# load the essential package
Packages <- c("tidyverse", "moments")
lapply(Packages, library, character.only = TRUE)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## [[1]]
## [1] "forcats" "stringr" "dplyr" "purrr" "readr" "tidyr"
## [7] "tibble" "ggplot2" "tidyverse" "stats" "graphics" "grDevices"
## [13] "utils" "datasets" "methods" "base"
##
## [[2]]
## [1] "moments" "forcats" "stringr" "dplyr" "purrr" "readr"
## [7] "tidyr" "tibble" "ggplot2" "tidyverse" "stats" "graphics"
## [13] "grDevices" "utils" "datasets" "methods" "base"
# load the dataset
df = read_csv('Data samples to analyze.csv')
## Rows: 580 Columns: 190
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (190): a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
sum(is.na(df)) # caculate the total missing value
## [1] 61
colSums(is.na(df)) # check which column contain missing value
## a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## a17 a18 a19 a20 a21 a22 a23 a24 a25 a26 a27 a28 a29 a30 a31 a32
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## a33 a34 a35 a36 a37 a38 a39 a40 a41 a42 a43 a44 a45 a46 a47 a48
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## a49 a50 a51 a52 a53 a54 a55 a56 a57 a58 a59 a60 a61 a62 a63 a64
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## a65 a66 a67 a68 a69 a70 a71 a72 a73 a74 a75 a76 a77 a78 a79 a80
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## a81 a82 a83 a84 a85 a86 a87 a88 a89 a90 a91 a92 a93 a94 a95 a96
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## a97 a98 a99 a100 a101 a102 a103 a104 a105 a106 a107 a108 a109 a110 a111 a112
## 22 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0
## a113 a114 a115 a116 a117 a118 a119 a120 a121 a122 a123 a124 a125 a126 a127 a128
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## a129 a130 a131 a132 a133 a134 a135 a136 a137 a138 a139 a140 a141 a142 a143 a144
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## a145 a146 a147 a148 a149 a150 a151 a152 a153 a154 a155 a156 a157 a158 a159 a160
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## a161 a162 a163 a164 a165 a166 a167 a168 a169 a170 a171 a172 a173 a174 a175 a176
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## a177 a178 a179 a180 a181 a182 a183 a184 a185 a186 a187 a188 a189 a190
## 0 0 0 18 0 0 0 0 0 0 0 0 0 0
#which(colSums(is.na(df))>0)
names(which(colSums(is.na(df))>0)) # get the column name of missing value
## [1] "a97" "a103" "a180"
library(magicfor) # load library
magic_for(silent = TRUE) # call magic_for()
#count all the min
for (i in names(df)){
smallest = min(df[i],na.rm = TRUE)
biggest = max(df[i],na.rm = TRUE)
average = mean(df[[i]],na.rm = TRUE)
middle = median(df[[i]],na.rm = TRUE)
quantile_25 = quantile(df[i],.25,na.rm = TRUE)
quantile_75 = quantile(df[i],.75,na.rm = TRUE)
stdev = sd(df[[i]],na.rm = TRUE)
variance = var(df[[i]],na.rm = TRUE)
skew = skewness(df[[i]],na.rm = TRUE)
put(smallest, biggest,average,middle, quantile_25,quantile_75,stdev,variance,skew)
}
df1 = magic_result_as_dataframe()
rownames(df1) <- df1$i
df1
## i smallest biggest average middle quantile_25 quantile_75 stdev
## a1 a1 2 34 18.177586 18.0 14.00 23.00 5.640145
## a2 a2 1 34 17.672414 18.0 14.00 22.00 5.649179
## a3 a3 3 34 17.434483 17.0 14.00 21.00 5.665170
## a4 a4 2 32 18.034483 18.0 14.00 22.00 5.838250
## a5 a5 2 33 18.124138 18.0 14.00 22.00 5.791880
## a6 a6 2 35 18.065517 18.0 14.00 22.00 5.903880
## a7 a7 4 34 17.534483 18.0 13.00 22.00 5.844311
## a8 a8 2 33 18.089655 18.0 14.00 23.00 5.856862
## a9 a9 1 34 18.234483 18.0 14.00 22.00 5.836593
## a10 a10 1 34 18.051724 18.0 14.00 22.00 5.759493
## a11 a11 3 35 18.379310 18.0 14.00 22.25 5.810263
## a12 a12 3 34 17.658621 18.0 13.75 22.00 5.616471
## a13 a13 4 32 17.518966 17.0 13.00 21.00 5.666127
## a14 a14 2 34 18.189655 18.0 14.00 22.00 5.787715
## a15 a15 3 34 17.870690 18.0 14.00 22.00 5.689323
## a16 a16 1 35 18.006897 18.0 14.00 22.00 5.935167
## a17 a17 3 35 17.743103 18.0 13.00 22.00 5.828393
## a18 a18 2 33 17.956897 18.0 14.00 22.00 5.745904
## a19 a19 2 34 17.915517 18.0 14.00 22.00 5.740933
## a20 a20 2 32 17.844828 18.0 14.00 22.00 5.718200
## a21 a21 2 33 17.874138 18.0 14.00 22.00 5.758198
## a22 a22 4 34 18.265517 18.0 14.00 22.00 5.642044
## a23 a23 2 34 17.831034 18.0 14.00 22.00 5.808612
## a24 a24 0 36 18.020690 16.0 8.00 28.00 11.750997
## a25 a25 1 34 17.793103 18.0 14.00 22.00 5.681101
## a26 a26 3 34 18.298276 19.0 14.00 22.00 5.634124
## a27 a27 4 34 18.139655 18.0 14.00 22.00 5.706961
## a28 a28 4 35 17.698276 18.0 13.00 22.00 5.897249
## a29 a29 1 35 17.948276 18.0 14.00 22.00 5.721886
## a30 a30 5 34 17.993103 18.0 14.00 22.00 5.468385
## a31 a31 2 36 17.813793 18.0 14.00 22.00 5.919420
## a32 a32 3 35 18.081034 18.0 14.00 22.00 5.559102
## a33 a33 2 32 18.203448 18.0 15.00 22.00 5.302913
## a34 a34 0 33 17.965517 18.0 14.00 22.00 5.826109
## a35 a35 3 34 17.408621 17.0 14.00 22.00 5.602577
## a36 a36 1 32 17.998276 18.0 14.00 22.00 5.623630
## a37 a37 0 9 4.487931 4.0 2.00 7.00 2.957650
## a38 a38 2 35 18.272414 18.0 14.00 22.00 5.839973
## a39 a39 2 35 18.475862 18.0 14.75 22.25 5.851747
## a40 a40 3 33 17.951724 18.0 14.00 22.00 5.610662
## a41 a41 4 34 17.855172 18.0 14.00 22.00 5.670856
## a42 a42 3 35 17.870690 18.0 14.00 21.25 5.760820
## a43 a43 3 35 18.025862 18.0 14.00 22.00 5.595861
## a44 a44 4 120 62.612069 61.0 36.00 90.00 30.936665
## a45 a45 3 34 18.053448 18.0 14.00 22.00 5.764423
## a46 a46 3 33 18.329310 18.0 15.00 22.00 5.506802
## a47 a47 2 33 17.801724 18.0 14.00 22.00 5.690066
## a48 a48 2 34 17.641379 18.0 14.00 22.00 5.709642
## a49 a49 3 35 18.022414 18.0 14.00 22.00 5.568960
## a50 a50 1 34 17.832759 18.0 14.00 21.00 5.729175
## a51 a51 2 33 18.003448 18.0 14.00 22.00 5.869036
## a52 a52 2 33 18.312069 18.0 14.00 23.00 5.822432
## a53 a53 1 32 18.025862 18.0 14.00 22.00 5.779275
## a54 a54 0 34 18.598276 19.0 15.00 23.00 5.946712
## a55 a55 4 35 17.858621 18.0 14.00 22.00 5.743271
## a56 a56 2 33 18.327586 18.0 15.00 22.00 5.460450
## a57 a57 3 34 17.363793 17.0 13.00 21.00 5.623820
## a58 a58 2 33 18.120690 18.0 14.00 22.00 5.652510
## a59 a59 2 34 17.639655 17.0 14.00 22.00 5.998804
## a60 a60 2 32 18.025862 18.0 14.00 22.00 5.799859
## a61 a61 3 36 17.910345 18.0 14.00 21.00 5.655226
## a62 a62 2 32 17.887931 18.0 14.00 22.00 5.688171
## a63 a63 2 26 13.151724 13.0 9.00 17.00 5.088619
## a64 a64 3 33 17.886207 18.0 14.00 22.00 5.929419
## a65 a65 5 34 18.305172 19.0 14.00 22.00 5.674685
## a66 a66 3 31 17.755172 18.0 14.00 22.00 5.485927
## a67 a67 1 34 18.144828 18.0 15.00 22.00 5.585547
## a68 a68 2 33 18.117241 18.0 14.00 22.00 5.661436
## a69 a69 2 34 18.341379 19.0 15.00 22.00 5.774461
## a70 a70 1 34 17.694828 17.5 14.00 22.00 5.852089
## a71 a71 3 32 18.208621 18.0 14.00 22.00 5.585844
## a72 a72 3 34 17.351724 17.0 13.00 21.00 5.789074
## a73 a73 4 33 18.020690 18.0 14.00 22.00 5.626509
## a74 a74 2 34 18.224138 18.0 14.00 22.00 5.891486
## a75 a75 3 34 17.927586 18.0 14.00 22.00 5.813295
## a76 a76 2 32 17.955172 18.0 14.00 22.00 5.730390
## a77 a77 3 35 17.906897 18.0 14.00 22.00 5.632525
## a78 a78 2 34 17.722414 18.0 14.00 22.00 5.577885
## a79 a79 3 34 17.565517 17.0 14.00 22.00 5.640728
## a80 a80 2 33 18.334483 19.0 15.00 22.00 5.496916
## a81 a81 2 35 18.196552 18.0 14.00 22.00 5.790766
## a82 a82 1 35 17.860345 18.0 14.00 22.00 5.772259
## a83 a83 0 9 4.350000 4.0 2.00 7.00 2.824134
## a84 a84 3 34 17.537931 17.5 14.00 21.00 5.484814
## a85 a85 1 34 18.243103 18.0 14.00 22.00 5.893517
## a86 a86 3 34 18.210345 18.0 14.00 22.00 5.786402
## a87 a87 3 35 17.887931 18.0 14.00 22.00 5.849836
## a88 a88 3 33 18.136207 18.0 14.00 22.00 5.650476
## a89 a89 1 36 17.963793 18.0 14.00 22.00 5.951189
## a90 a90 3 32 17.915517 18.0 14.00 22.00 5.600836
## a91 a91 3 33 17.812069 18.0 14.00 22.00 5.680089
## a92 a92 2 36 17.851724 18.0 13.00 22.00 5.808583
## a93 a93 2 34 18.439655 18.5 14.00 22.00 5.735519
## a94 a94 4 32 18.131034 18.0 14.00 22.00 5.584960
## a95 a95 3 34 18.250000 18.0 14.00 22.00 5.686203
## a96 a96 4 33 18.056897 18.0 14.00 22.00 5.631322
## a97 a97 2 34 18.084229 18.0 14.00 22.00 5.685826
## a98 a98 1 33 17.989655 18.0 14.00 22.00 5.933998
## a99 a99 1 33 17.986207 18.0 14.00 22.00 5.987882
## a100 a100 3 33 17.905172 18.0 14.00 22.00 5.533035
## a101 a101 3 34 18.013793 18.0 14.00 22.00 5.578439
## a102 a102 2 34 17.515517 17.0 14.00 22.00 5.655764
## a103 a103 3 34 18.078712 18.0 14.00 22.00 5.798983
## a104 a104 2 33 18.024138 18.0 14.00 22.00 5.595715
## a105 a105 2 34 18.100000 18.0 14.00 22.00 5.774729
## a106 a106 4 35 18.020690 18.0 14.00 22.00 5.608677
## a107 a107 3 33 17.808621 18.0 14.00 22.00 5.822320
## a108 a108 2 32 18.339655 19.0 14.00 22.25 5.694904
## a109 a109 5 118 62.410345 61.0 34.75 90.00 30.816644
## a110 a110 1 32 18.070690 18.0 14.00 22.00 5.768131
## a111 a111 0 34 18.106897 18.0 14.00 22.00 5.513584
## a112 a112 4 34 18.196552 18.0 14.00 22.00 5.697255
## a113 a113 6 121 62.437931 60.0 36.00 90.00 30.959184
## a114 a114 2 34 17.617241 18.0 13.00 22.00 5.881828
## a115 a115 4 43 22.970690 23.0 17.00 28.00 7.313661
## a116 a116 3 35 17.998276 18.0 13.00 22.00 6.059150
## a117 a117 1 32 18.063793 18.0 14.00 22.00 5.580722
## a118 a118 0 81 19.815517 12.0 2.00 32.75 20.757222
## a119 a119 3 33 17.932759 18.0 13.00 22.00 5.796845
## a120 a120 1 32 17.624138 17.0 14.00 22.00 5.718204
## a121 a121 1 35 17.967241 18.0 14.00 22.00 5.668502
## a122 a122 3 35 17.653448 17.5 14.00 22.00 5.668040
## a123 a123 3 32 18.034483 18.0 14.00 22.00 5.849776
## a124 a124 3 34 18.039655 18.0 14.00 22.00 5.696723
## a125 a125 3 33 18.156897 18.0 14.00 22.00 5.686806
## a126 a126 3 35 17.600000 18.0 14.00 21.25 5.744593
## a127 a127 1 34 17.525862 18.0 13.00 21.00 5.844356
## a128 a128 1 32 18.065517 18.0 14.75 22.00 5.779110
## a129 a129 2 36 17.660345 17.0 14.00 21.00 5.812572
## a130 a130 3 31 17.793103 18.0 14.00 21.25 5.414299
## a131 a131 3 33 17.772414 18.0 14.00 21.25 5.501446
## a132 a132 3 34 17.713793 17.0 14.00 22.00 5.974397
## a133 a133 2 33 18.212069 18.0 14.00 22.00 5.638033
## a134 a134 3 36 18.277586 18.0 14.00 22.00 5.884658
## a135 a135 2 34 18.141379 18.0 14.00 22.00 5.435190
## a136 a136 0 33 17.765517 18.0 13.75 22.00 6.028732
## a137 a137 1 36 17.936207 18.0 14.00 22.00 6.037682
## a138 a138 2 33 18.037931 18.0 14.00 22.00 5.680492
## a139 a139 1 36 18.322414 18.0 14.00 23.00 5.894687
## a140 a140 1 34 17.756897 18.0 14.00 22.00 5.755034
## a141 a141 0 9 4.505172 4.0 2.00 7.00 2.937749
## a142 a142 3 33 17.500000 18.0 13.00 22.00 5.800512
## a143 a143 0 31 17.977586 18.0 14.00 22.00 5.614979
## a144 a144 2 34 18.068966 18.0 14.00 22.00 5.685670
## a145 a145 2 35 17.837931 18.0 14.00 22.00 5.811484
## a146 a146 5 34 17.979310 18.0 14.00 22.00 5.500471
## a147 a147 0 9 4.296552 4.0 2.00 7.00 2.967001
## a148 a148 1 32 17.784483 18.0 14.00 22.00 5.739609
## a149 a149 3 33 18.020690 18.0 14.00 22.00 5.638161
## a150 a150 3 35 18.631034 19.0 15.00 23.00 5.927169
## a151 a151 2 43 22.950000 23.0 17.00 29.00 7.925337
## a152 a152 1 34 18.218966 18.0 14.00 22.00 5.805005
## a153 a153 2 33 18.144828 18.0 14.00 22.00 5.368274
## a154 a154 3 33 18.036207 18.0 14.00 22.00 5.504942
## a155 a155 3 35 18.236207 18.0 15.00 22.00 5.549999
## a156 a156 2 33 18.143103 18.0 14.00 22.00 5.802912
## a157 a157 2 34 17.550000 17.0 13.00 21.00 5.827922
## a158 a158 3 33 18.425862 18.0 14.00 22.00 5.622833
## a159 a159 2 35 18.068966 18.0 14.00 22.00 5.639921
## a160 a160 2 36 18.129310 18.0 14.00 22.00 5.759621
## a161 a161 3 34 18.434483 18.0 14.00 23.00 5.852610
## a162 a162 2 35 18.246552 18.0 14.00 22.00 5.841567
## a163 a163 2 34 17.924138 18.0 14.00 22.00 5.880600
## a164 a164 3 43 22.660345 23.0 17.00 28.00 7.391365
## a165 a165 3 34 17.839655 18.0 14.00 22.00 5.649841
## a166 a166 3 36 18.246552 18.0 14.00 23.00 5.847773
## a167 a167 1 35 18.124138 18.0 14.00 22.00 5.671348
## a168 a168 2 34 18.341379 18.0 15.00 22.00 5.664239
## a169 a169 4 33 17.860345 18.0 14.00 22.00 5.687864
## a170 a170 3 34 18.115517 18.0 14.00 22.00 5.602747
## a171 a171 4 32 18.201724 18.0 14.00 22.00 5.616931
## a172 a172 4 34 17.724138 18.0 14.00 22.00 5.719080
## a173 a173 3 34 18.068966 18.0 14.00 22.00 5.775184
## a174 a174 2 34 17.925862 18.0 14.00 22.00 5.665978
## a175 a175 2 33 18.172414 18.0 14.00 22.00 5.795710
## a176 a176 0 32 18.079310 18.0 14.00 22.00 5.709482
## a177 a177 1 32 17.765517 17.0 13.75 22.00 5.949719
## a178 a178 1 44 22.953448 23.0 17.00 29.00 7.743485
## a179 a179 3 34 17.660345 18.0 13.75 22.00 6.006917
## a180 a180 2 34 18.215302 18.0 14.00 22.00 5.831097
## a181 a181 2 34 18.229310 18.0 14.00 22.00 5.704663
## a182 a182 4 32 18.110345 18.0 14.00 22.00 5.761365
## a183 a183 2 33 18.481034 18.0 15.00 23.00 5.810299
## a184 a184 4 34 18.046552 18.0 14.00 22.00 5.746478
## a185 a185 3 36 18.205172 18.0 14.00 22.00 5.828668
## a186 a186 0 35 18.444828 19.0 14.00 23.00 5.934477
## a187 a187 3 35 18.070690 18.0 14.00 22.00 5.685801
## a188 a188 7 122 62.706897 60.0 37.00 89.00 30.832496
## a189 a189 4 34 18.341379 18.0 14.00 22.00 5.617701
## a190 a190 2 33 18.037931 18.0 14.00 22.00 6.105459
## variance skew
## a1 31.811241 -0.033171032
## a2 31.913227 0.137568276
## a3 32.094146 0.066922752
## a4 34.085165 -0.054304994
## a5 33.545876 0.008180447
## a6 34.855804 0.060994052
## a7 34.155976 0.019112968
## a8 34.302829 -0.058683120
## a9 34.065821 -0.076214501
## a10 33.171759 0.085407774
## a11 33.759157 0.010590387
## a12 31.544744 -0.011860837
## a13 32.104994 0.080686639
## a14 33.497648 -0.038010394
## a15 32.368397 0.021594988
## a16 35.226205 -0.009756053
## a17 33.970160 0.108289792
## a18 33.015410 0.012729070
## a19 32.958308 -0.131667026
## a20 32.697814 -0.072876919
## a21 33.156843 0.017772703
## a22 31.832660 0.105157537
## a23 33.739968 0.117513887
## a24 138.085927 0.029535952
## a25 32.274909 -0.132305492
## a26 31.743348 -0.074276039
## a27 32.569409 -0.017775031
## a28 34.777545 0.161182859
## a29 32.739980 -0.089675415
## a30 29.903234 0.143916865
## a31 35.039533 0.022826929
## a32 30.903612 0.008621030
## a33 28.120886 -0.033970075
## a34 33.943541 -0.110790907
## a35 31.388872 -0.097622743
## a36 31.625213 -0.122138609
## a37 8.747695 -0.016727374
## a38 34.105283 -0.026260179
## a39 34.242940 -0.105164862
## a40 31.479531 -0.064107556
## a41 32.158609 0.066974440
## a42 33.187050 0.092119587
## a43 31.313665 0.148154963
## a44 957.077229 0.071103725
## a45 33.228572 0.054797297
## a46 30.324873 -0.006748124
## a47 32.376854 -0.254578406
## a48 32.600012 -0.062332704
## a49 31.013314 -0.059155310
## a50 32.823450 0.133883292
## a51 34.445584 -0.050331455
## a52 33.900718 -0.051100139
## a53 33.400021 -0.116520067
## a54 35.363382 -0.136507646
## a55 32.985159 0.019307831
## a56 29.816509 -0.094434864
## a57 31.627357 0.115665173
## a58 31.950867 -0.048690619
## a59 35.985644 0.050049369
## a60 33.638363 -0.071148584
## a61 31.981585 0.023711718
## a62 32.355295 -0.020961838
## a63 25.894038 0.062337390
## a64 35.158013 -0.003564410
## a65 32.202046 -0.008242009
## a66 30.095396 -0.095307791
## a67 31.198332 -0.052338253
## a68 32.051861 -0.045079919
## a69 33.344399 -0.034110501
## a70 34.246951 -0.077166365
## a71 31.201653 0.066164451
## a72 33.513382 0.202786852
## a73 31.657602 -0.023967667
## a74 34.709606 -0.078597417
## a75 33.794402 0.157509101
## a76 32.837365 -0.043332537
## a77 31.725341 -0.023728179
## a78 31.112796 -0.078978213
## a79 31.817807 0.090962701
## a80 30.216080 -0.171920029
## a81 33.532976 -0.012488708
## a82 33.318977 -0.030962161
## a83 7.975734 0.027879670
## a84 30.083187 0.068331018
## a85 34.733545 0.067468873
## a86 33.482449 0.152008330
## a87 34.220579 -0.107322300
## a88 31.927875 -0.022309244
## a89 35.416649 0.022819657
## a90 31.369362 -0.127443653
## a91 32.263412 -0.001879013
## a92 33.739634 0.056271229
## a93 32.896180 -0.042698097
## a94 31.191781 -0.104296659
## a95 32.332902 -0.045268030
## a96 31.711783 -0.020029003
## a97 32.328620 -0.088133977
## a98 35.212328 -0.082970437
## a99 35.854732 0.013083930
## a100 30.614481 -0.034327268
## a101 31.118980 0.008488230
## a102 31.987669 -0.058665894
## a103 33.628202 -0.002053521
## a104 31.312024 0.029095573
## a105 33.347496 0.065608015
## a106 31.457257 0.163867802
## a107 33.899407 -0.140285708
## a108 32.431931 -0.200922023
## a109 949.665523 0.071927802
## a110 33.271333 -0.029728542
## a111 30.399607 -0.080538874
## a112 32.458710 -0.074744604
## a113 958.471098 0.068211390
## a114 34.595903 0.005298020
## a115 53.489640 -0.059412196
## a116 36.713296 -0.030298414
## a117 31.144455 -0.106935862
## a118 430.862280 1.005016026
## a119 33.603416 0.174995898
## a120 32.697862 -0.004397678
## a121 32.131913 -0.079574621
## a122 32.126672 0.042927920
## a123 34.219880 -0.038510734
## a124 32.452656 -0.030728570
## a125 32.339762 -0.022396163
## a126 33.000345 0.026039949
## a127 34.156498 0.037610642
## a128 33.398118 -0.215597186
## a129 33.785990 0.213161271
## a130 29.314633 -0.139010990
## a131 30.265904 0.054954871
## a132 35.693419 0.026655194
## a133 31.787419 -0.037267932
## a134 34.629203 0.060205956
## a135 29.541290 -0.048363476
## a136 36.345614 -0.151807319
## a137 36.453609 0.078511703
## a138 32.267989 -0.213471004
## a139 34.747338 0.008843810
## a140 33.120419 -0.108126906
## a141 8.630370 0.029535952
## a142 33.645941 0.049423253
## a143 31.527994 -0.168941342
## a144 32.326842 0.019987883
## a145 33.773343 -0.101650899
## a146 30.255184 0.013413072
## a147 8.803097 0.083542622
## a148 32.943109 0.039029499
## a149 31.788863 -0.074093879
## a150 35.131332 -0.070391677
## a151 62.810967 -0.010639045
## a152 33.698085 0.016542910
## a153 28.818367 -0.077806838
## a154 30.304386 -0.083007987
## a155 30.802486 -0.007387882
## a156 33.673787 -0.025397668
## a157 33.964680 0.154574049
## a158 31.616256 0.036686959
## a159 31.808707 0.010323023
## a160 33.173233 0.088451249
## a161 34.253040 0.096107620
## a162 34.123909 -0.038152423
## a163 34.581454 -0.011516680
## a164 54.632276 0.037549802
## a165 31.920705 -0.042346912
## a166 34.196448 0.035297066
## a167 32.164183 -0.028150153
## a168 32.083604 -0.094833387
## a169 32.351793 0.081318904
## a170 31.390778 0.105396718
## a171 31.549911 -0.088712881
## a172 32.707879 0.021816909
## a173 33.352748 0.014308387
## a174 32.103302 -0.116553197
## a175 33.590257 -0.053459068
## a176 32.598190 -0.103084423
## a177 35.399154 0.023468560
## a178 59.961560 0.020653308
## a179 36.083053 0.073260480
## a180 34.001691 0.031897555
## a181 32.543181 0.039029473
## a182 33.193330 -0.020761751
## a183 33.759571 -0.046403285
## a184 33.022009 0.008660971
## a185 33.973376 0.122827256
## a186 35.218022 -0.082218610
## a187 32.328328 -0.056638915
## a188 950.642785 0.047287837
## a189 31.558561 0.067116169
## a190 37.276624 -0.099832142
df2 = data.frame(t(df1[-1]))
df2
## a1 a2 a3 a4 a5
## smallest 2.00000000 1.0000000 3.00000000 2.00000000 2.000000000
## biggest 34.00000000 34.0000000 34.00000000 32.00000000 33.000000000
## average 18.17758621 17.6724138 17.43448276 18.03448276 18.124137931
## middle 18.00000000 18.0000000 17.00000000 18.00000000 18.000000000
## quantile_25 14.00000000 14.0000000 14.00000000 14.00000000 14.000000000
## quantile_75 23.00000000 22.0000000 21.00000000 22.00000000 22.000000000
## stdev 5.64014549 5.6491793 5.66516952 5.83825014 5.791880158
## variance 31.81124114 31.9132273 32.09414567 34.08516467 33.545875767
## skew -0.03317103 0.1375683 0.06692275 -0.05430499 0.008180447
## a6 a7 a8 a9 a10
## smallest 2.00000000 4.00000000 2.00000000 1.0000000 1.00000000
## biggest 35.00000000 34.00000000 33.00000000 34.0000000 34.00000000
## average 18.06551724 17.53448276 18.08965517 18.2344828 18.05172414
## middle 18.00000000 18.00000000 18.00000000 18.0000000 18.00000000
## quantile_25 14.00000000 13.00000000 14.00000000 14.0000000 14.00000000
## quantile_75 22.00000000 22.00000000 23.00000000 22.0000000 22.00000000
## stdev 5.90388039 5.84431146 5.85686169 5.8365933 5.75949292
## variance 34.85580370 34.15597642 34.30282890 34.0658210 33.17175868
## skew 0.06099405 0.01911297 -0.05868312 -0.0762145 0.08540777
## a11 a12 a13 a14 a15
## smallest 3.00000000 3.00000000 4.00000000 2.00000000 3.00000000
## biggest 35.00000000 34.00000000 32.00000000 34.00000000 34.00000000
## average 18.37931034 17.65862069 17.51896552 18.18965517 17.87068966
## middle 18.00000000 18.00000000 17.00000000 18.00000000 18.00000000
## quantile_25 14.00000000 13.75000000 13.00000000 14.00000000 14.00000000
## quantile_75 22.25000000 22.00000000 21.00000000 22.00000000 22.00000000
## stdev 5.81026305 5.61647080 5.66612687 5.78771523 5.68932305
## variance 33.75915669 31.54474421 32.10499375 33.49764755 32.36839676
## skew 0.01059039 -0.01186084 0.08068664 -0.03801039 0.02159499
## a16 a17 a18 a19 a20
## smallest 1.000000000 3.0000000 2.00000000 2.000000 2.00000000
## biggest 35.000000000 35.0000000 33.00000000 34.000000 32.00000000
## average 18.006896552 17.7431034 17.95689655 17.915517 17.84482759
## middle 18.000000000 18.0000000 18.00000000 18.000000 18.00000000
## quantile_25 14.000000000 13.0000000 14.00000000 14.000000 14.00000000
## quantile_75 22.000000000 22.0000000 22.00000000 22.000000 22.00000000
## stdev 5.935166764 5.8283925 5.74590376 5.740933 5.71820027
## variance 35.226204514 33.9701596 33.01541004 32.958308 32.69781431
## skew -0.009756053 0.1082898 0.01272907 -0.131667 -0.07287692
## a21 a22 a23 a24 a25
## smallest 2.0000000 4.0000000 2.0000000 0.00000000 1.0000000
## biggest 33.0000000 34.0000000 34.0000000 36.00000000 34.0000000
## average 17.8741379 18.2655172 17.8310345 18.02068966 17.7931034
## middle 18.0000000 18.0000000 18.0000000 16.00000000 18.0000000
## quantile_25 14.0000000 14.0000000 14.0000000 8.00000000 14.0000000
## quantile_75 22.0000000 22.0000000 22.0000000 28.00000000 22.0000000
## stdev 5.7581979 5.6420440 5.8086115 11.75099685 5.6811011
## variance 33.1568430 31.8326604 33.7399678 138.08592698 32.2749092
## skew 0.0177727 0.1051575 0.1175139 0.02953595 -0.1323055
## a26 a27 a28 a29 a30
## smallest 3.00000000 4.00000000 4.0000000 1.00000000 5.0000000
## biggest 34.00000000 34.00000000 35.0000000 35.00000000 34.0000000
## average 18.29827586 18.13965517 17.6982759 17.94827586 17.9931034
## middle 19.00000000 18.00000000 18.0000000 18.00000000 18.0000000
## quantile_25 14.00000000 14.00000000 13.0000000 14.00000000 14.0000000
## quantile_75 22.00000000 22.00000000 22.0000000 22.00000000 22.0000000
## stdev 5.63412350 5.70696147 5.8972489 5.72188603 5.4683849
## variance 31.74334763 32.56940921 34.7775445 32.73997975 29.9032339
## skew -0.07427604 -0.01777503 0.1611829 -0.08967542 0.1439169
## a31 a32 a33 a34 a35
## smallest 2.00000000 3.00000000 2.00000000 0.0000000 3.00000000
## biggest 36.00000000 35.00000000 32.00000000 33.0000000 34.00000000
## average 17.81379310 18.08103448 18.20344828 17.9655172 17.40862069
## middle 18.00000000 18.00000000 18.00000000 18.0000000 17.00000000
## quantile_25 14.00000000 14.00000000 15.00000000 14.0000000 14.00000000
## quantile_75 22.00000000 22.00000000 22.00000000 22.0000000 22.00000000
## stdev 5.91942000 5.55910173 5.30291299 5.8261086 5.60257727
## variance 35.03953308 30.90361205 28.12088619 33.9435412 31.38887201
## skew 0.02282693 0.00862103 -0.03397008 -0.1107909 -0.09762274
## a36 a37 a38 a39 a40
## smallest 1.0000000 0.00000000 2.00000000 2.0000000 3.00000000
## biggest 32.0000000 9.00000000 35.00000000 35.0000000 33.00000000
## average 17.9982759 4.48793103 18.27241379 18.4758621 17.95172414
## middle 18.0000000 4.00000000 18.00000000 18.0000000 18.00000000
## quantile_25 14.0000000 2.00000000 14.00000000 14.7500000 14.00000000
## quantile_75 22.0000000 7.00000000 22.00000000 22.2500000 22.00000000
## stdev 5.6236299 2.95765028 5.83997282 5.8517467 5.61066223
## variance 31.6252129 8.74769519 34.10528259 34.2429397 31.47953070
## skew -0.1221386 -0.01672737 -0.02626018 -0.1051649 -0.06410756
## a41 a42 a43 a44 a45
## smallest 4.00000000 3.00000000 3.000000 4.00000000 3.0000000
## biggest 34.00000000 35.00000000 35.000000 120.00000000 34.0000000
## average 17.85517241 17.87068966 18.025862 62.61206897 18.0534483
## middle 18.00000000 18.00000000 18.000000 61.00000000 18.0000000
## quantile_25 14.00000000 14.00000000 14.000000 36.00000000 14.0000000
## quantile_75 22.00000000 21.25000000 22.000000 90.00000000 22.0000000
## stdev 5.67085609 5.76082022 5.595861 30.93666480 5.7644229
## variance 32.15860878 33.18704961 31.313665 957.07722887 33.2285719
## skew 0.06697444 0.09211959 0.148155 0.07110372 0.0547973
## a46 a47 a48 a49 a50
## smallest 3.000000000 2.0000000 2.0000000 3.00000000 1.0000000
## biggest 33.000000000 33.0000000 34.0000000 35.00000000 34.0000000
## average 18.329310345 17.8017241 17.6413793 18.02241379 17.8327586
## middle 18.000000000 18.0000000 18.0000000 18.00000000 18.0000000
## quantile_25 15.000000000 14.0000000 14.0000000 14.00000000 14.0000000
## quantile_75 22.000000000 22.0000000 22.0000000 22.00000000 21.0000000
## stdev 5.506802470 5.6900662 5.7096420 5.56895984 5.7291753
## variance 30.324873444 32.3768537 32.6000119 31.01331368 32.8234501
## skew -0.006748124 -0.2545784 -0.0623327 -0.05915531 0.1338833
## a51 a52 a53 a54 a55
## smallest 2.00000000 2.00000000 1.0000000 0.0000000 4.00000000
## biggest 33.00000000 33.00000000 32.0000000 34.0000000 35.00000000
## average 18.00344828 18.31206897 18.0258621 18.5982759 17.85862069
## middle 18.00000000 18.00000000 18.0000000 19.0000000 18.00000000
## quantile_25 14.00000000 14.00000000 14.0000000 15.0000000 14.00000000
## quantile_75 22.00000000 23.00000000 22.0000000 23.0000000 22.00000000
## stdev 5.86903603 5.82243228 5.7792751 5.9467119 5.74327073
## variance 34.44558394 33.90071765 33.4000208 35.3633822 32.98515872
## skew -0.05033145 -0.05110014 -0.1165201 -0.1365076 0.01930783
## a56 a57 a58 a59 a60
## smallest 2.00000000 3.0000000 2.00000000 2.00000000 2.00000000
## biggest 33.00000000 34.0000000 33.00000000 34.00000000 32.00000000
## average 18.32758621 17.3637931 18.12068966 17.63965517 18.02586207
## middle 18.00000000 17.0000000 18.00000000 17.00000000 18.00000000
## quantile_25 15.00000000 13.0000000 14.00000000 14.00000000 14.00000000
## quantile_75 22.00000000 21.0000000 22.00000000 22.00000000 22.00000000
## stdev 5.46044951 5.6238205 5.65250976 5.99880356 5.79985886
## variance 29.81650884 31.6273569 31.95086654 35.98564410 33.63836281
## skew -0.09443486 0.1156652 -0.04869062 0.05004937 -0.07114858
## a61 a62 a63 a64 a65
## smallest 3.00000000 2.00000000 2.00000000 3.00000000 5.000000000
## biggest 36.00000000 32.00000000 26.00000000 33.00000000 34.000000000
## average 17.91034483 17.88793103 13.15172414 17.88620690 18.305172414
## middle 18.00000000 18.00000000 13.00000000 18.00000000 19.000000000
## quantile_25 14.00000000 14.00000000 9.00000000 14.00000000 14.000000000
## quantile_75 21.00000000 22.00000000 17.00000000 22.00000000 22.000000000
## stdev 5.65522638 5.68817146 5.08861852 5.92941930 5.674684638
## variance 31.98158537 32.35529450 25.89403847 35.15801322 32.202045739
## skew 0.02371172 -0.02096184 0.06233739 -0.00356441 -0.008242009
## a66 a67 a68 a69 a70
## smallest 3.00000000 1.00000000 2.00000000 2.0000000 1.00000000
## biggest 31.00000000 34.00000000 33.00000000 34.0000000 34.00000000
## average 17.75517241 18.14482759 18.11724138 18.3413793 17.69482759
## middle 18.00000000 18.00000000 18.00000000 19.0000000 17.50000000
## quantile_25 14.00000000 15.00000000 14.00000000 15.0000000 14.00000000
## quantile_75 22.00000000 22.00000000 22.00000000 22.0000000 22.00000000
## stdev 5.48592712 5.58554674 5.66143631 5.7744609 5.85208943
## variance 30.09539634 31.19833244 32.05186112 33.3443988 34.24695075
## skew -0.09530779 -0.05233825 -0.04507992 -0.0341105 -0.07716637
## a71 a72 a73 a74 a75
## smallest 3.00000000 3.0000000 4.00000000 2.00000000 3.0000000
## biggest 32.00000000 34.0000000 33.00000000 34.00000000 34.0000000
## average 18.20862069 17.3517241 18.02068966 18.22413793 17.9275862
## middle 18.00000000 17.0000000 18.00000000 18.00000000 18.0000000
## quantile_25 14.00000000 13.0000000 14.00000000 14.00000000 14.0000000
## quantile_75 22.00000000 21.0000000 22.00000000 22.00000000 22.0000000
## stdev 5.58584395 5.7890744 5.62650889 5.89148592 5.8132953
## variance 31.20165267 33.5133822 31.65760229 34.70960634 33.7944018
## skew 0.06616445 0.2027869 -0.02396767 -0.07859742 0.1575091
## a76 a77 a78 a79 a80
## smallest 2.00000000 3.00000000 2.00000000 3.0000000 2.000000
## biggest 32.00000000 35.00000000 34.00000000 34.0000000 33.000000
## average 17.95517241 17.90689655 17.72241379 17.5655172 18.334483
## middle 18.00000000 18.00000000 18.00000000 17.0000000 19.000000
## quantile_25 14.00000000 14.00000000 14.00000000 14.0000000 15.000000
## quantile_75 22.00000000 22.00000000 22.00000000 22.0000000 22.000000
## stdev 5.73038963 5.63252527 5.57788450 5.6407275 5.496916
## variance 32.83736526 31.72534096 31.11279555 31.8178072 30.216080
## skew -0.04333254 -0.02372818 -0.07897821 0.0909627 -0.171920
## a81 a82 a83 a84 a85
## smallest 2.00000000 1.00000000 0.00000000 3.00000000 1.00000000
## biggest 35.00000000 35.00000000 9.00000000 34.00000000 34.00000000
## average 18.19655172 17.86034483 4.35000000 17.53793103 18.24310345
## middle 18.00000000 18.00000000 4.00000000 17.50000000 18.00000000
## quantile_25 14.00000000 14.00000000 2.00000000 14.00000000 14.00000000
## quantile_75 22.00000000 22.00000000 7.00000000 21.00000000 22.00000000
## stdev 5.79076644 5.77225930 2.82413421 5.48481426 5.89351718
## variance 33.53297600 33.31897743 7.97573402 30.08318742 34.73354476
## skew -0.01248871 -0.03096216 0.02787967 0.06833102 0.06746887
## a86 a87 a88 a89 a90
## smallest 3.0000000 3.0000000 3.00000000 1.00000000 3.0000000
## biggest 34.0000000 35.0000000 33.00000000 36.00000000 32.0000000
## average 18.2103448 17.8879310 18.13620690 17.96379310 17.9155172
## middle 18.0000000 18.0000000 18.00000000 18.00000000 18.0000000
## quantile_25 14.0000000 14.0000000 14.00000000 14.00000000 14.0000000
## quantile_75 22.0000000 22.0000000 22.00000000 22.00000000 22.0000000
## stdev 5.7864021 5.8498359 5.65047565 5.95118886 5.6008358
## variance 33.4824489 34.2205795 31.92787505 35.41664880 31.3693616
## skew 0.1520083 -0.1073223 -0.02230924 0.02281966 -0.1274437
## a91 a92 a93 a94 a95
## smallest 3.000000000 2.00000000 2.0000000 4.0000000 3.00000000
## biggest 33.000000000 36.00000000 34.0000000 32.0000000 34.00000000
## average 17.812068966 17.85172414 18.4396552 18.1310345 18.25000000
## middle 18.000000000 18.00000000 18.5000000 18.0000000 18.00000000
## quantile_25 14.000000000 13.00000000 14.0000000 14.0000000 14.00000000
## quantile_75 22.000000000 22.00000000 22.0000000 22.0000000 22.00000000
## stdev 5.680089079 5.80858282 5.7355191 5.5849603 5.68620274
## variance 32.263411947 33.73963433 32.8961795 31.1917813 32.33290155
## skew -0.001879013 0.05627123 -0.0426981 -0.1042967 -0.04526803
## a96 a97 a98 a99 a100
## smallest 4.000000 2.00000000 1.00000000 1.00000000 3.00000000
## biggest 33.000000 34.00000000 33.00000000 33.00000000 33.00000000
## average 18.056897 18.08422939 17.98965517 17.98620690 17.90517241
## middle 18.000000 18.00000000 18.00000000 18.00000000 18.00000000
## quantile_25 14.000000 14.00000000 14.00000000 14.00000000 14.00000000
## quantile_75 22.000000 22.00000000 22.00000000 22.00000000 22.00000000
## stdev 5.631322 5.68582622 5.93399764 5.98788207 5.53303542
## variance 31.711783 32.32861978 35.21232803 35.85473170 30.61448097
## skew -0.020029 -0.08813398 -0.08297044 0.01308393 -0.03432727
## a101 a102 a103 a104 a105
## smallest 3.00000000 2.00000000 3.000000000 2.00000000 2.00000000
## biggest 34.00000000 34.00000000 34.000000000 33.00000000 34.00000000
## average 18.01379310 17.51551724 18.078711986 18.02413793 18.10000000
## middle 18.00000000 17.00000000 18.000000000 18.00000000 18.00000000
## quantile_25 14.00000000 14.00000000 14.000000000 14.00000000 14.00000000
## quantile_75 22.00000000 22.00000000 22.000000000 22.00000000 22.00000000
## stdev 5.57843889 5.65576423 5.798982835 5.59571482 5.77472906
## variance 31.11898041 31.98766899 33.628201922 31.31202430 33.34749568
## skew 0.00848823 -0.05866589 -0.002053521 0.02909557 0.06560802
## a106 a107 a108 a109 a110 a111
## smallest 4.0000000 3.0000000 2.000000 5.0000000 1.00000000 0.00000000
## biggest 35.0000000 33.0000000 32.000000 118.0000000 32.00000000 34.00000000
## average 18.0206897 17.8086207 18.339655 62.4103448 18.07068966 18.10689655
## middle 18.0000000 18.0000000 19.000000 61.0000000 18.00000000 18.00000000
## quantile_25 14.0000000 14.0000000 14.000000 34.7500000 14.00000000 14.00000000
## quantile_75 22.0000000 22.0000000 22.250000 90.0000000 22.00000000 22.00000000
## stdev 5.6086769 5.8223198 5.694904 30.8166436 5.76813079 5.51358386
## variance 31.4572569 33.8994074 32.431931 949.6655232 33.27133286 30.39960693
## skew 0.1638678 -0.1402857 -0.200922 0.0719278 -0.02972854 -0.08053887
## a112 a113 a114 a115 a116
## smallest 4.0000000 6.00000000 2.00000000 4.0000000 3.00000000
## biggest 34.0000000 121.00000000 34.00000000 43.0000000 35.00000000
## average 18.1965517 62.43793103 17.61724138 22.9706897 17.99827586
## middle 18.0000000 60.00000000 18.00000000 23.0000000 18.00000000
## quantile_25 14.0000000 36.00000000 13.00000000 17.0000000 13.00000000
## quantile_75 22.0000000 90.00000000 22.00000000 28.0000000 22.00000000
## stdev 5.6972546 30.95918438 5.88182817 7.3136612 6.05914976
## variance 32.4587100 958.47109761 34.59590257 53.4896403 36.71329581
## skew -0.0747446 0.06821139 0.00529802 -0.0594122 -0.03029841
## a117 a118 a119 a120 a121
## smallest 1.0000000 0.000000 3.0000000 1.000000000 1.00000000
## biggest 32.0000000 81.000000 33.0000000 32.000000000 35.00000000
## average 18.0637931 19.815517 17.9327586 17.624137931 17.96724138
## middle 18.0000000 12.000000 18.0000000 17.000000000 18.00000000
## quantile_25 14.0000000 2.000000 13.0000000 14.000000000 14.00000000
## quantile_75 22.0000000 32.750000 22.0000000 22.000000000 22.00000000
## stdev 5.5807218 20.757222 5.7968453 5.718204434 5.66850182
## variance 31.1444554 430.862280 33.6034155 32.697861950 32.13191293
## skew -0.1069359 1.005016 0.1749959 -0.004397678 -0.07957462
## a122 a123 a124 a125 a126
## smallest 3.00000000 3.00000000 3.00000000 3.00000000 3.00000000
## biggest 35.00000000 32.00000000 34.00000000 33.00000000 35.00000000
## average 17.65344828 18.03448276 18.03965517 18.15689655 17.60000000
## middle 17.50000000 18.00000000 18.00000000 18.00000000 18.00000000
## quantile_25 14.00000000 14.00000000 14.00000000 14.00000000 14.00000000
## quantile_75 22.00000000 22.00000000 22.00000000 22.00000000 21.25000000
## stdev 5.66803952 5.84977604 5.69672328 5.68680599 5.74459271
## variance 32.12667203 34.21987970 32.45265618 32.33976237 33.00034542
## skew 0.04292792 -0.03851073 -0.03072857 -0.02239616 0.02603995
## a127 a128 a129 a130 a131 a132
## smallest 1.00000000 1.0000000 2.0000000 3.000000 3.00000000 3.00000000
## biggest 34.00000000 32.0000000 36.0000000 31.000000 33.00000000 34.00000000
## average 17.52586207 18.0655172 17.6603448 17.793103 17.77241379 17.71379310
## middle 18.00000000 18.0000000 17.0000000 18.000000 18.00000000 17.00000000
## quantile_25 13.00000000 14.7500000 14.0000000 14.000000 14.00000000 14.00000000
## quantile_75 21.00000000 22.0000000 21.0000000 21.250000 21.25000000 22.00000000
## stdev 5.84435604 5.7791105 5.8125717 5.414299 5.50144566 5.97439696
## variance 34.15649753 33.3981180 33.7859895 29.314633 30.26590435 35.69341909
## skew 0.03761064 -0.2155972 0.2131613 -0.139011 0.05495487 0.02665519
## a133 a134 a135 a136 a137 a138
## smallest 2.00000000 3.00000000 2.00000000 0.0000000 1.0000000 2.000000
## biggest 33.00000000 36.00000000 34.00000000 33.0000000 36.0000000 33.000000
## average 18.21206897 18.27758621 18.14137931 17.7655172 17.9362069 18.037931
## middle 18.00000000 18.00000000 18.00000000 18.0000000 18.0000000 18.000000
## quantile_25 14.00000000 14.00000000 14.00000000 13.7500000 14.0000000 14.000000
## quantile_75 22.00000000 22.00000000 22.00000000 22.0000000 22.0000000 22.000000
## stdev 5.63803324 5.88465829 5.43518997 6.0287323 6.0376824 5.680492
## variance 31.78741886 34.62920314 29.54128998 36.3456137 36.4536091 32.267989
## skew -0.03726793 0.06020596 -0.04836348 -0.1518073 0.0785117 -0.213471
## a139 a140 a141 a142 a143
## smallest 1.00000000 1.0000000 0.00000000 3.00000000 0.0000000
## biggest 36.00000000 34.0000000 9.00000000 33.00000000 31.0000000
## average 18.32241379 17.7568966 4.50517241 17.50000000 17.9775862
## middle 18.00000000 18.0000000 4.00000000 18.00000000 18.0000000
## quantile_25 14.00000000 14.0000000 2.00000000 13.00000000 14.0000000
## quantile_75 23.00000000 22.0000000 7.00000000 22.00000000 22.0000000
## stdev 5.89468726 5.7550342 2.93774921 5.80051216 5.6149794
## variance 34.74733786 33.1204187 8.63037044 33.64594128 31.5279942
## skew 0.00884381 -0.1081269 0.02953595 0.04942325 -0.1689413
## a144 a145 a146 a147 a148
## smallest 2.00000000 2.0000000 5.00000000 0.00000000 1.0000000
## biggest 34.00000000 35.0000000 34.00000000 9.00000000 32.0000000
## average 18.06896552 17.8379310 17.97931034 4.29655172 17.7844828
## middle 18.00000000 18.0000000 18.00000000 4.00000000 18.0000000
## quantile_25 14.00000000 14.0000000 14.00000000 2.00000000 14.0000000
## quantile_75 22.00000000 22.0000000 22.00000000 7.00000000 22.0000000
## stdev 5.68566986 5.8114837 5.50047128 2.96700133 5.7396088
## variance 32.32684176 33.7733429 30.25518432 8.80309690 32.9431094
## skew 0.01998788 -0.1016509 0.01341307 0.08354262 0.0390295
## a149 a150 a151 a152 a153
## smallest 3.00000000 3.00000000 2.00000000 1.00000000 2.00000000
## biggest 33.00000000 35.00000000 43.00000000 34.00000000 33.00000000
## average 18.02068966 18.63103448 22.95000000 18.21896552 18.14482759
## middle 18.00000000 19.00000000 23.00000000 18.00000000 18.00000000
## quantile_25 14.00000000 15.00000000 17.00000000 14.00000000 14.00000000
## quantile_75 22.00000000 23.00000000 29.00000000 22.00000000 22.00000000
## stdev 5.63816132 5.92716899 7.92533704 5.80500519 5.36827412
## variance 31.78886308 35.13133226 62.81096718 33.69808528 28.81836698
## skew -0.07409388 -0.07039168 -0.01063904 0.01654291 -0.07780684
## a154 a155 a156 a157 a158
## smallest 3.00000000 3.000000000 2.00000000 2.000000 3.00000000
## biggest 33.00000000 35.000000000 33.00000000 34.000000 33.00000000
## average 18.03620690 18.236206897 18.14310345 17.550000 18.42586207
## middle 18.00000000 18.000000000 18.00000000 17.000000 18.00000000
## quantile_25 14.00000000 15.000000000 14.00000000 13.000000 14.00000000
## quantile_75 22.00000000 22.000000000 22.00000000 21.000000 22.00000000
## stdev 5.50494199 5.549998779 5.80291190 5.827922 5.62283343
## variance 30.30438628 30.802486451 33.67378655 33.964680 31.61625573
## skew -0.08300799 -0.007387882 -0.02539767 0.154574 0.03668696
## a159 a160 a161 a162 a163
## smallest 2.00000000 2.00000000 3.00000000 2.00000000 2.00000000
## biggest 35.00000000 36.00000000 34.00000000 35.00000000 34.00000000
## average 18.06896552 18.12931034 18.43448276 18.24655172 17.92413793
## middle 18.00000000 18.00000000 18.00000000 18.00000000 18.00000000
## quantile_25 14.00000000 14.00000000 14.00000000 14.00000000 14.00000000
## quantile_75 22.00000000 22.00000000 23.00000000 22.00000000 22.00000000
## stdev 5.63992084 5.75962088 5.85260970 5.84156731 5.88059983
## variance 31.80870705 33.17323268 34.25304032 34.12390864 34.58145435
## skew 0.01032302 0.08845125 0.09610762 -0.03815242 -0.01151668
## a164 a165 a166 a167 a168
## smallest 3.0000000 3.00000000 3.00000000 1.00000000 2.00000000
## biggest 43.0000000 34.00000000 36.00000000 35.00000000 34.00000000
## average 22.6603448 17.83965517 18.24655172 18.12413793 18.34137931
## middle 23.0000000 18.00000000 18.00000000 18.00000000 18.00000000
## quantile_25 17.0000000 14.00000000 14.00000000 14.00000000 15.00000000
## quantile_75 28.0000000 22.00000000 23.00000000 22.00000000 22.00000000
## stdev 7.3913650 5.64984111 5.84777287 5.67134756 5.66423908
## variance 54.6322762 31.92070454 34.19644750 32.16418319 32.08360431
## skew 0.0375498 -0.04234691 0.03529707 -0.02815015 -0.09483339
## a169 a170 a171 a172 a173
## smallest 4.0000000 3.0000000 4.00000000 4.00000000 3.00000000
## biggest 33.0000000 34.0000000 32.00000000 34.00000000 34.00000000
## average 17.8603448 18.1155172 18.20172414 17.72413793 18.06896552
## middle 18.0000000 18.0000000 18.00000000 18.00000000 18.00000000
## quantile_25 14.0000000 14.0000000 14.00000000 14.00000000 14.00000000
## quantile_75 22.0000000 22.0000000 22.00000000 22.00000000 22.00000000
## stdev 5.6878636 5.6027473 5.61693072 5.71908028 5.77518385
## variance 32.3517926 31.3907778 31.54991067 32.70787922 33.35274850
## skew 0.0813189 0.1053967 -0.08871288 0.02181691 0.01430839
## a174 a175 a176 a177 a178
## smallest 2.0000000 2.00000000 0.0000000 1.00000000 1.00000000
## biggest 34.0000000 33.00000000 32.0000000 32.00000000 44.00000000
## average 17.9258621 18.17241379 18.0793103 17.76551724 22.95344828
## middle 18.0000000 18.00000000 18.0000000 17.00000000 23.00000000
## quantile_25 14.0000000 14.00000000 14.0000000 13.75000000 17.00000000
## quantile_75 22.0000000 22.00000000 22.0000000 22.00000000 29.00000000
## stdev 5.6659776 5.79571020 5.7094824 5.94971884 7.74348499
## variance 32.1033024 33.59025669 32.5981895 35.39915431 59.96155976
## skew -0.1165532 -0.05345907 -0.1030844 0.02346856 0.02065331
## a179 a180 a181 a182 a183
## smallest 3.00000000 2.00000000 2.00000000 4.00000000 2.00000000
## biggest 34.00000000 34.00000000 34.00000000 32.00000000 33.00000000
## average 17.66034483 18.21530249 18.22931034 18.11034483 18.48103448
## middle 18.00000000 18.00000000 18.00000000 18.00000000 18.00000000
## quantile_25 13.75000000 14.00000000 14.00000000 14.00000000 15.00000000
## quantile_75 22.00000000 22.00000000 22.00000000 22.00000000 23.00000000
## stdev 6.00691713 5.83109686 5.70466308 5.76136527 5.81029867
## variance 36.08305342 34.00169055 32.54318087 33.19332976 33.75957060
## skew 0.07326048 0.03189756 0.03902947 -0.02076175 -0.04640329
## a184 a185 a186 a187 a188
## smallest 4.000000000 3.0000000 0.00000000 3.00000000 7.00000000
## biggest 34.000000000 36.0000000 35.00000000 35.00000000 122.00000000
## average 18.046551724 18.2051724 18.44482759 18.07068966 62.70689655
## middle 18.000000000 18.0000000 19.00000000 18.00000000 60.00000000
## quantile_25 14.000000000 14.0000000 14.00000000 14.00000000 37.00000000
## quantile_75 22.000000000 22.0000000 23.00000000 22.00000000 89.00000000
## stdev 5.746477949 5.8286684 5.93447736 5.68580053 30.83249560
## variance 33.022008814 33.9733756 35.21802156 32.32832768 950.64278483
## skew 0.008660971 0.1228273 -0.08221861 -0.05663892 0.04728784
## a189 a190
## smallest 4.00000000 2.00000000
## biggest 34.00000000 33.00000000
## average 18.34137931 18.03793103
## middle 18.00000000 18.00000000
## quantile_25 14.00000000 14.00000000
## quantile_75 22.00000000 22.00000000
## stdev 5.61770070 6.10545857
## variance 31.55856113 37.27662438
## skew 0.06711617 -0.09983214
write.csv(df2,'newfile.csv')
# add the range and size manual
data = read_csv('newfile2.csv')
## New names:
## Rows: 11 Columns: 191
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (191): ...1, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
dotchart(df$a1)
