Tests for the project "RSQLiteAdmin Visualisation Tools" in Google Summer of Code 2021

1. Easy Test

Problem Statement

Install the package rsqliteadmin from CRAN and play around with all the features. Create a database, add a table and import some data. Edit, search and export it.

Run the app.

Code

rsqliteadmin::run_rsqliteadmin()

Screenshots

Following are the screenshots of databas and tables I have created in RSQLiteAdmin

We can import the database by setting the database directory to the folder containing the sqlite db. Following are screenshots of a digital media store DB I downloaded from https://www.sqlitetutorial.net/sqlite-sample-database/ and have imported in RSqliteAdmin.

2. Medium Test 1

Problem Statement

Plot a time-series line chart on a dataset of your choice with different variables in a single chart. Customize it to make it clear and insightful.
In this test I have used the US economics time series dataset. Initially I have loaded the ggplot2 and set the minimalistic theme

Code

> library(ggplot2)
> theme_set(theme_minimal())

> head(economics)
# A tibble: 6 x 6
  date         pce    pop psavert uempmed
  <date>     <dbl>  <dbl>   <dbl>   <dbl>
1 1967-07-01  507. 198712    12.6     4.5
2 1967-08-01  510. 198911    12.6     4.7
3 1967-09-01  516. 199113    11.9     4.6
4 1967-10-01  512. 199311    12.9     4.9
5 1967-11-01  517. 199498    12.8     4.7
6 1967-12-01  525. 199657    11.8     4.8
# … with 1 more variable: unemploy <dbl>

Using the tidyverse package to prepare the data and gather the two variables ‘psavert’ and ‘uempmed’ into key-value pairs.

library("tidyverse")
> # For data preparation
> library("tidyverse")
── Attaching packages ──────── tidyverse 1.3.0 ──
✓ tibble  3.1.0     ✓ dplyr   1.0.5
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.1
✓ purrr   0.3.4     
── Conflicts ─────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
> df <- economics %>%
+     select(date, psavert, uempmed) %>%
+     gather(key = "variables", value = "value", -date)
> head(df)
# A tibble: 6 x 3
  date       variables value
  <date>     <chr>     <dbl>
1 1967-07-01 psavert    12.6
2 1967-08-01 psavert    12.6
3 1967-09-01 psavert    11.9
4 1967-10-01 psavert    12.9
5 1967-11-01 psavert    12.8
6 1967-12-01 psavert    11.8

Visualise the data showing personal savings rate and median duration of unemployment throughout the years using geom_line() from ggplot2 package and customize them using scale_color_manual() and ggtitle() to further add data labels and titles to the plot respectively.

> # Data Visualization
> ggplot(df, aes(x = date, y = value)) + 
+     geom_line(aes(color = variables)) + 
+     scale_color_manual(values = c("yellow", "green")) + ggtitle("Yearly plot showing personal savings rate and median duration of unemployment") 

Plot

Plot created by the above code is shown below:

3. Medium Test 2

Problem Statement

Code

4. Hard Test

Problem Statement

Code

Output