{ "cells": [ { "cell_type": "markdown", "id": "89d164dd-5948-459f-bbd0-e128081d9d95", "metadata": {}, "source": [ "# R for data science: Tidyverse\n", "\n", "The **tidyverse** is one of the most popular ecosystems for data science in R. It includes many R packages commonly used in everyday data analysis. The core packages are as follows:\n", "\n", "- **ggplot2** for visualization\n", "- **dplyr** for data manipulation\n", "- **tidyr** to tidy your data\n", "- **readr** to read data\n", "- **purrr** for functional programming\n", "- **tibble** for enhanced data frames\n", "- **stringr** for string manipulation\n", "- **forcats** for working with categorical data (factors)\n", "\n" ] }, { "cell_type": "markdown", "id": "d1a666f0-ff33-4498-8224-4d85773d0e5a", "metadata": {}, "source": [ "## Loading the tidyverse\n", "\n", "The **tidyverse** is a collection of packages. In R, to load a library or package, you simply use the `library()` function. This is similar to using the `import` keyword in Python." ] }, { "cell_type": "code", "execution_count": 1, "id": "1bd0f3a7-7a9e-48b8-9210-ded33a92263d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "── \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[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\u001b[39m\u001b[23m) to force all conflicts to become errors\n" ] } ], "source": [ "# install.packages(\"tidyverse\")\n", "library(\"tidyverse\")" ] }, { "cell_type": "markdown", "id": "40627419-94a4-4652-bb63-a3bed76d5230", "metadata": {}, "source": [ "## The pipe (`%>%`) operator" ] }, { "cell_type": "markdown", "id": "2d69ff88-acc6-401b-8f8b-1971d5afa5f0", "metadata": {}, "source": [ "One of the most powerful tools introduced in the **tidyverse** is the pipe (`%>%`) operator, which enables chaining functions in a clear, readable way—especially useful for data manipulation tasks.\n", "\n", "Here’s an example where we first apply a logical filter, then select specific columns, and finally sort the data:" ] }, { "cell_type": "code", "execution_count": 2, "id": "d918fd8a-9972-4684-b19b-c29fbbcd6df9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 2
petal_lengthpetal_width
<dbl><dbl>
16.92.3
26.72.2
36.72.0
46.62.1
56.42.0
66.31.8
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & petal\\_length & petal\\_width\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & 6.9 & 2.3\\\\\n", "\t2 & 6.7 & 2.2\\\\\n", "\t3 & 6.7 & 2.0\\\\\n", "\t4 & 6.6 & 2.1\\\\\n", "\t5 & 6.4 & 2.0\\\\\n", "\t6 & 6.3 & 1.8\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | petal_length <dbl> | petal_width <dbl> |\n", "|---|---|---|\n", "| 1 | 6.9 | 2.3 |\n", "| 2 | 6.7 | 2.2 |\n", "| 3 | 6.7 | 2.0 |\n", "| 4 | 6.6 | 2.1 |\n", "| 5 | 6.4 | 2.0 |\n", "| 6 | 6.3 | 1.8 |\n", "\n" ], "text/plain": [ " petal_length petal_width\n", "1 6.9 2.3 \n", "2 6.7 2.2 \n", "3 6.7 2.0 \n", "4 6.6 2.1 \n", "5 6.4 2.0 \n", "6 6.3 1.8 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Example with the iris dataset\n", "iris.df<-read.csv(\"https://raw.githubusercontent.com/mwaskom/seaborn-data/refs/heads/master/iris.csv\")\n", "\n", "iris.df.pipe <- # Save the resulting pipeline into this variable\n", " iris.df %>% # start with the original dataframe\n", " filter(species == \"virginica\") %>% # take observations corresponding to virginica\n", " select(petal_length, petal_width) %>% # select petal_length and petal_width colums\n", " arrange(desc(petal_length)) # sort data in descending order based on petal_length\n", "\n", "head(iris.df.pipe)" ] }, { "cell_type": "markdown", "id": "97db44fb-4a13-41bb-90fc-74bfdd13d112", "metadata": {}, "source": [ "## Data cleaning" ] }, { "cell_type": "code", "execution_count": 3, "id": "e0f0a6a6-16b9-4022-b9cc-0239322b9b87", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 4 × 2
xy
<dbl><dbl>
2NA
NANA
1 6
1 6
\n" ], "text/latex": [ "A data.frame: 4 × 2\n", "\\begin{tabular}{ll}\n", " x & y\\\\\n", " & \\\\\n", "\\hline\n", "\t 2 & NA\\\\\n", "\t NA & NA\\\\\n", "\t 1 & 6\\\\\n", "\t 1 & 6\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 4 × 2\n", "\n", "| x <dbl> | y <dbl> |\n", "|---|---|\n", "| 2 | NA |\n", "| NA | NA |\n", "| 1 | 6 |\n", "| 1 | 6 |\n", "\n" ], "text/plain": [ " x y \n", "1 2 NA\n", "2 NA NA\n", "3 1 6\n", "4 1 6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.to.clean = data.frame(x = c(2, NA, 1, 1), \n", " y = c(NA, NA, 6, 6)\n", " )\n", "df.to.clean" ] }, { "cell_type": "markdown", "id": "b160a650-4240-4a41-9269-fd66f8b30430", "metadata": {}, "source": [ "### `drop_na`: drop missing values" ] }, { "cell_type": "code", "execution_count": 4, "id": "af23dc2d-a2c6-434e-b5ef-cbc0632ea3ef", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\n", "
A data.frame: 2 × 2
xy
<dbl><dbl>
16
16
\n" ], "text/latex": [ "A data.frame: 2 × 2\n", "\\begin{tabular}{ll}\n", " x & y\\\\\n", " & \\\\\n", "\\hline\n", "\t 1 & 6\\\\\n", "\t 1 & 6\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 2 × 2\n", "\n", "| x <dbl> | y <dbl> |\n", "|---|---|\n", "| 1 | 6 |\n", "| 1 | 6 |\n", "\n" ], "text/plain": [ " x y\n", "1 1 6\n", "2 1 6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.to.clean %>% drop_na()" ] }, { "cell_type": "markdown", "id": "dbdc4f7b-f7bd-4b99-9985-28042b578c93", "metadata": {}, "source": [ "### `replace_na`: replace missing values" ] }, { "cell_type": "code", "execution_count": 5, "id": "ecd1c1fa-1d39-4a55-bb14-151ea55bbca9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 4 × 2
xy
<dbl><dbl>
20
00
16
16
\n" ], "text/latex": [ "A data.frame: 4 × 2\n", "\\begin{tabular}{ll}\n", " x & y\\\\\n", " & \\\\\n", "\\hline\n", "\t 2 & 0\\\\\n", "\t 0 & 0\\\\\n", "\t 1 & 6\\\\\n", "\t 1 & 6\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 4 × 2\n", "\n", "| x <dbl> | y <dbl> |\n", "|---|---|\n", "| 2 | 0 |\n", "| 0 | 0 |\n", "| 1 | 6 |\n", "| 1 | 6 |\n", "\n" ], "text/plain": [ " x y\n", "1 2 0\n", "2 0 0\n", "3 1 6\n", "4 1 6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.to.clean %>%replace_na(list(x=0, y=0))" ] }, { "cell_type": "markdown", "id": "07bdb6b6-961d-43c8-a171-ecd6b526bb4b", "metadata": {}, "source": [ "### `distinc`: drop duplicated data" ] }, { "cell_type": "code", "execution_count": 6, "id": "da0a2ec8-4668-4449-bd40-823af3d984c9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 3 × 2
xy
<dbl><dbl>
2NA
NANA
1 6
\n" ], "text/latex": [ "A data.frame: 3 × 2\n", "\\begin{tabular}{ll}\n", " x & y\\\\\n", " & \\\\\n", "\\hline\n", "\t 2 & NA\\\\\n", "\t NA & NA\\\\\n", "\t 1 & 6\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 2\n", "\n", "| x <dbl> | y <dbl> |\n", "|---|---|\n", "| 2 | NA |\n", "| NA | NA |\n", "| 1 | 6 |\n", "\n" ], "text/plain": [ " x y \n", "1 2 NA\n", "2 NA NA\n", "3 1 6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.to.clean %>% distinct()" ] }, { "cell_type": "markdown", "id": "67cc9cd0-ecd0-4b6b-b52c-f0fe578445b7", "metadata": {}, "source": [ "**Remember**: we can concatenate them together." ] }, { "cell_type": "code", "execution_count": 7, "id": "fdf9d334-4a41-4127-93d3-8e526161c420", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 3 × 2
xy
<dbl><dbl>
20
00
16
\n" ], "text/latex": [ "A data.frame: 3 × 2\n", "\\begin{tabular}{ll}\n", " x & y\\\\\n", " & \\\\\n", "\\hline\n", "\t 2 & 0\\\\\n", "\t 0 & 0\\\\\n", "\t 1 & 6\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 2\n", "\n", "| x <dbl> | y <dbl> |\n", "|---|---|\n", "| 2 | 0 |\n", "| 0 | 0 |\n", "| 1 | 6 |\n", "\n" ], "text/plain": [ " x y\n", "1 2 0\n", "2 0 0\n", "3 1 6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.to.clean %>% replace_na(list(x=0, y=0)) %>% distinct()" ] }, { "cell_type": "markdown", "id": "7b99a0ad-2792-44a6-aaed-2b6f9ccf46a3", "metadata": {}, "source": [ "## Data manipulation" ] }, { "cell_type": "code", "execution_count": 8, "id": "3fc119a9-a2b3-4be1-9b84-e455a631058c", "metadata": {}, "outputs": [], "source": [ "# let's load again our Iris dataframe\n", "iris.df<-read.csv(\"https://raw.githubusercontent.com/mwaskom/seaborn-data/refs/heads/master/iris.csv\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "85722a96", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 150
  2. 5
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 150\n", "\\item 5\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 150\n", "2. 5\n", "\n", "\n" ], "text/plain": [ "[1] 150 5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dim(iris.df)" ] }, { "cell_type": "code", "execution_count": 10, "id": "c2ac85e6-dd87-4793-8c15-f1eac303c1b0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t150 obs. of 5 variables:\n", " $ sepal_length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...\n", " $ sepal_width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...\n", " $ petal_length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...\n", " $ petal_width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...\n", " $ species : chr \"setosa\" \"setosa\" \"setosa\" \"setosa\" ...\n" ] } ], "source": [ "str(iris.df)" ] }, { "cell_type": "markdown", "id": "76f452da", "metadata": {}, "source": [ "### `filter`: Pick observations by their values\n", "\n", "This function allows you to subset observations based on specific conditions." ] }, { "cell_type": "code", "execution_count": 11, "id": "c6d0ab7c-5fe6-491b-979c-b3a92e8af02a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 5
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
<dbl><dbl><dbl><dbl><chr>
15.13.51.40.2setosa
24.93.01.40.2setosa
34.73.21.30.2setosa
44.63.11.50.2setosa
55.03.61.40.2setosa
65.43.91.70.4setosa
\n" ], "text/latex": [ "A data.frame: 6 × 5\n", "\\begin{tabular}{r|lllll}\n", " & sepal\\_length & sepal\\_width & petal\\_length & petal\\_width & species\\\\\n", " & & & & & \\\\\n", "\\hline\n", "\t1 & 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n", "\t2 & 4.9 & 3.0 & 1.4 & 0.2 & setosa\\\\\n", "\t3 & 4.7 & 3.2 & 1.3 & 0.2 & setosa\\\\\n", "\t4 & 4.6 & 3.1 & 1.5 & 0.2 & setosa\\\\\n", "\t5 & 5.0 & 3.6 & 1.4 & 0.2 & setosa\\\\\n", "\t6 & 5.4 & 3.9 & 1.7 & 0.4 & setosa\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 5\n", "\n", "| | sepal_length <dbl> | sepal_width <dbl> | petal_length <dbl> | petal_width <dbl> | species <chr> |\n", "|---|---|---|---|---|---|\n", "| 1 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |\n", "| 2 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |\n", "| 3 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |\n", "| 4 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |\n", "| 5 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |\n", "| 6 | 5.4 | 3.9 | 1.7 | 0.4 | setosa |\n", "\n" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "1 5.1 3.5 1.4 0.2 setosa \n", "2 4.9 3.0 1.4 0.2 setosa \n", "3 4.7 3.2 1.3 0.2 setosa \n", "4 4.6 3.1 1.5 0.2 setosa \n", "5 5.0 3.6 1.4 0.2 setosa \n", "6 5.4 3.9 1.7 0.4 setosa " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iris.df.setosa<-iris.df %>% filter(species==\"setosa\")\n", "\n", "head(iris.df.setosa)" ] }, { "cell_type": "code", "execution_count": 12, "id": "5d72b712", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 5
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
<dbl><dbl><dbl><dbl><chr>
15.13.51.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
55.43.91.70.4setosa
64.63.41.40.3setosa
\n" ], "text/latex": [ "A data.frame: 6 × 5\n", "\\begin{tabular}{r|lllll}\n", " & sepal\\_length & sepal\\_width & petal\\_length & petal\\_width & species\\\\\n", " & & & & & \\\\\n", "\\hline\n", "\t1 & 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n", "\t2 & 4.7 & 3.2 & 1.3 & 0.2 & setosa\\\\\n", "\t3 & 4.6 & 3.1 & 1.5 & 0.2 & setosa\\\\\n", "\t4 & 5.0 & 3.6 & 1.4 & 0.2 & setosa\\\\\n", "\t5 & 5.4 & 3.9 & 1.7 & 0.4 & setosa\\\\\n", "\t6 & 4.6 & 3.4 & 1.4 & 0.3 & setosa\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 5\n", "\n", "| | sepal_length <dbl> | sepal_width <dbl> | petal_length <dbl> | petal_width <dbl> | species <chr> |\n", "|---|---|---|---|---|---|\n", "| 1 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |\n", "| 2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |\n", "| 3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |\n", "| 4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |\n", "| 5 | 5.4 | 3.9 | 1.7 | 0.4 | setosa |\n", "| 6 | 4.6 | 3.4 | 1.4 | 0.3 | setosa |\n", "\n" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "1 5.1 3.5 1.4 0.2 setosa \n", "2 4.7 3.2 1.3 0.2 setosa \n", "3 4.6 3.1 1.5 0.2 setosa \n", "4 5.0 3.6 1.4 0.2 setosa \n", "5 5.4 3.9 1.7 0.4 setosa \n", "6 4.6 3.4 1.4 0.3 setosa " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Here filtering based on two columns\n", "iris.df.setosa.2<-iris.df %>% filter(species==\"setosa\", sepal_width>3)\n", "\n", "head(iris.df.setosa.2)" ] }, { "cell_type": "code", "execution_count": 13, "id": "9dd91a01", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 5
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
<dbl><dbl><dbl><dbl><chr>
14.93.01.40.2setosa
24.42.91.40.2setosa
34.83.01.40.1setosa
44.33.01.10.1setosa
55.03.01.60.2setosa
64.43.01.30.2setosa
\n" ], "text/latex": [ "A data.frame: 6 × 5\n", "\\begin{tabular}{r|lllll}\n", " & sepal\\_length & sepal\\_width & petal\\_length & petal\\_width & species\\\\\n", " & & & & & \\\\\n", "\\hline\n", "\t1 & 4.9 & 3.0 & 1.4 & 0.2 & setosa\\\\\n", "\t2 & 4.4 & 2.9 & 1.4 & 0.2 & setosa\\\\\n", "\t3 & 4.8 & 3.0 & 1.4 & 0.1 & setosa\\\\\n", "\t4 & 4.3 & 3.0 & 1.1 & 0.1 & setosa\\\\\n", "\t5 & 5.0 & 3.0 & 1.6 & 0.2 & setosa\\\\\n", "\t6 & 4.4 & 3.0 & 1.3 & 0.2 & setosa\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 5\n", "\n", "| | sepal_length <dbl> | sepal_width <dbl> | petal_length <dbl> | petal_width <dbl> | species <chr> |\n", "|---|---|---|---|---|---|\n", "| 1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |\n", "| 2 | 4.4 | 2.9 | 1.4 | 0.2 | setosa |\n", "| 3 | 4.8 | 3.0 | 1.4 | 0.1 | setosa |\n", "| 4 | 4.3 | 3.0 | 1.1 | 0.1 | setosa |\n", "| 5 | 5.0 | 3.0 | 1.6 | 0.2 | setosa |\n", "| 6 | 4.4 | 3.0 | 1.3 | 0.2 | setosa |\n", "\n" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "1 4.9 3.0 1.4 0.2 setosa \n", "2 4.4 2.9 1.4 0.2 setosa \n", "3 4.8 3.0 1.4 0.1 setosa \n", "4 4.3 3.0 1.1 0.1 setosa \n", "5 5.0 3.0 1.6 0.2 setosa \n", "6 4.4 3.0 1.3 0.2 setosa " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Here filtering with two logical conditions in the same column\n", "iris.df.3<-iris.df %>% filter(sepal_width> 2 & sepal_width <= 3)\n", "\n", "head(iris.df.3)" ] }, { "cell_type": "markdown", "id": "ce88ce1e", "metadata": {}, "source": [ "### `arrange`: sort data by value\n", "\n", "This function takes a dataframe, and a set of column names (or more complicated expressions) to order by (in ascending order by default). If more than one column name is provided, each additional column is used to break ties in the values of preceding columns." ] }, { "cell_type": "code", "execution_count": 14, "id": "5c5ec278", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 5
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
<dbl><dbl><dbl><dbl><chr>
14.33.01.10.1setosa
24.42.91.40.2setosa
34.43.01.30.2setosa
44.43.21.30.2setosa
54.52.31.30.3setosa
64.63.11.50.2setosa
\n" ], "text/latex": [ "A data.frame: 6 × 5\n", "\\begin{tabular}{r|lllll}\n", " & sepal\\_length & sepal\\_width & petal\\_length & petal\\_width & species\\\\\n", " & & & & & \\\\\n", "\\hline\n", "\t1 & 4.3 & 3.0 & 1.1 & 0.1 & setosa\\\\\n", "\t2 & 4.4 & 2.9 & 1.4 & 0.2 & setosa\\\\\n", "\t3 & 4.4 & 3.0 & 1.3 & 0.2 & setosa\\\\\n", "\t4 & 4.4 & 3.2 & 1.3 & 0.2 & setosa\\\\\n", "\t5 & 4.5 & 2.3 & 1.3 & 0.3 & setosa\\\\\n", "\t6 & 4.6 & 3.1 & 1.5 & 0.2 & setosa\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 5\n", "\n", "| | sepal_length <dbl> | sepal_width <dbl> | petal_length <dbl> | petal_width <dbl> | species <chr> |\n", "|---|---|---|---|---|---|\n", "| 1 | 4.3 | 3.0 | 1.1 | 0.1 | setosa |\n", "| 2 | 4.4 | 2.9 | 1.4 | 0.2 | setosa |\n", "| 3 | 4.4 | 3.0 | 1.3 | 0.2 | setosa |\n", "| 4 | 4.4 | 3.2 | 1.3 | 0.2 | setosa |\n", "| 5 | 4.5 | 2.3 | 1.3 | 0.3 | setosa |\n", "| 6 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |\n", "\n" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "1 4.3 3.0 1.1 0.1 setosa \n", "2 4.4 2.9 1.4 0.2 setosa \n", "3 4.4 3.0 1.3 0.2 setosa \n", "4 4.4 3.2 1.3 0.2 setosa \n", "5 4.5 2.3 1.3 0.3 setosa \n", "6 4.6 3.1 1.5 0.2 setosa " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This sorts the data based on sepal length first and then sepal_width\n", "iris.df.sorted<-iris.df %>% arrange(sepal_length, sepal_width)\n", "head(iris.df.sorted)" ] }, { "cell_type": "markdown", "id": "31fce8b6", "metadata": {}, "source": [ "If we want to reorder in descending order, we can use the function `desc`. This, in contrast to Python's Pandas, can be specified to single columns: " ] }, { "cell_type": "code", "execution_count": 15, "id": "78bb4fdb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 5
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
<dbl><dbl><dbl><dbl><chr>
17.93.86.42.0virginica
27.72.66.92.3virginica
37.72.86.72.0virginica
47.73.06.12.3virginica
57.73.86.72.2virginica
67.63.06.62.1virginica
\n" ], "text/latex": [ "A data.frame: 6 × 5\n", "\\begin{tabular}{r|lllll}\n", " & sepal\\_length & sepal\\_width & petal\\_length & petal\\_width & species\\\\\n", " & & & & & \\\\\n", "\\hline\n", "\t1 & 7.9 & 3.8 & 6.4 & 2.0 & virginica\\\\\n", "\t2 & 7.7 & 2.6 & 6.9 & 2.3 & virginica\\\\\n", "\t3 & 7.7 & 2.8 & 6.7 & 2.0 & virginica\\\\\n", "\t4 & 7.7 & 3.0 & 6.1 & 2.3 & virginica\\\\\n", "\t5 & 7.7 & 3.8 & 6.7 & 2.2 & virginica\\\\\n", "\t6 & 7.6 & 3.0 & 6.6 & 2.1 & virginica\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 5\n", "\n", "| | sepal_length <dbl> | sepal_width <dbl> | petal_length <dbl> | petal_width <dbl> | species <chr> |\n", "|---|---|---|---|---|---|\n", "| 1 | 7.9 | 3.8 | 6.4 | 2.0 | virginica |\n", "| 2 | 7.7 | 2.6 | 6.9 | 2.3 | virginica |\n", "| 3 | 7.7 | 2.8 | 6.7 | 2.0 | virginica |\n", "| 4 | 7.7 | 3.0 | 6.1 | 2.3 | virginica |\n", "| 5 | 7.7 | 3.8 | 6.7 | 2.2 | virginica |\n", "| 6 | 7.6 | 3.0 | 6.6 | 2.1 | virginica |\n", "\n" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species \n", "1 7.9 3.8 6.4 2.0 virginica\n", "2 7.7 2.6 6.9 2.3 virginica\n", "3 7.7 2.8 6.7 2.0 virginica\n", "4 7.7 3.0 6.1 2.3 virginica\n", "5 7.7 3.8 6.7 2.2 virginica\n", "6 7.6 3.0 6.6 2.1 virginica" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This sorts the data based on sepal length first, in descending order, and then sepal_width, in ascending order\n", "iris.df.sorted<-iris.df %>% arrange(desc(sepal_length), sepal_width)\n", "head(iris.df.sorted)" ] }, { "cell_type": "markdown", "id": "2fd0549c", "metadata": {}, "source": [ "### `select`: select columns\n", "\n", "It allows you to pick a subset of variables." ] }, { "cell_type": "code", "execution_count": 16, "id": "111402d5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 3
sepal_lengthsepal_widthspecies
<dbl><dbl><chr>
15.13.5setosa
24.93.0setosa
34.73.2setosa
44.63.1setosa
55.03.6setosa
65.43.9setosa
\n" ], "text/latex": [ "A data.frame: 6 × 3\n", "\\begin{tabular}{r|lll}\n", " & sepal\\_length & sepal\\_width & species\\\\\n", " & & & \\\\\n", "\\hline\n", "\t1 & 5.1 & 3.5 & setosa\\\\\n", "\t2 & 4.9 & 3.0 & setosa\\\\\n", "\t3 & 4.7 & 3.2 & setosa\\\\\n", "\t4 & 4.6 & 3.1 & setosa\\\\\n", "\t5 & 5.0 & 3.6 & setosa\\\\\n", "\t6 & 5.4 & 3.9 & setosa\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 3\n", "\n", "| | sepal_length <dbl> | sepal_width <dbl> | species <chr> |\n", "|---|---|---|---|\n", "| 1 | 5.1 | 3.5 | setosa |\n", "| 2 | 4.9 | 3.0 | setosa |\n", "| 3 | 4.7 | 3.2 | setosa |\n", "| 4 | 4.6 | 3.1 | setosa |\n", "| 5 | 5.0 | 3.6 | setosa |\n", "| 6 | 5.4 | 3.9 | setosa |\n", "\n" ], "text/plain": [ " sepal_length sepal_width species\n", "1 5.1 3.5 setosa \n", "2 4.9 3.0 setosa \n", "3 4.7 3.2 setosa \n", "4 4.6 3.1 setosa \n", "5 5.0 3.6 setosa \n", "6 5.4 3.9 setosa " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This selects columns by the given names of the columns. Here we just select sepal_length, sepal_width and species\n", "iris.df.select<-iris.df %>% select(sepal_length, sepal_width, species)\n", "\n", "head(iris.df.select)" ] }, { "cell_type": "markdown", "id": "e2e460f0", "metadata": {}, "source": [ "If you want to select consecutive columns, we can use the `:` operator, as in vectors." ] }, { "cell_type": "code", "execution_count": 17, "id": "047832aa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 4
sepal_lengthsepal_widthpetal_lengthpetal_width
<dbl><dbl><dbl><dbl>
15.13.51.40.2
24.93.01.40.2
34.73.21.30.2
44.63.11.50.2
55.03.61.40.2
65.43.91.70.4
\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & sepal\\_length & sepal\\_width & petal\\_length & petal\\_width\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t1 & 5.1 & 3.5 & 1.4 & 0.2\\\\\n", "\t2 & 4.9 & 3.0 & 1.4 & 0.2\\\\\n", "\t3 & 4.7 & 3.2 & 1.3 & 0.2\\\\\n", "\t4 & 4.6 & 3.1 & 1.5 & 0.2\\\\\n", "\t5 & 5.0 & 3.6 & 1.4 & 0.2\\\\\n", "\t6 & 5.4 & 3.9 & 1.7 & 0.4\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| | sepal_length <dbl> | sepal_width <dbl> | petal_length <dbl> | petal_width <dbl> |\n", "|---|---|---|---|---|\n", "| 1 | 5.1 | 3.5 | 1.4 | 0.2 |\n", "| 2 | 4.9 | 3.0 | 1.4 | 0.2 |\n", "| 3 | 4.7 | 3.2 | 1.3 | 0.2 |\n", "| 4 | 4.6 | 3.1 | 1.5 | 0.2 |\n", "| 5 | 5.0 | 3.6 | 1.4 | 0.2 |\n", "| 6 | 5.4 | 3.9 | 1.7 | 0.4 |\n", "\n" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width\n", "1 5.1 3.5 1.4 0.2 \n", "2 4.9 3.0 1.4 0.2 \n", "3 4.7 3.2 1.3 0.2 \n", "4 4.6 3.1 1.5 0.2 \n", "5 5.0 3.6 1.4 0.2 \n", "6 5.4 3.9 1.7 0.4 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This selects all columns between sepal_length and petal_width (inclusive)\n", "iris.df.select.2<-iris.df %>% select(sepal_length:petal_width)\n", "\n", "head(iris.df.select.2)" ] }, { "cell_type": "markdown", "id": "285a30ea", "metadata": {}, "source": [ "And you can use a minus (`-`) operator before the name of the columns to filter them out." ] }, { "cell_type": "code", "execution_count": 18, "id": "4f454cc7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 3
petal_lengthpetal_widthspecies
<dbl><dbl><chr>
11.40.2setosa
21.40.2setosa
31.30.2setosa
41.50.2setosa
51.40.2setosa
61.70.4setosa
\n" ], "text/latex": [ "A data.frame: 6 × 3\n", "\\begin{tabular}{r|lll}\n", " & petal\\_length & petal\\_width & species\\\\\n", " & & & \\\\\n", "\\hline\n", "\t1 & 1.4 & 0.2 & setosa\\\\\n", "\t2 & 1.4 & 0.2 & setosa\\\\\n", "\t3 & 1.3 & 0.2 & setosa\\\\\n", "\t4 & 1.5 & 0.2 & setosa\\\\\n", "\t5 & 1.4 & 0.2 & setosa\\\\\n", "\t6 & 1.7 & 0.4 & setosa\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 3\n", "\n", "| | petal_length <dbl> | petal_width <dbl> | species <chr> |\n", "|---|---|---|---|\n", "| 1 | 1.4 | 0.2 | setosa |\n", "| 2 | 1.4 | 0.2 | setosa |\n", "| 3 | 1.3 | 0.2 | setosa |\n", "| 4 | 1.5 | 0.2 | setosa |\n", "| 5 | 1.4 | 0.2 | setosa |\n", "| 6 | 1.7 | 0.4 | setosa |\n", "\n" ], "text/plain": [ " petal_length petal_width species\n", "1 1.4 0.2 setosa \n", "2 1.4 0.2 setosa \n", "3 1.3 0.2 setosa \n", "4 1.5 0.2 setosa \n", "5 1.4 0.2 setosa \n", "6 1.7 0.4 setosa " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This selects all columns but sepal_length and sepal_width\n", "iris.df.select.3<-iris.df %>% select(-sepal_length, -sepal_width)\n", "\n", "head(iris.df.select.3)" ] }, { "cell_type": "markdown", "id": "64325b1d-260b-49fb-9214-092f0ecc95e3", "metadata": {}, "source": [ "And you can also remove consecutive columns by combining both operators:" ] }, { "cell_type": "code", "execution_count": 19, "id": "8d0b72de-4f0f-46f9-9242-b1e4c80ea971", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 1
species
<chr>
1setosa
2setosa
3setosa
4setosa
5setosa
6setosa
\n" ], "text/latex": [ "A data.frame: 6 × 1\n", "\\begin{tabular}{r|l}\n", " & species\\\\\n", " & \\\\\n", "\\hline\n", "\t1 & setosa\\\\\n", "\t2 & setosa\\\\\n", "\t3 & setosa\\\\\n", "\t4 & setosa\\\\\n", "\t5 & setosa\\\\\n", "\t6 & setosa\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 1\n", "\n", "| | species <chr> |\n", "|---|---|\n", "| 1 | setosa |\n", "| 2 | setosa |\n", "| 3 | setosa |\n", "| 4 | setosa |\n", "| 5 | setosa |\n", "| 6 | setosa |\n", "\n" ], "text/plain": [ " species\n", "1 setosa \n", "2 setosa \n", "3 setosa \n", "4 setosa \n", "5 setosa \n", "6 setosa " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iris.df.select.4<-iris.df %>% select(-(sepal_length:petal_width))\n", "\n", "head(iris.df.select.4)" ] }, { "cell_type": "markdown", "id": "9f5d8319", "metadata": {}, "source": [ "### `mutate`: creating columns\n", "\n", "This function allows you to add new columns to data frames. It would be mostly similar to `assign` in Python's Pandas" ] }, { "cell_type": "code", "execution_count": 20, "id": "23f841fa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 6
sepal_lengthsepal_widthpetal_lengthpetal_widthspeciessepal_volume
<dbl><dbl><dbl><dbl><chr><dbl>
15.13.51.40.2setosa17.85
24.93.01.40.2setosa14.70
34.73.21.30.2setosa15.04
44.63.11.50.2setosa14.26
55.03.61.40.2setosa18.00
65.43.91.70.4setosa21.06
\n" ], "text/latex": [ "A data.frame: 6 × 6\n", "\\begin{tabular}{r|llllll}\n", " & sepal\\_length & sepal\\_width & petal\\_length & petal\\_width & species & sepal\\_volume\\\\\n", " & & & & & & \\\\\n", "\\hline\n", "\t1 & 5.1 & 3.5 & 1.4 & 0.2 & setosa & 17.85\\\\\n", "\t2 & 4.9 & 3.0 & 1.4 & 0.2 & setosa & 14.70\\\\\n", "\t3 & 4.7 & 3.2 & 1.3 & 0.2 & setosa & 15.04\\\\\n", "\t4 & 4.6 & 3.1 & 1.5 & 0.2 & setosa & 14.26\\\\\n", "\t5 & 5.0 & 3.6 & 1.4 & 0.2 & setosa & 18.00\\\\\n", "\t6 & 5.4 & 3.9 & 1.7 & 0.4 & setosa & 21.06\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 6\n", "\n", "| | sepal_length <dbl> | sepal_width <dbl> | petal_length <dbl> | petal_width <dbl> | species <chr> | sepal_volume <dbl> |\n", "|---|---|---|---|---|---|---|\n", "| 1 | 5.1 | 3.5 | 1.4 | 0.2 | setosa | 17.85 |\n", "| 2 | 4.9 | 3.0 | 1.4 | 0.2 | setosa | 14.70 |\n", "| 3 | 4.7 | 3.2 | 1.3 | 0.2 | setosa | 15.04 |\n", "| 4 | 4.6 | 3.1 | 1.5 | 0.2 | setosa | 14.26 |\n", "| 5 | 5.0 | 3.6 | 1.4 | 0.2 | setosa | 18.00 |\n", "| 6 | 5.4 | 3.9 | 1.7 | 0.4 | setosa | 21.06 |\n", "\n" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species sepal_volume\n", "1 5.1 3.5 1.4 0.2 setosa 17.85 \n", "2 4.9 3.0 1.4 0.2 setosa 14.70 \n", "3 4.7 3.2 1.3 0.2 setosa 15.04 \n", "4 4.6 3.1 1.5 0.2 setosa 14.26 \n", "5 5.0 3.6 1.4 0.2 setosa 18.00 \n", "6 5.4 3.9 1.7 0.4 setosa 21.06 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This creates a new column by multiplying summing sepal_length with sepal_width\n", "iris.df.volume<-iris.df %>% mutate(sepal_volume=sepal_length*sepal_width)\n", "head(iris.df.volume)" ] }, { "cell_type": "markdown", "id": "2a108362", "metadata": {}, "source": [ "We can always use a preceding created column to define a new column." ] }, { "cell_type": "code", "execution_count": 21, "id": "02c20b0b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 7
sepal_lengthsepal_widthpetal_lengthpetal_widthspeciessepal_volumesepal_volume_log
<dbl><dbl><dbl><dbl><chr><dbl><dbl>
15.13.51.40.2setosa17.852.882004
24.93.01.40.2setosa14.702.687847
34.73.21.30.2setosa15.042.710713
44.63.11.50.2setosa14.262.657458
55.03.61.40.2setosa18.002.890372
65.43.91.70.4setosa21.063.047376
\n" ], "text/latex": [ "A data.frame: 6 × 7\n", "\\begin{tabular}{r|lllllll}\n", " & sepal\\_length & sepal\\_width & petal\\_length & petal\\_width & species & sepal\\_volume & sepal\\_volume\\_log\\\\\n", " & & & & & & & \\\\\n", "\\hline\n", "\t1 & 5.1 & 3.5 & 1.4 & 0.2 & setosa & 17.85 & 2.882004\\\\\n", "\t2 & 4.9 & 3.0 & 1.4 & 0.2 & setosa & 14.70 & 2.687847\\\\\n", "\t3 & 4.7 & 3.2 & 1.3 & 0.2 & setosa & 15.04 & 2.710713\\\\\n", "\t4 & 4.6 & 3.1 & 1.5 & 0.2 & setosa & 14.26 & 2.657458\\\\\n", "\t5 & 5.0 & 3.6 & 1.4 & 0.2 & setosa & 18.00 & 2.890372\\\\\n", "\t6 & 5.4 & 3.9 & 1.7 & 0.4 & setosa & 21.06 & 3.047376\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 7\n", "\n", "| | sepal_length <dbl> | sepal_width <dbl> | petal_length <dbl> | petal_width <dbl> | species <chr> | sepal_volume <dbl> | sepal_volume_log <dbl> |\n", "|---|---|---|---|---|---|---|---|\n", "| 1 | 5.1 | 3.5 | 1.4 | 0.2 | setosa | 17.85 | 2.882004 |\n", "| 2 | 4.9 | 3.0 | 1.4 | 0.2 | setosa | 14.70 | 2.687847 |\n", "| 3 | 4.7 | 3.2 | 1.3 | 0.2 | setosa | 15.04 | 2.710713 |\n", "| 4 | 4.6 | 3.1 | 1.5 | 0.2 | setosa | 14.26 | 2.657458 |\n", "| 5 | 5.0 | 3.6 | 1.4 | 0.2 | setosa | 18.00 | 2.890372 |\n", "| 6 | 5.4 | 3.9 | 1.7 | 0.4 | setosa | 21.06 | 3.047376 |\n", "\n" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species sepal_volume\n", "1 5.1 3.5 1.4 0.2 setosa 17.85 \n", "2 4.9 3.0 1.4 0.2 setosa 14.70 \n", "3 4.7 3.2 1.3 0.2 setosa 15.04 \n", "4 4.6 3.1 1.5 0.2 setosa 14.26 \n", "5 5.0 3.6 1.4 0.2 setosa 18.00 \n", "6 5.4 3.9 1.7 0.4 setosa 21.06 \n", " sepal_volume_log\n", "1 2.882004 \n", "2 2.687847 \n", "3 2.710713 \n", "4 2.657458 \n", "5 2.890372 \n", "6 3.047376 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This creates a new column by multiplying multiplying sepal_length with sepal_width, and a second column with the logarithm\n", "iris.df.volume.2<- iris.df %>% mutate(sepal_volume=sepal_length*sepal_width, sepal_volume_log=log(sepal_volume))\n", "head(iris.df.volume.2)" ] }, { "cell_type": "code", "execution_count": 22, "id": "9a293c3d", "metadata": {}, "outputs": [ { "ename": "ERROR", "evalue": "\u001b[1m\u001b[33mError\u001b[39m in `mutate()`:\u001b[22m\n\u001b[1m\u001b[22m\u001b[36mℹ\u001b[39m In argument: `sepal_volume_log = log(sepal_volume)`.\n\u001b[1mCaused by error:\u001b[22m\n\u001b[33m!\u001b[39m objeto 'sepal_volume' no encontrado\n", "output_type": "error", "traceback": [ "\u001b[1m\u001b[33mError\u001b[39m in `mutate()`:\u001b[22m\n\u001b[1m\u001b[22m\u001b[36mℹ\u001b[39m In argument: `sepal_volume_log = log(sepal_volume)`.\n\u001b[1mCaused by error:\u001b[22m\n\u001b[33m!\u001b[39m objeto 'sepal_volume' no encontrado\nTraceback:\n", "1. mutate(., sepal_volume_log = log(sepal_volume), sepal_volume = sepal_length * \n . sepal_width)", "2. mutate.data.frame(., sepal_volume_log = log(sepal_volume), sepal_volume = sepal_length * \n . sepal_width)", "3. mutate_cols(.data, dplyr_quosures(...), by)", "4. withCallingHandlers(for (i in seq_along(dots)) {\n . poke_error_context(dots, i, mask = mask)\n . context_poke(\"column\", old_current_column)\n . new_columns <- mutate_col(dots[[i]], data, mask, new_columns)\n . }, error = dplyr_error_handler(dots = dots, mask = mask, bullets = mutate_bullets, \n . error_call = error_call, error_class = \"dplyr:::mutate_error\"), \n . warning = dplyr_warning_handler(state = warnings_state, mask = mask, \n . error_call = error_call))", "5. mutate_col(dots[[i]], data, mask, new_columns)", "6. mask$eval_all_mutate(quo)", "7. eval()", "8. .handleSimpleError(function (cnd) \n . {\n . local_error_context(dots, i = frame[[i_sym]], mask = mask)\n . if (inherits(cnd, \"dplyr:::internal_error\")) {\n . parent <- error_cnd(message = bullets(cnd))\n . }\n . else {\n . parent <- cnd\n . }\n . message <- c(cnd_bullet_header(action), i = if (has_active_group_context(mask)) cnd_bullet_cur_group_label())\n . abort(message, class = error_class, parent = parent, call = error_call)\n . }, \"objeto 'sepal_volume' no encontrado\", base::quote(NULL))", "9. h(simpleError(msg, call))", "10. abort(message, class = error_class, parent = parent, call = error_call)", "11. signal_abort(cnd, .file)", "12. signalCondition(cnd)" ] } ], "source": [ "# Obviously, this is going to give an error because we are trying to use a sepal_volume before it was created\n", "iris.df.volume.3<-iris.df %>% mutate(sepal_volume_log=log(sepal_volume), sepal_volume=sepal_length*sepal_width)\n", "head(iris.df.volume.3)" ] }, { "cell_type": "markdown", "id": "5f9099de", "metadata": {}, "source": [ "### `group_by` and `summarize`: collapse down to a single summary\n", "\n", "This would be similar to using `groupby` method in Pandas' dataframes" ] }, { "cell_type": "code", "execution_count": 23, "id": "99bbf3d5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 2
speciesmean_sepal_length
<chr><dbl>
setosa 5.006
versicolor5.936
virginica 6.588
\n" ], "text/latex": [ "A tibble: 3 × 2\n", "\\begin{tabular}{ll}\n", " species & mean\\_sepal\\_length\\\\\n", " & \\\\\n", "\\hline\n", "\t setosa & 5.006\\\\\n", "\t versicolor & 5.936\\\\\n", "\t virginica & 6.588\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 2\n", "\n", "| species <chr> | mean_sepal_length <dbl> |\n", "|---|---|\n", "| setosa | 5.006 |\n", "| versicolor | 5.936 |\n", "| virginica | 6.588 |\n", "\n" ], "text/plain": [ " species mean_sepal_length\n", "1 setosa 5.006 \n", "2 versicolor 5.936 \n", "3 virginica 6.588 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This first groups by species and then calculates the average sepal length within each category\n", "iris.df %>% group_by(species) %>% summarize(mean_sepal_length = mean(sepal_length))" ] }, { "cell_type": "code", "execution_count": 24, "id": "6e10cc2a-1245-4775-934c-baed921aa7a9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 3
speciesmean_sepal_lengthmedian_sepal_length
<chr><dbl><dbl>
setosa 5.0065.0
versicolor5.9365.9
virginica 6.5886.5
\n" ], "text/latex": [ "A tibble: 3 × 3\n", "\\begin{tabular}{lll}\n", " species & mean\\_sepal\\_length & median\\_sepal\\_length\\\\\n", " & & \\\\\n", "\\hline\n", "\t setosa & 5.006 & 5.0\\\\\n", "\t versicolor & 5.936 & 5.9\\\\\n", "\t virginica & 6.588 & 6.5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 3\n", "\n", "| species <chr> | mean_sepal_length <dbl> | median_sepal_length <dbl> |\n", "|---|---|---|\n", "| setosa | 5.006 | 5.0 |\n", "| versicolor | 5.936 | 5.9 |\n", "| virginica | 6.588 | 6.5 |\n", "\n" ], "text/plain": [ " species mean_sepal_length median_sepal_length\n", "1 setosa 5.006 5.0 \n", "2 versicolor 5.936 5.9 \n", "3 virginica 6.588 6.5 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# You can can have several aggregation methods too\n", "iris.df %>% group_by(species) %>% summarize(mean_sepal_length = mean(sepal_length),\n", " median_sepal_length = median(sepal_length))" ] }, { "cell_type": "markdown", "id": "ba14f961-2b97-4ebc-96a3-f4d75626cf7a", "metadata": {}, "source": [ "If you want to apply the same aggregation method to several columns, you need to use the `across` function:" ] }, { "cell_type": "code", "execution_count": 25, "id": "b8151b55-a423-4d50-a9f0-6ed970251f87", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 5
speciessepal_lengthsepal_widthpetal_lengthpetal_width
<chr><dbl><dbl><dbl><dbl>
setosa 5.0063.4281.4620.246
versicolor5.9362.7704.2601.326
virginica 6.5882.9745.5522.026
\n" ], "text/latex": [ "A tibble: 3 × 5\n", "\\begin{tabular}{lllll}\n", " species & sepal\\_length & sepal\\_width & petal\\_length & petal\\_width\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t setosa & 5.006 & 3.428 & 1.462 & 0.246\\\\\n", "\t versicolor & 5.936 & 2.770 & 4.260 & 1.326\\\\\n", "\t virginica & 6.588 & 2.974 & 5.552 & 2.026\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 5\n", "\n", "| species <chr> | sepal_length <dbl> | sepal_width <dbl> | petal_length <dbl> | petal_width <dbl> |\n", "|---|---|---|---|---|\n", "| setosa | 5.006 | 3.428 | 1.462 | 0.246 |\n", "| versicolor | 5.936 | 2.770 | 4.260 | 1.326 |\n", "| virginica | 6.588 | 2.974 | 5.552 | 2.026 |\n", "\n" ], "text/plain": [ " species sepal_length sepal_width petal_length petal_width\n", "1 setosa 5.006 3.428 1.462 0.246 \n", "2 versicolor 5.936 2.770 4.260 1.326 \n", "3 virginica 6.588 2.974 5.552 2.026 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This first groups by species and then calculates the average across all columns between sepal_length and petal_width\n", "iris.df %>% group_by(species) %>% summarize(across(sepal_length:petal_width, mean)) " ] }, { "cell_type": "markdown", "id": "55aacaff-164f-4e45-aa10-d63bdbffaf02", "metadata": {}, "source": [ "## `pivot_longer`: reshape data to long format\n", "\n", "This would be similart to `pd.melt` in Python's Pandas" ] }, { "cell_type": "code", "execution_count": 26, "id": "a2f5b34a-14f5-43bc-9c9e-ddd26fb0d118", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 3
speciesnamevalue
<chr><chr><dbl>
setosasepal_length5.1
setosasepal_width 3.5
setosapetal_length1.4
setosapetal_width 0.2
setosasepal_length4.9
setosasepal_width 3.0
\n" ], "text/latex": [ "A tibble: 6 × 3\n", "\\begin{tabular}{lll}\n", " species & name & value\\\\\n", " & & \\\\\n", "\\hline\n", "\t setosa & sepal\\_length & 5.1\\\\\n", "\t setosa & sepal\\_width & 3.5\\\\\n", "\t setosa & petal\\_length & 1.4\\\\\n", "\t setosa & petal\\_width & 0.2\\\\\n", "\t setosa & sepal\\_length & 4.9\\\\\n", "\t setosa & sepal\\_width & 3.0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 3\n", "\n", "| species <chr> | name <chr> | value <dbl> |\n", "|---|---|---|\n", "| setosa | sepal_length | 5.1 |\n", "| setosa | sepal_width | 3.5 |\n", "| setosa | petal_length | 1.4 |\n", "| setosa | petal_width | 0.2 |\n", "| setosa | sepal_length | 4.9 |\n", "| setosa | sepal_width | 3.0 |\n", "\n" ], "text/plain": [ " species name value\n", "1 setosa sepal_length 5.1 \n", "2 setosa sepal_width 3.5 \n", "3 setosa petal_length 1.4 \n", "4 setosa petal_width 0.2 \n", "5 setosa sepal_length 4.9 \n", "6 setosa sepal_width 3.0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iris.df.melt<-iris.df %>% pivot_longer(sepal_length:petal_width)\n", "\n", "head(iris.df.melt)" ] }, { "cell_type": "markdown", "id": "e85b9e78-464c-4cb5-8aaf-23cd96841693", "metadata": {}, "source": [ "## Data visualization" ] }, { "cell_type": "markdown", "id": "5f399152-7881-435e-9916-8404e1c58bb7", "metadata": {}, "source": [ "I don't want to finish this introduction to doing data science in R without briefly showing **ggplot2**.\n", "\n", "**ggplot2** is for data visualization, and personally, I think it is the best thing in R. It allows you to create magnificient and professional plots in an elegant and versatile way.\n", "\n", "It is based on grammar of graphics, a coherent system for describing and building graphs." ] }, { "cell_type": "markdown", "id": "29220b83-4dc9-4933-8f31-8224cedee3e6", "metadata": {}, "source": [ "To build a ggplot, you need to use the following basic template that can be used for different types of plots:\n", "\n", "\n", "> `ggplot(data = , mapping = aes()) + ()`\n", "\n", "or \n", "\n", "> `ggplot(data = ) + (mapping = aes())`\n", "\n", "\n", "In a nutshell:\n", "\n", "- One begins a plot with the function `ggplot`, which creates a coordinate system where you can add layers to. \n", "- The first argument of `ggplot` is the dataset to use in the graph. \n", "- The graph can be then completed by adding one or more layers to `ggplot` using a `geom` function.\n", "- Each `geom` function in **ggplot2** takes a mapping argument. This defines how variables in your dataset are mapped to visual properties. The mapping argument is always paired with the function `aes`, and the x and y arguments of `aes()` specify which variables to map to the x- and y-axes.\n", "- **ggplot2** comes with many `geom` functions that each add a different type of layer to a plot" ] }, { "cell_type": "code", "execution_count": 27, "id": "c8b4e2b1-34bc-4bc5-b209-360559cc9929", "metadata": {}, "outputs": [ { "data": { "image/png": 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8pqdV137nFDmnCEVvaCJQlDm4dU1ioOVHvv3i9b03L97ya3qkc5Lfc+f62vNNYO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\nAosOpL1l+4tfDGxNSGAuSAWDymra0f8NbgVCAosOJP3GnFnGP2Z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8pqdV137nFDmnCEVvaCJQlDm4dU1ioOVHvv3i9b03L97ya3qkc5Lfc+f62vNNYO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\niJAYCyBCYiyACImxACIkxgKIkBgLoP8H8RBXpGA6ZXIAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "iris.df %>% ggplot(aes(x=sepal_length, y=petal_length)) + geom_point()\n", "\n", "# or\n", "\n", "iris.df %>% ggplot() + geom_point(aes(x=sepal_length, y=petal_length))" ] }, { "cell_type": "code", "execution_count": 28, "id": "5e61f580-6882-4cad-8186-d7f4dedefec0", "metadata": {}, "outputs": [ { "data": { "image/png": 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iQnQipASGKEFI6QtAjJiZAKEJIYIYUj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\nQmCC", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "iris.df %>% ggplot(aes(x=petal_length)) + geom_histogram(binwidth = 0.1)" ] }, { "cell_type": "markdown", "id": "5520403a-18d5-480b-8a35-f9bfa3041fe1", "metadata": {}, "source": [ "We can assign the plot to a variable and render it any time we like:" ] }, { "cell_type": "code", "execution_count": 29, "id": "f0112447-94b6-449c-82c4-286681334110", "metadata": {}, "outputs": [ { "data": { "image/png": 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87uOdk5SEiEJK30QCow73RyiO+3xMyo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\niJAYCyBCYiyACImxACIkxgKIkBgLoP8H8RBXpGA6ZXIAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "g <- iris.df %>% ggplot(aes(x=sepal_length, y=petal_length)) + geom_point()\n", "g" ] }, { "cell_type": "markdown", "id": "6412edca-4871-4526-a22d-23915d5c4c0f", "metadata": {}, "source": [ "And we can keep adding layers to incorporate more graphics to the plot:" ] }, { "cell_type": "code", "execution_count": 30, "id": "a83f167f-48cd-4516-b379-b2c594206648", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22m`geom_smooth()` using formula = 'y ~ x'\n" ] }, { "data": { "image/png": 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8AbURQlEoncPQpPZ/wZEIlE+ICxjc/3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pR2UkEolk2bJlP/74o/LQw8Pjq6++aoca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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "g + geom_smooth(method='lm')" ] }, { "cell_type": "markdown", "id": "0e131643-d016-428c-8bcc-5bbef321c091", "metadata": {}, "source": [ "The full list of available layers (`geom_` functions) can be found at https://ggplot2.tidyverse.org/reference." ] }, { "cell_type": "markdown", "id": "dce44a35-68b0-4506-b0a3-68321180dddb", "metadata": {}, "source": [ "And as we mentioned, we can also control the **aesthetics** of the plot through the `aes` function. Examples of this include:\n", "\n", "- Position (i.e., on the x and y axes)\n", "- Color (“outside” color)\n", "- Fill (“inside” color)\n", "- Shape (of points)\n", "- Alpha (Transparency)\n", "- Line type\n", "- Size" ] }, { "cell_type": "code", "execution_count": 31, "id": "6e257ebf-1622-4cde-96c2-40f56d8c5dc6", "metadata": {}, "outputs": [ { "data": { "image/png": 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L/txDkPbFxW6jGwouqf4JY3MZXriKOW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\n24FZsckLt2IBQAEfNH5V3qbP1Vb39wf9J5Q6xB+r32qd6gghzYL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XL14csc/6+vq6ujpCyKBBZ6cxsSx7zz33RGzZ/k7OW1sS6WiwW716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\nAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "iris.df %>% ggplot(aes(x=sepal_length, y=petal_length, color=species)) + geom_point()" ] }, { "cell_type": "code", "execution_count": 32, "id": "299d4b3f-8479-4b3c-8959-49939f5513ed", "metadata": {}, "outputs": [ { "data": { "image/png": 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+vC8yuiKYtCDl69ZXFg7axwU5v1cDc/fss9G6fOKRUWTYvOKRN2tVqNXV9WqM4R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\nVUJYmnhs8uwScX5FNCxSz+8yVYWs15/SOmwAkBoIdgCQXH79lVU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DyGgHu8xupavL7MB12g8WVOmFgTeS1JLhEH2wS3ei3d9p8Wnb13dP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\nJWk0pFIpkSXmMoKDgx0cHMwHIx4+fKjT6SIjI00m0507dwAA5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Mg5c+b4qjsAP9lff9IoeHDv4J76H60ihwdjAQA8tW7dOucFFovlzTff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\n3+E6pxAHK7jZnagLAADgLwh2t5izRtk9w8Qnd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y4u7uvre/HFF+kmh8MJCQlhNhJYw/fff7979+7U1FSJRFJdXT00NLRnzx4Oh8N0LrCk\noaGhrq4uc9NoNKrVapVKJZPJmAsFVlFQUNDd3f32229zudzS0tI5c+bk5eUxHQosDyN2MGV9\nfX0BAQGhoaFMBwHrqq6uTkxMjI+PJ4S4u7vv3r27r6/P3d2d6VxgSWKxePLfcl1d3fz581HV\nsY/RaDxz5kx6enpwcDAh5JVXXlGr1Xq9XigUMh0NLAz32MGU0f/d79+/r9PpmM4C1nLz5s2b\nN28uX76cbkokkvz8fFR17Hb79u1jx4698847TAcBq+ByueaXFvH5fIy+sxVG7GBqKIrq6+tr\naGj4/PPPKYp6+umn33vvvcDAQKZzgYVptVoOh3P16tX8/Pz+/v4FCxaoVCpvb2+mc4EVHT58\nODo6WiKRMB0ELI/L5UZERNTX18+bN4/L5dbU1CxevBjDdayEETuYGq1Wa2dnFxgYWFFRUV5e\n7uvru3Pnzrt37zKdCyyM/p1WVVW9+eabubm5fD4/JydHr9cznQus5datW+3t7WvXrmU6CFiL\nSqXSarVZWVkbN27s7e3NyMhgOhFYBQo7mJq5c+ceO3YsJSVFLBa7uLhkZmYaDIbz588znQss\nzMHBgaKozMzMiIiIgICALVu23Lt37+zZs0znAmv56quvwsPD586dy3QQsAq9Xv/hhx9GRUUd\nOnSoqqpKoVBs3boV1+SshMIOZoTP57u6ug4NDTEdBCyMfg+Cj48P3XRwcHB1dR0cHGQ0FFjL\n+Ph4a2trTEwM00HAWs6fPz88PJyenu7s7CwSiZKTkwkhuFRjJRR2MDXt7e3vvvvu8PAw3dTr\n9f39/bj1in18fX2FQuHvv/9ON0dHR/v6+vD9kmx17tw5iqLw3iJ2MxqNBoNh8jKen2Albm5u\nLtMZwJbMnj27trb2ypUrYrF4cHCwpKREIBAkJSXhA4JleDzeyMhITU2Np6enTqcrLi62s7NL\nTU21s8PVIAsdP35cIBDExsYyHQSsxdXVtaWlpbOz083NTavVHjhwYGBgQKVS8fl8pqOBheEF\nxTBlt2/f3rdvX2dnJ5fLDQ0NVSqVs2bNYjoUWB5FUZWVlW1tbXq9PigoSKVSzZkzh+lQYBXp\n6ekymSwhIYHpIGBFf/31V0VFxS+//GIymRYtWpScnIwxeFZCYQcAAADAEphVAQAAAGAJFHYA\nAAAALIHCDgAAAIAlUNgBAAAAsAQKOwAAAACWQGEHAAAAwBIo7AAAAABYAoUdAExNVFRUZGSk\nZfe0IIVCER4e/i+fFADgCYHCDgBsW2Njo1KpHBkZYToIAADzUNgBgG379ddfDx48ODY2xnQQ\nAADmobAD+E+bmJh4or5X0GQyTUxMMJ0CAMBWobADsGE6nW779u0LFiwQCoX+/v4ffPDB6Oio\neWt3d3dCQoKfn5+zs3N0dPQ333xDrzcajRwOZ+/evRs3bhQKhUKhcNmyZZWVlZN71mg0MplM\nIpGIRKKQkJCysrKZp31QHkKIQqFYs2ZNdXW1h4eHvb29h4dHWlra8PCweYe2tja5XC4WiyMj\nI48ePapSqUJCQgghMTExW7ZsIYS4uLi89dZb5v0vXry4atUqV1dXDw+P1NTUu3fvzjw/AMCT\nj8d0AACYvvXr12s0mpdffjkpKenMmTOFhYVarXb//v2EkI6OjqioqFmzZq1fv14gENTW1q5e\nvbqkpCQtLY0+Ni8vb3BwUKlUurm51dXVJSUl3bp1a9u2bYSQioqK5OTkJUuWZGVlURRVX1+f\nlpbm7Oy8bt26aUd9ZJ5Lly41NjampKQEBwefPHmyrKzMZDLt27ePEPLdd98pFIqAgIDNmzd3\nd3cnJia6uLi4u7sTQtRqdWlpaXFxcX19vVQqpbvq7e2Ni4tLSEhQKBQNDQ379+/ncDgWqU0B\nAJ50FADYpqGhIQ6HQ9detJUrVz777LP0cmxsrI+Pz507d+imwWCQyWSOjo7Dw8Pmuc7m5mZ6\nq16vj4yMdHJy6u/vpygqPj7e2dlZq9XSW8fGxkQikUqlopsrVqxYunTp4yScvOdD8tDJCSFl\nZWXmY8PCwry9vc3LixYt0uv1dLO0tJQQEhwcTDcLCwsJIQMDA+YfAiFk7969k7uaN2/e4wQG\nALB1mIoFsFU8Hs/Ozq65ufnPP/+k15w4ceLy5cuEkKGhoZaWFpVKJRaLzTunp6ePjo7++OOP\n9Jro6OjY2Fh6WSAQ5OTkjIyMnDx5khBSU1PT29s7e/ZseqtWq52YmLh37960oz5OHicnJ6VS\naT4kKChIr9cTQrq6us6dO5eWliYQCOhNSqVSJBI95HROTk4bNmwwN+micNrhAQBsCAo7AFvl\n6OhYWFh45coVb2/vkJCQzMzMpqYmiqIIIb/99hshJDs7mzNJQkICIWRgYIA+PCgoaHJv9C1r\n169fJ4Q4OTl1dnbu2LHj9ddfDwsL8/Pzm2Fh9Dh5fHx8uFyu+RA7u/9/Ol27do0QYp5mJYTY\n29v7+fk95HS+vr7/2BUAAOvhHjsAG5aVlbVu3br6+vqmpqYvv/yyqKhILpefOHGCz+cTQrKz\ns59//vm/HbJw4cJ/7IrH4xFCxsfHCSE7d+7csWNHaGhobGxsfHx8aGjomjVrZpLzcfLY29v/\n47H0e0w4HM7klVwu12QyPeh0Dg4OM0kLAGC7UNgB2KrBwcHu7m6pVJqRkZGRkTE2NrZt2za1\nWq3RaGJiYgghPB7vueeeM+/f2dl54cKFsLAwutnR0TG5t4sXLxJCpFKpTqfLy8tLS0srKSkx\nbzUajTOJ6u/v/8g8DzJ//nxCyNWrV+mb5wghExMTN27c8PHxmUkkAABWwgwFgK3q6OgICwur\nqKigm3w+Pzo6mhDC4/FEIlFcXFxJSUlXVxe9Va/Xr169evv27UKhkF5z6tSpU6dO0ctjY2P5\n+fkODg5yubynp8dgMLi5uZlP1NbW1tvbO5Ooj5PnQaRSaWBgYFlZ2f379+k1hw4dunPnzt92\ne8gAHgDAfwdG7ABsVUREhFQq3bx5c0dHh1QqvXz5cn19/cKFC+lRsV27dkVHRy9fvjwhIcHB\nwaGmpubGjRtHjhwxz2l6eXkpFIoNGza4urrW1dVdunQpLy/Py8vLzc3N19e3qKhofHxcKpWe\nPXu2pqZGIpGcPn26ublZLpdPL+0j8zwIl8stKipauXJlVFTUq6++2tPT09DQ4O/vb566pR+k\nUKvVCoVixYoV04sHAMAOGLEDsFUCgaCxsfG1117TaDQfffRRW1tbYmJiS0uLk5MTISQ4OPjn\nn39etmzZ0aNHv/jiC4lEotFoJr+ITqlU7tmz5/Tp059++imfzy8vL8/JySGE2NvbazSapUuX\nlpSU5ObmDg8PX7hwYdeuXTqd7pNPPpl22kfmeQi5XN7U1PTUU08VFBRcu3bt22+/dXR0ND8Y\nu3btWplMplarjxw5Mu14AADswKGepG8TAoB/gdFo5PF42dnZ+fn5TGd5NIqiysrKpFKpTCaj\n1+h0Ok9PT5VK9dlnnzEaDQDgiYMROwB4onE4nMOHD7/00ktNTU06na67uzs9Pd1gMGRlZTEd\nDQDgiYN77ABgOiorK7du3fqQHZRK5ccff2yRc1VVVb3xxhtxcXF008vL6+uvv/b29rZI5wAA\nbIKpWID/HJPJtGnTphdeeGHVqlVMZ5mC69ev9/T0+Pj4+Pn54Z3DAAD/CIUdAAAAAEvgqhcA\nAACAJVDYAQAAALAECjsAAAAAlkBhBwAAAMASKOwAAAAAWAKFHQAAAABLoLADAAAAYAkUdgAA\nAAAs8T8RcUr5AdPkxgAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "iris.df %>% ggplot(aes(x=sepal_length, y=petal_length, color=species, size=petal_width)) + geom_point()" ] }, { "cell_type": "code", "execution_count": 33, "id": "60295c43-5f0e-4328-b5c1-4de3e87740f6", "metadata": {}, "outputs": [ { "data": { "image/png": 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RaQCgQkC0ixgFQgIFlAigWkAgHJmi4kRWrWQYrCiB4fDUjbJCAFA5IISF5ACgYk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\n6o8jIFXSULJw7ntXHJB8xrmfz9vzY+ccOOdid+8pyXd/6S5JDj7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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "iris.df %>% ggplot(aes(x=petal_length, fill=species)) + geom_histogram(binwidth = 0.1)" ] }, { "cell_type": "markdown", "id": "1f81cb66-010b-436a-99f9-0e96cedd11d6", "metadata": {}, "source": [ "I recommend visiting the [R graph gallery](https://r-graph-gallery.com/) for a complete list of recipes on creating graphs using **ggplot**. You'll have fun!" ] }, { "cell_type": "markdown", "id": "bd519e85-1f5d-4c58-9053-bd0a126dc93b", "metadata": {}, "source": [ "## Practice exercises" ] }, { "cell_type": "markdown", "id": "80a5671c", "metadata": {}, "source": [ "```{exercise}\n", ":label: tidyverse1\n", "\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- 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, `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 `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", "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 `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. You may find the following page useful for this: https://r-graph-gallery.com/4-barplot-with-error-bar.html\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "id": "3d73f011-2ba9-4d02-9195-5a95955a7fe1", "metadata": {}, "outputs": [], "source": [ "# Your answers from here" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.4.1" } }, "nbformat": 4, "nbformat_minor": 5 }