Mobile apps are distributed via online markets allowing practitioners to reach users worldwide; on the other side, users select what apps are more suitable for their preferences from a large set of apps offering similar features and capabilities. To facilitate that selection process, the distribution markets have different mechanisms, such as comments, ratings, and top-listed apps, including a curated list. As it is well known, apps stores metadata can provide insights for new, popular features or fixing existing bugs, as reported in previous works. However, to the best of our knowledge, app store data have not been used to identify possible predominant characteristics of successful apps using as a reference the aforementioned top lists. Thus, in this paper, we present a study that analyzes the metadata of apps belonging to Google Play top lists during 30 weeks in 4 countries to distill features of successful apps. Unfortunately, our results suggest that apps store metadata from top list apps do not provide enough information to identify those features.
In this repository, you can find all notebooks used to perform this analysis: Crème de la crème repository
Here you can find the main highlights of each RQs, including some results for not imputed and imputed data.
The following table presents the variables we selected in order to determine their prevalent characteristics:
| Type | Variable |
|---|---|
| Categorical | Content Rating |
| Android Version | |
| What's New | |
| Numerical | Name Length |
| Summary Length | |
| Description Length | |
| Rating | |
| 1 Star | |
| 2 Stars | |
| 3 Stars | |
| 4 Stars | |
| 5 Stars | |
| Days since last update | |
| Price (USD) | |
| Number of Installs |
We computed the predominant values for the general dataset, grouping by country, by top-lists and by top-lists and countries. Here we present some of them.
The following figure illustrates the possible values of Content Rating for the dataset without imputations. We found the predominant value is Everyone:
However, when we observed the dataset with imputations, we didn't find different predominant values, as presented above:
| LOCF | MFO |
|---|---|
 |
 |
After analyzing the dataset in general, we grouped it by countries to found whether there were differences between Brazil, Colombia, Germany and USA. As presented in the following figure, we found Everyone as the predominant content rating for Colombia and US, USK:All Ages for Germany and All Ages for Brazil:
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{"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Content Rating"}, "updatemenus": [{"buttons": [{"args": [{"visible": [true, true, true, true]}], "label": "All", "method": "update"}, {"args": [{"visible": [true, false, false, false]}], "label": "br", "method": "update"}, {"args": [{"visible": [false, true, false, false]}], "label": "co", "method": "update"}, {"args": [{"visible": [false, false, true, false]}], "label": "de", "method": "update"}, {"args": [{"visible": [false, false, false, true]}], "label": "us", "method": "update"}], "direction": "down", "pad": {"r": 10, "t": 10}, "showactive": true, "x": 0.1, 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-
Content Rating, grouped by country, for dataset with LOCF imputation: Content Rating LOCF
-
Content Rating, grouped by country, for dataset with MFO imputation: Content Rating MFO
Likewise, when analyzing by top-lists, we analyzed if Top free, Top selling and the Editor choice had differences. However, we found the predominant content rating is Everyone for all top-lists, as depicted in the following figure:
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"update"}, {"args": [{"visible": [false, true, false]}], "label": "topFree", "method": "update"}, {"args": [{"visible": [false, false, true]}], "label": "topSelling", "method": "update"}], "direction": "down", "pad": {"r": 10, "t": 10}, "showactive": true, "x": 0.1, "xanchor": "left", "y": 1.1, "yanchor": "top"}]}, {"responsive": true} ) }; </script>Additionally, we found the same predominant content rating when using the dataset with imputations:
Note: It's important to note that, for the datasets with imputations, we had to remove the information related with Colombia and Editors' Choice for the analysis related with top-lists. We had to remove that data because the data is missing for more than three weeks.
-
Content Rating, grouped by top, for dataset with LOCF imputation, without Colombia: Content Rating LOCF
-
Content Rating, grouped by top, for dataset with LOCF imputation, without Editors' Choice: Content Rating LOCF
-
Content Rating, grouped by top, for dataset with MFO imputation, without Colombia: Content Rating MFO
-
Content Rating, grouped by top, for dataset with MFO imputation, without Editors' Choice: Content Rating MFO
In conclusion, no matter if we are analyzing Top Free, Top selling or Editor choice, the predominant content rating is Everyone.
Finally, we grouped by country and top-lists. As depicted in the following figure, we found the predominant value is Everyone for all the pairs containing Colombia or USA, USK: All Ages for all pairs with Germany and All Ages for all pairs with Brazil.
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true]}], "label": "All", "method": "update"}, {"args": [{"visible": [true, false, false, false, false, false, false, false, false, false, false, false]}], "label": "('editorChoice', 'br')", "method": "update"}, {"args": [{"visible": [false, true, false, false, false, false, false, false, false, false, false, false]}], "label": "('editorChoice', 'co')", "method": "update"}, {"args": [{"visible": [false, false, true, false, false, false, false, false, false, false, false, false]}], "label": "('editorChoice', 'de')", "method": "update"}, {"args": [{"visible": [false, false, false, true, false, false, false, false, false, false, false, false]}], "label": "('editorChoice', 'us')", "method": "update"}, {"args": [{"visible": [false, false, false, false, true, false, false, false, false, false, false, false]}], "label": "('topFree', 'br')", "method": "update"}, {"args": [{"visible": [false, false, false, false, false, true, false, false, false, false, false, false]}], "label": "('topFree', 'co')", "method": "update"}, {"args": [{"visible": [false, false, false, false, false, false, true, false, false, false, false, false]}], "label": "('topFree', 'de')", "method": "update"}, {"args": [{"visible": [false, false, false, false, false, false, false, true, false, false, false, false]}], "label": "('topFree', 'us')", "method": "update"}, {"args": [{"visible": [false, false, false, false, false, false, false, false, true, false, false, false]}], "label": "('topSelling', 'br')", "method": "update"}, {"args": [{"visible": [false, false, false, false, false, false, false, false, false, true, false, false]}], "label": "('topSelling', 'co')", "method": "update"}, {"args": [{"visible": [false, false, false, false, false, false, false, false, false, false, true, false]}], "label": "('topSelling', 'de')", "method": "update"}, {"args": [{"visible": [false, false, false, false, false, false, false, false, false, false, false, true]}], "label": "('topSelling', 'us')", "method": "update"}], "direction": "down", "pad": {"r": 10, "t": 10}, "showactive": true, "x": 0.1, "xanchor": "left", "y": 1.1, "yanchor": "top"}]}, {"responsive": true} ) }; </script>However, when analyzing the dataset with imputations as can be observed here:
Note: It's important to note that, for the datasets with imputations, we had to remove the information related with Colombia and Editors' Choice for the analysis related with top-lists. We had to remove that data because the data is missing for more than three weeks.
-
Content Rating, grouped by country and top, for dataset with LOCF imputation, without Colombia: Content Rating LOCF
-
Content Rating, grouped by country and top, for dataset with LOCF imputation, without Editors' Choice: Content Rating LOCF
-
Content Rating, grouped by country and top, for dataset with MFO imputation, without Colombia: Content Rating MFO
-
Content Rating, grouped by country and top, for dataset with MFO imputation, without Editors' Choice: Content Rating MFO
In consequence, even when the behavior is the same for the rest of the groups, the group for Colombia and Editor choice is missing, in consequence, we cannot conclude that Everyone, USK:All Ages and All Ages are the predominant for all groups.
Regarding Android Version, the following figures illustrate the possible values for the dataset without imputations, with LOCF imputation and MFO imputation:
| LOCF | MFO |
|---|---|
 |
 |
It is possible to observe that the predominant Android Version for all the dataset is Ice Cream Sandwich and up, followed by Jelly Bean and up and, finally, Varies with device. It doesn't matter if the dataset has imputations or not, predominant values are the same.
The following figure depicts Android Version grouped by country:
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"ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Android Versions"}, "updatemenus": [{"buttons": [{"args": [{"visible": [true, true, true, true]}], "label": "All", "method": "update"}, {"args": [{"visible": [true, false, false, false]}], "label": "br", "method": "update"}, {"args": [{"visible": [false, true, false, false]}], "label": "co", "method": "update"}, {"args": [{"visible": [false, false, true, false]}], "label": "de", "method": "update"}, {"args": [{"visible": [false, false, false, true]}], "label": "us", "method": "update"}], "direction": "down", "pad": {"r": 10, "t": 10}, "showactive": true, "x": 0.1, "xanchor": "left", "y": 1.1, "yanchor": "top"}]}, {"responsive": true} ) }; </script>However, all countries have the same predominant version, Ice Cream Sandwich and up. Similarly, when using the datasets with imputations, we didn't find another predominant version, as can be found here:
-
Android Version, grouped by country, for dataset with LOCF imputation: Android Version LOCF
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Android Version, grouped by country, for dataset with MFO imputation: Android Version MFO
Android Version holds the same behavior, as the general dataset, for Top free and Top selling. However, Editor choice differs, because its prevalent version is Varies with device, as presented in the following figure:
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script> <script type="text/javascript"> window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById("9a4fe59f-db72-49b9-be1b-d95edd78b9e9")) { Plotly.newPlot( '9a4fe59f-db72-49b9-be1b-d95edd78b9e9', [{"name": "editorChoice", "orientation": "h", "type": "bar", "x": [2023, 1465, 784, 447, 143, 95, 64, 41, 38], "y": ["Varies with device", "Jelly Bean and up", "Ice Cream Sandwich and up", "KitKat and up", "Gingerbread and up", "Lollipop and up", "Honeycomb and up", "Froyo and up", null]}, {"name": "topFree", "orientation": "h", "type": "bar", "x": [125934, 120490, 57766, 32711, 24450, 15323, 8484, 2915, 1952, 1169, 816, 597, 421, 362, 23, 11], "y": ["Ice Cream Sandwich and up", "Jelly Bean and up", "Varies with device", "KitKat and up", "Gingerbread and up", "Lollipop and up", "Honeycomb and up", "Froyo and up", "Eclair and up", null, "Marshmallow and up", "Donut and up", "Cupcake and up", "Nougat and up", "Oreo and up", "Unnamed and up"]}, {"name": "topSelling", "orientation": "h", "type": "bar", "x": [81165, 54571, 29424, 29171, 17919, 16534, 11122, 10768, 9801, 5937, 2259, 1800, 1225, 565, 131, 125], "y": ["Ice Cream Sandwich and up", "Jelly Bean and up", "Varies with device", "Gingerbread and up", "KitKat and up", "Froyo and up", "Eclair and up", "Honeycomb and up", "Lollipop and up", "Donut and up", "Nougat and up", "Cupcake and up", "Marshmallow and up", null, "Oreo and up", "Unnamed and up"]}], {"template": {"data": {"bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "bar"}], "barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "barpolar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "histogram": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "histogram"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Android Versions"}, "updatemenus": [{"buttons": [{"args": [{"visible": [true, true, true]}], "label": "All", "method": "update"}, {"args": [{"visible": [true, false, false]}], "label": "editorChoice", "method": "update"}, {"args": [{"visible": [false, true, false]}], "label": "topFree", "method": "update"}, {"args": [{"visible": [false, false, true]}], "label": "topSelling", "method": "update"}], "direction": "down", "pad": {"r": 10, "t": 10}, "showactive": true, "x": 0.1, "xanchor": "left", "y": 1.1, "yanchor": "top"}]}, {"responsive": true} ) }; </script>In the same way, datasets with imputations keep the same prevalent android version for Top free and Top selling, and Varies with device for Editor choice:
Note: It's important to note that, for the datasets with imputations, we had to remove the information related with Colombia and Editors' Choice for the analysis related with top-lists. We had to remove that data because the data is missing for more than three weeks.
-
Android Version, grouped by top, for dataset with LOCF imputation, without Colombia: Android Version LOCF
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Android Version, grouped by top, for dataset with LOCF imputation, without Editors' Choice: Android Version LOCF
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Android Version, grouped by top, for dataset with MFO imputation, without Colombia: Android Version MFO
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Android Version, grouped by top, for dataset with MFO imputation, without Editors' Choice: Android Version MFO
When grouping by top-lists and country, we found they hold the behavior of top-lists, thus, all pairs containing Editor choice have Varies with device as predominant version, while the rest have Ice Cream Sandwich and up, as represented in the following figure:
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script> <script type="text/javascript"> window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById("f00b8f86-e098-4f41-9679-447919694b38")) { Plotly.newPlot( 'f00b8f86-e098-4f41-9679-447919694b38', [{"name": "('editorChoice', 'br')", "orientation": "h", "type": "bar", "x": [560, 355, 246, 76, 29, 20, 13, 5], "y": ["Varies with device", "Jelly Bean and up", "Ice Cream Sandwich and up", "KitKat and up", "Gingerbread and up", "Lollipop and up", null, "Froyo and up"]}, {"name": "('editorChoice', 'co')", "orientation": "h", "type": "bar", "x": [403, 364, 172, 102, 42, 32, 18, 15, 5], "y": ["Varies with device", "Jelly Bean and up", "Ice Cream Sandwich and up", "KitKat and up", "Gingerbread and up", "Honeycomb and up", "Froyo and up", "Lollipop and up", null]}, {"name": "('editorChoice', 'de')", "orientation": "h", "type": "bar", "x": [551, 344, 194, 109, 45, 30, 16], "y": ["Varies with device", "Jelly Bean and up", "Ice 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device as predominant variable. However, for all the groups with the rest of top lists, we can conclude that the predominant version is the same.
Note: It's important to note that, for the datasets with imputations, we had to remove the information related with Colombia and Editors' Choice for the analysis related with top-lists. We had to remove that data because the data is missing for more than three weeks.
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Android Version, grouped by top and country, for dataset with LOCF imputation, without Colombia: Android Version LOCF
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Android Version, grouped by top and country, for dataset with LOCF imputation, without Editors' Choice: Android Version LOCF
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Android Version, grouped by top and country, for dataset with MFO imputation, without Colombia: Android Version MFO
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Android Version, grouped by top and country, for dataset with MFO imputation, without Editors' Choice: Android Version MFO
The following figure illustrates the possible values of What's new for the dataset without imputations. We found 63.9% of the apps have description of changes:
It is worth noting that the same percentage holds for LOCF imputation and MFO imputation.
When grouping by country, we found USA have a higher percentage of apps with description of changes, followed by Germany, Brazil and Colombia, as depicted in the following image:
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"zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Has Whats New"}, "updatemenus": [{"buttons": [{"args": [{"visible": [true, false, false, false]}], "label": "br", "method": "update"}, {"args": [{"visible": [false, true, false, false]}], "label": "co", "method": "update"}, {"args": [{"visible": [false, false, true, false]}], "label": "de", "method": "update"}, {"args": [{"visible": [false, false, false, true]}], "label": "us", "method": "update"}], "direction": "down", "pad": {"r": 10, "t": 10}, "showactive": true, "x": 0.1, "xanchor": "left", "y": 1.1, "yanchor": "top"}]}, {"responsive": true} ) }; </script>Likewise, when using the datasets with imputations, we found the same order of percentages:
-
What's new, grouped by country, for dataset with LOCF imputation: What's New LOCF
-
What's new, grouped by country, for dataset with MFO imputation: What's New MFO
We grouped by top-list and noticed that Editor choice has a higher percentage of apps with description of changes, in the range of 80%, while Top free and Top selling keep a percentage between 60% and 70%, as represented in the following figure:
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script> <script type="text/javascript"> window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById("2d7ad537-a824-4958-b7b8-faf4f8663acd")) { Plotly.newPlot( '2d7ad537-a824-4958-b7b8-faf4f8663acd', [{"labels": [true, false], "name": "editorChoice", "type": "pie", "values": [4136, 964]}, {"labels": [true, false], "name": "topFree", "type": "pie", "values": [242187, 151237]}, {"labels": [true, false], "name": "topSelling", "type": "pie", "values": [182742, 89775]}], {"template": {"data": {"bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "bar"}], "barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "barpolar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": 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[{"args": [{"visible": [true, false, false]}], "label": "editorChoice", "method": "update"}, {"args": [{"visible": [false, true, false]}], "label": "topFree", "method": "update"}, {"args": [{"visible": [false, false, true]}], "label": "topSelling", "method": "update"}], "direction": "down", "pad": {"r": 10, "t": 10}, "showactive": true, "x": 0.1, "xanchor": "left", "y": 1.1, "yanchor": "top"}]}, {"responsive": true} ) }; </script>Likewise, when using the datasets with imputations, we found the same order of percentages, being Editor choice the highest followed by Top selling and finally Top free:
Note: It's important to note that, for the datasets with imputations, we had to remove the information related with Colombia and Editors' Choice for the analysis related with top-lists. We had to remove that data because the data is missing for more than three weeks.
-
What's new, grouped by country, for dataset with LOCF imputation, without Colombia: What's New LOCF
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What's new, grouped by country, for dataset with LOCF imputation, without Editors' Choice: What's New LOCF
-
What's new, grouped by country, for dataset with MFO imputation, without Colombia: What's New MFO
-
What's new, grouped by country, for dataset with MFO imputation, without Editors' Choice: What's New MFO
Finally, we compared pairs of country and tops lists and found out that, again, all pairs containing Editor choice had higher percentages, but the order of the countries changed. However, Colombia and Editor choice was removed, in consequence, we cannot determine which group has the highest value.
Regarding pairs with Top free, the order of the countries remained. But, for pairs with Top selling, US had the highest percentage, followed by Colombia, then Brazil and finally, Germany.
When analyzing the distribution of rating for countries and computing the effect size, we found out all have large effect size, which means, between countries there are significant differences. The following figure illustrates the distribution of rating grouped by countries:
In addition, when we observed the dataset with imputations, we found out that they also have significant differences between countries:
| LOCF | MFO |
|---|---|
 |
 |
Concerning groups of top-lists, we found out that the Editor choice only have apps with more than 4.0:
The same behavior holds for datasets with imputations, however, results differ for some imputations because Editor choice was removed:
| LOCF | MFO |
|---|---|
| Deleting CO records for the three tops | Deleting CO records for the three tops |
 |
 |
| Analyzing Top free and Top Selling only | Analyzing Top free and Top Selling only |
 |
 |
Regarding groups of countries and top-lists, we found out that the same behavior of Editor choice remains for all the pairs containing Editor choice:
It is interesting to highlight that Colombia and USA have similar distributions, while Brazil and Germany are different from Colombia and USA, but similar between them.
In addition, when using the datasets with imputations, but the same behavior of countries and top-lists holds, except in the case where Editor Choice was removed:
| LOCF | MFO |
|---|---|
| Deleting CO records for the three tops | Deleting CO records for the three tops |
 |
 |
| Analyzing Top free and Top Selling only | Analyzing Top free and Top Selling only |
 |
 |
The following figures present the distribution of number of installs divided by country:
All countries seem to have the same distribution, however, when computing the effect size, we found out that all countries have large differences between them. Which means, they have different distribution and behavior, but it cannot be seen clearly since there are apps with high number of installs.
Nevertheless, when observing number of installs for the datasets with imputations, behavior was the same, with large differences between countries:
| LOCF | MFO |
|---|---|
 |
 |
For top-lists, we noticed different behavior between tops, as the following figure depicts:
It can be observed that the Editor choice have a higher number of installs, followed by Top free and, finally, Top selling. This means, apps beloging to the Editor choice list have more number of installs with respect to apps belonging to Top free or Top selling.
Similarly, datasets with imputations present the same behavior, with the exception of the dataset where Editor Choice was removed:
| LOCF | MFO |
|---|---|
| Deleting CO records for the three tops | Deleting CO records for the three tops |
 |
 |
| Analyzing Top free and Top Selling only | Analyzing Top free and Top Selling only |
 |
 |
Finally, when grouping by country and top-lists, we saw that the predominant behavior is the one from top-lists, found previously, with Editor choice with the highest value:
We would expect that Colombia and Brazil had similar behavior, since both countries belong to South America. However, it is also to note that Colombia and USA again have a similar behavior between them, as well as Germany and Brazil. This could be because Colombia and USA belong to the same content rating classification.
On the other hand, when looking at the datasets with imputations, almost all the groups had the same behavior as the dataset without imputations, with the exception of Colombia and Editor Choice:
| LOCF | MFO |
|---|---|
| Deleting CO records for the three tops | Deleting CO records for the three tops |
 |
 |
| Analyzing Top free and Top Selling only | Analyzing Top free and Top Selling only |
 |
 |
Main highlights: The majority of apps in top lists at GP belong to content rating categories suitable for a broad age group. Besides, we can conclude that, no matter the top list, description of changes positively affects success, but in Latin American countries, is slightly less important than in other countries. Regarding Android version, no matter the country, top apps support a broader set of versions instead of use new features and improvements of newer Android versions. In addition, we can conclude that numerical variables related to length are not descriptive enough to characterize successful apps. On the other hand, we can identify that shorter release intervals (less than six months) may be related to being in a top list, in particular for Editors' Choice list. Regarding rating and amount of stars, Latin American countries tend to rate less and with lower ratings and Editors’ Choice apps have higher ratings and are more rated than the other tops. Finally, even when number of installs doesn't seem as an important selection criteria for being in the Top Free or Top Selling list, for the Editors' Choice list in BR and DE, the number is important and is higher than the other countries.
Regarding categorical features, some prevalent characteristics were found; however, most of them could be related to the nature of the market and tops lists and cannot completely help or give a hint when characterizing top apps. In particular, we identified some prevalence of content rating that encompasses a broader population; the same behavior happens with Android version, promoting compatibility with a broader range of devices. Regarding the main Editors' Choice findings, the absence of unrated apps is highly related to the nature of this top, since GP curates it, the apps that belong to this category have to meet best practices, one of them is establishing the content rating for the app. For numerical variables, we found some insights related to price at the country level; others related to the number of installs and ratings across different granularities. Nonetheless, these significant differences between Editors' Choice and the other two could be a consequence, not a cause of belonging to this top. In addition, these two features (i.e., number of installs and ratings) cannot be controlled by a developer to grant success; instead, these two features could indicate the app success.
To answer this question, we generated different time intervals. Since the dataset had 30 weeks, we generated intervals based on the divisor of 30: 15, 10, 6, 5, 3, 2 and 1. Here we will present some of them.
When analyzing What's New at a general level, we fount the percentage of apps with description of changes was between 60% and 70%, however, when analyzing at a granular level of 15 weeks, we discover the percentage decreased for the last 15 weeks, as presented by the following figure:
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"ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Has Whats New"}, "updatemenus": [{"buttons": [{"args": [{"visible": [true, false]}], "label": "1", "method": "update"}, {"args": [{"visible": [false, true]}], "label": "2", "method": "update"}], "direction": "down", "pad": {"r": 10, "t": 10}, "showactive": true, "x": 0.1, "xanchor": "left", "y": 1.1, "yanchor": "top"}]}, {"responsive": true} ) }; </script>Similarly, when using imputed data, the percentage of apps with description of changed for the last 15 weeks also decreased:
-
What's new, grouped by 15 weeks, for dataset with LOCF imputation: What's New LOCF
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What's new, grouped by 15 weeks, for dataset with MFO imputation: What's New MFO
Thus, we analyzed at a 10 weeks-level to found out which weeks were different, and we noticed the last 10 weeks decreased:
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"gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Has Whats New"}, "updatemenus": [{"buttons": [{"args": [{"visible": [true, false, false]}], "label": "1", "method": "update"}, {"args": [{"visible": [false, true, false]}], "label": "2", "method": "update"}, {"args": [{"visible": [false, false, true]}], "label": "3", "method": "update"}], "direction": "down", "pad": {"r": 10, "t": 10}, "showactive": true, "x": 0.1, "xanchor": "left", "y": 1.1, "yanchor": "top"}]}, {"responsive": true} ) }; </script>Similarly, when using imputed data, the percentage of apps with description of changed for the last 10 weeks also decreased:
-
What's new, grouped by 10 weeks, for dataset with LOCF imputation: What's New LOCF
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What's new, grouped by 10 weeks, for dataset with MFO imputation: What's New MFO
Then, we analyzed at a level of 6 weeks and observed the last 12 weeks decreased:
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"ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Has Whats New"}, "updatemenus": [{"buttons": [{"args": [{"visible": [true, false, false, false, false]}], "label": "1", "method": "update"}, {"args": [{"visible": [false, true, false, false, false]}], "label": "2", "method": "update"}, {"args": [{"visible": [false, false, true, false, false]}], "label": "3", "method": "update"}, {"args": [{"visible": [false, false, false, true, false]}], "label": "4", "method": "update"}, {"args": [{"visible": [false, false, false, false, true]}], "label": "5", "method": "update"}], "direction": "down", "pad": {"r": 10, "t": 10}, "showactive": true, "x": 0.1, "xanchor": "left", "y": 1.1, "yanchor": "top"}]}, {"responsive": true} ) }; </script>Likewise, when using imputed data, the percentage of apps with description of changed for the last 12 weeks also decreased:
-
What's new, grouped by 6 weeks, for dataset with LOCF imputation: What's New LOCF
-
What's new, grouped by 6 weeks, for dataset with MFO imputation: What's New MFO
This behavior could be related with the importance of including description of changes, maybe it lost some importance when including apps in top charts. Thus, apps without What's New, enter to the top.
Similar to what happened with What's New, we found a change in the last 15 weeks when analyzing Summary Length, but in this case, it was an increase. This means, for the last 15 weeks the length of the summary increased:
This increase happened also in the imputed data:
| LOCF | MFO |
|---|---|
 |
 |
When we observed at a different granularity, 10 weeks, we found the same increase but for the last 10 weeks, for data without imputations, but also for data with them:
| LOCF | MFO |
|---|---|
 |
 |
However, when we analyzed at a 6 week level, we noticed that the last 6 weeks presented a higher increase:
| LOCF | MFO |
|---|---|
 |
 |
Thus, we compared the summary of apps for the first weeks with respect to the last weeks and we discovered the summary was the same as the description. That's why the summary increased its length. However, this could happen because of a change in the Google Play web page which takes the summary as a part of the description but, when clicking on it, it shows the full description, thus the summary is the description.
The following figures present the Rating at a 15 week level for imputed data and not imputed data:
| LOCF | MFO |
|---|---|
 |
 |
In addition, we computed the effect size between the groups and obtained that there are significant (large) differences between the first group (first 15 weeks) and the second group (last 15 weeks), this means the Rating changes at a time interval of 15 weeks.
To check if Rating changed at different time intervals, we computed the distribution at a 10 week level:
| LOCF | MFO |
|---|---|
 |
 |
In this case, we also found large differences between the groups. However, it is important to note that the last 10 weeks have higher ratings with respect to the first 20 weeks.
Thus, we analyzed at a 6 week level, for imputed and not imputed data:
| LOCF | MFO |
|---|---|
 |
 |
In this case, we noted that only the last 6 weeks of the study have higher rating. This could mean that, while What's New became less important, the Rating became important for an app to be in the top charts. In consequence, we can conclue Rating evolved over time and obtained more importance across weeks.
Main highlights: After analyzing the data at different time intervals, we conclude that content rating, Android version, name's length, description's length and days since last update remain consistent in time in top apps. However, what's new, summary's length and price tend to evolve mostly at the final weeks of the analysis period. Number of install stabilizes at the end and, lastly, rating values change constantly in top apps.
Discussion RQ2: when analyzing at a larger time interval the analyzed variables, it is clear that apps were not having a strong influence during time. For example, the price was stable during the 30 weeks, \emph{what's new} decreased its size along time, and sadly, \emph{rating} variable was not able to be correctly analyzed due to the missing data.
To answer this question for each app we analyzed some survivability events to understand survivability behavior patterns in the analyzed apps for some granularities (Country, top, category).
A TLO indicates the number of survivability events for an app.
TLO(Country) distributions by country are all different, except USA and Colombia, but all the differences were negligible. As it is possible to se in the next figures all the distributtions were similar between not imputed data and imputations. The following images illustrates the TLO distributions:
| LOCF | MFO |
|---|---|
 |
 |
The table illustrates the effect sizes and if we were able to reject the null hypotesis:
| Comparision | H0 rejected | Cliff's delta size |
|---|---|---|
| BR vs CO | True | Negligible |
| BR vs DE | True | Negligible |
| BR vs US | True | Negligible |
| CO vs DE | True | Negligible |
| CO vs US | False | -- |
| DE vs US | True | Negligible |
TLO(Top) distributions by top are all different and no difference was considerable. In the next figures it is possible to see the TLO distribution by top and for each dataset (not imputed and imputed dataset), as we pointed out, for the imputed data we took the two approaches already mentioned.
TLO distribution by top-list for not imputed data
When we compared each pair of distributions we found that all were different. These results are presented in the next table:
| Comparision | H0 rejected | Cliff's delta size |
|---|---|---|
| Top selling vs Top free | True | Small |
| Top selling vs Editor's choice | True | Medium |
| Top free vs Editors' choice | True | Small |
In the following tables and images, in this section, we present the analysis for the imputed datasets:
| LOCF | MFO |
|---|---|
| Deleting CO records for the three tops | Deleting CO records for the three tops |
 |
 |
The next table presents the results obtained in both imputed datasets (LOCF and MOF) deleting al the CO registers for the three tops. Between each pairs of tops a difference was found, however only the difference between Top selling and Editors' choice was Large the other two were small, also be aware that the outliers with value 3 in Editors' choice were removed when deleting CO registers.
| Comparision | H0 rejected | Cliff's delta size |
|---|---|---|
| Top selling vs Top free | True | Small |
| Top selling vs Editors' choice | True | Large |
| Top free vs Editors' choice | True | Small |
As you can see when we removed registers related to Colombia, the difference found between Top selling vs Editors' choice change from Medium to Large, the other differences between remained the same.
| LOCF | MFO |
|---|---|
| Analyzing Top free and Top Selling only | Analyzing Top free and Top Selling only |
 |
 |
The following table presents the results obtained in both imputed datasets (LOCF and MOF) deleting al the Edtors' choice registers for the three tops.
| Comparision | H0 rejected | Cliff's delta size |
|---|---|---|
| Top selling vs Top free | True | Small |
A EST refers to the duration of each TLO event, while A TBSE is the time between two consecutive TLO events. In the subsequent subsections it is possible to see the results for each aggregations and event (ie., EST and TBSE). In addition, we present the result for the imputed and not imputed data.
In the next figure it is possible to see EST and TBSE by country for not imputed data.
For EST its important to point out that some apps stayed 30 consecutive weeks in a top-list and in the opposite another apps only stayed for a single week. Conserning TBSE, some apps left the top for a single week (TBSE=1), while some apps had the opposite behavior and left a top for 28 weeks. The following images illustrates the EST and TBSE distributions:
| LOCF | MFO |
|---|---|
 |
 |
Regarding the differences we did found a diffeence between each pair od distribution, however all of them where negligibe. These results are the same for not imputed and imputed data.
When analysing EST and TBSE events by top-list, we found that Editors' choice is a distribution that behaves way different than the other two. This behaviour was the same across the three approaches (imputed data).
EST and TBSE distribution by top-list for not imputed data by country
After comparing pairwaise the distributions, we corroborated that these differnces were statistically significant and were not trivial, excepting TBSE(Top selling) vs TBSE(Top free). In fact the dissimilarities that involved Editors' choice were medium or large.
| Event | Comparision | H0 rejected | Cliff's delta size |
|---|---|---|---|
| EST | Top selling vs Top free | True | Small |
| EST | Top selling vs Editors' choice | True | Large |
| EST | Top free vs Editors' choice | True | Large |
| TBSE | Top selling vs Top free | True | Negligible |
| TBSE | Top selling vs Editors' choice | True | Medium |
| TBSE | Top free vs Editors' choice | True | Medium |
Regarding the imputed datasets, in the following tables and images of this section we present the aformentioned analysis in each datasets by the two approached already mentioned (deleting all registers form CO and deleting Editors' choice).
| LOCF | MFO |
|---|---|
| Analyzing Top free and Top Selling only | Analyzing Top free and Top Selling only |
 |
 |
| Deleting CO records for the three tops | Deleting CO records for the three tops |
 |
 |
As you may notice in the Editors' choice EST distribution when we removed CO registers the minium values changes from 5 to 13 and for TBSE the maximum value, outliers with the value of 5 were removed. Regarding the difference of sizes between distributions we found that the difference between pairs in EST distributions stayed the same, on the other side the differences in TBSE distrbutions between Editors' choie and the other tops increase from medium to Large.
When analysing by category we found that for EST distribution for Dating Category was the distribution that had the greatest number of non trivial differences with other categories a total of 25. For simplification we used a heatmap and we only ilustrated the first diagonal, because is redundant the other half.
EST distribution by category for not imputed data
| LOCF | MFO |
|---|---|
 |
 |
Main highlights: taking into account that different survivability events can help us to understand behaviors of the apps in the studied time. For instance, (i) Editors' Choice is the most stable and less diverse top list: it has the lowest number of apps being at the list and the highest survival times (i.e., TLO); (ii) between the countries, the events' distributions can differ but the effect size, in most cases, is trivial (i.e., d<0.2); (iii) if an app returns after leaving a top list, it returns in most cases for Top Free and Top Selling in 4 weeks or less, and (iv) analyzing these events by category show that the differences in the distribution events are, in the majority, negligible.
Discussion RQ3: In all three survivability events we found some insights, having as the main insight that Editors' Choice is the most stable. However, this could be related to the intrinsic nature of the top list. With this we meant, it could not be ignored that Editors' Choice contains some apps handpicked by Store Editors in a periodic way, which could lead to a more stable top than the other two