Introduction to R and R-Studio Toy Program #2 Excel to R & Basic Descriptives

Transcription

1 Introduction to R and R-Studio Toy Program #2 Basic Descriptives Summary The goal of this toy program is to give you a boiler for working with your own excel data. So, I m hoping you ll try!. In this illustration, you will: 1. Import excel data and save to an.rdata dataset 2. Produce some basic numerical descriptive statistics 3. Produce some basic graphs. Packages Used in This Illustration To install these packages (one time), at the console window, type install.packages( openxlsx ) install.packages( summarytools ) install.packages( stargazer ) install.packages( ggplot2 ) install.packages( psych ) Tips 1) When installing packages, don t forget. Install.packages has a period between install and packages 2) When installing packages, the package name must be enclosed in quotes 3) If you think it might be helpful, skim the summary at the end of this illustration before starting your session Note Prefacing command name with packagename:: In the illustration that follows, often, you will see a command that is of the form packagename::command. Strictly speaking, I do not need to provide the package name. It s optional. For us beginners, however, I find this to be a helpful learning tool. Preliminary - Before you begin Download from the course website the excel file depress_small.xlsx and place this on your working directory. Preliminary - Set working directory. setwd("/users/cbigelow/desktop") R handout Fall 2018 Toy Program 2.docx Page 1 of 11

2 2. Import Excel Data Using Package openxlsx. Output/save to an R dataset ( name.rdata ) # Following assumes that the following package has been installed: openxlsx # Also assumes depress_small.xlsx has been downloaded and is on your desktop # library(openxlsx) depress_small <- read.xlsx("depress_small.xlsx") save(depress_small, file="depress_small.rdata") str(depress_small) ## 'data.frame': 294 obs. of 3 variables: ## $ id : num ## $ age : num ## $ depressed: num # Quick look using command summary() that comes with basic installation summary(depress_small) ## id age depressed ## Min. : 1.00 Min. :18.00 Min. : ## 1st Qu.: st Qu.: st Qu.: ## Median : Median :42.50 Median : ## Mean : Mean :44.41 Mean : ## 3rd Qu.: rd Qu.: rd Qu.: ## Max. : Max. :89.00 Max. : ONE VARIABLE Descriptive Statistics Using Packages summarytools and stargazer # Following assumes the following packages have been installed: summarytools and stargazer # library(summarytools) library(stargazer) #3a - I. Single continuous variable using package summarytools and command descr() summarytools::descr(depress_small$age, stats = c("n.valid","mean", "sd", "min", "med", "max"), transpose = TR UE) ## Descriptive Statistics ## depress_small$age ## N: 294 ## ## N.Valid Mean Std.Dev Min Median Max ## ## age R handout Fall 2018 Toy Program 2.docx Page 2 of 11

3 #3a - II. Single continuous variable using package stargazer and command stargazer() stargazer::stargazer(depress_small[c("age")], type = "text",summary.stat=c("n", "mean", "sd", "min", "p25", " median", "p75", "max")) ## ## ============================================================== ## Statistic N Mean St. Dev. Min Pctl(25) Median Pctl(75) Max ## ## age ## #3b. Single discrete variable using package summarytools and command freq(). Note: order= freq is optional. summarytools::freq(depress_small$depressed, order = "freq") ## Frequencies ## depress_small$depressed ## Type: Numeric ## ## Freq % Valid % Valid Cum. % Total % Total Cum. ## ## ## ## <NA> ## Total ONE VARIABLE Graphical Summaries Using Package ggplot2 # Following assumes the following package has been installed: ggplot2r # #4a - I. Single continuous variable - HISTOGRAM # Tip - Develop plot layer by layer p <- ggplot(data=depress_small, aes(x=age)) + geom_histogram(color="black", fill="blue", binwidth=2) p <- p + ggtitle("histogram of Age") p1 <- p + theme_bw() p1 R handout Fall 2018 Toy Program 2.docx Page 3 of 11

4 #4a - II. Single continuous variable BOXPLOT (See summary for basic command. Here I have added options) # Tip - Develop plot layer by layer p <- ggplot(data=depress_small, aes(x=1, y=age)) + geom_boxplot(color="black", fill="blue") p <- p + xlab(".") p <- p + ylab("age (years)") p <- p + ggtitle("box Plot of Age") p2 <- p + theme_bw() p2 #4b - I. Single discrete variable - BARPLOT of frequencies (Again, see summary for basic command) # Tip - Develop plot layer by layer p <- ggplot(data=depress_small, aes(x=factor(depressed))) p <- p + geom_bar(color="black", fill="blue") p <- p + xlab("depression Status (0=No, 1=Yes)") p <- p + ylab("frequency (#)") p <- p + ggtitle("depression Dataset") p3 <- p + theme_bw() p3 R handout Fall 2018 Toy Program 2.docx Page 4 of 11

5 #4b - II. Single discrete variable - BARPLOT of relative frequencies (See summary for basic command) # Tip - Develop plot layer by layer p <- ggplot(data=depress_small, aes(x=factor(depressed)), stat="count") p <- p + scale_y_continuous(labels = scales::percent_format()) p <- p + geom_bar(color="black", fill="blue") p <- p + xlab("depression Status (0=No, 1=Yes)") p <- p + ylab("relative Frequency (%)") p <- p + ggtitle("depression Dataset") p4 <- p + theme_bw() p4 5. ONE CONTINUOUS & ONE GROUPING VARIABLE Descriptives Using Package psych # Following assumes the following packages have been installed: psych # library(psych) #5a - I. Using package psych and command describeby() depress_small$depressed <- as.factor(depress_small$depressed) psych::describeby(depress_small$age, group = depress_small$depressed) ## $`0` ## vars n mean sd median trimmed mad min max range skew kurtosis ## X ## se ## X ## ## $`1` ## vars n mean sd median trimmed mad min max range skew kurtosis ## X ## se ## X ## ## attr(,"call") ## by.default(data = x, INDICES = group, FUN = describe, type = type) R handout Fall 2018 Toy Program 2.docx Page 5 of 11

6 6. ONE CONTINUOUS & ONE GROUPING VARIABLE Graphical Summary Using Package ggplot2 # Following assumes the following package has been installed: ggplot2r # TAKE CARE - packages are installed from the CONSOLE, never from within an R Markdown # install.packages("ggplot2") # #6a. Side- by- side BOX PLOTS using command ggplot()(see summary for basic command) p <- ggplot(data=depress_small, aes(x=as.factor(depressed), y=age)) p <- p + geom_boxplot(color="black", fill="blue") p <- p + xlab("depression Status (0=No, 1=Yes") p <- p + ylab("age (years)") p <- p + ggtitle("side- by Side Box Plot of Age (years), by Depression Status") p5 <- p + theme_bw() p5 #6b - I. Side- by- side HISTOGRAMS, stacked, using command ggplot(). (See summary for basic command) p <- ggplot(data=depress_small, aes(x=age, fill=depressed)) p <- p + geom_histogram(color="black", fill="blue",binwidth=5) p <- p + facet_wrap(~depressed, ncol=1) p <- p + ggtitle("histogram of Age (years), by Depression Status (0=No, 1=Yes)") p6 <- p + theme_bw() p6 R handout Fall 2018 Toy Program 2.docx Page 6 of 11

7 #6b - II. Side- by- side HISTOGRAMS, overlay, using command ggplot()(see summary for basic command) p <- ggplot(data=depress_small, aes(x=age, fill=depressed, color=depressed)) + geom_histogram(position="ident ity", alpha=0.4, binwidth=5) p <- p + ggtitle("histogram of Age (years), over Depression Status (0=No, 1=Yes)") p7 <- p + theme_bw() p7 R handout Fall 2018 Toy Program 2.docx Page 7 of 11

8 Toy Program #2 Basic Descriptives Summary Notes 1) This summary describes the basics. Some of the options used on previous pages are not shown. It s always possible to add options. 2) To see options, at right, click on the help tab and enter the command name. You ll get an expanded help page. 3) If you like you can precede a command with the package name followed by two colons (as I did in this handout). This is OPTIONAL! 4) What you edit is shaded in yellow. A. WORKING WITH DATA B. NUMERICAL DESCRIPTIVES C. DATA VISUALIZATION A. WORKING WITH DATA 1. Import Excel Data library(openxlsx) currentdataframee <- read.xlsx("excelfilename.xlsx") depress_small <- read.xlsx("depress_small.xlsx") 2. Save Current Data to R Dataset save(currentdataframe, file="rdatasetname.rdata") save(depress_small, file="depress_small.rdata") B. NUMERICAL DESCRIPTIVES Take care!!! The statistic for the sample size goes by different names, depending on the package!! In summarytools, it is n.valid. In stargazer, it is n. 1. Single Continuous Variable Using Package summarytools library(summarytools) descr(dataframe$variable, stats = c("statistic, statistic )) descr(depress_small$age, stats = c("n.valid","mean", "sd", "min", "med", "max")) 2. Single Continuous Variable Using Package stargazer library(stargazer) stargazer(dataframe[c("variable")], type = "text",summary.stat=c("statistic", "statistic")) stargazer(depress_small[c("age")], type = "text",summary.stat=c("n", "mean", "sd", "min", "p25", "median", "p 75", "max")) R handout Fall 2018 Toy Program 2.docx Page 8 of 11

9 3. Single Discrete Variable Using Package summarytools library(summarytools) freq(dataframe$variable) freq(depress_small$depressed, order = "freq") 4. One Continuous Variable & One Grouping Variable Using Package psych Tip! The grouping variable must be declared to be a factor variable. So I always do this, needed or not. library(psych) dataframe$groupvariable <- as.factor(dataframe$groupvariable) describeby(dataframe$continuousvariable, group = dataframe$groupvariable) depress_small$depressed <- as.factor(depress_small$depressed) describeby(depress_small$age, group = depress_small$depressed) C. DATA VISUALIZATION Suggestion! I find it the least error prone if I build my graphs layer by layer. I start with a basic graph which I echo. Then, if I like it, I add an embellishment. I echo that and edit until I m happy. And so on. Try it! 1. Single Continuous Variable HISOTGRAM Using Package ggplot2 p <- ggplot(data=dataframe, aes(x=variable)) + geom_histogram() p <- ggplot(data=depress_small, aes(x=age)) + geom_histogram(color="black", fill="blue", binwidth=2) p <- p + ggtitle("histogram of Age") p1 <- p + theme_bw() p1 2. Single Continuous Variable BOXPLOT Using Package ggplot2 p <- ggplot(data=dataframe, aes(x=1, y=variable)) + geom_boxplot() p <- ggplot(data=depress_small, aes(x=1, y=age)) + geom_boxplot(color="black", fill="blue") p <- p + xlab(".") p <- p + ylab("age (years)") p <- p + ggtitle("box Plot of Age") p2 <- p + theme_bw() p2 R handout Fall 2018 Toy Program 2.docx Page 9 of 11

10 2. Single Discrete Variable BAR PLOT of frequencies (also called bar chart) Using Package ggplot2 p <- ggplot(data=dataframe, aes(x=factor(discretevariable))) p <- p + geom_bar() p <- ggplot(data=depress_small, aes(x=factor(depressed))) p <- p + geom_bar(color="black", fill="blue") p <- p + xlab("depression Status (0=No, 1=Yes)") p <- p + ylab("frequency (#)") p <- p + ggtitle("depression Dataset") p3 <- p + theme_bw() p3 3. Single Discrete Variable BAR PLOT of relative frequencies (also called bar chart) Using Package ggplot2 p <- ggplot(data=dataframe, aes(x=factor(discretevariable)), stat="count") p <- p + scale_y_continuous(labels = scales::percent_format()) p <- p + geom_bar() p <- ggplot(data=depress_small, aes(x=factor(depressed)), stat="count") p <- p + scale_y_continuous(labels = scales::percent_format()) p <- p + geom_bar(color="black", fill="blue") p <- p + xlab("depression Status (0=No, 1=Yes)") p <- p + ylab("relative Frequency (%)") p <- p + ggtitle("depression Dataset") p4 <- p + theme_bw() p4 4. One Continuous Variable & One Grouping Variable SIDE-BY-SIDE BOX PLOT Using Package ggplot2 p <- ggplot(data=dataframe, aes(x=as.factor(groupvariable), y=continuousvariable)) p <- p + geom_boxplot( ) p <- ggplot(data=depress_small, aes(x=as.factor(depressed), y=age)) p <- p + geom_boxplot(color="black", fill="blue") p <- p + xlab("depression Status (0=No, 1=Yes") p <- p + ylab("age (years)") p <- p + ggtitle("side- by Side Box Plot of Age (years), by Depression Status") p5 <- p + theme_bw() p5 R handout Fall 2018 Toy Program 2.docx Page 10 of 11

11 5. One Continuous Variable & One Grouping Variable SIDE-BY-SIDE HISTOGRAMS stacked Using Package ggplot2 p <- ggplot(data=dataframe, aes(x=continuousvariable, fill=groupvariable)) p <- p + geom_histogram() p <- p + facet_wrap(~groupvariable, ncol=1) p <- ggplot(data=depress_small, aes(x=age, fill=depressed)) p <- p + geom_histogram(color="black", fill="blue",binwidth=5) p <- p + facet_wrap(~depressed, ncol=1) p <- p + ggtitle("histogram of Age (years), by Depression Status (0=No, 1=Yes)") p6 <- p + theme_bw() p6 6. One Continuous Variable & One Grouping Variable SIDE-BY-SIDE HISTOGRAMS overlaid Using Package ggplot2 p <- ggplot(data=dataframe, aes(x=continuousvariable, fill=groupvariable)) p <- p + geom_histogram(position="identity") p <- p + facet_wrap(~groupvariable, ncol=1) p <- ggplot(data=depress_small, aes(x=age, fill=depressed, color=depressed)) p <- p + geom_histogram(position="identity", alpha=0.4, binwidth=5) p <- p + ggtitle("histogram of Age (years), over Depression Status (0=No, 1=Yes)") p7 <- p + theme_bw() p7 R handout Fall 2018 Toy Program 2.docx Page 11 of 11