R/temp.R
In rOpenGov/edudata: Data package containing yaml-files for edu packages exercises
# # # #
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Tee taulukko jossa F-kirjaimella alkavien maiden osalta datassa olevat vuodet.
# # #
# # # library(dplyr)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat %>%
# # # filter(grepl("^F", cname)) %>%
# # # count(cname)
# # # # Or
# # # dat %>%
# # # filter(grepl("^F", cname)) %>%
# # # group_by(cname) %>%
# # # summarise(n = n())
# # #
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Valitse undp_hdi muuttuja, poista puuttuvat arvot ja tee taulukko jossa A-kirjaimella alkavien maiden osalta datassa olevien vuosien määrä.
# # #
# # # library(dplyr)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat %>%
# # # filter(grepl("^A", cname),
# # # !is.na(undp_hdi)) %>%
# # # count(cname)
# # # # Or
# # # dat %>%
# # # filter(grepl("^F", cname),
# # # !is.na(undp_hdi)) %>%
# # # group_by(cname) %>%
# # # summarise(n = n())
# # #
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Tee taulukko jossa y-kirjaimeen loppuvien maiden korkein undp_hdi-arvo ja ko. vuosi.
# # #
# # # # cname undp_hdi year
# # # # Germany 0.936 2017
# # # # Hungary 0.838 2017
# # # # Italy 0.88 2017
# # # # Norway 0.953 2017
# # # # Paraguay 0.702 2015
# # # # Paraguay 0.702 2016
# # # # Paraguay 0.702 2017
# # # # Turkey 0.791 2017
# # # # Uruguay 0.804 2017
# # #
# # # library(dplyr)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat %>%
# # # filter(grepl("y$", cname)) %>%
# # # group_by(cname) %>%
# # # filter(undp_hdi == max(undp_hdi, na.rm = TRUE)) %>%
# # # select(cname,undp_hdi,year) %>%
# # # ungroup()
# # #
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Tee taulukko jossa o-kirjaimeen loppuvien maiden tuorein wdi_homicides-arvo ja ko. vuosi.
# # #
# # # # cname wdi_homicides year
# # # # Congo 9.32 2015
# # # # Lesotho 41.2 2015
# # # # Mexico 19.3 2016
# # # # Monaco 0 2015
# # # # Montenegro 4.46 2016
# # # # Morocco 1.24 2015
# # # # San Marino 0 2011
# # # # Togo 9.00 2015
# # # # Trinidad and Tobago 30.9 2015
# # # # Burkina Faso 0.370 2015
# # #
# # # library(dplyr)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat %>%
# # # select(cname,year,wdi_homicides) %>%
# # # filter(!is.na(wdi_homicides),
# # # grepl("o$", cname)) %>%
# # # group_by(cname) %>%
# # # filter(year == max(year, na.rm = TRUE)) %>%
# # # select(cname,wdi_homicides,year) %>%
# # # ungroup()
# # #
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Tee taulukko jossa d-kirjaimeen loppuvien maiden osalta top3 maat muuttujan wdi_internet-suhteen paremmuusjärjestyksessä kunakin vuonna 2014,2015,2016 ja 2017.
# # #
# # # # cname year wdi_internet
# # # # Iceland 2014 98.2
# # # # Switzerland 2014 87.4
# # # # Finland 2014 86.5
# # # # Iceland 2015 98.2
# # # # New Zealand 2015 88.2
# # # # Switzerland 2015 87.5
# # # # Iceland 2016 98.2
# # # # Switzerland 2016 89.1
# # # # New Zealand 2016 88.5
# # # # Switzerland 2017 93.7
# # # # Finland 2017 87.5
# # # # Ireland 2017 84.5
# # #
# # # library(dplyr)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat %>%
# # # select(cname,year,wdi_internet) %>%
# # # filter(!is.na(wdi_internet),
# # # grepl("d$", cname),
# # # year %in% 2014:2017) %>%
# # # group_by(year) %>%
# # # arrange(desc(wdi_internet)) %>%
# # # slice(1:3) %>%
# # # ungroup()
# # #
# # #
# # #
# # # library(nycflights13)
# # # library(dplyr)
# # # left_join(flights, airlines) %>%
# # # filter(name == "Hawaiian Airlines Inc.",
# # # year == 2013,
# # # origin == "JFK") %>%
# # # summarise(n=n())
# # #
# # #
# # # left_join(flights, airlines) %>%
# # # left_join(planes) %>%
# # # filter(name == "US Airways Inc.",
# # # year == 2013,
# # # origin == "JFK") %>%
# # # summarise(seats = mean(seats, na.rm = TRUE))
# # #
# # #
# # #
# # #
# # # # Visualisointiharjoitukset
# # #
# # # visu_h19_k4_1
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Miten saat seuraavanlaisen kuvan: http://courses.markuskainu.fi/utur2019/kuvat/visu_h19_k4_1.png
# # #
# # # library(dplyr)
# # # library(ggplot2)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat2 <- dat %>%
# # # select(year,cname,wdi_homicides) %>%
# # # filter(!is.na(wdi_homicides),
# # # grepl("o$", cname))
# # # ggplot(dat2, aes(x = year,
# # # y = wdi_homicides,
# # # color = cname)) +
# # # geom_point() +
# # # geom_line() +
# # # theme(legend.position = "none") +
# # # geom_text(data = dat2 %>%
# # # group_by(cname) %>%
# # # filter(year == max(year, na.rm = TRUE)) %>%
# # # ungroup(),
# # # aes(label = cname))
# # #
# # #
# # # visu_h19_k4_2
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Miten saat seuraavanlaisen kuvan: http://courses.markuskainu.fi/utur2019/kuvat/visu_h19_k4_2.png
# # #
# # # library(dplyr)
# # # library(ggplot2)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat2 <- dat %>%
# # # select(year,cname,wdi_internet,wdi_elerenew) %>%
# # # filter(year %in% c(2008,2010,2012,2014))
# # #
# # # ggplot(dat2, aes(x = wdi_internet,
# # # y = wdi_elerenew,
# # # color = cname,
# # # group = 1)) +
# # # geom_point() +
# # # facet_wrap(~year) +
# # # geom_smooth(method = "lm") +
# # # theme(legend.position = "none")
# # #
# # #
# # # visu_h19_k4_3
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Miten saat seuraavanlaisen kuvan: http://courses.markuskainu.fi/utur2019/kuvat/visu_h19_k4_3.png
# # #
# # library(dplyr)
# # library(ggplot2)
# # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # dat2 <- dat %>%
# # select(year,cname,wdi_fertility,wdi_empagr) %>%
# # filter(year %in% c(2008,2010,2012,2014))
# #
# # dat_cor <- dat2 %>%
# # na.omit() %>%
# # group_by(year) %>%
# # summarise(corr = cor(wdi_empagr,wdi_fertility, method = "pearson"),
# # wdi_fertility = mean(wdi_fertility, na.rm = TRUE),
# # wdi_empagr = mean(wdi_empagr, na.rm = TRUE))
# #
# # p <- ggplot(dat2, aes(x = wdi_empagr,
# # y = wdi_fertility)) +
# # geom_point() +
# # facet_wrap(~year) +
# # geom_smooth() +
# # geom_text(data = dat_cor,
# # aes(label = paste0("r. ", round(corr, 3))),
# # color = "dim grey",
# # size = 15, alpha = .6)
# library(dplyr)
# library(ggplot2)
# library(forcats)
# library(tidyr)
#
# kuvadata <- dplyr::starwars %>%
# unnest(films) %>%
# select(name,gender,films) %>%
# filter(!is.na(gender)) %>%
# group_by(films) %>%
# count(gender) %>%
# mutate(osuus = round(n/sum(n)*100,1))
#
#
# # suput esiintymisasteen (kaikissa elokuvissa) mukaan
# suput <- kuvadata %>%
# group_by(gender) %>%
# summarise(osuus = sum(osuus)) %>%
# arrange(desc(osuus)) %>%
# pull(gender)
#
# # elokuvat miesten esiintymisasteen mukaan
# elokuvat <- kuvadata %>%
# filter(gender == "male") %>%
# arrange(desc(osuus)) %>%
# pull(films)
#
# # asetetaan faktorilevelit
# kuvadata$gender <- factor(kuvadata$gender, levels = rev(suput))
# kuvadata$films <- factor(kuvadata$films, levels = elokuvat)
#
# ggplot(kuvadata, aes(x = films,
# y = osuus,
# fill = gender)) +
# geom_col() +
# labs(title = "Star Wars elokuvat ja hahmojen 'sukupuoliosuudet'",
# subtitle = "Elokuvat järjestetty miesten suhteellisen osuuden mukaan",
# y = "naisten määrä") +
# theme(axis.text.x = element_text(angle = 90))
#
# ggsave(filename = "~/btsync/kela/utur2019/utur2019_site/kuvat/h19_k4_factor_4.png", plot = p)
# #
#
#
# dplyr::starwars %>%
# mutate(eye_color_base = gsub('-.+$|,.+$', "", eye_color)) %>%
# filter(grepl("-|,", eye_color)) %>%
# select(name,eye_color,eye_color_base)
rOpenGov/edudata documentation built on May 29, 2019, 2:53 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# # # #
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Tee taulukko jossa F-kirjaimella alkavien maiden osalta datassa olevat vuodet.
# # #
# # # library(dplyr)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat %>%
# # # filter(grepl("^F", cname)) %>%
# # # count(cname)
# # # # Or
# # # dat %>%
# # # filter(grepl("^F", cname)) %>%
# # # group_by(cname) %>%
# # # summarise(n = n())
# # #
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Valitse undp_hdi muuttuja, poista puuttuvat arvot ja tee taulukko jossa A-kirjaimella alkavien maiden osalta datassa olevien vuosien määrä.
# # #
# # # library(dplyr)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat %>%
# # # filter(grepl("^A", cname),
# # # !is.na(undp_hdi)) %>%
# # # count(cname)
# # # # Or
# # # dat %>%
# # # filter(grepl("^F", cname),
# # # !is.na(undp_hdi)) %>%
# # # group_by(cname) %>%
# # # summarise(n = n())
# # #
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Tee taulukko jossa y-kirjaimeen loppuvien maiden korkein undp_hdi-arvo ja ko. vuosi.
# # #
# # # # cname undp_hdi year
# # # # Germany 0.936 2017
# # # # Hungary 0.838 2017
# # # # Italy 0.88 2017
# # # # Norway 0.953 2017
# # # # Paraguay 0.702 2015
# # # # Paraguay 0.702 2016
# # # # Paraguay 0.702 2017
# # # # Turkey 0.791 2017
# # # # Uruguay 0.804 2017
# # #
# # # library(dplyr)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat %>%
# # # filter(grepl("y$", cname)) %>%
# # # group_by(cname) %>%
# # # filter(undp_hdi == max(undp_hdi, na.rm = TRUE)) %>%
# # # select(cname,undp_hdi,year) %>%
# # # ungroup()
# # #
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Tee taulukko jossa o-kirjaimeen loppuvien maiden tuorein wdi_homicides-arvo ja ko. vuosi.
# # #
# # # # cname wdi_homicides year
# # # # Congo 9.32 2015
# # # # Lesotho 41.2 2015
# # # # Mexico 19.3 2016
# # # # Monaco 0 2015
# # # # Montenegro 4.46 2016
# # # # Morocco 1.24 2015
# # # # San Marino 0 2011
# # # # Togo 9.00 2015
# # # # Trinidad and Tobago 30.9 2015
# # # # Burkina Faso 0.370 2015
# # #
# # # library(dplyr)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat %>%
# # # select(cname,year,wdi_homicides) %>%
# # # filter(!is.na(wdi_homicides),
# # # grepl("o$", cname)) %>%
# # # group_by(cname) %>%
# # # filter(year == max(year, na.rm = TRUE)) %>%
# # # select(cname,wdi_homicides,year) %>%
# # # ungroup()
# # #
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Tee taulukko jossa d-kirjaimeen loppuvien maiden osalta top3 maat muuttujan wdi_internet-suhteen paremmuusjärjestyksessä kunakin vuonna 2014,2015,2016 ja 2017.
# # #
# # # # cname year wdi_internet
# # # # Iceland 2014 98.2
# # # # Switzerland 2014 87.4
# # # # Finland 2014 86.5
# # # # Iceland 2015 98.2
# # # # New Zealand 2015 88.2
# # # # Switzerland 2015 87.5
# # # # Iceland 2016 98.2
# # # # Switzerland 2016 89.1
# # # # New Zealand 2016 88.5
# # # # Switzerland 2017 93.7
# # # # Finland 2017 87.5
# # # # Ireland 2017 84.5
# # #
# # # library(dplyr)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat %>%
# # # select(cname,year,wdi_internet) %>%
# # # filter(!is.na(wdi_internet),
# # # grepl("d$", cname),
# # # year %in% 2014:2017) %>%
# # # group_by(year) %>%
# # # arrange(desc(wdi_internet)) %>%
# # # slice(1:3) %>%
# # # ungroup()
# # #
# # #
# # #
# # # library(nycflights13)
# # # library(dplyr)
# # # left_join(flights, airlines) %>%
# # # filter(name == "Hawaiian Airlines Inc.",
# # # year == 2013,
# # # origin == "JFK") %>%
# # # summarise(n=n())
# # #
# # #
# # # left_join(flights, airlines) %>%
# # # left_join(planes) %>%
# # # filter(name == "US Airways Inc.",
# # # year == 2013,
# # # origin == "JFK") %>%
# # # summarise(seats = mean(seats, na.rm = TRUE))
# # #
# # #
# # #
# # #
# # # # Visualisointiharjoitukset
# # #
# # # visu_h19_k4_1
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Miten saat seuraavanlaisen kuvan: http://courses.markuskainu.fi/utur2019/kuvat/visu_h19_k4_1.png
# # #
# # # library(dplyr)
# # # library(ggplot2)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat2 <- dat %>%
# # # select(year,cname,wdi_homicides) %>%
# # # filter(!is.na(wdi_homicides),
# # # grepl("o$", cname))
# # # ggplot(dat2, aes(x = year,
# # # y = wdi_homicides,
# # # color = cname)) +
# # # geom_point() +
# # # geom_line() +
# # # theme(legend.position = "none") +
# # # geom_text(data = dat2 %>%
# # # group_by(cname) %>%
# # # filter(year == max(year, na.rm = TRUE)) %>%
# # # ungroup(),
# # # aes(label = cname))
# # #
# # #
# # # visu_h19_k4_2
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Miten saat seuraavanlaisen kuvan: http://courses.markuskainu.fi/utur2019/kuvat/visu_h19_k4_2.png
# # #
# # # library(dplyr)
# # # library(ggplot2)
# # # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # # dat2 <- dat %>%
# # # select(year,cname,wdi_internet,wdi_elerenew) %>%
# # # filter(year %in% c(2008,2010,2012,2014))
# # #
# # # ggplot(dat2, aes(x = wdi_internet,
# # # y = wdi_elerenew,
# # # color = cname,
# # # group = 1)) +
# # # geom_point() +
# # # facet_wrap(~year) +
# # # geom_smooth(method = "lm") +
# # # theme(legend.position = "none")
# # #
# # #
# # # visu_h19_k4_3
# # # # Lataa QOG-projektin Basic data (dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019) ). Miten saat seuraavanlaisen kuvan: http://courses.markuskainu.fi/utur2019/kuvat/visu_h19_k4_3.png
# # #
# # library(dplyr)
# # library(ggplot2)
# # dat <- rqog::read_qog(which_data = "basic", data_type = "time-series", year = 2019)
# # dat2 <- dat %>%
# # select(year,cname,wdi_fertility,wdi_empagr) %>%
# # filter(year %in% c(2008,2010,2012,2014))
# #
# # dat_cor <- dat2 %>%
# # na.omit() %>%
# # group_by(year) %>%
# # summarise(corr = cor(wdi_empagr,wdi_fertility, method = "pearson"),
# # wdi_fertility = mean(wdi_fertility, na.rm = TRUE),
# # wdi_empagr = mean(wdi_empagr, na.rm = TRUE))
# #
# # p <- ggplot(dat2, aes(x = wdi_empagr,
# # y = wdi_fertility)) +
# # geom_point() +
# # facet_wrap(~year) +
# # geom_smooth() +
# # geom_text(data = dat_cor,
# # aes(label = paste0("r. ", round(corr, 3))),
# # color = "dim grey",
# # size = 15, alpha = .6)
# library(dplyr)
# library(ggplot2)
# library(forcats)
# library(tidyr)
#
# kuvadata <- dplyr::starwars %>%
# unnest(films) %>%
# select(name,gender,films) %>%
# filter(!is.na(gender)) %>%
# group_by(films) %>%
# count(gender) %>%
# mutate(osuus = round(n/sum(n)*100,1))
#
#
# # suput esiintymisasteen (kaikissa elokuvissa) mukaan
# suput <- kuvadata %>%
# group_by(gender) %>%
# summarise(osuus = sum(osuus)) %>%
# arrange(desc(osuus)) %>%
# pull(gender)
#
# # elokuvat miesten esiintymisasteen mukaan
# elokuvat <- kuvadata %>%
# filter(gender == "male") %>%
# arrange(desc(osuus)) %>%
# pull(films)
#
# # asetetaan faktorilevelit
# kuvadata$gender <- factor(kuvadata$gender, levels = rev(suput))
# kuvadata$films <- factor(kuvadata$films, levels = elokuvat)
#
# ggplot(kuvadata, aes(x = films,
# y = osuus,
# fill = gender)) +
# geom_col() +
# labs(title = "Star Wars elokuvat ja hahmojen 'sukupuoliosuudet'",
# subtitle = "Elokuvat järjestetty miesten suhteellisen osuuden mukaan",
# y = "naisten määrä") +
# theme(axis.text.x = element_text(angle = 90))
#
# ggsave(filename = "~/btsync/kela/utur2019/utur2019_site/kuvat/h19_k4_factor_4.png", plot = p)
# #
#
#
# dplyr::starwars %>%
# mutate(eye_color_base = gsub('-.+$|,.+$', "", eye_color)) %>%
# filter(grepl("-|,", eye_color)) %>%
# select(name,eye_color,eye_color_base)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.