To install the latest version of inst.research from github:
# install.packages("devtools")
devtools::install_github("nietsnel/inst.research")bulk_import()A function to import multiple .txt or .csv files into R. (website example forthcoming).usm_labels()labels variables and raw values using one of three different methods.
usm_labels is a function to attach labels to a dataframe currently loaded in memory. This process can be facilitated using one of three methods: (1) by using the default MHEC labels included with the inst.research package; (2) by using custom user defined value labels in the R-console; or (3) by loading an external dataframe frame into R that contains the value-label pairings. These three methods are presented in the examples below.
The "inst.research" package includes an unlabeled "example_dataset" (?example_dataset for more info). To attach the default MHEC label pairings to this dataset follow the example below.
##### Example 1.
library(inst.research) #Attach the inst.research package
print(head(example_dataset, 12), row.names=FALSE) #View the example dataset.
# IDType Degree Gender UScitizen
# 2 60 2 1
# 2 60 2 2
# 2 40 2 1
# 3 81 2 1
# 2 81 1 2
# 2 81 1 2
# 2 60 1 1
# 3 81 2 1
# 1 40 1 2
# 1 40 2 1
# 1 60 2 2
# 2 40 2 2
usm_labels(dataset = example_dataset, label_values = TRUE, label_variables = TRUE)
##note that there are options to re-label the values and or the variables themselves.
##See usm_labels help to view all parameter options.
print(head(output_file, 12), row.names=FALSE) #View the example dataset with labels attached.
# IDType Degree Gender US Citizen
# 1 Doc. R/S Female Foreign
# 2 Doc. R/S Female US Citizen
# 2 Bachelors Male US Citizen
# 2 Masters Male Foreign
# 2 Bachelors Female Foreign
# 1 Masters Female US Citizen
# 1 Doc. R/S Female US Citizen
# 1 Doc. R/S Female Foreign
# 2 Bachelors Male US Citizen
# 2 Masters Male US Citizen
# 1 Doc. R/S Male Foreign
# 2 Bachelors Male US CitizenSecondly, value-label pairs can be written directly in R. This is useful when the list of value-label pairings is short. This method can utilized by following the simple formatting shown in the example below.
##### Example 2.
library(inst.research) #Attach the inst.research package
data_def<- c("var.name_IDType" , 1, "Student", 2, "faculty", 3, "staff",
"var.name_USCitizen", 1, "Yes", 2, "No",
"var.name_Gender", 1, "male", 2, "female",
"var.name_Degree", 40, "BA", 60, "MA", 81, "AA")
# Note: each variable name must follow the "var.name_" prefix. Secondly, each value (e.g., 1, 2, etc) must be
# paired with a label (eg., "student"). Once the variables have been defined, call the object using the
# **manual_label_input** parameter in the usm_labels function as shown below.
usm_labels(dataset=example_dataset, label_variables = FALSE, label_values=FALSE, manual_label_input=data_def)
##Attach user-defined labels to example dataset.
print(head(output_file, 12), row.names=FALSE) #View the example dataset with labels attached.
# IDType Degree Gender UScitizen
# Student AA female 2
# faculty AA female 1
# faculty BA male 1
# faculty MA male 2
# faculty BA female 2
# Student MA female 1
# Student AA female 1
# Student AA female 2
# faculty BA male 1
# faculty MA male 1
# Student AA male 2
# faculty BA male 1A dataframe containing value-label pairs can also be used for relabeling. This is useful when there are a large amount of value-label pairings stored in an external file (e.g, comma separated file.)
The value-label pairings must be in the following format.
"Degree", "86", "Doc. Other",
"Degree", "87", "Non-Deg Grad",
"Degree", "99", "Multi Major",
"DependStatus", "0", "Unknown",
"DependStatus", "1", "Dependent",
"DependStatus", "2", "Independent",
"DistEdFlag", "1", "Exclusively",
"DistEdFlag", "2", "Some",
"Gender", "1", "Male",
"Gender, "2", "Female"
Note: Each line must begin with the variable name corresponding to the value-label pair.
The "inst.research" package includes an unlabeled "example_dataset" (?example_dataset for more info) which we can combine with a second included dataset called "example_external_labels". You can try this process using the procedure shown in the following example.
##### Example 3.
##### Step 1.
# load the inst.research package and import your value-label pairings into R (e.g., read_csv()). Because
# inst.research contains an example labels dataframe this step can be skipped. You can also view both of the
# example datasets using the print() function.
library(inst.research) ##Attach inst.research library
print(head(example_dataset, 12), row.names=FALSE) #View the example dataset.
# IDType Degree Gender UScitizen
# 2 60 2 1
# 2 60 2 2
# 2 40 2 1
# 3 81 2 1
# 2 81 1 2
# 2 81 1 2
# 2 60 1 1
# 3 81 2 1
# 1 40 1 2
# 1 40 2 1
# 1 60 2 2
# 2 40 2 2
print(example_external_labels, row.names=FALSE) #View the example external labels.
# V1 V2 V3
# Degree 40 BA
# Degree 60 MA
# Degree 81 AA
# DependStatus 0 Unknown
# DependStatus 1 Dependent
# DependStatus 2 Independent
# DistEdFlag 1 Exclusively
# DistEdFlag 2 Some
# Gender 1 Male
# Gender 2 Female
# UScitizen 1 Yes
# UScitizen 2 No
##### Step 2.
# Label the example_dataset using the usm_labels() function.
usm_labels(dataset=example_dataset, label_variables = FALSE, label_values=FALSE,
label_matrix=example_external_labels)
# You can then view the results below.
print(head(output_file, 15), row.names=FALSE) #View the example dataset..
# IDType Degree Gender UScitizen
# 1 AA Female No
# 2 AA Female Yes
# 2 BA Male Yes
# 2 MA Male No
# 2 BA Female No
# 1 MA Female Yes
# 1 AA Female Yes
# 1 AA Female No
# 2 BA Male Yes
# 2 MA Male Yes
# 1 AA Male No
# 2 BA Male Yes
# 3 AA Female Yes
# 2 BA Male Yes
# 3 BA Male No