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jrasero committed Nov 15, 2024
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2 changes: 1 addition & 1 deletion _sources/chapters/module-5/051-intro_to_R.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Hellow world program\n",
"## Hello world program\n",
"\n",
"As is common in any first lesson on programming, one of the first things to do is write a \"Hello, World!\" program.\n",
"\n",
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},
"source": [
"```{exercise}\n",
"\n",
":label: Rloops1\n",
"\n",
"1- Write a piece of code that prints \"x is an even number\" if a variable x (which you define) is even; otherwise, print “x is an odd number.” Test your code by setting x to both an odd and an even number. (Hint: You may find the modulus operator helpful here.)\n",
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1 change: 0 additions & 1 deletion _sources/chapters/module-5/054-functions.ipynb
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"metadata": {},
"source": [
"```{exercise}\n",
"\n",
":label: Rfunctions3\n",
"\n",
"3- Create a function named \"mysquare\", which takes two arguments, such that it first takes the square of the first one, and then adds the second one to it. Set the second argument to take the value 2 as default. Test it. \n",
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88 changes: 45 additions & 43 deletions _sources/chapters/module-5/055-tidyverse.ipynb
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"── \u001b[1mAttaching core tidyverse packages\u001b[22m ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──\n",
"── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n",
"\u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.1.4 \u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 2.1.5\n",
"\u001b[32m✔\u001b[39m \u001b[34mforcats \u001b[39m 1.0.0 \u001b[32m✔\u001b[39m \u001b[34mstringr \u001b[39m 1.5.1\n",
"\u001b[32m✔\u001b[39m \u001b[34mggplot2 \u001b[39m 3.5.1 \u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.2.1\n",
"\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.3 \u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.3.1\n",
"\u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 1.0.2 \n",
"── \u001b[1mConflicts\u001b[22m ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──\n",
"── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
"\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
"\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n",
"\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n"
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"\n",
"1- Import the data from \"https://vincentarelbundock.github.io/Rdatasets/csv/psych/sat.act.csv\", which contains SAT and ACT scores for a sample of students. Save it to a variable named sat.dat.\n",
"\n",
"2- Using the pipe (`%>%`) operator, perform the following operations in sequence:\n",
"2- Convert \"education\" and \"gender\" columns to factor type. (Hint: use `as.factor` function)\n",
"\n",
"3- Using the pipe (`%>%`) operator, perform the following operations in sequence:\n",
"\n",
"- Filter the data to include only observations where `age` is between 18 and 45 years.\n",
"- Create a new variable, `SATV.avg`, representing the average of `SATQ` and `SATV`.\n",
"- Select the columns `gender`, `education`, and `SATV.avg`.\n",
"- Create a new variable, `SAT.avg`, representing the average of `SATQ` and `SATV`.\n",
"- Select the columns `gender`, `education`, and `SAT.avg`.\n",
"- Group the dataframe by `gender` and `education`.\n",
"- Summarize the dataframe by computing the mean and standard deviation of `SATV.avg`.\n",
"- Summarize the dataframe by computing the mean and standard deviation of `SAT.avg`. Here, since there are some missing information, you will need to remove these observations. You can actually do this when using the `mean` and `sd` functions. Look into their documentation to figure out how to do this.\n",
"\n",
"Save the resulting dataframe in a variable named `sat.dat.preprocessed` and display it.\n",
"\n",
"3- With the resulting dataframe, create a barplot using `geom_bar`:\n",
"4- With the resulting dataframe, create a barplot using `geom_bar`:\n",
"\n",
"- Set the x position to each level of `education`, and the `height` (y position) to the mean of `SATV.avg`.\n",
"- Set the x position to each level of `education`, and the `height` (y position) to the mean of `SAT.avg`.\n",
"- To display a separate bar for each `gender`, set `fill = gender` in the aesthetics, and use `position = \"dodge\"` to place the bars side by side rather than stacked.\n",
"\n",
"Adjust the plot aesthetics to make it more visually appealing.\n",
"Adjust the plot aesthetics to make it more visually appealing. You may find the following page useful for this: https://r-graph-gallery.com/4-barplot-with-error-bar.html\n",
"```"
]
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"metadata": {},
"outputs": [],
"source": [
"# Your answers here"
"# Your answers from here"
]
}
],
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