The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
'Google Trends' provides cross-sectional and time-series data on searches, but lacks readily available longitudinal data. Researchers, who want to create longitudinal 'Google Trends' on their own, face practical challenges, such as normalized counts that make it difficult to combine cross-sectional and time-series data and limitations in data formats and timelines that limit data granularity over extended time periods. This package addresses these issues and enables researchers to generate longitudinal 'Google Trends' data. This package is built on 'pytrends', a Python library that acts as the unofficial 'Google Trends API' to collect 'Google Trends' data. As long as the 'Google Trends API', 'pytrends' and all their dependencies are working, this package will work. During testing, we noticed that for the same input (keyword, topic, data_format, timeline), the output index can vary from time to time. Besides, if the keyword is not very popular, then the resulting dataset will contain a lot of zeros, which will greatly affect the final result. While this package has no control over the accuracy or quality of 'Google Trends' data, once the data is created, this package coverts it to longitudinal data. In addition, the user may encounter a 429 Too Many Requests error when using cross_section() and time_series() to collect 'Google Trends' data. This error indicates that the user has exceeded the rate limits set by the 'Google Trends API'. For more information about the 'Google Trends API' - 'pytrends', visit <https://pypi.org/project/pytrends/>.
| Version: | 0.1.4 |
| Imports: | lubridate, jsonlite, reticulate, utils |
| Suggests: | knitr, rmarkdown |
| Published: | 2024-09-17 |
| DOI: | 10.32614/CRAN.package.PytrendsLongitudinalR |
| Author: | Taeyong Park [cre, cph, aut], Malika Dixit [aut] |
| Maintainer: | Taeyong Park <taeyongp at andrew.cmu.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| SystemRequirements: | Python (>= 3.7), pip (for installing Python packages), pytrends, pandas, requests |
| Materials: | README |
| CRAN checks: | PytrendsLongitudinalR results |
| Reference manual: | PytrendsLongitudinalR.pdf |
| Vignettes: |
PytrendsLongitudinalR (source, R code) |
| Package source: | PytrendsLongitudinalR_0.1.4.tar.gz |
| Windows binaries: | r-devel: PytrendsLongitudinalR_0.1.4.zip, r-release: PytrendsLongitudinalR_0.1.4.zip, r-oldrel: PytrendsLongitudinalR_0.1.4.zip |
| macOS binaries: | r-release (arm64): PytrendsLongitudinalR_0.1.4.tgz, r-oldrel (arm64): PytrendsLongitudinalR_0.1.4.tgz, r-release (x86_64): PytrendsLongitudinalR_0.1.4.tgz, r-oldrel (x86_64): PytrendsLongitudinalR_0.1.4.tgz |
| Old sources: | PytrendsLongitudinalR archive |
Please use the canonical form https://CRAN.R-project.org/package=PytrendsLongitudinalR to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.