c()Table 9.1 shows COVID for three states in Germany:
| state | Bavaria | North Rhine-Westphalia | Baden-Württemberg |
|---|---|---|---|
| deaths (in mio) | 4,92M | 5,32M | 3,69M |
| cases | 24.111 | 25.466 | 16.145 |
Write down the code you would need to put into the R-console…
df_covid.tbl_covid.The script uses the following functions: c, data.frame, tibble.
Set up R, RStudio, and R packages
Open this interactive tutorial and work through it.
%in% operator, and the pipe |>mtcars dataset, write code to create a new dataframe that includes only the rows where the number of cylinders is either 4 or 6, and the weight (wt) is less than 3.5.Do this in two different ways using:
%in% operator and the pipe |> .|>.Compare the resulting dataframes using the identical() function.
mtcars dataset, generate a logical variable that indicates with TRUE all cars with either 4 or 6 cylinders that wt is less than 3.5 and add this variable to a new dataset.The script uses the following functions: c, filter, identical, if_else, mutate, subset, transform, with.
Use the mtcars dataset. It is part of the package datasets and can be called with
mtcarsCreate a new tibble called mtcars_new using the pipe operator |>. Generate a new dummy variable called d_cyl_6to8 that takes the value 1 if the number of cylinders (cyl) is greater than 6, and 0 otherwise. Do all of this in a single pipe.
Generate a new dummy variable called posercar that takes a value of 1 if a car has more than 6 cylinders (cyl) and can drive less than 18 miles per gallon (mpg), and 0 otherwise. Add this variable to the tibble mtcars_new.
Remove the variable d_cyl_6to8 from the data frame.
The script uses the following functions: as_tibble, if_else, mutate, rownames_to_column, select.
mtcars dataset by entering mtcars.mtcars dataset in an object named cars.cars?mtcars dataset?mpg in cars to MPG. Use rename().rename_all, and the toupper function.rownames_to_column.ends_with().wt, qsec, and hp and assign this object to carsSub. (Use select().)carsSub? (Use dim().)carsSub to all upper case. Use rename_all(), and toupper() (or colnames()).mpg) of fuel efficiency. How many are there? (Use filter().)mpg) of fuel efficiency and have more than 100 horsepower (hp). How many are there? (Use filter() and the pipe operator.)wt, qsec, and hp for cars with 8 cylinders (cyl) and reassign this object to carsSub. What are the dimensions of this dataset? Do not use the pipe operator.wt, qsec, and hp for cars with 8 cylinders (cyl) and reassign this object to carsSub2. Use the pipe operator.carsSub by weight (wt) in increasing order. (Use arrange().)carsSub called wt2, which is equal to wt\(^2\), using mutate() and piping |>.The script uses the following functions: arrange, class, dim, ends_with, filter, mutate, ncol, nrow, rename, rename_all, rownames_to_column, select.
| v1 | v2 | v3 | v4 |
|---|---|---|---|
| 1 | A | NA | |
| 2 | NA | 0.2 | 0.4 |
| C | 0.3 | 0.1 | |
| 4 | D | NA | 3 |
| 5 | E | 0.5 | 1.5 |
Consider the data of Table Table 9.2 and solve the following exercises:
misone that is 1 if there is a missing and 0 otherwise. (Hint: Use case_when and is.na().)library(dplyr)
df <- df |>
mutate(misone = case_when(
v1 == "" | is.na(v1) ~ 1,
v2 == "" | is.na(v2) ~ 1,
v3 == "" | is.na(v3) ~ 1,
v4 == "" | is.na(v4) ~ 1,
TRUE ~ 0
))
tt(df, output="markdown")| v1 | v2 | v3 | v4 | misone |
|---|---|---|---|---|
| 1 | A | NA | 1 | |
| 2 | NA | 0.2 | 0.4 | 1 |
| C | 0.3 | 0.1 | 1 | |
| 4 | D | NA | 3 | 1 |
| 5 | E | 0.5 | 1.5 | 0 |
miscount that counts how many observations are missing in each row. (Hint: Use mutate_all, rowSums, and pick(everything()))test_df <- df |>
mutate_all(~ if_else(is.na(.) | . == "", 1, 0)) |>
mutate(miscount = rowSums(pick(everything())))
test_df_miscount <- test_df |>
select(miscount)
df <- bind_cols(df, test_df_miscount)
tt(df, output="markdown")| v1 | v2 | v3 | v4 | misone | miscount |
|---|---|---|---|---|---|
| 1 | A | NA | 1 | 2 | |
| 2 | NA | 0.2 | 0.4 | 1 | 1 |
| C | 0.3 | 0.1 | 1 | 1 | |
| 4 | D | NA | 3 | 1 | 1 |
| 5 | E | 0.5 | 1.5 | 0 | 0 |
rowwise to calculate the NA and "" observations. (Hint: Use is.na and pick(everything()).)df <- df |>
rowwise() |>
mutate(countNA = sum(is.na(pick(everything())))) |>
mutate(countOK = sum(pick(everything()) == "", na.rm = TRUE)) |>
ungroup()
tt(df, output="markdown")| v1 | v2 | v3 | v4 | misone | miscount | countNA | countOK |
|---|---|---|---|---|---|---|---|
| 1 | A | NA | 1 | 2 | 1 | 1 | |
| 2 | NA | 0.2 | 0.4 | 1 | 1 | 1 | 0 |
| C | 0.3 | 0.1 | 1 | 1 | 0 | 1 | |
| 4 | D | NA | 3 | 1 | 1 | 1 | 0 |
| 5 | E | 0.5 | 1.5 | 0 | 0 | 0 | 0 |
mispercent that measures the percentage of missings and a variable mis30up that is 1 if the percentage is above 30%. (Hint: Use mutate, select, ifelse, and bind_cols.)test_df_mis30up <- test_df |>
mutate(fraction = miscount / 4) |>
mutate(mis30up = ifelse(fraction > 0.3, 1, 0)) |>
select(mis30up, fraction)
df <- bind_cols(df, test_df_mis30up)
tt(df, output="markdown")| v1 | v2 | v3 | v4 | misone | miscount | countNA | countOK | mis30up | fraction |
|---|---|---|---|---|---|---|---|---|---|
| 1 | A | NA | 1 | 2 | 1 | 1 | 1 | 0.50 | |
| 2 | NA | 0.2 | 0.4 | 1 | 1 | 1 | 0 | 0 | 0.25 |
| C | 0.3 | 0.1 | 1 | 1 | 0 | 1 | 0 | 0.25 | |
| 4 | D | NA | 3 | 1 | 1 | 1 | 0 | 0 | 0.25 |
| 5 | E | 0.5 | 1.5 | 0 | 0 | 0 | 0 | 0 | 0.00 |
v1, v3, and v4. Name the variable average. (Hint: Use as.numeric, rowwise, and mean.)df <- df |>
mutate(
v1 = as.numeric(v1),
v4 = as.numeric(v4)
)
df <- df |>
rowwise() |>
mutate(average = mean(c(v1, v3, v4), na.rm = TRUE)) |>
ungroup()
test_df <- df |>
select(v1, v3, v4, average)
tt(df, output="markdown")| v1 | v2 | v3 | v4 | misone | miscount | countNA | countOK | mis30up | fraction | average |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | A | NA | NA | 1 | 2 | 1 | 1 | 1 | 0.50 | 1.0000000 |
| 2 | NA | 0.2 | 0.4 | 1 | 1 | 1 | 0 | 0 | 0.25 | 0.8666667 |
| NA | C | 0.3 | 0.1 | 1 | 1 | 0 | 1 | 0 | 0.25 | 0.2000000 |
| 4 | D | NA | 3.0 | 1 | 1 | 1 | 0 | 0 | 0.25 | 3.5000000 |
| 5 | E | 0.5 | 1.5 | 0 | 0 | 0 | 0 | 0 | 0.00 | 2.3333333 |
https://raw.githubusercontent.com/hubchev/courses/main/dta/PCEPI.csv The data stem from the Federal Reserve Bank of St. Louis (FRED).
You should always try to use most recent data and you should not work outside of R. Thus, it would be optimal to download the data directly from FRED and using a R package. If this would be serious research, I would not recommend to use the data that I have downloaded from FRED, I’d recommend to use the fredr package which allows to fetch the observation directly from FRED database using the function fredr.
Here is an excerpt of the data:
DATE PCEPI_NBD20190101
1 1965-01-01 15.92229
2 1966-01-01 16.32477
3 1967-01-01 16.73491
4 1968-01-01 17.38977
5 1969-01-01 18.17313
6 1970-01-01 19.02267Divide the variable PCEPI_NBD20190101 by 100 and name the resulting variable pce. Additionally, generate a new variable year that contains the respective year. Save the modified dataframe as pce_cl.
Make the following plot:
year pce inflation_rate
1 1965 0.1592229 NA
2 1966 0.1632477 2.527777
3 1967 0.1673491 2.512378
4 1968 0.1738977 3.913137
5 1969 0.1817313 4.504717
6 1970 0.1902267 4.674704
Calculate the average inflation rate over all years.
Calculate the average inflation rate for each decade:
# A tibble: 7 × 2
decade avg_inf
<chr> <dbl>
1 1960s 3.36
2 1970s 6.43
3 1980s 5.03
4 1990s 2.32
5 2000s 2.14
6 2010s 1.57
7 2020s 2.53
The script uses the following functions: aes, as.integer, case_when, filter, geom_bar, geom_line, geom_point, ggplot, group_by, lag, mean, mutate, read.csv, select, str_sub, summarize.
if (!require(pacman)) install.packages("pacman")
pacman::p_load(tidyverse, janitor, expss)
# setwd("~/yourdirectory-of-choice")
rm(list = ls())
# read in raw data
# PCEPI <- read.csv("PCEPI.csv")
PCEPI <- read.csv("https://raw.githubusercontent.com/hubchev/courses/main/dta/PCEPI.csv")
# clean data
pce_cl <- PCEPI |>
mutate(pce = PCEPI_NBD20190101/100) |>
mutate(year = str_sub(DATE, 1, 4)) |>
mutate(year = as.integer(year)) |>
select(pce, year)
# make a plot
pce_cl |>
ggplot( aes(x=year, y=pce))+
geom_line() +
geom_point()
# make a barplot
pce_cl |>
ggplot( aes(x=year, y=pce))+
geom_bar(stat="identity")
# make a plot for all years from 2000 onwards
pce_cl |>
filter(year > 2000 ) |>
ggplot( aes(x=year, y=pce)) +
geom_bar(stat="identity")
# make a plot for all years except the 90s
pce_cl |>
filter(year > 2000 | year <1990) |>
ggplot( aes(x=year, y=pce)) +
geom_bar(stat="identity")
# calculate yearly inflation
pce_cl <- pce_cl |>
mutate(inflation_rate = (pce/lag(pce)-1)*100 )
# plot the inflation rate
pce_cl |>
ggplot( aes(x=year, y=inflation_rate))+
geom_bar(stat="identity")Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_bar()`).
# what is the avergage inflation rate
pce_cl |>
summarize(avg_mpg = mean(inflation_rate, na.rm = TRUE)) avg_mpg
1 3.454529# make a variable that indicates the decades 1 for 60s, 2 for 70s, etc.
pce_cl <- pce_cl |>
mutate(decade = case_when(
year < 1970 ~ "1960s",
(year > 1969 & year < 1980) ~ "1970s",
(year > 1979 & year < 1990) ~ "1980s",
(year > 1989 & year < 2000) ~ "1990s",
(year > 1999 & year < 2010) ~ "2000s",
(year > 2009 & year < 2020) ~ "2010s",
(year > 2019 & year < 2030) ~ "2020s",
))
pce_decade <- pce_cl |>
group_by(decade) |>
summarize(avg_inf = mean(inflation_rate, na.rm = TRUE))
pce_decade# A tibble: 7 × 2
decade avg_inf
<chr> <dbl>
1 1960s 3.36
2 1970s 6.43
3 1980s 5.03
4 1990s 2.32
5 2000s 2.14
6 2010s 1.57
7 2020s 2.53pce_decade |>
ggplot( aes(x=decade, y=avg_inf))+
geom_bar(stat="identity")
Create a scatter plot illustrating the relationship between the price and weight of a car. Provide a meaningful title for the graph and try to make it clear which car each observation corresponds to.
Save this graph in the formats of .png and .pdf.
Create a variable lp100km that indicates the fuel consumption of an average car in liters per 100 kilometers. (Note: One gallon is approximately equal to 3.8 liters, and one mile is about 1.6 kilometers.)
Create a dummy variable larger6000 that is equal to 1 if the price of a car is above $6000.
Now, search for the “most unreasonable poser car” that costs no more than $6000. A poser car is defined as one that is expensive, has a large turning radius, consumes a lot of fuel, and is often defective (rep78 is low). For this purpose, create a metric indicator for each corresponding variable that indicates a value of 1 for the car that is the most unreasonable in that variable and 0 for the most reasonable car. All other cars should fall between 0 and 1.
The script uses the following functions: aes, arrange, desc, dir.create, dir.exists, filter, geom_point, geom_text_repel, ggplot, head, ifelse, max, min, min_max_norm, mutate, na.omit, read_dta, select, tail, theme_minimal, xlab, ylab.
# Load the required libraries
if (!require(pacman)) install.packages("pacman")
pacman::p_load(tidyverse, haven, ggrepel)
# setwd("~/Dropbox/hsf/23-ws/R_begin")
rm(list = ls())
# Read the Stata dataset
auto <- read_dta("http://www.stata-press.com/data/r8/auto.dta")
# Create a scatter plot of price vs. weight
scatter_plot <- ggplot(auto, aes(x = mpg, y = price, label = make)) +
geom_point() +
geom_text_repel() +
xlab("Miles per Gallon") +
ylab("Price in Dollar") +
theme_minimal()
scatter_plotWarning: ggrepel: 43 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
# Create "fig" directory if it doesn't already exist
if (!dir.exists("fig")) {
dir.create("fig")
}
# Save the scatter plot in different formats
# ggsave("fig/scatter_plot.png", plot = scatter_plot, device = "png")
# ggsave("fig/scatter_plot.pdf", plot = scatter_plot, device = "pdf")
# Create 'lp100km' variable for fuel consumption
n_auto <- auto |>
mutate(lp100km = (1 / (mpg * 1.6 / 3.8)) * 100)
# Create 'larger6000' dummy variable
n_auto <- n_auto |>
mutate(larger6000 = ifelse(price > 6000, 1, 0))
# Normalize variables
## Do it slowly
n_auto <- n_auto |>
mutate(sprice = (price - min(auto$price)) / (max(auto$price) - min(auto$price)))
n_auto <- n_auto |>
filter(larger6000 == 0)
## Do it with a self-written function
min_max_norm <- function(x) {
(x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
}
n_auto <- n_auto |>
mutate(smpg = min_max_norm(mpg)) |>
mutate(sturn = min_max_norm(turn)) |>
mutate(slp100km = min_max_norm(lp100km)) |>
mutate(sprice = min_max_norm(price)) |>
mutate(srep78 = min_max_norm(rep78))
## With a loop:
# vars_to_normalize <- c("mpg", "turn", "lp100km", "price", "rep78")
#
# # Loop through the selected variables and apply min_max_norm
# for (var in c("mpg", "turn", "lp100km", "price", "rep78")) {
# auto <- auto |>
# mutate(!!paste0("s", var) := min_max_norm(!!sym(var))) |>
# select(make, starts_with("s"))
# }
## mpg and rep78 need to be changed because a SMALL value is poser-like
n_auto <- n_auto |>
mutate(smpg = 1 - smpg) |>
mutate(srep78 = 1 - srep78)
## create the poser (composite) indicator
n_auto <- n_auto |>
mutate(poser = (sturn + smpg + sprice + srep78) / 4)
## filter results
n_auto |>
arrange(desc(poser)) |>
select(make, poser) |>
head(5)# A tibble: 5 × 2
make poser
<chr> <dbl>
1 Dodge Magnum 0.888
2 Pont. Firebird 0.782
3 Merc. Cougar 0.763
4 Buick LeSabre 0.754
5 Pont. Grand Prix 0.720df_poser <- n_auto |>
filter(larger6000 == 0) |>
arrange(desc(poser)) |>
select(make, poser) |>
na.omit()
# Five top poser cars
head(df_poser, 15)# A tibble: 15 × 2
make poser
<chr> <dbl>
1 Dodge Magnum 0.888
2 Pont. Firebird 0.782
3 Merc. Cougar 0.763
4 Buick LeSabre 0.754
5 Pont. Grand Prix 0.720
6 Chev. Impala 0.702
7 Dodge Diplomat 0.690
8 Chev. Monte Carlo 0.684
9 Pont. Catalina 0.678
10 Olds Cutl Supr 0.671
11 Plym. Volare 0.665
12 Buick Regal 0.663
13 Olds Cutlass 0.629
14 Olds Starfire 0.626
15 AMC Pacer 0.619# Five top non-poser cars
tail(df_poser, 5)# A tibble: 5 × 2
make poser
<chr> <dbl>
1 VW Diesel 0.261
2 Dodge Colt 0.227
3 Toyota Corolla 0.195
4 Datsun 210 0.195
5 Subaru 0.178# unload packages
suppressMessages(pacman::p_unload(tidyverse, haven, ggrepel))Load the packages datasauRus and tidyverse. If necessary, install these packages.
The packagedatasauRus comes with a dataset in two different formats: datasaurus_dozen and datasaurus_dozen_wide. Store them as ds and ds_wide.
Open and read the R vignette of the datasauRus package. Also open the R documentation of the dataset datasaurus_dozen.
Explore the dataset: What are the dimensions of this dataset? Look at the descriptive statistics.
How many unique values does the variable dataset of the tibble ds have? Hint: The function unique() return the unique values of a variable and the function length() returns the length of a vector, such as the unique elements.
Compute the mean values of the x and y variables for each entry in dataset. Hint: Use the group_by() function to group the data by the appropriate column and then the summarise() function to calculate the mean.
Compute the standard deviation, the correlation, and the median in the same way. Round the numbers.
What can you conclude?
Plot all datasets of ds. Hide the legend. Hint: Use the facet_wrap() and the theme() function.
Create a loop that generates separate scatter plots for each unique datatset of the tibble ds. Export each graph as a png file.
Watch the video Animating the Datasaurus Dozen Dataset in R from The Data Digest on YouTube.
The script uses the following functions: aes, cor, dim, dir.create, dir.exists, facet_wrap, filter, geom_point, ggplot, ggsave, glimpse, group_by, head, labs, length, mean, median, paste, paste0, round, sd, select, summarise, summary, theme, theme_bw, unique, view.
# setwd("/home/sthu/Dropbox/hsf/23-ws/ds_mim/")
rm(list = ls())
# Load the packages datasauRus and tidyverse. If necessary, install these packages.
# load packages
if (!require(pacman)) install.packages("pacman")
pacman::p_load(datasauRus, tidyverse)
# The packagedatasauRus comes with a dataset in two different formats:
# datasaurus_dozen and datasaurus_dozen_wide. Store them as ds and ds_wide.
ds <- datasaurus_dozen
ds_wide <- datasaurus_dozen_wide
# Open and read the R vignette of the datasauRus package.
# Also open the R documentation of the dataset datasaurus_dozen.
??datasaurus
# Explore the dataset: What are the dimensions of this dataset? Look at the descriptive statistics.
ds# A tibble: 1,846 × 3
dataset x y
<chr> <dbl> <dbl>
1 dino 55.4 97.2
2 dino 51.5 96.0
3 dino 46.2 94.5
4 dino 42.8 91.4
5 dino 40.8 88.3
6 dino 38.7 84.9
7 dino 35.6 79.9
8 dino 33.1 77.6
9 dino 29.0 74.5
10 dino 26.2 71.4
# ℹ 1,836 more rowsdim(ds)[1] 1846 3head(ds)# A tibble: 6 × 3
dataset x y
<chr> <dbl> <dbl>
1 dino 55.4 97.2
2 dino 51.5 96.0
3 dino 46.2 94.5
4 dino 42.8 91.4
5 dino 40.8 88.3
6 dino 38.7 84.9glimpse(ds)Rows: 1,846
Columns: 3
$ dataset <chr> "dino", "dino", "dino", "dino", "dino", "dino", "dino", "dino"…
$ x <dbl> 55.3846, 51.5385, 46.1538, 42.8205, 40.7692, 38.7179, 35.6410,…
$ y <dbl> 97.1795, 96.0256, 94.4872, 91.4103, 88.3333, 84.8718, 79.8718,…view(ds)
summary(ds) dataset x y
Length:1846 Min. :15.56 Min. : 0.01512
Class :character 1st Qu.:41.07 1st Qu.:22.56107
Mode :character Median :52.59 Median :47.59445
Mean :54.27 Mean :47.83510
3rd Qu.:67.28 3rd Qu.:71.81078
Max. :98.29 Max. :99.69468 # How many unique values does the variable dataset of the tibble ds have?
# Hint: The function unique() return the unique values of a variable and the
# function length() returns the length of a vector, such as the unique elements.
unique(ds$dataset) [1] "dino" "away" "h_lines" "v_lines" "x_shape"
[6] "star" "high_lines" "dots" "circle" "bullseye"
[11] "slant_up" "slant_down" "wide_lines"unique(ds$dataset) |>
length()[1] 13# Compute the mean values of the x and y variables for each entry in dataset.
# Hint: Use the group_by() function to group the data by the appropriate column and
# then the summarise() function to calculate the mean.
ds |>
group_by(dataset) |>
summarise(
mean_x = mean(x),
mean_y = mean(y)
)# A tibble: 13 × 3
dataset mean_x mean_y
<chr> <dbl> <dbl>
1 away 54.3 47.8
2 bullseye 54.3 47.8
3 circle 54.3 47.8
4 dino 54.3 47.8
5 dots 54.3 47.8
6 h_lines 54.3 47.8
7 high_lines 54.3 47.8
8 slant_down 54.3 47.8
9 slant_up 54.3 47.8
10 star 54.3 47.8
11 v_lines 54.3 47.8
12 wide_lines 54.3 47.8
13 x_shape 54.3 47.8# Compute the standard deviation, the correlation, and the median in the same way. Round the numbers.
ds |>
group_by(dataset) |>
summarise(
mean_x = round(mean(x), 2),
mean_y = round(mean(y), 2),
sd_x = round(sd(x), 2),
sd_y = round(sd(y), 2),
med_x = round(median(x), 2),
med_y = round(median(y), 2),
cor = round(cor(x, y), digits = 4)
)# A tibble: 13 × 8
dataset mean_x mean_y sd_x sd_y med_x med_y cor
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 away 54.3 47.8 16.8 26.9 53.3 47.5 -0.0641
2 bullseye 54.3 47.8 16.8 26.9 53.8 47.4 -0.0686
3 circle 54.3 47.8 16.8 26.9 54.0 51.0 -0.0683
4 dino 54.3 47.8 16.8 26.9 53.3 46.0 -0.0645
5 dots 54.3 47.8 16.8 26.9 51.0 51.3 -0.0603
6 h_lines 54.3 47.8 16.8 26.9 53.1 50.5 -0.0617
7 high_lines 54.3 47.8 16.8 26.9 54.2 32.5 -0.0685
8 slant_down 54.3 47.8 16.8 26.9 53.1 46.4 -0.069
9 slant_up 54.3 47.8 16.8 26.9 54.3 45.3 -0.0686
10 star 54.3 47.8 16.8 26.9 56.5 50.1 -0.063
11 v_lines 54.3 47.8 16.8 26.9 50.4 47.1 -0.0694
12 wide_lines 54.3 47.8 16.8 26.9 64.6 46.3 -0.0666
13 x_shape 54.3 47.8 16.8 26.9 47.1 39.9 -0.0656# What can you conclude?
# --> The standard deviation, the mean, and the correlation are basically the
# same for all datasets. The median is different.
# Plot all datasets of ds. Hide the legend. Hint: Use the facet_wrap() and the theme() function.
ggplot(ds, aes(x = x, y = y)) +
geom_point() +
facet_wrap(~dataset, ncol = 3) +
theme(legend.position = "none")
# Create a loop that generates separate scatter plots for each unique datatset of the tibble ds.
# Export each graph as a png file.
# Assuming uni_ds is a vector of unique values for the 'dataset' variable
uni_ds <- unique(ds$dataset)
# Create the 'pic' folder if it doesn't exist
if (!dir.exists("fig")) {
dir.create("fig")
}
for (uni_v in uni_ds) {
# Select data for the current value
subset_ds <- ds |>
filter(dataset == uni_v) |>
select(x, y)
# Make plot
graph <- ggplot(subset_ds, aes(x = x, y = y)) +
geom_point() +
labs(
title = paste("Dataset:", uni_v),
x = "X",
y = "Y"
) +
theme_bw()
# Save the plot as a PNG file
filename <- paste0("fig/", "plot_ds_", uni_v, ".png")
ggsave(filename, plot = graph)
}Saving 7 x 5 in image
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Saving 7 x 5 in image
Saving 7 x 5 in image# unload packages
suppressMessages(pacman::p_unload(datasauRus, tidyverse))The dataset convergence.dta, see https://github.com/hubchev/courses/blob/main/dta/convergence.dta, contains the per capita GDP of 1960 (gdppc60) and the average growth rate of GDP per capita between 1960 and 1995 (growth) for different countries (country), as well as 3 dummy variables indicating the belonging of a country to the region Asia (asia), Western Europe (weurope) or Africa (africa).
gdpgrowth) on the y-axis and the 1960 GDP per capita (gdppc60) on the x-axis. Add to the same graph the estimated linear relationship. You do not need to label the graph further, just two things: title the graph world and label the individual observations with the country names.weurope, africa and asia.The script uses the following functions: aes, as.numeric, c, cor, describe, diff, filter, gather, geom_point, geom_text, ggarrange, ggplot, ggtitle, group_by, head, ifelse, lag, list, lm, log, mean, mutate, names, read_dta, select, set_label, starts_with, stat_smooth, stat.desc, str, subset, substr, summarise, summarise_all, summarise_at, summary, tab_model, tail, tbl_summary, vars, view.
# Convergence
# set working directory
# setwd("/home/sthu/Dropbox/hsf/github/courses/")
# clear the environment
rm(list = ls())
# load packages
if (!require(pacman)) install.packages("pacman")
pacman::p_load(
haven, tidyverse, vtable, gtsummary, pastecs, Hmisc,
sjlabelled, tis, ggpubr, sjPlot, psych
)
# import data
data <- read_dta("https://github.com/hubchev/courses/raw/main/dta/convergence.dta")
# inspect data
names(data) [1] "country" "gdppc60" "gdppc65" "gdppc70" "gdppc75" "gdppc80" "gdppc85"
[8] "gdppc90" "gdppc95" "africa" "asia" "weurope" "growth" str(data)tibble [107 × 13] (S3: tbl_df/tbl/data.frame)
$ country: chr [1:107] "Algeria" "Angola" "Argentina" "Australia" ...
..- attr(*, "format.stata")= chr "%24s"
$ gdppc60: num [1:107] 2848 2642 7879 11436 7842 ...
..- attr(*, "label")= chr "real gdp per capita 1960"
..- attr(*, "format.stata")= chr "%9.0g"
$ gdppc65: num [1:107] 3536 3072 8802 13192 9387 ...
..- attr(*, "label")= chr "real gdp per capita 1965"
..- attr(*, "format.stata")= chr "%9.0g"
$ gdppc70: num [1:107] 3670 3558 9903 15842 11946 ...
..- attr(*, "label")= chr "real gdp per capita 1970"
..- attr(*, "format.stata")= chr "%9.0g"
$ gdppc75: num [1:107] 3917 2230 10609 16716 14198 ...
..- attr(*, "label")= chr "real gdp per capita 1975"
..- attr(*, "format.stata")= chr "%9.0g"
$ gdppc80: num [1:107] 5094 2059 11359 18300 16869 ...
..- attr(*, "label")= chr "real gdp per capita 1980"
..- attr(*, "format.stata")= chr "%9.0g"
$ gdppc85: num [1:107] 5876 1988 9246 19669 17919 ...
..- attr(*, "label")= chr "real gdp per capita 1985"
..- attr(*, "format.stata")= chr "%9.0g"
$ gdppc90: num [1:107] 5307 2081 7716 21446 21178 ...
..- attr(*, "label")= chr "real gdp per capita 1990"
..- attr(*, "format.stata")= chr "%9.0g"
$ gdppc95: num [1:107] 4935 1339 10973 23827 22474 ...
..- attr(*, "label")= chr "real gdp per capita 1995"
..- attr(*, "format.stata")= chr "%9.0g"
$ africa : num [1:107] 1 1 0 0 0 0 0 0 1 0 ...
..- attr(*, "label")= chr "=1 if in Africa"
..- attr(*, "format.stata")= chr "%8.0g"
$ asia : num [1:107] 0 0 0 0 0 1 0 0 0 0 ...
..- attr(*, "label")= chr "=1 if in Asia"
..- attr(*, "format.stata")= chr "%8.0g"
$ weurope: num [1:107] 0 0 0 0 0 0 0 1 0 0 ...
..- attr(*, "label")= chr "=1 if in Western Europe"
..- attr(*, "format.stata")= chr "%8.0g"
$ growth : num [1:107] 0.55 -0.68 0.331 0.734 1.053 ...
..- attr(*, "format.stata")= chr "%9.0g"data# A tibble: 107 × 13
country gdppc60 gdppc65 gdppc70 gdppc75 gdppc80 gdppc85 gdppc90 gdppc95
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Algeria 2848. 3536. 3670. 3917. 5094. 5876. 5307. 4935.
2 Angola 2642. 3072. 3558. 2230. 2059. 1988. 2081. 1339.
3 Argentina 7879. 8802. 9903. 10609. 11359. 9246. 7716. 10973.
4 Australia 11436. 13192. 15842. 16716. 18300. 19669. 21446. 23827.
5 Austria 7842. 9387. 11946. 14198. 16869. 17919. 21178. 22474.
6 Bangladesh 1130. 1164. 1181. 1030. 1040. 1245. 1366. 1568.
7 Barbados 3632. 4632. 6456. 8827. 10911. 11090. 14411. 14636.
8 Belgium 8314. 10454. 12980. 15024. 17451. 18109. 21246. 22356.
9 Benin 1140. 1188. 1170. 1048. 1069. 1252. 1069. 1139.
10 Bolivia 2516. 2880. 2670. 3124. 3264. 2718. 2615. 2795.
# ℹ 97 more rows
# ℹ 4 more variables: africa <dbl>, asia <dbl>, weurope <dbl>, growth <dbl>head(data)# A tibble: 6 × 13
country gdppc60 gdppc65 gdppc70 gdppc75 gdppc80 gdppc85 gdppc90 gdppc95 africa
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Algeria 2848. 3536. 3670. 3917. 5094. 5876. 5307. 4935. 1
2 Angola 2642. 3072. 3558. 2230. 2059. 1988. 2081. 1339. 1
3 Argent… 7879. 8802. 9903. 10609. 11359. 9246. 7716. 10973. 0
4 Austra… 11436. 13192. 15842. 16716. 18300. 19669. 21446. 23827. 0
5 Austria 7842. 9387. 11946. 14198. 16869. 17919. 21178. 22474. 0
6 Bangla… 1130. 1164. 1181. 1030. 1040. 1245. 1366. 1568. 0
# ℹ 3 more variables: asia <dbl>, weurope <dbl>, growth <dbl>tail(data)# A tibble: 6 × 13
country gdppc60 gdppc65 gdppc70 gdppc75 gdppc80 gdppc85 gdppc90 gdppc95 africa
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 United… 10341. 11633. 12917. 14072. 15302. 16878. 19585. 20963. 0
2 United… 13118. 15697. 17478. 19284. 22806. 25251. 28281. 30366. 0
3 Uruguay 6279. 5936. 6553. 6949. 8580. 6625. 7763. 9399. 0
4 Venezu… 8381. 10618. 11253. 8815. 8516. 7274. 7431. 7582. 0
5 Zambia 1290. 1564. 1427. 1446. 1324. 1167. 1091. 870. 1
6 Zimbab… 1317. 1539. 2303. 2694. 2816. 2923. 3115. 2832. 1
# ℹ 3 more variables: asia <dbl>, weurope <dbl>, growth <dbl>summary(data) country gdppc60 gdppc65 gdppc70
Length:107 Min. : 407.8 Min. : 513.6 Min. : 354.5
Class :character 1st Qu.: 1153.2 1st Qu.: 1364.5 1st Qu.: 1488.0
Mode :character Median : 2484.7 Median : 2884.4 Median : 3072.2
Mean : 3634.3 Mean : 4367.5 Mean : 5128.4
3rd Qu.: 4354.0 3rd Qu.: 5873.3 3rd Qu.: 6994.6
Max. :16010.3 Max. :18928.9 Max. :22030.9
gdppc75 gdppc80 gdppc85 gdppc90
Min. : 617.9 Min. : 473.6 Min. : 542.3 Min. : 527.7
1st Qu.: 1480.7 1st Qu.: 1708.6 1st Qu.: 1598.8 1st Qu.: 1829.0
Median : 3741.7 Median : 4306.2 Median : 4200.7 Median : 4034.0
Mean : 5759.1 Mean : 6553.6 Mean : 6900.3 Mean : 7775.1
3rd Qu.: 8355.8 3rd Qu.: 9968.6 3rd Qu.:10037.2 3rd Qu.:11716.2
Max. :21808.9 Max. :23860.1 Max. :25251.4 Max. :28744.1
gdppc95 africa asia weurope
Min. : 499.3 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.: 1673.7 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median : 4467.9 Median :0.0000 Median :0.0000 Median :0.0000
Mean : 8468.2 Mean :0.3738 Mean :0.1308 Mean :0.1402
3rd Qu.:13627.8 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
Max. :36741.1 Max. :1.0000 Max. :1.0000 Max. :1.0000
growth
Min. :-0.6888
1st Qu.: 0.2458
Median : 0.6587
Mean : 0.6345
3rd Qu.: 1.0505
Max. : 2.3493 view(data)
# library(vtable)
# vtable(data, missing=TRUE)
# library(pastecs)
stat.desc(data) country gdppc60 gdppc65 gdppc70 gdppc75
nbr.val NA 1.070000e+02 1.070000e+02 1.070000e+02 1.070000e+02
nbr.null NA 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
nbr.na NA 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
min NA 4.078180e+02 5.135667e+02 3.545075e+02 6.178639e+02
max NA 1.601025e+04 1.892888e+04 2.203095e+04 2.180892e+04
range NA 1.560243e+04 1.841531e+04 2.167644e+04 2.119105e+04
sum NA 3.888715e+05 4.673224e+05 5.487424e+05 6.162241e+05
median NA 2.484720e+03 2.884388e+03 3.072176e+03 3.741725e+03
mean NA 3.634313e+03 4.367500e+03 5.128433e+03 5.759103e+03
SE.mean NA 3.314566e+02 4.021934e+02 4.736475e+02 5.272377e+02
CI.mean NA 6.571449e+02 7.973875e+02 9.390523e+02 1.045300e+03
var NA 1.175539e+07 1.730827e+07 2.400459e+07 2.974381e+07
std.dev NA 3.428613e+03 4.160321e+03 4.899448e+03 5.453789e+03
coef.var NA 9.434006e-01 9.525635e-01 9.553499e-01 9.469857e-01
gdppc80 gdppc85 gdppc90 gdppc95 africa
nbr.val 1.070000e+02 1.070000e+02 1.070000e+02 1.070000e+02 107.00000000
nbr.null 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 67.00000000
nbr.na 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.00000000
min 4.735793e+02 5.422725e+02 5.277151e+02 4.993415e+02 0.00000000
max 2.386009e+04 2.525136e+04 2.874414e+04 3.674105e+04 1.00000000
range 2.338651e+04 2.470909e+04 2.821642e+04 3.624171e+04 1.00000000
sum 7.012400e+05 7.383373e+05 8.319308e+05 9.061030e+05 40.00000000
median 4.306217e+03 4.200733e+03 4.034010e+03 4.467940e+03 0.00000000
mean 6.553645e+03 6.900349e+03 7.775054e+03 8.468253e+03 0.37383178
SE.mean 6.018749e+02 6.552251e+02 7.711596e+02 8.456513e+02 0.04699273
CI.mean 1.193276e+03 1.299048e+03 1.528899e+03 1.676586e+03 0.09316766
var 3.876112e+07 4.593724e+07 6.363152e+07 7.651850e+07 0.23628990
std.dev 6.225843e+03 6.777701e+03 7.976937e+03 8.747486e+03 0.48609659
coef.var 9.499817e-01 9.822259e-01 1.025965e+00 1.032974e+00 1.30030838
asia weurope growth
nbr.val 107.00000000 107.00000000 107.0000000
nbr.null 93.00000000 92.00000000 0.0000000
nbr.na 0.00000000 0.00000000 0.0000000
min 0.00000000 0.00000000 -0.6887722
max 1.00000000 1.00000000 2.3493433
range 1.00000000 1.00000000 3.0381155
sum 14.00000000 15.00000000 67.8899760
median 0.00000000 0.00000000 0.6586871
mean 0.13084112 0.14018692 0.6344858
SE.mean 0.03275433 0.03372119 0.0601857
CI.mean 0.06493865 0.06685553 0.1193240
var 0.11479457 0.12167166 0.3875881
std.dev 0.33881347 0.34881465 0.6225657
coef.var 2.58950297 2.48821120 0.9812131# library(Hmisc)
describe(data) vars n mean sd median trimmed mad min max
country* 1 107 54.00 31.03 54.00 54.00 40.03 1.00 107.00
gdppc60 2 107 3634.31 3428.61 2484.72 3032.19 2027.76 407.82 16010.25
gdppc65 3 107 4367.50 4160.32 2884.39 3673.42 2579.50 513.57 18928.88
gdppc70 4 107 5128.43 4899.45 3072.18 4370.29 2854.11 354.51 22030.95
gdppc75 5 107 5759.10 5453.79 3741.72 4977.54 3708.25 617.86 21808.92
gdppc80 6 107 6553.64 6225.84 4306.22 5707.40 4476.29 473.58 23860.09
gdppc85 7 107 6900.35 6777.70 4200.73 5929.46 4382.44 542.27 25251.36
gdppc90 8 107 7775.05 7976.94 4034.01 6660.00 4258.37 527.72 28744.14
gdppc95 9 107 8468.25 8747.49 4467.94 7235.12 4935.09 499.34 36741.05
africa 10 107 0.37 0.49 0.00 0.34 0.00 0.00 1.00
asia 11 107 0.13 0.34 0.00 0.05 0.00 0.00 1.00
weurope 12 107 0.14 0.35 0.00 0.06 0.00 0.00 1.00
growth 13 107 0.63 0.62 0.66 0.63 0.59 -0.69 2.35
range skew kurtosis se
country* 106.00 0.00 -1.23 3.00
gdppc60 15602.43 1.53 1.55 331.46
gdppc65 18415.31 1.41 1.16 402.19
gdppc70 21676.44 1.29 0.74 473.65
gdppc75 21191.05 1.15 0.09 527.24
gdppc80 23386.51 1.07 -0.11 601.87
gdppc85 24709.09 1.14 0.01 655.23
gdppc90 28216.42 1.13 -0.10 771.16
gdppc95 36241.71 1.14 0.12 845.65
africa 1.00 0.51 -1.75 0.05
asia 1.00 2.16 2.69 0.03
weurope 1.00 2.04 2.20 0.03
growth 3.04 0.15 0.07 0.06# library(gtsummary)
tbl_summary(data)| Characteristic | N = 1071 |
|---|---|
| country | |
| Algeria | 1 (0.9%) |
| Angola | 1 (0.9%) |
| Argentina | 1 (0.9%) |
| Australia | 1 (0.9%) |
| Austria | 1 (0.9%) |
| Bangladesh | 1 (0.9%) |
| Barbados | 1 (0.9%) |
| Belgium | 1 (0.9%) |
| Benin | 1 (0.9%) |
| Bolivia | 1 (0.9%) |
| Botswana | 1 (0.9%) |
| Brazil | 1 (0.9%) |
| Burkina Faso | 1 (0.9%) |
| Burundi | 1 (0.9%) |
| Cameroon | 1 (0.9%) |
| Canada | 1 (0.9%) |
| Cape Verde | 1 (0.9%) |
| Central African Republic | 1 (0.9%) |
| Chad | 1 (0.9%) |
| Chile | 1 (0.9%) |
| China | 1 (0.9%) |
| Colombia | 1 (0.9%) |
| Comoros | 1 (0.9%) |
| Congo, Republic of | 1 (0.9%) |
| Costa Rica | 1 (0.9%) |
| Cote d'lvoire | 1 (0.9%) |
| Cyprus | 1 (0.9%) |
| Denmark | 1 (0.9%) |
| Dominican Republic | 1 (0.9%) |
| Ecuador | 1 (0.9%) |
| Egypt | 1 (0.9%) |
| El Salvador | 1 (0.9%) |
| Ethiopia | 1 (0.9%) |
| Fiji | 1 (0.9%) |
| Finland | 1 (0.9%) |
| France | 1 (0.9%) |
| Gabon | 1 (0.9%) |
| Gambia, The | 1 (0.9%) |
| Ghana | 1 (0.9%) |
| Greece | 1 (0.9%) |
| Guatemala | 1 (0.9%) |
| Guinea | 1 (0.9%) |
| Guinea-Bissau | 1 (0.9%) |
| Guyana | 1 (0.9%) |
| Honduras | 1 (0.9%) |
| Hong Kong | 1 (0.9%) |
| Iceland | 1 (0.9%) |
| India | 1 (0.9%) |
| Indonesia | 1 (0.9%) |
| Iran | 1 (0.9%) |
| Ireland | 1 (0.9%) |
| Israel | 1 (0.9%) |
| Italy | 1 (0.9%) |
| Jamaica | 1 (0.9%) |
| Japan | 1 (0.9%) |
| Jordan | 1 (0.9%) |
| Kenya | 1 (0.9%) |
| Lesotho | 1 (0.9%) |
| Luxembourg | 1 (0.9%) |
| Madagascar | 1 (0.9%) |
| Malawi | 1 (0.9%) |
| Malaysia | 1 (0.9%) |
| Mali | 1 (0.9%) |
| Mauritania | 1 (0.9%) |
| Mauritius | 1 (0.9%) |
| Mexico | 1 (0.9%) |
| Morocco | 1 (0.9%) |
| Mozambique | 1 (0.9%) |
| Namibia | 1 (0.9%) |
| Nepal | 1 (0.9%) |
| Netherlands | 1 (0.9%) |
| New Zealand | 1 (0.9%) |
| Nicaragua | 1 (0.9%) |
| Niger | 1 (0.9%) |
| Nigeria | 1 (0.9%) |
| Norway | 1 (0.9%) |
| Pakistan | 1 (0.9%) |
| Panama | 1 (0.9%) |
| Papua New Guinea | 1 (0.9%) |
| Paraguay | 1 (0.9%) |
| Peru | 1 (0.9%) |
| Philippines | 1 (0.9%) |
| Portugal | 1 (0.9%) |
| Romania | 1 (0.9%) |
| Rwanda | 1 (0.9%) |
| Senegal | 1 (0.9%) |
| Seychelles | 1 (0.9%) |
| Singapore | 1 (0.9%) |
| South Africa | 1 (0.9%) |
| South Korea | 1 (0.9%) |
| Spain | 1 (0.9%) |
| Sri Lanka | 1 (0.9%) |
| Sweden | 1 (0.9%) |
| Switzerland | 1 (0.9%) |
| Syria | 1 (0.9%) |
| Tanzania | 1 (0.9%) |
| Thailand | 1 (0.9%) |
| Togo | 1 (0.9%) |
| Trinidad & Tobago | 1 (0.9%) |
| Turkey | 1 (0.9%) |
| Uganda | 1 (0.9%) |
| United Kingdom | 1 (0.9%) |
| United States of America | 1 (0.9%) |
| Uruguay | 1 (0.9%) |
| Venezuela | 1 (0.9%) |
| Zambia | 1 (0.9%) |
| Zimbabwe | 1 (0.9%) |
| real gdp per capita 1960 | 2,485 (1,153, 4,354) |
| real gdp per capita 1965 | 2,884 (1,365, 5,873) |
| real gdp per capita 1970 | 3,072 (1,488, 6,995) |
| real gdp per capita 1975 | 3,742 (1,481, 8,356) |
| real gdp per capita 1980 | 4,306 (1,709, 9,969) |
| real gdp per capita 1985 | 4,201 (1,599, 10,037) |
| real gdp per capita 1990 | 4,034 (1,829, 11,716) |
| real gdp per capita 1995 | 4,468 (1,674, 13,628) |
| =1 if in Africa | 40 (37%) |
| =1 if in Asia | 14 (13%) |
| =1 if in Western Europe | 15 (14%) |
| growth | 0.66 (0.25, 1.05) |
| 1 n (%); Median (IQR) | |
# check the assignments of countries to continents
data |>
select(country, africa, asia, weurope) |>
view()
data <- mutate(data, x_1 = africa + asia + weurope)
data |>
filter(x_1 == 0) |>
select(africa, asia, weurope, country) |>
view()
# correct the assignment manually
data$weurope[data$country == "Austria"] <- 1
data$weurope[data$country == "Greece"] <- 1
data$weurope[data$country == "Cyprus"] <- 1
filter(data, data$weurope == 1) # check changes# A tibble: 18 × 14
country gdppc60 gdppc65 gdppc70 gdppc75 gdppc80 gdppc85 gdppc90 gdppc95
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Austria 7842. 9387. 11946. 14198. 16869. 17919. 21178. 22474.
2 Belgium 8314. 10454. 12980. 15024. 17451. 18109. 21246. 22356.
3 Cyprus 3178. 4261. 5638. 4827. 8302. 10228. 13798. 17169.
4 Denmark 11745. 14749. 17143. 17750. 19558. 21596. 23308. 25293.
5 Finland 8007. 9851. 12198. 14884. 16621. 18585. 21667. 20084.
6 France 8364. 10497. 13186. 14951. 17335. 18429. 21403. 21502.
7 Greece 4454. 6549. 9022. 11121. 12672. 12287. 12794. 13332.
8 Iceland 8786. 11403. 11678. 15235. 19440. 20414. 22502. 21901.
9 Ireland 5490. 6413. 7760. 9064. 10649. 11641. 15133. 18456.
10 Italy 7364. 9097. 12072. 13386. 16286. 17518. 20638. 21691.
11 Luxembourg 12510. 14019. 16163. 17384. 19089. 21414. 28744. 36741.
12 Netherlands 9883. 11702. 14237. 15803. 17339. 17974. 20823. 22320.
13 Norway 8808. 10478. 11959. 14873. 17977. 20630. 21855. 25538.
14 Portugal 3665. 4866. 6730. 7951. 9667. 9847. 13155. 13924.
15 Spain 4956. 7459. 9701. 11970. 12294. 12583. 15475. 17434.
16 Sweden 10870. 13552. 15850. 17588. 18348. 20001. 22219. 22122.
17 Switzerland 16010. 18929. 22031. 21809. 23860. 24844. 27931. 26227.
18 United Kingd… 10341. 11633. 12917. 14072. 15302. 16878. 19585. 20963.
# ℹ 5 more variables: africa <dbl>, asia <dbl>, weurope <dbl>, growth <dbl>,
# x_1 <dbl># In the following, I do the same with a loop
# c_europe <- c("Austria","Greece","Cyprus")
# sum(data$weurope) # check changes
# for (i in c_europe){
# print(i)
# data$weurope[data$country == i] <- 1
# }
# sum(data$weurope) # check changes
# data$weurope[data$country == "Austria"] # check changes
# create a category for the remaining countries
# use ifelse -- ifelse(condition, result if TRUE, result if FALSE)
data$rest <- ifelse(data$africa == 0 & data$asia == 0 & data$weurope == 0, 1, 0)
data$rest <- set_label(data$rest, label = "=1 if not in Africa, W.Europe, or Asia")
# create table with means across country groups
table_gdp <- data |>
group_by(africa, asia, weurope) |>
summarise_at(vars(gdppc60:gdppc95), list(name = mean))
data |>
group_by(africa, asia, weurope) |>
select(gdppc60:gdppc95) |>
summarise_all(mean)Adding missing grouping variables: `africa`, `asia`, `weurope`# A tibble: 4 × 11
# Groups: africa, asia [3]
africa asia weurope gdppc60 gdppc65 gdppc70 gdppc75 gdppc80 gdppc85 gdppc90
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 0 0 4288. 5034. 5727. 6411. 7042. 7185. 7457.
2 0 0 1 8366. 10294. 12401. 13994. 16059. 17272. 20192.
3 0 1 0 1739. 2247. 3090. 3760. 4905. 5761. 7501.
4 1 0 0 1596. 1860. 2046. 2182. 2426. 2382. 2562.
# ℹ 1 more variable: gdppc95 <dbl># create growth rate
data$gr1 <- (data$gdppc95 - data$gdppc60) / data$gdppc60
data$gr2 <- log(data$gdppc95) - log(data$gdppc60)
cor(data$gr1, data$gr2)[1] 0.9008887ggplot(data, aes(x = gdppc60, y = growth, label = country)) +
geom_point() +
geom_text(hjust = 0, vjust = 0)
p1 <- ggplot(data, aes(x = gdppc60, y = growth, label = country)) +
geom_point() +
stat_smooth(formula = y ~ x, method = "lm", se = FALSE, colour = "red", linetype = 1) +
# geom_text(hjust=0, vjust=0) +
ggtitle("World")
p2 <- data |>
filter(weurope == 1) |>
ggplot(aes(x = gdppc60, y = growth, label = country)) +
geom_point() +
stat_smooth(formula = y ~ x, method = "lm", se = FALSE, colour = "red", linetype = 1) +
# geom_text(hjust=0, vjust=0) +
ggtitle("Western Europe")
p3 <- data |>
filter(asia == 1) |>
ggplot(aes(x = gdppc60, y = growth, label = country)) +
geom_point() +
stat_smooth(formula = y ~ x, method = "lm", se = FALSE, colour = "red", linetype = 1) +
# geom_text(hjust=0, vjust=0) +
ggtitle("Asia")
p4 <- data |>
filter(africa == 1) |>
ggplot(aes(x = gdppc60, y = growth, label = country)) +
geom_point() +
stat_smooth(formula = y ~ x, method = "lm", se = FALSE, colour = "red", linetype = 1) +
# geom_text( hjust=0, vjust=0) +
ggtitle("Africa")
ggarrange(p1, p2, p3, p4,
labels = c("A", "B", "C", "D"),
ncol = 2, nrow = 2
)Warning: The following aesthetics were dropped during statistical transformation: label.
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
The following aesthetics were dropped during statistical transformation: label.
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
The following aesthetics were dropped during statistical transformation: label.
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
The following aesthetics were dropped during statistical transformation: label.
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
# Regression analysis
m1 <- lm(growth ~ gdppc60, data = data)
m2 <- lm(growth ~ gdppc60, data = subset(data, weurope == 1))
m3 <- lm(growth ~ gdppc60, data = subset(data, asia == 1))
m4 <- lm(growth ~ gdppc60, data = subset(data, africa == 1))
tab_model(m1, m2, m3, m4,
p.style = "stars",
p.threshold = c(0.2, 0.1, 0.05),
show.ci = FALSE,
show.se = FALSE,
show.aic = TRUE,
dv.labels = c("World", "W.Europe", "Asia", "Africa")
)| World | W.Europe | Asia | Africa | |
| Predictors | Estimates | Estimates | Estimates | Estimates |
| (Intercept) | 0.54 *** | 1.59 *** | 0.91 *** | 0.20 |
| real gdp per capita 1960 | 0.00 * | -0.00 *** | 0.00 * | 0.00 |
| Observations | 107 | 18 | 14 | 40 |
| R2 / R2 adjusted | 0.021 / 0.012 | 0.727 / 0.710 | 0.158 / 0.088 | 0.002 / -0.024 |
| AIC | 204.917 | -14.237 | 31.220 | 76.318 |
| * p<0.2 ** p<0.1 *** p<0.05 | ||||
# reshape data (see: https://stackoverflow.com/questions/2185252/reshaping-data-frame-from-wide-to-long-format)
data_long <- gather(data, condition, measurement, gdppc60:gdppc95, factor_key = TRUE)Warning: attributes are not identical across measure variables; they will be
droppeddata_long$year <- as.numeric(substr(data_long$condition, 6, 7))
data_long$gr_long <- data_long |>
select(country, measurement) |>
group_by(country) |>
mutate(gr = c(NA, diff(measurement)) / lag(measurement, 1))
# erase all helping variables
data <- select(data, -starts_with("h_"))
# generate and remove variables in a dataframe
data <- mutate(data, Land = country)
data <- select(data, -country)
data |>
summarise(
y65 = mean(gdppc65, na.rm = TRUE),
y70 = mean(gdppc70, na.rm = TRUE),
y75 = mean(gdppc75, na.rm = TRUE),
y80 = mean(gdppc80, na.rm = TRUE),
y85 = mean(gdppc85, na.rm = TRUE),
y90 = mean(gdppc90, na.rm = TRUE),
y95 = mean(gdppc95, na.rm = TRUE)
)# A tibble: 1 × 7
y65 y70 y75 y80 y85 y90 y95
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 4367. 5128. 5759. 6554. 6900. 7775. 8468.suppressMessages(pacman::p_unload(
haven, tidyverse, vtable, gtsummary, pastecs, Hmisc,
sjlabelled, tis, ggpubr, sjPlot
))The following exercise was a former exam.
Please answer all (!) questions in an R script. Normal text should be written as comments, using the ‘#’ to comment out text. Make sure the script runs without errors before submitting it. Each task (starting with 1) is worth five points. You have a total of 120 minutes of editing time. Please do not forget to number your answers.
When you are done with your work, save the R script, export the script to pdf format and upload the pdf file.
Suppose you aim to empirically examine unemployment and GDP for Germany and France. The data set that we use in the following is ‘forest.Rdata’.
Write down your name, matriculation number, and date.
Set your working directory.
load(url("https://github.com/hubchev/courses/raw/main/dta/forest.Rdata"))If that is not working, you can also download the data from ILIAS, save it in your working directory and load it from there with:
# load("forest.Rdata")Show the first eight observations of the dataset df.
Show the last observation of the dataset df.
Which type of data do we have here (Panel, cross-section,time series, …)? Name the variable(s) that are necessary to identify the observations in the dataset.
Explain what the assignment operator in R is and what it is good for.
Write down the R code to store the number of observations and the number of variables that are in the dataset df. Name the object in which you store these numbers ‘observations_df’.
In the dataset df, rename the variable ‘country.x’ to ‘nation’ and the variable ‘date’ to ‘year’.
Explain what the pipe operator in R is and what it is good for.
For the upcoming analysis you are only interested the following variables that are part of the dataframe df: nation, year, gdp, pop, gdppc, and unemployment. Drop all other variables from the dataframe df.
Create a variable that indicates the GDP per capita (‘gdp’ divided by ‘pop’). Name the variable ‘gdp_pc’. (Hint: If you fail here, use the variable ‘gdppc’ which is already in the dataset as a replacement for ‘gdp_pc’ in the following tasks.)
For the upcoming analysis you are only interested the following countries that are part of the dataframe df: Germany and France. Drop all other countries from the dataframe df.
Create a table showing the average unemployment rate and GDP per capita for Germany and France in the given years. Use the pipe operator. (Hint: See below for how your results should look like.)
# A tibble: 2 × 3
nation `mean(unemployment)` `mean(gdppc)`
<chr> <dbl> <dbl>
1 France 9.75 34356.
2 Germany 7.22 36739.# A tibble: 2 × 3
nation `mean(unemployment)` `mean(gdppc)`
<chr> <dbl> <dbl>
1 France 8.01 35786.
2 Germany 3.81 41315.# A tibble: 2 × 3
nation `max(unemployment)` `max(gdppc)`
<chr> <dbl> <dbl>
1 France 12.6 38912.
2 Germany 11.2 43329.# A tibble: 2 × 3
nation `sd(gdppc)` `sd(unemployment)`
<chr> <dbl> <dbl>
1 France 2940. 1.58
2 Germany 4015. 2.37# A tibble: 2 × 4
nation `sd(unemployment)` `mean(unemployment)` cov
<chr> <dbl> <dbl> <dbl>
1 France 1.58 9.75 0.162
2 Germany 2.37 7.22 0.328# A tibble: 2 × 4
nation `sd(gdppc)` `mean(gdppc)` cov
<chr> <dbl> <dbl> <dbl>
1 France 2940. 34356. 0.0856
2 Germany 4015. 36739. 0.109 |> of what your result may look like).
Suppose you want to visualize the simultaneous evolution of the unemployment rate and GDP per capita over time for Germany as well.
Suppose further that you have found the following lines of code that create the kind of chart you are looking for.
# Data
x <- c(1, 2, 3, 4, 5, 4, 7, 8, 9)
y <- c(12, 16, 14, 18, 16, 13, 15, 20, 22)
labels <- 1970:1978
# Connected scatter plot with text
plot(x, y, type = "b", xlab = "Var 1", ylab = "Var 2")
text(x + 0.4, y + 0.1, labels)
Use these lines of code and customize them to create the co-movement visualization for Germany using the available df data. The result should look something like this:
The script uses the following functions: aes, c, dim, filter, geom_line, ggplot, ggtitle, group_by, head, load, max, mean, mutate, plot, rename, sd, select, summarise, tail, text, title, url.
# setwd("/home/sthu/Dropbox/hsf/exams/22-11/scr/")
rm(list = ls())
if (!require(pacman)) install.packages("pacman")
pacman::p_load(tidyverse, ggpubr, sjPlot)
load(url("https://github.com/hubchev/courses/raw/main/dta/forest.Rdata"))
head(df, 8)# A tibble: 8 × 11
# Groups: country.x [1]
country.x date gdp gdp_growth unemployment region income forest pop
<chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
1 United Arab… 1992 1.26e11 -2.48 1.84 Middl… High … 3.63 2.05e6
2 United Arab… 1993 1.27e11 -4.34 1.85 Middl… High … 3.72 2.17e6
3 United Arab… 1994 1.36e11 1.25 1.81 Middl… High … 3.81 2.29e6
4 United Arab… 1995 1.45e11 1.35 1.80 Middl… High … 3.90 2.42e6
5 United Arab… 1996 1.54e11 0.631 1.90 Middl… High … 3.99 2.54e6
6 United Arab… 1997 1.66e11 2.83 1.98 Middl… High … 4.08 2.67e6
7 United Arab… 1998 1.67e11 -4.77 2.14 Middl… High … 4.18 2.81e6
8 United Arab… 1999 1.72e11 -2.40 2.22 Middl… High … 4.27 2.97e6
# ℹ 2 more variables: unemployment_dif <dbl>, gdppc <dbl>tail(df, 1)# A tibble: 1 × 11
# Groups: country.x [1]
country.x date gdp gdp_growth unemployment region income forest pop
<chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
1 Zimbabwe 2020 1.94e10 -7.62 5.35 Sub-S… Lower… 45.1 1.49e7
# ℹ 2 more variables: unemployment_dif <dbl>, gdppc <dbl># panel data set
# date and country.x
observations_df <- dim(df)
df <- rename(df, nation = country.x)
df <- rename(df, year = date)
df <- df |>
select(nation, year, gdp, pop, gdppc, unemployment)
df <- df |>
mutate(gdp_pc = gdp / pop)
df <- df |> filter(nation == "Germany" | nation == "France")
df |>
group_by(nation) |>
summarise(mean(unemployment), mean(gdppc))# A tibble: 2 × 3
nation `mean(unemployment)` `mean(gdppc)`
<chr> <dbl> <dbl>
1 France 9.75 34356.
2 Germany 7.22 36739.df |>
filter(year == 2020) |>
group_by(nation) |>
summarise(mean(unemployment), mean(gdppc))# A tibble: 2 × 3
nation `mean(unemployment)` `mean(gdppc)`
<chr> <dbl> <dbl>
1 France 8.01 35786.
2 Germany 3.81 41315.df |>
group_by(nation) |>
summarise(max(unemployment), max(gdppc))# A tibble: 2 × 3
nation `max(unemployment)` `max(gdppc)`
<chr> <dbl> <dbl>
1 France 12.6 38912.
2 Germany 11.2 43329.df |>
group_by(nation) |>
summarise(sd(gdppc), sd(unemployment))# A tibble: 2 × 3
nation `sd(gdppc)` `sd(unemployment)`
<chr> <dbl> <dbl>
1 France 2940. 1.58
2 Germany 4015. 2.37df |>
group_by(nation) |>
summarise(sd(unemployment), mean(unemployment), cov = sd(unemployment) / mean(unemployment))# A tibble: 2 × 4
nation `sd(unemployment)` `mean(unemployment)` cov
<chr> <dbl> <dbl> <dbl>
1 France 1.58 9.75 0.162
2 Germany 2.37 7.22 0.328df |>
group_by(nation) |>
summarise(sd(gdppc), mean(gdppc), cov = sd(gdppc) / mean(gdppc))# A tibble: 2 × 4
nation `sd(gdppc)` `mean(gdppc)` cov
<chr> <dbl> <dbl> <dbl>
1 France 2940. 34356. 0.0856
2 Germany 4015. 36739. 0.109 df |>
filter(nation == "Germany") |>
ggplot(aes(x = year, y = unemployment)) +
geom_line() +
ggtitle("Germany")
labels <- 1992:2020
dfra <- df |> filter(nation == "France")
plot(dfra$gdppc, dfra$unemployment,
type = "b",
xlab = "GDP per capita", ylab = "Unemployment rate"
)
text(dfra$gdppc + 0.1, dfra$unemployment + 0.1, labels)
title("France")
# Data
x <- c(1, 2, 3, 4, 5, 4, 7, 8, 9)
y <- c(12, 16, 14, 18, 16, 13, 15, 20, 22)
labels <- 1970:1978
# Connected scatter plot with text
plot(x, y, type = "b", xlab = "Var 1", ylab = "Var 2")
text(x + 0.4, y + 0.1, labels)
dfger <- df |> filter(nation == "Germany")
labels <- 1992:2020
plot(dfger$gdppc, dfger$unemployment,
type = "b",
xlab = "Var 1", ylab = "Var 2"
)
text(dfger$gdppc + 0.7, dfger$unemployment + 0.4, labels)
title("Germany")
# rmarkdown::render("22-11_dsda_exam.Rmd", "all")
# knitr::purl(input = "22-11_dsda_exam.Rmd", output = "22-11_dsda_solution.R",documentation = 0)
suppressMessages(pacman::p_unload(tidyverse, ggpubr, sjPlot))Reproduce Figure 3 of Hortaçsu & Syverson (2015, p. 99) using R. Write a clear report about your work, i.e., document everything with a R script or a R Markdown file.
Here are the required steps:
ggplot().The script uses the following functions: aes, download.file, geom_line, ggplot, pivot_longer, read_excel, unzip.
# setwd("~/Dropbox/hsf/courses/Rlang/hortacsu")
rm(list = ls())
# install and load packages
if (!require(pacman)) install.packages("pacman")
pacman::p_load(tidyverse, readxl)
# Define the URL of the ZIP file
zip_f <- "https://github.com/hubchev/courses/raw/main/dta/113962-V1.zip"
# Download the ZIP file
download.file(zip_f, destfile = "113962-V1.zip")
# Unzip the contents
unzip("113962-V1.zip")
df_curves <- read_excel("Hortacsu_Syverson_JEP_Retail/diffusion_curves_figure.xlsx",
sheet = "Data and Predictions", range = "N3:Y60"
)
df <- df_curves |>
pivot_longer(
cols = "Music and Video":"Food and Beverages",
names_to = "industry",
values_to = "value"
)
# Plot
df |>
ggplot(aes(x = Year, y = value, group = industry, color = industry)) +
geom_line()Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).
# unload packages
suppressMessages(pacman::p_unload(tidyverse, readxl))In the statistic course of WS 2020, I asked 23 students about their weight, height, sex, and number of siblings. I wonder how good the height can explain the weight of students. Examine with corelations and a regression analysis the association. Load the data as follows:
library("haven")
Attaching package: 'haven'The following objects are masked from 'package:expss':
is.labelled, read_spssclassdata <- read.csv("https://raw.githubusercontent.com/hubchev/courses/main/dta/classdata.csv")The script uses the following functions: aes, c, coef, fitted, geom_abline, geom_point, ggplot, head, lm, plot, read.csv, residuals, show, stat_smooth, subset, summary, tab_model.
## ---- echo = TRUE--------------------------------------------------
# install and load packages
if (!require(pacman)) install.packages("pacman")
pacman::p_load(tidyverse, haven, ggplot2, sjPlot)
classdata <- read.csv("https://raw.githubusercontent.com/hubchev/courses/main/dta/classdata.csv")
head(classdata) id sex weight height siblings row
1 1 w 53 156 1 g
2 2 w 73 170 1 g
3 3 m 68 169 1 g
4 4 w 67 166 1 g
5 5 w 65 175 1 g
6 6 w 48 161 0 g## ---- echo = TRUE--------------------------------------------------
summary(classdata) id sex weight height
Min. : 1.0 Length:23 Min. :48.00 Min. :156.0
1st Qu.: 6.5 Class :character 1st Qu.:64.50 1st Qu.:168.0
Median :12.0 Mode :character Median :70.00 Median :175.0
Mean :12.0 Mean :70.61 Mean :173.7
3rd Qu.:17.5 3rd Qu.:81.00 3rd Qu.:180.0
Max. :23.0 Max. :90.00 Max. :194.0
siblings row
Min. :0.000 Length:23
1st Qu.:1.000 Class :character
Median :1.000 Mode :character
Mean :1.391
3rd Qu.:2.000
Max. :4.000 ## ----pressure, echo=TRUE-------------------------------------------
ggplot(classdata, aes(x = height, y = weight)) +
geom_point()
## ---- echo=TRUE----------------------------------------------------
ggplot(classdata, aes(x = height, y = weight)) +
geom_point() +
stat_smooth(formula = y ~ x, method = "lm", se = FALSE, colour = "red", linetype = 1)
## ---- echo=TRUE----------------------------------------------------
## baseline regression model
model <- lm(weight ~ height + sex, data = classdata)
show(model)
Call:
lm(formula = weight ~ height + sex, data = classdata)
Coefficients:
(Intercept) height sexw
-29.5297 0.5923 -5.7894 interm <- model$coefficients[1]
slope <- model$coefficients[2]
interw <- model$coefficients[1] + model$coefficients[3]
## ---- echo=TRUE----------------------------------------------------
summary(model)
Call:
lm(formula = weight ~ height + sex, data = classdata)
Residuals:
Min 1Q Median 3Q Max
-17.086 -3.730 2.850 7.245 12.914
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -29.5297 47.6606 -0.620 0.5425
height 0.5923 0.2671 2.217 0.0383 *
sexw -5.7894 4.4773 -1.293 0.2107
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.942 on 20 degrees of freedom
Multiple R-squared: 0.4124, Adjusted R-squared: 0.3537
F-statistic: 7.019 on 2 and 20 DF, p-value: 0.004904## ---- echo=TRUE----------------------------------------------------
ggplot(classdata, aes(x = height, y = weight, shape = sex)) +
geom_point() +
geom_abline(slope = slope, intercept = interw, linetype = 2, size = 1.5) +
geom_abline(slope = slope, intercept = interm, linetype = 2, size = 1.5) +
geom_abline(slope = coef(model)[[2]], intercept = coef(model)[[1]])Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
## ---- echo=TRUE----------------------------------------------------
ggplot(classdata, aes(x = height, y = weight, shape = sex)) +
geom_point(aes(size = 2)) +
stat_smooth(
formula = y ~ x, method = "lm",
se = FALSE, colour = "red", linetype = 1
)
## ---- echo=TRUE----------------------------------------------------
ggplot(classdata, aes(x = height, y = weight, shape = sex)) +
geom_point(aes(size = siblings))
## ---- echo=TRUE----------------------------------------------------
## baseline model
model <- lm(weight ~ height + sex, data = classdata)
ggplot(classdata, aes(x = height, y = weight, shape = sex)) +
geom_point(aes(size = 2)) +
stat_smooth(
formula = y ~ x,
method = "lm",
se = T,
colour = "red",
linetype = 1
)
## ---- echo=TRUE, results='hide'------------------------------------
m1 <- lm(weight ~ height, data = classdata)
m2 <- lm(weight ~ height + sex, data = classdata)
m3 <- lm(weight ~ height + sex + height * sex, data = classdata)
m4 <- lm(weight ~ height + sex + height * sex + siblings, data = classdata)
m5 <- lm(weight ~ height + sex + height * sex, data = subset(classdata, siblings < 4))
tab_model(m1, m2, m3, m4, m5,
p.style = "stars",
p.threshold = c(0.2, 0.1, 0.05),
show.ci = FALSE,
show.se = FALSE
)| weight | weight | weight | weight | weight | |
| Predictors | Estimates | Estimates | Estimates | Estimates | Estimates |
| (Intercept) | -65.44 * | -29.53 | 47.14 | 50.27 | 27.69 |
| height | 0.78 *** | 0.59 *** | 0.16 | 0.16 | 0.28 |
| sex [w] | -5.79 | -153.96 ** | -161.92 ** | -134.51 * | |
| height × sex [w] | 0.85 * | 0.89 * | 0.74 * | ||
| siblings | -1.16 | ||||
| Observations | 23 | 23 | 23 | 23 | 21 |
| R2 / R2 adjusted | 0.363 / 0.333 | 0.412 / 0.354 | 0.487 / 0.407 | 0.496 / 0.385 | 0.572 / 0.497 |
| * p<0.2 ** p<0.1 *** p<0.05 | |||||
## ---- echo=FALSE---------------------------------------------------
tab_model(m1, m2, m3, m4,
p.style = "stars",
p.threshold = c(0.2, 0.1, 0.05),
show.ci = FALSE,
show.se = FALSE
)| weight | weight | weight | weight | |
| Predictors | Estimates | Estimates | Estimates | Estimates |
| (Intercept) | -65.44 * | -29.53 | 47.14 | 50.27 |
| height | 0.78 *** | 0.59 *** | 0.16 | 0.16 |
| sex [w] | -5.79 | -153.96 ** | -161.92 ** | |
| height × sex [w] | 0.85 * | 0.89 * | ||
| siblings | -1.16 | |||
| Observations | 23 | 23 | 23 | 23 |
| R2 / R2 adjusted | 0.363 / 0.333 | 0.412 / 0.354 | 0.487 / 0.407 | 0.496 / 0.385 |
| * p<0.2 ** p<0.1 *** p<0.05 | ||||
## ---- echo=FALSE---------------------------------------------------
tab_model(m3, m5,
p.style = "stars",
p.threshold = c(0.2, 0.1, 0.05),
show.ci = FALSE,
show.se = FALSE
)| weight | weight | |
| Predictors | Estimates | Estimates |
| (Intercept) | 47.14 | 27.69 |
| height | 0.16 | 0.28 |
| sex [w] | -153.96 ** | -134.51 * |
| height × sex [w] | 0.85 * | 0.74 * |
| Observations | 23 | 21 |
| R2 / R2 adjusted | 0.487 / 0.407 | 0.572 / 0.497 |
| * p<0.2 ** p<0.1 *** p<0.05 | ||
## ---- echo=T-------------------------------------------------------
plot(residuals(m3), fitted(m3))
plot(residuals(m3), classdata$siblings)
# unload packages
suppressMessages(pacman::p_unload(tidyverse, haven, sjPlot))tidyverseThe following table stems from a survey carried out at the Campus of the German Sport University of Cologne at Opening Day (first day of the new semester) between 8:00am and 8:20am. The survey consists of 6 individuals with the following information:
| id | sex | age | weight | calories | sport |
|---|---|---|---|---|---|
| 1 | f | 21 | 48 | 1700 | 60 |
| 2 | f | 19 | 55 | 1800 | 120 |
| 3 | f | 23 | 50 | 2300 | 180 |
| 4 | m | 18 | 71 | 2000 | 60 |
| 5 | m | 20 | 77 | 2800 | 240 |
| 6 | m | 61 | 85 | 2500 | 30 |
Data Description:
df. If you fail here, read in the data using this line of code:df <- read_csv("https://raw.githubusercontent.com/hubchev/courses/main/dta/df-calories.csv")Rows: 6 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): sex
dbl (4): age, weight, calories, sport
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Show for all numerical variables the summary statistics including the mean, median, minimum, and the maximum.
Show for all numerical variables the summary statistics including the mean and the standard deviation, separated by male and female. Use therefore the pipe operator.
Suppose you want to analyze the general impact of average calories consumption per day on the weight. Discuss if the sample design is appropriate to draw conclusions on the population. What may cause some bias in the data? Discuss possibilities to improve the sampling and the survey, respectively.
The following plot visualizes the two variables weight and calories. Discuss what can be improved in the graphical visualization.
Make a scatterplot matrix containing all numerical variables.
Calculate the Pearson Correlation Coefficient of the two variables
calories and sportweight and caloriesMake a scatterplot with weight in the y-axis and calories on the x-axis. Additionally, the plot should contain a linear fit and the points should be labeled with the sex just like in the figure shown above.
Estimate the following regression specification using the OLS method: [weight_i=_0+_1 calories_i+ _i]
Show a summary of the estimates that look like the following:
Call:
lm(formula = weight ~ calories, data = df)
Residuals:
1 2 3 4 5 6
-5.490 -1.182 -6.640 9.435 -6.099 9.976
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.730275 20.197867 0.383 0.7214
calories 0.026917 0.009107 2.956 0.0417 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.68 on 4 degrees of freedom
Multiple R-squared: 0.6859, Adjusted R-squared: 0.6074
F-statistic: 8.735 on 1 and 4 DF, p-value: 0.04174Interpret the results. In particular, interpret how many kg the estimated weight increases—on average and ceteris paribus—if calories increase by 100 calories. Additionally, discuss the statistical properties of the estimated coefficient \(\hat{\beta_1}\) and the meaning of the Adjusted R-squared.
OLS estimates can suffer from omitted variable bias. State the two conditions that need to be fulfilled for omitted bias to occur.
Discuss potential confounding variables that may cause omitted variable bias. Given the dataset above how can some of the confounding variables be controlled for?
The script uses the following functions: aes, c, cor, data.frame, geom_point, geom_text, ggplot, group_by, lm, mean, plot, read_csv, sd, stat_smooth, summarise, summary.
# 1
# Stephan Huber, 000, 2020-May-30
# 2
# setwd("/home/sthu/Dropbox/hsf/22-ss/dsb_bac/work/")
# 3
rm(list = ls())
# 4
if (!require(pacman)) install.packages("pacman")
pacman::p_load(tidyverse, haven)
# 5
# cross-section
# 6
sex <- c("f", "f", "f", "m", "m", "m")
age <- c(21, 19, 23, 18, 20, 61)
weight <- c(48, 55, 63, 71, 77, 85)
calories <- c(1700, 1800, 2300, 2000, 2800, 2500)
sport <- c(60, 120, 180, 60, 240, 30)
df <- data.frame(sex, age, weight, calories, sport)
# write_csv(df, file = "/home/sthu/Dropbox/hsf/exams/21-04/stuff/df.csv")
# write_csv(df, file = "/home/sthu/Dropbox/hsf/github/courses/dta/df-calories.csv")
df <- read_csv("https://raw.githubusercontent.com/hubchev/courses/main/dta/df-calories.csv")Rows: 6 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): sex
dbl (4): age, weight, calories, sport
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# 7
summary(df) sex age weight calories sport
Length:6 Min. :18.00 Min. :48.0 Min. :1700 Min. : 30
Class :character 1st Qu.:19.25 1st Qu.:57.0 1st Qu.:1850 1st Qu.: 60
Mode :character Median :20.50 Median :67.0 Median :2150 Median : 90
Mean :27.00 Mean :66.5 Mean :2183 Mean :115
3rd Qu.:22.50 3rd Qu.:75.5 3rd Qu.:2450 3rd Qu.:165
Max. :61.00 Max. :85.0 Max. :2800 Max. :240 # 8
df |>
group_by(sex) |>
summarise(
mcal = mean(calories),
sdcal = sd(calories),
mweight = mean(weight),
sdweight = sd(weight)
)# A tibble: 2 × 5
sex mcal sdcal mweight sdweight
<chr> <dbl> <dbl> <dbl> <dbl>
1 f 1933. 321. 55.3 7.51
2 m 2433. 404. 77.7 7.02# 9
# discussed in class
# 10
# Many things can be mentioned here such as the use of colors
# (red/blue is not a good choice for color blind people),
# the legend makes no sense as red and green both refer to \textit{sport},
# the label of `f' and `m' is not explained in the legend,
# rotating the labels of the y-axis would increase readability, and
# both axes do not start at zero which is hard to see.
# Also, it is a common to draw the variable you want to explain
# (here: calories) on the y-axis.
# 11
plot(df)
# 12
cor(df$calories, df$sport, method = c("pearson"))[1] 0.5330615cor(df$weight, df$calories, method = c("pearson"))[1] 0.8281972# 13
ggplot(df, aes(x = calories, y = weight, label = sex)) +
geom_point() +
geom_text(hjust = 0, vjust = 0) +
stat_smooth(formula = y ~ x, method = "lm", se = FALSE)Warning: The following aesthetics were dropped during statistical transformation: label.
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
# 14
reg_base <- lm(weight ~ calories, data = df)
summary(reg_base)
Call:
lm(formula = weight ~ calories, data = df)
Residuals:
1 2 3 4 5 6
-5.490 -1.182 -6.640 9.435 -6.099 9.976
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.730275 20.197867 0.383 0.7214
calories 0.026917 0.009107 2.956 0.0417 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.68 on 4 degrees of freedom
Multiple R-squared: 0.6859, Adjusted R-squared: 0.6074
F-statistic: 8.735 on 1 and 4 DF, p-value: 0.04174# 15
# 1) An increase of 100 calories (taken on average on a daily basis) is associated
# - on average and ceteris paribus - with 2.69 more of kg the participants are
# pretended to weight.
# 2) The estimated coefficient $beta_1$ is statistically significantly different to zero
# on a significance level of 5%.
# 3) About 60 % of the variation of the weight is explained by the
# estimated coefficients of the empirical model.
# 16
# For omitted variable bias to occur, the omitted variable `Z` must satisfy
# two conditions:
# 1) The omitted variable is correlated with the included regressor
# 2) The omitted variable is a determinant of the dependent variable
# 17
# discussed in class
# unload packages
suppressMessages(pacman::p_unload(tidyverse, haven))Open the script that you find here and work on the following tasks:
Set your working directory.
Clear th environment.
Install and load the bundesligR and tidyverse.
Read in the data bundesligR as a tibble.
Replace “Bor. Moenchengladbach” with “Borussia Moenchengladbach.”
Check for the data class.
View the data.
Glimpse on the data.
Show the first and last observations.
Show summary statistics to all variables.
How many teams have played in the league over the years?
Which teams have played Bundesliga so far?
How many teams have played Bundesliga?
How often has each team played in the Bundesliga?
Show summary statistics of variable Season only.
Show summary statistics of all numeric variables (Team is a character).
What is the highest number of points ever received by a team? Show only the name of the club with the highest number of points ever received.
Create a new tibble using liga removing the variable Pts_pre_95 from the data.
Create a new tibble using liga renaming W, D, and L to Win, Draw, and Loss. Additionally rename GF, GA, GD to Goals_shot, Goals_received, Goal_difference.
Create a new tibble using liga without the variable Pts_pre_95 and only observations before the year 1996.
Remove the three tibbles just created from the environment.
Rename all variables of liga to lowercase and store it as dfb.
Show the winner and the runner up after the year 2010. Additionally show the points received.
Create a variable that counts how often a team was ranked first.
How often has each team played in the Bundesliga?
Make a ranking that shows which team has played the Bundesliga most often.
Add a variable to dfb that contains the number of appearances of a team in the league.
Create a number that indicates how often a team has played Bundesliga in a given year.
Make a ranking with the number of titles of all teams that ever won the league.
Create a numeric identifying variable for each team.
When a team is in the league, what is the probability that it wins the league?
Make a scatterplot with points on the y-axis and position on the x-axis.
Make a scatterplot with points on the y-axis and position on the x-axis. Additionally, only consider seasons with 18 teams and add lines that make clear how many points you needed to be placed in between rank 2 and 15.
Remove all objects from the environment except dfb and liga.
In Figure Figure 9.2, the ranking history of 1. FC Kaiserslautern is shown. Replicate that plot.
png file.The script uses the following functions: aes, arrange, as_tibble, as.numeric, between, c, case_when, class, complete, desc, dir.create, dir.exists, element_blank, facet_wrap, factor, filter, geom_hline, geom_line, geom_point, geom_vline, ggplot, ggsave, glimpse, group_by, head, ifelse, is.na, labs, list, max, mutate, n_distinct, paste, print, rename, rename_all, row_number, scale_x_continuous, scale_y_continuous, scale_y_reverse, select, seq, setdiff, slice_head, subset, sum, summarise, summary, table, tail, theme, theme_classic, theme_minimal, unique, unlink, view, xlim.
# In dfb.R I analyze German soccer results
# set working directory
# setwd("~/Dropbox/hsf/23-ws/dsda/scripts")
# clear environment
rm(list = ls())
# (Install and) load packagages
if (!require(pacman)) install.packages("pacman")
pacman::p_load(
bundesligR,
tidyverse
)
# Read in the data as tibble
liga <- as_tibble(bundesligR)
# --------------------------------------
# !!! ERRORS / ISSUES:
# "Borussia Moenchengladbach" is also entitled "Bor. Moenchengladbach"!
# Leverkusen is falsly entitled "SV Bayer 04 Leverkusen"
# Uerdingen has changed its name several times
# Stuttgarter Kickers are named differently
# How often is "Bor. Moenchengladbach" in the data?
sum(liga$Team == "Bor. Moenchengladbach")[1] 2# show the entries
liga |>
filter(Team == "Bor. Moenchengladbach")# A tibble: 2 × 12
Season Position Team Played W D L GF GA GD Points
<dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1989 15 Bor. Moench… 34 11 8 15 37 45 -8 41
2 1976 1 Bor. Moench… 34 17 10 7 58 34 24 61
# ℹ 1 more variable: Pts_pre_95 <dbl># Replace "Bor. Moenchengladbach" with "Borussia Moenchengladbach"
liga <- liga |>
mutate(Team = ifelse(Team == "Bor. Moenchengladbach",
"Borussia Moenchengladbach",
Team
)) |>
mutate(Team = ifelse(Team == "SV Bayer 04 Leverkusen",
"TSV Bayer 04 Leverkusen",
Team
)) |>
mutate(Team = ifelse(Team == "FC Bayer 05 Uerdingen" |
Team == "Bayer 05 Uerdingen",
"KFC Uerdingen 05",
Team
)) |>
mutate(Team = ifelse(Team == "SV Stuttgarter Kickers",
"Stuttgarter Kickers",
Team
))
# ------------------------------------
# Check for the data class
class(liga)[1] "tbl_df" "tbl" "data.frame"# view data
view(liga)
# Glimpse on the data
glimpse(liga)Rows: 952
Columns: 12
$ Season <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015,…
$ Position <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
$ Team <chr> "FC Bayern Muenchen", "Borussia Dortmund", "Bayer 04 Leverk…
$ Played <dbl> 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,…
$ W <dbl> 28, 24, 18, 17, 15, 14, 14, 12, 10, 11, 10, 9, 10, 9, 9, 9,…
$ D <dbl> 4, 6, 6, 4, 7, 8, 8, 9, 13, 8, 10, 11, 8, 11, 10, 9, 6, 4, …
$ L <dbl> 2, 4, 10, 13, 12, 12, 12, 13, 11, 15, 14, 14, 16, 14, 15, 1…
$ GF <dbl> 80, 82, 56, 67, 51, 46, 42, 47, 38, 40, 33, 42, 50, 38, 39,…
$ GA <dbl> 17, 34, 40, 50, 49, 42, 42, 49, 42, 46, 42, 52, 65, 53, 54,…
$ GD <dbl> 63, 48, 16, 17, 2, 4, 0, -2, -4, -6, -9, -10, -15, -15, -15…
$ Points <dbl> 88, 78, 60, 55, 52, 50, 50, 45, 43, 41, 40, 38, 38, 38, 37,…
$ Pts_pre_95 <dbl> 60, 54, 42, 38, 37, 36, 36, 33, 33, 30, 30, 29, 28, 29, 28,…# first and last observations
head(liga)# A tibble: 6 × 12
Season Position Team Played W D L GF GA GD Points
<dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2015 1 FC Bayern M… 34 28 4 2 80 17 63 88
2 2015 2 Borussia Do… 34 24 6 4 82 34 48 78
3 2015 3 Bayer 04 Le… 34 18 6 10 56 40 16 60
4 2015 4 Borussia Mo… 34 17 4 13 67 50 17 55
5 2015 5 FC Schalke … 34 15 7 12 51 49 2 52
6 2015 6 1. FSV Main… 34 14 8 12 46 42 4 50
# ℹ 1 more variable: Pts_pre_95 <dbl>tail(liga)# A tibble: 6 × 12
Season Position Team Played W D L GF GA GD Points
<dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1963 11 Eintracht B… 30 11 6 13 36 49 -13 39
2 1963 12 1. FC Kaise… 30 10 6 14 48 69 -21 36
3 1963 13 Karlsruher … 30 8 8 14 42 55 -13 32
4 1963 14 Hertha BSC 30 9 6 15 45 65 -20 33
5 1963 15 Preussen Mu… 30 7 9 14 34 52 -18 30
6 1963 16 1. FC Saarb… 30 6 5 19 44 72 -28 23
# ℹ 1 more variable: Pts_pre_95 <dbl># summary statistics
summary(liga) Season Position Team Played
Min. :1963 Min. : 1.000 Length:952 Min. :30.00
1st Qu.:1976 1st Qu.: 5.000 Class :character 1st Qu.:34.00
Median :1989 Median : 9.000 Mode :character Median :34.00
Mean :1989 Mean : 9.486 Mean :33.95
3rd Qu.:2002 3rd Qu.:14.000 3rd Qu.:34.00
Max. :2015 Max. :20.000 Max. :38.00
W D L GF
Min. : 2.00 Min. : 2.000 Min. : 1.00 Min. : 15.00
1st Qu.: 9.75 1st Qu.: 7.000 1st Qu.:10.00 1st Qu.: 42.00
Median :12.00 Median : 9.000 Median :13.00 Median : 50.00
Mean :12.61 Mean : 8.733 Mean :12.61 Mean : 52.01
3rd Qu.:15.00 3rd Qu.:11.000 3rd Qu.:15.00 3rd Qu.: 61.00
Max. :29.00 Max. :18.000 Max. :28.00 Max. :101.00
GA GD Points Pts_pre_95
Min. :10.0 Min. :-60.0000 Min. :10.00 Min. : 8.00
1st Qu.:43.0 1st Qu.:-13.0000 1st Qu.:38.00 1st Qu.:29.00
Median :51.0 Median : -2.0000 Median :44.00 Median :33.00
Mean :51.7 Mean : 0.3015 Mean :46.56 Mean :33.95
3rd Qu.:60.0 3rd Qu.: 13.0000 3rd Qu.:55.00 3rd Qu.:39.00
Max. :93.0 Max. : 80.0000 Max. :91.00 Max. :62.00 # How many teams have played in the league over the years?
table(liga$Season)
1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978
16 16 18 18 18 18 18 18 18 18 18 18 18 18 18 18
1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
18 18 18 18 18 18 18 18 18 18 18 18 20 18 18 18
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
2011 2012 2013 2014 2015
18 18 18 18 18 # Which teams have played Bundesliga
unique(liga$Team) [1] "FC Bayern Muenchen" "Borussia Dortmund"
[3] "Bayer 04 Leverkusen" "Borussia Moenchengladbach"
[5] "FC Schalke 04" "1. FSV Mainz 05"
[7] "Hertha BSC" "VfL Wolfsburg"
[9] "1. FC Koeln" "Hamburger SV"
[11] "FC Ingolstadt 04" "FC Augsburg"
[13] "Werder Bremen" "SV Darmstadt 98"
[15] "TSG 1899 Hoffenheim" "Eintracht Frankfurt"
[17] "VfB Stuttgart" "Hannover 96"
[19] "SC Freiburg" "SC Paderborn 07"
[21] "1. FC Nuernberg" "Eintracht Braunschweig"
[23] "Fortuna Duesseldorf" "SpVgg Greuther Fuerth"
[25] "1. FC Kaiserslautern" "FC St. Pauli"
[27] "VfL Bochum" "Energie Cottbus"
[29] "Karlsruher SC" "Arminia Bielefeld"
[31] "Hansa Rostock" "MSV Duisburg"
[33] "Alemannia Aachen" "TSV 1860 Muenchen"
[35] "SpVgg Unterhaching" "SSV Ulm 1846"
[37] "KFC Uerdingen 05" "Dynamo Dresden"
[39] "SG Wattenscheid 09" "VfB Leipzig"
[41] "1. FC Saarbruecken" "TSV Bayer 04 Leverkusen"
[43] "SV Werder Bremen" "1. FC Dynamo Dresden"
[45] "Stuttgarter Kickers" "FC Hansa Rostock"
[47] "SV Waldhof Mannheim" "FC 08 Homburg"
[49] "FC Homburg" "Blau-Weiss 90 Berlin"
[51] "Kickers Offenbach" "Tennis Borussia Berlin"
[53] "Rot-Weiss Essen" "Wuppertaler SV"
[55] "SC Fortuna Koeln" "Rot-Weiss Oberhausen"
[57] "SC Rot-Weiss Oberhausen" "Borussia Neunkirchen"
[59] "Meidericher SV" "SC Tasmania 1900 Berlin"
[61] "Preussen Muenster" # How many teams have played Bundesliga
n_distinct(liga$Team)[1] 61# How often has each team played in the Bundesliga
table(liga$Team)
1. FC Dynamo Dresden 1. FC Kaiserslautern 1. FC Koeln
1 44 45
1. FC Nuernberg 1. FC Saarbruecken 1. FSV Mainz 05
32 5 10
Alemannia Aachen Arminia Bielefeld Bayer 04 Leverkusen
4 17 30
Blau-Weiss 90 Berlin Borussia Dortmund Borussia Moenchengladbach
1 49 48
Borussia Neunkirchen Dynamo Dresden Eintracht Braunschweig
3 3 21
Eintracht Frankfurt Energie Cottbus FC 08 Homburg
47 6 2
FC Augsburg FC Bayern Muenchen FC Hansa Rostock
5 51 1
FC Homburg FC Ingolstadt 04 FC Schalke 04
1 1 48
FC St. Pauli Fortuna Duesseldorf Hamburger SV
8 23 53
Hannover 96 Hansa Rostock Hertha BSC
28 11 33
Karlsruher SC KFC Uerdingen 05 Kickers Offenbach
24 14 7
Meidericher SV MSV Duisburg Preussen Muenster
3 25 1
Rot-Weiss Essen Rot-Weiss Oberhausen SC Fortuna Koeln
7 3 1
SC Freiburg SC Paderborn 07 SC Rot-Weiss Oberhausen
16 1 1
SC Tasmania 1900 Berlin SG Wattenscheid 09 SpVgg Greuther Fuerth
1 4 1
SpVgg Unterhaching SSV Ulm 1846 Stuttgarter Kickers
2 1 2
SV Darmstadt 98 SV Waldhof Mannheim SV Werder Bremen
3 7 1
Tennis Borussia Berlin TSG 1899 Hoffenheim TSV 1860 Muenchen
2 8 20
TSV Bayer 04 Leverkusen VfB Leipzig VfB Stuttgart
7 1 51
VfL Bochum VfL Wolfsburg Werder Bremen
34 19 51
Wuppertaler SV
3 # summary of variable Season only
summary(liga$Season) Min. 1st Qu. Median Mean 3rd Qu. Max.
1963 1976 1989 1989 2002 2015 # summary of numeric of variables (Team is a character)
liga |>
select(Season, Position, Played, W, D, L, GF, GA, GD, Points, Pts_pre_95) |>
summary() Season Position Played W
Min. :1963 Min. : 1.000 Min. :30.00 Min. : 2.00
1st Qu.:1976 1st Qu.: 5.000 1st Qu.:34.00 1st Qu.: 9.75
Median :1989 Median : 9.000 Median :34.00 Median :12.00
Mean :1989 Mean : 9.486 Mean :33.95 Mean :12.61
3rd Qu.:2002 3rd Qu.:14.000 3rd Qu.:34.00 3rd Qu.:15.00
Max. :2015 Max. :20.000 Max. :38.00 Max. :29.00
D L GF GA
Min. : 2.000 Min. : 1.00 Min. : 15.00 Min. :10.0
1st Qu.: 7.000 1st Qu.:10.00 1st Qu.: 42.00 1st Qu.:43.0
Median : 9.000 Median :13.00 Median : 50.00 Median :51.0
Mean : 8.733 Mean :12.61 Mean : 52.01 Mean :51.7
3rd Qu.:11.000 3rd Qu.:15.00 3rd Qu.: 61.00 3rd Qu.:60.0
Max. :18.000 Max. :28.00 Max. :101.00 Max. :93.0
GD Points Pts_pre_95
Min. :-60.0000 Min. :10.00 Min. : 8.00
1st Qu.:-13.0000 1st Qu.:38.00 1st Qu.:29.00
Median : -2.0000 Median :44.00 Median :33.00
Mean : 0.3015 Mean :46.56 Mean :33.95
3rd Qu.: 13.0000 3rd Qu.:55.00 3rd Qu.:39.00
Max. : 80.0000 Max. :91.00 Max. :62.00 # shorter alternative
liga |>
select(Season, Position, Played:Pts_pre_95) |>
summary() Season Position Played W
Min. :1963 Min. : 1.000 Min. :30.00 Min. : 2.00
1st Qu.:1976 1st Qu.: 5.000 1st Qu.:34.00 1st Qu.: 9.75
Median :1989 Median : 9.000 Median :34.00 Median :12.00
Mean :1989 Mean : 9.486 Mean :33.95 Mean :12.61
3rd Qu.:2002 3rd Qu.:14.000 3rd Qu.:34.00 3rd Qu.:15.00
Max. :2015 Max. :20.000 Max. :38.00 Max. :29.00
D L GF GA
Min. : 2.000 Min. : 1.00 Min. : 15.00 Min. :10.0
1st Qu.: 7.000 1st Qu.:10.00 1st Qu.: 42.00 1st Qu.:43.0
Median : 9.000 Median :13.00 Median : 50.00 Median :51.0
Mean : 8.733 Mean :12.61 Mean : 52.01 Mean :51.7
3rd Qu.:11.000 3rd Qu.:15.00 3rd Qu.: 61.00 3rd Qu.:60.0
Max. :18.000 Max. :28.00 Max. :101.00 Max. :93.0
GD Points Pts_pre_95
Min. :-60.0000 Min. :10.00 Min. : 8.00
1st Qu.:-13.0000 1st Qu.:38.00 1st Qu.:29.00
Median : -2.0000 Median :44.00 Median :33.00
Mean : 0.3015 Mean :46.56 Mean :33.95
3rd Qu.: 13.0000 3rd Qu.:55.00 3rd Qu.:39.00
Max. : 80.0000 Max. :91.00 Max. :62.00 # shortest alternative
liga |>
select(-Team) |>
filter(Season == 1999 | Season == 2010) |>
summary() Season Position Played W D
Min. :1999 Min. : 1.0 Min. :34 Min. : 4.00 Min. : 3.000
1st Qu.:1999 1st Qu.: 5.0 1st Qu.:34 1st Qu.: 9.75 1st Qu.: 6.000
Median :2004 Median : 9.5 Median :34 Median :12.00 Median : 8.000
Mean :2004 Mean : 9.5 Mean :34 Mean :12.83 Mean : 8.333
3rd Qu.:2010 3rd Qu.:14.0 3rd Qu.:34 3rd Qu.:14.25 3rd Qu.:10.250
Max. :2010 Max. :18.0 Max. :34 Max. :23.00 Max. :15.000
L GF GA GD
Min. : 3.00 Min. :31.00 Min. :22.00 Min. :-34.00
1st Qu.:10.75 1st Qu.:41.00 1st Qu.:44.00 1st Qu.:-10.25
Median :13.00 Median :47.00 Median :48.50 Median : -3.00
Mean :12.83 Mean :49.42 Mean :49.42 Mean : 0.00
3rd Qu.:16.00 3rd Qu.:54.25 3rd Qu.:59.00 3rd Qu.: 4.75
Max. :21.00 Max. :81.00 Max. :71.00 Max. : 45.00
Points Pts_pre_95
Min. :22.00 Min. :18.00
1st Qu.:39.75 1st Qu.:29.75
Median :44.00 Median :32.00
Mean :46.83 Mean :34.00
3rd Qu.:50.75 3rd Qu.:37.50
Max. :75.00 Max. :52.00 # Most points ever received by a team
liga |>
filter(Points == max(Points))# A tibble: 1 × 12
Season Position Team Played W D L GF GA GD Points
<dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2012 1 FC Bayern M… 34 29 4 1 98 18 80 91
# ℹ 1 more variable: Pts_pre_95 <dbl># Show only the team name
liga |>
filter(Points == max(Points)) |>
select(Team) |>
print()# A tibble: 1 × 1
Team
<chr>
1 FC Bayern Muenchen# remove the variable `Pts_pre_95` from the data
liga_post95 <- liga |>
select(-Pts_pre_95)
# rename W, D, and L to Win, Draw, and Loss
# additionally rename GF, GA, GD to Goals_shot, Goals_received, Goal_difference
liga_longnames <- liga |>
rename(Win = W, Draw = D, Loss = L) |>
rename(Goals_shot = GF, Goals_received = GA, Goal_difference = GD)
# Remove the variable `Pts_pre_95` from `liga`
# additionally remove all observations before the year 1996
liga_no3point <- liga |>
select(-Pts_pre_95) |>
filter(Season >= 1996)
# Remove the objects liga_post95, liga_longnames, and liga_no3point from the environment
rm(liga_post95, liga_longnames, liga_no3point)
# Rename all variables of `liga`to lower cases and store it as `dfb`
dfb <- liga |>
rename_all(tolower)
# Show the winner and the runner up after 2010
# additionally show the points
dfb |>
filter(season > 2010) |>
group_by(season) |>
arrange(desc(points)) |>
slice_head(n = 2) |>
select(team, points, position)# A tibble: 10 × 4
# Groups: season [5]
season team points position
<dbl> <chr> <dbl> <dbl>
1 2011 Borussia Dortmund 81 1
2 2011 FC Bayern Muenchen 73 2
3 2012 FC Bayern Muenchen 91 1
4 2012 Borussia Dortmund 66 2
5 2013 FC Bayern Muenchen 90 1
6 2013 Borussia Dortmund 71 2
7 2014 FC Bayern Muenchen 79 1
8 2014 VfL Wolfsburg 69 2
9 2015 FC Bayern Muenchen 88 1
10 2015 Borussia Dortmund 78 2# Create a variable that counts how often a team was ranked first
dfb <- dfb |>
group_by(team) |>
mutate(meister_count = sum(position == 1))
# How often has each team played in the Bundesliga
table(liga$Team)
1. FC Dynamo Dresden 1. FC Kaiserslautern 1. FC Koeln
1 44 45
1. FC Nuernberg 1. FC Saarbruecken 1. FSV Mainz 05
32 5 10
Alemannia Aachen Arminia Bielefeld Bayer 04 Leverkusen
4 17 30
Blau-Weiss 90 Berlin Borussia Dortmund Borussia Moenchengladbach
1 49 48
Borussia Neunkirchen Dynamo Dresden Eintracht Braunschweig
3 3 21
Eintracht Frankfurt Energie Cottbus FC 08 Homburg
47 6 2
FC Augsburg FC Bayern Muenchen FC Hansa Rostock
5 51 1
FC Homburg FC Ingolstadt 04 FC Schalke 04
1 1 48
FC St. Pauli Fortuna Duesseldorf Hamburger SV
8 23 53
Hannover 96 Hansa Rostock Hertha BSC
28 11 33
Karlsruher SC KFC Uerdingen 05 Kickers Offenbach
24 14 7
Meidericher SV MSV Duisburg Preussen Muenster
3 25 1
Rot-Weiss Essen Rot-Weiss Oberhausen SC Fortuna Koeln
7 3 1
SC Freiburg SC Paderborn 07 SC Rot-Weiss Oberhausen
16 1 1
SC Tasmania 1900 Berlin SG Wattenscheid 09 SpVgg Greuther Fuerth
1 4 1
SpVgg Unterhaching SSV Ulm 1846 Stuttgarter Kickers
2 1 2
SV Darmstadt 98 SV Waldhof Mannheim SV Werder Bremen
3 7 1
Tennis Borussia Berlin TSG 1899 Hoffenheim TSV 1860 Muenchen
2 8 20
TSV Bayer 04 Leverkusen VfB Leipzig VfB Stuttgart
7 1 51
VfL Bochum VfL Wolfsburg Werder Bremen
34 19 51
Wuppertaler SV
3 # Make a ranking
dfb |>
group_by(team) |>
summarise(appearances = n_distinct(season)) |>
arrange(desc(appearances)) |>
print(n = Inf)# A tibble: 61 × 2
team appearances
<chr> <int>
1 Hamburger SV 53
2 FC Bayern Muenchen 51
3 VfB Stuttgart 51
4 Werder Bremen 51
5 Borussia Dortmund 49
6 Borussia Moenchengladbach 48
7 FC Schalke 04 48
8 Eintracht Frankfurt 47
9 1. FC Koeln 45
10 1. FC Kaiserslautern 44
11 VfL Bochum 34
12 Hertha BSC 33
13 1. FC Nuernberg 32
14 Bayer 04 Leverkusen 30
15 Hannover 96 28
16 MSV Duisburg 25
17 Karlsruher SC 24
18 Fortuna Duesseldorf 23
19 Eintracht Braunschweig 21
20 TSV 1860 Muenchen 20
21 VfL Wolfsburg 19
22 Arminia Bielefeld 17
23 SC Freiburg 16
24 KFC Uerdingen 05 14
25 Hansa Rostock 11
26 1. FSV Mainz 05 10
27 FC St. Pauli 8
28 TSG 1899 Hoffenheim 8
29 Kickers Offenbach 7
30 Rot-Weiss Essen 7
31 SV Waldhof Mannheim 7
32 TSV Bayer 04 Leverkusen 7
33 Energie Cottbus 6
34 1. FC Saarbruecken 5
35 FC Augsburg 5
36 Alemannia Aachen 4
37 SG Wattenscheid 09 4
38 Borussia Neunkirchen 3
39 Dynamo Dresden 3
40 Meidericher SV 3
41 Rot-Weiss Oberhausen 3
42 SV Darmstadt 98 3
43 Wuppertaler SV 3
44 FC 08 Homburg 2
45 SpVgg Unterhaching 2
46 Stuttgarter Kickers 2
47 Tennis Borussia Berlin 2
48 1. FC Dynamo Dresden 1
49 Blau-Weiss 90 Berlin 1
50 FC Hansa Rostock 1
51 FC Homburg 1
52 FC Ingolstadt 04 1
53 Preussen Muenster 1
54 SC Fortuna Koeln 1
55 SC Paderborn 07 1
56 SC Rot-Weiss Oberhausen 1
57 SC Tasmania 1900 Berlin 1
58 SSV Ulm 1846 1
59 SV Werder Bremen 1
60 SpVgg Greuther Fuerth 1
61 VfB Leipzig 1# Add a variable to `dfb` that contains the number of appearances of a team in the league
dfb <- dfb |>
group_by(team) |>
mutate(appearances = n_distinct(season))
# create a number that indicates how often a team has played Bundesliga in a given year
dfb <- dfb |>
arrange(team, season) |>
group_by(team) |>
mutate(team_in_liga_count = row_number())
# Make a ranking with the number of titles of all teams that ever won the league
dfb |>
filter(team_in_liga_count == 1) |>
filter(meister_count != 0) |>
arrange(desc(meister_count)) |>
select(meister_count, team)# A tibble: 12 × 2
# Groups: team [12]
meister_count team
<int> <chr>
1 25 FC Bayern Muenchen
2 5 Borussia Dortmund
3 5 Borussia Moenchengladbach
4 4 Werder Bremen
5 3 Hamburger SV
6 3 VfB Stuttgart
7 2 1. FC Kaiserslautern
8 2 1. FC Koeln
9 1 1. FC Nuernberg
10 1 Eintracht Braunschweig
11 1 TSV 1860 Muenchen
12 1 VfL Wolfsburg # Create a numeric identifying variable for each team
dfb_teamid <- dfb |>
mutate(team_id = as.numeric(factor(team)))
# When a team is in the league, what is the probability that it wins the league
dfb |>
filter(team_in_liga_count == 1) |>
mutate(prob_win = meister_count / appearances) |>
filter(prob_win > 0) |>
arrange(desc(prob_win)) |>
select(meister_count, prob_win, team)# A tibble: 12 × 3
# Groups: team [12]
meister_count prob_win team
<int> <dbl> <chr>
1 25 0.490 FC Bayern Muenchen
2 5 0.104 Borussia Moenchengladbach
3 5 0.102 Borussia Dortmund
4 4 0.0784 Werder Bremen
5 3 0.0588 VfB Stuttgart
6 3 0.0566 Hamburger SV
7 1 0.0526 VfL Wolfsburg
8 1 0.05 TSV 1860 Muenchen
9 1 0.0476 Eintracht Braunschweig
10 2 0.0455 1. FC Kaiserslautern
11 2 0.0444 1. FC Koeln
12 1 0.0312 1. FC Nuernberg # make a scatterplot with points on the y-axis and position on the x-axis
ggplot(dfb, aes(x = position, y = points)) +
geom_point()
# Make a scatterplot with points on the y-axis and position on the x-axis.
# Additionally, only consider seasons with 18 teams and
# add lines that make clear how many points you needed to be placed
# in between rank 2 and 15.
dfb_18 <- dfb |>
group_by(season) |>
mutate(teams_in_league = n_distinct(team)) |>
filter(teams_in_league == 18)
h_1 <- dfb_18 |>
filter(position == 16) |>
mutate(ma = max(points))
max_points_rank_16 <- max(h_1$ma) + 1
h_2 <- dfb_18 |>
filter(position == 2) |>
mutate(mb = max(points))
min_points_rank_2 <- max(h_2$mb) + 1
dfb_18 <- dfb_18 |>
mutate(season_category = case_when(
season < 1970 ~ 1,
between(season, 1970, 1979) ~ 2,
between(season, 1980, 1989) ~ 3,
between(season, 1990, 1999) ~ 4,
between(season, 2000, 2009) ~ 5,
between(season, 2010, 2019) ~ 6,
TRUE ~ 7 # Adjust this line based on the actual range of your data
))
ggplot(dfb_18, aes(x = position, y = points)) +
geom_point() +
labs(
title = "Scatterplot of Points and Position",
x = "Position",
y = "Points"
) +
geom_vline(xintercept = c(1.5, 15.5), linetype = "dashed", color = "red") +
geom_hline(yintercept = max_points_rank_16, linetype = "dashed", color = "blue") +
geom_hline(yintercept = min_points_rank_2, linetype = "dashed", color = "blue") +
scale_y_continuous(breaks = c(min_points_rank_2, max_points_rank_16, seq(0, max(dfb_18$points), by = 5))) +
scale_x_continuous(breaks = c(seq(0, max(dfb_18$points), by = 1))) +
theme_classic()
# Remove all objects except liga and dfb
rm(list = setdiff(ls(), c("liga", "dfb")))
# Rank "1. FC Kaiserslautern" over time
dfb_bal <- dfb |>
select(season, team, position) |>
as_tibble() |>
complete(season, team)
table(dfb_bal$team)
1. FC Dynamo Dresden 1. FC Kaiserslautern 1. FC Koeln
53 53 53
1. FC Nuernberg 1. FC Saarbruecken 1. FSV Mainz 05
53 53 53
Alemannia Aachen Arminia Bielefeld Bayer 04 Leverkusen
53 53 53
Blau-Weiss 90 Berlin Borussia Dortmund Borussia Moenchengladbach
53 53 53
Borussia Neunkirchen Dynamo Dresden Eintracht Braunschweig
53 53 53
Eintracht Frankfurt Energie Cottbus FC 08 Homburg
53 53 53
FC Augsburg FC Bayern Muenchen FC Hansa Rostock
53 53 53
FC Homburg FC Ingolstadt 04 FC Schalke 04
53 53 53
FC St. Pauli Fortuna Duesseldorf Hamburger SV
53 53 53
Hannover 96 Hansa Rostock Hertha BSC
53 53 53
Karlsruher SC KFC Uerdingen 05 Kickers Offenbach
53 53 53
Meidericher SV MSV Duisburg Preussen Muenster
53 53 53
Rot-Weiss Essen Rot-Weiss Oberhausen SC Fortuna Koeln
53 53 53
SC Freiburg SC Paderborn 07 SC Rot-Weiss Oberhausen
53 53 53
SC Tasmania 1900 Berlin SG Wattenscheid 09 SpVgg Greuther Fuerth
53 53 53
SpVgg Unterhaching SSV Ulm 1846 Stuttgarter Kickers
53 53 53
SV Darmstadt 98 SV Waldhof Mannheim SV Werder Bremen
53 53 53
Tennis Borussia Berlin TSG 1899 Hoffenheim TSV 1860 Muenchen
53 53 53
TSV Bayer 04 Leverkusen VfB Leipzig VfB Stuttgart
53 53 53
VfL Bochum VfL Wolfsburg Werder Bremen
53 53 53
Wuppertaler SV
53 dfb_fck <- dfb_bal |>
filter(team == "1. FC Kaiserslautern")
ggplot(dfb_fck, aes(x = season, y = position)) +
geom_point() +
geom_line() +
scale_y_reverse(breaks = seq(1, 18, by = 1))
# Make the plot nice
# consider different rules for having to leave the league:
dfb_fck <- dfb_fck |>
mutate(godown = ifelse(season <= 1964, 14.5, NA)) |>
mutate(godown = ifelse(season > 1964 & season <= 1973, 16.5, godown)) |>
mutate(godown = ifelse(season > 1973 & season <= 1980, 15.5, godown)) |>
mutate(godown = ifelse(season > 1980 & season <= 1990, 16, godown)) |>
mutate(godown = ifelse(season == 1991, 16.5, godown)) |>
mutate(godown = ifelse(season > 1991 & season <= 2008, 15.5, godown)) |>
mutate(godown = ifelse(season > 2008, 16, godown))
ggplot(dfb_fck, aes(x = season)) +
geom_point(aes(y = position)) +
geom_line(aes(y = position)) +
geom_point(aes(y = godown), shape = 25) +
scale_y_reverse(breaks = seq(1, 18, by = 1)) +
theme_minimal() +
theme(panel.grid.minor = element_blank()) +
geom_hline(yintercept = 1.5, linetype = "dashed", color = "blue")
dfb_bal <- dfb_bal |>
mutate(godown = ifelse(season <= 1964, 14.5, NA)) |>
mutate(godown = ifelse(season > 1964 & season <= 1973, 16.5, godown)) |>
mutate(godown = ifelse(season > 1973 & season <= 1980, 15.5, godown)) |>
mutate(godown = ifelse(season > 1980 & season <= 1990, 16, godown)) |>
mutate(godown = ifelse(season == 1991, 16.5, godown)) |>
mutate(godown = ifelse(season > 1991 & season <= 2008, 15.5, godown)) |>
mutate(godown = ifelse(season > 2008, 16, godown)) |>
mutate(inliga = ifelse(is.na(position), 0, 1))
rank_plot <- ggplot(dfb_bal, aes(x = season)) +
geom_point(aes(y = position), shape = 1) +
# geom_line(aes(y = position)) +
geom_point(aes(y = godown), shape = 25) +
scale_y_reverse(breaks = seq(1, 20, by = 1), limits = c(20, 1)) +
xlim(1963, 2015) +
theme(panel.grid.minor = element_blank()) +
geom_hline(yintercept = 1.5, linetype = "dashed", color = "gray") +
geom_point(aes(y = position), shape = 1)
rank_plot
# !--> in 1979 is a gap! Error?
# No. Reason: two clubs shared the third place.
rank_plot +
facet_wrap(~team)
# Create "test" directory if it doesn't already exist
if (!dir.exists("test")) {
dir.create("test")
}
plots <- list()
for (club in unique(dfb_bal$team)) {
dfb_subset <- subset(dfb_bal, team == club)
p <- ggplot(dfb_subset, aes(x = season)) +
geom_point(aes(y = position), shape = 15) +
geom_line(aes(y = position)) +
geom_point(aes(y = godown), shape = 25) +
scale_y_reverse(breaks = seq(1, 20, by = 1), limits = c(20, 1)) +
xlim(1963, 2015) +
theme(panel.grid.minor = element_blank()) +
geom_hline(yintercept = 1.5, linetype = "dashed", color = "gray") +
geom_point(aes(y = position), shape = 1) +
labs(title = paste("Ranking History:", club))
ggsave(filename = paste("test/r_", club, ".png", sep = ""))
plots[[club]] <- p
}
print(plots$`Meidericher SV`)
print(plots$`1. FC Koeln`)
# unload packages
suppressMessages(pacman::p_unload(
bundesligR,
tidyverse
))
# Remove the "test" directory and its contents after saving all graphs
unlink("test", recursive = TRUE)Suppose you aim to empirically examine unemployment and GDP for Germany and France. The data set that we use in the following is ‘forest.Rdata’ and should already been known to you from the lecture.
Write down your name, matriculation number, and date.
Set your working directory.
load(url("https://github.com/hubchev/courses/raw/main/dta/forest.Rdata"))If that is not working, you can also download the data from ILIAS, save it in your working directory and load it from there with:
load("forest.Rdata")Show the first eight observations of the dataset df.
Show the last observation of the dataset df.
Which type of data do we have here (Panel, cross-section,time series, …)? Name the variable(s) that are necessary to identify the observations in the dataset.
Explain what the assignment operator in R is and what it is good for.
Write down the R code to store the number of observations and the number of variables that are in the dataset df. Name the object in which you store these numbers observations_df.
In the dataset df, rename the variable ‘country.x’ to ‘nation’ and the variable ‘date’ to ‘year’.
Explain what the pipe operator in R is and what it is good for.
For the upcoming analysis you are only interested the following variables that are part of the dataframe df: nation, year, gdp, pop, gdppc, and unemployment. Drop all other variables from the dataframe df.
Create a variable that indicates the GDP per capita (‘gdp’ divided by ‘pop’). Name the variable ‘gdp_pc’. (Hint: If you fail here, use the variable ‘gdppc’ which is already in the dataset as a replacement for ‘gdp_pc’ in the following tasks.)
For the upcoming analysis you are only interested the following countries that are part of the dataframe df: Germany and France. Drop all other countries from the dataframe df.
Create a table showing the average unemployment rate and GDP per capita for Germany and France in the given years. Use the pipe operator. (Hint: See below for how your results should look like.)
# A tibble: 2 × 3
nation `mean(unemployment)` `mean(gdppc)`
<chr> <dbl> <dbl>
1 France 9.75 34356.
2 Germany 7.22 36739.# A tibble: 2 × 3
nation `mean(unemployment)` `mean(gdppc)`
<chr> <dbl> <dbl>
1 France 8.01 35786.
2 Germany 3.81 41315.# A tibble: 2 × 3
nation `max(unemployment)` `max(gdppc)`
<chr> <dbl> <dbl>
1 France 12.6 38912.
2 Germany 11.2 43329.# A tibble: 2 × 3
nation `sd(gdppc)` `sd(unemployment)`
<chr> <dbl> <dbl>
1 France 2940. 1.58
2 Germany 4015. 2.37# A tibble: 2 × 4
nation `sd(unemployment)` `mean(unemployment)` cov
<chr> <dbl> <dbl> <dbl>
1 France 1.58 9.75 0.162
2 Germany 2.37 7.22 0.328look like.)
# A tibble: 2 × 4
nation `sd(gdppc)` `mean(gdppc)` cov
<chr> <dbl> <dbl> <dbl>
1 France 2940. 34356. 0.0856
2 Germany 4015. 36739. 0.109 ggtitle("Germany"). Please note that you may choose any type of graphical representation. (Hint: Below you can see one of many possible examples of what your result may look like).
Suppose you want to visualize the simultaneous evolution of the unemployment rate and GDP per capita over time for Germany as well.
Suppose further that you have found the following lines of code that create the kind of chart you are looking for.
# Data
x <- c(1, 2, 3, 4, 5, 4, 7, 8, 9)
y <- c(12, 16, 14, 18, 16, 13, 15, 20, 22)
labels <- 1970:1978
# Connected scatter plot with text
plot(x, y, type = "b", xlab = "Var 1", ylab = "Var 2")
text(x + 0.4, y + 0.1, labels)
Use these lines of code and customize them to create the co-movement visualization for Germany using the available df data. The result should look something like this:
The script uses the following functions: aes, c, dim, filter, geom_line, ggplot, ggtitle, group_by, head, load, max, mean, mutate, plot, rename, sd, select, summarise, tail, text, title, url.
# setwd("/home/sthu/Dropbox/hsf/exams/22-11/scr/")
rm(list = ls())
# load packages
if (!require(pacman)) install.packages("pacman")
pacman::p_load(tidyverse, ggpubr, sjPlot)
load(url("https://github.com/hubchev/courses/raw/main/dta/forest.Rdata"))
head(df, 8)# A tibble: 8 × 11
# Groups: country.x [1]
country.x date gdp gdp_growth unemployment region income forest pop
<chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
1 United Arab… 1992 1.26e11 -2.48 1.84 Middl… High … 3.63 2.05e6
2 United Arab… 1993 1.27e11 -4.34 1.85 Middl… High … 3.72 2.17e6
3 United Arab… 1994 1.36e11 1.25 1.81 Middl… High … 3.81 2.29e6
4 United Arab… 1995 1.45e11 1.35 1.80 Middl… High … 3.90 2.42e6
5 United Arab… 1996 1.54e11 0.631 1.90 Middl… High … 3.99 2.54e6
6 United Arab… 1997 1.66e11 2.83 1.98 Middl… High … 4.08 2.67e6
7 United Arab… 1998 1.67e11 -4.77 2.14 Middl… High … 4.18 2.81e6
8 United Arab… 1999 1.72e11 -2.40 2.22 Middl… High … 4.27 2.97e6
# ℹ 2 more variables: unemployment_dif <dbl>, gdppc <dbl>tail(df, 1)# A tibble: 1 × 11
# Groups: country.x [1]
country.x date gdp gdp_growth unemployment region income forest pop
<chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
1 Zimbabwe 2020 1.94e10 -7.62 5.35 Sub-S… Lower… 45.1 1.49e7
# ℹ 2 more variables: unemployment_dif <dbl>, gdppc <dbl># panel data set
# date and country.x
observations_df <- dim(df)
df <- rename(df, nation = country.x)
df <- rename(df, year = date)
df <- df |>
select(nation, year, gdp, pop, gdppc, unemployment)
df <- df |>
mutate(gdp_pc = gdp / pop)
df <- df |> filter(nation == "Germany" | nation == "France")
df |>
group_by(nation) |>
summarise(mean(unemployment), mean(gdppc))# A tibble: 2 × 3
nation `mean(unemployment)` `mean(gdppc)`
<chr> <dbl> <dbl>
1 France 9.75 34356.
2 Germany 7.22 36739.df |>
filter(year == 2020) |>
group_by(nation) |>
summarise(mean(unemployment), mean(gdppc))# A tibble: 2 × 3
nation `mean(unemployment)` `mean(gdppc)`
<chr> <dbl> <dbl>
1 France 8.01 35786.
2 Germany 3.81 41315.df |>
group_by(nation) |>
summarise(max(unemployment), max(gdppc))# A tibble: 2 × 3
nation `max(unemployment)` `max(gdppc)`
<chr> <dbl> <dbl>
1 France 12.6 38912.
2 Germany 11.2 43329.df |>
group_by(nation) |>
summarise(sd(gdppc), sd(unemployment))# A tibble: 2 × 3
nation `sd(gdppc)` `sd(unemployment)`
<chr> <dbl> <dbl>
1 France 2940. 1.58
2 Germany 4015. 2.37df |>
group_by(nation) |>
summarise(sd(unemployment), mean(unemployment), cov = sd(unemployment) / mean(unemployment))# A tibble: 2 × 4
nation `sd(unemployment)` `mean(unemployment)` cov
<chr> <dbl> <dbl> <dbl>
1 France 1.58 9.75 0.162
2 Germany 2.37 7.22 0.328df |>
group_by(nation) |>
summarise(sd(gdppc), mean(gdppc), cov = sd(gdppc) / mean(gdppc))# A tibble: 2 × 4
nation `sd(gdppc)` `mean(gdppc)` cov
<chr> <dbl> <dbl> <dbl>
1 France 2940. 34356. 0.0856
2 Germany 4015. 36739. 0.109 df_pger <- df |>
filter(nation == "Germany")
pger <- ggplot(df_pger, aes(x = year, y = unemployment)) +
geom_line() +
ggtitle("Germany")
plot(pger)
labels <- 1992:2020
dfra <- df |> filter(nation == "France")
plot(dfra$gdppc, dfra$unemployment,
type = "b",
xlab = "GDP per capita", ylab = "Unemployment rate"
)
text(dfra$gdppc + 0.1, dfra$unemployment + 0.1, labels)
title("France")
# Data
x <- c(1, 2, 3, 4, 5, 4, 7, 8, 9)
y <- c(12, 16, 14, 18, 16, 13, 15, 20, 22)
labels <- 1970:1978
# Connected scatter plot with text
plot(x, y, type = "b", xlab = "Var 1", ylab = "Var 2")
text(x + 0.4, y + 0.1, labels)
dfger <- df |> filter(nation == "Germany")
labels <- 1992:2020
plot(dfger$gdppc, dfger$unemployment,
type = "b",
xlab = "Var 1", ylab = "Var 2"
)
text(dfger$gdppc + 0.7, dfger$unemployment + 0.4, labels)
title("Germany")
suppressMessages(pacman::p_unload(tidyverse, ggpubr, sjPlot))pacman, tidyverse, janitor, babynames, stringr).df_2022 and df_2022_error.relocate function so that surname, name, and age appear first. Save the changed data in a tibble called df.surname, name, and age.born that contains the year of birth. How is the born variable calculated?id that identifies each person by surname, name, and their birth year (born). Why is this identifier useful?The script uses the following functions: anti_join, arrange, c, cur_group_id, desc, dim, distinct, filter, get_dupes, glimpse, group_by, head, load, max, mutate, n, paste, relocate, row_number, setdiff, summary, tail, ungroup, url.
The data under investigation includes population information for various German cities, identified by the variable stadt, spanning the years 1970, 1987, and 2010. The variable status provides details about the legislative status of the cities, and the variable state (Bundesland) indicates the state in which each respective city is situated.
Preamble
Read in, inspect, and clean the data
df <- read_dta(
"https://github.com/hubchev/courses/raw/main/dta/city.dta",
encoding = "latin1"
) |>
as_tibble()If that is not working, you can also download the data from ILIAS, save it in your working directory and load it from there with:
load("city.RData")Show the first six and the last six observations of the dataset df.
How many observations (rows) and variables (columns) are in the dataset?
Show for all numerical variables the summary statistics including the mean, median, minimum, and the maximum.
Rename the variable stadt to city.
Remove the variables pop1970 and pop1987.
Replicate the following table which contains some summary statistics.
# A tibble: 17 × 3
state `mean(pop2011)` `sum(pop2011)`
<chr> <dbl> <dbl>
1 Baden-Wrttemberg 7580 7580
2 Baden-Württemberg 23680. 7837917
3 Bayern 23996. 7558677
4 Berlin 3292365 3292365
5 Brandenburg 18472. 1865632
6 Bremen 325432. 650863
7 Hamburg 1706696 1706696
8 Hessen 22996. 5036121
9 Mecklenburg-Vorpommern 27034. 811005
10 Niedersachsen 24107. 6219515
11 Nordrhein-Westfalen 47465. 18036727
12 Rheinland-Pfalz 25644. 1871995
13 Saarland NA NA
14 Sachsen 27788. 2973351
15 Sachsen-Anhalt 21212. 1993915
16 Schleswig-Holstein 24157. 1739269
17 Th_ringen 29192. 1167692# A tibble: 16 × 3
state `mean(pop2011)` `sum(pop2011)`
<chr> <dbl> <dbl>
1 Baden-Württemberg 23631. 7845497
2 Bayern 23996. 7558677
3 Berlin 3292365 3292365
4 Brandenburg 18472. 1865632
5 Bremen 325432. 650863
6 Hamburg 1706696 1706696
7 Hessen 22996. 5036121
8 Mecklenburg-Vorpommern 27034. 811005
9 Niedersachsen 24107. 6219515
10 Nordrhein-Westfalen 47465. 18036727
11 Rheinland-Pfalz 25644. 1871995
12 Saarland NA NA
13 Sachsen 27788. 2973351
14 Sachsen-Anhalt 21212. 1993915
15 Schleswig-Holstein 24157. 1739269
16 Thüringen 29192. 1167692NAs for Saarland, examine all cities within Saarland. Therefore, please display all observations for cities in Saarland in the Console, as illustrated below.# A tibble: 47 × 5
city status state pop2011 rankX
<chr> <chr> <chr> <dbl> <dbl>
1 Perl Commune Saarland 7775 2003
2 Freisen Commune Saarland 8270 1894
3 Großrosseln Commune Saarland 8403 1868
4 Nonnweiler Commune Saarland 8844 1775
5 Nalbach Commune Saarland 9302 1678
6 Wallerfangen Commune Saarland 9542 1642
7 Kirkel Commune Saarland 10058 1541
8 Merchweiler Commune Saarland 10219 1515
9 Nohfelden Commune Saarland 10247 1511
10 Friedrichsthal City Saarland 10409 1489
11 Marpingen Commune Saarland 10590 1461
12 Mandelbachtal Commune Saarland 11107 1390
13 Kleinblittersdorf Commune Saarland 11396 1354
14 Überherrn Commune Saarland 11655 1317
15 Mettlach Commune Saarland 12180 1241
16 Tholey Commune Saarland 12385 1217
17 Saarwellingen Commune Saarland 13348 1104
18 Quierschied Commune Saarland 13506 1088
19 Spiesen-Elversberg Commune Saarland 13509 1086
20 Rehlingen-Siersburg Commune Saarland 14526 996
21 Riegelsberg Commune Saarland 14763 982
22 Ottweiler City Saarland 14934 969
23 Beckingen Commune Saarland 15355 931
24 Losheim am See Commune Saarland 15906 887
25 Schiffweiler Commune Saarland 15993 882
26 Wadern City Saarland 16181 874
27 Schmelz Commune Saarland 16435 857
28 Sulzbach/Saar City Saarland 16591 849
29 Illingen Commune Saarland 16978 827
30 Schwalbach Commune Saarland 17320 812
31 Eppelborn Commune Saarland 17726 793
32 Wadgassen Commune Saarland 17885 785
33 Bexbach City Saarland 18038 777
34 Heusweiler Commune Saarland 18201 762
35 Püttlingen City Saarland 19134 718
36 Lebach City Saarland 19484 701
37 Dillingen/Saar City Saarland 20253 654
38 Blieskastel City Saarland 21255 601
39 St. Wendel City Saarland 26220 460
40 Merzig City Saarland 29727 392
41 Saarlouis City Saarland 34479 323
42 St. Ingbert City Saarland 36645 299
43 Völklingen City Saarland 38809 279
44 Homburg City Saarland 41502 247
45 Neunkirchen City Saarland 46172 206
46 Saarbrücken City Saarland 175853 43
47 Perl Commune Saarland NA NAWith reference to the table above, we have identified an entry for the city of Perl that solely consists of NAs. This city is duplicated in the dataset, appearing at positions 1 and 47. The latter duplicate contains only NAs and can be safely removed without the loss of valuable information. Please eliminate this duplification and regenerate the list of all cities in the Saarland.
Calculate the total population and average size of cities in Saarland.
Check if any other city is recorded more than once in the dataset. To do so, reproduce the table below.
# A tibble: 23 × 5
# Groups: city [11]
city status state pop2011 unique_count
<chr> <chr> <chr> <dbl> <int>
1 Bonn City with County Rights Nordrhein-Westfalen 305765 3
2 Bonn City with County Rights Nordrhein-Westfalen 305765 3
3 Bonn City with County Rights Nordrhein-Westfalen 305765 3
4 Brühl Commune Baden-Württemberg 13805 2
5 Brühl City Nordrhein-Westfalen 43568 2
6 Erbach City Baden-Württemberg 13024 2
7 Erbach City Hessen 13245 2
8 Fürth City with County Rights Bayern 115613 2
9 Fürth Commune Hessen 10481 2
10 Lichtenau City Nordrhein-Westfalen 10473 2
11 Lichtenau Commune Sachsen 7544 2
12 Münster Commune Hessen 14071 2
13 Münster City with County Rights Nordrhein-Westfalen 289576 2
14 Neunkirchen Commune Nordrhein-Westfalen 13930 2
15 Neunkirchen City Saarland 46172 2
16 Neuried Commune Baden-Württemberg 9383 2
17 Neuried Commune Bayern 8277 2
18 Petersberg Commune Hessen 14766 2
19 Petersberg Commune Sachsen-Anhalt 10097 2
20 Senden City Bayern 21560 2
21 Senden Commune Nordrhein-Westfalen 19976 2
22 Staufenberg City Hessen 8114 2
23 Staufenberg Commune Niedersachsen 7983 2Data analysis (Zipf’s Law)
*Note: If you have failed to solve the data cleaning tasks above, you can download the cleaned data from ILIAS, save it in your working directory and load it from there with: load("city_clean.RData")
In the following, you aim to examine the validity of Zipf’s Law for Germany. Zipf’s Law postulates how the size of cities is distributed. The “law” states that there is a special relationship between the size of a city and the rank it occupies in a series sorted by city size. In the estimation equation \[ \log(M_j) = c - q \log(R_j), \] the law postulates a coefficient of \(( q=1 )\). \(c\) is a constant; \(M_j\) is the size of city \(j\); \(R_j\) is the rank that city \(j\) occupies in a series sorted by city size.
Create a variable named rank that includes a ranking of cities based on the population size in the year 2011. Therefore, Berlin should have a rank of 1, Hamburg a rank of 2, Munich a rank of 3, and so on.
Note: If you cannot solve this task, use the variable rankX as a substitute for the variable rank that was not generated.
# A tibble: 6 × 3
city pop2011 rank
<chr> <dbl> <int>
1 Berlin 3292365 1
2 Hamburg 1706696 2
3 München [Munich] 1348335 3
4 Köln [Cologne] 1005775 4
5 Frankfurt am Main 667925 5
6 Düsseldorf [Dusseldorf] 586291 6pop2011 and rank. The result should be:[1] -0.2948903rank, and on the y-axis, plot pop2011. Add a regression line representing the observed relationship to the same scatter plot.
rank and pop2011. Title the new variables as lnrank and lnpop2011, respectively. Here is a snapshot of the resulting variables:# A tibble: 6 × 5
city rank lnrank pop2011 lnpop2011
<chr> <int> <dbl> <dbl> <dbl>
1 Berlin 1 0 3292365 15.0
2 Hamburg 2 0.693 1706696 14.4
3 München [Munich] 3 1.10 1348335 14.1
4 Köln [Cologne] 4 1.39 1005775 13.8
5 Frankfurt am Main 5 1.61 667925 13.4
6 Düsseldorf [Dusseldorf] 6 1.79 586291 13.3lnpop2011 and lnrank. The result should be:[1] -0.9990053lnrank, and on the y-axis, plot lnpop2011. Add a regression line representing the observed relationship to the same scatter plot. Additionally, add a title and label the axes like is shown here:
Call:
lm(formula = lnpop2011 ~ lnrank, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.28015 -0.01879 0.01083 0.02005 0.25973
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.947859 0.005141 2908 <2e-16 ***
lnrank -0.780259 0.000766 -1019 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03454 on 2067 degrees of freedom
Multiple R-squared: 0.998, Adjusted R-squared: 0.998
F-statistic: 1.038e+06 on 1 and 2067 DF, p-value: < 2.2e-16df <- df |>
mutate(prediction = predict(zipf, newdata = df)) |>
mutate(pred_pop = exp(prediction))
df |>
select(city, pop2011, pred_pop) |>
filter(city == "Regensburg")# A tibble: 1 × 3
city pop2011 pred_pop
<chr> <dbl> <dbl>
1 Regensburg 135403 134194.The script uses the following functions: aes, arrange, as_tibble, c, case_when, cor, desc, dim, exp, filter, geom_point, geom_smooth, ggplot, group_by, head, is.na, labs, lm, log, mean, mutate, n, predict, print, read_dta, rename, row_number, save, select, starts_with, sum, summarise, summary, tail, ungroup.
# load packages
if (!require(pacman)) install.packages("pacman")
suppressMessages(pacman::p_unload(all))
# setwd("~/Dropbox/hsf/exams/24-01/Rmd")
rm(list = ls())
pacman::p_load(tidyverse, haven, janitor, jtools)
df <- read_dta("https://github.com/hubchev/courses/raw/main/dta/city.dta",
encoding = "latin1"
) |>
as_tibble()
head(df)# A tibble: 6 × 7
stadt status state pop1970 pop1987 pop2011 rankX
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Vohenstrauß City Bayern 7349 7059 7500 2069
2 Stockstadt a. Main Commune Bayern 6416 6615 7504 2068
3 Jesteburg Commune Niedersachsen 4141 5818 7510 2067
4 Bordesholm Commune Schleswig-Holstein 6011 6726 7513 2066
5 Herrieden City Bayern 5631 6250 7516 2065
6 Weida City Th_ringen NA NA 7522 2064tail(df)# A tibble: 6 × 7
stadt status state pop1970 pop1987 pop2011 rankX
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Frankfurt am Main City with County Rights Hessen 699297 618266 667925 5
2 Köln [Cologne] City with County Rights Nordr… 994705 928309 1005775 4
3 München [Munich] City with County Rights Bayern 1293599 1185421 1348335 3
4 Hamburg City with County Rights Hambu… 1793823 1592770 1706696 2
5 Berlin City with County Rights Berlin 3210000 3260000 3292365 1
6 Perl Commune Saarl… NA NA NA NAdim(df)[1] 2072 7summary(df) stadt status state pop1970
Length:2072 Length:2072 Length:2072 Min. : 1604
Class :character Class :character Class :character 1st Qu.: 8149
Mode :character Mode :character Mode :character Median : 11912
Mean : 30504
3rd Qu.: 21318
Max. :3210000
NA's :355
pop1987 pop2011 rankX
Min. : 4003 Min. : 7500 Min. : 1.0
1st Qu.: 9194 1st Qu.: 9998 1st Qu.: 516.5
Median : 13118 Median : 13937 Median :1034.0
Mean : 30854 Mean : 30772 Mean :1034.0
3rd Qu.: 23074 3rd Qu.: 24096 3rd Qu.:1551.5
Max. :3260000 Max. :3292365 Max. :2069.0
NA's :248 NA's :1 NA's :1 df <- df |>
rename(city = stadt)
df <- df |>
select(-pop1970, -pop1987)
df |>
group_by(state) |>
summarise(
mean(pop2011),
sum(pop2011)
)# A tibble: 17 × 3
state `mean(pop2011)` `sum(pop2011)`
<chr> <dbl> <dbl>
1 Baden-Wrttemberg 7580 7580
2 Baden-Württemberg 23680. 7837917
3 Bayern 23996. 7558677
4 Berlin 3292365 3292365
5 Brandenburg 18472. 1865632
6 Bremen 325432. 650863
7 Hamburg 1706696 1706696
8 Hessen 22996. 5036121
9 Mecklenburg-Vorpommern 27034. 811005
10 Niedersachsen 24107. 6219515
11 Nordrhein-Westfalen 47465. 18036727
12 Rheinland-Pfalz 25644. 1871995
13 Saarland NA NA
14 Sachsen 27788. 2973351
15 Sachsen-Anhalt 21212. 1993915
16 Schleswig-Holstein 24157. 1739269
17 Th_ringen 29192. 1167692df <- df |>
mutate(state = case_when(
state == "Baden-Wrttemberg" ~ "Baden-Württemberg",
state == "Th_ringen" ~ "Thüringen",
TRUE ~ state
))
df |>
group_by(state) |>
summarise(
mean(pop2011),
sum(pop2011)
)# A tibble: 16 × 3
state `mean(pop2011)` `sum(pop2011)`
<chr> <dbl> <dbl>
1 Baden-Württemberg 23631. 7845497
2 Bayern 23996. 7558677
3 Berlin 3292365 3292365
4 Brandenburg 18472. 1865632
5 Bremen 325432. 650863
6 Hamburg 1706696 1706696
7 Hessen 22996. 5036121
8 Mecklenburg-Vorpommern 27034. 811005
9 Niedersachsen 24107. 6219515
10 Nordrhein-Westfalen 47465. 18036727
11 Rheinland-Pfalz 25644. 1871995
12 Saarland NA NA
13 Sachsen 27788. 2973351
14 Sachsen-Anhalt 21212. 1993915
15 Schleswig-Holstein 24157. 1739269
16 Thüringen 29192. 1167692df |>
filter(state == "Saarland") |>
print(n = 100)# A tibble: 47 × 5
city status state pop2011 rankX
<chr> <chr> <chr> <dbl> <dbl>
1 Perl Commune Saarland 7775 2003
2 Freisen Commune Saarland 8270 1894
3 Großrosseln Commune Saarland 8403 1868
4 Nonnweiler Commune Saarland 8844 1775
5 Nalbach Commune Saarland 9302 1678
6 Wallerfangen Commune Saarland 9542 1642
7 Kirkel Commune Saarland 10058 1541
8 Merchweiler Commune Saarland 10219 1515
9 Nohfelden Commune Saarland 10247 1511
10 Friedrichsthal City Saarland 10409 1489
11 Marpingen Commune Saarland 10590 1461
12 Mandelbachtal Commune Saarland 11107 1390
13 Kleinblittersdorf Commune Saarland 11396 1354
14 Überherrn Commune Saarland 11655 1317
15 Mettlach Commune Saarland 12180 1241
16 Tholey Commune Saarland 12385 1217
17 Saarwellingen Commune Saarland 13348 1104
18 Quierschied Commune Saarland 13506 1088
19 Spiesen-Elversberg Commune Saarland 13509 1086
20 Rehlingen-Siersburg Commune Saarland 14526 996
21 Riegelsberg Commune Saarland 14763 982
22 Ottweiler City Saarland 14934 969
23 Beckingen Commune Saarland 15355 931
24 Losheim am See Commune Saarland 15906 887
25 Schiffweiler Commune Saarland 15993 882
26 Wadern City Saarland 16181 874
27 Schmelz Commune Saarland 16435 857
28 Sulzbach/Saar City Saarland 16591 849
29 Illingen Commune Saarland 16978 827
30 Schwalbach Commune Saarland 17320 812
31 Eppelborn Commune Saarland 17726 793
32 Wadgassen Commune Saarland 17885 785
33 Bexbach City Saarland 18038 777
34 Heusweiler Commune Saarland 18201 762
35 Püttlingen City Saarland 19134 718
36 Lebach City Saarland 19484 701
37 Dillingen/Saar City Saarland 20253 654
38 Blieskastel City Saarland 21255 601
39 St. Wendel City Saarland 26220 460
40 Merzig City Saarland 29727 392
41 Saarlouis City Saarland 34479 323
42 St. Ingbert City Saarland 36645 299
43 Völklingen City Saarland 38809 279
44 Homburg City Saarland 41502 247
45 Neunkirchen City Saarland 46172 206
46 Saarbrücken City Saarland 175853 43
47 Perl Commune Saarland NA NAdf <- df |>
filter(!(city == "Perl" & is.na(pop2011)))
df |>
filter(state == "Saarland") |>
print(n = 100)# A tibble: 46 × 5
city status state pop2011 rankX
<chr> <chr> <chr> <dbl> <dbl>
1 Perl Commune Saarland 7775 2003
2 Freisen Commune Saarland 8270 1894
3 Großrosseln Commune Saarland 8403 1868
4 Nonnweiler Commune Saarland 8844 1775
5 Nalbach Commune Saarland 9302 1678
6 Wallerfangen Commune Saarland 9542 1642
7 Kirkel Commune Saarland 10058 1541
8 Merchweiler Commune Saarland 10219 1515
9 Nohfelden Commune Saarland 10247 1511
10 Friedrichsthal City Saarland 10409 1489
11 Marpingen Commune Saarland 10590 1461
12 Mandelbachtal Commune Saarland 11107 1390
13 Kleinblittersdorf Commune Saarland 11396 1354
14 Überherrn Commune Saarland 11655 1317
15 Mettlach Commune Saarland 12180 1241
16 Tholey Commune Saarland 12385 1217
17 Saarwellingen Commune Saarland 13348 1104
18 Quierschied Commune Saarland 13506 1088
19 Spiesen-Elversberg Commune Saarland 13509 1086
20 Rehlingen-Siersburg Commune Saarland 14526 996
21 Riegelsberg Commune Saarland 14763 982
22 Ottweiler City Saarland 14934 969
23 Beckingen Commune Saarland 15355 931
24 Losheim am See Commune Saarland 15906 887
25 Schiffweiler Commune Saarland 15993 882
26 Wadern City Saarland 16181 874
27 Schmelz Commune Saarland 16435 857
28 Sulzbach/Saar City Saarland 16591 849
29 Illingen Commune Saarland 16978 827
30 Schwalbach Commune Saarland 17320 812
31 Eppelborn Commune Saarland 17726 793
32 Wadgassen Commune Saarland 17885 785
33 Bexbach City Saarland 18038 777
34 Heusweiler Commune Saarland 18201 762
35 Püttlingen City Saarland 19134 718
36 Lebach City Saarland 19484 701
37 Dillingen/Saar City Saarland 20253 654
38 Blieskastel City Saarland 21255 601
39 St. Wendel City Saarland 26220 460
40 Merzig City Saarland 29727 392
41 Saarlouis City Saarland 34479 323
42 St. Ingbert City Saarland 36645 299
43 Völklingen City Saarland 38809 279
44 Homburg City Saarland 41502 247
45 Neunkirchen City Saarland 46172 206
46 Saarbrücken City Saarland 175853 43df |>
filter(state == "Saarland") |>
summarise(
mean(pop2011),
sum(pop2011)
)# A tibble: 1 × 2
`mean(pop2011)` `sum(pop2011)`
<dbl> <dbl>
1 20850. 959110df |>
group_by(city) |>
mutate(unique_count = n()) |>
arrange(city, state) |>
filter(unique_count > 1) |>
select(city, status, state, starts_with("pop"), unique_count) |>
print(n = 100)# A tibble: 23 × 5
# Groups: city [11]
city status state pop2011 unique_count
<chr> <chr> <chr> <dbl> <int>
1 Bonn City with County Rights Nordrhein-Westfalen 305765 3
2 Bonn City with County Rights Nordrhein-Westfalen 305765 3
3 Bonn City with County Rights Nordrhein-Westfalen 305765 3
4 Brühl Commune Baden-Württemberg 13805 2
5 Brühl City Nordrhein-Westfalen 43568 2
6 Erbach City Baden-Württemberg 13024 2
7 Erbach City Hessen 13245 2
8 Fürth City with County Rights Bayern 115613 2
9 Fürth Commune Hessen 10481 2
10 Lichtenau City Nordrhein-Westfalen 10473 2
11 Lichtenau Commune Sachsen 7544 2
12 Münster Commune Hessen 14071 2
13 Münster City with County Rights Nordrhein-Westfalen 289576 2
14 Neunkirchen Commune Nordrhein-Westfalen 13930 2
15 Neunkirchen City Saarland 46172 2
16 Neuried Commune Baden-Württemberg 9383 2
17 Neuried Commune Bayern 8277 2
18 Petersberg Commune Hessen 14766 2
19 Petersberg Commune Sachsen-Anhalt 10097 2
20 Senden City Bayern 21560 2
21 Senden Commune Nordrhein-Westfalen 19976 2
22 Staufenberg City Hessen 8114 2
23 Staufenberg Commune Niedersachsen 7983 2df |>
group_by(city, state) |>
mutate(unique_count = n()) |>
arrange(city, state) |>
filter(unique_count > 1) |>
select(city, status, state, starts_with("pop"), unique_count) |>
print(n = 100)# A tibble: 3 × 5
# Groups: city, state [1]
city status state pop2011 unique_count
<chr> <chr> <chr> <dbl> <int>
1 Bonn City with County Rights Nordrhein-Westfalen 305765 3
2 Bonn City with County Rights Nordrhein-Westfalen 305765 3
3 Bonn City with County Rights Nordrhein-Westfalen 305765 3df <- df |>
group_by(city, state) |>
mutate(n_row = row_number()) |>
filter(n_row == 1) |>
select(-n_row)
df |>
group_by(city, state) |>
mutate(unique_count = n()) |>
arrange(city, state) |>
filter(unique_count > 1) |>
select(city, status, state, starts_with("pop"), unique_count) |>
print(n = 100)# A tibble: 0 × 5
# Groups: city, state [0]
# ℹ 5 variables: city <chr>, status <chr>, state <chr>, pop2011 <dbl>,
# unique_count <int>save(df, file = "city_clean.RData")
df <- df |>
ungroup() |>
arrange(desc(pop2011)) |>
mutate(rank = row_number())
df |>
select(-rankX, -status, -state) |>
head()# A tibble: 6 × 3
city pop2011 rank
<chr> <dbl> <int>
1 Berlin 3292365 1
2 Hamburg 1706696 2
3 München [Munich] 1348335 3
4 Köln [Cologne] 1005775 4
5 Frankfurt am Main 667925 5
6 Düsseldorf [Dusseldorf] 586291 6cor(df$pop2011, df$rank, method = c("pearson"))[1] -0.2948903ggplot(df, aes(x = rank, y = pop2011)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "blue")`geom_smooth()` using formula = 'y ~ x'
df <- df |>
mutate(lnrank = log(rank)) |>
mutate(lnpop2011 = log(pop2011))
df |>
select(city, rank, lnrank, pop2011, lnpop2011) |>
head()# A tibble: 6 × 5
city rank lnrank pop2011 lnpop2011
<chr> <int> <dbl> <dbl> <dbl>
1 Berlin 1 0 3292365 15.0
2 Hamburg 2 0.693 1706696 14.4
3 München [Munich] 3 1.10 1348335 14.1
4 Köln [Cologne] 4 1.39 1005775 13.8
5 Frankfurt am Main 5 1.61 667925 13.4
6 Düsseldorf [Dusseldorf] 6 1.79 586291 13.3cor(df$lnpop2011, df$lnrank, method = c("pearson"))[1] -0.9990053ggplot(df, aes(x = lnrank, y = lnpop2011)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "blue") +
labs(
title = "Scatterplot with Regression Line",
x = "lnrank (Logarithmized Rank)",
y = "lnpop2011 (Logarithmized Population 2011)"
)`geom_smooth()` using formula = 'y ~ x'
zipf <- lm(lnpop2011 ~ lnrank, data = df)
summary(zipf)
Call:
lm(formula = lnpop2011 ~ lnrank, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.28015 -0.01879 0.01083 0.02005 0.25973
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.947859 0.005141 2908 <2e-16 ***
lnrank -0.780259 0.000766 -1019 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03454 on 2067 degrees of freedom
Multiple R-squared: 0.998, Adjusted R-squared: 0.998
F-statistic: 1.038e+06 on 1 and 2067 DF, p-value: < 2.2e-16df <- df |>
mutate(prediction = predict(zipf, newdata = df)) |>
mutate(pred_pop = exp(prediction))
df |>
select(city, pop2011, pred_pop) |>
filter(city == "Regensburg")# A tibble: 1 × 3
city pop2011 pred_pop
<chr> <dbl> <dbl>
1 Regensburg 135403 134194.suppressMessages(pacman::p_unload(tidyverse, haven, janitor, jtools))
# rmarkdown::render("24-01_dsda.Rmd", "all")
# knitr::purl(input = "24-01_dsda.Rmd", output = "24-01_dsda_solution.R",documentation = 0)