class: center, middle, inverse, title-slide # STA 517 3.0 Programming and Statistical Computing with R ## Welcome! ### Dr Thiyanga Talagala ### 2020-08-16 --- background-image: url('me.png') background-position: center background-size: cover --- background-image: url('courseweb_msc.png') background-position: center background-size: cover --- background-image: url('courseweb.png') background-position: center background-size: cover --- background-image: url('book.png') background-position: center background-size: contain --- background-image: url('RLadies.jpg') background-position: center background-size: cover # Toulouse, France, June-2019 --- background-image: url('rl1.png') background-position: center background-size: cover ## @RLadiesColombo --- background-image: url('rl2.png') background-position: center background-size: cover ## @RLadiesColombo --- background-image: url('scs.png') background-position: center background-size: cover # https://scs-fas-sjp.netlify.app/ --- background-image: url('classoverview.png') background-position: center background-size: cover # Class overview - precourse survey --- # Class overview - precourse survey ```r software <- c("R", "Python", "MINITAB", "SPSS", "C++", "STATA", "SAS") count <- c(4, 0, 10, 12, 1, 1, 1) df <- data.frame(software, count) df ``` ``` software count 1 R 4 2 Python 0 3 MINITAB 10 4 SPSS 12 5 C++ 1 6 STATA 1 7 SAS 1 ``` --- # Class overview - precourse survey ```r library(ggplot2) p1 <- ggplot(data=df, aes(x=software, y=count)) + geom_bar(stat="identity") p1 ``` <!-- --> --- # Class overview - precourse survey ```r df$software <- factor(df$software, levels=c("SPSS", "MINITAB", "R", "SAS", "STATA", "C++", "Python")) p1 <- ggplot(data=df, aes(x=software, y=count)) + geom_bar(stat="identity") p1 ``` <!-- --> --- # Class overview - precourse survey ```r df$software <- factor(df$software, levels=c("SPSS", "MINITAB", "R", "SAS", "STATA", "C++", "")) p1 <- ggplot(data=df, aes(x=software, y=count)) + geom_bar(stat="identity", fill="forestgreen") p1 ``` <!-- --> --- # Class overview - precourse survey ```r df$software <- factor(df$software, levels=c("SPSS", "MINITAB", "R", "SAS", "STATA", "C++", "")) p1 <- p1 + coord_flip() p1 ``` <!-- --> --- # Class overview - precourse survey ```r library(plotly) ggplotly(p1) ``` <div id="htmlwidget-a8832f190053a296c478" style="width:504px;height:360px;" class="plotly html-widget"></div> <script type="application/json" data-for="htmlwidget-a8832f190053a296c478">{"x":{"data":[{"orientation":"h","width":[0.9,0.9,0.9,0.9,0.9,0.9,0.9],"base":[0,0,0,0,0,0,0],"x":[4,0,10,12,1,1,1],"y":[3,7,2,1,6,5,4],"text":["software: R<br />count: 4","software: NA<br />count: 0","software: MINITAB<br />count: 10","software: SPSS<br />count: 12","software: C++<br />count: 1","software: STATA<br />count: 1","software: SAS<br />count: 1"],"type":"bar","marker":{"autocolorscale":false,"color":"rgba(34,139,34,1)","line":{"width":1.88976377952756,"color":"transparent"}},"showlegend":false,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null}],"layout":{"margin":{"t":26.2283105022831,"r":7.30593607305936,"b":40.1826484018265,"l":66.4840182648402},"plot_bgcolor":"rgba(235,235,235,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187},"xaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[-0.6,12.6],"tickmode":"array","ticktext":["0.0","2.5","5.0","7.5","10.0","12.5"],"tickvals":[0,2.5,5,7.5,10,12.5],"categoryorder":"array","categoryarray":["0.0","2.5","5.0","7.5","10.0","12.5"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"y","title":{"text":"count","font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187}},"hoverformat":".2f"},"yaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.4,7.6],"tickmode":"array","ticktext":["SPSS","MINITAB","R","SAS","STATA","C++"],"tickvals":[1,2,3,4,5,6],"categoryorder":"array","categoryarray":["SPSS","MINITAB","R","SAS","STATA","C++"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.65296803652968,"tickwidth":0.66417600664176,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.689497716895},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176,"zeroline":false,"anchor":"x","title":{"text":"software","font":{"color":"rgba(0,0,0,1)","family":"","size":14.6118721461187}},"hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":1,"y0":0,"y1":1}],"showlegend":false,"legend":{"bgcolor":"rgba(255,255,255,1)","bordercolor":"transparent","borderwidth":1.88976377952756,"font":{"color":"rgba(0,0,0,1)","family":"","size":11.689497716895}},"hovermode":"closest","barmode":"relative"},"config":{"doubleClick":"reset","showSendToCloud":false},"source":"A","attrs":{"20273f8e7ac":{"x":{},"y":{},"type":"bar"}},"cur_data":"20273f8e7ac","visdat":{"20273f8e7ac":["function (y) ","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script> --- background-image: url('horse.png') background-position: center background-size: cover