From 9d76652209023c594c0f85427244925cde579309 Mon Sep 17 00:00:00 2001 From: Gabriel Demetrios Lafis <33261275+galafis@users.noreply.github.com> Date: Thu, 22 May 2025 14:23:27 -0300 Subject: [PATCH 1/2] Add files via upload --- .../unnamed-chunk-11-1.png | Bin 0 -> 4354 bytes .../unnamed-chunk-15-1.png | Bin 0 -> 6875 bytes .../unnamed-chunk-3-1.png | Bin 0 -> 3857 bytes .../unnamed-chunk-6-1.png | Bin 0 -> 5011 bytes 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 PA template files figure-hmtl/unnamed-chunk-11-1.png create mode 100644 PA template files figure-hmtl/unnamed-chunk-15-1.png create mode 100644 PA template files figure-hmtl/unnamed-chunk-3-1.png create mode 100644 PA template files figure-hmtl/unnamed-chunk-6-1.png diff --git a/PA template files figure-hmtl/unnamed-chunk-11-1.png b/PA template files figure-hmtl/unnamed-chunk-11-1.png new file mode 100644 index 0000000000000000000000000000000000000000..428a2cb80cfcfe1abda452966ead2b88f14d787c GIT binary patch literal 4354 zcmdT|c{r47{~wxR8cS2DEZHK<6rCF35aSUh*~XT}k|PaSvSeq5JWh%vr6f!9MBe5o z9E5B`M^c2bWy#hu8VQ{+m}K6EbAH#``CaGtdzb5a-|Kq+c&_{VJoop$@9+2X{e150 zzMmwLHBk&BjX@v~V&+Fo><|b+2!Rk7M{j_?SlR6pf}4#bO9xZ9?dj>^^ZCOkZ}ruz|zf40Eft&>D)^h`MRR0`epu=(Od08D?+L@$Hxot_nMR| zPqpHeU z`dhR|=av%{IbT->4ZhA4Z5EASgh?fT{_^vbhfMH;H&t8xnutj?;E)a8Whc+3YI8H5^XfhucI5^)>e}_@bsId--2L2P zZ%gm9<88TO)o$701Fa_AXRD0s%5>`iC*RzXQ)NR9Aq9|PF0le+XSFmm_X+2nd>5P_ zL$Ea@q^nR?8H0P;?4G;H+rLyk6LI!g`|9L(hhOeXs&la~@moE9264ymNm_(@*4Nfh zfUqfmkli{H%xeqLi>@v%nyH3zJ;LLr3wwMvRZF@wXqh&AQjd{S6Sv*2Ko2>QeRBNr z`TZWn(2b!mCq=PO>5wh=Hbj1EmSGQOT&+2*>6^aL=_h%?A~AQQe@kc}S;nV`7;Uea zve5}sGp1r1N9pku;ki#)pxW`;Wtk$*_pSPhL*@EByhmj*&e?PEP|VGKCZ)*|SR9)T z0k`|{FF`jR=ax~Zof&@3d$yDEY~r~5D71kD6K_jOq8 zEF9s+ygi>()9|^X^#VCugpyi!fSsmaVcz6r*n7ckqoY-(WA#`*!`CymA@~+F=Al36 zJzlTkhsG_Rp$c{Qoji1?B;@jyAceS#s-bwV;BUuJAWf5+~h; zVm14j9k*RoOC-!T7xsG}q8GcPcI($NEai^A^7*Hq zQMy*akE$mtl8#5!y74M+&a1CF?s#~yTi&qVVUktzzwfTp`}l%_iPwYoBZX0A%9K{`8B8HLYidO#h6Lu zucf?|&aJbf-Iq+bAhQe-DPMcvm^Ivs)aaZ!?8f@Ix@#v`3=dP@!v50SYMQr_b~aGz z!lhc3Uv-6Ou(z;U#MS542kddmFPq02vrS?gk+j$EJKzL5kuF#XKsZy8(T6 z+S9p;LzlU~XrMvN#rwV0)Oo8%Ya`tnUgQj5k%lhBM(TdJa<|#1{hFab*N4}}en{?%bh!^XXC7tj( zfT>dWLlxCDd$Z5GY(j2JtCPQ;ewPj>;Oey|sr7Oz@Ldm;G{G$swtHsRSSiP;Lw)#KMgICeYS&<@_KK zn2L{Hocf;>jM#ddeKvFJcu2_WPe{)D!DuWui{&)O{c*Ie(At8ZQlr;M+5b1SNUnUy z&fl}VK0^xaqppO^^g3pV%tv>0*|QbL;ncq&d64Lu#3buTon)#otg^5*k{d01T`kGW zq)s)BNNo#E8|=wWxC~VSGp!6Lq21diP`pKTVk~hDMAEXQ-MwF5Z|%r)q{8xj`WMB#Nh;fQ?z)*sa3z9RI zX0LHd94fZA`1>^x)K08RDGL@bvceDEssB#LBV(512~Br1R@ZKx`prH_|MgCdIa)&P zme8Pg1a_HpsA^6=g&7!xJSYI;$ZgNUH6dLlypXFtH_%TD=pXw9m@89?B?nJSIf&ydJDeY@?2iqkRQI zJ)cx)!*6jYF*c$<-2EOD|1sp@i$$?iNJW8>K5-ut9Xcoq$K&q6BKhZR{C&|DX&s&- z+>OpMWYM1c&r@_FCEX(jvD4JIE!6#prKd}h5{=v6mPaC7NMirzJ*OBOI?>h$jSKo{ zN6B6D?8~`AR|7}&-@jE&h88=Pmy&jI>O%};^U%?=f2V6-*gt++G26#ENVgjyh}W|B z==kF+%hcu)ZN}SFYH6)YLrF8sl&ks39gmotYZC0L;RjE+w&U*cLFqPTXlOU z)<+AGD!!MQHj-i+&S+LQLs>Dk@H}}i)mY=h-MAl$B7Mc_4e9f-OEmBKNFqy@s(Jci z`I)#@CF$1awu;ddo2%6Qex>x7sdAnB=o;b?Cg))6hmsjm57Ru);LXv!IcMISyo@eR zWt!?UODcsgi(4%Qm{%@j_|;3%JwB<&62#k`m1}>gx1UlsOqI#wH%MA+V*ZAFbK=0@%l)SZ0;^0W1*YxbxI%thI{KcAkBubY z9$ef?zV(rJZBJINC#g6g;DL^(nCjG$Y4r~GJI(h36N^(Xm9XCQ$SeFf-D<-w&EM`W q6syr-e91<>?w|Z8@TZmH3ogXVU0+JIzYPDsK$x3an-m-W0R9zwjYcg1 literal 0 HcmV?d00001 diff --git a/PA template files figure-hmtl/unnamed-chunk-15-1.png b/PA template files figure-hmtl/unnamed-chunk-15-1.png new file mode 100644 index 0000000000000000000000000000000000000000..88bac707c644a1c3b6f409ca8b13891abf8eee7e GIT binary patch literal 6875 zcma)>dpuNI|M=IQF@)T2(V3h=ZlN-Ca>-UGBtzpiB$dlVOkya+lvBBcA{1fgIxZu3 znGBUEgh^v6lA7BJ^_=JRJkR%cp5OQP$E>|(eLm~6*81$v-s}BYI~srFppbx^ z002P9{E&$)04M|i=)(pyx5dg+9BP)gs&`{`@3xMH1{f9s+O9vEv6@@zxnKI zP3~#zn$kNp9^$O+*P44Es?uVG)?%&pPQT6N_S6U7{q+XX4>jKe>t~;de>%BI=7uKM zd}EmH(`%%?rzVB_iYUXMUqlbRwT(R?NzV8~)uhI)Ggmt`C?rDCFH`YFRJxIu-M)kG zpDgP?3jK z;IElNjBwu9N3-JCt*x0=A7=9GV`PjnsLpJgFL(7c0(m#bTysAes(Y}G{YyO=jcd3( z3#B~(p0wG{dUB#RqNhWq2>b>u`kP&0&X3)N)7x{bX*p$t;pIqLNxv2|4r@X z9|0XyX^~bZH>yfwXc>Xfm)A@w`cE7YIPaGMQ>=K$aZD&;ck|qNd%bj%YN6V z-N#bTy*?0;ZvvkYjS`R<3U-3N}b4qdp{A{!uVr)CDd|&G2e$!YKG~&p-Y{9S(asr?EcRBXp0xAD z;7f|=AgxAMIyQ5oQI7xuerj+Fh6JnY757p5FihFV+E#B}W*ezijw120YRx)T+fBo%^UND6{ zR@d1y$5*|4BR&1}0t?Ri%PZdK8DE;Qbxe$o2EgDuINvXvzb|jZOWOqOPHODYerpXf zwn+I9^E~tCYrp<@-D?aHZYJ+BziHRNOipL)D1QDehYkb~?xKGcX(;xcyHZj`#Kiv5ycs_vcnH_0!t1|X6J?QVWN=q!seNOYHBMeFgB#NMYn z=au2K)Hv5c)kf0d?)%%C5^7K+A=XK;!e1j3`Ks{IMZc=OfYfm7f0`Adx1u0C=sAhO z+x<0>U&H;g;9M=gbKU6C^B3<(Whk1OkH>~@Q%}R!p1e5>irR=NLOALACI#Zr=v-TGP^#?S*gMmj6obhzEx42&w>!#P5PWUkx(84dr6i~o;W+lKag-4~O-mWS2EL&QJl-`jit{bKBaFB(@#{5d)X z4}AXwFvSs?DWUnIxN~FaJN>J^*(u=j^cmP$1>mLZfuV+X`X(nHm@GK5%=gSw4Wzxv z{k;iY(jAEH;Tu_&;S*_3Irx2t+VgK<>=>kR-7lCz=3Ho-JDf5cM6|y^dImJiZFuRt5EpVB4mO88_)0*phoNY-4d~rKO1wG@h>cF%X>#N-?3G=_r`%9?B=eG{ z7S#dcg_kUbE3v&`AcRJZ9 zj;d#6M+9*a=TV5myyt{%-Ja5@TIbWlL;WFq!9pq44*80`4z}S~W@-ZW7CEF>+eVgZ z*~$gQ>$)C~47&R$;_)i2bq$k1Gq&vJC4JVYTu7)$zz{&-OARR5xBLM)fNfiLj6+~I zQMU{7!Clo~LI_cnb>)(`ptLj1JFf7uA{bkd3FW%I-ny4`;%ulj>;BcKG;=X{*iq>k zt;Bg>hsQSk$2rc-wjoYm|7lcp`^;!0M4876j!awy!NR9!$SZNY?Dpc?XKmy+E^~2dRu>hKb26}g9zTm; zhn6}B!~I%QzEh_>HXw=bTwC{2J1ge4*eWAy=K33G&b7@l;WBt-dO|-_V;ePONeONr zc+l>riW>d+2{7nuJnhvt*7U1F=B^v=1Q{m`MeuutY1&fDbrZv`gA*&tSwT!*tJnGd zdO)uuL=HK*-aeTzAc|gEH43}#@6)Rk^ikt8*P*sP>k>!CgFo#y@;C`a7kH_wpC7OjApv^5vU-BG;gi9b+!A z6)($#EshKhB)yq39q#^$VIMn;CaM--@Lu;zD5G6rlRPt{?@N@drp}BB*E0_*jbC&b zHC&0VX|34i`xQH)KT>|I?clCta**xh@AF43RyHA~=e24wZ6Zr|jXP*JCUE9j-RThm z<_k7MvE{V^p+af24s9Kh(_q8`r>yXv{U?_}tj{_$i}pVCU;yh%UUrwYDR>u?#Di-L z7C{b#SG+lKbelSLOPMP#$uBFPhK44hi1DX$G>=1Pbz>TpsX>m`fPLZYCJc$*V2EYu zeA6B3H(7KL1`}o6S?D?zC)5N3cKc-!+Ih>QY|T~?&Jg5V5szUHgqZTjiqHb6gDab{ z)py5_KqJR>c{yrwU^TST&G}+u61}6kn*<@9`t_FCEpol*H3R@f^Yi;qDu&f9;|Q@0 ze!~mtki=PZXwO~~(`?Ai`psh$%ZupSMJI?UllPkfTm@)Wu55!r^K9^gtC>?^sd^?el$vDZD;?|^xUEGwPT;>?#$L7O3yy3 zP$!hh-9r=S8p$QW75~X_628TC!x6p{h-1X#Q8dF~RX+4#;KSXIir)M}JOx-k4KJ3a z#Z8H%3=|(LcPo+p0l7~s!$OTz{yc(bzmP0FFkZaYMq!Zg^3H5Rj(9| ziATh1&K^qTqI-%6jb}`h;mFMg+anIi`Cn=57ro^u<-F>-T~!DQ-|ts;Qb=d*T=>6y z^gl2-#;*E5S(yvLBKre=0kC7aaOHpc|9|WJpP;RQS_*F*YJKs}ty!)Jj4UsAWgYGi zvM;%(mUF0e%&f&PJl8-kxf%h<;{EWzoCI7xik?|r)4kX& z95cIHot#+qzhM3UwVpQ74~GhVGMS7z=fvM-11wLUqFjR4g4kc|+2NGKy*cV7APs}t zAq$vK{=z>x)$Gxc=olSIDvIW*&Mmxsn%*34k$3yaj!sG{a)9}iD2rOrFVAYk=}J(G z%2KX@+MGUFF4tL>Xf$bQI-EZh?)|67;<^#pX}5m1>L_5ZeEJs4M$}3)M9ja)K*?n! zFeCoUwtDJ90YvnBV}JW&OmDAo2G@%!6*1H8+v}AwBxWDzEC%BYM-8|RIl{}%MxU38s9 zOfpVmSV>TIAt0p#oDl6tU8gW0j8b*)iUPyjzGUMq_<}$n2}?OC-8P$;)6b~^L?Z=2 zOjtN;zH>EQmUYT$v~Y%UJ%u!_x?1!Tt>z)lX&suZOC)dT(Ma8qAP+oXE6ggwV+Bt! zxbx|5YF-2@{yl;)S(wCP1H`Vsbfcdz>9!M~b9E5`)?vhj0482fw0)SQ@+JfV;>(FP zgooC5^1dkE{4l7>?n{1LM=foY1oVts8}K4l|LBOqi&Xo;$DG}R9B8T^J)sc=K=Ir1 zOWjxuUqI9oVYNAZMH2w~r)dMYf7E{`;=FuXj`cl*9192ucG-(Y@K6Oqn)SiZOAfDU zI&sqsj-a^TbVj8W8hdm+RLq_`v=x~;j!jqHH+9D=^#nCWgyigOvqY!;X_Zl(DutE3?z=b&N_(dvk^C6LKTz595*RJbCZczV0LPQGw87EP)8OY zL@!H%V|m=+IPBA#1xPFP4Lot%rucBxyLfPnJle=Be{BipU_sa#MYvoi4Quo7f#l@> zJasHJiT~634nVl1bK#?)W>-TsKc3$!$@*j~QLEbp7M#_ks+Dy@gzQD0*gNg#a`XI) z&qZ68E-u7I^H>Boy!1Vn0|@%a$614$w*=rJWgV^2i-5^^IBO!MpIH#vk^@}%mC z=+Nh>-N}L1mRb(yhRj>)dUXnWQC=AROoW}C+dtndH1^5cv~SrRMd?AJ9V(=v{o^U2 zHdfRTvBwO{=g-w z^mm^ei;rSO+`|qzR_QY-A*x>Lk0bEBC>rVF#q|a4MB$_5+$^b+4_nY9SE%_RHByGB zfK`n}G6p%Wii}h3rGlpl62B-$ZD@>qztY0ma{y=$`?l;Vaofta)9CC5oTVU8H`o_L63u z37kfc^Ac*Li`*Xc)LYJT^|FJC_GCdBX?h=@)N6p}0XzNAUsBi+~b z{@ahNK?qmZe_EY^Mg}$()ZekJ-^{KX51!}8uxvQqr+I`9X(A{35^pakWmo!(Ann6m zGFhR}tjyY%KxpYe>O#l~9+5{}#|2*1LgSKdQU-f+^-HIJ^!<8;yZBC^2rr_kA+O&a zpz2;|vd|{w;(190rTJ$XtMBc2n$?c-mNrXD?l7e;srJnM_@MwonAJnQG6ypRXaTS1 z9mrWCBsU(GV2R=DeW80s&O|}U8$|~Pu!^?UdgGc0fo~OWaK=3exT|)Kz2it^SIt%n z*b^){EV@D}IwQ>-P55=j7&SlY4Mf`$IwC&6-Kzqu1N?snhJL=KPW61mT7Df~TvpUu z5mJOgLNVtg3h%+0OKT3z?3bBcqXv0GaDwA%V@>C7?tZO7s1pvwi-jG+?i6aVTIApyP08*p`m@KR8&9 z9I}>~lRar=BsP7-BF>|JMtO27H1r6$f2=3$Ze`5SPoA%K($*O9WZoN(88tdktj{^Y znK{3`70YMVed0{UF{-W^-MmhxL5q)d6bLnR{0`FWUzm2Pjjf}zbv-`O z*va+FnXJ$P<}EJHSNKJI)y`g%L{41$4PZ}TSdkp0cB&>s2BF(TJYz_^Us42gwA?(l zroDG}n0E1}WhvpPC3Wg{*S5D(zgn=3yAs5yDU97wt@FO}aDckG zwX?oK{O~X;&FgE@$kFe8REBkOk3#*vraiT~K+fpP`c9;MtB#1*g$q}w$n;}5;wwQH z^}T6FR2xR3PQj_cle1`caKn^$M=}NP5#}JoM`(V&&9(YAFG2jF{PmdX4kE;EoR$+J z4Zk`3FtbnG;{&sxsxfAJ(BpF5`fgHO3?)nFhl=9#mg?8@i;4lGXxydN(%1=3Z4)QM zr?M<+IU};``_8(d6w|52h78I_|GioRdWx`rzlSE$yrm?9xIKQ~bP=PZb>jEU*Co%) zCzDFt0!!-ysxy_Qt=noWr(LkUjw{9_4R3@P>= z{S@O^DN!qd4Tn_8=SsuAawop zdeYpp{S8Ameke57Di~^cfB)TjU-LZg%!H|-(n6mLM3c}F*yjL3RNkFy*7lO_SJI(5 z-7oc$*P;q{fmB_APCZmC>3F5`hCMo*V+4lT zLXValFy&fGE;61V7~oY`7%Dk0y}_+eRuH$G1cf0(50BDA9N&o-= literal 0 HcmV?d00001 diff --git a/PA template files figure-hmtl/unnamed-chunk-3-1.png b/PA template files figure-hmtl/unnamed-chunk-3-1.png new file mode 100644 index 0000000000000000000000000000000000000000..29b2738fced1dbb7195c97174360d4586eed13bc GIT binary patch literal 3857 zcmeHKc~nzZ8jm4EKm-g+vFwP7paNPz8XyD(BU&IL3WA`Pf&pYJ`x0V`1=*ywD99Q` zB@k9w6e3H9A_@XQ2)hJOAP8X#Ng$Y{FR{*EfW%A0AlB8APx+Y=x>~CfU&QyPaqI5S|61O!Jy;k zLO>vyR^hXyJ&^bvFe$gSwsMNhWxSsJ_MYjEW0R#ETrau$hTPBF)1De^TxGt`f`1_= zae2hoU&rEV%c})3P_%@?UD)0u+URS?2oh;Xm@sMzR~IM+P-$=Jco+H z$Qsp(sVUn84XL~t*Q1n0D9^;u7P@-ZuOoCQjfHz_YOh zn~G9D%MK5F)+0%U*ovDiZ;^d|7K4Gz&Rev+*DiJE-3w6l4&j#(7ZHwiHl7@6YTGWG zWY$6dmqXcP)3=P;VfDJ;o%7S<%2TIO`^@OiTr-`&537xUn^I~J=`&jg2F(S|W3i~Z z9n(*S8!JwE;L1&kXM0Ry6snD80yIuny@)0o$VP<-SeG7t+f03a(wGr;fHnxf+K>3n zY0=TY?&LM(g{4?Q{hftF_JY+|i)gI-<8T8phN?HGz?~UdCtDA%scCH=v65Gs_GR^Sq)oAnahjIl2iQ=x^PY$M-QwBzi=3NU=%*vESg!l-2 zRu->MHoxr#{zc54kHslr!WbOLi0P9+-_6LZQT*+Fy=HIAYIP%C)RFApHby88dIxl! zSzPQjXU;>om|QJTQa-N+&{8+ z$=7}G%5!St8A9>My&){t!HmK+OtJ1@p3@kf(F!c@@(dYbCn-Q{^NT&|xz`blD){|{ z?Ys6sv-3{Ig?Gv@+I5yT=aJQ0I!NyN!M=-iV>uVHu5eIMWgV@h5hq!y(;fYZW9M>M zl{T`B)_`r)+)9^ezB;ueAeSbC66dN+zVJA%iyg)KjNC^C$tV()?QJ54`t6@E%V=NVzxEBRp3{a^TB{{0m${>&>awAfkO zM5MIKZZ)v+#ap2g{a61TcoB0eUxjJ+lQhKFP@TRC9gyh1Xi8aYjXP{b5Qmf;{X=-- z`5gM*w54z*K(FCM4{Ev8Uka~Wc>)gcC7j#sv6~OYD_?h>8%VML!=f;K$}(2JnRQQ| z`0J1!c8n`piYKl4Fx?E`c;#)DvL$MZjsPvFE#L%6_`moozJcMB6LSW=L4!vK;+~|8 z5*2a+c?Z~dhwFp>ryI;5=a1(tI{8>>;rVy!`CA(G9G*S(-UMv?+__kO+@STf3|?7* zwB}i;i{(FXR3W6v}L_l!gnb@7^_+F(HOd|OUuraae>$OX2?8eOzK(3>pVY&YgVmY#KO$qEDIbEu>sSj9PHR!r!Fe|-tYRcW z;rYZREy`S^dQBp-h;(89PUS^800lDuu*CVR7YSrApi1lNKT7CZ(JTg(w1vj{W#hyI z@>XzOfFhk=-SD!(97Yd?i~ve7j4a+BMX-Rt)$r`?e}Ii(MP6l#*T8fVvgl0`R~Q{B z3@!>IAq*|@`srW*-J0LDw}P2m)LGgokGDSz^sx5v$ti8hT!I)wkh}UJRv#p~@j@Ek z8sH8SMp2Pk<7w!w7}FQ8Nv!QF)LDJMzq@dqCkdlFfA|9bC5=xf{5dom`k>jRsY0^d z8pIX7^kb8%xkzmpPFLzP(l-Pqj2I9i%&pfm!nWGbCE0%!8I~h2OnMU?v>2G`FY3{w zxxy5F{IxJyKfwS6iOw8UpzWdbo~-fHr&>iMNQK_%F^eQ3q3N-M=S$)jw<*yN6+{Gq zZ)s9AbL8nQL3n*;6jTQ-36_5JHD2Z~SYGb+3~r+5A^mdz9?Ys#cdywAn!;{%eTo-i zX#%2WMX)l_q2MRM?lVjodx9K9`MVIX+KxZJ%p{9*=h_SaKg=BQ>=G5)$4#n@MtWL` z_FYcv#ZJ!CQ!WP|v<$6>jc`=laQ8uxFr4&OOp->MnCtz7|M0(#g20)hEkUN5*%9VS zcixnTc<`+y*T(+TuyI!B6E6xlLi_uScdKtImp0z1jC}dnJGeW?Lrf$5(Ne)71lu}u z!(aQdyegXhF2b^iEzP(#*B6=)Rc&k8Rh}IjBhCb0hE6z@5~K33G;tFmn>e+@S>1+# zdg3exZr4VX!HNyoZeE4Wib;#1hq*b76u2iNISx$zy#?a}EN@RN1Zx+)_Ck*6ojvV$ zHwDV&zG< z2kpEeWkD*-^WoeWr6+RzX`(~+s!F?lP%y{@$~!XN+APnBf{#9ir{@@Lrgb>;&gxV` zKl{xIQo`nY9YbH#ZA+$ECg!==l+`@-@=*jvFAG83L!2N{byzU;>>e(?R_^4yTSX5` z*9&)v+T89~e4u%NRmGF4UO6z(saa;}AxD=f%|u0Gy#<@4r`Z$v6;`Oqv+BA63GQ>T zF4~m#?GvQP>|(w`b973&i(b`KQnY&8(r=E%ih@9?p7_O9SGD*sK1?cN+rS2D|JXs7 iS&gco|Mj}a|J(5)fl&uqZw|Q5f^2Q&G{NZ;;z^hyU literal 0 HcmV?d00001 diff --git a/PA template files figure-hmtl/unnamed-chunk-6-1.png b/PA template files figure-hmtl/unnamed-chunk-6-1.png new file mode 100644 index 0000000000000000000000000000000000000000..dd49d0ffb204d2dab783e1b1d927b809f11fb4a9 GIT binary patch literal 5011 zcmcIodpuOz+o$$0#%)|DWQ@B;QOGT~UF6Q>R^*bR#{FKHNPDOwp-3);?MTK%N+Or| zMY<48j1VaalNk3yGUJ^(@ANnw!mCm+#iu!IU0K>O&(6iL$GtP;PjiDn`J}A?>Q1*xRrYp7ne}` zkB9qt7{!l^OHA9^9CV4jIa`=^y%dk;TG%`n|GMXL=h0gMj4M6CFdpu`6751^ZmHkNltjV!!`xf-xL`)R^*i44AZ^@`@O(^G zdy_t07zCAmYU>@Ejk0Kq%rt15%9>S-`_$;_G^u_=9ZOfBRvOs3QPY?F!VAT*LU-!d z*Rx+lDGe)iiWk|jc24SN+`jiPJmuOhiPoDdb>;*_kF!~L;6BYGPdb+I39+`NK>DK5 zgua{?cht&q=R{kM^GWq0@mBBNe3qYJc1~a^p3)=)DT86M(@^|G3qU`ao7mYt{;f(uDS5i=3zv!qlq=mbI>na|5GeT z4n|VS)r?EyZNdLyTPG~`*~QA?TF3T|*{GqOS*-JQZ=;4!C9sCzNnTEPMRiL3 zlvn(bYQfi$W!iaZ^_f8W()I=Jo+)G+=}E@%L7c}fe}_(`1N6aHi9Z8gMa35h+q znT?v3Q0H4e`l}ixcgo|kaJa_BGt9;MraED&Ts$jJOGM{}d~ondQ}=eivX-m@+`)kyvUvW+U;_G?IafBV; zYo971@jpkl&DRn@M)V9Jx&j0rnpfUA`Yw z_k7Hp5EBi=40SNWPeustLWukmH{qv;(pbK`i^vQAFrol}D=(;wqZk&DZ-q5I4eM1q zs)mZ+R1`cV3AkE1($)uF{-%E&N$v3i3rW(W=fpYjPZ9Vc)PE!9?6?NRIG)iN949DH z34FkA&BBy3aSGb1FRUAUlWGQ0)tA_qt$!Rt5* z7tqfOB5)KFB%TXg0SVklsxXB5GxSQ(Q%ON>;ID@NgOC3WYF5X|{gu$4^N0snt!gZT z!CgX%&~%?XWr;oTI!NlTSvwBkD%iHeCzd>Tz!l#DYqJ)SqdMIJKiz#1tzA^S-~Q9| zrI1oC9FJoN7+Nk=>q<;K6o95nm5Ho@PkIKII5U<7Yq&GIgYx$)))8+=0133t+ty?3 z=IcWZA#Mx^t9Kl1>luvDk{EOyYuCV)iqslBg@6 z^C+h1Xr8kp4xBkJRaVQ5<f=T>{n2_M3jP~CYqmt45Vrwm)7eXjws33O$EoH$sQ;NdLsRUQm2RrLAs%JiV7@gjtA^% zdq})s`RBcym*!;iM#N>e!L{mk=Xfr)O6^Y3h(DFR|_-qC&Jhx*4=ze@O36`@+s1yC6 zF3iKwJJRNWZo3R23Y~;rmH=CsCjjHfCFW>0l8OzLEZ#}nfrlKBdTFtK8%OSHrdWdm z+qHp&lr*+U&R4rP1-BbecXv{{?5uEVkei+tM_fH3LN^hP8jWZe70-p+^x0%891sAo ztV~>K8?Pqag~0yy?&FT&_!OF61NRp+Oev%*+sGK6TS|dU4&DTyXLLkNrbQ9iDC~@I z*7~Zm)^1BePIF(JWNisEp*yNRZ^{Rd^$xdwbiPuI-eDVeNcu#12iiq(E;?{;)P&d} z(v=G#(@@=tok+C9=$Ds!9_sGoi%;1?|xsy zuiiN`d!@m-Pj}hF%BK;#+`9K>IzENg@sfyyX^r~I=nr^`t)%&w%Ayi6Dz833TR5Gs z@iY7dSuo?`{wQA@NQheBP~X%FS(`sC2J&JqqZEmbaht}Q1gq8Nu&&qgM2}b*@WGj3 zN^;x{-Lr#A#9g+fu*T{uHk24Ir*j6E0=?BsFSei!82Gp`oh=tqY#Y8O@SaVD2eQ9} z_>0t*e#{DQr-?78IM_fc#;IGuQZ?omdShMG?e5SrQ zYc2#lJm*MU0*q@QHt+^Rr-RPo^j4dfZT6p6m2zAXD&W;J6N%+AJ7hd2!;p{_F1CP( zd9xsf@_5%ATYn@KPVwVd*q-2=yiM%Y9ON|K=gthK_Hqj&77t@X1=E3dGS~Q+~IvI4eO6EZdXwq*-wAY zO$Z4ByvPF|mY!88B8)HUr8xVWn4Rs$$`fZt;&=TBr7*IR836X>oV&y9+Bop)op#C_ zHzx{RU>7eQ*m$q{VzU0yF1R`V*vxlFJkIyk$5_20RM4%gNnAPFG{!Ilffd zPea7Eb8Z;qg@**%e#E#)zBF@P^GF3Blz1J^^e;zBe@5HM>XdEw3b)o5(P&Z=^bpl) z39+!IwKZt9z75*~Idt#i&S^~6wvEsmXlm-TL>0sxQlv=p1Kq|FAs^Hk*KCcT9&=~f z8lz#2c0FsApZbfQxs4$cNlkHZy#-Ac@5T{hryg$w`}5U_(R&i(NDn(8cH$m9^C9OV z<^ipjzVAjAA^J)C-F6^8gWVR+-~Qq#@2|0 zhUX&DF$mq~%3Y?#;_0VuT)H?$0=iELMSoVms z@hD2@6{Q0FU-)N{WYxW~1cm22k>t_}bgkrf5#h4ppD0B=yCFik~L$Nh!L_iZb#64?57 zVPcz$oH*{;s2cVkF5d6xD4g@P5Ev!UAS-~+i zc%)5D|4ZuM8U`!)%y|CQ`otT|a>rr2@7f#65u=6eIgu6%uG)>-HHk}i!?wH*aNjrG zP?(tYvE{YH-ip;eb&XayyYfnmCsnDI-4c9&HKQw^#?LnFxWzO%>8p@_S>qBlud)}G z;=T8b2WkulBg%_P%C|q;?XLUv7}{k=N593ss2wVWu{_4v_CZ((fBO;1W|%LYUHZQH ztPmiZOLPRQEr9;COqliZZbv|Q5a4Q)41e-OQu8@K_$j3*c5#>}{p%2;+zR6oY2T%< znZJZNIoqYO?Ccm@9{g!Is>3`!EXKiVGBB(>l4ebZQ)>kcmFZyKLBBhzFGhP literal 0 HcmV?d00001 From 523da230bcd2606d3c21125fe3ab9137537eb5f7 Mon Sep 17 00:00:00 2001 From: Gabriel Demetrios Lafis <33261275+galafis@users.noreply.github.com> Date: Thu, 22 May 2025 14:27:52 -0300 Subject: [PATCH 2/2] Add files via upload --- PA1_template.Rmd | 188 ++++++++++++++--- PA1_template.html | 527 ++++++++++++++++++++++++++++++++++++++++++++++ PA1_template.md | 208 ++++++++++++++++++ README.md | 1 + 4 files changed, 899 insertions(+), 25 deletions(-) create mode 100644 PA1_template.html create mode 100644 PA1_template.md diff --git a/PA1_template.Rmd b/PA1_template.Rmd index d5cc677c93d..220cf7a774b 100644 --- a/PA1_template.Rmd +++ b/PA1_template.Rmd @@ -1,25 +1,163 @@ ---- -title: "Reproducible Research: Peer Assessment 1" -output: - html_document: - keep_md: true ---- - - -## Loading and preprocessing the data - - - -## What is mean total number of steps taken per day? - - - -## What is the average daily activity pattern? - - - -## Imputing missing values - - - -## Are there differences in activity patterns between weekdays and weekends? +--- +title: "Reproducible Research: Peer Assessment 1" +output: + html_document: + keep_md: true +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) + +#libraries +library(dplyr) +library(ggplot2) + +# settings +Sys.setlocale("LC_TIME", "C") +``` + +## Loading and preprocessing the data + +In this first section, I load the data and process the variable date to get a correct format of date. + +```{r} +# read data +activity <- read.csv("activity.csv") +activity$date <- as.Date(activity$date) + +``` + +## What is mean total number of steps taken per day? + +I aggregate the data to determine the total number of steps taken each day. The dplyr package is necessary for this. + +```{r} +# manage data (aggregate per day) +act_day <- activity %>% + group_by(date)%>% + summarise(all_steps = sum(steps)) +``` + +### Histogram + +I use the previous data to plot the histogram of the total number of steps taken each day. + +```{r} +hist(act_day$all_steps, breaks = 10, + main = "Histogram of the total number of steps taken each day", + xlab = "Steps") +``` + +### Mean and median of the total number of steps taken per day + +Also, I use the previous data to obtain the mean and median of the total number of steps taken per day. + +```{r} +paste("Mean:", round(mean(act_day$all_steps, na.rm = TRUE), 2)) +paste("Median:", median(act_day$all_steps, na.rm = TRUE)) +``` + +## What is the average daily activity pattern? + +First, I obtain the mean of number of steps taken per interval. I ommit the NA values. + +```{r} +act_interval <- activity %>% + group_by(interval)%>% + summarise(mean_steps = mean(steps, na.rm = T )) +``` + +Now, I can plot the time series. + +```{r} +plot(act_interval$interval, act_interval$mean_steps, type = "l", + main = "Mean of number of steps per interval", xlab = "Interval", ylab = "Steps") +``` + +Finally, I find the maximum number of steps and the interval that contains this maximum. + +***Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?*** + +The maximum number of steps (average across all the days in the dataset) is 206.17 and it is taken in the interval 835. + +```{r} +max_act <- max(act_interval$mean_steps) +act_interval[act_interval$mean_steps == max_act, ] +``` + +## Imputing missing values + +I obtain the total of NAs in the dataset. + +```{r} +paste("Total of NAs in the dataset:", sum(is.na(activity$steps))) +``` + +To impute the missing values in the dataset, I use the mean of the intervals obtained in the previous section, and then assign this mean to each NA value. Then I get the new dataset without NAs. + +```{r} +data_NA <- merge(activity[is.na(activity$steps),], act_interval, by = "interval") +data_NA$steps <-data_NA$mean_steps +data_NA <- data_NA[,1:3] + +data_NA <- rbind(data_NA,activity[!is.na(activity$steps),] ) + +``` + +Now, I obtain the same aggregation for the histogram and the mean and median of the dataset. + +```{r} +# manage data (aggregate per day) +act_day_NA <- data_NA %>% + group_by(date)%>% + summarise(all_steps = sum(steps)) +``` + +### Histogram without NAs + +```{r} +hist(act_day_NA$all_steps, breaks = 10, + main = "Histogram of the total number of steps taken each day \n (without NAs)") +``` + +### Mean and median of the total number of steps taken per day (without NAs) + +```{r} +paste("Mean (without NAs) :", round(mean(act_day_NA$all_steps, na.rm = TRUE), 2)) +paste("Median (without NAs):", median(act_day_NA$all_steps, na.rm = TRUE)) +``` + +***Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?*** + +The mean is the same, but the median differs a little. The mean doesn't differ because the number missing values are the same in all the intervals and days, if we fill them with the mean of each interval, nether the mean of interval and the mean of day change. + +## Are there differences in activity patterns between weekdays and weekends? + +I calculate the new factor variable ("weekday" and "weekend"), + +```{r} +data_NA$weekday_s <- weekdays(data_NA$date) +data_NA$weekday <- ifelse(data_NA$weekday_s == "Sunday" | data_NA$weekday_s == "Saturday", "Weekend", "Weekday" ) +data_NA$weekday <- as.factor(data_NA$weekday) +``` + +I aggregate the data by interval. + +```{r} +# manage data (aggregate per interval-weekday factor) +act_day_weekday <- data_NA %>% + group_by(weekday,interval)%>% + summarise(mean_steps = mean(steps),.groups = "drop") +``` + +Finnaly, I plot the number of steps taken by interval differencing between weekdays and weekends + +```{r} +ggplot(act_day_weekday, aes(x = interval, y = mean_steps)) + + geom_line() + + facet_wrap(~ weekday, ncol = 1) + + theme_minimal() + + labs(title = "Total number of steps taken each interval", + x = "Interval", y = "Steps") + + theme( plot.title = element_text(hjust = 0.5) ) +``` diff --git a/PA1_template.html b/PA1_template.html new file mode 100644 index 00000000000..895c2307a57 --- /dev/null +++ b/PA1_template.html @@ -0,0 +1,527 @@ + + + + + + + + + + + + + +Reproducible Research: Peer Assessment 1 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Loading and preprocessing the data

+

In this first section, I load the data and process the variable date +to get a correct format of date.

+
# read data
+activity <- read.csv("activity.csv")
+activity$date <- as.Date(activity$date)
+
+
+

What is mean total number of steps taken per day?

+

I aggregate the data to determine the total number of steps taken +each day. The dplyr package is necessary for this.

+
# manage data (aggregate per day)
+act_day <- activity %>% 
+  group_by(date)%>% 
+  summarise(all_steps = sum(steps))
+
+

Histogram

+

I use the previous data to plot the histogram of the total number of +steps taken each day.

+
hist(act_day$all_steps, breaks = 10,
+     main = "Histogram of the total number of steps taken each day", 
+     xlab = "Steps")
+

+
+
+

Mean and median of the total number of steps taken per day

+

Also, I use the previous data to obtain the mean and median of the +total number of steps taken per day.

+
paste("Mean:", round(mean(act_day$all_steps, na.rm = TRUE), 2))
+
## [1] "Mean: 10766.19"
+
paste("Median:", median(act_day$all_steps, na.rm = TRUE))
+
## [1] "Median: 10765"
+
+
+
+

What is the average daily activity pattern?

+

First, I obtain the mean of number of steps taken per interval. I +ommit the NA values.

+
act_interval <- activity %>% 
+  group_by(interval)%>% 
+  summarise(mean_steps = mean(steps, na.rm = T ))
+

Now, I can plot the time series.

+
plot(act_interval$interval, act_interval$mean_steps, type = "l", 
+     main = "Mean of number of steps per interval", xlab = "Interval", ylab = "Steps")
+

+

Finally, I find the maximum number of steps and the interval that +contains this maximum.

+

Which 5-minute interval, on average across all the days +in the dataset, contains the maximum number of steps?

+

The maximum number of steps (average across all the days in the +dataset) is 206.17 and it is taken in the interval 835.

+
max_act <- max(act_interval$mean_steps)
+act_interval[act_interval$mean_steps == max_act, ]
+
## # A tibble: 1 × 2
+##   interval mean_steps
+##      <int>      <dbl>
+## 1      835       206.
+
+
+

Imputing missing values

+

I obtain the total of NAs in the dataset.

+
paste("Total of NAs in the dataset:", sum(is.na(activity$steps)))
+
## [1] "Total of NAs in the dataset: 2304"
+

To impute the missing values in the dataset, I use the mean of the +intervals obtained in the previous section, and then assign this mean to +each NA value. Then I get the new dataset without NAs.

+
data_NA <- merge(activity[is.na(activity$steps),], act_interval, by = "interval")
+data_NA$steps <-data_NA$mean_steps
+data_NA <- data_NA[,1:3]
+
+data_NA <- rbind(data_NA,activity[!is.na(activity$steps),] )
+

Now, I obtain the same aggregation for the histogram and the mean and +median of the dataset.

+
# manage data (aggregate per day)
+act_day_NA <- data_NA %>% 
+  group_by(date)%>% 
+  summarise(all_steps = sum(steps))
+
+

Histogram without NAs

+
hist(act_day_NA$all_steps, breaks = 10,
+     main = "Histogram of the total number of steps taken each day \n (without NAs)")
+

+
+
+

Mean and median of the total number of steps taken per day (without +NAs)

+
paste("Mean (without NAs) :", round(mean(act_day_NA$all_steps, na.rm = TRUE), 2))
+
## [1] "Mean (without NAs) : 10766.19"
+
paste("Median (without NAs):", median(act_day_NA$all_steps, na.rm = TRUE))
+
## [1] "Median (without NAs): 10766.1886792453"
+

Do these values differ from the estimates from the first +part of the assignment? What is the impact of imputing missing data on +the estimates of the total daily number of steps?

+

The mean is the same, but the median differs a little. The mean +doesn’t differ because the number missing values are the same in all the +intervals and days, if we fill them with the mean of each interval, +nether the mean of interval and the mean of day change.

+
+
+
+

Are there differences in activity patterns between weekdays and +weekends?

+

I calculate the new factor variable (“weekday” and “weekend”),

+
data_NA$weekday_s <- weekdays(data_NA$date)
+data_NA$weekday <- ifelse(data_NA$weekday_s == "Sunday" | data_NA$weekday_s == "Saturday", "Weekend", "Weekday" )
+data_NA$weekday <- as.factor(data_NA$weekday)
+

I aggregate the data by interval.

+
# manage data (aggregate per interval-weekday factor)
+act_day_weekday <- data_NA %>% 
+  group_by(weekday,interval)%>% 
+  summarise(mean_steps = mean(steps),.groups = "drop")
+

Finnaly, I plot the number of steps taken by interval differencing +between weekdays and weekends

+
ggplot(act_day_weekday, aes(x = interval, y = mean_steps)) +
+  geom_line() +
+  facet_wrap(~ weekday, ncol = 1) +
+  theme_minimal() +
+  labs(title = "Total number of steps taken each interval",
+       x = "Interval", y = "Steps") +
+  theme( plot.title = element_text(hjust = 0.5) )
+

+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/PA1_template.md b/PA1_template.md new file mode 100644 index 00000000000..b95c95be740 --- /dev/null +++ b/PA1_template.md @@ -0,0 +1,208 @@ +--- +title: "Reproducible Research: Peer Assessment 1" +output: + html_document: + keep_md: true +--- + + + +## Loading and preprocessing the data + +In this first section, I load the data and process the variable date to get a correct format of date. + + +``` r +# read data +activity <- read.csv("activity.csv") +activity$date <- as.Date(activity$date) +``` + +## What is mean total number of steps taken per day? + +I aggregate the data to determine the total number of steps taken each day. The dplyr package is necessary for this. + + +``` r +# manage data (aggregate per day) +act_day <- activity %>% + group_by(date)%>% + summarise(all_steps = sum(steps)) +``` + +### Histogram + +I use the previous data to plot the histogram of the total number of steps taken each day. + + +``` r +hist(act_day$all_steps, breaks = 10, + main = "Histogram of the total number of steps taken each day", + xlab = "Steps") +``` + +![](PA1_template_files/figure-html/unnamed-chunk-3-1.png) + +### Mean and median of the total number of steps taken per day + +Also, I use the previous data to obtain the mean and median of the total number of steps taken per day. + + +``` r +paste("Mean:", round(mean(act_day$all_steps, na.rm = TRUE), 2)) +``` + +``` +## [1] "Mean: 10766.19" +``` + +``` r +paste("Median:", median(act_day$all_steps, na.rm = TRUE)) +``` + +``` +## [1] "Median: 10765" +``` + +## What is the average daily activity pattern? + +First, I obtain the mean of number of steps taken per interval. I ommit the NA values. + + +``` r +act_interval <- activity %>% + group_by(interval)%>% + summarise(mean_steps = mean(steps, na.rm = T )) +``` + +Now, I can plot the time series. + + +``` r +plot(act_interval$interval, act_interval$mean_steps, type = "l", + main = "Mean of number of steps per interval", xlab = "Interval", ylab = "Steps") +``` + +![](PA1_template_files/figure-html/unnamed-chunk-6-1.png) + +Finally, I find the maximum number of steps and the interval that contains this maximum. + +***Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?*** + +The maximum number of steps (average across all the days in the dataset) is 206.17 and it is taken in the interval 835. + + +``` r +max_act <- max(act_interval$mean_steps) +act_interval[act_interval$mean_steps == max_act, ] +``` + +``` +## # A tibble: 1 × 2 +## interval mean_steps +## +## 1 835 206. +``` + +## Imputing missing values + +I obtain the total of NAs in the dataset. + + +``` r +paste("Total of NAs in the dataset:", sum(is.na(activity$steps))) +``` + +``` +## [1] "Total of NAs in the dataset: 2304" +``` + +To impute the missing values in the dataset, I use the mean of the intervals obtained in the previous section, and then assign this mean to each NA value. Then I get the new dataset without NAs. + + +``` r +data_NA <- merge(activity[is.na(activity$steps),], act_interval, by = "interval") +data_NA$steps <-data_NA$mean_steps +data_NA <- data_NA[,1:3] + +data_NA <- rbind(data_NA,activity[!is.na(activity$steps),] ) +``` + +Now, I obtain the same aggregation for the histogram and the mean and median of the dataset. + + +``` r +# manage data (aggregate per day) +act_day_NA <- data_NA %>% + group_by(date)%>% + summarise(all_steps = sum(steps)) +``` + +### Histogram without NAs + + +``` r +hist(act_day_NA$all_steps, breaks = 10, + main = "Histogram of the total number of steps taken each day \n (without NAs)") +``` + +![](PA1_template_files/figure-html/unnamed-chunk-11-1.png) + +### Mean and median of the total number of steps taken per day (without NAs) + + +``` r +paste("Mean (without NAs) :", round(mean(act_day_NA$all_steps, na.rm = TRUE), 2)) +``` + +``` +## [1] "Mean (without NAs) : 10766.19" +``` + +``` r +paste("Median (without NAs):", median(act_day_NA$all_steps, na.rm = TRUE)) +``` + +``` +## [1] "Median (without NAs): 10766.1886792453" +``` + +***Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?*** + +The mean is the same, but the median differs a little. The mean doesn't differ because the number missing values are the same in all the intervals and days, if we fill them with the mean of each interval, nether the mean of interval and the mean of day change. + +## Are there differences in activity patterns between weekdays and weekends? + +I calculate the new factor variable ("weekday" and "weekend"), + + +``` r +data_NA$weekday_s <- weekdays(data_NA$date) +data_NA$weekday <- ifelse(data_NA$weekday_s == "Sunday" | data_NA$weekday_s == "Saturday", "Weekend", "Weekday" ) +data_NA$weekday <- as.factor(data_NA$weekday) +``` + +I aggregate the data by interval. + + +``` r +# manage data (aggregate per interval-weekday factor) +act_day_weekday <- data_NA %>% + group_by(weekday,interval)%>% + summarise(mean_steps = mean(steps),.groups = "drop") +``` + +Finnaly, I plot the number of steps taken by interval differencing between weekdays and weekends + + +``` r +ggplot(act_day_weekday, aes(x = interval, y = mean_steps)) + + geom_line() + + facet_wrap(~ weekday, ncol = 1) + + theme_minimal() + + labs(title = "Total number of steps taken each interval", + x = "Interval", y = "Steps") + + theme( plot.title = element_text(hjust = 0.5) ) +``` + +![](PA1_template_files/figure-html/unnamed-chunk-15-1.png) diff --git a/README.md b/README.md index 05763414e69..43e24a0787b 100644 --- a/README.md +++ b/README.md @@ -166,3 +166,4 @@ https://github.com/rdpeng/RepData_PeerAssessment1 7c376cc5447f11537f8740af8e07d6facc3d9645 ``` +Feito por Gabriel Demetrios Lafis