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.
rsqliteadmin::run_rsqliteadmin()
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.
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
> 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 created by the above code is shown below:
