From 5ea736c25e4c096f3b7f5990d80b9afa12694326 Mon Sep 17 00:00:00 2001 From: Edward Wang Date: Tue, 27 Sep 2016 13:52:58 -0400 Subject: [PATCH] finish following instructors --- .gitignore | 4 ++++ Class 7 Instructions.Rmd | 40 +++++++++++++++++++++++++++++---------- Rplot.png | Bin 0 -> 18548 bytes class7.Rproj | 13 +++++++++++++ 4 files changed, 47 insertions(+), 10 deletions(-) create mode 100644 .gitignore create mode 100644 Rplot.png create mode 100644 class7.Rproj diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5b6a065 --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +.Rproj.user +.Rhistory +.RData +.Ruserdata diff --git a/Class 7 Instructions.Rmd b/Class 7 Instructions.Rmd index 5ae641a..e2308a0 100644 --- a/Class 7 Instructions.Rmd +++ b/Class 7 Instructions.Rmd @@ -11,14 +11,14 @@ date: "February 13, 2016" We will use two packages: tidyr and dplyr ```{r} #Insall packages -install.packages("tidyr", "dplyr") +#install.packages("tidyr", "dplyr") #Load packages library(tidyr, dplyr) ``` ##Upload wide format instructor data (instructor_activity_wide.csv) ```{r} -data_wide <- read.table("~/Documents/NYU/EDCT2550/Assignments/Assignment 3/instructor_activity_wide.csv", sep = ",", header = TRUE) +data_wide <- read.table("instructor_activity_wide.csv", sep = ",", header = TRUE) #Now view the data you have uploaded and notice how its structure: each variable is a date and each row is a type of measure. View(data_wide) @@ -59,7 +59,9 @@ instructor_data <- spread(data_long, variables, measure) ##Now we have a workable instructor data set!The next step is to create a workable student data set. Upload the data "student_activity.csv". View your file once you have uploaded it and then draw on a piece of paper the structure that you want before you attempt to code it. Write the code you use in the chunk below. (Hint: you can do it in one step) ```{r} - +student_data <- read.table("student_activity.csv", sep = ",", header = TRUE) +student_data_wide <- spread(student_data, variable, measure) +View(student_data_wide) ``` ##Now that you have workable student data set, subset it to create a data set that only includes data from the second class. @@ -69,13 +71,17 @@ To do this we will use the dplyr package (We will need to call dplyr in the comm Notice that the way we subset is with a logical rule, in this case date == 20160204. In R, when we want to say that something "equals" something else we need to use a double equals sign "==". (A single equals sign means the same as <-). ```{r} -student_data_2 <- dplyr::filter(student_data, date == 20160204) +student_data_2 <- dplyr::filter(student_data_wide, date == 20160204) +View(student_data_2) ``` Now subset the student_activity data frame to create a data frame that only includes students who have sat at table 4. Write your code in the following chunk: ```{r} - +student_table <- dplyr::filter(student_data_2, variable == "table") +View(student_table) +student_table_4 <- dplyr::filter(student_table, measure == 4) +View(student_table_4) ``` ##Make a new variable @@ -84,22 +90,27 @@ It is useful to be able to make new variables for analysis. We can either apend ```{r} instructor_data <- dplyr::mutate(instructor_data, total_sleep = s_deep + s_light) +View(instructor_data) ``` Now, refering to the cheat sheet, create a data frame called "instructor_sleep" that contains ONLY the total_sleep variable. Write your code in the following code chunk: ```{r} - +instructor_sleep <- dplyr::select(instructor_data, contains("total_sleep")) +View(instructor_sleep) ``` Now, we can combine several commands together to create a new variable that contains a grouping. The following code creates a weekly grouping variable called "week" in the instructor data set: ```{r} instructor_data <- dplyr::mutate(instructor_data, week = dplyr::ntile(date, 3)) +View(instructor_data) ``` Create the same variables for the student data frame, write your code in the code chunk below: ```{r} +student_data_3 <- dplyr::mutate(student_data_wide, week = dplyr::ntile(date,3)) +View(student_data_3) ``` @@ -107,17 +118,20 @@ Create the same variables for the student data frame, write your code in the cod Next we will summarize the student data. First we can simply take an average of one of our student variables such as motivation: ```{r} -student_data %>% dplyr::summarise(mean(motivation)) +student_data_wide %>% dplyr::summarise(mean(motivation)) #That isn't super interesting, so let's break it down by week: -student_data %>% dplyr::group_by(date) %>% dplyr::summarise(mean(motivation)) +student_data_wide %>% dplyr::group_by(date) %>% dplyr::summarise(mean(motivation)) ``` Create two new data sets using this method. One that sumarizes average motivation for students for each week (student_week) and another than sumarizes "m_active_time" for the instructor per week (instructor_week). Write your code in the following chunk: ```{r} - +student_week <- student_data_3 %>% dplyr::group_by(week) %>% dplyr::summarise(mean(motivation)) +View(student_week) +instructor_week <- instructor_data %>% dplyr::group_by(week) %>% dplyr::summarise(mean(m_active_time)) +View(instructor_week) ``` ##Merging @@ -125,13 +139,19 @@ Now we will merge these two data frames using dplyr. ```{r} merge <- dplyr::full_join(instructor_week, student_week, "week") +View(merge) ``` ##Visualize Visualize the relationship between these two variables (mean motivation and mean instructor activity) with the "plot" command and then run a Pearson correlation test (hint: cor.test()). Write the code for the these commands below: ```{r} - +x_value <- merge$"mean(motivation)" +x_value +y_value <- merge$"mean(m_active_time)" +y_value +plot(x_value, y_value) +cor.test(x_value,y_value) ``` Fnally save your markdown document and your plot to this folder and comit, push and pull your repo to submit. diff --git a/Rplot.png b/Rplot.png new file mode 100644 index 0000000000000000000000000000000000000000..05e6e9af9317a1484586379da3c0aba3311b2fde GIT binary patch literal 18548 zcmeIabySsa*DeYOh=7Wsgo+>%Dk&)qDoP_DNP~2PG}2gib1i)LyT38Mz0VorJ7$-h;Pv-7%{B!si7#PRJ#cs=E zU|@M+U|@d0!-d}n^3A(qU|=6NzIE%K_^n%1_pB`qjLq~hFmCwyDQXy9P&^}8W@C7n zrdh#^do3$^&*l0|%$b_X zVh^e->nHmK3s}PNY5O{7XjV^41rHyB$DF3ejrD|pphyxuq@Atv|gskE_ z6lbX(QR?LXd`sXRfVq^W*kF(OcsqzvXh!wohR#&+b>SM8#~*&^c7L`Jbn`Pd7-t;q z@WxS8F!7Ec3TXWD>ki@0_1rG4L}`=RcT*EdIx%*GiSMlzAMPiZR~tQBHUFH}5|tqM zbn&734g)9c z!dI8vCZBSxGLABNw{TB>Q>gCAco*^|w%Wd?m{WCSg%7*xWvN>9a;^1_xkCKPXb*E0L@(%JHR@kDSuGB8)ps(qokFbpn^|Ox79lS6Z(PNY77J@vra} zL^V}cd=5 zZv;JJ!=!e4#V@@Pav_gcZ*jXv!c0_?pSx@Cqc6LBYK-V0PeECt_5u4fDRqVJOvV#M z&o4Jz-NDf)-~FofUVWO8Tdru7Jg)RX^On}u>@M2|!>&p4i2L}1v?1a2AkpiCrar*mCk&`d-Q|%MY zv37PTzNcH{$R*5ne!;`D_Ob<8%eSLLI5$?G_T}|gQv`{* z5R7zg&TUj~vpKr$bxse|*kn#Jc@%Q<#Z?Y3+I9&FA7o${`nh8!Cb%fx)84nlz!1g| zzb$g#5p#a2W%xTT#Qip_fUVIU^=k_QGB9u@NAhJ51N%aYQsPYGjSViO@hd@-u2xUv5HnQBBE z^NGmO2ZEw^QGdt7Ejjx8LFC6#0wO}*4=Y!BQO^sr)(ITGT-igrwK`0;4gK~fA`5@M zhDf-o8VlR&M7GN-f7D=z2(3^49u@NA1PK+Ht<{8r?LWgJ686vzbVdz=$SN13I?;UHvm_yCxjmit-2^iJd`@Pl% zsC+~<1xd!M;#D0LD)|MvWBHOd3aM2#@3!5gtfxr{I*Zf$~IN0 zD6BA($5ATtP*pYh4AZuxl%Va@z3KK8AO5APBY8Ss@E7+zG$TXM30@GZfN)#;%y>g@ zzG1_{U>Q4(i)mM8P;xSjxVZS$x;ZPRKhv>#3)8!#_z~Cn_<62I+jVNuS7Khc$H(h} zE=T#lKZ44mcm?oSqUavKmY!bnTyH+Tg2_DBP^n$U?oaN@dRt!#^z0I0b&fF2Om1Ct z8b3ANos*zm;leXWzot^VKHDAP?d@&5Sapz-S-?81Ik?HeKdU)+G29?T0PiXbdRu`?W%`8oml$una$;w zmAI7D8P|pD=TVz;j|l5F1>$)@|Vmw???xe1kAn2N`S^ii)MdIwLrrObvkP!X0 zB+)#_WgV3=`z)tsOA%q~^2aJ7z)h2hGf~Cv&Q!Vl@Zm$1XxS(>(Nc#+W}C_8 zuw%q@dQs2gP@9T@MO`#YCnF&dEaHW$e;&^yCH#uq&C$_Oy@_vOo!3z__ozCKZAMC1 zWZErrnwZ$~Ub5DSEVW`8lke~Jqx`W^FY^e&G3%Bd$k7sN^H_rDZ?ANBXQ#VIA8>|A zwX^Yxpw);64Gm4*%0z=`S#U3ED=@M1NOLt%5K;=N8}5U_nmxpGoi5p&RuL2 z$589#sZ9dQ^)_;=WNK}DeO7<>*K)XBk>#-9*6wP{1T=^5r6jRXD@CP@V@6K+nANV$ z_&DLZB)2PvhP8E`%F|NE<$<$V4{|hNRrwVwFK20^TCPj87@K;e6Mu;3H5 zDtjg`mWp3Vx9{F$G_2|%VyTYO3rTJ`*G`M3>}sFN5}WJvt7~#&9USI z+v6utqraku*^lGJ`{-6g#8tJT%rjTzZ{wc0V1&1`HXY)>Qhk&%bg}Y4a8Pgvz3_O+ zNC=XhI(2EbD{D3U)2gZ==8i>Q+y!#-=`x>g)ci28$g$114ertV+b#|b`kwE+?q%AY z9oF{dZo_ze$V6YE>FM+585oo@S;z1UTjJwhfA}bhniSE}S!COEF^Am`T#4u6cx=8l zp7Zzd33+oj;M-fdH#HO1Q46}R^V2ssm{@br_!`^o_s3Mamp#dA@nEIO*z9KR_rBMC zF<{?sE)OSNOR}B;wcQw=jL3c)sGQzxh9V0Y(>Iji0dpD692)HtEuG38#6I1$)0L%` zayQ_j^}+y;La2U2xHzP!`#G8#*A(B;FsT*<`km+AJ`)mgICm8!ItTLfeF}^_?zFeJ zOF3~_4l$NHZ-y@|S#?PBoA+^au=Nz0KEV2vX%g#-`k)7#p3;|-EeV#YI1$VmCa+!x z#C6oR8V61}U0jA@)RZ~OWAjt&#R&?RR}GTrnP6&R_7@Ub^cOYpYt$%sccjTmw!{mR zdnOuIGE2pUhK@TmIG`W)WJJ;vl#@<==U?q9lBqZ1cW$E)baK3xLajEShKvmZ16-3HeDb7-kz<&XWaQVC`y!Hua4-dR>emoT|vsGxqiJ( zL8b!Tnc(SNf+Kye`{Y3GLocal4sl#UvcPQ7R^naJyLV%{ZpJAd)-kWN$V$=OfC@v!t9vXV4EiGuu#s?yZ38~N(H5WtfI|l%U^Ua3o+@h96wP^>ge07{ zGxVxLvh(Hwo5O;76zjvA;l@2^d_hH}Q)V#=FpZqU=w6X$qLRtW)3h|q%rZ*Z>f#iK zp*0{}WnZmbWw~Q;gJ<(cfH=C@Kr5ZjhhC+^bH&l?|-P>L#K8_b~QQ+F%&L+1m zmfeaNAcH2~(IZDsh~2#Tgi)(Py?`MKjf;e-oRP%X6vG{%QD$Ecuu6Yvq}m!@BbDOg z=NI-pDL^hr!jb9E(^sxzy+*Lenr|He+R4K_My2?JWZKuKvy=UreisQT6K zJpK0V;5&Zjkg`Qc&UV{2+!CTVQ?*b6KxeuRDwAP&p2veql&O&BZ`PAror!ZpyUN3s z=}G(u3omb90m)3J4m#^$c+UMsigM8$#?-4nn?e9RhyemhPEM|iVmE9obuTpS4u5|9 zyn&XE{~;W3{EeEdDKB2M2oE*x%}eP`@sWwRs_@k&@-MVV@*6EqJZv-eQJr8##luM2 zA7GeoAdc>AenG+b4GiBXi9bPy`&C$7B;5b?$-p<-OS@aEd8XaW;?mM#5OF(?wyWqL zwoO;XSl{`x3Rc-3T7xx!5rgRC9dDAbXqqM44#YFhC$(V3-*}|s**v1(W;{8R8H&Pm zSS;G)gqnbq7s?g|OYG;Pr66G9oqm#s_yBgU`+QP}5g<#LO1=S(pkZ%pkZ4-I=Rx>B-(=OZ)O z>?ey3Byt=^uWHN6Jtd3sbtPgvgIz1YhvT)B?x9Go8Z5PAzu3IFNlqHqyh&h<4iKsu zY)1M(qrkM5T>-aU$JC_C%^_!l0qrc+LN*HBqo~1o2rm&R7ZDEGb!(g^C%+2MAex1dC{L-PT-4W6o)3V?I^!o*I_jg@Pj%)9-wQnQu#=(FWdAzgl4foD zFRk?czR-u0FsollPfzcq)fGBi0wRKOUb0R$3W3M>ku2C4#V(?LI}aVYRDp3G`rPjY zO)MaJObg~`CW7Hfh^2z-_2!P>={@|f z^Fa2rSGYR2*`0tM;_~{;cXp;H+nR!(QZ4 zpgwD4NGzaHiE9tCV?zSQug-U-D@e}{19^3uGA!adm;Sdi(?jKDjd6V41w;m6&(Xjc zdUH1%uXB!j>pAY6x~sN+B7_==d8^Wtw1b3v6Bek=+$erb>n4!cum{U5n=6&k&_|9S_%e~KT zd)qH@pKf3I2n99fx+l;a@&!h6><0G|B7*}1sUVM$nz!lwOT}O#+g+}uOaY>2D5`|h zv@4WJtw;=-gmEOdjBTb$#~7#{&##mk@t;zVEwHoxwe%ykO!>%n-`C@*MxFW8@7k^Gj}X!;X&T?ev^CK)>Z;4cx3fI9Q-MaNBLI15 zsi|*LU%o7=hW{5Ok%{Chp!uSgJ$WzPQo~p;Z*HIeo)O{l>&J_v!LM=)g?sqA3@Ula z({$O&@2P!cNblfSN&gu~Zn?(<_USmrm5qMO2B^ssX$fv@W7vhJMUY($QnT&5XU@6f zluY03fcAq9@4H8?YN1Ig=RL2RP2G{#b!j*`3#syJ3ejmt4Ii?bNxJQ$NhrS2UEHp7 z{XnXXhsX*Bkua%7iY5@`jWhCU1oo*D{5b(PHQ0E)wRUm^al7NI$=h_ z3nX#Of6-pAuW9#6XA<0KiRP5FsMs=??o1DX1aP-GK}gH!B5ZG>h}W*Ft!i1(UyB2L z4IwVTkgL3R7RLgFxbp2NUDxFDr`B$r6refJZPB>jxMllPG;Q@*c*h^S>~R@~vFxRy zXzj6?e7E)4ff5@?jnpc++MX(!sWNfAEAz#}BxDz19mzOJ|M0??NAZ50CEVtndhAwO zksuh;GhscLTh_H{)tvq0+ZA?Xo`vx|?*qESk7u-#ob zKIkya86uAGj!?P6AbB-d0t=1BqUFZSnRF%gNfwGH9#Q0Iy$N!wv?C z{_a|XNr&~!o2?%7M)DCT%TTp_^1CR|$G;-oP11SYSq0}w^g=V=VrWjqwuBbRw+)~t z#B6PCU#rLXpnDDbGr4BuW*w=1Qx9^@SSok(j!ETzYvCSt(N#Tq@T*oR`m@8pqZd<| zhJWFn-w~WCq~z#aY&pyf6iod6Y;_f2?82BY_ep0Fs|)U}DdY<=GBOT6`k#rANI0(2 zFl+ltHLT5QVy{jYLk=2YsDq?(`OAr9D znOc>aLvB0f)4ul>Ij*k+Umqqv;VL0awPM0; zlJ{`D*j%@5+q!NZE)eDcHlrFYx5a>w!PNew(7}8DF^I|($SUE}E796O3ADetdy>at zA(rQRo_+((wQCaZ9^^1XcX*mY_U+bR$oIp0h?Te1-+?HQ+RU)8uGpT*fL3*xvE`kJ za&Mk~K0nFZI5;lXeYDe3Qlb=Q##SB!YQHGL4NOiHmw7Z$G%}_3^Af^jS8SV6wBaQN zfEuud^FL47Xt^vuvz_aaqmzv{BXM4UJjAGySHrlX9>}{o*}|Rm1fiT-MB+J3!#m%~ z-Qp0tz-ONcc%Yq;H!I+447;I>h=HFedcTT@;rt5FngFi-xY1|20@%-6@W>5*esv&z zX&@cOJB}XfEpy1BH?D9SE;5e+lC8=4xa*7*U`+ZWyxtc-Se{F^)K} zPHj!SzV$+HvN_JcuQK1TCHnf4zH8md;>6@aZXfAYDqOqM0vOdl++)@#)yr;6SD;rZ zwM};{^|MQBm^f|DgRnFe6JEsV9ezy}Si!>-b2{$Eqnk+aUaZ*3o+1;7dwbn#M0iz` z)U`|cNWsDW;sMv#=E8?Df3xP$%|SG&(-a~GwVvMZ9A|K3BpErIp7%6w*WIq^8?`_v zf7A+OMM^ZP*-peT?)E6-3w?b_2~b6ZHYmOO(0L#*!gTZ)LRkNW_Bo zrD)}Dy_U#NpTj-1KUKGkeY)(7uM0YB;3)$KqE6~S;T-W_7W@2LE7WGII z1s|yNuwV6O(HuQ_F?3_TUowbVG#Q9^`5aAwborE5PB~d#uv@?D77)*w8f8i|AS4u1 z2W$O#!4+)Jf&z|?e#f7UYe2Fs^Z@mdfA2@bwMy-iv zM?|u6Pt!_>sjJ6ks+X{J&IYXfIpD$iljr6<1U zIIjg5p4<=UXC^c2&W@v@qf`Bs{cl>%h?(c|Sgj;*OXCv}T3nx-@4qok4u}b#j~a3u^OF+) z7S6<=QED3m1x6fc{3uR_Q2-kg!)?ioKvMv#4FI<%K-!3!q2}UJQp(X}x_PyIgEo=-?y(_*G`+0)F62%JEqGA_O{@ke#=+&u%D1H5$0iOE;~l^4l1ttYSk3LRq_5Jb5TmxxJMoDpKZ_m8>i#8Qcq7;mjNsg|JHa2!VORN zynpj?e|xq!BKR^Y6<}bg-~;{j%a3~~5CLVtwi@HFfl}6x-y+Cx`L6$&`O*|l)`rIh z^r(&)dUxRcTrb2~R;6V;Lo{n>i}j{jUniZ6^7r#=%F8`Gt3+ZfK|Kb2m6~V08v!-w_4nzS%#_4vS_L!)ezw*fco9JILe6{yoWHb zQs>#<>XSjijS_bO-OW}M5T5_ea0k?5V5SY2McTNa z|HdKY2}a-=LjFPiIXGUh{emqn;{#m@O)k!4L=mEWuN|SHU#1`QnI3w_UX%lttS zY^DS!$k}T6gPAW~5&>GC_T|f$DrcfJ4g>BY!qVE!bOwQ{QW;^Y}Cm%nnz&nGa=@ z|GSP_F4yd!aU4_#q;o#`4;|Gd`uBZ{YXz z*)#p=zv!O_bW)Ll`seHq0h~7ygb2-Z{9Gu|JYlLyw~qly83HF!z83Xb3@q;70saqt z^HC(ojR)v--gL!`aDCp>=-eX%sfYVfrQ6=^BcA}yO8jfl{#E691Vz%!OZ?T`{6p&l zbFSZ=t%103$oU;})hpcu6s7?x>jM=A+$bYiVL*uOtb2a5#fr1h1`~ zxzkP7nWAg!S`FHD6s-Y&?Oz?C_V4nbN<$J?l&vg}o)+Sx&_~P_|K_9k&kI3N3oBpI zqOBr`-Q#zG{5QKt^{%EZFAt9j=-y`OX|v~2Q&Uw_FCQvFjfkvtmR*Q6u1ho%1RVnv z!|h*u8vTK2LJoKZIJqYs-H^^KcXjf*J%}q|kVc&HKB498s_@^!!Z!dA4bFa2e=4Lf z5fn0Hs$NUX)RKSJPFoF0Ht=A-v!CzZ4r}Lzq|DLB-jfm$a$=j zr@oPz;VGlcDOpuF{%O>ObC2x~AM7RYuXidS!2>j27{U@3(3?YCSZ@wG996TgmkmTd zc)xNEm?yCLb!I$h*%dvb*Q|H+AN=BSO1aP^>=-uUc{$6V#L?YKB61vy&tZWPX?wTa z*|nIP#c$oLve(5`bp6wom$+=@l~l86e!Oz|j&ESuLa8J`ah5->h3B_%Hh zz^Bx$O?QBCfIe#@dGE(b_rQsU2xkz~5w8R(p+#?=XkUTxWn*LGRN?IlKZeSkB^@0< zOy#kImK6df7jX#uV|e+Q*8d#Lr@>XgqcVbQ)SM@F>eMOg(VCZx0q==1A!?pfLoJI@ zs)lKZfvH9xEwnwF>v?ZbH3vN4HHRUmG+@|j(S8D=U@RyUq3};nr3mQVR~HR$jod*Z zso1=q4iNHcxOlDD+FY-~D^gjZT{%OF+32T#D#+8kDv)C*zCX|k12rp^;U^qp}{s?x17gj!-usu>nli~(&7bPl}#TZ_!u=xJNwNu`81d#j2aN_f;~K9^Phlv8Y&Dn z5E`Jhbpr$pzBbPWv-R;6>DHd$0V7f>#j`G<e@3>vn|QtYyt+~(N!t{;dWdpQ zBlZ{qf3amt_pH^57Ir5uCnskI{?0G#91rFlfH#~a)2T5}xI@Mn$qIZrZD}e~WM*8>cvCHr%nWac>3g3ji~xZi(Qi@4 zbc22Kz(%oVk1~_tt&P}EA}a#I^ITFuebJVFxE3RTz19}^;v{8wb}}>0n{FuH(i7_p6&a(j88h1Fh8+X=A@@7p z78=Xp-vhHG#`}^pC?HB?24=}>rOV_09CJA9SA>Cnz^eHvkIMUyKZt-x_6wv$A)7=D z@R$-1#Ym#Ls%pBbqz>dY^pc*iwZH&!$WqnI)8BAd!44kDj5t5>Z52EC?c2BKX~&d_ zj!hAZZ7dG60ONz7q#ks(48EW*>uVhgdQ`%gr7w?o*QcZAhWWpGJb@>k78DGVLcKDO?(gSA(*gagj95CC@vd&j!u}>sQBFFO7!fJ zx&G&d@-CJDyl&SgDf50kZJ5XttN!s34>U_r=u1|t@deeYuu9U|-QE0m#TBYVk4H63 zw?uZS$rORlagv6|_|y_U5#i&jj&iM7puiRl*mg*VN4V~9O-_WXS=6u04mmHbySV{n zhxj#-juuk1?3#_4=UFlEz#y#+h-1fP=PcuxG(ReXbQC%*xtWi1r)rqDBO?Wb^dl$v zsUW9D08J`kW|rx){{6u;TX1yrH6Y@wSDUycfixZ8-&u@EaNpSPc+EvrAI^->$%N(v^PMLYn-z=2%5O2TJUxj z=!&(}t?k(y>2nTCql2|R1INt3q&HzvZ3Dtcy<(OsX9OQv0lyqHI~i)l7DKjBk8A*8 zH*f-FyC~=|vS5AGli9)N4GaoQk%=)qKimn=*e*NF^;}AD-G~Be`}Xsbl<^tiPu&l8 zhJWS^Lur$MHIq7pP(ce?-tzD6U$qP>I6GVOl6d6{sg~pBGPMI%-^^02(7qfnPw^SV zdWwdRsm-hH4h#*Nr}w{vX4tI0&cv}+HC({BJ>VH13+_wjn-1C!HBA{_TLA6KndLcH z=D@wUPC5Ux`{9L>a_*1xqt8CBcd3n@7{Bcl#H z5cGa-G6@-KUeo-AM+C!ZMIzET z?%9hMK4srEHJv8TMhQ3r+p+s$)(Ma0ZaaZoW2IYgB8-oQYw~BRZ{kIl=+A(8; zLK5#IySEZFHzRf6)h=D@2;^nIltk+>LLkrCbUWR)I1YX)QE}UGnRYP}Ga9wF<{Qc) zM5oJI+g-ZAR+m^l2j-5JsD*9}FYSA@D@7Bj&9-0((k*TyX}%J;*jvi;d%x~!ZO|Hvp0}fDKz%r1|%pt(Bpk0RNicI1ul(U`c7N* z+;+;5yk2}+sxsb=>@VF~?t=vezrDo|KVf$Wq~>2<=Txi4pQ4oRv(8}>1E+9B6ipIS zA^6=la3^_xNR|b&TWs5ecj(_^i}%_rA#VM_b*@G>p1+syr4y*xqVDng8^x7T#3l{9 z895c#z#@H}fzhZ1Xiqh%mRD|H1yXq(NPj9^q7B?i<4t~b_u)-(VMCtMqj@pe{=WGn z>ns`q3uV*3y7Rcp6ntaOvE5rNR&RJa5;YGxR?OS7RPs{?oF?OfLqkIkqQ>y2vDXYG z4P%=@P5`P$_v?sBMxy_vPW?pNj_vwiHmN)7hJFd`VOM&jXK&6LfI4IlUW~hV?$>l4 z2SNwe`|iJ+G0Qn~f>d<%00-#-`qEQ^z7tvUL>aqHCuN~v@oB(vEo zI1Nqe?MkN-=U{h}zx+a<&LOT#Toyo*@8?-i zq`CLrNYd-;s}Cs8%6mB%Jvj?>Oo)oTWbM5j3*Na=a3Us|O3k!O^U6tfpIaNWWfC9E zGnrnOt~E7;Q^orx{#H^b?`MPyiK2UD^n(|g1Mm+?Y$k;ID|S4yvBTh3`++y`wzPxvXk-U?}fRfqc)n@h(OrO7#>nb99+Fe2-d*l|^# zo)JvA?@zk3`RTVYGj8@hQTe$Ubob(QCX#JE?7bF=gCjA}>ytaDhA8!{JP=l+DrhdS zt9K}qi<@p#)F@CsT50+qtVq+yAsm-VR$x7J(bzHWBo_Is)V!VYGr=!+)xvC7yu18l zX4(>12BW#mP3W#pjedEoZs;{@rW9vB0J^SA9c^0UmBh-i0tRL7tN=(OF{1C*9pYu+ ze(wmPl@XrfsADX*gS6lyL+3PK*xg9ksA>pSMz)+*RzY@{w)P)f3`9cXy)Vc>u#rGIkgt3XBlgyzN16BlKttOIV@lpK{P z8}LeV4O!w1z*?ZH?v(y2nl^Z(y~%Bp*LXTx8`AJa2h*Ib%$@G+hQXA@_GkXRSoDD* zyf!SRqwlmTE7DKy@w6w{{Hg_cYkn*fGQble5{R zZF(54Br!X5mv3S%Xb(i+i? z)&J^SxrrFqg4#{YRL%XR7eaax+dz*X4nF-2JYD^Cm)AU^4Ux6F9*1b0g2vN0gml@HOq2Z#2dcDXFEj&S12Mtm9F0* zA0|bFOn{ri8rIuu#=RA;W$I=2O>^C2m3#A+6O#$<;RsempoQhf<>%lmOWC<#2`!W7 zN4zsF4KKDBp`Rj}!$Q1wz-Qc5EiihBl@C^ghM>!L#}P}ANRrUjV%wV+_cN8{5Yr(b z1Wrm7kWGO@P<^~HXRLZqvAZG(Ciy4FcsZ-RP6`@fl_CDL1WCQ9J8CD_&jGDY=lK0L z$md~#uG@8BcDf3gxMjuWxAi~#sbh2L@GOs8Db{J3x4J~awfND{%mH~?dx9xPqWWmw8 zW(IJTT_h7X3ft@AS-AEgtO@-2YErG?DPeFpf+;YVRMSxeoLvza7G0P}aO*T22KBMZ zb8}e{k0RLapE=yChW{jl1o^)J2GsW9D$~vVP4|QDf>C3Px4O7?9}_iMxMSQlAU2Y8 zFRn9;{p{C|TP9nC@brwuV>MuDuOA<|Tp|&AwQs=yo7d_yj-B^O42=US)d<_XT35mZfQM#v5Q3fxDMxs z#$PED4-pR@)+M+Si6`0&r$dA(7o1K`6JZae8b$z9H<=DbghfAo-!W%kw3^H&;O3|txV7n!OH@UY4MrJ1iih18Ks8~Nr4<(w3j|M8 zhmFLlMnq7XN^O9^M&#UZ<#3~5*M!+xp<;$^De-S{?Sm&q^~iCn8&5H?Hb`3^xtt3% zPE5&i?u$-iazw70k&i`$oJPDi?H?ZLrURxeF!}y%rwx#ColT2?VRJ^l|C^*FDbk#x zI}82AvM$}n*IdLRlD6Q|DocRS{s&`m7o@!gPus=?u3@{bB_RCt!GPbMC~}0jX6D>; z*eXtqq3P4Bh}2h?B7ADIk^2F&P($r+TEYdJpZ%qs*=v8MhQ{ zmL9Up$km?CLkJR2gkwmin?!RAW^`5UY+Mc kX8eDMSoyCQ%C3;3C@m3}j$Q-&Z$cR2qB6I$Z)!jJAIn8>=Kufz literal 0 HcmV?d00001 diff --git a/class7.Rproj b/class7.Rproj new file mode 100644 index 0000000..8e3c2eb --- /dev/null +++ b/class7.Rproj @@ -0,0 +1,13 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX