README.md
In jjchern/meda: Display metadata records for datasets
About meda
This package provides R functions that replicates the behavior of four Stata commands: (1) d, (2) lookfor, (3) codebook, compact, and (4) l, sepby(). These commands present metadata records for datasets in a cleaned way.
The package is built upon two R packages: haven, which provides a labelled class that supports labelled data frames, and labelled, which makes it easier to work with labelled data frames.
Installation
Since the labelled package is still not up on CRAN, we might also install it as well.
devtools::install_github("larmarange/labelled")
devtools::install_github("jjchern/meda")
Usage
Describe and Look for Variables
library(meda)
nlsw88 = haven::read_dta("http://www.stata-press.com/data/r13/nlsw88.dta")
cb(nlsw88) # show summary statistics of a data frame
#> Source: local data frame [17 x 8]
#>
#> var obs unique mean std.dev min max
#> (chr) (int) (int) (dbl) (dbl) (dbl) (dbl)
#> 1 idcode 2246 2246 2612.65 1480.86 1.00 5159.00
#> 2 age 2246 13 39.15 3.06 34.00 46.00
#> 3 race 2246 3 1.28 0.48 1.00 3.00
#> 4 married 2246 2 0.64 0.48 0.00 1.00
#> 5 never_married 2246 2 0.10 0.31 0.00 1.00
#> 6 grade 2244 16 13.10 2.52 0.00 18.00
#> 7 collgrad 2246 2 0.24 0.43 0.00 1.00
#> 8 south 2246 2 0.42 0.49 0.00 1.00
#> 9 smsa 2246 2 0.70 0.46 0.00 1.00
#> 10 c_city 2246 2 0.29 0.45 0.00 1.00
#> 11 industry 2232 12 8.19 3.01 1.00 12.00
#> 12 occupation 2237 13 4.64 3.41 1.00 13.00
#> 13 union 1878 2 0.25 0.43 0.00 1.00
#> 14 wage 2246 967 7.77 5.76 1.00 40.75
#> 15 hours 2242 62 37.22 10.51 1.00 80.00
#> 16 ttl_exp 2246 1546 12.53 4.61 0.12 28.88
#> 17 tenure 2231 259 5.98 5.51 0.00 25.92
#> Variables not shown: var_label (chr)
d(nlsw88) # shows variable labels, and whether value label exists for certain variables
#> Source: local data frame [17 x 6]
#>
#> var type class val_label label
#> (chr) (chr) (chr) (lgl) (chr)
#> 1 idcode int int FALSE NLS id
#> 2 age int int FALSE age in current year
#> 3 race int lbl TRUE race
#> 4 married int lbl TRUE married
#> 5 never_married int int FALSE never married
#> 6 grade int int FALSE current grade complete...
#> 7 collgrad int lbl TRUE college graduate
#> 8 south int int FALSE lives in south
#> 9 smsa int lbl TRUE lives in SMSA
#> 10 c_city int int FALSE lives in central city...
#> 11 industry int lbl TRUE industry
#> 12 occupation int lbl TRUE occupation
#> 13 union int lbl TRUE union worker
#> 14 wage dbl nmr FALSE hourly wage
#> 15 hours int int FALSE usual hours worked
#> 16 ttl_exp dbl nmr FALSE total work experience...
#> 17 tenure dbl nmr FALSE job tenure (years)
#> Variables not shown: head (chr)
# Note that there's a value label for the variable "race", thus we can checkout the values
labelled::val_labels(nlsw88$race)
#> white black other
#> 1 2 3
# Note also that if a variable label is too long, we can find out the whole label with
labelled::var_label(nlsw88$grade)
#> [1] "current grade completed"
# Or look at all the variable labels
labelled::var_label(nlsw88)
#> $idcode
#> [1] "NLS id"
#>
#> $age
#> [1] "age in current year"
#>
#> $race
#> [1] "race"
#>
#> $married
#> [1] "married"
#>
#> $never_married
#> [1] "never married"
#>
#> $grade
#> [1] "current grade completed"
#>
#> $collgrad
#> [1] "college graduate"
#>
#> $south
#> [1] "lives in south"
#>
#> $smsa
#> [1] "lives in SMSA"
#>
#> $c_city
#> [1] "lives in central city"
#>
#> $industry
#> [1] "industry"
#>
#> $occupation
#> [1] "occupation"
#>
#> $union
#> [1] "union worker"
#>
#> $wage
#> [1] "hourly wage"
#>
#> $hours
#> [1] "usual hours worked"
#>
#> $ttl_exp
#> [1] "total work experience"
#>
#> $tenure
#> [1] "job tenure (years)"
lookfor(nlsw88, "mar") # search for variables that are related to marriage
#> Source: local data frame [2 x 6]
#>
#> var type class val_label label head
#> (chr) (chr) (chr) (lgl) (chr) (chr)
#> 1 married int lbl TRUE married 0 0 0 1 1...
#> 2 never_married int int FALSE never married 0 0 1 0 0...
List Observations and Separated by Some ID Variable
library(dplyr)
abdata = haven::read_dta("http://www.stata-press.com/data/r13/abdata.dta")
abdata %>% select(id, year, wage) %>% l(by = "id", n = 30)
#> id year wage
#> 1 1 1977 13.1515998840332
#> 2 1 1978 12.3017997741699
#> 3 1 1979 12.8395004272461
#> 4 1 1980 13.8038997650146
#> 5 1 1981 14.2896995544434
#> 6 1 1982 14.8681001663208
#> 7 1 1983 13.7784004211426
#> 8
#> 9 2 1977 14.7909002304077
#> 10 2 1978 14.1035995483398
#> 11 2 1979 14.9533996582031
#> 12 2 1980 15.4910001754761
#> 13 2 1981 16.1968994140625
#> 14 2 1982 16.1313991546631
#> 15 2 1983 16.3050994873047
#> 16
#> 17 3 1977 22.6919994354248
#> 18 3 1978 20.6937999725342
#> 19 3 1979 21.2047996520996
#> 20 3 1980 22.19700050354
#> 21 3 1981 24.8714008331299
#> 22 3 1982 24.8446998596191
#> 23 3 1983 28.9076995849609
#> 24
#> 25 4 1977 14.8283004760742
#> 26 4 1978 14.8379001617432
#> 27 4 1979 14.875599861145
#> 28 4 1980 15.2332000732422
#> 29 4 1981 17.2528991699219
#> 30 4 1982 19.3141994476318
jjchern/meda documentation built on May 19, 2019, 11:38 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
About meda
This package provides R functions that replicates the behavior of four Stata commands: (1) d, (2) lookfor, (3) codebook, compact, and (4) l, sepby(). These commands present metadata records for datasets in a cleaned way.
The package is built upon two R packages: haven, which provides a labelled class that supports labelled data frames, and labelled, which makes it easier to work with labelled data frames.
Installation
Since the labelled package is still not up on CRAN, we might also install it as well.
devtools::install_github("larmarange/labelled")
devtools::install_github("jjchern/meda")
Usage
Describe and Look for Variables
library(meda)
nlsw88 = haven::read_dta("http://www.stata-press.com/data/r13/nlsw88.dta")
cb(nlsw88) # show summary statistics of a data frame
#> Source: local data frame [17 x 8]
#>
#> var obs unique mean std.dev min max
#> (chr) (int) (int) (dbl) (dbl) (dbl) (dbl)
#> 1 idcode 2246 2246 2612.65 1480.86 1.00 5159.00
#> 2 age 2246 13 39.15 3.06 34.00 46.00
#> 3 race 2246 3 1.28 0.48 1.00 3.00
#> 4 married 2246 2 0.64 0.48 0.00 1.00
#> 5 never_married 2246 2 0.10 0.31 0.00 1.00
#> 6 grade 2244 16 13.10 2.52 0.00 18.00
#> 7 collgrad 2246 2 0.24 0.43 0.00 1.00
#> 8 south 2246 2 0.42 0.49 0.00 1.00
#> 9 smsa 2246 2 0.70 0.46 0.00 1.00
#> 10 c_city 2246 2 0.29 0.45 0.00 1.00
#> 11 industry 2232 12 8.19 3.01 1.00 12.00
#> 12 occupation 2237 13 4.64 3.41 1.00 13.00
#> 13 union 1878 2 0.25 0.43 0.00 1.00
#> 14 wage 2246 967 7.77 5.76 1.00 40.75
#> 15 hours 2242 62 37.22 10.51 1.00 80.00
#> 16 ttl_exp 2246 1546 12.53 4.61 0.12 28.88
#> 17 tenure 2231 259 5.98 5.51 0.00 25.92
#> Variables not shown: var_label (chr)
d(nlsw88) # shows variable labels, and whether value label exists for certain variables
#> Source: local data frame [17 x 6]
#>
#> var type class val_label label
#> (chr) (chr) (chr) (lgl) (chr)
#> 1 idcode int int FALSE NLS id
#> 2 age int int FALSE age in current year
#> 3 race int lbl TRUE race
#> 4 married int lbl TRUE married
#> 5 never_married int int FALSE never married
#> 6 grade int int FALSE current grade complete...
#> 7 collgrad int lbl TRUE college graduate
#> 8 south int int FALSE lives in south
#> 9 smsa int lbl TRUE lives in SMSA
#> 10 c_city int int FALSE lives in central city...
#> 11 industry int lbl TRUE industry
#> 12 occupation int lbl TRUE occupation
#> 13 union int lbl TRUE union worker
#> 14 wage dbl nmr FALSE hourly wage
#> 15 hours int int FALSE usual hours worked
#> 16 ttl_exp dbl nmr FALSE total work experience...
#> 17 tenure dbl nmr FALSE job tenure (years)
#> Variables not shown: head (chr)
# Note that there's a value label for the variable "race", thus we can checkout the values
labelled::val_labels(nlsw88$race)
#> white black other
#> 1 2 3
# Note also that if a variable label is too long, we can find out the whole label with
labelled::var_label(nlsw88$grade)
#> [1] "current grade completed"
# Or look at all the variable labels
labelled::var_label(nlsw88)
#> $idcode
#> [1] "NLS id"
#>
#> $age
#> [1] "age in current year"
#>
#> $race
#> [1] "race"
#>
#> $married
#> [1] "married"
#>
#> $never_married
#> [1] "never married"
#>
#> $grade
#> [1] "current grade completed"
#>
#> $collgrad
#> [1] "college graduate"
#>
#> $south
#> [1] "lives in south"
#>
#> $smsa
#> [1] "lives in SMSA"
#>
#> $c_city
#> [1] "lives in central city"
#>
#> $industry
#> [1] "industry"
#>
#> $occupation
#> [1] "occupation"
#>
#> $union
#> [1] "union worker"
#>
#> $wage
#> [1] "hourly wage"
#>
#> $hours
#> [1] "usual hours worked"
#>
#> $ttl_exp
#> [1] "total work experience"
#>
#> $tenure
#> [1] "job tenure (years)"
lookfor(nlsw88, "mar") # search for variables that are related to marriage
#> Source: local data frame [2 x 6]
#>
#> var type class val_label label head
#> (chr) (chr) (chr) (lgl) (chr) (chr)
#> 1 married int lbl TRUE married 0 0 0 1 1...
#> 2 never_married int int FALSE never married 0 0 1 0 0...
List Observations and Separated by Some ID Variable
library(dplyr)
abdata = haven::read_dta("http://www.stata-press.com/data/r13/abdata.dta")
abdata %>% select(id, year, wage) %>% l(by = "id", n = 30)
#> id year wage
#> 1 1 1977 13.1515998840332
#> 2 1 1978 12.3017997741699
#> 3 1 1979 12.8395004272461
#> 4 1 1980 13.8038997650146
#> 5 1 1981 14.2896995544434
#> 6 1 1982 14.8681001663208
#> 7 1 1983 13.7784004211426
#> 8
#> 9 2 1977 14.7909002304077
#> 10 2 1978 14.1035995483398
#> 11 2 1979 14.9533996582031
#> 12 2 1980 15.4910001754761
#> 13 2 1981 16.1968994140625
#> 14 2 1982 16.1313991546631
#> 15 2 1983 16.3050994873047
#> 16
#> 17 3 1977 22.6919994354248
#> 18 3 1978 20.6937999725342
#> 19 3 1979 21.2047996520996
#> 20 3 1980 22.19700050354
#> 21 3 1981 24.8714008331299
#> 22 3 1982 24.8446998596191
#> 23 3 1983 28.9076995849609
#> 24
#> 25 4 1977 14.8283004760742
#> 26 4 1978 14.8379001617432
#> 27 4 1979 14.875599861145
#> 28 4 1980 15.2332000732422
#> 29 4 1981 17.2528991699219
#> 30 4 1982 19.3141994476318
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