missing data
Steen Flammild Harsted & Søren O’Neill
The Workflow
The Workflow
Missing data
What?
Where?
Why?
How much?
pattern or missing at random?
How much?
name genus vore order conservation
Length :83 Length :83 Length :83 Length :83 Length :83
N.unique :83 N.unique :77 N.unique : 4 N.unique :19 N.unique : 6
N.blank : 0 N.blank : 0 N.blank : 0 N.blank : 0 N.blank : 0
Min.nchar: 3 Min.nchar: 3 Min.nchar: 4 Min.nchar: 6 Min.nchar: 2
Max.nchar:30 Max.nchar:13 Max.nchar: 7 Max.nchar:15 Max.nchar:12
NAs : 7 NAs :29
sleep_total sleep_rem sleep_cycle awake
Min. : 1.90 Min. :0.100 Min. :0.1167 Min. : 4.10
1st Qu.: 7.85 1st Qu.:0.900 1st Qu.:0.1833 1st Qu.:10.25
Median :10.10 Median :1.500 Median :0.3333 Median :13.90
Mean :10.43 Mean :1.875 Mean :0.4396 Mean :13.57
3rd Qu.:13.75 3rd Qu.:2.400 3rd Qu.:0.5792 3rd Qu.:16.15
Max. :19.90 Max. :6.600 Max. :1.5000 Max. :22.10
NAs :22 NAs :51
brainwt bodywt
Min. :0.00014 Min. : 0.005
1st Qu.:0.00290 1st Qu.: 0.174
Median :0.01240 Median : 1.670
Mean :0.28158 Mean : 166.136
3rd Qu.:0.12550 3rd Qu.: 41.750
Max. :5.71200 Max. :6654.000
NAs :27 How much?
name height mass hair_color skin_color eye_color birth_year sex
[1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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gender homeworld species films vehicles starships
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# A tibble: 1 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<int> <int> <int> <int> <int> <int> <int> <int> <int>
1 0 6 28 5 0 0 44 4 4
# ℹ 5 more variables: homeworld <int>, species <int>, films <int>,
# vehicles <int>, starships <int>The naniar package
The naniar package vis_miss()
The naniar package vis_miss()
Use arrange() to get more information
The naniar package gg_miss_upset()
