data_dim | R Documentation |
This is a set of function are designed to help the user to deal with new data sets.
data_dim(data)
data_which_na(data)
data_names(data)
data_sorter_names(data, max = 5, newnames)
data |
a data frame |
max |
the maximum number of characters allowed |
newnames |
New names if not abbreviated required |
The function data_dim()
gives the dimensions and whether missing values exist.
The function data_which_na()
gives the number of missing observation for each variable in the data
The function data_names()
gives the names of the variables
The function data_sorter_names()
takes the current names and abbreviates to max
characters.
The function data_dim()
print the dimensions and whether missing values exist.
The function data_which_na()
prints the number of missing observation for each variable in the data
The function data_names()
prints the names of the variables
The function data_sorter_names()
takes the current names and abbreviates to max
characters and return a new data frame
Mikis Stasinopoulos, Bob Rigby and Fernanda De Bastiani
Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.
Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07/.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.
(see also https://www.gamlss.com/).
data_cor
data_dim(rent)
data_which_na(rent)
data_names(rent)
data_sorter_names(rent)
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