library(haven)
library(readr)
library(writexl)
library(tidyverse)
library(gt)

1.5 Export datasets

Upon completing data cleaning and pre-processing, the subsequent step involves exporting the DataFrame to a file. In this demonstration, we showcase the simplicity of exporting by covering the following methods:

  • 1.5.1 Export to CSV

  • 1.5.2 Export to Excel

  • 1.5.3 Export to Stata

  • 1.5.4 Export to SPSS

This practical guide will equip you with the skills to export your processed data to various file formats, ensuring accessibility and compatibility for future analyses or sharing.

1.5.1 Export to CSV

Utilize the write_csv() function from the readr library to export a DataFrame from R to a CSV formatted file.

df = mtcars
write_csv(x = df, file = "K:/data1.csv")

The df data frame will be saved on local drive K.

1.5.2 Export to Excel

In this Section, we show how to following:

  • 1.5.2.1 Export a single DataFrame

  • 1.5.2.2 Export multiple DataFrames to separate Excel worksheet

1.5.2.1 Export a single DataFrame

write_xlsx(
    x = df,
    path = "K:/data2.xlsx"
)

The df data frame will be saved on local drive K.

1.5.2.2 Export multiple DataFrames to separate Excel worksheet

Load a dataset set to use for illustration.

df = readRDS(file = "~/STEMResearch/datasets/hh_income.RDS")
gt(df)
id race gender married education age income expenditure ses
1 White Female Yes Masters 32 528.87 417.77 High
2 Other Male Yes Bachelors 46 422.02 501.42 High
3 White Female No Doctoral 43 466.81 336.07 Middle
4 Black Female No Masters 35 598.32 454.08 Low
5 Black Male Yes Bachelors 36 510.16 340.33 Middle
6 Other Female Yes Doctoral 44 517.48 372.84 Middle
7 White Male Yes Bachelors 30 399.02 348.82 Low
8 Black Male No Bachelors 42 546.79 393.82 Low
9 Other Female Yes Masters 30 467.69 460.62 Low
10 Black Male Yes Doctoral 28 551.13 353.95 High

Let us begin by creating two different subset data frames of female and male respondents from the above df dataframe.

df_female <- df %>% 
    filter(gender == "Female")
gt(df_female)
id race gender married education age income expenditure ses
1 White Female Yes Masters 32 528.87 417.77 High
3 White Female No Doctoral 43 466.81 336.07 Middle
4 Black Female No Masters 35 598.32 454.08 Low
6 Other Female Yes Doctoral 44 517.48 372.84 Middle
9 Other Female Yes Masters 30 467.69 460.62 Low
 
df_male <- df %>% 
    filter(gender == "Male")
gt(df_male)
id race gender married education age income expenditure ses
2 Other Male Yes Bachelors 46 422.02 501.42 High
5 Black Male Yes Bachelors 36 510.16 340.33 Middle
7 White Male Yes Bachelors 30 399.02 348.82 Low
8 Black Male No Bachelors 42 546.79 393.82 Low
10 Black Male Yes Doctoral 28 551.13 353.95 High

Make a list of the 3 DataFrames df, df_female, df_male created above.

dfs <- list(
    "All" = df,
    "Female" = df_female,
    "Male" = df_male
)

Now export the DataFrames specified in the dfs list above

write_xlsx(
    x = dfs,
    path = "K:/hh_income3.xlsx"
)

The three data frames df, df_female and df_male will be saved on local drive K. Each of these data frames will appear as different worksheets of the hh_income3.xlsx workbook .

1.5.3 Export to Stata

Efficiently exporting DataFrames to Stata is accomplished by utilizing the write_dta() function available in the haven library.

write_dta(
    data = df,
    path = "K:/hh_income.dta"
)

The df data frame will be saved on local drive K.

1.5.4 Export to SPSS

In addition to its functionality for Stata export, the haven library includes the write_sav() function, specifically designed for exporting DataFrames to SPSS format.

write_sav(
    data = df,
    path = "K:/hh_income.sav"
)

The df data frame will be saved on local drive K.