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Merge branch 'master' of https://github.com/kruggles7/RAJAN
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kruggles7 committed Feb 8, 2014
2 parents d90c22a + 9a33f0d commit 80f7755
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176 changes: 176 additions & 0 deletions percent_hm_graph_weights/PO_use.m
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%QUESTION 75

cd ..
cd ..
sex=importdata('sex-NaN.txt', '\t');
race=importdata('race-NaN.txt', '\t');
weight=importdata('weight.txt','\t');
grade=importdata('grade-NaN.txt','\t');
weight=weight(5:6,:);
race=race(5:6,:);
sex=sex(5:6,:);
grade=grade(5:6,:);
cd results_020314
cd NaN
question_mat=importdata('Q79--NaN.txt', '\t');
[r,c]=size(question_mat);


for i=1:r
% total(i)=TOTAL(i,1);
index_yes{i}=find(question_mat(i,:)==1);
index_girls{i}=find(sex(i,:)==1);
index_boys{i}=find(sex(i,:)==2);
index_W{i}=find(race(i,:)== 1 );
index_B{i}=find(race(i,:)== 2 );
index_H{i}=find(race(i,:)== 3 );
index_O{i}=find(race(i,:)== 4 );
index_9{i}=find(grade(i,:)== 1 );
index_10{i}=find(grade(i,:)== 2 );
index_11{i}=find(grade(i,:)== 3 );
index_12{i}=find(grade(i,:)== 4 );
end
% ^for each desired variable we created an array of the indexes (ie. students) who are a "yes" in that variable


for i=1:r
index_missQ{i}=find(question_mat(i,:)==9); %students who didn't answer the Q
index_nomiss{i}=find(question_mat(i,:)==0 | question_mat(i,:)==1); %answers that were NOT missing (ie. 0's and 1's / no's and yes's)
end

for i=1:r
missQ(i)=length(index_missQ{i}); %number of students who answered the question each year
end


for i=1:r
index_total_b{i}=intersect(index_nomiss{i},index_boys{i}); %index of all boys who answered
index_total_g{i}=intersect(index_nomiss{i},index_girls{i}); %index of all girls who answered
end

% totals of each race/sex/combo: (for percentages- to compare those who said yes to total questioned)
for i=1:r
w=weight(i,:)';
total_ans(i)=nansum(w(index_nomiss{i}));
total_girls(i)=nansum(w(index_total_g{i})); %total # of girls who answered
total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered
total_W{i}=nansum(w(intersect(index_nomiss{i}, index_W{i}))); %total # of white students who answered
total_B{i}=nansum(w(intersect(index_nomiss{i}, index_B{i}))); %total # of black students who answered
total_H{i}=nansum(w(intersect(index_nomiss{i}, index_H{i}))); %total # of hispanic students who answered
total_O{i}=nansum(w(intersect(index_nomiss{i}, index_O{i}))); %total # of "other" students who answered
total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i})));
total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i})));
total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i})));
total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i})));
total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i})));
total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i})));
total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i})));
total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i})));
total_9(i)=nansum(w(intersect((index_9{i}),index_nomiss{i})));
total_10(i)=nansum(w(intersect((index_10{i}),index_nomiss{i})));
total_11(i)=nansum(w(intersect((index_11{i}),index_nomiss{i})));
total_12(i)=nansum(w(intersect((index_12{i}),index_nomiss{i})));
total_9G(i)=nansum(w(intersect(index_9{i},index_total_g{i})));
total_10G(i)=nansum(w(intersect(index_10{i},index_total_g{i})));
total_11G(i)=nansum(w(intersect(index_11{i},index_total_g{i})));
total_12G(i)=nansum(w(intersect(index_12{i},index_total_g{i})));
total_9B(i)=nansum(w(intersect(index_9{i},index_total_b{i})));
total_10B(i)=nansum(w(intersect(index_10{i},index_total_b{i})));
total_11B(i)=nansum(w(intersect(index_11{i},index_total_b{i})));
total_12B(i)=nansum(w(intersect(index_12{i},index_total_b{i})));
end

% %yes-es:
for i=1:r
w=weight(i,:)';
index_yesgirls{i}=intersect(index_yes{i},index_girls{i});
index_yesboys{i}=intersect(index_yes{i},index_boys{i});
yes_girls(i)=nansum(w(index_yesgirls{i}));
yes_boys(i)=nansum(w(index_yesboys{i}));
yes_W(i)=nansum(w(intersect(index_yes{i}, index_W{i})));
yes_B(i)=nansum(w(intersect(index_yes{i}, index_B{i})));
yes_H(i)=nansum(w(intersect(index_yes{i}, index_H{i})));
yes_O(i)=nansum(w(intersect(index_yes{i}, index_O{i})));
yes_WG(i)=nansum(w(intersect(index_yesgirls{i},index_W{i})));
yes_BG(i)=nansum(w(intersect(index_yesgirls{i},index_B{i})));
yes_HG(i)=nansum(w(intersect(index_yesgirls{i},index_H{i})));
yes_OG(i)=nansum(w(intersect(index_yesgirls{i},index_O{i})));
yes_WB(i)=nansum(w(intersect(index_yesboys{i},index_W{i})));
yes_BB(i)=nansum(w(intersect(index_yesboys{i},index_B{i})));
yes_HB(i)=nansum(w(intersect(index_yesboys{i},index_H{i})));
yes_OB(i)=nansum(w(intersect(index_yesboys{i},index_O{i})));
yes_9(i)=nansum(w(intersect(index_yes{i},index_9{i})));
yes_10(i)=nansum(w(intersect(index_yes{i},index_10{i})));
yes_11(i)=nansum(w(intersect(index_yes{i},index_11{i})));
yes_12(i)=nansum(w(intersect(index_yes{i},index_12{i})));
yes_9B(i)=nansum(w(intersect(index_yesboys{i},index_9{i})));
yes_10B(i)=nansum(w(intersect(index_yesboys{i},index_10{i})));
yes_11B(i)=nansum(w(intersect(index_yesboys{i},index_11{i})));
yes_12B(i)=nansum(w(intersect(index_yesboys{i},index_12{i})));
yes_9G(i)=nansum(w(intersect(index_yesgirls{i},index_9{i})));
yes_10G(i)=nansum(w(intersect(index_yesgirls{i},index_10{i})));
yes_11G(i)=nansum(w(intersect(index_yesgirls{i},index_11{i})));
yes_12G(i)=nansum(w(intersect(index_yesgirls{i},index_12{i})));
total_yes(i)=nansum(w(index_yes{i}));
total_w(i)=total_W{i};
total_b(i)=total_B{i};
total_h(i)=total_H{i};
total_o(i)=total_O{i};
end

%put them all into a matrix as percent values:
per_mat = zeros(27,r);
for i=1:r
per_mat(1, i)=total_yes(i)/total_ans(i)*100; %total
per_mat(2, i)=yes_boys(i)/total_boys(i)*100; %boys
per_mat(3, i)=yes_girls(i)/total_girls(i)*100; %girls
per_mat(4, i)=yes_W(i)/total_w(i)*100; %whites
per_mat(5, i)=yes_B(i)/total_b(i)*100; %blacks
per_mat(6, i)=yes_H(i)/total_h(i)*100; %hispanics
per_mat(7, i)=yes_O(i)/total_o(i)*100; %other
per_mat(8, i)=yes_WB(i)/total_Wb(i)*100; %WB
per_mat(9, i)=yes_WG(i)/total_Wg(i)*100; %WG
per_mat(10, i)=yes_BB(i)/total_Bb(i)*100; %BB
per_mat(11, i)=yes_BG(i)/total_Bg(i)*100; %BG
per_mat(12, i)=yes_HB(i)/total_Hb(i)*100; %HB
per_mat(13, i)=yes_HG(i)/total_Hg(i)*100; %HG
per_mat(14, i)=yes_OB(i)/total_Ob(i)*100; %OB
per_mat(15, i)=yes_OG(i)/total_Og(i)*100; %OG
per_mat(16, i)=yes_9(i)/total_9(i)*100;
per_mat(17, i)=yes_10(i)/total_10(i)*100;
per_mat(18, i)=yes_11(i)/total_11(i)*100;
per_mat(19, i)=yes_12(i)/total_12(i)*100;
per_mat(20, i)=yes_9B(i)/total_9B(i)*100;
per_mat(21, i)=yes_10B(i)/total_10B(i)*100;
per_mat(22, i)=yes_11B(i)/total_11B(i)*100;
per_mat(23, i)=yes_12B(i)/total_12B(i)*100;
per_mat(24, i)=yes_9G(i)/total_9G(i)*100;
per_mat(25, i)=yes_10G(i)/total_10G(i)*100;
per_mat(26, i)=yes_11G(i)/total_11G(i)*100;
per_mat(27, i)=yes_12G(i)/total_12G(i)*100;
end

%plot grade increases
xlab={'9th','10th','11th','12th'};
x=[1 2 3 4];
f=figure;
plot(x,per_mat(16:19,2), '-ko', 'MarkerFaceColor', 'k');
hold on
plot(x,per_mat(20:23,2),'--ks', 'MarkerFaceColor', 'k');
plot(x,per_mat(24:27,2), ':kd', 'MarkerFaceColor', 'k');
hold off
xlim([0.5 4.5]);
max3=max(per_mat(16:19,2));
max4=max(per_mat(20:23,2));
max5=max(per_mat(24:27,2));
maxT=max([max3; max4; max5]);
maxT=maxT+0.1*maxT;
ylim([0 maxT]);
set(gca, 'XTick', x);
set(gca, 'XTickLabels', xlab);

legend ('Overall', 'Girls','Boys','Location', 'SouthWest');
xlabel('Grade', 'FontSize',12);
ylabel('% of Respondents Answering Positively', 'FontSize',12);
set (gca, 'FontSize',12);

176 changes: 176 additions & 0 deletions percent_hm_graph_weights/PO_use_more20.m
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%redo everything with categorical data (>=5)-----------------------
cd ..
cd ..
sex=importdata('sex-NaN.txt', '\t');
race=importdata('race-NaN.txt', '\t');
weight=importdata('weight.txt','\t');
grade=importdata('grade-NaN.txt','\t');
weight=weight(5:6,:);
race=race(5:6,:);
sex=sex(5:6,:);
grade=grade(5:6,:);
cd results_020314
cd NaN
cd ..
cd cat
question_mat=importdata('Q79-cat-NaN.txt', '\t');
[r,c]=size(question_mat);


for i=1:r
% total(i)=TOTAL(i,1);
index_yes{i}=find(question_mat(i,:)>4);
index_girls{i}=find(sex(i,:)==1);
index_boys{i}=find(sex(i,:)==2);
index_W{i}=find(race(i,:)== 1 );
index_B{i}=find(race(i,:)== 2 );
index_H{i}=find(race(i,:)== 3 );
index_O{i}=find(race(i,:)== 4 );
index_9{i}=find(grade(i,:)== 1 );
index_10{i}=find(grade(i,:)== 2 );
index_11{i}=find(grade(i,:)== 3 );
index_12{i}=find(grade(i,:)== 4 );
end
% ^for each desired variable we created an array of the indexes (ie. students) who are a "yes" in that variable


for i=1:r
index_missQ{i}=find(question_mat(i,:)== 0); %students who didn't answer the Q
index_nomiss{i}=find(question_mat(i,:)>0 & question_mat(i,:)<7); %answers that were NOT missing (ie. 0's and 1's / no's and yes's)
end

% for i=1:r
% missQ(i)=length(index_missQ{i}); %number of students who answered the question each year
% end


for i=1:r
index_total_b{i}=intersect(index_nomiss{i},index_boys{i}); %index of all boys who answered
index_total_g{i}=intersect(index_nomiss{i},index_girls{i}); %index of all girls who answered
end

% totals of each race/sex/combo: (for percentages- to compare those who said yes to total questioned)
for i=1:r
w=weight(i,:)';
total_ans(i)=nansum(w(index_nomiss{i}));
total_girls(i)=nansum(w(index_total_g{i})); %total # of girls who answered
total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered
total_W{i}=nansum(w(intersect(index_nomiss{i}, index_W{i}))); %total # of white students who answered
total_B{i}=nansum(w(intersect(index_nomiss{i}, index_B{i}))); %total # of black students who answered
total_H{i}=nansum(w(intersect(index_nomiss{i}, index_H{i}))); %total # of hispanic students who answered
total_O{i}=nansum(w(intersect(index_nomiss{i}, index_O{i}))); %total # of "other" students who answered
total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i})));
total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i})));
total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i})));
total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i})));
total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i})));
total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i})));
total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i})));
total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i})));
total_9(i)=nansum(w(intersect((index_9{i}),index_nomiss{i})));
total_10(i)=nansum(w(intersect((index_10{i}),index_nomiss{i})));
total_11(i)=nansum(w(intersect((index_11{i}),index_nomiss{i})));
total_12(i)=nansum(w(intersect((index_12{i}),index_nomiss{i})));
total_9G(i)=nansum(w(intersect(index_9{i},index_total_g{i})));
total_10G(i)=nansum(w(intersect(index_10{i},index_total_g{i})));
total_11G(i)=nansum(w(intersect(index_11{i},index_total_g{i})));
total_12G(i)=nansum(w(intersect(index_12{i},index_total_g{i})));
total_9B(i)=nansum(w(intersect(index_9{i},index_total_b{i})));
total_10B(i)=nansum(w(intersect(index_10{i},index_total_b{i})));
total_11B(i)=nansum(w(intersect(index_11{i},index_total_b{i})));
total_12B(i)=nansum(w(intersect(index_12{i},index_total_b{i})));
end

% %yes-es:
for i=1:r
w=weight(i,:)';
index_yesgirls{i}=intersect(index_yes{i},index_girls{i});
index_yesboys{i}=intersect(index_yes{i},index_boys{i});
yes_girls(i)=nansum(w(index_yesgirls{i}));
yes_boys(i)=nansum(w(index_yesboys{i}));
yes_W(i)=nansum(w(intersect(index_yes{i}, index_W{i})));
yes_B(i)=nansum(w(intersect(index_yes{i}, index_B{i})));
yes_H(i)=nansum(w(intersect(index_yes{i}, index_H{i})));
yes_O(i)=nansum(w(intersect(index_yes{i}, index_O{i})));
yes_WG(i)=nansum(w(intersect(index_yesgirls{i},index_W{i})));
yes_BG(i)=nansum(w(intersect(index_yesgirls{i},index_B{i})));
yes_HG(i)=nansum(w(intersect(index_yesgirls{i},index_H{i})));
yes_OG(i)=nansum(w(intersect(index_yesgirls{i},index_O{i})));
yes_WB(i)=nansum(w(intersect(index_yesboys{i},index_W{i})));
yes_BB(i)=nansum(w(intersect(index_yesboys{i},index_B{i})));
yes_HB(i)=nansum(w(intersect(index_yesboys{i},index_H{i})));
yes_OB(i)=nansum(w(intersect(index_yesboys{i},index_O{i})));
yes_9(i)=nansum(w(intersect(index_yes{i},index_9{i})));
yes_10(i)=nansum(w(intersect(index_yes{i},index_10{i})));
yes_11(i)=nansum(w(intersect(index_yes{i},index_11{i})));
yes_12(i)=nansum(w(intersect(index_yes{i},index_12{i})));
yes_9B(i)=nansum(w(intersect(index_yesboys{i},index_9{i})));
yes_10B(i)=nansum(w(intersect(index_yesboys{i},index_10{i})));
yes_11B(i)=nansum(w(intersect(index_yesboys{i},index_11{i})));
yes_12B(i)=nansum(w(intersect(index_yesboys{i},index_12{i})));
yes_9G(i)=nansum(w(intersect(index_yesgirls{i},index_9{i})));
yes_10G(i)=nansum(w(intersect(index_yesgirls{i},index_10{i})));
yes_11G(i)=nansum(w(intersect(index_yesgirls{i},index_11{i})));
yes_12G(i)=nansum(w(intersect(index_yesgirls{i},index_12{i})));
total_yes(i)=nansum(w(index_yes{i}));
total_w(i)=total_W{i};
total_b(i)=total_B{i};
total_h(i)=total_H{i};
total_o(i)=total_O{i};
end

%put them all into a matrix as percent values:
per_mat = zeros(27,r);
for i=1:r
per_mat(1, i)=total_yes(i)/total_ans(i)*100; %total
per_mat(2, i)=yes_boys(i)/total_boys(i)*100; %boys
per_mat(3, i)=yes_girls(i)/total_girls(i)*100; %girls
per_mat(4, i)=yes_W(i)/total_w(i)*100; %whites
per_mat(5, i)=yes_B(i)/total_b(i)*100; %blacks
per_mat(6, i)=yes_H(i)/total_h(i)*100; %hispanics
per_mat(7, i)=yes_O(i)/total_o(i)*100; %other
per_mat(8, i)=yes_WB(i)/total_Wb(i)*100; %WB
per_mat(9, i)=yes_WG(i)/total_Wg(i)*100; %WG
per_mat(10, i)=yes_BB(i)/total_Bb(i)*100; %BB
per_mat(11, i)=yes_BG(i)/total_Bg(i)*100; %BG
per_mat(12, i)=yes_HB(i)/total_Hb(i)*100; %HB
per_mat(13, i)=yes_HG(i)/total_Hg(i)*100; %HG
per_mat(14, i)=yes_OB(i)/total_Ob(i)*100; %OB
per_mat(15, i)=yes_OG(i)/total_Og(i)*100; %OG
per_mat(16, i)=yes_9(i)/total_9(i)*100;
per_mat(17, i)=yes_10(i)/total_10(i)*100;
per_mat(18, i)=yes_11(i)/total_11(i)*100;
per_mat(19, i)=yes_12(i)/total_12(i)*100;
per_mat(20, i)=yes_9B(i)/total_9B(i)*100;
per_mat(21, i)=yes_10B(i)/total_10B(i)*100;
per_mat(22, i)=yes_11B(i)/total_11B(i)*100;
per_mat(23, i)=yes_12B(i)/total_12B(i)*100;
per_mat(24, i)=yes_9G(i)/total_9G(i)*100;
per_mat(25, i)=yes_10G(i)/total_10G(i)*100;
per_mat(26, i)=yes_11G(i)/total_11G(i)*100;
per_mat(27, i)=yes_12G(i)/total_12G(i)*100;
end

%plot grade increases
xlab={'9th','10th','11th','12th'};
x=[1 2 3 4];
f=figure;
plot(x,per_mat(16:19,2), '-ko', 'MarkerFaceColor', 'k');
hold on
plot(x,per_mat(20:23,2),'--ks', 'MarkerFaceColor', 'k');
plot(x,per_mat(24:27,2), ':kd', 'MarkerFaceColor', 'k');
hold off
xlim([0.5 4.5]);
max3=max(per_mat(16:19,2));
max4=max(per_mat(20:23,2));
max5=max(per_mat(24:27,2));
maxT=max([max3; max4; max5]);
maxT=maxT+0.1*maxT;
ylim([0 maxT]);
set(gca, 'XTick', x);
set(gca, 'XTickLabels', xlab);

legend ('Overall', 'Girls','Boys','Location', 'SouthWest');
xlabel('Grade', 'FontSize',12);
ylabel('% of Respondents using prescription drugs >20 times', 'FontSize',12);
set (gca, 'FontSize',12);
26 changes: 26 additions & 0 deletions statitsics/regression_over_years.m
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files=dir(fullfile('C:','Users','Kelly','Documents','MATLAB', 'rajan', 'parsed_questions_111413', 'NaN', '*.txt'));
% reads all the text files in the folder 'binary_NaN_files' and saves them in an array called files
% make sure that folder contains only the NaN files for the questions you want to run create_hm_graph for
N=length(files);
for i=1%4%1:N
cd ..
cd ..
cd parsed_questions_111413
cd NaN
question_mat=importdata(files(i).name, '\t');
%%%
cd ..
cd ..
cd programs
cd statistics
filename='';
a=char(files(i).name);
b=strfind(a,'-');
for p=1:(b(1)-1)
c=a(p);
filename=[filename c];
end
%%% ^ this piece of the program just makes the variable 'filename' out of everything before the '-' in 'Q#--NaN'
[ per_mat_map, indx ] = regression_years ( question_mat, filename );
end
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