## [136] 0.0167130919 0.0260115607 0.0221238938 0.0187793427 0.0464396285
## [141] 0.0524934383 0.0199667221 0.0366972477 0.0361990950 0.0538461538
## [146] 0.0352422907 0.0464285714 0.0404040404 0.0098684211 0.0177514793
## [151] 0.0189189189 0.0363128492 0.0188235294 0.0279720280 0.0668523677
## [156] 0.0588235294 0.0215053763 0.0244444444 NA 0.0535947712
## [161] 0.0507726269 0.0365853659 0.0151515152 0.0109090909 0.0288888889
## [166] 0.0034542314 0.0140515222 0.0270602706 0.0176991150 0.0199203187
## [171] 0.0533707865 0.0279503106 0.0570719603 0.0342857143 0.0373134328
## [176] 0.0607966457 NA 0.0352941176 0.0470262794 0.0411764706
## [181] 0.0495867769 0.0427698574 0.0566502463 0.0316205534 0.0358422939
## [186] 0.0420792079 0.0408805031 0.0237131290 0.0279329609 0.0318107667
## [191] 0.0480591497 0.0313479624 0.0389221557 0.0429864253 0.0329218107
## [196] 0.0282916213 0.0251215559 0.0135693215 0.0578778135 0.0280701754
## [201] 0.0020576132 0.0635838150 0.0387491502 NA NA
## [206] 0.0759162304 0.0544217687 NA NA 0.0163934426
## [211] 0.0053705693 0.0165517241 0.0133333333 0.0109704641 0.0062611807
## [216] 0.0013297872 0.0009460738 0.0197628458 0.0221300138 0.0023529412
## [221] 0.0352941176 0.0314960630 0.0098591549 0.0127620784 0.0353982301
## [226] 0.0285132383 0.0226928896 0.0184501845 0.0433386838 0.0470588235
## [231] 0.0195121951 0.0263736264 0.0309734513 0.0138408304 0.0138408304
## [236] 0.0318021201 0.0050632911 0.0054844607 0.0182724252 0.0525059666
## [241] 0.0291666667 0.0160734788 0.0320404722 0.0258064516 0.0233281493
## [246] 0.0406852248 0.0261437908 0.0196428571 0.0059701493 0.0186757216
## [251] 0.0055710306 0.0091324201 0.0280528053 0.0289855072 0.0453074434
## [256] 0.0442477876 0.0170827858 0.0188679245 0.0056497175 0.0292164675
## [261] 0.0204429302 0.0141342756 0.0449640288 0.0407725322 0.0304259635
## [266] 0.0253623188 0.0503144654 0.0132450331 0.0291970803 0.0298165138
## [271] 0.0222222222 0.0144230769 0.0557768924 0.0250000000 0.0480000000
## [276] 0.0260869565 0.0335305720 0.0343137255 0.0362694301 0.0268562401
## [281] 0.0124777184 0.0235690236 0.0184842884 0.0285714286 0.0564102564
## [286] 0.0222672065 0.0123456790 0.0281373101 0.0559210526 0.0232974910
## [291] 0.0316901408 0.0200364299 0.0279503106 0.0262295082 0.0364025696
## [296] 0.0168612192 0.0364238411 0.0066006601 0.0189393939 0.0171526587
## [301] 0.0100864553 0.0111835974 0.0089163237 0.0217391304 0.0191938580
## [306] 0.0303738318 0.0254506893 0.0062015504 0.0310077519 0.0174927114
## [311] 0.0120898100 0.0079787234 0.0130434783 0.0119760479 0.0080591001
## [316] NA 0.0337197050 0.0268361582 0.0290322581 0.0120048019
## [321] 0.0378071834 0.0214723926 0.0141414141 0.0000000000 0.0229132570
## [326] 0.0205223881 0.0290135397 0.0287277702 0.0422535211 0.0434782609
## [331] 0.0094637224 0.0270833333 0.0456273764 0.0033333333 0.0196353436
## [336] 0.0170940171 0.0279823270 0.0162454874 0.0229540918 0.0061983471
## [341] 0.0102214651 0.0181219110 0.0193050193 0.0059880240 0.0262626263
## [346] 0.0115606936 0.0345368917 0.0411099692 0.0029239766 0.0135658915
## [351] 0.0261437908 0.0116959064 0.0169491525 0.0233393178 0.0261437908
## [356] 0.0223880597 0.0493601463 0.0288888889 0.0115384615 0.0335917313
## [361] 0.0394574599 0.0162454874 0.0370370370 0.0369230769 0.0077777778
## [366] 0.0238313474 0.0378787879 0.0239520958 0.0132275132 0.0266666667
## [371] 0.0000000000 0.0410447761 0.0311614731 0.0226757370 0.0144766147
## [376] 0.0145530146 0.0350877193 0.0159235669 0.0411861614 0.0180327869
## [381] 0.0397727273 0.0290556901 0.0061255743 0.0209424084 0.0132669983
## [386] 0.0065627564 0.0321592649 0.0161943320 0.0142348754 0.0345394737
## [391] 0.0101010101 0.0168269231 0.0280898876 0.0096463023 0.0303797468
## [396] 0.0114547537 0.0479041916 0.0593471810 0.0371428571 0.0331950207
## [401] 0.0555555556 0.0357781753 0.0454545455 0.0416666667 0.0400000000
## [406] 0.0171673820 0.0256410256 0.0228571429 0.0215053763 0.0120000000
## [411] 0.0274442539 0.0338983051 0.0265060241 0.0351758794 0.0422960725
## [416] 0.0308123249 0.0632911392 0.0251256281 0.0291545190 0.0374753452
## [421] 0.0181818182 0.0298953662 0.0384615385 0.0032626427 0.0126382306
## [426] 0.0132275132 0.0227272727 0.0429042904 0.0237068966 0.0199619772
## [431] 0.0355618777 0.0135135135 0.0206185567 0.0412698413 0.0198675497
## [436] 0.0414507772 0.0458015267 0.0439560440 0.0078431373 0.0202976996
## [441] 0.0290456432 0.0091324201 0.0231362468 0.0170575693 0.0128205128
## [446] 0.0059970015 0.0249376559 0.0352303523 0.0303867403 0.0191897655
## [451] 0.0151515152 0.0374531835 0.0266666667 0.0212264151 0.0122199593
## [456] 0.0332749562 0.0288065844 0.0212765957 0.0206185567 0.0084745763
## [461] 0.0168918919 0.0064308682 0.0209059233 0.0284191829 0.0109589041
## [466] 0.0234657040 0.0333333333 0.0208333333 0.0191897655 0.0160714286
## [471] 0.0314232902 0.0189393939 0.0170278638 0.0305010893 0.0248447205
## [476] 0.0163934426 0.0496083551 0.0191570881 0.0406504065 0.0263929619
## [481] 0.0249110320 0.0102389078 0.0235988201 0.0304182510 0.0258064516
## [486] 0.0674486804 0.0409090909 0.0089766607 0.0378787879 0.0542168675
## [491] 0.0347222222 0.0097402597 0.0256410256 0.0206896552 0.0296610169
## [496] 0.0285714286 0.0305010893 0.0202952030 0.0075471698 0.0184162063
## [501] 0.0157894737 0.0113500597 0.0093323762 0.0034482759 0.0136830103
## [506] 0.0176415970 0.0082742317 0.0381165919 0.0337078652 0.0334346505
## [511] 0.0190217391 0.0113452188 0.0110565111 0.0304487179 0.0327380952
## [516] 0.0169014085 0.0340909091 0.0227272727 0.0290909091 0.0193452381
## [521] 0.0250000000 0.0158730159 0.0431547619 0.0456273764 0.0133333333
## [526] 0.0347222222 0.0174291939 0.0119760479 0.0186170213 0.0033305579
## [531] 0.0174418605 0.0156599553 0.0139275766 0.0147492625 0.0063291139
## [536] 0.0338345865 0.0178082192 0.0369230769 0.0084388186 0.0234493192
## [541] 0.0276243094 0.0345528455 0.0075187970 0.0534591195 0.0263157895
## [546] 0.0485714286 0.0110803324 0.0153374233 0.0049140049 0.0149572650
## [551] 0.0219178082 0.0393939394 0.0346153846 0.0238095238 0.0209424084
## [556] 0.0124610592 0.0203252033 0.0173913043 0.0101010101 0.0185528757
## [561] 0.0189393939 0.0280112045 0.0112994350 0.0182704019 0.0255183413
## [566] 0.0273348519 0.0225563910 0.0143312102 0.0342205323 0.0196936543
## [571] 0.0237037037 0.0198019802 0.0279069767 0.0291858679 0.0441176471
## [576] 0.0411700975 0.0275689223 0.0133531157 0.0145833333 0.0057088487
## [581] 0.0450160772 0.0313152401 0.0000000000 0.0000000000 0.0167364017
## [586] 0.0000000000 0.0142857143 0.0168674699 0.0072222222 0.0237659963
## [591] 0.0322580645 0.0246406571 0.0347394541 0.0374331551 0.0280898876
## [596] 0.0338541667 0.0576923077 0.0430107527 0.0199692780 0.0170731707
## [601] 0.0438596491 0.0229276896 0.0653153153 0.0462184874 0.0233644860
## [606] 0.0176470588 0.0225352113 0.0271317829 0.0408921933 0.0320284698
## [611] 0.0347826087 0.0432382705 0.0445103858 0.0190839695 0.0370370370
## [616] 0.0418994413 0.0131578947 0.0092213115 0.0099290780 0.0104438642
## [621] 0.0339506173 0.0174216028 0.0145348837 0.0272000000 0.0381944444
## [626] 0.0506756757 0.0193905817 0.0379464286 0.0249376559 0.0186335404
## [631] 0.0289855072 0.0285234899 0.0313901345 0.0258064516 0.0276679842
## [636] 0.0271002710 0.0350877193 0.0310173697 0.0046685341 0.0187793427
## [641] 0.0321285141 0.0157232704 0.0371428571 0.0315315315 0.0205882353
## [646] 0.0263157895 0.0246575342 0.0121951220 0.0145772595 0.0214592275
## [651] NA 0.0158478605 0.0275229358 0.0502512563 0.0570469799
## [656] 0.0168776371 0.0157116451 0.0297872340 0.0375275938 0.0305343511
## [661] 0.0315533981 0.0479274611 0.0063752277 0.0237510238 0.0000000000
## [666] 0.0084447572 0.0065298507 0.0024813896 0.0066476733 0.0389610390
## [671] 0.0093271153 0.0234899329 0.0398481973 0.0222717149 0.0422222222
## [676] 0.0197215777 0.0149253731 0.0123839009 0.0098765432 0.0173611111
## [681] 0.0098039216 0.0314960630 0.0302613480 0.0298507463 0.0218772054
## [686] 0.0430463576 0.0437636761 0.0187500000 0.0550458716 0.0095693780
## [691] 0.0097323601 0.0505617978 0.0380047506 0.0406504065 0.0348027842
## [696] 0.0365853659 0.0216606498 0.0221088435 0.0497925311 0.0545454545
## [701] 0.0227272727 0.0418604651 0.0502283105 0.0295202952 0.0236220472
## [706] 0.0457516340 0.0000000000 0.0292682927 0.0107526882 0.0299401198
## [711] 0.0346820809 0.0487804878 0.0252100840 0.0294906166 0.0233918129
## [716] 0.0351437700 0.0328638498 0.0400000000 0.0152905199 0.0497237569
## [721] 0.0215517241 0.0638547159 0.0372093023 NA 0.0588235294
## [726] 0.0606741573 0.0294117647 0.0299145299 0.0183486239 0.0160256410
## [731] NA 0.0360655738 0.0271739130 0.0454545455 0.0226628895
## [736] 0.0682302772 0.0409356725 0.0154639175 0.0347490347 0.0336749634
## [741] 0.0291970803 0.0358649789 0.0155038760 0.0347826087 0.0488505747
## [746] 0.0107671602 0.0093984962 0.0265339967 0.0278688525 0.0168421053
## [751] 0.0157232704 0.0105401845 0.0054151625 0.0092936803 0.0203703704
## [756] 0.0068621335 0.0125418060 0.0095057034 0.0081967213 0.0343642612
## [761] 0.0188087774 0.0086642599 0.0111940299 0.0163339383 0.0224632068
## [766] 0.0300187617 0.0375722543 0.0101651842 0.0200333890 0.0105555556
## [771] 0.0072904010 0.0303468208 0.0124045802 0.0269709544 0.0216216216
## [776] 0.0130293160 0.0107758621 0.0372208437 0.0110803324 0.0109409190
## [781] 0.0057251908 0.0325443787 0.0190476190 0.0274725275 0.0100628931
## [786] 0.0238805970 0.0339622642 0.0255183413 0.0092307692 0.0086580087
## [791] 0.0005555556 0.0422297297 0.0254545455 0.0213089802 0.0465116279
## [796] 0.0486111111 0.0538720539 0.0145985401 0.0429042904 0.0363196126
## [801] 0.0762463343 0.0339366516 0.0564784053 0.0045662100 0.0328767123
## [806] 0.0319634703 0.0081300813 0.0287081340 0.0268456376 0.0281954887
## [811] 0.0276679842 0.0437017995 0.0074696545 0.0385487528 0.0162790698
## [816] 0.0196721311 0.0140845070 0.0078048780 0.0322580645 0.0204841713
## [821] 0.0179028133 0.0150943396 0.0565610860 0.0159362550 0.0344827586
## [826] 0.0578313253 0.0202020202 0.0557029178 0.0628742515 0.0361010830
## [831] 0.0326086957 0.0326086957 0.0454545455 0.0415224913 0.0235294118
## [836] 0.0251572327 0.0248226950 0.0190476190 0.0337552743 0.0603674541
## [841] 0.0260869565 0.0544747082 0.0379146919 0.0568720379 0.0315457413
## [846] 0.0196078431 0.0312500000 0.0108303249 0.0738916256 0.0490797546
## [851] 0.0436507937 0.0239912759 0.0322580645 0.0293542074 0.0329575022
## [856] 0.0242424242 0.0367346939 0.0328849028 0.0225763612 0.0060711188
## [861] 0.0094043887 0.0255319149 0.0543293718 0.0141129032 0.0179104478
## [866] 0.0492424242 0.0416666667 0.0360824742 0.0716612378 0.0557275542
## [871] 0.0335365854 0.0440528634 0.0384615385 0.0118764846 0.0684624018
## [876] 0.0516528926 0.0411764706 0.0574948665 0.0077821012 0.0452488688
## [881] 0.0231213873 0.0180722892 0.0122324159 0.0614886731 0.0120898100
## [886] NA NA NA 0.0056818182 0.0016666667
## [891] NA NA 0.0333919156 0.0127877238 0.0263157895
## [896] 0.0220883534 0.0171428571 0.0093720712 0.0113122172 0.0042238648
## [901] 0.0185758514 0.0068143101 0.0141643059 0.0344827586 0.0368271955
## [906] 0.0180180180 0.0472103004 0.0221130221 NA 0.0175438596
## [911] 0.0406698565 0.0255754476 0.0201149425 0.0323383085 0.0187969925
## [916] 0.0516431925 0.0460251046 0.0000000000 0.0112044818 0.0240549828
## [921] 0.0072992701 0.0351758794 0.0264423077
- Why would one use a visualization for exploratory analysis?
To have a better sight for exploring biological differences and to use diagram as an instrument for presenting data and support results
- What information about the nature of our data can a histogram tell us?
It will help us to get a sense of the distribution of our data
- Make a histogram of center time from the crusio1 data frame. Is it skewed or normally distributed? Try confirming with pastecs::stat.desc (hint set norm = TRUE as an argument).
hist(crusio1$center_time, col= "darkorchid")
round(pastecs::stat.desc(crusio1$center_time, norm=T),2)## nbr.val nbr.null nbr.na min max range
## 914.00 1.00 9.00 0.00 98.00 98.00
## sum median mean SE.mean CI.mean.0.95 var
## 29384.00 32.00 32.15 0.42 0.83 163.27
## std.dev coef.var skewness skew.2SE kurtosis kurt.2SE
## 12.78 0.40 0.51 3.13 1.05 3.25
## normtest.W normtest.p
## 0.98 0.00
- Try making a QQPlot to assess the normality of the same variable from the previous question. Is the distribution normal? How can you tell?
qqnorm(crusio1$center_time, main = "Normal QQ Plot")
qqline(crusio1$center_time)
# most of the data lands on the line so this might suggest that our data is
# follows a normal distribution
- Make a boxplot of bodyweight by sex in crusio1:
boxplot(crusio1$body_wt ~ crusio1$sex)