From f39a2a413ad4e8c9cc47b253604c823a11333ff9 Mon Sep 17 00:00:00 2001 From: ludekstehlik2 Date: Sun, 8 Apr 2018 13:18:29 +0200 Subject: [PATCH] No comment --- ...e_psychologie_Inteligence-checkpoint.ipynb | 917 ------------------ .../Data_Inteligence.csv | 0 ...minar_obecne_psychologie_Inteligence.ipynb | 0 ...Rproj => Seminare-obecne-psychologie.Rproj | 0 4 files changed, 917 deletions(-) delete mode 100644 .ipynb_checkpoints/Seminar_obecne_psychologie_Inteligence-checkpoint.ipynb rename Data_Inteligence.csv => Inteligence/Data_Inteligence.csv (100%) rename Seminar_obecne_psychologie_Inteligence.ipynb => Inteligence/Seminar_obecne_psychologie_Inteligence.ipynb (100%) rename Seminar_obecne_psychologie_Inteligence.Rproj => Seminare-obecne-psychologie.Rproj (100%) diff --git a/.ipynb_checkpoints/Seminar_obecne_psychologie_Inteligence-checkpoint.ipynb b/.ipynb_checkpoints/Seminar_obecne_psychologie_Inteligence-checkpoint.ipynb deleted file mode 100644 index 66fe5bf..0000000 --- a/.ipynb_checkpoints/Seminar_obecne_psychologie_Inteligence-checkpoint.ipynb +++ /dev/null @@ -1,917 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Seminář obecné psychologie - Inteligence\n", - "\n", - "### Výzkumné cíle: \n", - "- Srovnání výsledků skupiny studentů s dostupnými normami, resp. s výsledky získanými v rámci validizačních studií\n", - "- Odhad míry nezávislosti konstruktů měřených jednotlivými testy\n", - "\n", - "### Měřené proměnné: \n", - "- Obecná inteligence\n", - "- Emoční inteligence\n", - "- Sociální inteligence (Social Information Processing, Social Skills, Social Awareness)\n", - "- Kognitivní reflexivita (viz jeden z předchozích seminářů)\n", - "- Tendence ne/podléhat kognitivním zkreslením (viz jeden z předchozích seminářů)\n", - "- Osobnostní rysy Extraverze, Přívětivosti, Svědomitosti, Emoční stability a Otevřenost ke zkušenosti (viz jeden z předchozích seminářů)\n", - "\n", - "### Metody: \n", - "- 16 PF (Faktor B) (Cattell, 1998)\n", - "- Measure of Emotional Intelligence (Schutte et al., 1998)\n", - "- Tromsø Social Intelligence Scale (Silvera, Martinussen, & Dahl, 2001)\n", - "- CRT-L2 (Frederick, 2005; Primi et al., 2015; Toplak, West, & Stanovich, 2014)\n", - "- Sada 6 úloh inspirovaných výzkumem heuristik a zkreslení, které zjišťují některé aspekty racionálního myšlení (Toplak, West, & Stanovich, 2011)\n", - "- TIPI (Gosling, Rentfrow, & Swann, 2003)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "-- Attaching packages --------------------------------------- tidyverse 1.2.1 --\n", - " ggplot2 2.2.1 purrr 0.2.4\n", - " tibble 1.4.2 dplyr 0.7.4\n", - " tidyr 0.8.0 stringr 1.3.0\n", - " readr 1.1.1 forcats 0.3.0\n", - "-- Conflicts ------------------------------------------ tidyverse_conflicts() --\n", - "x dplyr::filter() masks stats::filter()\n", - "x dplyr::lag() masks stats::lag()\n", - "Parsed with column specification:\n", - "cols(\n", - " Vek = col_integer(),\n", - " Pohlavi = col_integer(),\n", - " Obecna_inteligence = col_integer(),\n", - " CRT_Long = col_integer(),\n", - " Cognitive_Biases_Sum = col_integer(),\n", - " Emocni_inteligence = col_integer(),\n", - " Social_information_processing = col_integer(),\n", - " Social_skills = col_integer(),\n", - " Social_awareness = col_integer(),\n", - " Extraverze = col_double(),\n", - " Privetivost = col_double(),\n", - " Svedomitost = col_double(),\n", - " Emocni_stabilita = col_double(),\n", - " Otevrenost = col_double()\n", - ")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Observations: 65\n", - "Variables: 14\n", - "$ Vek 20, 20, 21, 21, 23, 21, 19, 20, 19, 2...\n", - "$ Pohlavi 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...\n", - "$ Obecna_inteligence 11, NA, 10, 12, NA, 12, 11, 13, 13, 1...\n", - "$ CRT_Long 5, NA, 8, 6, NA, 5, 8, 5, NA, 3, NA, ...\n", - "$ Cognitive_Biases_Sum 2, NA, 2, 4, NA, 2, 3, 2, NA, 2, NA, ...\n", - "$ Emocni_inteligence 127, NA, 161, 101, NA, 133, 144, 121,...\n", - "$ Social_information_processing 33, NA, 48, 29, NA, 45, 42, 38, 44, 3...\n", - "$ Social_skills 33, NA, 47, 21, NA, 45, 36, 37, 26, 3...\n", - "$ Social_awareness 36, NA, 36, 22, NA, 43, 44, 46, 38, 4...\n", - "$ Extraverze 4.0, 5.5, 6.0, NA, NA, 6.0, 4.5, NA, ...\n", - "$ Privetivost 5.5, 4.5, 5.5, NA, NA, 5.0, 4.5, NA, ...\n", - "$ Svedomitost 6.5, 3.5, 6.5, NA, NA, 7.0, 5.5, NA, ...\n", - "$ Emocni_stabilita 6.0, 4.0, 6.0, NA, NA, 5.5, 1.5, NA, ...\n", - "$ Otevrenost 4.5, 6.5, 6.5, NA, NA, 4.5, 5.5, NA, ...\n" - ] - } - ], - "source": [ - "# Načtěme si knihovny, které budeme potřebovat pro načtení dat a jejich přípravu na následnou analýzu a vizualizaci. \n", - "library(tidyverse)\n", - "\n", - "# Načtěme si naše data a podívejme se na jejich strukturu.\n", - "myData <- read_delim('Data_Inteligence.csv', delim = \";\")\n", - "glimpse(myData)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Upravme proměnnou 'Pohlaví' na kategoriální proměnnou se smyslupným označením jejích jednotlivých úrovní \n", - "myData$Pohlavi <- as.factor(ifelse(myData$Pohlavi == 1, \"M\", \"F\"))\n", - "\n", - "# Přeškálujme výsledky 'Cognitive_Biases_Sum' tak, aby vyšší skór odpovídal větší schopnosti nepodléhat kognitivním zkreslením\n", - "myData$Cognitive_Biases_Sum <- (6 - myData$Cognitive_Biases_Sum)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Popis výzkumného souboru" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Loading required package: lattice\n", - "Loading required package: survival\n", - "Loading required package: Formula\n", - "\n", - "Attaching package: 'Hmisc'\n", - "\n", - "The following objects are masked from 'package:dplyr':\n", - "\n", - " src, summarize\n", - "\n", - "The following objects are masked from 'package:base':\n", - "\n", - " format.pval, units\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - ". \n", - "\n", - " 1 Variables 65 Observations\n", - "--------------------------------------------------------------------------------\n", - "Vek \n", - " n missing distinct Info Mean Gmd .05 .10 \n", - " 65 0 10 0.94 21.06 2.42 19.0 19.0 \n", - " .25 .50 .75 .90 .95 \n", - " 20.0 20.0 21.0 22.6 24.8 \n", - " \n", - "Value 19 20 21 22 23 24 25 26 29 43\n", - "Frequency 15 22 12 9 1 2 1 1 1 1\n", - "Proportion 0.231 0.338 0.185 0.138 0.015 0.031 0.015 0.015 0.015 0.015\n", - "--------------------------------------------------------------------------------" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Z hlediska věku probandů\n", - "library(Hmisc)\n", - "myData %>%\n", - "select(Vek) %>%\n", - "Hmisc::describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "myData %>%\n", - "select(Vek) %>%\n", - "ggplot(aes(Vek))+\n", - "geom_histogram(colour=\"white\", fill=\"lightblue\", binwidth = 1)+\n", - "scale_x_continuous(breaks = seq(18, 46, 2), limits = c(18, 46))+\n", - "xlab(\"Věk\")+\n", - "ylab(\"Četnost\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - " F M \n", - "55 10 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "\n", - " F M \n", - "0.85 0.15 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Z hlediska pohlaví probandů\n", - "table(myData$Pohlavi)\n", - "round(prop.table(table(myData$Pohlavi)),2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Popisná statistika sledovaných proměnných" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Attaching package: 'psych'\n", - "\n", - "The following object is masked from 'package:Hmisc':\n", - "\n", - " describe\n", - "\n", - "The following objects are masked from 'package:ggplot2':\n", - "\n", - " %+%, alpha\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
varsnmeansdmediantrimmedmadminmaxrangeskewkurtosisse
Obecna_inteligence 1 46 11.565217 1.5585203 12.0 11.552632 1.48260 9.0 15.0 6.0 0.070929719-0.78236706 0.2297913
CRT_Long 2 37 5.918919 2.0999785 6.0 6.032258 2.96520 1.0 9.0 8.0 -0.333766051-0.65458327 0.3452343
Cognitive_Biases_Sum 3 37 4.081081 1.0375821 4.0 4.064516 1.48260 2.0 6.0 4.0 -0.010816303-0.72373223 0.1705774
Emocni_inteligence 4 47 126.744681 12.1984819 125.0 126.641026 8.89560 99.0 161.0 62.0 0.297770704 0.35777018 1.7793315
Social_information_processing 5 47 39.489362 4.6197701 39.0 39.641026 4.44780 29.0 49.0 20.0 -0.373280330 0.01462936 0.6738627
Social_skills 6 47 33.127660 7.9799123 33.0 33.256410 10.37820 16.0 47.0 31.0 -0.170828653-1.02684616 1.1639898
Social_awareness 7 45 38.755556 5.8002961 39.0 39.351351 5.93040 22.0 48.0 26.0 -0.897669515 0.53583624 0.8646571
Extraverze 8 42 4.142857 1.3264923 4.0 4.147059 1.48260 1.0 6.5 5.5 -0.064171692-0.76031006 0.2046822
Privetivost 9 42 5.297619 0.7813335 5.0 5.264706 0.74130 3.5 7.0 3.5 0.145744518-0.80681918 0.1205624
Svedomitost10 42 5.369048 1.2200536 5.5 5.470588 1.48260 1.5 7.0 5.5 -0.951216912 0.88877406 0.1882584
Emocni_stabilita11 42 4.047619 1.4725518 4.0 4.058824 1.85325 1.5 6.5 5.0 -0.001530203-1.28300261 0.2272197
Otevrenost12 42 5.190476 1.1736559 5.5 5.294118 0.74130 2.5 7.0 4.5 -0.682064270-0.33740730 0.1810990
\n" - ], - "text/latex": [ - "\\begin{tabular}{r|lllllllllllll}\n", - " & vars & n & mean & sd & median & trimmed & mad & min & max & range & skew & kurtosis & se\\\\\n", - "\\hline\n", - "\tObecna\\_inteligence & 1 & 46 & 11.565217 & 1.5585203 & 12.0 & 11.552632 & 1.48260 & 9.0 & 15.0 & 6.0 & 0.070929719 & -0.78236706 & 0.2297913 \\\\\n", - "\tCRT\\_Long & 2 & 37 & 5.918919 & 2.0999785 & 6.0 & 6.032258 & 2.96520 & 1.0 & 9.0 & 8.0 & -0.333766051 & -0.65458327 & 0.3452343 \\\\\n", - "\tCognitive\\_Biases\\_Sum & 3 & 37 & 4.081081 & 1.0375821 & 4.0 & 4.064516 & 1.48260 & 2.0 & 6.0 & 4.0 & -0.010816303 & -0.72373223 & 0.1705774 \\\\\n", - "\tEmocni\\_inteligence & 4 & 47 & 126.744681 & 12.1984819 & 125.0 & 126.641026 & 8.89560 & 99.0 & 161.0 & 62.0 & 0.297770704 & 0.35777018 & 1.7793315 \\\\\n", - "\tSocial\\_information\\_processing & 5 & 47 & 39.489362 & 4.6197701 & 39.0 & 39.641026 & 4.44780 & 29.0 & 49.0 & 20.0 & -0.373280330 & 0.01462936 & 0.6738627 \\\\\n", - "\tSocial\\_skills & 6 & 47 & 33.127660 & 7.9799123 & 33.0 & 33.256410 & 10.37820 & 16.0 & 47.0 & 31.0 & -0.170828653 & -1.02684616 & 1.1639898 \\\\\n", - "\tSocial\\_awareness & 7 & 45 & 38.755556 & 5.8002961 & 39.0 & 39.351351 & 5.93040 & 22.0 & 48.0 & 26.0 & -0.897669515 & 0.53583624 & 0.8646571 \\\\\n", - "\tExtraverze & 8 & 42 & 4.142857 & 1.3264923 & 4.0 & 4.147059 & 1.48260 & 1.0 & 6.5 & 5.5 & -0.064171692 & -0.76031006 & 0.2046822 \\\\\n", - "\tPrivetivost & 9 & 42 & 5.297619 & 0.7813335 & 5.0 & 5.264706 & 0.74130 & 3.5 & 7.0 & 3.5 & 0.145744518 & -0.80681918 & 0.1205624 \\\\\n", - "\tSvedomitost & 10 & 42 & 5.369048 & 1.2200536 & 5.5 & 5.470588 & 1.48260 & 1.5 & 7.0 & 5.5 & -0.951216912 & 0.88877406 & 0.1882584 \\\\\n", - "\tEmocni\\_stabilita & 11 & 42 & 4.047619 & 1.4725518 & 4.0 & 4.058824 & 1.85325 & 1.5 & 6.5 & 5.0 & -0.001530203 & -1.28300261 & 0.2272197 \\\\\n", - "\tOtevrenost & 12 & 42 & 5.190476 & 1.1736559 & 5.5 & 5.294118 & 0.74130 & 2.5 & 7.0 & 4.5 & -0.682064270 & -0.33740730 & 0.1810990 \\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "| | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | \n", - "|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| Obecna_inteligence | 1 | 46 | 11.565217 | 1.5585203 | 12.0 | 11.552632 | 1.48260 | 9.0 | 15.0 | 6.0 | 0.070929719 | -0.78236706 | 0.2297913 | \n", - "| CRT_Long | 2 | 37 | 5.918919 | 2.0999785 | 6.0 | 6.032258 | 2.96520 | 1.0 | 9.0 | 8.0 | -0.333766051 | -0.65458327 | 0.3452343 | \n", - "| Cognitive_Biases_Sum | 3 | 37 | 4.081081 | 1.0375821 | 4.0 | 4.064516 | 1.48260 | 2.0 | 6.0 | 4.0 | -0.010816303 | -0.72373223 | 0.1705774 | \n", - "| Emocni_inteligence | 4 | 47 | 126.744681 | 12.1984819 | 125.0 | 126.641026 | 8.89560 | 99.0 | 161.0 | 62.0 | 0.297770704 | 0.35777018 | 1.7793315 | \n", - "| Social_information_processing | 5 | 47 | 39.489362 | 4.6197701 | 39.0 | 39.641026 | 4.44780 | 29.0 | 49.0 | 20.0 | -0.373280330 | 0.01462936 | 0.6738627 | \n", - "| Social_skills | 6 | 47 | 33.127660 | 7.9799123 | 33.0 | 33.256410 | 10.37820 | 16.0 | 47.0 | 31.0 | -0.170828653 | -1.02684616 | 1.1639898 | \n", - "| Social_awareness | 7 | 45 | 38.755556 | 5.8002961 | 39.0 | 39.351351 | 5.93040 | 22.0 | 48.0 | 26.0 | -0.897669515 | 0.53583624 | 0.8646571 | \n", - "| Extraverze | 8 | 42 | 4.142857 | 1.3264923 | 4.0 | 4.147059 | 1.48260 | 1.0 | 6.5 | 5.5 | -0.064171692 | -0.76031006 | 0.2046822 | \n", - "| Privetivost | 9 | 42 | 5.297619 | 0.7813335 | 5.0 | 5.264706 | 0.74130 | 3.5 | 7.0 | 3.5 | 0.145744518 | -0.80681918 | 0.1205624 | \n", - "| Svedomitost | 10 | 42 | 5.369048 | 1.2200536 | 5.5 | 5.470588 | 1.48260 | 1.5 | 7.0 | 5.5 | -0.951216912 | 0.88877406 | 0.1882584 | \n", - "| Emocni_stabilita | 11 | 42 | 4.047619 | 1.4725518 | 4.0 | 4.058824 | 1.85325 | 1.5 | 6.5 | 5.0 | -0.001530203 | -1.28300261 | 0.2272197 | \n", - "| Otevrenost | 12 | 42 | 5.190476 | 1.1736559 | 5.5 | 5.294118 | 0.74130 | 2.5 | 7.0 | 4.5 | -0.682064270 | -0.33740730 | 0.1810990 | \n", - "\n", - "\n" - ], - "text/plain": [ - " vars n mean sd median trimmed \n", - "Obecna_inteligence 1 46 11.565217 1.5585203 12.0 11.552632\n", - "CRT_Long 2 37 5.918919 2.0999785 6.0 6.032258\n", - "Cognitive_Biases_Sum 3 37 4.081081 1.0375821 4.0 4.064516\n", - "Emocni_inteligence 4 47 126.744681 12.1984819 125.0 126.641026\n", - "Social_information_processing 5 47 39.489362 4.6197701 39.0 39.641026\n", - "Social_skills 6 47 33.127660 7.9799123 33.0 33.256410\n", - "Social_awareness 7 45 38.755556 5.8002961 39.0 39.351351\n", - "Extraverze 8 42 4.142857 1.3264923 4.0 4.147059\n", - "Privetivost 9 42 5.297619 0.7813335 5.0 5.264706\n", - "Svedomitost 10 42 5.369048 1.2200536 5.5 5.470588\n", - "Emocni_stabilita 11 42 4.047619 1.4725518 4.0 4.058824\n", - "Otevrenost 12 42 5.190476 1.1736559 5.5 5.294118\n", - " mad min max range skew \n", - "Obecna_inteligence 1.48260 9.0 15.0 6.0 0.070929719\n", - "CRT_Long 2.96520 1.0 9.0 8.0 -0.333766051\n", - "Cognitive_Biases_Sum 1.48260 2.0 6.0 4.0 -0.010816303\n", - "Emocni_inteligence 8.89560 99.0 161.0 62.0 0.297770704\n", - "Social_information_processing 4.44780 29.0 49.0 20.0 -0.373280330\n", - "Social_skills 10.37820 16.0 47.0 31.0 -0.170828653\n", - "Social_awareness 5.93040 22.0 48.0 26.0 -0.897669515\n", - "Extraverze 1.48260 1.0 6.5 5.5 -0.064171692\n", - "Privetivost 0.74130 3.5 7.0 3.5 0.145744518\n", - "Svedomitost 1.48260 1.5 7.0 5.5 -0.951216912\n", - "Emocni_stabilita 1.85325 1.5 6.5 5.0 -0.001530203\n", - "Otevrenost 0.74130 2.5 7.0 4.5 -0.682064270\n", - " kurtosis se \n", - "Obecna_inteligence -0.78236706 0.2297913\n", - "CRT_Long -0.65458327 0.3452343\n", - "Cognitive_Biases_Sum -0.72373223 0.1705774\n", - "Emocni_inteligence 0.35777018 1.7793315\n", - "Social_information_processing 0.01462936 0.6738627\n", - "Social_skills -1.02684616 1.1639898\n", - "Social_awareness 0.53583624 0.8646571\n", - "Extraverze -0.76031006 0.2046822\n", - "Privetivost -0.80681918 0.1205624\n", - "Svedomitost 0.88877406 0.1882584\n", - "Emocni_stabilita -1.28300261 0.2272197\n", - "Otevrenost -0.33740730 0.1810990" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Celkový přehled\n", - "library(psych)\n", - "myData %>%\n", - "select(-Vek, -Pohlavi) %>%\n", - "psych::describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Obecná inteligence" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "\"Removed 19 rows containing non-finite values (stat_boxplot).\"Warning message:\n", - "\"Removed 19 rows containing non-finite values (stat_summary).\"Warning message:\n", - "\"Removed 19 rows containing missing values (geom_point).\"Warning message:\n", - "\"Removed 19 rows containing non-finite values (stat_bin).\"" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "library(gridExtra)\n", - "\n", - "graf_IQ1 <- ggplot(myData, aes(Obecna_inteligence))+\n", - " geom_histogram(colour=\"white\", fill=\"lightblue\", binwidth = 1)+\n", - " scale_x_continuous(expand = c(0,0), limit = c(0, 15.5), breaks = seq(0,15,1))+\n", - " scale_y_continuous(expand = c(0,0), limit = c(0, 16), breaks = seq(0,16,2))+\n", - " xlab(\"Obecná inteligence (hrubý skór)\")+\n", - " ylab(\"Četnost\")\n", - " \n", - "graf_IQ2 <- ggplot(myData, aes(x=1, y= Obecna_inteligence))+\n", - " geom_boxplot(outlier.shape = NA)+\n", - " geom_jitter(color = \"grey\", width = 0.3, height = 0.05)+\n", - " stat_summary(fun.y=mean, colour=\"red\", geom=\"point\", shape=18, size=3)+\n", - " coord_flip()+\n", - " scale_y_continuous(expand = c(0,0), limit = c(0, 15.5), breaks = seq(0,15,1))+\n", - " theme(axis.text.y=element_blank(),\n", - " axis.ticks.y=element_blank())+\n", - " xlab(\"Probandi\")+\n", - " ylab(\"Obecná inteligence (hrubý skór)\")\n", - " \n", - "grid.arrange(graf_IQ2, graf_IQ1, ncol=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Emoční inteligence" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "\"Removed 18 rows containing non-finite values (stat_boxplot).\"Warning message:\n", - "\"Removed 18 rows containing non-finite values (stat_summary).\"Warning message:\n", - "\"Removed 18 rows containing missing values (geom_point).\"Warning message:\n", - "\"Removed 18 rows containing non-finite values (stat_bin).\"" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "graf_EQ1 <- ggplot(myData, aes(Emocni_inteligence))+\n", - " geom_histogram(colour=\"white\", fill=\"lightblue\", binwidth = 3)+\n", - " scale_x_continuous(expand = c(0,0), breaks = seq(33, 165, 12), limits = c(32.5, 165.5))+\n", - " scale_y_continuous(expand = c(0,0), limit = c(0, 10), breaks = seq(0,10,1))+\n", - " xlab(\"Emoční inteligence (hrubý skór)\")+\n", - " ylab(\"Četnost\")\n", - " \n", - "graf_EQ2 <- ggplot(myData, aes(x=1, y= Emocni_inteligence))+\n", - " geom_boxplot(outlier.shape = NA)+\n", - " geom_jitter(color = \"grey\", width = 0.3, height = 0.05)+\n", - " stat_summary(fun.y=mean, colour=\"red\", geom=\"point\", shape=18, size=3)+\n", - " coord_flip()+\n", - " scale_y_continuous(expand = c(0,0), breaks = seq(33, 165, 12), limits = c(32.5, 165.5))+\n", - " theme(axis.text.y=element_blank(),\n", - " axis.ticks.y=element_blank())+\n", - " xlab(\"Probandi\")+\n", - " ylab(\"Emoční inteligence (hrubý skór)\")\n", - " \n", - "grid.arrange(graf_EQ2, graf_EQ1, ncol=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Sociální inteligence" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "\"Removed 56 rows containing non-finite values (stat_boxplot).\"Warning message:\n", - "\"Removed 56 rows containing non-finite values (stat_summary).\"Warning message:\n", - "\"Removed 56 rows containing missing values (geom_point).\"" - ] - }, - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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+rIcVflxOQX9Ch2/Hyxpu\na/duyP59a366rj3YdlmkZmz867pV/8H6hux/zU/9x9qvt2cMD8IARFLkj9Utz1O1vA5MgUiK\nvPXG7ApRX2pd77tCFIikx+lkcDHfXmp9La8JUyCSHpXJxIFfr7crMIgAkQASgEgACUAkgAQg\nEkACEAkgAYgEkABEAkgAIgEk4P/idn7qf0B7KAAAAABJRU5ErkJggg==", - "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Upravme si formát dat z \"širokého\" na \"dlouhý\"\n", - "myData2 <- myData %>%\n", - "select(Social_information_processing, Social_skills, Social_awareness) %>%\n", - "gather(Type_Of_Intelligence, Score, Social_information_processing:Social_awareness, factor_key = T)\n", - "\n", - "ggplot(myData2, aes(Type_Of_Intelligence, Score))+\n", - " geom_boxplot(outlier.shape = NA)+\n", - " geom_jitter(color = \"grey\", width = 0.3, height = 0.05)+\n", - " stat_summary(fun.y=mean, colour=\"red\", geom=\"point\", shape=18, size=3)+\n", - " ylab(\"Hrubý skór\")+\n", - " xlab(\"Subškály sociální inteligence\")+\n", - " theme(axis.title.x=element_text(margin=margin(20,0,0,0)),\n", - " axis.title.y=element_text(margin=margin(0,15,0,0)))+\n", - " scale_y_continuous(breaks = seq(7, 49, 3), limits = c(6.5, 49.5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Kognitivní reflexivita" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "\"Removed 28 rows containing non-finite values (stat_boxplot).\"Warning message:\n", - "\"Removed 28 rows containing non-finite values (stat_summary).\"Warning message:\n", - "\"Removed 28 rows containing missing values (geom_point).\"Warning message:\n", - "\"Removed 28 rows containing non-finite values (stat_bin).\"" - ] - }, - { - "data": { - "image/png": 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cUGiRGQFEZnkFj+lhgBSWG0Bild8kzqgoD00J4CJEByGEv2PQVIE23rm7fjAiRA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\nAiSEBAIkhAQCJIQE+n9wY0MemYXt8QAAAABJRU5ErkJggg==", - "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "graf_CRT1 <- ggplot(myData, aes(CRT_Long))+\n", - " geom_histogram(colour=\"white\", fill=\"lightblue\", binwidth = 1)+\n", - " scale_x_continuous(expand = c(0,0), limit = c(0, 9.5), breaks = seq(0,9,1))+\n", - " scale_y_continuous(expand = c(0,0), limit = c(0, 10), breaks = seq(0,10,1))+\n", - " xlab(\"Kognitivní reflexivita (hrubý skór)\")+\n", - " ylab(\"Četnost\")\n", - " \n", - "graf_CRT2 <- ggplot(myData, aes(x=1, y= CRT_Long))+\n", - " geom_boxplot(outlier.shape = NA)+\n", - " geom_jitter(color = \"grey\", width = 0.3, height = 0.05)+\n", - " stat_summary(fun.y=mean, colour=\"red\", geom=\"point\", shape=18, size=3)+\n", - " coord_flip()+\n", - " scale_y_continuous(expand = c(0,0), limit = c(0, 9.5), breaks = seq(0,9,1))+\n", - " theme(axis.text.y=element_blank(),\n", - " axis.ticks.y=element_blank())+\n", - " xlab(\"Probandi\")+\n", - " ylab(\"Kognitivní reflexivita (hrubý skór)\")\n", - " \n", - "grid.arrange(graf_CRT2, graf_CRT1, ncol=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Tendence nepodléhat kognitivním zkreslením" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "\"Removed 28 rows containing non-finite values (stat_boxplot).\"Warning message:\n", - "\"Removed 28 rows containing non-finite values (stat_summary).\"Warning message:\n", - "\"Removed 28 rows containing missing values (geom_point).\"Warning message:\n", - "\"Removed 28 rows containing non-finite values (stat_bin).\"" - ] - }, - { - "data": { - "image/png": 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YgUipZ1G5FiRiNSKFrWbUSKGY1IoWhZ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\nF9YyQYYoI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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "graf_CB1 <- ggplot(myData, aes(Cognitive_Biases_Sum))+\n", - " geom_histogram(colour=\"white\", fill=\"lightblue\", binwidth = 1)+\n", - " scale_x_continuous(expand = c(0,0), limit = c(0, 6.5), breaks = seq(0,6,1))+\n", - " scale_y_continuous(expand = c(0,0), limit = c(0, 14), breaks = seq(0,14,2))+\n", - " xlab(\"Tendence nepodléhat kognitivním zkreslením (hrubý skór)\")+\n", - " ylab(\"Četnost\")\n", - " \n", - "graf_CB2 <- ggplot(myData, aes(x=1, y= Cognitive_Biases_Sum))+\n", - " geom_boxplot(outlier.shape = NA)+\n", - " geom_jitter(color = \"grey\", width = 0.3, height = 0.05)+\n", - " stat_summary(fun.y=mean, colour=\"red\", geom=\"point\", shape=18, size=3)+\n", - " coord_flip()+\n", - " scale_y_continuous(expand = c(0,0), limit = c(0, 6.5), breaks = seq(0,6,1))+\n", - " theme(axis.text.y=element_blank(),\n", - " axis.ticks.y=element_blank())+\n", - " xlab(\"Probandi\")+\n", - " ylab(\"Tendence nepodléhat kognitivním zkreslením (hrubý skór)\")\n", - " \n", - "grid.arrange(graf_CB2, graf_CB1, ncol=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Osobnostní charakteristiky (Big Five)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "\"Removed 115 rows containing non-finite values (stat_boxplot).\"Warning message:\n", - "\"Removed 115 rows containing non-finite values (stat_summary).\"Warning message:\n", - "\"Removed 115 rows containing missing values (geom_point).\"" - ] - }, - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Upravme si formát dat z \"širokého\" na \"dlouhý\"\n", - "myData3 <- myData %>%\n", - "select(Extraverze, Privetivost, Svedomitost, Emocni_stabilita, Otevrenost) %>%\n", - "gather(Osobnostni_faktor, Skor, Extraverze:Otevrenost, factor_key = T)\n", - "\n", - "ggplot(myData3, aes(Osobnostni_faktor, Skor))+\n", - " geom_boxplot(outlier.shape = NA)+\n", - " geom_jitter(color = \"grey\", width = 0.3, height = 0.05)+\n", - " stat_summary(fun.y=mean, colour=\"red\", geom=\"point\", shape=18, size=3)+\n", - " ylab(\"Hrubý skór\")+\n", - " xlab(\"Osobnostní faktory (Big Five)\")+\n", - " theme(axis.title.x=element_text(margin=margin(20,0,0,0)),\n", - " axis.title.y=element_text(margin=margin(0,15,0,0)))+\n", - " scale_y_continuous(breaks = seq(1, 7, 1), limits = c(0.5, 7.5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Inferenční statistika" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
Obecna_inteligenceCRT_LongCognitive_Biases_SumEmocni_inteligenceSocial_information_processingSocial_skillsSocial_awarenessExtraverzePrivetivostSvedomitostEmocni_stabilitaOtevrenost
Obecna_inteligence 1.00 0.36 0.25-0.27 0.08-0.28-0.06 0.19 0.02-0.40-0.27-0.25
CRT_Long 0.36 1.00 0.37 0.07-0.09-0.15-0.21-0.09 0.05-0.14-0.20 0.17
Cognitive_Biases_Sum 0.25 0.37 1.00 0.09-0.06 0.02-0.02 0.03 0.16-0.16 0.19-0.37
Emocni_inteligence-0.27 0.07 0.09 1.00 0.53 0.52 0.34 0.43 0.02 0.54 0.18 0.45
Social_information_processing 0.08-0.09-0.06 0.53 1.00 0.40 0.63 0.38-0.24 0.29-0.13 0.33
Social_skills-0.28-0.15 0.02 0.52 0.40 1.00 0.40 0.56-0.33 0.12 0.22 0.35
Social_awareness-0.06-0.21-0.02 0.34 0.63 0.40 1.00-0.01-0.06 0.34 0.02 0.37
Extraverze 0.19-0.09 0.03 0.43 0.38 0.56-0.01 1.00-0.11-0.08-0.01 0.19
Privetivost 0.02 0.05 0.16 0.02-0.24-0.33-0.06-0.11 1.00 0.18 0.01-0.12
Svedomitost-0.40-0.14-0.16 0.54 0.29 0.12 0.34-0.08 0.18 1.00 0.14-0.08
Emocni_stabilita-0.27-0.20 0.19 0.18-0.13 0.22 0.02-0.01 0.01 0.14 1.00 0.07
Otevrenost-0.25 0.17-0.37 0.45 0.33 0.35 0.37 0.19-0.12-0.08 0.07 1.00
\n" - ], - "text/latex": [ - "\\begin{tabular}{r|llllllllllll}\n", - " & Obecna\\_inteligence & CRT\\_Long & Cognitive\\_Biases\\_Sum & Emocni\\_inteligence & Social\\_information\\_processing & Social\\_skills & Social\\_awareness & Extraverze & Privetivost & Svedomitost & Emocni\\_stabilita & Otevrenost\\\\\n", - "\\hline\n", - "\tObecna\\_inteligence & 1.00 & 0.36 & 0.25 & -0.27 & 0.08 & -0.28 & -0.06 & 0.19 & 0.02 & -0.40 & -0.27 & -0.25\\\\\n", - "\tCRT\\_Long & 0.36 & 1.00 & 0.37 & 0.07 & -0.09 & -0.15 & -0.21 & -0.09 & 0.05 & -0.14 & -0.20 & 0.17\\\\\n", - "\tCognitive\\_Biases\\_Sum & 0.25 & 0.37 & 1.00 & 0.09 & -0.06 & 0.02 & -0.02 & 0.03 & 0.16 & -0.16 & 0.19 & -0.37\\\\\n", - "\tEmocni\\_inteligence & -0.27 & 0.07 & 0.09 & 1.00 & 0.53 & 0.52 & 0.34 & 0.43 & 0.02 & 0.54 & 0.18 & 0.45\\\\\n", - "\tSocial\\_information\\_processing & 0.08 & -0.09 & -0.06 & 0.53 & 1.00 & 0.40 & 0.63 & 0.38 & -0.24 & 0.29 & -0.13 & 0.33\\\\\n", - "\tSocial\\_skills & -0.28 & -0.15 & 0.02 & 0.52 & 0.40 & 1.00 & 0.40 & 0.56 & -0.33 & 0.12 & 0.22 & 0.35\\\\\n", - "\tSocial\\_awareness & -0.06 & -0.21 & -0.02 & 0.34 & 0.63 & 0.40 & 1.00 & -0.01 & -0.06 & 0.34 & 0.02 & 0.37\\\\\n", - "\tExtraverze & 0.19 & -0.09 & 0.03 & 0.43 & 0.38 & 0.56 & -0.01 & 1.00 & -0.11 & -0.08 & -0.01 & 0.19\\\\\n", - "\tPrivetivost & 0.02 & 0.05 & 0.16 & 0.02 & -0.24 & -0.33 & -0.06 & -0.11 & 1.00 & 0.18 & 0.01 & -0.12\\\\\n", - "\tSvedomitost & -0.40 & -0.14 & -0.16 & 0.54 & 0.29 & 0.12 & 0.34 & -0.08 & 0.18 & 1.00 & 0.14 & -0.08\\\\\n", - "\tEmocni\\_stabilita & -0.27 & -0.20 & 0.19 & 0.18 & -0.13 & 0.22 & 0.02 & -0.01 & 0.01 & 0.14 & 1.00 & 0.07\\\\\n", - "\tOtevrenost & -0.25 & 0.17 & -0.37 & 0.45 & 0.33 & 0.35 & 0.37 & 0.19 & -0.12 & -0.08 & 0.07 & 1.00\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "| | Obecna_inteligence | CRT_Long | Cognitive_Biases_Sum | Emocni_inteligence | Social_information_processing | Social_skills | Social_awareness | Extraverze | Privetivost | Svedomitost | Emocni_stabilita | Otevrenost | \n", - "|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| Obecna_inteligence | 1.00 | 0.36 | 0.25 | -0.27 | 0.08 | -0.28 | -0.06 | 0.19 | 0.02 | -0.40 | -0.27 | -0.25 | \n", - "| CRT_Long | 0.36 | 1.00 | 0.37 | 0.07 | -0.09 | -0.15 | -0.21 | -0.09 | 0.05 | -0.14 | -0.20 | 0.17 | \n", - "| Cognitive_Biases_Sum | 0.25 | 0.37 | 1.00 | 0.09 | -0.06 | 0.02 | -0.02 | 0.03 | 0.16 | -0.16 | 0.19 | -0.37 | \n", - "| Emocni_inteligence | -0.27 | 0.07 | 0.09 | 1.00 | 0.53 | 0.52 | 0.34 | 0.43 | 0.02 | 0.54 | 0.18 | 0.45 | \n", - "| Social_information_processing | 0.08 | -0.09 | -0.06 | 0.53 | 1.00 | 0.40 | 0.63 | 0.38 | -0.24 | 0.29 | -0.13 | 0.33 | \n", - "| Social_skills | -0.28 | -0.15 | 0.02 | 0.52 | 0.40 | 1.00 | 0.40 | 0.56 | -0.33 | 0.12 | 0.22 | 0.35 | \n", - "| Social_awareness | -0.06 | -0.21 | -0.02 | 0.34 | 0.63 | 0.40 | 1.00 | -0.01 | -0.06 | 0.34 | 0.02 | 0.37 | \n", - "| Extraverze | 0.19 | -0.09 | 0.03 | 0.43 | 0.38 | 0.56 | -0.01 | 1.00 | -0.11 | -0.08 | -0.01 | 0.19 | \n", - "| Privetivost | 0.02 | 0.05 | 0.16 | 0.02 | -0.24 | -0.33 | -0.06 | -0.11 | 1.00 | 0.18 | 0.01 | -0.12 | \n", - "| Svedomitost | -0.40 | -0.14 | -0.16 | 0.54 | 0.29 | 0.12 | 0.34 | -0.08 | 0.18 | 1.00 | 0.14 | -0.08 | \n", - "| Emocni_stabilita | -0.27 | -0.20 | 0.19 | 0.18 | -0.13 | 0.22 | 0.02 | -0.01 | 0.01 | 0.14 | 1.00 | 0.07 | \n", - "| Otevrenost | -0.25 | 0.17 | -0.37 | 0.45 | 0.33 | 0.35 | 0.37 | 0.19 | -0.12 | -0.08 | 0.07 | 1.00 | \n", - "\n", - "\n" - ], - "text/plain": [ - " Obecna_inteligence CRT_Long Cognitive_Biases_Sum\n", - "Obecna_inteligence 1.00 0.36 0.25 \n", - "CRT_Long 0.36 1.00 0.37 \n", - "Cognitive_Biases_Sum 0.25 0.37 1.00 \n", - "Emocni_inteligence -0.27 0.07 0.09 \n", - "Social_information_processing 0.08 -0.09 -0.06 \n", - "Social_skills -0.28 -0.15 0.02 \n", - "Social_awareness -0.06 -0.21 -0.02 \n", - "Extraverze 0.19 -0.09 0.03 \n", - "Privetivost 0.02 0.05 0.16 \n", - "Svedomitost -0.40 -0.14 -0.16 \n", - "Emocni_stabilita -0.27 -0.20 0.19 \n", - "Otevrenost -0.25 0.17 -0.37 \n", - " Emocni_inteligence Social_information_processing\n", - "Obecna_inteligence -0.27 0.08 \n", - "CRT_Long 0.07 -0.09 \n", - "Cognitive_Biases_Sum 0.09 -0.06 \n", - "Emocni_inteligence 1.00 0.53 \n", - "Social_information_processing 0.53 1.00 \n", - "Social_skills 0.52 0.40 \n", - "Social_awareness 0.34 0.63 \n", - "Extraverze 0.43 0.38 \n", - "Privetivost 0.02 -0.24 \n", - "Svedomitost 0.54 0.29 \n", - "Emocni_stabilita 0.18 -0.13 \n", - "Otevrenost 0.45 0.33 \n", - " Social_skills Social_awareness Extraverze\n", - "Obecna_inteligence -0.28 -0.06 0.19 \n", - "CRT_Long -0.15 -0.21 -0.09 \n", - "Cognitive_Biases_Sum 0.02 -0.02 0.03 \n", - "Emocni_inteligence 0.52 0.34 0.43 \n", - "Social_information_processing 0.40 0.63 0.38 \n", - "Social_skills 1.00 0.40 0.56 \n", - "Social_awareness 0.40 1.00 -0.01 \n", - "Extraverze 0.56 -0.01 1.00 \n", - "Privetivost -0.33 -0.06 -0.11 \n", - "Svedomitost 0.12 0.34 -0.08 \n", - "Emocni_stabilita 0.22 0.02 -0.01 \n", - "Otevrenost 0.35 0.37 0.19 \n", - " Privetivost Svedomitost Emocni_stabilita\n", - "Obecna_inteligence 0.02 -0.40 -0.27 \n", - "CRT_Long 0.05 -0.14 -0.20 \n", - "Cognitive_Biases_Sum 0.16 -0.16 0.19 \n", - "Emocni_inteligence 0.02 0.54 0.18 \n", - "Social_information_processing -0.24 0.29 -0.13 \n", - "Social_skills -0.33 0.12 0.22 \n", - "Social_awareness -0.06 0.34 0.02 \n", - "Extraverze -0.11 -0.08 -0.01 \n", - "Privetivost 1.00 0.18 0.01 \n", - "Svedomitost 0.18 1.00 0.14 \n", - "Emocni_stabilita 0.01 0.14 1.00 \n", - "Otevrenost -0.12 -0.08 0.07 \n", - " Otevrenost\n", - "Obecna_inteligence -0.25 \n", - "CRT_Long 0.17 \n", - "Cognitive_Biases_Sum -0.37 \n", - "Emocni_inteligence 0.45 \n", - "Social_information_processing 0.33 \n", - "Social_skills 0.35 \n", - "Social_awareness 0.37 \n", - "Extraverze 0.19 \n", - "Privetivost -0.12 \n", - "Svedomitost -0.08 \n", - "Emocni_stabilita 0.07 \n", - "Otevrenost 1.00 " - ] - }, - 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sc/2bQthPZkuVc0n89PTk6S4z134ABFUU6fPt3rkvPk73rNe4ssy1dfffX09LQz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\nmifk6hoL7eOrKG0DUDUA4F+NRiOTyaiLRNjaReuG4fzO47rt1WI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DRJTJZDqdTiaTUVN0RkZGWq2WLpEvhkaj0el0arVar5/tpfrkY0S0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3N6PRaH9/fzAY1JbG+3K8EgqFrrjiinK5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\n1Btlvs0gi31ZvXk17h7ksIRZIzDUmpC8BDFG3uo+/Je7G+nqSrJsyT18P4Q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- "text/plain": [ - "Plot with title \"\"" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "library(corrplot)\n", - "\n", - "M <- myData %>%\n", - "select(-Vek, -Pohlavi) %>%\n", - "cor(use=\"pairwise.complete.obs\", method=\"pearson\")\n", - "\n", - "res <- myData %>%\n", - "select(-Vek, -Pohlavi) %>%\n", - "cor.mtest(conf.level = .95)\n", - "\n", - "round(M,2)\n", - "\n", - "col <- colorRampPalette(c(\"#BB4444\", \"#EE9988\", \"#FFFFFF\", \"#77AADD\", \"#4477AA\"))\n", - "corrplot(M, method = \"color\", col = col(200),\n", - " type = \"upper\", order = \"AOE\", number.cex = .7, # AOE, hclust, FPC\n", - " addCoef.col = \"black\", # Add coefficient of correlation\n", - " tl.col = \"black\", tl.srt = 90, # Text label color and rotation\n", - " # Combine with significance\n", - " p.mat = res$p, sig.level = 0.05, insig = \"blank\", \n", - " # hide correlation coefficient on the principal diagonal\n", - " diag = FALSE)" - ] - } - ], - "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": "3.4.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Data_Inteligence.csv b/Inteligence/Data_Inteligence.csv similarity index 100% rename from Data_Inteligence.csv rename to Inteligence/Data_Inteligence.csv diff --git a/Seminar_obecne_psychologie_Inteligence.ipynb b/Inteligence/Seminar_obecne_psychologie_Inteligence.ipynb similarity index 100% rename from Seminar_obecne_psychologie_Inteligence.ipynb rename to Inteligence/Seminar_obecne_psychologie_Inteligence.ipynb diff --git a/Seminar_obecne_psychologie_Inteligence.Rproj b/Seminare-obecne-psychologie.Rproj similarity index 100% rename from Seminar_obecne_psychologie_Inteligence.Rproj rename to Seminare-obecne-psychologie.Rproj