smplot: An R Package for Easy and Elegant Data Visualization

R, a programming language, is an attractive tool for data visualization because it is free and open source. However, learning R can be intimidating and cumbersome for many. In this report, we introduce an R package called “smplot” for easy and elegant data visualization. The R package “smplot” generates graphs with defaults that are visually pleasing and informative. Although it requires basic knowledge of R and ggplot2, it significantly simplifies the process of plotting a bar graph, a violin plot, a correlation plot, a slope chart, a Bland-Altman plot and a raincloud plot. The aesthetics of the plots generated from the package are elegant, highly customisable and adhere to important practices of data visualization. The functions from smplot can be used in a modular fashion, thereby allowing the user to further customise the aesthetics. The smplot package is open source under the MIT license and available on Github (https://github.com/smin95/smplot), where updates will be posted. All the example figures in this report are reproducible and the codes and data are provided for the reader in a separate online guide (https://smin95.github.io/dataviz/).

Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Autoregressive Integrated Moving Average (ARIMA)

There are various types of investment in Indonesia, one of which is the Indeks Harga Saham Gabungan (IHSG) or in English it is called the Indonesia Composite Index, ICI, or IDX Composite. IHSG is an important parameter to consider when making an investment considering that IHSG is a joint stock. This study aims to predict the price of the IHSG with data mining techniques using an algorithm that can be used as a reference for investors when making an investment. ARIMA is a model for generating estimates from historical data. Data in this study were collected from the monthly IHSG from January 4, 2010 - November 26, 2019. Based on the correlation plot, two autocorrelations (lag 1, lag 32) were found to be significant. This model can predict with an average percentage error of 0.004 so that this prediction is considered good enough to predict the stock price of the IHSG.

Time series forecasting for the adobe software company’s stock prices using ARIMA (BOX-JENKIN’) model

The time series forecasting strategy, Auto-Regressive Integrated Moving Average (ARIMA) model, is applied on the time series data consisting of Adobe stock prices, in order to forecast the future prices for a period of one year. ARIMA model is used due to its simple and flexible implementation for short term predictions of future stock prices. In order to achieve stationarity, the time series data requires second-order differencing. The comparison and parameterization of the ARIMA model has been done using auto-correlation plot, partial auto-correlation plot and auto.arima() function provided in R (which automatically finds the best fitting model based on the AIC and BIC values). The ARIMA (0, 2, 1) (0, 0, 2) [12] is chosen as the best fitting model, with a very less MAPE (Mean Absolute Percentage Error) of 3.854958%.

An intercomparison of CH₃O₂ measurements by fluorescence assay by gas expansion and cavity ring-down spectroscopy within HIRAC (Highly Instrumented Reactor for Atmospheric Chemistry)

Simultaneous measurements of CH3O2 radical concentrations have been performed using two different methods in the Leeds HIRAC (Highly Instrumented Reactor for Atmospheric Chemistry) chamber at 295 K and in 80 mbar of a mixture of 3:1 He∕O2 and 100 or 1000 mbar of synthetic air. The first detection method consisted of the indirect detection of CH3O2 using the conversion of CH3O2 into CH3O by excess NO with subsequent detection of CH3O by fluorescence assay by gas expansion (FAGE). The FAGE instrument was calibrated for CH3O2 in two ways. In the first method, a known concentration of CH3O2 was generated using the 185 nm photolysis of water vapour in synthetic air at atmospheric pressure followed by the conversion of the generated OH radicals to CH3O2 by reaction with CH4∕O2. This calibration can be used for experiments performed in HIRAC at 1000 mbar in air. In the second method, calibration was achieved by generating a near steady state of CH3O2 and then switching off the photolysis lamps within HIRAC and monitoring the subsequent decay of CH3O2, which was controlled via its self-reaction, and analysing the decay using second-order kinetics. This calibration could be used for experiments performed at all pressures. In the second detection method, CH3O2 was measured directly using cavity ring-down spectroscopy (CRDS) using the absorption at 7487.98 cm−1 in the A←X (ν12) band with the optical path along the ∼1.4 m chamber diameter. Analysis of the second-order kinetic decays of CH3O2 by self-reaction monitored by CRDS has been used for the determination of the CH3O2 absorption cross section at 7487.98 cm−1, both at 100 mbar of air and at 80 mbar of a 3:1 He∕O2 mixture, from which σCH3O2=(1.49±0.19)×10-20 cm2 molecule−1 was determined for both pressures. The absorption spectrum of CH3O2 between 7486 and 7491 cm−1 did not change shape when the total pressure was increased to 1000 mbar, from which we determined that σCH3O2 is independent of pressure over the pressure range 100–1000 mbar in air. CH3O2 was generated in HIRAC using either the photolysis of Cl2 with UV black lamps in the presence of CH4 and O2 or the photolysis of acetone at 254 nm in the presence of O2. At 1000 mbar of synthetic air the correlation plot of [CH3O2]FAGE against [CH3O2]CRDS gave a gradient of 1.09±0.06. At 100 mbar of synthetic air the FAGE–CRDS correlation plot had a gradient of 0.95±0.024, and at 80 mbar of 3:1 He∕O2 mixture the correlation plot gradient was 1.03±0.05. These results provide a validation of the FAGE method to determine concentrations of CH3O2.

Multi-lab intrinsic solubility measurement reproducibility in CheqSol and shake-flask methods

This commentary compares 233 CheqSol intrinsic solubility values (log S0) reported in the Wiki-pS0 database for 145 different druglike molecules to the 838 log S0 values determined mostly by the saturation shake-flask (SSF) method for 124 of the molecules from the CheqSol set. The range of log S0 spans from -1.0 to -10.6 (log molar units), averaging at -3.8. The correlation plot between the two methods indicates r2 = 0.96, RMSE = 0.34 log unit, and a slight bias of -0.07 log unit. The average interlaboratory standard deviation (SDi) is slightly better for the CheqSol set than that of the SSF set: SDiCS = 0.15 and SDiSSF = 0.24. The intralaboratory errors reported in the CheqSol method (0.05 log) need to be multiplied by a factor of 3 to match the expected interlaboratory errors for the method. The scale factor, in part, relates to the hidden systematic errors in the single-lab values. It is expected that improved standardizations in the ‘gold standard’ SSF method, as suggested in the recent ‘white paper’ on solubility measurement methodology, should make the SDi of both methods be about ~0.15 log unit. The multi-lab averaged log S0 (and the corresponding SDi) values could be helpful additions to existing training-set molecules used to predict the intrinsic solubility of drugs and druglike molecules.

Simulating Siberian Radioheliograph response to the quiet Sun

The Siberian Radioheliograph (SRH) correlation plot is the time dependence of the sum of absolute values of complex correlations over all baselines. These plots are built for each operating frequency of SRH. The correlation is related not only to the spatial coherence of the incident microwave emission but also to antenna gains. That is why we have to consider real SRH antenna gains and shadowings. Correlation plots obtained by SRH are related to microwave flux density of the Sun and spatial features of microwave sources. Also the correlation plots show variability of SRH beam pattern in time with constant flux density and spatial structure of sources. The SRH beam pattern depends on position of the Sun with respect to SRH, which changes with time. This leads to variations of these plots, which can be confused, for example, with the quasi-harmonic oscillations of the microwave flux produced by sources located above sunspots. Because the solar disk is an extended source, the correlation plot variability is mostly due to the SRH response to the quiet Sun. The smaller is the microwave source, the smaller are the correlation plot variations caused by a change of the beam pattern. Relatively fast variations result from long baseline responses, so it is undesirable to exclude them from the plots. Moreover, the sensitivity of the plots is better when all baselines are taken in account. The impact of the correlation plot variations on the eruptive event response is especially strong because variations of microwave flux during such events are comparable with those of the correlation plots in magnitude and time. From the above it seems reasonable to simulate the SRH response to the quiet solar disk and correct the correlation plots. In this work, we propose a method for simulating correlation plots, which allows us to correct their variations caused by time and frequency dependence of SRH response to the solar disk. The correlation plots are simulated either by summing all model antenna pair responses to the model solar disk or by summing the corresponding values of the solar disk visibility under the assumption that the visibility is ~J1(x)/x, where J1(x) is the Bessel function of the first kind. Also we consider the shadowing of antennas nearest to the center of the SRH antenna array.

Simulating Siberian Radioheliograph response to the quiet Sun

The Siberian Radioheliograph (SRH) correlation plot is the time dependence of the sum of absolute values of complex correlations over all baselines. These plots are built for each operating frequency of SRH. The correlation is related not only to the spatial coherence of the incident microwave emission but also to antenna gains. That is why we have to consider real SRH antenna gains and shadowings. Correlation plots obtained by SRH are related to microwave flux density of the Sun and spatial features of microwave sources. Also the correlation plots show variability of SRH beam pattern in time with constant flux density and spatial structure of sources. The SRH beam pattern depends on position of the Sun with respect to SRH, which changes with time. This leads to variations of these plots, which can be confused, for example, with the quasi-harmonic oscillations of the microwave flux produced by sources located above sunspots. Because the solar disk is an extended source, the correlation plot variability is mostly due to the SRH response to the quiet Sun. The smaller is the microwave source, the smaller are the correlation plot variations caused by a change of the beam pattern. Relatively fast variations result from long baseline responses, so it is undesirable to exclude them from the plots. Moreover, the sensitivity of the plots is better when all baselines are taken in account. The impact of the correlation plot variations on the eruptive event response is especially strong because variations of microwave flux during such events are comparable with those of the correlation plots in magnitude and time. From the above it seems reasonable to simulate the SRH response to the quiet solar disk and correct the correlation plots. In this work, we propose a method for simulating correlation plots, which allows us to correct their variations caused by time and frequency dependence of SRH response to the solar disk. The correlation plots are simulated either by summing all model antenna pair responses to the model solar disk or by summing the corresponding values of the solar disk visibility under the assumption that the visibility is ~J1(x)/x, where J1(x) is the Bessel function of the first kind. Also we consider the shadowing of antennas nearest to the center of the SRH antenna array.

First space-borne measurements of methanol inside aged southern tropical to mid-latitude biomass burning plumes using the ACE-FTS instrument

First measurements from space of upper tropospheric and lower stratospheric methanol profiles within aged fire plumes are reported. Elevated levels of methanol at 0–45° S from 30 September to 3 November 2004 have been measured by the high resolution infrared spectrometer ACE-FTS onboard the SCISAT satellite. Methanol volume mixing ratios higher than 4000 pptv are detected and are strongly correlated with other fire products such as CO, C2H6, and HCN. A sensitivity study of the methanol retrieval, accounting for random and systematic contributions, shows that the retrieved methanol profile for a single occultation exceeds 100% error above 16.5 km, with an accuracy of about 20% for measurements inside polluted air masses. The upper tropospheric enhancement ratio of methanol with respect to CO is estimated from the correlation plot between methanol and CO for aged tropical biomass burning plumes. This ratio is in good agreement with the ratio measured in the free troposphere (up to 12 km) by recent aircraft studies and does not suggest any secondary production of methanol by oxidation in aged biomass burning plumes.

First space-borne measurements of methanol inside aged tropical biomass burning plumes using the ACE-FTS instrument

First measurements from space of upper tropospheric and lower stratospheric methanol profiles within aged fire plumes are reported. Elevated levels of methanol at 0–45° S from 30 September to 3 November 2004 have been measured by the high resolution infrared spectrometer ACE-FTS onboard the SCISAT satellite. Methanol volume mixing ratios higher than 4000 pptv are detected and are strongly correlated with other fire products such as CO, C2H6, and HCN. A sensitivity study of the methanol retrieval, accounting for random and systematic contributions, shows that the retrieved methanol profile is reliable from 8.5 to 16.5 km, with an accuracy of about 20% for measurements inside polluted air masses. The upper tropospheric enhancement ratio of methanol with respect to CO is estimated from the correlation plot between methanol and CO for aged tropical biomass burning plumes. This ratio is in good agreement with the ratio measured in the free troposphere (up to 12 km) by recent aircraft studies and does not suggest any secondary production of methanol by oxidation in aged biomass burning plumes.